diff --git a/.eslintignore b/.eslintignore new file mode 100644 index 00000000000..1cfd9487674 --- /dev/null +++ b/.eslintignore @@ -0,0 +1,4 @@ +extensions +extensions-disabled +repositories +venv \ No newline at end of file diff --git a/.eslintrc.js b/.eslintrc.js new file mode 100644 index 00000000000..2e7258f6b13 --- /dev/null +++ b/.eslintrc.js @@ -0,0 +1,98 @@ +/* global module */ +module.exports = { + env: { + browser: true, + es2021: true, + }, + extends: "eslint:recommended", + parserOptions: { + ecmaVersion: "latest", + }, + rules: { + "arrow-spacing": "error", + "block-spacing": "error", + "brace-style": "error", + "comma-dangle": ["error", "only-multiline"], + "comma-spacing": "error", + "comma-style": ["error", "last"], + "curly": ["error", "multi-line", "consistent"], + "eol-last": "error", + "func-call-spacing": "error", + "function-call-argument-newline": ["error", "consistent"], + "function-paren-newline": ["error", "consistent"], + "indent": ["error", 4], + "key-spacing": "error", + "keyword-spacing": "error", + "linebreak-style": ["error", "unix"], + "no-extra-semi": "error", + "no-mixed-spaces-and-tabs": "error", + "no-multi-spaces": "error", + "no-redeclare": ["error", {builtinGlobals: false}], + "no-trailing-spaces": "error", + "no-unused-vars": "off", + "no-whitespace-before-property": "error", + "object-curly-newline": ["error", {consistent: true, multiline: true}], + "object-curly-spacing": ["error", "never"], + "operator-linebreak": ["error", "after"], + "quote-props": ["error", "consistent-as-needed"], + "semi": ["error", "always"], + "semi-spacing": "error", + "semi-style": ["error", "last"], + "space-before-blocks": "error", + "space-before-function-paren": ["error", "never"], + "space-in-parens": ["error", "never"], + "space-infix-ops": "error", + "space-unary-ops": "error", + "switch-colon-spacing": "error", + "template-curly-spacing": ["error", "never"], + "unicode-bom": "error", + }, + globals: { + //script.js + gradioApp: "readonly", + executeCallbacks: "readonly", + onAfterUiUpdate: "readonly", + onOptionsChanged: "readonly", + onUiLoaded: "readonly", + onUiUpdate: "readonly", + uiCurrentTab: "writable", + uiElementInSight: "readonly", + uiElementIsVisible: "readonly", + //ui.js + opts: "writable", + all_gallery_buttons: "readonly", + selected_gallery_button: "readonly", + selected_gallery_index: "readonly", + switch_to_txt2img: "readonly", + switch_to_img2img_tab: "readonly", + switch_to_img2img: "readonly", + switch_to_sketch: "readonly", + switch_to_inpaint: "readonly", + switch_to_inpaint_sketch: "readonly", + switch_to_extras: "readonly", + get_tab_index: "readonly", + create_submit_args: "readonly", + restart_reload: "readonly", + updateInput: "readonly", + onEdit: "readonly", + //extraNetworks.js + requestGet: "readonly", + popup: "readonly", + // profilerVisualization.js + createVisualizationTable: "readonly", + // from python + localization: "readonly", + // progrssbar.js + randomId: "readonly", + requestProgress: "readonly", + // imageviewer.js + modalPrevImage: "readonly", + modalNextImage: "readonly", + // localStorage.js + localSet: "readonly", + localGet: "readonly", + localRemove: "readonly", + // resizeHandle.js + setupResizeHandle: "writable" + } +}; diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs new file mode 100644 index 00000000000..4104da632b8 --- /dev/null +++ b/.git-blame-ignore-revs @@ -0,0 +1,2 @@ +# Apply ESlint +9c54b78d9dde5601e916f308d9a9d6953ec39430 \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index 50c54210a52..00000000000 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,32 +0,0 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: bug-report -assignees: '' - ---- - -**Describe the bug** -A clear and concise description of what the bug is. - -**To Reproduce** -Steps to reproduce the behavior: -1. Go to '...' -2. Click on '....' -3. Scroll down to '....' -4. See error - -**Expected behavior** -A clear and concise description of what you expected to happen. - -**Screenshots** -If applicable, add screenshots to help explain your problem. - -**Desktop (please complete the following information):** - - OS: [e.g. Windows, Linux] - - Browser [e.g. chrome, safari] - - Commit revision [looks like this: e68484500f76a33ba477d5a99340ab30451e557b; can be seen when launching webui.bat, or obtained manually by running `git rev-parse HEAD`] - -**Additional context** -Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 00000000000..c86bd8a680b --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,105 @@ +name: Bug Report +description: You think something is broken in the UI +title: "[Bug]: " +labels: ["bug-report"] + +body: + - type: markdown + attributes: + value: | + > The title of the bug report should be short and descriptive. + > Use relevant keywords for searchability. + > Do not leave it blank, but also do not put an entire error log in it. + - type: checkboxes + attributes: + label: Checklist + description: | + Please perform basic debugging to see if extensions or configuration is the cause of the issue. + Basic debug procedure + 　1. Disable all third-party extensions - check if extension is the cause + 　2. Update extensions and webui - sometimes things just need to be updated + 　3. Backup and remove your config.json and ui-config.json - check if the issue is caused by bad configuration + 　4. Delete venv with third-party extensions disabled - sometimes extensions might cause wrong libraries to be installed + 　5. Try a fresh installation webui in a different directory - see if a clean installation solves the issue + Before making a issue report please, check that the issue hasn't been reported recently. + options: + - label: The issue exists after disabling all extensions + - label: The issue exists on a clean installation of webui + - label: The issue is caused by an extension, but I believe it is caused by a bug in the webui + - label: The issue exists in the current version of the webui + - label: The issue has not been reported before recently + - label: The issue has been reported before but has not been fixed yet + - type: markdown + attributes: + value: | + > Please fill this form with as much information as possible. Don't forget to "Upload Sysinfo" and "What browsers" and provide screenshots if possible + - type: textarea + id: what-did + attributes: + label: What happened? + description: Tell us what happened in a very clear and simple way + placeholder: | + txt2img is not working as intended. + validations: + required: true + - type: textarea + id: steps + attributes: + label: Steps to reproduce the problem + description: Please provide us with precise step by step instructions on how to reproduce the bug + placeholder: | + 1. Go to ... + 2. Press ... + 3. ... + validations: + required: true + - type: textarea + id: what-should + attributes: + label: What should have happened? + description: Tell us what you think the normal behavior should be + placeholder: | + WebUI should ... + validations: + required: true + - type: dropdown + id: browsers + attributes: + label: What browsers do you use to access the UI ? + multiple: true + options: + - Mozilla Firefox + - Google Chrome + - Brave + - Apple Safari + - Microsoft Edge + - Android + - iOS + - Other + - type: textarea + id: sysinfo + attributes: + label: Sysinfo + description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file. + placeholder: | + 1. Go to WebUI Settings -> Sysinfo -> Download system info. + If WebUI fails to launch, use --dump-sysinfo commandline argument to generate the file + 2. Upload the Sysinfo as a attached file, Do NOT paste it in as plain text. + validations: + required: true + - type: textarea + id: logs + attributes: + label: Console logs + description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occurred. If it's very long, provide a link to pastebin or similar service. + render: Shell + validations: + required: true + - type: textarea + id: misc + attributes: + label: Additional information + description: | + Please provide us with any relevant additional info or context. + Examples: + 　I have updated my GPU driver recently. diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 00000000000..f58c94a9be6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: false +contact_links: + - name: WebUI Community Support + url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions + about: Please ask and answer questions here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md deleted file mode 100644 index eda42fa7d50..00000000000 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -name: Feature request -about: Suggest an idea for this project -title: '' -labels: 'suggestion' -assignees: '' - ---- - -**Is your feature request related to a problem? Please describe.** -A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] - -**Describe the solution you'd like** -A clear and concise description of what you want to happen. - -**Describe alternatives you've considered** -A clear and concise description of any alternative solutions or features you've considered. - -**Additional context** -Add any other context or screenshots about the feature request here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml new file mode 100644 index 00000000000..35a887408c1 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -0,0 +1,40 @@ +name: Feature request +description: Suggest an idea for this project +title: "[Feature Request]: " +labels: ["enhancement"] + +body: + - type: checkboxes + attributes: + label: Is there an existing issue for this? + description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit. + options: + - label: I have searched the existing issues and checked the recent builds/commits + required: true + - type: markdown + attributes: + value: | + *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible* + - type: textarea + id: feature + attributes: + label: What would your feature do ? + description: Tell us about your feature in a very clear and simple way, and what problem it would solve + validations: + required: true + - type: textarea + id: workflow + attributes: + label: Proposed workflow + description: Please provide us with step by step information on how you'd like the feature to be accessed and used + value: | + 1. Go to .... + 2. Press .... + 3. ... + validations: + required: true + - type: textarea + id: misc + attributes: + label: Additional information + description: Add any other context or screenshots about the feature request here. diff --git a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md deleted file mode 100644 index 86009613eed..00000000000 --- a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md +++ /dev/null @@ -1,28 +0,0 @@ -# Please read the [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) before submitting a pull request! - -If you have a large change, pay special attention to this paragraph: - -> Before making changes, if you think that your feature will result in more than 100 lines changing, find me and talk to me about the feature you are proposing. It pains me to reject the hard work someone else did, but I won't add everything to the repo, and it's better if the rejection happens before you have to waste time working on the feature. - -Otherwise, after making sure you're following the rules described in wiki page, remove this section and continue on. - -**Describe what this pull request is trying to achieve.** - -A clear and concise description of what you're trying to accomplish with this, so your intent doesn't have to be extracted from your code. - -**Additional notes and description of your changes** - -More technical discussion about your changes go here, plus anything that a maintainer might have to specifically take a look at, or be wary of. - -**Environment this was tested in** - -List the environment you have developed / tested this on. As per the contributing page, changes should be able to work on Windows out of the box. - - OS: [e.g. Windows, Linux] - - Browser [e.g. chrome, safari] - - Graphics card [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB] - -**Screenshots or videos of your changes** - -If applicable, screenshots or a video showing off your changes. If it edits an existing UI, it should ideally contain a comparison of what used to be there, before your changes were made. - -This is **required** for anything that touches the user interface. \ No newline at end of file diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 00000000000..c9fcda2e279 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,15 @@ +## Description + +* a simple description of what you're trying to accomplish +* a summary of changes in code +* which issues it fixes, if any + +## Screenshots/videos: + + +## Checklist: + +- [ ] I have read [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) +- [ ] I have performed a self-review of my own code +- [ ] My code follows the [style guidelines](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style) +- [ ] My code passes [tests](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests) diff --git a/.github/workflows/on_pull_request.yaml b/.github/workflows/on_pull_request.yaml index 5270cba4ca7..9326c6a45e9 100644 --- a/.github/workflows/on_pull_request.yaml +++ b/.github/workflows/on_pull_request.yaml @@ -1,36 +1,38 @@ -# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file -name: Run Linting/Formatting on Pull Requests +name: Linter on: - push - pull_request - # See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs - # if you want to filter out branches, delete the `- pull_request` and uncomment these lines : - # pull_request: - # branches: - # - master - # branches-ignore: - # - development jobs: - lint: + lint-python: + name: ruff runs-on: ubuntu-latest + if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name steps: - name: Checkout Code - uses: actions/checkout@v3 - - name: Set up Python 3.10 - uses: actions/setup-python@v3 + uses: actions/checkout@v4 + - uses: actions/setup-python@v5 with: - python-version: 3.10.6 - - name: Install PyLint - run: | - python -m pip install --upgrade pip - pip install pylint - # This lets PyLint check to see if it can resolve imports - - name: Install dependencies - run : | - export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit" - python launch.py - - name: Analysing the code with pylint - run: | - pylint $(git ls-files '*.py') + python-version: 3.11 + # NB: there's no cache: pip here since we're not installing anything + # from the requirements.txt file(s) in the repository; it's faster + # not to have GHA download an (at the time of writing) 4 GB cache + # of PyTorch and other dependencies. + - name: Install Ruff + run: pip install ruff==0.3.3 + - name: Run Ruff + run: ruff . + lint-js: + name: eslint + runs-on: ubuntu-latest + if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name + steps: + - name: Checkout Code + uses: actions/checkout@v4 + - name: Install Node.js + uses: actions/setup-node@v4 + with: + node-version: 18 + - run: npm i --ci + - run: npm run lint diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml new file mode 100644 index 00000000000..0610f4f5436 --- /dev/null +++ b/.github/workflows/run_tests.yaml @@ -0,0 +1,81 @@ +name: Tests + +on: + - push + - pull_request + +jobs: + test: + name: tests on CPU with empty model + runs-on: ubuntu-latest + if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name + steps: + - name: Checkout Code + uses: actions/checkout@v4 + - name: Set up Python 3.10 + uses: actions/setup-python@v5 + with: + python-version: 3.10.6 + cache: pip + cache-dependency-path: | + **/requirements*txt + launch.py + - name: Cache models + id: cache-models + uses: actions/cache@v4 + with: + path: models + key: "2023-12-30" + - name: Install test dependencies + run: pip install wait-for-it -r requirements-test.txt + env: + PIP_DISABLE_PIP_VERSION_CHECK: "1" + PIP_PROGRESS_BAR: "off" + - name: Setup environment + run: python launch.py --skip-torch-cuda-test --exit + env: + PIP_DISABLE_PIP_VERSION_CHECK: "1" + PIP_PROGRESS_BAR: "off" + TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu + WEBUI_LAUNCH_LIVE_OUTPUT: "1" + PYTHONUNBUFFERED: "1" + - name: Print installed packages + run: pip freeze + - name: Start test server + run: > + python -m coverage run + --data-file=.coverage.server + launch.py + --skip-prepare-environment + --skip-torch-cuda-test + --test-server + --do-not-download-clip + --no-half + --disable-opt-split-attention + --use-cpu all + --api-server-stop + 2>&1 | tee output.txt & + - name: Run tests + run: | + wait-for-it --service 127.0.0.1:7860 -t 20 + python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test + - name: Kill test server + if: always() + run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10 + - name: Show coverage + run: | + python -m coverage combine .coverage* + python -m coverage report -i + python -m coverage html -i + - name: Upload main app output + uses: actions/upload-artifact@v4 + if: always() + with: + name: output + path: output.txt + - name: Upload coverage HTML + uses: actions/upload-artifact@v4 + if: always() + with: + name: htmlcov + path: htmlcov diff --git a/.github/workflows/warns_merge_master.yml b/.github/workflows/warns_merge_master.yml new file mode 100644 index 00000000000..ae2aab6ba8c --- /dev/null +++ b/.github/workflows/warns_merge_master.yml @@ -0,0 +1,19 @@ +name: Pull requests can't target master branch + +"on": + pull_request: + types: + - opened + - synchronize + - reopened + branches: + - master + +jobs: + check: + runs-on: ubuntu-latest + steps: + - name: Warning marge into master + run: | + echo -e "::warning::This pull request directly merge into \"master\" branch, normally development happens on \"dev\" branch." + exit 1 diff --git a/.gitignore b/.gitignore index 69785b3e29e..e81ad31f53a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,8 @@ __pycache__ *.ckpt +*.safetensors *.pth +.DS_Store /ESRGAN/* /SwinIR/* /repositories @@ -27,3 +29,16 @@ __pycache__ notification.mp3 /SwinIR /textual_inversion +.vscode +/extensions +/test/stdout.txt +/test/stderr.txt +/cache.json* +/config_states/ +/node_modules +/package-lock.json +/.coverage* +/test/test_outputs +/cache +trace.json +/sysinfo-????-??-??-??-??.json diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000000..5f7e59351cb --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,1085 @@ +## 1.10.1 + +### Bug Fixes: +* fix image upscale on cpu ([#16275](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16275)) + + +## 1.10.0 + +### Features: +* A lot of performance improvements (see below in Performance section) +* Stable Diffusion 3 support ([#16030](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16030), [#16164](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16164), [#16212](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16212)) + * Recommended Euler sampler; DDIM and other timestamp samplers currently not supported + * T5 text model is disabled by default, enable it in settings +* New schedulers: + * Align Your Steps ([#15751](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15751)) + * KL Optimal ([#15608](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608)) + * Normal ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149)) + * DDIM ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149)) + * Simple ([#16142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16142)) + * Beta ([#16235](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16235)) +* New sampler: DDIM CFG++ ([#16035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16035)) + +### Minor: +* Option to skip CFG on early steps ([#15607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15607)) +* Add --models-dir option ([#15742](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15742)) +* Allow mobile users to open context menu by using two fingers press ([#15682](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15682)) +* Infotext: add Lora name as TI hashes for bundled Textual Inversion ([#15679](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15679)) +* Check model's hash after downloading it to prevent corruped downloads ([#15602](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15602)) +* More extension tag filtering options ([#15627](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15627)) +* When saving AVIF, use JPEG's quality setting ([#15610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15610)) +* Add filename pattern: `[basename]` ([#15978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15978)) +* Add option to enable clip skip for clip L on SDXL ([#15992](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15992)) +* Option to prevent screen sleep during generation ([#16001](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16001)) +* ToggleLivePriview button in image viewer ([#16065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16065)) +* Remove ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153)) +* option to disable save button log.csv ([#16242](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16242)) + +### Extensions and API: +* Add process_before_every_sampling hook ([#15984](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15984)) +* Return HTTP 400 instead of 404 on invalid sampler error ([#16140](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16140)) + +### Performance: +* [Performance 1/6] use_checkpoint = False ([#15803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15803)) +* [Performance 2/6] Replace einops.rearrange with torch native ops ([#15804](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15804)) +* [Performance 4/6] Precompute is_sdxl_inpaint flag ([#15806](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15806)) +* [Performance 5/6] Prevent unnecessary extra networks bias backup ([#15816](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15816)) +* [Performance 6/6] Add --precision half option to avoid casting during inference ([#15820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15820)) +* [Performance] LDM optimization patches ([#15824](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15824)) +* [Performance] Keep sigmas on CPU ([#15823](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15823)) +* Check for nans in unet only once, after all steps have been completed +* Added pption to run torch profiler for image generation + +### Bug Fixes: +* Fix for grids without comprehensive infotexts ([#15958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15958)) +* feat: lora partial update precede full update ([#15943](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15943)) +* Fix bug where file extension had an extra '.' under some circumstances ([#15893](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15893)) +* Fix corrupt model initial load loop ([#15600](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15600)) +* Allow old sampler names in API ([#15656](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15656)) +* more old sampler scheduler compatibility ([#15681](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15681)) +* Fix Hypertile xyz ([#15831](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15831)) +* XYZ CSV skipinitialspace ([#15832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15832)) +* fix soft inpainting on mps and xpu, torch_utils.float64 ([#15815](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15815)) +* fix extention update when not on main branch ([#15797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15797)) +* update pickle safe filenames +* use relative path for webui-assets css ([#15757](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15757)) +* When creating a virtual environment, upgrade pip in webui.bat/webui.sh ([#15750](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15750)) +* Fix AttributeError ([#15738](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15738)) +* use script_path for webui root in launch_utils ([#15705](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15705)) +* fix extra batch mode P Transparency ([#15664](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15664)) +* use gradio theme colors in css ([#15680](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15680)) +* Fix dragging text within prompt input ([#15657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15657)) +* Add correct mimetype for .mjs files ([#15654](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15654)) +* QOL Items - handle metadata issues more cleanly for SD models, Loras and embeddings ([#15632](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15632)) +* replace wsl-open with wslpath and explorer.exe ([#15968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15968)) +* Fix SDXL Inpaint ([#15976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15976)) +* multi size grid ([#15988](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15988)) +* fix Replace preview ([#16118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16118)) +* Possible fix of wrong scale in weight decomposition ([#16151](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16151)) +* Ensure use of python from venv on Mac and Linux ([#16116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16116)) +* Prioritize python3.10 over python3 if both are available on Linux and Mac (with fallback) ([#16092](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16092)) +* stoping generation extras ([#16085](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16085)) +* Fix SD2 loading ([#16078](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16078), [#16079](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16079)) +* fix infotext Lora hashes for hires fix different lora ([#16062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16062)) +* Fix sampler scheduler autocorrection warning ([#16054](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16054)) +* fix ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153)) +* fix upscale logic ([#16239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16239)) +* [bug] do not break progressbar on non-job actions (add wrap_gradio_call_no_job) ([#16202](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16202)) +* fix OSError: cannot write mode P as JPEG ([#16194](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16194)) + +### Other: +* fix changelog #15883 -> #15882 ([#15907](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15907)) +* ReloadUI backgroundColor --background-fill-primary ([#15864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15864)) +* Use different torch versions for Intel and ARM Macs ([#15851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15851)) +* XYZ override rework ([#15836](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15836)) +* scroll extensions table on overflow ([#15830](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15830)) +* img2img batch upload method ([#15817](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15817)) +* chore: sync v1.8.0 packages according to changelog ([#15783](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15783)) +* Add AVIF MIME type support to mimetype definitions ([#15739](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15739)) +* Update imageviewer.js ([#15730](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15730)) +* no-referrer ([#15641](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15641)) +* .gitignore trace.json ([#15980](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15980)) +* Bump spandrel to 0.3.4 ([#16144](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16144)) +* Defunct --max-batch-count ([#16119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16119)) +* docs: update bug_report.yml ([#16102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16102)) +* Maintaining Project Compatibility for Python 3.9 Users Without Upgrade Requirements. ([#16088](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16088), [#16169](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16169), [#16192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16192)) +* Update torch for ARM Macs to 2.3.1 ([#16059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16059)) +* remove deprecated setting dont_fix_second_order_samplers_schedule ([#16061](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16061)) +* chore: fix typos ([#16060](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16060)) +* shlex.join launch args in console log ([#16170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16170)) +* activate venv .bat ([#16231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16231)) +* add ids to the resize tabs in img2img ([#16218](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16218)) +* update installation guide linux ([#16178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16178)) +* Robust sysinfo ([#16173](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16173)) +* do not send image size on paste inpaint ([#16180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16180)) +* Fix noisy DS_Store files for MacOS ([#16166](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16166)) + + +## 1.9.4 + +### Bug Fixes: +* pin setuptools version to fix the startup error ([#15882](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15882)) + +## 1.9.3 + +### Bug Fixes: +* fix get_crop_region_v2 ([#15594](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15594)) + +## 1.9.2 + +### Extensions and API: +* restore 1.8.0-style naming of scripts + +## 1.9.1 + +### Minor: +* Add avif support ([#15582](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15582)) +* Add filename patterns: `[sampler_scheduler]` and `[scheduler]` ([#15581](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15581)) + +### Extensions and API: +* undo adding scripts to sys.modules +* Add schedulers API endpoint ([#15577](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15577)) +* Remove API upscaling factor limits ([#15560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15560)) + +### Bug Fixes: +* Fix images do not match / Coordinate 'right' is less than 'left' ([#15534](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15534)) +* fix: remove_callbacks_for_function should also remove from the ordered map ([#15533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15533)) +* fix x1 upscalers ([#15555](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15555)) +* Fix cls.__module__ value in extension script ([#15532](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15532)) +* fix typo in function call (eror -> error) ([#15531](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15531)) + +### Other: +* Hide 'No Image data blocks found.' message ([#15567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15567)) +* Allow webui.sh to be runnable from arbitrary directories containing a .git file ([#15561](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15561)) +* Compatibility with Debian 11, Fedora 34+ and openSUSE 15.4+ ([#15544](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15544)) +* numpy DeprecationWarning product -> prod ([#15547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15547)) +* get_crop_region_v2 ([#15583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15583), [#15587](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15587)) + + +## 1.9.0 + +### Features: +* Make refiner switchover based on model timesteps instead of sampling steps ([#14978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14978)) +* add an option to have old-style directory view instead of tree view; stylistic changes for extra network sorting/search controls +* add UI for reordering callbacks, support for specifying callback order in extension metadata ([#15205](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15205)) +* Sgm uniform scheduler for SDXL-Lightning models ([#15325](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15325)) +* Scheduler selection in main UI ([#15333](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15333), [#15361](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15361), [#15394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15394)) + +### Minor: +* "open images directory" button now opens the actual dir ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947)) +* Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973)) +* make extra network card description plaintext by default, with an option to re-enable HTML as it was +* resize handle for extra networks ([#15041](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15041)) +* cmd args: `--unix-filenames-sanitization` and `--filenames-max-length` ([#15031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15031)) +* show extra networks parameters in HTML table rather than raw JSON ([#15131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15131)) +* Add DoRA (weight-decompose) support for LoRA/LoHa/LoKr ([#15160](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15160), [#15283](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15283)) +* Add '--no-prompt-history' cmd args for disable last generation prompt history ([#15189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15189)) +* update preview on Replace Preview ([#15201](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15201)) +* only fetch updates for extensions' active git branches ([#15233](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15233)) +* put upscale postprocessing UI into an accordion ([#15223](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15223)) +* Support dragdrop for URLs to read infotext ([#15262](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15262)) +* use diskcache library for caching ([#15287](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15287), [#15299](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15299)) +* Allow PNG-RGBA for Extras Tab ([#15334](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15334)) +* Support cover images embedded in safetensors metadata ([#15319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15319)) +* faster interrupt when using NN upscale ([#15380](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15380)) +* Extras upscaler: an input field to limit maximul side length for the output image ([#15293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15293), [#15415](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15415), [#15417](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15417), [#15425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15425)) +* add an option to hide postprocessing options in Extras tab + +### Extensions and API: +* ResizeHandleRow - allow overriden column scale parametr ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004)) +* call script_callbacks.ui_settings_callback earlier; fix extra-options-section built-in extension killing the ui if using a setting that doesn't exist +* make it possible to use zoom.js outside webui context ([#15286](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15286), [#15288](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15288)) +* allow variants for extension name in metadata.ini ([#15290](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15290)) +* make reloading UI scripts optional when doing Reload UI, and off by default +* put request: gr.Request at start of img2img function similar to txt2img +* open_folder as util ([#15442](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15442)) +* make it possible to import extensions' script files as `import scripts.` ([#15423](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15423)) + +### Performance: +* performance optimization for extra networks HTML pages +* optimization for extra networks filtering +* optimization for extra networks sorting + +### Bug Fixes: +* prevent escape button causing an interrupt when no generation has been made yet +* [bug] avoid doble upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966)) +* possible fix for reload button not appearing in some cases for extra networks. +* fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006)) +* Fix resize-handle visability for vertical layout (mobile) ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010)) +* register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012)) +* Protect alphas_cumprod during refiner switchover ([#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979)) +* Fix EXIF orientation in API image loading ([#15062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15062)) +* Only override emphasis if actually used in prompt ([#15141](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15141)) +* Fix emphasis infotext missing from `params.txt` ([#15142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15142)) +* fix extract_style_text_from_prompt #15132 ([#15135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15135)) +* Fix Soft Inpaint for AnimateDiff ([#15148](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15148)) +* edit-attention: deselect surrounding whitespace ([#15178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15178)) +* chore: fix font not loaded ([#15183](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15183)) +* use natural sort in extra networks when ordering by path +* Fix built-in lora system bugs caused by torch.nn.MultiheadAttention ([#15190](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15190)) +* Avoid error from None in get_learned_conditioning ([#15191](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15191)) +* Add entry to MassFileLister after writing metadata ([#15199](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15199)) +* fix issue with Styles when Hires prompt is used ([#15269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15269), [#15276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15276)) +* Strip comments from hires fix prompt ([#15263](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15263)) +* Make imageviewer event listeners browser consistent ([#15261](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15261)) +* Fix AttributeError in OFT when trying to get MultiheadAttention weight ([#15260](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15260)) +* Add missing .mean() back ([#15239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15239)) +* fix "Restore progress" button ([#15221](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15221)) +* fix ui-config for InputAccordion [custom_script_source] ([#15231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15231)) +* handle 0 wheel deltaY ([#15268](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15268)) +* prevent alt menu for firefox ([#15267](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15267)) +* fix: fix syntax errors ([#15179](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15179)) +* restore outputs path ([#15307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15307)) +* Escape btn_copy_path filename ([#15316](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15316)) +* Fix extra networks buttons when filename contains an apostrophe ([#15331](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15331)) +* escape brackets in lora random prompt generator ([#15343](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15343)) +* fix: Python version check for PyTorch installation compatibility ([#15390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15390)) +* fix typo in call_queue.py ([#15386](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15386)) +* fix: when find already_loaded model, remove loaded by array index ([#15382](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15382)) +* minor bug fix of sd model memory management ([#15350](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15350)) +* Fix CodeFormer weight ([#15414](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15414)) +* Fix: Remove script callbacks in ordered_callbacks_map ([#15428](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15428)) +* fix limited file write (thanks, Sylwia) +* Fix extra-single-image API not doing upscale failed ([#15465](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15465)) +* error handling paste_field callables ([#15470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15470)) + +### Hardware: +* Add training support and change lspci for Ascend NPU ([#14981](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14981)) +* Update to ROCm5.7 and PyTorch ([#14820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14820)) +* Better workaround for Navi1, removing --pre for Navi3 ([#15224](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15224)) +* Ascend NPU wiki page ([#15228](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15228)) + +### Other: +* Update comment for Pad prompt/negative prompt v0 to add a warning about truncation, make it override the v1 implementation +* support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002)) +* Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995)) +* Use `absolute` path for normalized filepath ([#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035)) +* resizeHandle handle double tap ([#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065)) +* --dat-models-path cmd flag ([#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039)) +* Add a direct link to the binary release ([#15059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15059)) +* upscaler_utils: Reduce logging ([#15084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15084)) +* Fix various typos with crate-ci/typos ([#15116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15116)) +* fix_jpeg_live_preview ([#15102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15102)) +* [alternative fix] can't load webui if selected wrong extra option in ui ([#15121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15121)) +* Error handling for unsupported transparency ([#14958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14958)) +* Add model description to searched terms ([#15198](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15198)) +* bump action version ([#15272](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15272)) +* PEP 604 annotations ([#15259](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15259)) +* Automatically Set the Scale by value when user selects an Upscale Model ([#15244](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15244)) +* move postprocessing-for-training into builtin extensions ([#15222](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15222)) +* type hinting in shared.py ([#15211](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15211)) +* update ruff to 0.3.3 +* Update pytorch lightning utilities ([#15310](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15310)) +* Add Size as an XYZ Grid option ([#15354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15354)) +* Use HF_ENDPOINT variable for HuggingFace domain with default ([#15443](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15443)) +* re-add update_file_entry ([#15446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15446)) +* create_infotext allow index and callable, re-work Hires prompt infotext ([#15460](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15460)) +* update restricted_opts to include more options for --hide-ui-dir-config ([#15492](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15492)) + + +## 1.8.0 + +### Features: +* Update torch to version 2.1.2 +* Soft Inpainting ([#14208](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14208)) +* FP8 support ([#14031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14031), [#14327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14327)) +* Support for SDXL-Inpaint Model ([#14390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14390)) +* Use Spandrel for upscaling and face restoration architectures ([#14425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14425), [#14467](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14467), [#14473](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14473), [#14474](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14474), [#14477](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14477), [#14476](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14476), [#14484](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14484), [#14500](https://github.com/AUTOMATIC1111/stable-difusion-webui/pull/14500), [#14501](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14501), [#14504](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14504), [#14524](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14524), [#14809](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14809)) +* Automatic backwards version compatibility (when loading infotexts from old images with program version specified, will add compatibility settings) +* Implement zero terminal SNR noise schedule option (**[SEED BREAKING CHANGE](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Seed-breaking-changes#180-dev-170-225-2024-01-01---zero-terminal-snr-noise-schedule-option)**, [#14145](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14145), [#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979)) +* Add a [✨] button to run hires fix on selected image in the gallery (with help from [#14598](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14598), [#14626](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14626), [#14728](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14728)) +* [Separate assets repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets); serve fonts locally rather than from google's servers +* Official LCM Sampler Support ([#14583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14583)) +* Add support for DAT upscaler models ([#14690](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14690), [#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039)) +* Extra Networks Tree View ([#14588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14588), [#14900](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14900)) +* NPU Support ([#14801](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14801)) +* Prompt comments support + +### Minor: +* Allow pasting in WIDTHxHEIGHT strings into the width/height fields ([#14296](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14296)) +* add option: Live preview in full page image viewer ([#14230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14230), [#14307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14307)) +* Add keyboard shortcuts for generate/skip/interrupt ([#14269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14269)) +* Better TCMALLOC support on different platforms ([#14227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14227), [#14883](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14883), [#14910](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14910)) +* Lora not found warning ([#14464](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14464)) +* Adding negative prompts to Loras in extra networks ([#14475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14475)) +* xyz_grid: allow varying the seed along an axis separate from axis options ([#12180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12180)) +* option to convert VAE to bfloat16 (implementation of [#9295](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9295)) +* Better IPEX support ([#14229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14229), [#14353](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14353), [#14559](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14559), [#14562](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14562), [#14597](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14597)) +* Option to interrupt after current generation rather than immediately ([#13653](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13653), [#14659](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14659)) +* Fullscreen Preview control fading/disable ([#14291](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14291)) +* Finer settings freezing control ([#13789](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13789)) +* Increase Upscaler Limits ([#14589](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14589)) +* Adjust brush size with hotkeys ([#14638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14638)) +* Add checkpoint info to csv log file when saving images ([#14663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14663)) +* Make more columns resizable ([#14740](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14740), [#14884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14884)) +* Add an option to not overlay original image for inpainting for #14727 +* Add Pad conds v0 option to support same generation with DDIM as before 1.6.0 +* Add "Interrupting..." placeholder. +* Button for refresh extensions list ([#14857](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14857)) +* Add an option to disable normalization after calculating emphasis. ([#14874](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14874)) +* When counting tokens, also include enabled styles (can be disabled in settings to revert to previous behavior) +* Configuration for the [📂] button for image gallery ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947)) +* Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973)) +* support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002)) + +### Extensions and API: +* Removed packages from requirements: basicsr, gfpgan, realesrgan; as well as their dependencies: absl-py, addict, beautifulsoup4, future, gdown, grpcio, importlib-metadata, lmdb, lpips, Markdown, platformdirs, PySocks, soupsieve, tb-nightly, tensorboard-data-server, tomli, Werkzeug, yapf, zipp, soupsieve +* Enable task ids for API ([#14314](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14314)) +* add override_settings support for infotext API +* rename generation_parameters_copypaste module to infotext_utils +* prevent crash due to Script __init__ exception ([#14407](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14407)) +* Bump numpy to 1.26.2 ([#14471](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14471)) +* Add utility to inspect a model's dtype/device ([#14478](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14478)) +* Implement general forward method for all method in built-in lora ext ([#14547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14547)) +* Execute model_loaded_callback after moving to target device ([#14563](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14563)) +* Add self to CFGDenoiserParams ([#14573](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14573)) +* Allow TLS with API only mode (--nowebui) ([#14593](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14593)) +* New callback: postprocess_image_after_composite ([#14657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14657)) +* modules/api/api.py: add api endpoint to refresh embeddings list ([#14715](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14715)) +* set_named_arg ([#14773](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14773)) +* add before_token_counter callback and use it for prompt comments +* ResizeHandleRow - allow overridden column scale parameter ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004)) + +### Performance: +* Massive performance improvement for extra networks directories with a huge number of files in them in an attempt to tackle #14507 ([#14528](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14528)) +* Reduce unnecessary re-indexing extra networks directory ([#14512](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14512)) +* Avoid unnecessary `isfile`/`exists` calls ([#14527](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14527)) + +### Bug Fixes: +* fix multiple bugs related to styles multi-file support ([#14203](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14203), [#14276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14276), [#14707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14707)) +* Lora fixes ([#14300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14300), [#14237](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14237), [#14546](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14546), [#14726](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14726)) +* Re-add setting lost as part of e294e46 ([#14266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14266)) +* fix extras caption BLIP ([#14330](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14330)) +* include infotext into saved init image for img2img ([#14452](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14452)) +* xyz grid handle axis_type is None ([#14394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14394)) +* Update Added (Fixed) IPV6 Functionality When there is No Webui Argument Passed webui.py ([#14354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14354)) +* fix API thread safe issues of txt2img and img2img ([#14421](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14421)) +* handle selectable script_index is None ([#14487](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14487)) +* handle config.json failed to load ([#14525](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14525), [#14767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14767)) +* paste infotext cast int as float ([#14523](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14523)) +* Ensure GRADIO_ANALYTICS_ENABLED is set early enough ([#14537](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14537)) +* Fix logging configuration again ([#14538](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14538)) +* Handle CondFunc exception when resolving attributes ([#14560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14560)) +* Fix extras big batch crashes ([#14699](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14699)) +* Fix using wrong model caused by alias ([#14655](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14655)) +* Add # to the invalid_filename_chars list ([#14640](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14640)) +* Fix extension check for requirements ([#14639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14639)) +* Fix tab indexes are reset after restart UI ([#14637](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14637)) +* Fix nested manual cast ([#14689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14689)) +* Keep postprocessing upscale selected tab after restart ([#14702](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14702)) +* XYZ grid: filter out blank vals when axis is int or float type (like int axis seed) ([#14754](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14754)) +* fix CLIP Interrogator topN regex ([#14775](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14775)) +* Fix dtype error in MHA layer/change dtype checking mechanism for manual cast ([#14791](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14791)) +* catch load style.csv error ([#14814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14814)) +* fix error when editing extra networks card +* fix extra networks metadata failing to work properly when you create the .json file with metadata for the first time. +* util.walk_files extensions case insensitive ([#14879](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14879)) +* if extensions page not loaded, prevent apply ([#14873](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14873)) +* call the right function for token counter in img2img +* Fix the bugs that search/reload will disappear when using other ExtraNetworks extensions ([#14939](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14939)) +* Gracefully handle mtime read exception from cache ([#14933](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14933)) +* Only trigger interrupt on `Esc` when interrupt button visible ([#14932](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14932)) +* Disable prompt token counters option actually disables token counting rather than just hiding results. +* avoid double upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966)) +* Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995)) +* fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006)) +* Fix resize-handle for mobile ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010), [#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065)) + +### Other: +* Assign id for "extra_options". Replace numeric field with slider. ([#14270](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14270)) +* change state dict comparison to ref compare ([#14216](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14216)) +* Bump torch-rocm to 5.6/5.7 ([#14293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14293)) +* Base output path off data path ([#14446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14446)) +* reorder training preprocessing modules in extras tab ([#14367](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14367)) +* Remove `cleanup_models` code ([#14472](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14472)) +* only rewrite ui-config when there is change ([#14352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14352)) +* Fix lint issue from 501993eb ([#14495](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14495)) +* Update README.md ([#14548](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14548)) +* hires button, fix seeds () +* Logging: set formatter correctly for fallback logger too ([#14618](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14618)) +* Read generation info from infotexts rather than json for internal needs (save, extract seed from generated pic) ([#14645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14645)) +* improve get_crop_region ([#14709](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14709)) +* Bump safetensors' version to 0.4.2 ([#14782](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14782)) +* add tooltip create_submit_box ([#14803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14803)) +* extensions tab table row hover highlight ([#14885](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14885)) +* Always add timestamp to displayed image ([#14890](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14890)) +* Added core.filemode=false so doesn't track changes in file permission… ([#14930](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14930)) +* Normalize command-line argument paths ([#14934](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14934), [#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035)) +* Use original App Title in progress bar ([#14916](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14916)) +* register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012)) + +## 1.7.0 + +### Features: +* settings tab rework: add search field, add categories, split UI settings page into many +* add altdiffusion-m18 support ([#13364](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13364)) +* support inference with LyCORIS GLora networks ([#13610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13610)) +* add lora-embedding bundle system ([#13568](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13568)) +* option to move prompt from top row into generation parameters +* add support for SSD-1B ([#13865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13865)) +* support inference with OFT networks ([#13692](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13692)) +* script metadata and DAG sorting mechanism ([#13944](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13944)) +* support HyperTile optimization ([#13948](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13948)) +* add support for SD 2.1 Turbo ([#14170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14170)) +* remove Train->Preprocessing tab and put all its functionality into Extras tab +* initial IPEX support for Intel Arc GPU ([#14171](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14171)) + +### Minor: +* allow reading model hash from images in img2img batch mode ([#12767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12767)) +* add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818)) +* extra field for lora metadata viewer: `ss_output_name` ([#12838](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12838)) +* add action in settings page to calculate all SD checkpoint hashes ([#12909](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12909)) +* add button to copy prompt to style editor ([#12975](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12975)) +* add --skip-load-model-at-start option ([#13253](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13253)) +* write infotext to gif images +* read infotext from gif images ([#13068](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13068)) +* allow configuring the initial state of InputAccordion in ui-config.json ([#13189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13189)) +* allow editing whitespace delimiters for ctrl+up/ctrl+down prompt editing ([#13444](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13444)) +* prevent accidentally closing popup dialogs ([#13480](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13480)) +* added option to play notification sound or not ([#13631](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13631)) +* show the preview image in the full screen image viewer if available ([#13459](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13459)) +* support for webui.settings.bat ([#13638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13638)) +* add an option to not print stack traces on ctrl+c +* start/restart generation by Ctrl (Alt) + Enter ([#13644](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13644)) +* update prompts_from_file script to allow concatenating entries with the general prompt ([#13733](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13733)) +* added a visible checkbox to input accordion +* added an option to hide all txt2img/img2img parameters in an accordion ([#13826](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13826)) +* added 'Path' sorting option for Extra network cards ([#13968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13968)) +* enable prompt hotkeys in style editor ([#13931](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13931)) +* option to show batch img2img results in UI ([#14009](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14009)) +* infotext updates: add option to disregard certain infotext fields, add option to not include VAE in infotext, add explanation to infotext settings page, move some options to infotext settings page +* add FP32 fallback support on sd_vae_approx ([#14046](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046)) +* support XYZ scripts / split hires path from unet ([#14126](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14126)) +* allow use of multiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125)) +* make extra network card description plaintext by default, with an option (Treat card description as HTML) to re-enable HTML as it was (originally by [#13241](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13241)) + +### Extensions and API: +* update gradio to 3.41.2 +* support installed extensions list api ([#12774](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12774)) +* update pnginfo API to return dict with parsed values +* add noisy latent to `ExtraNoiseParams` for callback ([#12856](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12856)) +* show extension datetime in UTC ([#12864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12864), [#12865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12865), [#13281](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13281)) +* add an option to choose how to combine hires fix and refiner +* include program version in info response. ([#13135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13135)) +* sd_unet support for SDXL +* patch DDPM.register_betas so that users can put given_betas in model yaml ([#13276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13276)) +* xyz_grid: add prepare ([#13266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13266)) +* allow multiple localization files with same language in extensions ([#13077](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13077)) +* add onEdit function for js and rework token-counter.js to use it +* fix the key error exception when processing override_settings keys ([#13567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13567)) +* ability for extensions to return custom data via api in response.images ([#13463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13463)) +* call state.jobnext() before postproces*() ([#13762](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13762)) +* add option to set notification sound volume ([#13884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13884)) +* update Ruff to 0.1.6 ([#14059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14059)) +* add Block component creation callback ([#14119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14119)) +* catch uncaught exception with ui creation scripts ([#14120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14120)) +* use extension name for determining an extension is installed in the index ([#14063](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14063)) +* update is_installed() from launch_utils.py to fix reinstalling already installed packages ([#14192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14192)) + +### Bug Fixes: +* fix pix2pix producing bad results +* fix defaults settings page breaking when any of main UI tabs are hidden +* fix error that causes some extra networks to be disabled if both and are present in the prompt +* fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working +* prevent duplicate resize handler ([#12795](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12795)) +* small typo: vae resolve bug ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12797)) +* hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12792)) +* don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12780)) +* fix style editing dialog breaking if it's opened in both img2img and txt2img tabs +* hide --gradio-auth and --api-auth values from /internal/sysinfo report +* add missing infotext for RNG in options ([#12819](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12819)) +* fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834)) +* honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832)) +* don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12833), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855)) +* get progressbar to display correctly in extensions tab +* keep order in list of checkpoints when loading model that doesn't have a checksum +* fix inpainting models in txt2img creating black pictures +* fix generation params regex ([#12876](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12876)) +* fix batch img2img output dir with script ([#12926](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12926)) +* fix #13080 - Hypernetwork/TI preview generation ([#13084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13084)) +* fix bug with sigma min/max overrides. ([#12995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12995)) +* more accurate check for enabling cuDNN benchmark on 16XX cards ([#12924](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12924)) +* don't use multicond parser for negative prompt counter ([#13118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13118)) +* fix data-sort-name containing spaces ([#13412](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13412)) +* update card on correct tab when editing metadata ([#13411](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13411)) +* fix viewing/editing metadata when filename contains an apostrophe ([#13395](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13395)) +* fix: --sd_model in "Prompts from file or textbox" script is not working ([#13302](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13302)) +* better Support for Portable Git ([#13231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13231)) +* fix issues when webui_dir is not work_dir ([#13210](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13210)) +* fix: lora-bias-backup don't reset cache ([#13178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13178)) +* account for customizable extra network separators whyen removing extra network text from the prompt ([#12877](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12877)) +* re fix batch img2img output dir with script ([#13170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13170)) +* fix `--ckpt-dir` path separator and option use `short name` for checkpoint dropdown ([#13139](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13139)) +* consolidated allowed preview formats, Fix extra network `.gif` not woking as preview ([#13121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13121)) +* fix venv_dir=- environment variable not working as expected on linux ([#13469](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13469)) +* repair unload sd checkpoint button +* edit-attention fixes ([#13533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13533)) +* fix bug when using --gfpgan-models-path ([#13718](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13718)) +* properly apply sort order for extra network cards when selected from dropdown +* fixes generation restart not working for some users when 'Ctrl+Enter' is pressed ([#13962](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13962)) +* thread safe extra network list_items ([#13014](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13014)) +* fix not able to exit metadata popup when pop up is too big ([#14156](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14156)) +* fix auto focal point crop for opencv >= 4.8 ([#14121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14121)) +* make 'use-cpu all' actually apply to 'all' ([#14131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14131)) +* extras tab batch: actually use original filename +* make webui not crash when running with --disable-all-extensions option + +### Other: +* non-local condition ([#12814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12814)) +* fix minor typos ([#12827](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12827)) +* remove xformers Python version check ([#12842](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12842)) +* style: file-metadata word-break ([#12837](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12837)) +* revert SGM noise multiplier change for img2img because it breaks hires fix +* do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854)) +* [RC 1.6.0 - zoom is partly hidden] Update style.css ([#12839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12839)) +* chore: change extension time format ([#12851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12851)) +* WEBUI.SH - Use torch 2.1.0 release candidate for Navi 3 ([#12929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12929)) +* add Fallback at images.read_info_from_image if exif data was invalid ([#13028](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13028)) +* update cmd arg description ([#12986](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12986)) +* fix: update shared.opts.data when add_option ([#12957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12957), [#13213](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13213)) +* restore missing tooltips ([#12976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12976)) +* use default dropdown padding on mobile ([#12880](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12880)) +* put enable console prompts option into settings from commandline args ([#13119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13119)) +* fix some deprecated types ([#12846](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12846)) +* bump to torchsde==0.2.6 ([#13418](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13418)) +* update dragdrop.js ([#13372](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13372)) +* use orderdict as lru cache:opt/bug ([#13313](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13313)) +* XYZ if not include sub grids do not save sub grid ([#13282](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13282)) +* initialize state.time_start befroe state.job_count ([#13229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13229)) +* fix fieldname regex ([#13458](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13458)) +* change denoising_strength default to None. ([#13466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13466)) +* fix regression ([#13475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13475)) +* fix IndexError ([#13630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13630)) +* fix: checkpoints_loaded:{checkpoint:state_dict}, model.load_state_dict issue in dict value empty ([#13535](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13535)) +* update bug_report.yml ([#12991](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12991)) +* requirements_versions httpx==0.24.1 ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839)) +* fix parenthesis auto selection ([#13829](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13829)) +* fix #13796 ([#13797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13797)) +* corrected a typo in `modules/cmd_args.py` ([#13855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13855)) +* feat: fix randn found element of type float at pos 2 ([#14004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14004)) +* adds tqdm handler to logging_config.py for progress bar integration ([#13996](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13996)) +* hotfix: call shared.state.end() after postprocessing done ([#13977](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13977)) +* fix dependency address patch 1 ([#13929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13929)) +* save sysinfo as .json ([#14035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14035)) +* move exception_records related methods to errors.py ([#14084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14084)) +* compatibility ([#13936](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13936)) +* json.dump(ensure_ascii=False) ([#14108](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14108)) +* dir buttons start with / so only the correct dir will be shown and no… ([#13957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13957)) +* alternate implementation for unet forward replacement that does not depend on hijack being applied +* re-add `keyedit_delimiters_whitespace` setting lost as part of commit e294e46 ([#14178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14178)) +* fix `save_samples` being checked early when saving masked composite ([#14177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14177)) +* slight optimization for mask and mask_composite ([#14181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14181)) +* add import_hook hack to work around basicsr/torchvision incompatibility ([#14186](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14186)) + +## 1.6.1 + +### Bug Fixes: + * fix an error causing the webui to fail to start ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839)) + +## 1.6.0 + +### Features: + * refiner support [#12371](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) + * add NV option for Random number generator source setting, which allows to generate same pictures on CPU/AMD/Mac as on NVidia videocards + * add style editor dialog + * hires fix: add an option to use a different checkpoint for second pass ([#12181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12181)) + * option to keep multiple loaded models in memory ([#12227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12227)) + * new samplers: Restart, DPM++ 2M SDE Exponential, DPM++ 2M SDE Heun, DPM++ 2M SDE Heun Karras, DPM++ 2M SDE Heun Exponential, DPM++ 3M SDE, DPM++ 3M SDE Karras, DPM++ 3M SDE Exponential ([#12300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12300), [#12519](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12519), [#12542](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12542)) + * rework DDIM, PLMS, UniPC to use CFG denoiser same as in k-diffusion samplers: + * makes all of them work with img2img + * makes prompt composition possible (AND) + * makes them available for SDXL + * always show extra networks tabs in the UI ([#11808](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11808)) + * use less RAM when creating models ([#11958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11958), [#12599](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12599)) + * textual inversion inference support for SDXL + * extra networks UI: show metadata for SD checkpoints + * checkpoint merger: add metadata support + * prompt editing and attention: add support for whitespace after the number ([ red : green : 0.5 ]) (seed breaking change) ([#12177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12177)) + * VAE: allow selecting own VAE for each checkpoint (in user metadata editor) + * VAE: add selected VAE to infotext + * options in main UI: add own separate setting for txt2img and img2img, correctly read values from pasted infotext, add setting for column count ([#12551](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12551)) + * add resize handle to txt2img and img2img tabs, allowing to change the amount of horizontable space given to generation parameters and resulting image gallery ([#12687](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12687), [#12723](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12723)) + * change default behavior for batching cond/uncond -- now it's on by default, and is disabled by an UI setting (Optimizatios -> Batch cond/uncond) - if you are on lowvram/medvram and are getting OOM exceptions, you will need to enable it + * show current position in queue and make it so that requests are processed in the order of arrival ([#12707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12707)) + * add `--medvram-sdxl` flag that only enables `--medvram` for SDXL models + * prompt editing timeline has separate range for first pass and hires-fix pass (seed breaking change) ([#12457](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12457)) + +### Minor: + * img2img batch: RAM savings, VRAM savings, .tif, .tiff in img2img batch ([#12120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12120), [#12514](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12514), [#12515](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12515)) + * postprocessing/extras: RAM savings ([#12479](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12479)) + * XYZ: in the axis labels, remove pathnames from model filenames + * XYZ: support hires sampler ([#12298](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12298)) + * XYZ: new option: use text inputs instead of dropdowns ([#12491](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12491)) + * add gradio version warning + * sort list of VAE checkpoints ([#12297](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12297)) + * use transparent white for mask in inpainting, along with an option to select the color ([#12326](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12326)) + * move some settings to their own section: img2img, VAE + * add checkbox to show/hide dirs for extra networks + * Add TAESD(or more) options for all the VAE encode/decode operation ([#12311](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12311)) + * gradio theme cache, new gradio themes, along with explanation that the user can input his own values ([#12346](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12346), [#12355](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12355)) + * sampler fixes/tweaks: s_tmax, s_churn, s_noise, s_tmax ([#12354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12354), [#12356](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12356), [#12357](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12357), [#12358](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12358), [#12375](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12375), [#12521](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12521)) + * update README.md with correct instructions for Linux installation ([#12352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12352)) + * option to not save incomplete images, on by default ([#12338](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12338)) + * enable cond cache by default + * git autofix for repos that are corrupted ([#12230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12230)) + * allow to open images in new browser tab by middle mouse button ([#12379](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12379)) + * automatically open webui in browser when running "locally" ([#12254](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12254)) + * put commonly used samplers on top, make DPM++ 2M Karras the default choice + * zoom and pan: option to auto-expand a wide image, improved integration ([#12413](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12413), [#12727](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12727)) + * option to cache Lora networks in memory + * rework hires fix UI to use accordion + * face restoration and tiling moved to settings - use "Options in main UI" setting if you want them back + * change quicksettings items to have variable width + * Lora: add Norm module, add support for bias ([#12503](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12503)) + * Lora: output warnings in UI rather than fail for unfitting loras; switch to logging for error output in console + * support search and display of hashes for all extra network items ([#12510](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12510)) + * add extra noise param for img2img operations ([#12564](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12564)) + * support for Lora with bias ([#12584](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12584)) + * make interrupt quicker ([#12634](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12634)) + * configurable gallery height ([#12648](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12648)) + * make results column sticky ([#12645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12645)) + * more hash filename patterns ([#12639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12639)) + * make image viewer actually fit the whole page ([#12635](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12635)) + * make progress bar work independently from live preview display which results in it being updated a lot more often + * forbid Full live preview method for medvram and add a setting to undo the forbidding + * make it possible to localize tooltips and placeholders + * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818)) + * Restore faces and Tiling generation parameters have been moved to settings out of main UI + * if you want to put them back into main UI, use `Options in main UI` setting on the UI page. + +### Extensions and API: + * gradio 3.41.2 + * also bump versions for packages: transformers, GitPython, accelerate, scikit-image, timm, tomesd + * support tooltip kwarg for gradio elements: gr.Textbox(label='hello', tooltip='world') + * properly clear the total console progressbar when using txt2img and img2img from API + * add cmd_arg --disable-extra-extensions and --disable-all-extensions ([#12294](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12294)) + * shared.py and webui.py split into many files + * add --loglevel commandline argument for logging + * add a custom UI element that combines accordion and checkbox + * avoid importing gradio in tests because it spams warnings + * put infotext label for setting into OptionInfo definition rather than in a separate list + * make `StableDiffusionProcessingImg2Img.mask_blur` a property, make more inline with PIL `GaussianBlur` ([#12470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12470)) + * option to make scripts UI without gr.Group + * add a way for scripts to register a callback for before/after just a single component's creation + * use dataclass for StableDiffusionProcessing + * store patches for Lora in a specialized module instead of inside torch + * support http/https URLs in API ([#12663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12663), [#12698](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12698)) + * add extra noise callback ([#12616](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12616)) + * dump current stack traces when exiting with SIGINT + * add type annotations for extra fields of shared.sd_model + +### Bug Fixes: + * Don't crash if out of local storage quota for javascriot localStorage + * XYZ plot do not fail if an exception occurs + * fix missing TI hash in infotext if generation uses both negative and positive TI ([#12269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12269)) + * localization fixes ([#12307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12307)) + * fix sdxl model invalid configuration after the hijack + * correctly toggle extras checkbox for infotext paste ([#12304](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12304)) + * open raw sysinfo link in new page ([#12318](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12318)) + * prompt parser: Account for empty field in alternating words syntax ([#12319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12319)) + * add tab and carriage return to invalid filename chars ([#12327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12327)) + * fix api only Lora not working ([#12387](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12387)) + * fix options in main UI misbehaving when there's just one element + * make it possible to use a sampler from infotext even if it's hidden in the dropdown + * fix styles missing from the prompt in infotext when making a grid of batch of multiplie images + * prevent bogus progress output in console when calculating hires fix dimensions + * fix --use-textbox-seed + * fix broken `Lora/Networks: use old method` option ([#12466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12466)) + * properly return `None` for VAE hash when using `--no-hashing` ([#12463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12463)) + * MPS/macOS fixes and optimizations ([#12526](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12526)) + * add second_order to samplers that mistakenly didn't have it + * when refreshing cards in extra networks UI, do not discard user's custom resolution + * fix processing error that happens if batch_size is not a multiple of how many prompts/negative prompts there are ([#12509](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12509)) + * fix inpaint upload for alpha masks ([#12588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12588)) + * fix exception when image sizes are not integers ([#12586](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12586)) + * fix incorrect TAESD Latent scale ([#12596](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12596)) + * auto add data-dir to gradio-allowed-path ([#12603](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12603)) + * fix exception if extensuions dir is missing ([#12607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12607)) + * fix issues with api model-refresh and vae-refresh ([#12638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12638)) + * fix img2img background color for transparent images option not being used ([#12633](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12633)) + * attempt to resolve NaN issue with unstable VAEs in fp32 mk2 ([#12630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12630)) + * implement missing undo hijack for SDXL + * fix xyz swap axes ([#12684](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12684)) + * fix errors in backup/restore tab if any of config files are broken ([#12689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12689)) + * fix SD VAE switch error after model reuse ([#12685](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12685)) + * fix trying to create images too large for the chosen format ([#12667](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12667)) + * create Gradio temp directory if necessary ([#12717](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12717)) + * prevent possible cache loss if exiting as it's being written by using an atomic operation to replace the cache with the new version + * set devices.dtype_unet correctly + * run RealESRGAN on GPU for non-CUDA devices ([#12737](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) + * prevent extra network buttons being obscured by description for very small card sizes ([#12745](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12745)) + * fix error that causes some extra networks to be disabled if both and are present in the prompt + * fix defaults settings page breaking when any of main UI tabs are hidden + * fix incorrect save/display of new values in Defaults page in settings + * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working + * fix an error that prevents VAE being reloaded after an option change if a VAE near the checkpoint exists ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) + * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) + * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737)) + * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs + * fix a bug allowing users to bypass gradio and API authentication (reported by vysecurity) + * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834)) + * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832)) + * don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855)) + * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854)) + * get progressbar to display correctly in extensions tab + + +## 1.5.2 + +### Bug Fixes: + * fix memory leak when generation fails + * update doggettx cross attention optimization to not use an unreasonable amount of memory in some edge cases -- suggestion by MorkTheOrk + + +## 1.5.1 + +### Minor: + * support parsing text encoder blocks in some new LoRAs + * delete scale checker script due to user demand + +### Extensions and API: + * add postprocess_batch_list script callback + +### Bug Fixes: + * fix TI training for SD1 + * fix reload altclip model error + * prepend the pythonpath instead of overriding it + * fix typo in SD_WEBUI_RESTARTING + * if txt2img/img2img raises an exception, finally call state.end() + * fix composable diffusion weight parsing + * restyle Startup profile for black users + * fix webui not launching with --nowebui + * catch exception for non git extensions + * fix some options missing from /sdapi/v1/options + * fix for extension update status always saying "unknown" + * fix display of extra network cards that have `<>` in the name + * update lora extension to work with python 3.8 + + +## 1.5.0 + +### Features: + * SD XL support + * user metadata system for custom networks + * extended Lora metadata editor: set activation text, default weight, view tags, training info + * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension) + * show github stars for extensions + * img2img batch mode can read extra stuff from png info + * img2img batch works with subdirectories + * hotkeys to move prompt elements: alt+left/right + * restyle time taken/VRAM display + * add textual inversion hashes to infotext + * optimization: cache git extension repo information + * move generate button next to the generated picture for mobile clients + * hide cards for networks of incompatible Stable Diffusion version in Lora extra networks interface + * skip installing packages with pip if they all are already installed - startup speedup of about 2 seconds + +### Minor: + * checkbox to check/uncheck all extensions in the Installed tab + * add gradio user to infotext and to filename patterns + * allow gif for extra network previews + * add options to change colors in grid + * use natural sort for items in extra networks + * Mac: use empty_cache() from torch 2 to clear VRAM + * added automatic support for installing the right libraries for Navi3 (AMD) + * add option SWIN_torch_compile to accelerate SwinIR upscale + * suppress printing TI embedding info at start to console by default + * speedup extra networks listing + * added `[none]` filename token. + * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs) + * add always_discard_next_to_last_sigma option to XYZ plot + * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag. + +### Extensions and API: + * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop + * allow Script to have custom metaclass + * add model exists status check /sdapi/v1/options + * rename --add-stop-route to --api-server-stop + * add `before_hr` script callback + * add callback `after_extra_networks_activate` + * disable rich exception output in console for API by default, use WEBUI_RICH_EXCEPTIONS env var to enable + * return http 404 when thumb file not found + * allow replacing extensions index with environment variable + +### Bug Fixes: + * fix for catch errors when retrieving extension index #11290 + * fix very slow loading speed of .safetensors files when reading from network drives + * API cache cleanup + * fix UnicodeEncodeError when writing to file CLIP Interrogator batch mode + * fix warning of 'has_mps' deprecated from PyTorch + * fix problem with extra network saving images as previews losing generation info + * fix throwing exception when trying to resize image with I;16 mode + * fix for #11534: canvas zoom and pan extension hijacking shortcut keys + * fixed launch script to be runnable from any directory + * don't add "Seed Resize: -1x-1" to API image metadata + * correctly remove end parenthesis with ctrl+up/down + * fixing --subpath on newer gradio version + * fix: check fill size none zero when resize (fixes #11425) + * use submit and blur for quick settings textbox + * save img2img batch with images.save_image() + * prevent running preload.py for disabled extensions + * fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included + + +## 1.4.1 + +### Bug Fixes: + * add queue lock for refresh-checkpoints + +## 1.4.0 + +### Features: + * zoom controls for inpainting + * run basic torch calculation at startup in parallel to reduce the performance impact of first generation + * option to pad prompt/neg prompt to be same length + * remove taming_transformers dependency + * custom k-diffusion scheduler settings + * add an option to show selected settings in main txt2img/img2img UI + * sysinfo tab in settings + * infer styles from prompts when pasting params into the UI + * an option to control the behavior of the above + +### Minor: + * bump Gradio to 3.32.0 + * bump xformers to 0.0.20 + * Add option to disable token counters + * tooltip fixes & optimizations + * make it possible to configure filename for the zip download + * `[vae_filename]` pattern for filenames + * Revert discarding penultimate sigma for DPM-Solver++(2M) SDE + * change UI reorder setting to multiselect + * read version info form CHANGELOG.md if git version info is not available + * link footer API to Wiki when API is not active + * persistent conds cache (opt-in optimization) + +### Extensions: + * After installing extensions, webui properly restarts the process rather than reloads the UI + * Added VAE listing to web API. Via: /sdapi/v1/sd-vae + * custom unet support + * Add onAfterUiUpdate callback + * refactor EmbeddingDatabase.register_embedding() to allow unregistering + * add before_process callback for scripts + * add ability for alwayson scripts to specify section and let user reorder those sections + +### Bug Fixes: + * Fix dragging text to prompt + * fix incorrect quoting for infotext values with colon in them + * fix "hires. fix" prompt sharing same labels with txt2img_prompt + * Fix s_min_uncond default type int + * Fix for #10643 (Inpainting mask sometimes not working) + * fix bad styling for thumbs view in extra networks #10639 + * fix for empty list of optimizations #10605 + * small fixes to prepare_tcmalloc for Debian/Ubuntu compatibility + * fix --ui-debug-mode exit + * patch GitPython to not use leaky persistent processes + * fix duplicate Cross attention optimization after UI reload + * torch.cuda.is_available() check for SdOptimizationXformers + * fix hires fix using wrong conds in second pass if using Loras. + * handle exception when parsing generation parameters from png info + * fix upcast attention dtype error + * forcing Torch Version to 1.13.1 for RX 5000 series GPUs + * split mask blur into X and Y components, patch Outpainting MK2 accordingly + * don't die when a LoRA is a broken symlink + * allow activation of Generate Forever during generation + + +## 1.3.2 + +### Bug Fixes: + * fix files served out of tmp directory even if they are saved to disk + * fix postprocessing overwriting parameters + +## 1.3.1 + +### Features: + * revert default cross attention optimization to Doggettx + +### Bug Fixes: + * fix bug: LoRA don't apply on dropdown list sd_lora + * fix png info always added even if setting is not enabled + * fix some fields not applying in xyz plot + * fix "hires. fix" prompt sharing same labels with txt2img_prompt + * fix lora hashes not being added properly to infotex if there is only one lora + * fix --use-cpu failing to work properly at startup + * make --disable-opt-split-attention command line option work again + +## 1.3.0 + +### Features: + * add UI to edit defaults + * token merging (via dbolya/tomesd) + * settings tab rework: add a lot of additional explanations and links + * load extensions' Git metadata in parallel to loading the main program to save a ton of time during startup + * update extensions table: show branch, show date in separate column, and show version from tags if available + * TAESD - another option for cheap live previews + * allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings + * calculate hashes for Lora + * add lora hashes to infotext + * when pasting infotext, use infotext's lora hashes to find local loras for `` entries whose hashes match loras the user has + * select cross attention optimization from UI + +### Minor: + * bump Gradio to 3.31.0 + * bump PyTorch to 2.0.1 for macOS and Linux AMD + * allow setting defaults for elements in extensions' tabs + * allow selecting file type for live previews + * show "Loading..." for extra networks when displaying for the first time + * suppress ENSD infotext for samplers that don't use it + * clientside optimizations + * add options to show/hide hidden files and dirs in extra networks, and to not list models/files in hidden directories + * allow whitespace in styles.csv + * add option to reorder tabs + * move some functionality (swap resolution and set seed to -1) to client + * option to specify editor height for img2img + * button to copy image resolution into img2img width/height sliders + * switch from pyngrok to ngrok-py + * lazy-load images in extra networks UI + * set "Navigate image viewer with gamepad" option to false by default, by request + * change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...> + * allow hiding buttons in ui-config.json + +### Extensions: + * add /sdapi/v1/script-info api + * use Ruff to lint Python code + * use ESlint to lint Javascript code + * add/modify CFG callbacks for Self-Attention Guidance extension + * add command and endpoint for graceful server stopping + * add some locals (prompts/seeds/etc) from processing function into the Processing class as fields + * rework quoting for infotext items that have commas in them to use JSON (should be backwards compatible except for cases where it didn't work previously) + * add /sdapi/v1/refresh-loras api checkpoint post request + * tests overhaul + +### Bug Fixes: + * fix an issue preventing the program from starting if the user specifies a bad Gradio theme + * fix broken prompts from file script + * fix symlink scanning for extra networks + * fix --data-dir ignored when launching via webui-user.bat COMMANDLINE_ARGS + * allow web UI to be ran fully offline + * fix inability to run with --freeze-settings + * fix inability to merge checkpoint without adding metadata + * fix extra networks' save preview image not adding infotext for jpeg/webm + * remove blinking effect from text in hires fix and scale resolution preview + * make links to `http://<...>.git` extensions work in the extension tab + * fix bug with webui hanging at startup due to hanging git process + + +## 1.2.1 + +### Features: + * add an option to always refer to LoRA by filenames + +### Bug Fixes: + * never refer to LoRA by an alias if multiple LoRAs have same alias or the alias is called none + * fix upscalers disappearing after the user reloads UI + * allow bf16 in safe unpickler (resolves problems with loading some LoRAs) + * allow web UI to be ran fully offline + * fix localizations not working + * fix error for LoRAs: `'LatentDiffusion' object has no attribute 'lora_layer_mapping'` + +## 1.2.0 + +### Features: + * do not wait for Stable Diffusion model to load at startup + * add filename patterns: `[denoising]` + * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for + * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metadata of the file, if present, instead of filename (both can be used to activate LoRA) + * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active + * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer) + * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss) + * add version to infotext, footer and console output when starting + * add links to wiki for filename pattern settings + * add extended info for quicksettings setting and use multiselect input instead of a text field + +### Minor: + * bump Gradio to 3.29.0 + * bump PyTorch to 2.0.1 + * `--subpath` option for gradio for use with reverse proxy + * Linux/macOS: use existing virtualenv if already active (the VIRTUAL_ENV environment variable) + * do not apply localizations if there are none (possible frontend optimization) + * add extra `None` option for VAE in XYZ plot + * print error to console when batch processing in img2img fails + * create HTML for extra network pages only on demand + * allow directories starting with `.` to still list their models for LoRA, checkpoints, etc + * put infotext options into their own category in settings tab + * do not show licenses page when user selects Show all pages in settings + +### Extensions: + * tooltip localization support + * add API method to get LoRA models with prompt + +### Bug Fixes: + * re-add `/docs` endpoint + * fix gamepad navigation + * make the lightbox fullscreen image function properly + * fix squished thumbnails in extras tab + * keep "search" filter for extra networks when user refreshes the tab (previously it showed everything after you refreshed) + * fix webui showing the same image if you configure the generation to always save results into same file + * fix bug with upscalers not working properly + * fix MPS on PyTorch 2.0.1, Intel Macs + * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events + * prevent Reload UI button/link from reloading the page when it's not yet ready + * fix prompts from file script failing to read contents from a drag/drop file + + +## 1.1.1 +### Bug Fixes: + * fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle + +## 1.1.0 +### Features: + * switch to PyTorch 2.0.0 (except for AMD GPUs) + * visual improvements to custom code scripts + * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]` + * add support for saving init images in img2img, and record their hashes in infotext for reproducibility + * automatically select current word when adjusting weight with ctrl+up/down + * add dropdowns for X/Y/Z plot + * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs + * support Gradio's theme API + * use TCMalloc on Linux by default; possible fix for memory leaks + * add optimization option to remove negative conditioning at low sigma values #9177 + * embed model merge metadata in .safetensors file + * extension settings backup/restore feature #9169 + * add "resize by" and "resize to" tabs to img2img + * add option "keep original size" to textual inversion images preprocess + * image viewer scrolling via analog stick + * button to restore the progress from session lost / tab reload + +### Minor: + * bump Gradio to 3.28.1 + * change "scale to" to sliders in Extras tab + * add labels to tool buttons to make it possible to hide them + * add tiled inference support for ScuNET + * add branch support for extension installation + * change Linux installation script to install into current directory rather than `/home/username` + * sort textual inversion embeddings by name (case-insensitive) + * allow styles.csv to be symlinked or mounted in docker + * remove the "do not add watermark to images" option + * make selected tab configurable with UI config + * make the extra networks UI fixed height and scrollable + * add `disable_tls_verify` arg for use with self-signed certs + +### Extensions: + * add reload callback + * add `is_hr_pass` field for processing + +### Bug Fixes: + * fix broken batch image processing on 'Extras/Batch Process' tab + * add "None" option to extra networks dropdowns + * fix FileExistsError for CLIP Interrogator + * fix /sdapi/v1/txt2img endpoint not working on Linux #9319 + * fix disappearing live previews and progressbar during slow tasks + * fix fullscreen image view not working properly in some cases + * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it + * fix prompt schedule for second order samplers + * fix image mask/composite for weird resolutions #9628 + * use correct images for previews when using AND (see #9491) + * one broken image in img2img batch won't stop all processing + * fix image orientation bug in train/preprocess + * fix Ngrok recreating tunnels every reload + * fix `--realesrgan-models-path` and `--ldsr-models-path` not working + * fix `--skip-install` not working + * use SAMPLE file format in Outpainting Mk2 & Poorman + * do not fail all LoRAs if some have failed to load when making a picture + +## 1.0.0 + * everything diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 00000000000..2c781aff450 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,7 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it as below." +authors: + - given-names: AUTOMATIC1111 +title: "Stable Diffusion Web UI" +date-released: 2022-08-22 +url: "https://github.com/AUTOMATIC1111/stable-diffusion-webui" diff --git a/CODEOWNERS b/CODEOWNERS index 935fedcf231..7438c9bc69d 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -1 +1,12 @@ * @AUTOMATIC1111 + +# if you were managing a localization and were removed from this file, this is because +# the intended way to do localizations now is via extensions. See: +# https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions +# Make a repo with your localization and since you are still listed as a collaborator +# you can add it to the wiki page yourself. This change is because some people complained +# the git commit log is cluttered with things unrelated to almost everyone and +# because I believe this is the best overall for the project to handle localizations almost +# entirely without my oversight. + + diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 00000000000..14577543864 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,663 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (c) 2023 AUTOMATIC1111 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/README.md b/README.md index 859a91b6f6f..bc62945c0c5 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,7 @@ # Stable Diffusion web UI -A browser interface based on Gradio library for Stable Diffusion. +A web interface for Stable Diffusion, implemented using Gradio library. -![](txt2img_Screenshot.png) - -Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) wiki page for extra scripts developed by users. +![](screenshot.png) ## Features [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features): @@ -11,24 +9,26 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - One click install and run script (but you still must install python and git) - Outpainting - Inpainting +- Color Sketch - Prompt Matrix - Stable Diffusion Upscale - Attention, specify parts of text that the model should pay more attention to - - a man in a ((tuxedo)) - will pay more attention to tuxedo - - a man in a (tuxedo:1.21) - alternative syntax - - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user) + - a man in a `((tuxedo))` - will pay more attention to tuxedo + - a man in a `(tuxedo:1.21)` - alternative syntax + - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user) - Loopback, run img2img processing multiple times -- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters +- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters - Textual Inversion - have as many embeddings as you want and use any names you like for them - use multiple embeddings with different numbers of vectors per token - works with half precision floating point numbers + - train embeddings on 8GB (also reports of 6GB working) - Extras tab with: - GFPGAN, neural network that fixes faces - CodeFormer, face restoration tool as an alternative to GFPGAN - RealESRGAN, neural network upscaler - ESRGAN, neural network upscaler with a lot of third party models - - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers + - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers - LDSR, Latent diffusion super resolution upscaling - Resizing aspect ratio options - Sampling method selection @@ -37,21 +37,21 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - Interrupt processing at any time - 4GB video card support (also reports of 2GB working) - Correct seeds for batches -- Prompt length validation - - get length of prompt in tokens as you type - - get a warning after generation if some text was truncated +- Live prompt token length validation - Generation parameters - parameters you used to generate images are saved with that image - in PNG chunks for PNG, in EXIF for JPEG - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI - can be disabled in settings + - drag and drop an image/text-parameters to promptbox +- Read Generation Parameters Button, loads parameters in promptbox to UI - Settings page -- Running arbitrary python code from UI (must run with --allow-code to enable) +- Running arbitrary python code from UI (must run with `--allow-code` to enable) - Mouseover hints for most UI elements - Possible to change defaults/mix/max/step values for UI elements via text config -- Random artist button - Tiling support, a checkbox to create images that can be tiled like textures - Progress bar and live image generation preview + - Can use a separate neural network to produce previews with almost none VRAM or compute requirement - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image - Styles, a way to save part of prompt and easily apply them via dropdown later - Variations, a way to generate same image but with tiny differences @@ -59,49 +59,103 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - CLIP interrogator, a button that tries to guess prompt from an image - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway - Batch Processing, process a group of files using img2img -- Img2img Alternative +- Img2img Alternative, reverse Euler method of cross attention control - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly -- Checkpoint Merger, a tab that allows you to merge two checkpoints into one +- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once - separate prompts using uppercase `AND` - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2` - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) -- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args) -- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) +- DeepDanbooru integration, creates danbooru style tags for anime prompts +- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args) +- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI +- Generate forever option +- Training tab + - hypernetworks and embeddings options + - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) +- Clip skip +- Hypernetworks +- Loras (same as Hypernetworks but more pretty) +- A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt +- Can select to load a different VAE from settings screen +- Estimated completion time in progress bar +- API +- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML +- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) +- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions +- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions +- Now without any bad letters! +- Load checkpoints in safetensors format +- Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64 +- Now with a license! +- Reorder elements in the UI from settings screen +- [Segmind Stable Diffusion](https://huggingface.co/segmind/SSD-1B) support ## Installation and Running -Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. +Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for: +- [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) +- [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. +- [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page) +- [Ascend NPUs](https://github.com/wangshuai09/stable-diffusion-webui/wiki/Install-and-run-on-Ascend-NPUs) (external wiki page) + +Alternatively, use online services (like Google Colab): -Alternatively, use Google Colab: +- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) -- [Colab, maintained by Akaibu](https://colab.research.google.com/drive/1kw3egmSn-KgWsikYvOMjJkVDsPLjEMzl) -- [Colab, original by me, outdated](https://colab.research.google.com/drive/1Iy-xW9t1-OQWhb0hNxueGij8phCyluOh). +### Installation on Windows 10/11 with NVidia-GPUs using release package +1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract its contents. +2. Run `update.bat`. +3. Run `run.bat`. +> For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) ### Automatic Installation on Windows -1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH" +1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH". 2. Install [git](https://git-scm.com/download/win). 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`. -4. Place `model.ckpt` in the `models` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it). -5. _*(Optional)*_ Place `GFPGANv1.4.pth` in the base directory, alongside `webui.py` (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it). -6. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. +4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. ### Automatic Installation on Linux 1. Install the dependencies: ```bash # Debian-based: -sudo apt install wget git python3 python3-venv +sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0 # Red Hat-based: -sudo dnf install wget git python3 +sudo dnf install wget git python3 gperftools-libs libglvnd-glx +# openSUSE-based: +sudo zypper install wget git python3 libtcmalloc4 libglvnd # Arch-based: sudo pacman -S wget git python3 ``` -2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run: +If your system is very new, you need to install python3.11 or python3.10: +```bash +# Ubuntu 24.04 +sudo add-apt-repository ppa:deadsnakes/ppa +sudo apt update +sudo apt install python3.11 + +# Manjaro/Arch +sudo pacman -S yay +yay -S python311 # do not confuse with python3.11 package + +# Only for 3.11 +# Then set up env variable in launch script +export python_cmd="python3.11" +# or in webui-user.sh +python_cmd="python3.11" +``` +2. Navigate to the directory you would like the webui to be installed and execute the following command: ```bash -bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh) +wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh +``` +Or just clone the repo wherever you want: +```bash +git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui ``` +3. Run `webui.sh`. +4. Check `webui-user.sh` for options. ### Installation on Apple Silicon Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). @@ -110,26 +164,42 @@ Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-w Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) ## Documentation + The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). +For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki). + ## Credits -- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers +Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file. + +- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers, https://github.com/mcmonkey4eva/sd3-ref - k-diffusion - https://github.com/crowsonkb/k-diffusion.git -- GFPGAN - https://github.com/TencentARC/GFPGAN.git -- CodeFormer - https://github.com/sczhou/CodeFormer -- ESRGAN - https://github.com/xinntao/ESRGAN -- SwinIR - https://github.com/JingyunLiang/SwinIR -- Swin2SR - https://github.com/mv-lab/swin2sr +- Spandrel - https://github.com/chaiNNer-org/spandrel implementing + - GFPGAN - https://github.com/TencentARC/GFPGAN.git + - CodeFormer - https://github.com/sczhou/CodeFormer + - ESRGAN - https://github.com/xinntao/ESRGAN + - SwinIR - https://github.com/JingyunLiang/SwinIR + - Swin2SR - https://github.com/mv-lab/swin2sr - LDSR - https://github.com/Hafiidz/latent-diffusion +- MiDaS - https://github.com/isl-org/MiDaS - Ideas for optimizations - https://github.com/basujindal/stable-diffusion -- Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. -- InvokeAI, lstein - Cross Attention layer optimization - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) -- Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). +- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. +- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) +- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention) +- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - xformers - https://github.com/facebookresearch/xformers - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru +- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6) +- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix +- Security advice - RyotaK +- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC +- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd +- LyCORIS - KohakuBlueleaf +- Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling +- Hypertile - tfernd - https://github.com/tfernd/HyperTile - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You) diff --git a/_typos.toml b/_typos.toml new file mode 100644 index 00000000000..1c63fe70331 --- /dev/null +++ b/_typos.toml @@ -0,0 +1,5 @@ +[default.extend-words] +# Part of "RGBa" (Pillow's pre-multiplied alpha RGB mode) +Ba = "Ba" +# HSA is something AMD uses for their GPUs +HSA = "HSA" diff --git a/artists.csv b/artists.csv deleted file mode 100644 index 99cdbdc6099..00000000000 --- a/artists.csv +++ /dev/null @@ -1,3048 +0,0 @@ -artist,score,category -Peter Max,0.99715996,weird -Roy Lichtenstein,0.98272276,cartoon -Romero Britto,0.9498342,scribbles -Keith Haring,0.9431302,weird -Hiroshige,0.93995106,ukioe -Joan Miró,0.9169429,scribbles -Jean-Michel Basquiat,0.90080947,scribbles -Katsushika Hokusai,0.8887236,ukioe -Paul Klee,0.8868682,scribbles -Marc Chagall,0.8868168,scribbles -Karl Schmidt-Rottluff,0.88444495,scribbles -Howard Hodgkin,0.8808578,scribbles -Jean Metzinger,0.88056004,scribbles -Alma Thomas,0.87658304,weird -Rufino Tamayo,0.8749848,scribbles -Utagawa Hiroshige,0.8728796,ukioe -Chagall,0.8718535,scribbles -Harumi Hironaka,0.86914605,scribbles -Hans Hofmann,0.8686159,scribbles -Kawanabe Kyōsai,0.86612236,ukioe -Andy Warhol,0.8654825,scribbles -Barbara Takenaga,0.86223894,scribbles -Tatsuro Kiuchi,0.8597267,cartoon -Vincent Van Gogh,0.85538065,scribbles -Wassily Kandinsky,0.85490596,scribbles -Georges Seurat,0.8534801,scribbles -Karel Appel,0.8529153,scribbles -Sonia Delaunay,0.8506156,scribbles -Hokusai,0.85046995,ukioe -Eduardo Kobra,0.85036755,weird -Fra Angelico,0.84984255,fineart -Milton Avery,0.849746,scribbles -David Hockney,0.8496144,scribbles -Hiroshi Nagai,0.847129,cartoon -Aristarkh Lentulov,0.846537,scribbles -Lyonel Feininger,0.84573764,scribbles -Mary Blair,0.845709,scribbles -Ellsworth Kelly,0.8455428,scribbles -Jun Kaneko,0.8448367,scribbles -Roz Chast,0.8432013,weird -Ida Rentoul Outhwaite,0.84275174,scribbles -Robert Motherwell,0.8409468,scribbles -Garry Winogrand,0.83994275,black-white -Andrei Rublev,0.83950496,fineart -Alexander Calder,0.83832693,scribbles -Tomokazu Matsuyama,0.8376121,scribbles -August Macke,0.8362022,scribbles -Kazimir Malevich,0.8356527,scribbles -Richard Scarry,0.83554685,scribbles -Victor Vasarely,0.8335438,scribbles -Kitagawa Utamaro,0.83333457,ukioe -Matt Bors,0.83252287,scribbles -Emil Nolde,0.8323225,scribbles -Patrick Caulfield,0.8322225,scribbles -Charles Blackman,0.83200824,scribbles -Peter Doig,0.83111644,scribbles -Alexej von Jawlensky,0.8308932,scribbles -Rumiko Takahashi,0.8301817,anime -Eileen Agar,0.82945526,scribbles -Ernst Ludwig Kirchner,0.82756275,scribbles -Nicolas Delort,0.8261329,scribbles -Marsden Hartley,0.8250993,scribbles -Keith Negley,0.8212553,scribbles -Jamini Roy,0.8212199,scribbles -Quentin Blake,0.82115215,scribbles -Andy Kehoe,0.82063186,cartoon -George barbier,0.82046914,fineart -Frans Masereel,0.81997275,scribbles -Umberto Boccioni,0.81921184,scribbles -Conrad Roset,0.8190752,cartoon -Paul Ranson,0.81903255,scribbles -Yayoi Kusama,0.81886625,weird -Tomi Ungerer,0.81848705,scribbles -Saul Steinberg,0.81778854,scribbles -Jon Klassen,0.81773067,scribbles -W.W. Denslow,0.81708044,fineart -Helen Frankenthaler,0.81704986,scribbles -Jean Jullien,0.816437,scribbles -Brett Whiteley,0.81601924,scribbles -Giotto Di Bondone,0.81427747,fineart -Takashi Murakami,0.81338763,weird -Howard Finster,0.81333554,scribbles -Eduardo Paolozzi,0.81312317,scribbles -Charles Rennie Mackintosh,0.81297064,scribbles -Brandon Mably,0.8128239,weird -Rebecca Louise Law,0.81214285,weird -Victo Ngai,0.81195843,cartoon -Hanabusa Itchō II,0.81187993,ukioe -Edmund Dulac,0.81104875,scribbles -Ben Shahn,0.8104582,scribbles -Howard Arkley,0.8103746,scribbles -Wilfredo Lam,0.8096211,scribbles -Michael Deforge,0.8095954,scribbles -John Hoyland,0.8094592,fineart -Francesco Clemente,0.8090387,scribbles -Leonetto Cappiello,0.8087691,scribbles -Norman Ackroyd,0.80788493,scribbles -Bhupen Khakhar,0.8077607,scribbles -Jeremiah Ketner,0.8075384,cartoon -Chris Ofili,0.8073793,scribbles -Banksy,0.80695426,scribbles -Tom Whalen,0.805867,scribbles -Ernst Wilhelm Nay,0.805295,scribbles -Henri Rousseau,0.8049866,scribbles -Kunisada,0.80493814,ukioe -Naoko Takeuchi,0.80482674,anime -Kaethe Butcher,0.80406916,scribbles -Hasui Kawase,0.8040483,ukioe -Alvin Langdon Coburn,0.8035004,black-white -Stanley Donwood,0.8033054,scribbles -Agnes Martin,0.8028028,scribbles -Osamu Tezuka,0.8005524,cartoon -Frank Stella,0.80049455,scribbles -Dale Chihuly,0.79982775,digipa-high-impact -Evgeni Gordiets,0.79967916,scribbles -Janek Sedlar,0.7993992,fineart -Alasdair Gray,0.7992301,scribbles -Yasuo Kuniyoshi,0.79870003,ukioe -Edward Gorey,0.7984938,scribbles -Johannes Itten,0.798481,scribbles -Cuno Amiet,0.7979497,scribbles -M.C. 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Bridgman,0.5592567,fineart -Piet Mondrian,0.71990657,scribbles -Michelangelo Merisi Da Caravaggio,0.7094674,fineart -Marie Spartali Stillman,0.71986604,fineart -Gertrude Abercrombie,0.7196962,scribbles -Louis Icart,0.7195913,fineart -David Driskell,0.719564,scribbles -Paula Modersohn-Becker,0.7193769,scribbles -George Hurrell,0.57496595,digipa-high-impact -Andrea Mantegna,0.7190254,fineart -Silvestro Lega,0.71891177,fineart -Junji Ito,0.7188978,anime -Jacob Hashimoto,0.7186867,digipa-high-impact -Benjamin West,0.6642946,fineart -David Teniers the Elder,0.7181293,fineart -Roberto Matta,0.71808386,fineart -Chiho Aoshima,0.71801454,anime -Amedeo Modigliani,0.71788836,scribbles -Raja Ravi Varma,0.71788085,fineart -Roberto Ferri,0.538221,nudity -Winslow Homer,0.7176876,fineart -Horace Vernet,0.65729,fineart -Lucas Cranach the Elder,0.71738195,fineart -Godfried Schalcken,0.625893,fineart -Affandi,0.7170285,nudity -Diane Arbus,0.655138,digipa-high-impact -Joseph Ducreux,0.65247905,digipa-high-impact -Berthe Morisot,0.7165984,fineart -Hilma AF Klint,0.71643853,scribbles -Hilma af Klint,0.71643853,scribbles -Filippino Lippi,0.7163017,fineart -Leonid Afremov,0.7163005,fineart -Chris Ware,0.71628594,scribbles -Marius Borgeaud,0.7162446,scribbles -M.W. 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Hendricks,0.69986427,n -Yoshiyuki Tomino,0.6998095,anime -Guido Reni,0.6416875,fineart -Lynd Ward,0.69958556,fineart -John Constable,0.6907788,fineart -František Kupka,0.6993329,fineart -Pieter Bruegel The Elder,0.6992879,scribbles -Benjamin Gerritsz Cuyp,0.6992173,fineart -Nicolas Mignard,0.6988214,fineart -Augustus Edwin Mulready,0.6482165,fineart -Andrea del Sarto,0.698532,fineart -Edward Steichen,0.69837445,black-white -James Abbott McNeill Whistler,0.69836813,fineart -Alphonse Legros,0.6983243,fineart -Ivan Aivazovsky,0.64588225,fineart -Giovanni Francesco Barbieri,0.6981316,fineart -Grace Cossington Smith,0.69811064,fineart -Bert Stern,0.53411555,scribbles -Mary Cassatt,0.6980135,fineart -Jules Bastien-Lepage,0.69796044,fineart -Max Ernst,0.69777006,fineart -Kentaro Miura,0.697743,anime -Georges Rouault,0.69758564,scribbles -Josephine Wall,0.6973667,fineart -Anne-Louis Girodet,0.58104825,nudity -Bert Hardy,0.6972966,black-white -Adriaen van de Velde,0.69716156,fineart -Andreas 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Solomon,0.6920825,fineart -Georges Lacombe,0.6944455,fineart -Gwen John,0.6944161,fineart -Terada Katsuya,0.6944026,cartoon -James Gillray,0.6871335,fineart -Robert Crumb,0.69420326,fineart -Bruce Pennington,0.6545669,fineart -David Firth,0.69400465,scribbles -Arthur Boyd,0.69399726,fineart -Antonin Artaud,0.67321455,fineart -Giuseppe Arcimboldo,0.6937329,fineart -Jim Mahfood,0.6936606,cartoon -Ossip Zadkine,0.6494374,scribbles -Atelier Olschinsky,0.69349927,fineart -Carl Frederik von Breda,0.57274634,fineart -Ken Sugimori,0.6932626,anime -Chris Friel,0.5399168,fineart -Andrew Macara,0.69307995,fineart -Alexander Jansson,0.69298327,scribbles -Anne Brigman,0.6865817,black-white -George Ault,0.66756654,fineart -Arkhyp Kuindzhi,0.6928072,digipa-high-impact -Emiliano Ponzi,0.69278395,scribbles -William Holman Hunt,0.6927663,fineart -Tamara Lempicka,0.6386007,scribbles -Mark Ryden,0.69259655,fineart -Giovanni Paolo Pannini,0.6802902,fineart -Carl Barks,0.6923666,cartoon -Fritz 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Paschke,0.68974686,scribbles -Guy Rose,0.68960273,fineart -Barbara Hepworth,0.68958247,fineart -Edward Henry Potthast,0.6895703,fineart -Francis Bacon,0.6895397,scribbles -Pawel Kuczynski,0.6894536,fineart -Bjarke Ingels,0.68933153,digipa-high-impact -Henry Ossawa Tanner,0.68932164,fineart -Alessandro Allori,0.6892961,fineart -Abraham van Calraet,0.63841593,fineart -Egon Schiele,0.6891415,scribbles -Tim Doyle,0.5474768,digipa-high-impact -Grandma Moses,0.6890782,fineart -John Frederick Kensett,0.61981744,fineart -Giacomo Balla,0.68893707,fineart -Jamie Baldridge,0.6546651,digipa-high-impact -Max Beckmann,0.6884731,scribbles -Cornelis van Haarlem,0.6677613,fineart -Edward Hopper,0.6884258,special -Barkley Hendricks,0.6883637,n -Patrick Dougherty,0.688321,digipa-high-impact -Karol Bak,0.6367705,fineart -Pierre Puvis de Chavannes,0.6880703,fineart -Antoni Tàpies,0.685689,fineart -Alexander Nasmyth,0.57695735,fineart -Laurent Grasso,0.5793272,fineart -Camille Walala,0.6076875,digipa-high-impact -Fairfield Porter,0.68790644,fineart -Alex Colville,0.68787855,fineart -Herb Ritts,0.51471305,scribbles -Gerhard Munthe,0.687658,fineart -Susan Seddon Boulet,0.68762136,scribbles -Liu Ye,0.68760437,fineart -Robert Antoine Pinchon,0.68744636,fineart -Fujiwara Nobuzane,0.6873439,fineart -Frederick Carl Frieseke,0.6873361,fineart -Aert van der Neer,0.6159286,fineart -Allen Jones,0.6869935,scribbles -Anja Millen,0.6064488,digipa-high-impact -Esaias van de Velde,0.68673944,fineart -Gyoshū Hayami,0.68665624,anime -William Hogarth,0.6720842,fineart -Frederic Church,0.6865637,fineart -Cyril Rolando,0.68644965,cartoon -Frederic Edwin Church,0.6863009,fineart -Thomas Rowlandson,0.66726154,fineart -Joachim Brohm,0.68601763,digipa-high-impact -Cristofano Allori,0.6858083,fineart -Adrianus Eversen,0.58259964,fineart -Richard Dadd,0.68546164,fineart -Ambrosius Bosschaert II,0.6854217,fineart -Paolo Veronese,0.68422073,fineart -Abraham van den 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Tan,0.6830649,fineart -Anthony van Dyck,0.6577681,fineart -Neil Welliver,0.68297863,nudity -Robert McCall,0.68294585,fineart -Sandra Chevrier,0.68284667,scribbles -Yinka Shonibare,0.68256056,n -Arthur Tress,0.6301861,digipa-high-impact -Richard McGuire,0.6820089,scribbles -Anni Albers,0.65708244,digipa-high-impact -Aleksey Savrasov,0.65207493,fineart -Wayne Barlowe,0.6537874,fineart -Giorgio De Chirico,0.6815907,fineart -Giorgio de Chirico,0.6815907,fineart -Ernest Procter,0.6815795,fineart -Adriaen Brouwer,0.6815058,fineart -Ilya Glazunov,0.6813533,fineart -Alison Bechdel,0.68096143,scribbles -Carl Holsoe,0.68082225,fineart -Alfred Edward Chalon,0.6464571,fineart -Gerard David,0.68058,fineart -Basil Blackshaw,0.6805679,fineart -Gerrit Adriaenszoon Berckheyde,0.67340267,fineart -George Hendrik Breitner,0.6804209,fineart -Abraham Bloemaert,0.68036544,fineart -Ferdinand Van Kessel,0.67742276,fineart -Hugo Simberg,0.68031186,fineart -Gaston Bussière,0.665221,fineart -Shawn 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Bartlett,0.67947805,fineart -David A Hardy,0.5554935,fineart -C. R. W. Nevinson,0.67946506,fineart -Man Ray,0.6507145,scribbles -Albert Bierstadt,0.67935765,fineart -Charles Le Brun,0.6758479,fineart -Lovis Corinth,0.67913896,fineart -Herbert Abrams,0.5507507,digipa-high-impact -Giorgio Morandi,0.6789025,fineart -Agnolo Bronzino,0.6787985,fineart -Abraham Pether,0.66922426,fineart -John Bauer,0.6786695,fineart -Arthur Stanley Wilkinson,0.67860866,fineart -Arthur Wardle,0.5510789,fineart -George Romney,0.62868094,fineart -Laurie Lipton,0.5201844,fineart -Mickalene Thomas,0.45433685,digipa-low-impact -Alice Rahon,0.6777824,scribbles -Gustave Van de Woestijne,0.6777346,scribbles -Laurel Burch,0.67766285,fineart -Hendrik Gerritsz Pot,0.67750573,fineart -John William Waterhouse,0.677472,fineart -Conor Harrington,0.5967809,fineart -Gabriel Ba,0.6773366,cartoon -Franz Xaver Winterhalter,0.62229514,fineart -George Cruikshank,0.6473593,fineart -Hyacinthe Rigaud,0.67717785,fineart -Cornelis Claesz van Wieringen,0.6770269,fineart -Adriaen van 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Giger,0.6456823,weird -Adolph Menzel,0.6455246,fineart -Aliza Razell,0.5863178,digipa-high-impact -Gerard Seghers,0.6205679,fineart -David Aja,0.62812066,scribbles -Gustave Courbet,0.64476407,fineart -Alexandre Cabanel,0.63849115,fineart -Albert Marquet,0.64471006,fineart -Harold Harvey,0.64464307,fineart -William Wegman,0.6446265,scribbles -Harold Gilman,0.6445966,fineart -Jeremy Geddes,0.57839495,digipa-high-impact -Abraham van Beijeren,0.6356113,fineart -Eugène Isabey,0.6160607,fineart -Jorge Jacinto,0.58618563,fineart -Frederic Leighton,0.64383554,fineart -Dave McKean,0.6438012,cartoon -Hiromu Arakawa,0.64371413,anime -Aaron Douglas,0.6437089,fineart -Adolf Dietrich,0.590169,fineart -Frederik de Moucheron,0.6435952,fineart -Siya Oum,0.6435919,cartoon -Alberto Morrocco,0.64352196,fineart -Robert Vonnoh,0.6433115,fineart -Tom Bagshaw,0.5322264,fineart -Guerrilla Girls,0.64309967,digipa-high-impact -Johann Wolfgang von Goethe,0.6429888,fineart -Charles Le Roux,0.6426594,fineart -Auguste Toulmouche,0.64261353,fineart -Cindy Sherman,0.58666563,digipa-high-impact -Federico Zuccari,0.6425021,fineart -Mike Mignola,0.642346,cartoon -Cecily Brown,0.6421981,fineart -Brian K. Vaughan,0.64147836,cartoon -RETNA (Marquis Lewis),0.47963,n -Klaus Janson,0.64129144,cartoon -Alessandro Galli Bibiena,0.6412889,fineart -Jeremy Lipking,0.64123213,fineart -Stephen Shore,0.64108944,digipa-high-impact -Heinz Edelmann,0.51325977,digipa-med-impact -Joaquín Sorolla,0.6409732,fineart -Bella Kotak,0.6409608,digipa-high-impact -Cornelis Engebrechtsz,0.64091057,fineart -Bruce Munro,0.64084166,digipa-high-impact -Marjane Satrapi,0.64076495,fineart -Jeremy Mann,0.557744,digipa-high-impact -Heinrich Maria Davringhausen,0.6403986,fineart -Kengo Kuma,0.6402023,digipa-high-impact -Alfred Manessier,0.640153,fineart -Antonio Galli Bibiena,0.6399247,digipa-high-impact -Eduard von Grützner,0.6397164,fineart -Bunny Yeager,0.5455078,digipa-high-impact -Adolphe Willette,0.6396935,fineart -Wangechi Mutu,0.6394607,n -Peter Milligan,0.6391612,digipa-high-impact -Dalí,0.45400402,digipa-low-impact -Élisabeth Vigée Le Brun,0.6388982,fineart -Beth Conklin,0.6388204,digipa-high-impact -Charles Alphonse du Fresnoy,0.63881266,fineart -Thomas Benjamin Kennington,0.56668127,fineart -Jim Woodring,0.5625168,fineart -Francisco Oller,0.63846034,fineart -Csaba Markus,0.6384506,fineart -Botero,0.63843524,scribbles -Bill Henson,0.5394536,digipa-high-impact -Anna Bocek,0.6382304,scribbles -Hugo van der Goes,0.63822484,fineart -Robert William Hume,0.5433574,fineart -Chip Zdarsky,0.6381826,cartoon -Daniel Seghers,0.53494316,fineart -Richard Doyle,0.6377541,fineart -Hendrick Terbrugghen,0.63773805,fineart -Joe Madureira,0.6377177,special -Floris van Schooten,0.6376191,fineart -Jeff Simpson,0.3959046,fineart -Albert Joseph Moore,0.6374316,fineart -Arthur Merric Boyd,0.6373228,fineart -Amadeo de Souza Cardoso,0.5927926,fineart -Os Gemeos,0.6368859,digipa-high-impact -Giovanni Boldini,0.6368698,fineart -Albert Goodwin,0.6368695,fineart -Hans Eduard von Berlepsch-Valendas,0.61562145,fineart -Edmond Xavier Kapp,0.5758474,fineart -François Quesnel,0.6365935,fineart -Nathan Coley,0.6365817,digipa-high-impact -Jasmine Becket-Griffith,0.6365083,digipa-high-impact -Raphaelle Peale,0.6364422,fineart -Candido Portinari,0.63634276,fineart -Edward Dugmore,0.63179636,fineart -Anders Zorn,0.6361722,fineart -Ed Emshwiller,0.63615763,fineart -Francis Coates Jones,0.6361159,fineart -Ernst Haas,0.6361123,digipa-high-impact -Dirck van Baburen,0.6213001,fineart -René Lalique,0.63594735,fineart -Sydney Prior Hall,0.6359345,fineart -Brad Kunkle,0.5659712,fineart -Corneille,0.6356381,fineart -Henry Lamb,0.63560975,fineart -Dirck Hals,0.63559663,fineart -Alex Grey,0.62908936,nudity -Michael Heizer,0.63555753,fineart -Yiannis Moralis,0.61731136,fineart -Emily Murray Paterson,0.4392335,fineart -Georg Friedrich Kersting,0.6256248,fineart -Frances Hodgkins,0.6352128,fineart -Charles Cundall,0.6349486,fineart -Henry Wallis,0.63478243,fineart -Goro Fujita,0.6346491,cartoon -Jean-Léon Gérôme,0.5954844,fineart -August von Pettenkofen,0.60910493,fineart -Abbott Handerson Thayer,0.63428533,fineart -Martin John Heade,0.5926603,fineart -Ellen Jewett,0.63420236,digipa-high-impact -Hidari Jingorō,0.63388014,fareast -Taiyō Matsumoto,0.63372946,special -Emanuel Leutze,0.6007246,fineart -Adam Martinakis,0.48973057,digipa-med-impact -Will Eisner,0.63349223,cartoon -Alexander Stirling Calder,0.6331682,fineart -Saturno Butto,0.6331184,nudity -Cecilia Beaux,0.6330725,fineart -Amandine Van Ray,0.6174208,digipa-high-impact -Bob Eggleton,0.63277495,digipa-high-impact -Sherree Valentine Daines,0.63274443,fineart -Frederick Lord Leighton,0.6299176,fineart -Daniel Ridgway Knight,0.63251615,fineart -Gaetano Previati,0.61743724,fineart -John Berkey,0.63226986,fineart -Richard Misrach,0.63201725,digipa-high-impact -Aaron Jasinski,0.57948315,fineart -"Edward Otho Cresap Ord, II",0.6317712,fineart -Evelyn De Morgan,0.6317376,fineart -Noelle Stevenson,0.63159716,digipa-high-impact -Edward Robert Hughes,0.6315573,fineart -Allan Ramsay,0.63150716,fineart -Balthus,0.6314323,scribbles -Hendrick Cornelisz Vroom,0.63143134,digipa-high-impact -Ilya Repin,0.6313043,fineart -George Lambourn,0.6312267,fineart -Arthur Hughes,0.6310194,fineart -Antonio J. Manzanedo,0.53841716,fineart -John Singleton Copley,0.6264835,fineart -Dennis Miller Bunker,0.63078755,fineart -Ernie Barnes,0.6307126,cartoon -Alison Kinnaird,0.6306353,digipa-high-impact -Alex Toth,0.6305541,digipa-high-impact -Henry Raeburn,0.6155551,fineart -Alice Bailly,0.6305177,fineart -Brian Kesinger,0.63037646,scribbles -Antoine Blanchard,0.63036835,fineart -Ron Walotsky,0.63035095,fineart -Kent Monkman,0.63027304,fineart -Naomi Okubo,0.5782754,fareast -Hercules Seghers,0.62957174,fineart -August Querfurt,0.6295643,fineart -Samuel Melton Fisher,0.6283333,fineart -David Burdeny,0.62950236,digipa-high-impact -George Bain,0.58519644,fineart -Peter Holme III,0.62938106,fineart -Grayson Perry,0.62928164,digipa-high-impact -Chris Claremont,0.6292076,digipa-high-impact -Dod Procter,0.6291759,fineart -Huang Tingjian,0.6290358,fareast -Dorothea Warren O'Hara,0.6290113,fineart -Ivan Albright,0.6289551,fineart -Hubert von Herkomer,0.6288955,fineart -Barbara Nessim,0.60589516,digipa-high-impact -Henry Scott Tuke,0.6286309,fineart -Ditlev Blunck,0.6282925,fineart -Sven Nordqvist,0.62828535,fineart -Lee Madgwick,0.6281731,fineart -Hubert van Eyck,0.6281529,fineart -Edmond Bille,0.62339354,fineart -Ejnar Nielsen,0.6280824,fineart -Arturo Souto,0.6280583,fineart -Jean Giraud,0.6279888,fineart -Storm Thorgerson,0.6277394,digipa-high-impact -Ed Benedict,0.62764007,digipa-high-impact -Christoffer Wilhelm Eckersberg,0.6014842,fineart -Clarence Holbrook Carter,0.5514105,fineart -Dorothy Lockwood,0.6273235,fineart -John Singer Sargent,0.6272487,fineart -Brigid Derham,0.6270125,digipa-high-impact -Henricus Hondius II,0.6268505,fineart -Gertrude Harvey,0.5903887,fineart -Grant Wood,0.6266253,fineart -Fyodor Vasilyev,0.5234919,digipa-med-impact -Cagnaccio di San Pietro,0.6261671,fineart -Cagnaccio Di San Pietro,0.6261671,fineart -Doris Boulton-Maude,0.62593174,fineart -Adolf Hirémy-Hirschl,0.5946784,fineart -Harold von Schmidt,0.6256755,fineart -Martine Johanna,0.6256161,digipa-high-impact -Gerald Kelly,0.5579602,digipa-high-impact -Ub Iwerks,0.625396,cartoon -Dirck van der Lisse,0.6253871,fineart -Edouard Riou,0.6250113,fineart -Ilya Yefimovich Repin,0.62491584,fineart -Martin Johnson Heade,0.59421235,fineart -Afarin Sajedi,0.62475824,scribbles -Alfred Thompson Bricher,0.6247515,fineart -Edwin G. Lucas,0.5553578,fineart -Georges Emile Lebacq,0.56175387,fineart -Francis Davis Millet,0.5988504,fineart -Bill Sienkiewicz,0.6125557,digipa-high-impact -Giocondo Albertolli,0.62441677,fineart -Victor Nizovtsev,0.6242258,fineart -Squeak Carnwath,0.62416434,digipa-high-impact -Bill Viola,0.62409425,digipa-high-impact -Annie Abernethie Pirie Quibell,0.6240767,fineart -Jason Edmiston,0.62405366,fineart -Al Capp,0.6239494,fineart -Kobayashi Kiyochika,0.6239368,anime -Albert Anker,0.62389827,fineart -Iain Faulkner,0.62376785,fineart -Todd Schorr,0.6237408,fineart -Charles Ginner,0.62370133,fineart -Emile Auguste Carolus-Duran,0.62353987,fineart -John Philip Falter,0.623418,cartoon -Chizuko Yoshida,0.6233001,fareast -Anna Dittmann,0.62327325,cartoon -Henry Snell Gamley,0.62319934,fineart -Edmund Charles Tarbell,0.6230626,fineart -Rob Gonsalves,0.62298363,fineart -Gladys Dawson,0.6228511,fineart -Tomma Abts,0.61153626,fineart -Kate Beaton,0.53993124,digipa-high-impact -Gustave Buchet,0.62243867,fineart -Gareth Pugh,0.6223551,digipa-high-impact -Caspar van Wittel,0.57871693,fineart -Anton Otto Fischer,0.6222941,fineart -Albert Guillaume,0.56529653,fineart -Felix Octavius Carr Darley,0.62223387,fineart -Bernard van Orley,0.62221646,fineart -Edward John Poynter,0.60147405,fineart -Walter Percy Day,0.62207425,fineart -Franciszek Starowieyski,0.5709621,fineart -Auguste Baud-Bovy,0.6219854,fineart -Chris LaBrooy,0.45497298,digipa-low-impact -Abraham de Vries,0.5859101,fineart -Antoni Gaudi,0.62162614,fineart -Joe Jusko,0.62156093,digipa-high-impact -Lynda Barry,0.62154603,digipa-high-impact -Michal Karcz,0.62154436,digipa-high-impact -Raymond Briggs,0.62150294,fineart -Herbert James Gunn,0.6210927,fineart -Dwight William Tryon,0.620984,fineart -Paul Henry,0.5752968,fineart -Helio Oiticica,0.6203739,digipa-high-impact -Sebastian Errazuriz,0.62036186,digipa-high-impact -Lucian Freud,0.6203146,nudity -Frank Auerbach,0.6201102,weird -Andre-Charles Boulle,0.6200789,fineart -Franz Fedier,0.5669752,fineart -Austin Briggs,0.57675314,fineart -Hugo Sánchez Bonilla,0.61978436,digipa-high-impact -Caroline Chariot-Dayez,0.6195682,digipa-high-impact -Bill Ward,0.61953044,digipa-high-impact -Charles Bird King,0.6194487,fineart -Adrian Ghenie,0.6193521,digipa-high-impact -Agnes Cecile,0.6192814,digipa-high-impact -Augustus John,0.6191995,fineart -Jeffrey T. Larson,0.61913544,fineart -Alexis Simon Belle,0.3190395,digipa-low-impact -Jean-Baptiste Monge,0.5758537,fineart -Adolf Bierbrauer,0.56129396,fineart -Ayako Rokkaku,0.61891204,fareast -Lisa Keene,0.54570895,digipa-high-impact -Edmond Aman-Jean,0.57168096,fineart -Marc Davis,0.61837333,cartoon -Cerith Wyn Evans,0.61829346,digipa-high-impact -George Wyllie,0.61829203,fineart -George Luks,0.6182724,fineart -William-Adolphe Bouguereau,0.618265,c -Grigoriy Myasoyedov,0.61801606,fineart -Hashimoto Gahō,0.61795104,fineart -Charles Ragland Bunnell,0.61772746,fineart -Ambrose McCarthy Patterson,0.61764514,fineart -Bill Brauer,0.5824066,fineart -Mikko Lagerstedt,0.591015,digipa-high-impact -Koson Ohara,0.53635323,fineart -Evaristo Baschenis,0.5857368,fineart -Martin Ansin,0.5294119,fineart -Cory Loftis,0.6168619,cartoon -Joseph Stella,0.6166778,fineart -André Pijet,0.5768274,fineart -Jeff Wall,0.6162895,digipa-high-impact -Eleanor Layfield Davis,0.6158844,fineart -Saul Tepper,0.61579347,fineart -Alex Hirsch,0.6157384,cartoon -Alexandre Falguière,0.55011404,fineart -Malcolm Liepke,0.6155646,fineart -Georg Friedrich Schmidt,0.60364646,fineart -Hendrik Kerstens,0.55099905,digipa-high-impact -Félix Bódog Widder,0.6153954,fineart -Marie Guillemine Benoist,0.61532974,fineart -Kelly Mckernan,0.60047054,digipa-high-impact -Ignacio Zuloaga,0.6151608,fineart -Hubert van Ravesteyn,0.61489964,fineart -Angus McKie,0.61487424,digipa-high-impact -Colin Campbell Cooper,0.6147882,fineart -Pieter Aertsen,0.61454165,fineart -Jan Brett,0.6144608,fineart -Kazuo Koike,0.61438507,fineart -Edith Grace Wheatley,0.61428297,fineart -Ogawa Kazumasa,0.61427975,fareast -Giovanni Battista Cipriani,0.6022825,fineart -André Bauchant,0.57124996,fineart -George Abe,0.6140447,digipa-high-impact -Georges Lemmen,0.6139967,scribbles -Frank Leonard Brooks,0.6139327,fineart -Gai Qi,0.613744,anime -Frank Gehry,0.6136776,digipa-high-impact -Anton Domenico Gabbiani,0.55471313,fineart -Cassandra Austen,0.6135781,fineart -Paul Gustav Fischer,0.613273,fineart -Emiliano Di Cavalcanti,0.6131207,fineart -Meryl McMaster,0.6129995,digipa-high-impact -Domenico di Pace Beccafumi,0.6129922,fineart -Ludwig Mies van der Rohe,0.6126692,fineart -Étienne-Louis Boullée,0.6126158,fineart -Dali,0.5928694,nudity -Shinji Aramaki,0.61246127,anime -Giovanni Fattori,0.59544694,fineart -Bapu,0.6122084,c -Raphael Lacoste,0.5539114,digipa-high-impact -Scarlett Hooft Graafland,0.6119631,digipa-high-impact -Rene Laloux,0.61190474,fineart -Julius Horsthuis,0.59037095,fineart -Gerald van Honthorst,0.6115939,fineart -Dino Valls,0.611533,fineart -Tony DiTerlizzi,0.6114657,cartoon -Michael Cheval,0.61138546,anime -Charles Schulz,0.6113759,digipa-high-impact -Alvar Aalto,0.61122143,digipa-high-impact -Gu Kaizhi,0.6110798,fareast -Eugene von Guerard,0.6109776,fineart -John Cassaday,0.610949,fineart -Elizabeth Forbes,0.61092335,fineart -Edmund Greacen,0.6109115,fineart -Eugène Burnand,0.6107876,fineart -Boris Grigoriev,0.6107853,scribbles -Norman Rockwell,0.6107638,fineart -Barthélemy Menn,0.61064315,fineart -George Biddle,0.61058354,fineart -Edgar Ainsworth,0.5525424,digipa-high-impact -Alfred Leyman,0.5887217,fineart -Tex Avery,0.6104007,cartoon -Beatrice Ethel Lithiby,0.61030364,fineart -Grace Pailthorpe,0.61026484,digipa-high-impact -Brian Oldham,0.396231,digipa-low-impact -Android Jones,0.61023116,fareast -François Girardon,0.5830649,fineart -Ib Eisner,0.61016303,digipa-high-impact -Armand Point,0.610156,fineart -Henri Alphonse Barnoin,0.59465057,fineart -Jean Marc Nattier,0.60987425,fineart -Francisco de Holanda,0.6091294,fineart -Marco Mazzoni,0.60970783,fineart -Esaias Boursse,0.6093308,fineart -Alexander Deyneka,0.55000365,fineart -John Totleben,0.60883725,fineart -Al Feldstein,0.6087723,fineart -Adam Hughes,0.60854626,anime -Ernest Zobole,0.6085073,fineart -Alex Gross,0.60837066,digipa-high-impact -George Jamesone,0.6079673,fineart -Frank Lloyd Wright,0.60793245,scribbles -Brooke DiDonato,0.47680336,digipa-med-impact -Hans Gude,0.60780364,fineart -Ethel Schwabacher,0.60748273,fineart -Gladys Kathleen Bell,0.60747695,fineart -Adolf Fényes,0.54192233,fineart -Carel Willink,0.58120143,fineart -George Henry,0.6070727,digipa-high-impact -Ronald Balfour,0.60697085,fineart -Elsie Dalton Hewland,0.6067718,digipa-high-impact -Alex Maleev,0.6067118,fineart -Anish Kapoor,0.6067015,digipa-high-impact -Aleksandr Ivanovich Laktionov,0.606544,fineart -Kim Keever,0.6037775,digipa-high-impact -Aleksi Briclot,0.46056762,fineart -Raymond Leech,0.6062721,fineart -Richard Eurich,0.6062664,fineart -Phil Jimenez,0.60625625,cartoon -Gao Cen,0.60618126,nudity -Mike Deodato,0.6061201,cartoon -Charles Haslewood Shannon,0.6060581,fineart -Alexandre Jacovleff,0.3991747,digipa-low-impact -André Beauneveu,0.584062,fineart -Hiroshi Honda,0.60507596,digipa-high-impact -Charles Joshua Chaplin,0.60498774,fineart -Domenico Zampieri,0.6049726,fineart -Gusukuma Seihō,0.60479784,fareast -Nikolina Petolas,0.46318632,digipa-low-impact -Casey Weldon,0.6047672,cartoon -Elmyr de Hory,0.6046374,fineart -Nan Goldin,0.6046119,digipa-high-impact -Charles McAuley,0.6045995,fineart -Archibald Skirving,0.6044234,fineart -Elizabeth York Brunton,0.6043737,fineart -Dugald Sutherland MacColl,0.6042907,fineart -Titian,0.60426414,fineart -Ignacy Witkiewicz,0.6042259,fineart -Allie Brosh,0.6042061,digipa-high-impact -H.P. Lovecraft,0.6039597,digipa-high-impact -Andrée Ruellan,0.60395086,fineart -Ralph McQuarrie,0.60380936,fineart -Mead Schaeffer,0.6036558,fineart -Henri-Julien Dumont,0.571257,fineart -Kieron Gillen,0.6035093,fineart -Maginel Wright Enright Barney,0.6034306,nudity -Vincent Di Fate,0.6034131,fineart -Briton Rivière,0.6032918,fineart -Hajime Sorayama,0.60325956,nudity -Béla Czóbel,0.6031023,fineart -Edmund Blampied,0.603072,fineart -E. Simms Campbell,0.6030443,fineart -Hisui Sugiura,0.603034,fareast -Alan Davis,0.6029676,fineart -Glen Keane,0.60287905,cartoon -Frank Holl,0.6027312,fineart -Abbott Fuller Graves,0.6025608,fineart -Albert Servaes,0.60250103,black-white -Hovsep Pushman,0.5937487,fineart -Brian M. Viveros,0.60233414,fineart -Charles Fremont Conner,0.6023278,fineart -Francesco Furini,0.6022654,digipa-high-impact -Camille-Pierre Pambu Bodo,0.60191673,fineart -Yasushi Nirasawa,0.6016714,nudity -Charles Uzzell-Edwards,0.6014683,fineart -Abram Efimovich Arkhipov,0.60128385,fineart -Hedda Sterne,0.6011857,digipa-high-impact -Ben Aronson,0.6011548,fineart -Frank Frazetta,0.551121,nudity -Elizabeth Durack,0.6010842,fineart -Ian Miller,0.42153555,fareast -Charlie Bowater,0.4410439,special -Michael Carson,0.60039437,fineart -Walter Langley,0.6002273,fineart -Cornelis Anthonisz,0.6001956,fineart -Dorothy Elizabeth Bradford,0.6001929,fineart -J.C. Leyendecker,0.5791972,fineart -Willem van Haecht,0.59990716,fineart -Anna and Elena Balbusso,0.59955937,digipa-low-impact -Harrison Fisher,0.59952044,fineart -Bill Medcalf,0.59950054,fineart -Edward Arthur Walton,0.59945667,fineart -Alois Arnegger,0.5991994,fineart -Ray Caesar,0.59902894,digipa-high-impact -Karen Wallis,0.5990094,fineart -Emmanuel Shiu,0.51082766,digipa-med-impact -Thomas Struth,0.5988324,digipa-high-impact -Barbara Longhi,0.5985706,fineart -Richard Deacon,0.59851056,fineart -Constantin Hansen,0.5984213,fineart -Harold Shapinsky,0.5984175,fineart -George Dionysus Ehret,0.5983857,fineart -Doug Wildey,0.5983639,digipa-high-impact -Fernand Toussaint,0.5982694,fineart -Horatio Nelson Poole,0.5982614,fineart -Caesar van Everdingen,0.5981566,fineart -Eva Gonzalès,0.5981396,fineart -Franz Vohwinkel,0.5448179,fineart -Margaret Mee,0.5979592,fineart -Francis Focer Brown,0.59779185,fineart -Henry Moore,0.59767926,nudity -Scott Listfield,0.58795893,fineart -Nikolai Ge,0.5973643,fineart -Jacek Yerka,0.58198756,fineart -Margaret Brundage,0.5969077,fineart -JC Leyendecker,0.5620243,fineart -Ben Templesmith,0.5498991,digipa-high-impact -Armin Hansen,0.59669334,anime -Jean-Louis Prevost,0.5966897,fineart -Daphne Allen,0.59666026,fineart -Franz Karl Basler-Kopp,0.59663445,fineart -"Henry Ives Cobb, Jr.",0.596385,fineart -Michael Sowa,0.546285,fineart -Anna Füssli,0.59600973,fineart -György Rózsahegyi,0.59580946,fineart -Luis Royo,0.59566617,fineart -Émile Gallé,0.5955559,fineart -Antonio Mora,0.5334297,digipa-high-impact -Edward P. Beard Jr.,0.59543866,fineart -Jessica Rossier,0.54958373,special -André Thomkins,0.5343785,digipa-high-impact -David Macbeth Sutherland,0.5949968,fineart -Charles Liu,0.5949787,digipa-high-impact -Edi Rama,0.5949226,digipa-high-impact -Jacques Le Moyne,0.5948843,fineart -Egbert van der Poel,0.59488285,fineart -Georg Jensen,0.594782,digipa-high-impact -Anne Sudworth,0.5947539,fineart -Jan Pietersz Saenredam,0.59472525,fineart -Henryk Stażewski,0.5945748,fineart -André François,0.58402044,fineart -Alexander Runciman,0.5944449,digipa-high-impact -Thomas Kinkade,0.594391,fineart -Robert Williams,0.5567989,digipa-high-impact -George Gardner Symons,0.57431924,fineart -D. Alexander Gregory,0.5334464,fineart -Gerald Brom,0.52473724,fineart -Robert Hagan,0.59406,fineart -Ernest Crichlow,0.5940588,fineart -Viviane Sassen,0.5939927,digipa-high-impact -Enrique Simonet,0.5937546,fineart -Esther Blaikie MacKinnon,0.593747,digipa-high-impact -Jeff Kinney,0.59372896,scribbles -Igor Morski,0.5936732,digipa-high-impact -John Currin,0.5936216,fineart -Bob Ringwood,0.5935273,digipa-high-impact -Jordan Grimmer,0.44948143,digipa-low-impact -François Barraud,0.5933471,fineart -Helen Binyon,0.59331006,digipa-high-impact -Brenda Chamberlain,0.5932333,fineart -Candido Bido,0.59310603,fineart -Abraham Storck,0.5929502,fineart -Raphael,0.59278333,fineart -Larry Sultan,0.59273386,digipa-high-impact -Agostino Tassi,0.59265685,fineart -Alexander V. Kuprin,0.5925917,fineart -Frans Koppelaar,0.5658725,fineart -Richard Corben,0.59251785,fineart -David Gilmour Blythe,0.5924247,digipa-high-impact -František Kaván,0.5924211,fineart -Rob Liefeld,0.5921167,fineart -Ernő Rubik,0.5920297,fineart -Byeon Sang-byeok,0.59200096,fareast -Johfra Bosschart,0.5919376,fineart -Emil Lindenfeld,0.5761086,fineart -Howard Mehring,0.5917471,fineart -Gwenda Morgan,0.5915571,digipa-high-impact -Henry Asencio,0.5915404,fineart -"George Barret, Sr.",0.5914306,fineart -Andrew Ferez,0.5911011,fineart -Ed Brubaker,0.5910869,digipa-high-impact -George Reid,0.59095883,digipa-high-impact -Derek Gores,0.51769906,digipa-med-impact -Charles Rollier,0.5539186,fineart -Terry Oakes,0.590443,fineart -Thomas Blackshear,0.5078616,fineart -Albert Benois,0.5902705,nudity -Krenz Cushart,0.59026587,special -Jeff Koons,0.5902637,digipa-high-impact -Akihiko Yoshida,0.5901294,special -Anja Percival,0.45039332,digipa-low-impact -Eduard von Steinle,0.59008586,fineart -Alex Russell Flint,0.5900352,digipa-high-impact -Edward Okuń,0.5897297,fineart -Emma Lampert Cooper,0.5894849,fineart -Stuart Haygarth,0.58132994,digipa-high-impact -George French Angas,0.5434376,fineart -Edmund F. Ward,0.5892848,fineart -Eleanor Vere Boyle,0.58925456,digipa-high-impact -Evelyn Cheston,0.58924586,fineart -Edwin Dickinson,0.58921975,digipa-high-impact -Christophe Vacher,0.47325426,fineart -Anne Dewailly,0.58905107,fineart -Gertrude Greene,0.5862596,digipa-high-impact -Boris Groh,0.5888809,digipa-high-impact -Douglas Smith,0.588804,digipa-high-impact -Ian Hamilton Finlay,0.5887713,fineart -Derek Jarman,0.5887292,digipa-high-impact -Archibald Thorburn,0.5882001,fineart -Gillis d'Hondecoeter,0.58813053,fineart -I Ketut Soki,0.58801544,digipa-high-impact -Alex Schomburg,0.46614102,digipa-low-impact -Bastien L. Deharme,0.583349,special -František Jakub Prokyš,0.58782333,fineart -Jesper Ejsing,0.58782053,fineart -Odd Nerdrum,0.53551745,digipa-high-impact -Tom Lovell,0.5877577,fineart -Ayami Kojima,0.5877416,fineart -Peter Sculthorpe,0.5875696,fineart -Bernard D’Andrea,0.5874042,fineart -Denis Eden,0.58739066,digipa-high-impact -Alfons Walde,0.58728385,fineart -Jovana Rikalo,0.47006977,digipa-low-impact -Franklin Booth,0.5870834,fineart -Mat Collishaw,0.5870676,digipa-high-impact -Joseph Lorusso,0.586858,fineart -Helen Stevenson,0.454647,digipa-low-impact -Delaunay,0.58657396,fineart -H.R. Millar,0.58655745,fineart -E. Charlton Fortune,0.586376,fineart -Alson Skinner Clark,0.58631575,fineart -Stan And Jan Berenstain,0.5862361,digipa-high-impact -Howard Lyon,0.5862271,fineart -John Blanche,0.586182,fineart -Bernardo Cavallino,0.5858575,fineart -Tomasz Alen Kopera,0.5216588,fineart -Peter Gric,0.58583695,fineart -Guo Pei,0.5857794,fareast -James Turrell,0.5853901,digipa-high-impact -Alexandr Averin,0.58533764,fineart -Bertalan Székely,0.5548113,digipa-high-impact -Brothers Hildebrandt,0.5850233,fineart -Ed Roth,0.5849769,digipa-high-impact -Enki Bilal,0.58492255,fineart -Alan Lee,0.5848701,fineart -Charles H. Woodbury,0.5848688,fineart -André Charles Biéler,0.5847876,fineart -Annie Rose Laing,0.5597829,fineart -Matt Fraction,0.58463776,cartoon -Charles Alston,0.58453286,fineart -Frank Xavier Leyendecker,0.545465,fineart -Alfred Richard Gurrey,0.584306,fineart -Dan Mumford,0.5843051,cartoon -Francisco Martín,0.5842005,fineart -Alvaro Siza,0.58406967,digipa-high-impact -Frank J. Girardin,0.5839858,fineart -Henry Carr,0.58397424,digipa-high-impact -Charles Furneaux,0.58394694,fineart -Daniel F. Gerhartz,0.58389103,fineart -Gilberto Soren Zaragoza,0.5448442,fineart -Bart Sears,0.5838427,cartoon -Allison Bechdel,0.58383805,digipa-high-impact -Frank O'Meara,0.5837992,fineart -Charles Codman,0.5836579,fineart -Francisco Zúñiga,0.58359766,fineart -Vladimir Kush,0.49075457,fineart -Arnold Mesches,0.5834257,fineart -Frank McKelvey,0.5831641,fineart -Allen Butler Talcott,0.5830911,fineart -Eric Zener,0.58300316,fineart -Noah Bradley,0.44176096,digipa-low-impact -Robert Childress,0.58289623,fineart -Frances C. Fairman,0.5827239,fineart -Kathryn Morris Trotter,0.465856,digipa-low-impact -Everett Raymond Kinstler,0.5824819,fineart -Edward Mitchell Bannister,0.5804899,fineart -"George Barret, Jr.",0.5823128,fineart -Greg Hildebrandt,0.4271311,fineart -Anka Zhuravleva,0.5822078,digipa-high-impact -Rolf Armstrong,0.58217514,fineart -Eric Wallis,0.58191466,fineart -Clemens Ascher,0.5480207,digipa-high-impact -Hugo Kārlis Grotuss,0.5818766,fineart -Albert Paris Gütersloh,0.5817827,fineart -Hilda May Gordon,0.5817449,fineart -Hendrik Martenszoon Sorgh,0.5817126,fineart -Pipilotti Rist,0.5816868,digipa-high-impact -Hiroyuki Tajima,0.5816242,fareast -Igor Zenin,0.58159757,digipa-high-impact -Genevieve Springston Lynch,0.4979099,digipa-med-impact -Dan Witz,0.44476372,fineart -David Roberts,0.5255326,fineart -Frieke Janssens,0.5706969,digipa-high-impact -Arnold Schoenberg,0.56520367,fineart -Inoue Naohisa,0.5809933,fareast -Elfriede Lohse-Wächtler,0.58097905,fineart -Alex Ross,0.42460668,digipa-low-impact -Robert Irwin,0.58078,c -Charles Angrand,0.58077514,fineart -Anne Nasmyth,0.54221964,fineart -Henri Bellechose,0.5773891,fineart -De Hirsh Margules,0.58059025,fineart -Hiromitsu Takahashi,0.5805599,fareast -Ilya Kuvshinov,0.5805521,special -Cassius Marcellus Coolidge,0.5805516,c -Dorothy Burroughes,0.5804835,fineart -Emanuel de Witte,0.58027405,fineart -George Herbert Baker,0.5799624,digipa-high-impact -Cheng Zhengkui,0.57990086,fareast -Bernard Fleetwood-Walker,0.57987773,digipa-high-impact -Philippe Parreno,0.57985014,digipa-high-impact -Thornton Oakley,0.57969713,fineart -Greg Rutkowski,0.5203395,special -Ike no Taiga,0.5795857,anime -Eduardo Lefebvre Scovell,0.5795808,fineart -Adolfo Müller-Ury,0.57944727,fineart -Patrick Woodroffe,0.5228063,fineart -Wim Crouwel,0.57933235,digipa-high-impact -Colijn de Coter,0.5792779,fineart -François Boquet,0.57924724,fineart -Gerbrand van den Eeckhout,0.57897866,fineart -Eugenio Granell,0.5392264,fineart -Kuang Hong,0.5782304,digipa-high-impact -Justin Gerard,0.46685404,fineart -Tokujin Yoshioka,0.5779153,digipa-high-impact -Alan Bean,0.57788515,fineart -Ernest Biéler,0.5778079,fineart -Martin Deschambault,0.44401115,digipa-low-impact -Anna Boch,0.577735,fineart -Jack Davis,0.5775291,fineart -Félix Labisse,0.5775142,fineart -Greg Simkins,0.5679761,fineart -David Lynch,0.57751054,digipa-low-impact -Eizō Katō,0.5774127,digipa-high-impact -Grethe Jürgens,0.5773412,digipa-high-impact -Heinrich Bichler,0.5770147,fineart -Barbara Nasmyth,0.5446056,fineart -Domenico Induno,0.5583946,fineart -Gustave Baumann,0.5607866,fineart -Mike Mayhew,0.5765857,cartoon -Delmer J. Yoakum,0.576538,fineart -Aykut Aydogdu,0.43111503,digipa-low-impact -George Barker,0.5763551,fineart -Ernő Grünbaum,0.57634187,fineart -Eliseu Visconti,0.5763241,fineart -Esao Andrews,0.5761547,fineart -JennyBird Alcantara,0.49165845,digipa-med-impact -Joan Tuset,0.5761051,fineart -Angela Barrett,0.55976534,digipa-high-impact -Syd Mead,0.5758396,fineart -Ignacio Bazan-Lazcano,0.5757512,fineart -Franciszek Kostrzewski,0.57570386,fineart -Eero Järnefelt,0.57540673,fineart -Loretta Lux,0.56217635,digipa-high-impact -Gaudi,0.57519895,fineart -Charles Gleyre,0.57490873,fineart -Antoine Verney-Carron,0.56386137,fineart -Albert Edelfelt,0.57466495,fineart -Fabian Perez,0.57444525,fineart -Kevin Sloan,0.5737548,fineart -Stanislav Poltavsky,0.57434607,fineart -Abraham Hondius,0.574326,fineart -Tadao Ando,0.57429105,fareast -Fyodor Slavyansky,0.49796474,digipa-med-impact -David Brewster,0.57385933,digipa-high-impact -Cliff Chiang,0.57375133,digipa-high-impact -Drew Struzan,0.5317983,digipa-high-impact -Henry O. Tanner,0.5736586,fineart -Alberto Sughi,0.5736495,fineart -Albert J. Welti,0.5736257,fineart -Charles Mahoney,0.5735923,digipa-high-impact -Exekias,0.5734506,fineart -Felipe Seade,0.57342744,digipa-high-impact -Henriette Wyeth,0.57330644,digipa-high-impact -Harold Sandys Williamson,0.5443646,fineart -Eddie Campbell,0.57329535,digipa-high-impact -Gao Fenghan,0.5732926,fareast -Cynthia Sheppard,0.51099646,fineart -Henriette Grindat,0.573179,fineart -Yasutomo Oka,0.5731342,fareast -Celia Frances Bedford,0.57313216,fineart -Les Edwards,0.42068473,fineart -Edwin Deakin,0.5031717,fineart -Eero Saarinen,0.5725142,digipa-high-impact -Franciszek Smuglewicz,0.5722554,fineart -Doris Blair,0.57221186,fineart -Seb Mckinnon,0.51721895,digipa-med-impact -Gregorio Lazzarini,0.57204294,fineart -Gerard Sekoto,0.5719927,fineart -Francis Ernest Jackson,0.5506009,fineart -Simon Birch,0.57171595,digipa-high-impact -Bayard Wu,0.57171166,fineart -François Clouet,0.57162094,fineart -Christopher Wren,0.5715372,fineart -Evgeny Lushpin,0.5714827,special -Art Green,0.5714495,digipa-high-impact -Amy Judd,0.57142305,digipa-high-impact -Art Brenner,0.42619684,digipa-low-impact -Travis Louie,0.43916368,digipa-low-impact -James Jean,0.5457318,digipa-high-impact -Ewald Rübsamen,0.57083976,fineart -Donato Giancola,0.57052535,fineart -Carl Arnold Gonzenbach,0.5703996,fineart -Bastien Lecouffe-Deharme,0.5201288,fineart -Howard Chandler Christy,0.5702813,nudity -Dean Cornwell,0.56977296,fineart -Don Maitz,0.4743015,fineart -James Montgomery Flagg,0.56974065,fineart -Andreas Levers,0.42125136,digipa-low-impact -Edgar Schofield Baum,0.56965977,fineart -Alan Parry,0.5694952,digipa-high-impact -An Zhengwen,0.56942475,fareast -Alayna Lemmer,0.48293802,fineart -Edward Marshall Boehm,0.5530143,fineart -Henri Biva,0.54013556,nudity -Fiona Rae,0.4646715,digipa-low-impact -Elizabeth Jane Lloyd,0.5688463,digipa-high-impact -Franklin Carmichael,0.5687844,digipa-high-impact -Dionisius,0.56875896,fineart -Edwin Georgi,0.56868523,fineart -Jenny Saville,0.5686633,fineart -Ernest Hébert,0.56859314,fineart -Stephan Martiniere,0.56856346,digipa-high-impact -Huang Binhong,0.56841767,fineart -August Lemmer,0.5683548,fineart -Camille Bouvagne,0.5678048,fineart -Olga Skomorokhova,0.39401102,digipa-low-impact -Sacha Goldberger,0.5675477,digipa-high-impact -Hilda Annetta Walker,0.5675261,digipa-high-impact -Harvey Pratt,0.51314723,digipa-med-impact -Jean Bourdichon,0.5670543,fineart -Noriyoshi Ohrai,0.56690073,fineart -Kadir Nelson,0.5669006,n -Ilya Ostroukhov,0.5668801,fineart -Eugène Brands,0.56681967,fineart -Achille Leonardi,0.56674325,fineart -Franz Cižek,0.56670356,fineart -George Paul Chalmers,0.5665988,digipa-high-impact -Serge Marshennikov,0.5665971,digipa-high-impact -Mike Worrall,0.56641084,fineart -Dirck van Delen,0.5661764,fineart -Peter Andrew Jones,0.5661655,fineart -Rafael Albuquerque,0.56541103,fineart -Daniel Buren,0.5654043,fineart -Giuseppe Grisoni,0.5432699,fineart -George Fiddes Watt,0.55861616,fineart -Stan Lee,0.5651268,digipa-high-impact -Dorning Rasbotham,0.56511617,fineart -Albert Lynch,0.56497896,fineart -Lorenz Hideyoshi,0.56494075,fineart -Fenghua Zhong,0.56492203,fareast -Caroline Lucy Scott,0.49190843,digipa-med-impact -Victoria Crowe,0.5647996,digipa-high-impact -Hasegawa Settan,0.5647092,fareast -Dennis H. Farber,0.56453323,digipa-high-impact -Dick Bickenbach,0.5644289,fineart -Art Frahm,0.56439924,fineart -Edith Edmonds,0.5643151,fineart -Alfred Heber Hutty,0.56419206,fineart -Henry Tonks,0.56410825,fineart -Peter Howson,0.5640759,fineart -Albert Dorne,0.56395364,fineart -Arthur Adams,0.5639404,fineart -Bernt Tunold,0.56383425,digipa-high-impact -Gianluca Foli,0.5637317,digipa-high-impact -Vittorio Matteo Corcos,0.5636767,fineart -Béla Iványi-Grünwald,0.56355745,nudity -Feng Zhu,0.5634973,fineart -Sam Kieth,0.47251505,digipa-low-impact -Charles Crodel,0.5633834,fineart -Elsie Henderson,0.56310076,digipa-high-impact -George Earl Ortman,0.56295705,fineart -Tari Márk Dávid,0.562937,fineart -Betty Merken,0.56281745,digipa-high-impact -Cecile Walton,0.46672013,digipa-low-impact -Bracha L. Ettinger,0.56237936,fineart -Ken Fairclough,0.56230986,digipa-high-impact -Phil Koch,0.56224954,digipa-high-impact -George Pirie,0.56213045,digipa-high-impact -Chad Knight,0.56194013,digipa-high-impact -Béla Kondor,0.5427164,digipa-high-impact -Barclay Shaw,0.53689134,digipa-high-impact -Tim Hildebrandt,0.47194147,fineart -Hermann Rüdisühli,0.56104004,digipa-high-impact -Ian McQue,0.5342066,digipa-high-impact -Yanjun Cheng,0.5607171,fineart -Heinrich Hofmann,0.56060636,fineart -Henry Raleigh,0.5605958,fineart -Ernest Buckmaster,0.5605704,fineart -Charles Ricketts,0.56055415,fineart -Juergen Teller,0.56051147,digipa-high-impact -Auguste Mambour,0.5604873,fineart -Sean Yoro,0.5601486,digipa-high-impact -Sheilah Beckett,0.55995446,digipa-high-impact -Eugene Tertychnyi,0.5598978,fineart -Dr. Seuss,0.5597466,c -Adolf Wölfli,0.5372333,digipa-high-impact -Enrique Tábara,0.559323,fineart -Dionisio Baixeras Verdaguer,0.5590695,fineart -Aleksander Gierymski,0.5590013,fineart -Augustus Dunbier,0.55872476,fineart -Adolf Born,0.55848217,fineart -Chris Turnham,0.5584234,digipa-high-impact -James C Christensen,0.55837405,fineart -Daphne Fedarb,0.5582459,digipa-high-impact -Andre Kohn,0.5581832,special -Ron Mueck,0.5581811,nudity -Glenn Fabry,0.55786383,fineart -Elizabeth Polunin,0.5578102,digipa-high-impact -Charles S. Kaelin,0.5577954,fineart -Arthur Radebaugh,0.5577016,fineart -Ai Yazawa,0.55768114,fareast -Charles Roka,0.55762553,fineart -Ai Weiwei,0.5576034,digipa-high-impact -Dorothy Bradford,0.55760014,digipa-high-impact -Alfred Leslie,0.557555,fineart -Heinrich Herzig,0.5574423,fineart -Eliot Hodgkin,0.55740607,digipa-high-impact -Albert Kotin,0.55737317,fineart -Carlo Carlone,0.55729353,fineart -Chen Rong,0.5571221,fineart -Ikuo Hirayama,0.5570225,digipa-high-impact -Edward Corbett,0.55701995,nudity -Eugeniusz Żak,0.556925,nudity -Ettore Tito,0.556875,fineart -Helene Knoop,0.5567731,fineart -Amanda Sage,0.37731662,fareast -Annick Bouvattier,0.54647046,fineart -Harvey Dunn,0.55663586,fineart -Hans Sandreuter,0.5562575,digipa-high-impact -Ruan Jia,0.5398549,special -Anton Räderscheidt,0.55618906,fineart -Tyler Shields,0.4081434,digipa-low-impact -Darek Zabrocki,0.49975997,digipa-med-impact -Frank Montague Moore,0.5556432,fineart -Greg Staples,0.5555332,fineart -Endre Bálint,0.5553731,fineart -Augustus Vincent Tack,0.5136602,fineart -Marc Simonetti,0.48602036,fineart -Carlo Randanini,0.55493265,digipa-high-impact -Diego Dayer,0.5549119,fineart -Kelly Freas,0.55476534,fineart -Thomas Saliot,0.5139967,digipa-med-impact -Gijsbert d'Hondecoeter,0.55455256,fineart -Walter Kim,0.554521,digipa-high-impact -Francesco Cozza,0.5155097,digipa-med-impact -Bill Watterson,0.5542879,digipa-high-impact -Mark Keathley,0.4824056,fineart -Béni Ferenczy,0.55405354,digipa-high-impact -Amadou Opa Bathily,0.5536976,n -Giuseppe Antonio Petrini,0.55340284,fineart -Enzo Cucchi,0.55331933,digipa-high-impact -Adolf Schrödter,0.55316544,fineart -George Benjamin Luks,0.548566,fineart -Glenys Cour,0.55304,digipa-high-impact -Andrew Robertson,0.5529603,digipa-high-impact -Claude Rogers,0.55272067,digipa-high-impact -Alexandre Antigna,0.5526737,fineart -Aimé Barraud,0.55265915,digipa-high-impact -György Vastagh,0.55258965,fineart -Bruce Nauman,0.55257386,digipa-high-impact -Benjamin Block,0.55251944,digipa-high-impact -Gonzalo Endara Crow,0.552346,digipa-high-impact -Dirck de Bray,0.55221736,fineart -Gerald Kelley,0.5521059,digipa-high-impact -Dave Gibbons,0.5520954,digipa-high-impact -Béla Nagy Abodi,0.5520624,digipa-high-impact -Faith 47,0.5517006,digipa-high-impact -Anna Razumovskaya,0.5229187,digipa-med-impact -Archibald Robertson,0.55129635,digipa-high-impact -Louise Dahl-Wolfe,0.55120385,digipa-high-impact -Simon Bisley,0.55119276,digipa-high-impact -Eric Fischl,0.55107886,fineart -Hu Zaobin,0.5510481,fareast -Béla Pállik,0.5507963,digipa-high-impact -Eugene J. Martin,0.55078864,fineart -Friedrich Gauermann,0.55063415,fineart -Fritz Baumann,0.5341434,fineart -Michal Lisowski,0.5505639,fineart -Paolo Roversi,0.5503342,digipa-high-impact -Andrew Atroshenko,0.55009747,fineart -Gyula Derkovits,0.5500315,fineart -Hugh Adam Crawford,0.55000615,digipa-high-impact -Béla Apáti Abkarovics,0.5499799,digipa-high-impact -Paul Chadeisson,0.389151,digipa-low-impact -Aurél Bernáth,0.54968774,fineart -Albert Henry Krehbiel,0.54952574,fineart -Piet Hein Eek,0.54918796,digipa-high-impact -Yoshitaka Amano,0.5491855,fareast -Antonio Rotta,0.54909515,fineart -Józef Mehoffer,0.50760424,fineart -Donald Sherwood,0.5490415,digipa-high-impact -Catrin G Grosse,0.5489286,digipa-high-impact -Arthur Webster Emerson,0.5478842,fineart -Incarcerated Jerkfaces,0.5488423,digipa-high-impact -Emanuel Büchel,0.5487217,fineart -Andrew Loomis,0.54854584,fineart -Charles Hopkinson,0.54853606,fineart -Gabor Szikszai,0.5485203,digipa-high-impact -Archibald Standish Hartrick,0.54850936,digipa-high-impact -Aleksander Orłowski,0.546705,nudity -Hans Hinterreiter,0.5483628,fineart -Fred Williams,0.54544824,fineart -Fred A. Precht,0.5481606,fineart -Camille Souter,0.5213742,fineart -Emil Fuchs,0.54807395,fineart -Francesco Bonsignori,0.5478936,fineart -H. R. (Hans Ruedi) Giger,0.547799,fineart -Harriet Zeitlin,0.5477388,digipa-high-impact -Christian Jane Fergusson,0.5396168,fineart -Edward Kemble,0.5476892,fineart -Bernard Aubertin,0.5475396,fineart -Augustyn Mirys,0.5474162,fineart -Alejandro Burdisio,0.47482288,special -Erin Hanson,0.4343264,digipa-low-impact -Amalia Lindegren,0.5471987,digipa-high-impact -Alberto Seveso,0.47735062,fineart -Bartholomeus Strobel,0.54703736,fineart -Jim Davis,0.54703003,digipa-high-impact -Antony Gormley,0.54696125,digipa-high-impact -Charles Marion Russell,0.54696095,fineart -George B. Sutherland,0.5467901,fineart -Almada Negreiros,0.54670584,fineart -Edward Armitage,0.54358315,fineart -Bruno Walpoth,0.546167,digipa-high-impact -Richard Hamilton,0.5461275,nudity -Charles Harold Davis,0.5460415,digipa-high-impact -Fernand Verhaegen,0.54601514,fineart -Bernard Meninsky,0.5302034,digipa-high-impact -Fede Galizia,0.5456873,digipa-high-impact -Alfred Kelsner,0.5455753,nudity -Fritz Puempin,0.5452847,fineart -Alfred Charles Parker,0.54521024,fineart -Ahmed Yacoubi,0.544767,digipa-high-impact -Arthur B. Carles,0.54447794,fineart -Alice Prin,0.54435575,digipa-high-impact -Carl Gustaf Pilo,0.5443212,digipa-high-impact -Ross Tran,0.5259248,special -Hideyuki Kikuchi,0.544193,fareast -Art Fitzpatrick,0.49847245,fineart -Cherryl Fountain,0.5440454,fineart -Skottie Young,0.5440119,cartoon -NC Wyeth,0.54382974,digipa-high-impact -Rudolf Freund,0.5437342,fineart -Mort Kunstler,0.5433619,digipa-high-impact -Ben Goossens,0.53002644,digipa-high-impact -Andreas Rocha,0.49621177,special -Gérard Ernest Schneider,0.5429964,fineart -Francesco Filippini,0.5429598,digipa-high-impact -Alejandro Jodorowsky,0.5429065,digipa-high-impact -Friedrich Traffelet,0.5428817,fineart -Honor C. Appleton,0.5428735,digipa-high-impact -Jason A. Engle,0.542821,fineart -Henry Otto Wix,0.54271996,fineart -Gregory Manchess,0.54270375,fineart -Ann Stookey,0.54269934,digipa-high-impact -Henryk Rodakowski,0.542589,fineart -Albert Welti,0.5425134,digipa-high-impact -Gerard Houckgeest,0.5424413,digipa-high-impact -Dorothy Hood,0.54226196,digipa-high-impact -Frank Schoonover,0.51056194,fineart -Erlund Hudson,0.5422107,digipa-high-impact -Alexander Litovchenko,0.54210097,fineart -Sakai Hōitsu,0.5420294,digipa-high-impact -Benito Quinquela Martín,0.54194224,fineart -David Watson Stevenson,0.54191554,fineart -Ann Thetis Blacker,0.5416629,digipa-high-impact -Frank DuMond,0.51004076,digipa-med-impact -David Dougal Williams,0.5410126,digipa-high-impact -Robert Mcginnis,0.54098356,fineart -Ernest Briggs,0.5408636,fineart -Ferenc Joachim,0.5408625,fineart -Carlos Saenz de Tejada,0.47332364,digipa-low-impact -David Burton-Richardson,0.49659324,digipa-med-impact -Ernest Heber Thompson,0.54039246,digipa-high-impact -Albert Bertelsen,0.54038215,nudity -Giorgio Giulio Clovio,0.5403708,fineart -Eugene Leroy,0.54019785,digipa-high-impact -Anna Findlay,0.54018176,digipa-high-impact -Roy Gjertson,0.54012,digipa-high-impact -Charmion von Wiegand,0.5400893,fineart -Arnold Bronckhorst,0.526247,fineart -Boris Vallejo,0.487253,fineart -Adélaïde Victoire Hall,0.539939,fineart -Earl Norem,0.5398575,fineart -Sanford Kossin,0.53977877,digipa-high-impact -Aert de Gelder,0.519166,digipa-med-impact -Carl Eugen Keel,0.539739,digipa-high-impact -Francis Bourgeois,0.5397272,digipa-high-impact -Bojan Jevtic,0.41141546,fineart -Edward Avedisian,0.5393925,fineart -Gao Xiang,0.5392419,fareast -Charles Hinman,0.53911865,digipa-high-impact -Frits Van den Berghe,0.53896487,fineart -Carlo Martini,0.5384833,digipa-high-impact -Elina Karimova,0.5384318,digipa-high-impact -Anto Carte,0.4708289,digipa-low-impact -Andrey Yefimovich Martynov,0.537721,fineart -Frances Jetter,0.5376904,fineart -Yuri Ivanovich Pimenov,0.5342793,fineart -Gaston Anglade,0.537608,digipa-high-impact -Albert Swinden,0.5375844,fineart -Bob Byerley,0.5375774,fineart -A.B. Frost,0.5375025,fineart -Jaya Suberg,0.5372893,digipa-high-impact -Josh Keyes,0.53654516,digipa-high-impact -Juliana Huxtable,0.5364195,n -Everett Warner,0.53641814,digipa-high-impact -Hugh Kretschmer,0.45171157,digipa-low-impact -Arnold Blanch,0.535774,fineart -Ryan McGinley,0.53572595,digipa-high-impact -Alfons Karpiński,0.53564656,fineart -George Aleef,0.5355317,digipa-high-impact -Hal Foster,0.5351446,fineart -Stuart Immonen,0.53501946,digipa-high-impact -Craig Thompson,0.5346844,digipa-high-impact -Bartolomeo Vivarini,0.53465015,fineart -Hermann Feierabend,0.5346168,digipa-high-impact -Antonio Donghi,0.4610982,digipa-low-impact -Adonna Khare,0.4858036,digipa-med-impact -James Stokoe,0.5015107,digipa-med-impact -Art & Language,0.5341332,digipa-high-impact -Agustín Fernández,0.53403986,fineart -Germán Londoño,0.5338712,fineart -Emmanuelle Moureaux,0.5335641,digipa-high-impact -Conrad Marca-Relli,0.5148334,digipa-med-impact -Gyula Batthyány,0.5332407,fineart -Francesco Raibolini,0.53314835,fineart -Apelles,0.5166026,fineart -Marat Latypov,0.45811993,fineart -Andrei Markin,0.5328752,fineart -Einar Hakonarson,0.5328311,digipa-high-impact -Beatrice Huntington,0.5328165,digipa-high-impact -Coppo di Marcovaldo,0.5327443,fineart -Gregorio Prestopino,0.53250784,fineart -A.D.M. Cooper,0.53244877,digipa-high-impact -Horatio McCulloch,0.53244334,digipa-high-impact -Wes Anderson,0.5318741,digipa-high-impact -Moebius,0.53178746,digipa-high-impact -Gerard Soest,0.53160626,fineart -Charles Ellison,0.53152347,digipa-high-impact -Wojciech Ostrycharz,0.5314213,fineart -Doug Chiang,0.5313724,fineart -Anne Savage,0.5310638,digipa-high-impact -Cor Melchers,0.53099334,fineart -Gordon Browne,0.5308195,digipa-high-impact -Augustus Earle,0.49196815,fineart -Carlos Francisco Chang Marín,0.5304734,fineart -Larry Elmore,0.53032553,fineart -Adolf Hölzel,0.5303149,fineart -David Ligare,0.5301894,fineart -Jan Luyken,0.52985555,fineart -Earle Bergey,0.5298525,fineart -David Ramsay Hay,0.52974963,digipa-high-impact -Alfred East,0.5296565,digipa-high-impact -A. R. Middleton Todd,0.50988734,fineart -Giorgio De Vincenzi,0.5291678,fineart -Hugh William Williams,0.5291014,digipa-high-impact -Erwin Bowien,0.52895796,digipa-high-impact -Victor Adame Minguez,0.5288686,fineart -Yoji Shinkawa,0.5287015,anime -Clara Weaver Parrish,0.5284487,digipa-high-impact -Albert Eckhout,0.5284096,fineart -Dorothy Coke,0.5282345,digipa-high-impact -Jerzy Duda-Gracz,0.5279943,digipa-high-impact -Byron Galvez,0.39178842,fareast -Alson S. Clark,0.5278568,digipa-high-impact -Adolf Ulric Wertmüller,0.5278296,digipa-high-impact -Bruce Coville,0.5277226,digipa-high-impact -Gong Kai,0.5276811,digipa-high-impact -Andréi Arinouchkine,0.52763486,digipa-high-impact -Florence Engelbach,0.5273161,digipa-high-impact -Brian Froud,0.5270276,fineart -Charles Thomson,0.5270127,digipa-high-impact -Bessie Wheeler,0.5269164,digipa-high-impact -Anton Lehmden,0.5268611,fineart -Emilia Wilk,0.5264961,fineart -Carl Eytel,0.52646196,digipa-high-impact -Alfred Janes,0.5264481,digipa-high-impact -Julie Bell,0.49962538,fineart -Eugenio de Arriba,0.52613926,digipa-high-impact -Samuel and Joseph Newsom,0.52595663,digipa-high-impact -Hans Falk,0.52588874,digipa-high-impact -Guillermo del Toro,0.52565175,digipa-high-impact -Félix Arauz,0.52555984,digipa-high-impact -Gyula Basch,0.52524436,digipa-high-impact -Haroon Mirza,0.5252279,digipa-high-impact -Du Jin,0.5249934,digipa-med-impact -Harry Shoulberg,0.5249456,digipa-med-impact -Arie Smit,0.5249027,fineart -Ahmed Karahisari,0.4259451,digipa-low-impact -Brian and Wendy Froud,0.5246335,fineart -E. William Gollings,0.52461207,digipa-med-impact -Bo Bartlett,0.51341593,digipa-med-impact -Hans Burgkmair,0.52416867,digipa-med-impact -David Macaulay,0.5241233,digipa-med-impact -Benedetto Caliari,0.52370214,digipa-med-impact -Eliott Lilly,0.5235398,digipa-med-impact -Vincent Tanguay,0.48578292,digipa-med-impact -Ada Hill Walker,0.52207166,fineart -Christopher Wood,0.49360397,digipa-med-impact -Kris Kuksi,0.43938053,digipa-low-impact -Chen Yifei,0.5217867,fineart -Margaux Valonia,0.5217782,digipa-med-impact -Antoni Pitxot,0.40582713,digipa-low-impact -Jhonen Vasquez,0.5216471,digipa-med-impact -Emilio Grau Sala,0.52156484,fineart -Henry B. Christian,0.52153796,fineart -Jacques Nathan-Garamond,0.52144086,digipa-med-impact -Eddie Mendoza,0.4949638,digipa-med-impact -Grzegorz Rutkowski,0.48906532,special -Beeple,0.40085253,digipa-low-impact -Giorgio Cavallon,0.5209209,digipa-med-impact -Godfrey Blow,0.52062386,digipa-med-impact -Gabriel Dawe,0.5204431,fineart -Emile Lahner,0.5202367,digipa-med-impact -Steve Dillon,0.5201676,digipa-med-impact -Lee Quinones,0.4626683,digipa-low-impact -Hale Woodruff,0.52000225,digipa-med-impact -Tom Hammick,0.5032626,digipa-med-impact -Hamilton Sloan,0.5197798,digipa-med-impact -Caesar Andrade Faini,0.51971483,digipa-med-impact -Sam Spratt,0.48991,digipa-med-impact -Chris Cold,0.4753577,fineart -Alejandro Obregón,0.5190562,digipa-med-impact -Dan Flavin,0.51901346,digipa-med-impact -Arthur Sarnoff,0.5189428,fineart -Elenore Abbott,0.5187141,digipa-med-impact -Andrea Kowch,0.51822996,digipa-med-impact -Demetrios Farmakopoulos,0.5181248,digipa-med-impact -Alexis Grimou,0.41958088,digipa-low-impact -Lesley Vance,0.5177536,digipa-med-impact -Gyula Aggházy,0.517747,fineart -Georgina Hunt,0.46105456,digipa-low-impact -Christian W. Staudinger,0.4684662,digipa-low-impact -Abraham Begeyn,0.5172538,digipa-med-impact -Charles Mozley,0.5171356,digipa-med-impact -Elias Ravanetti,0.38719344,digipa-low-impact -Herman van Swanevelt,0.5168748,digipa-med-impact -David Paton,0.4842217,digipa-med-impact -Hans Werner Schmidt,0.51671976,digipa-med-impact -Bob Ross,0.51628315,fineart -Sou Fujimoto,0.5162528,fareast -Balcomb Greene,0.5162045,digipa-med-impact -Glen Angus,0.51609933,digipa-med-impact -Buckminster Fuller,0.51607454,digipa-med-impact -Andrei Ryabushkin,0.5158933,fineart -Almeida Júnior,0.515856,digipa-med-impact -Tim White,0.4182697,digipa-low-impact -Hans Beat Wieland,0.51553553,digipa-med-impact -Jakub Różalski,0.5154904,digipa-med-impact -John Whitcomb,0.51523805,digipa-med-impact -Dorothy King,0.5150925,digipa-med-impact -Richard S. Johnson,0.51500344,fineart -Aniello Falcone,0.51475304,digipa-med-impact -Henning Jakob Henrik Lund,0.5147134,c -Robert M Cunningham,0.5144858,digipa-med-impact -Nick Knight,0.51447505,digipa-med-impact -David Chipperfield,0.51424,digipa-med-impact -Bartolomeo Cesi,0.5136737,digipa-med-impact -Bettina Heinen-Ayech,0.51334465,digipa-med-impact -Annabel Kidston,0.51327646,digipa-med-impact -Charles Schridde,0.51308405,digipa-med-impact -Samuel Earp,0.51305825,digipa-med-impact -Eugene Montgomery,0.5128343,digipa-med-impact -Alfred Parsons,0.5127445,digipa-med-impact -Anton Möller,0.5127209,digipa-med-impact -Craig Davison,0.499598,special -Cricorps Grégoire,0.51267076,fineart -Celia Fiennes,0.51266706,digipa-med-impact -Raymond Swanland,0.41350424,fineart -Howard Knotts,0.5122062,digipa-med-impact -Helmut Federle,0.51201206,digipa-med-impact -Tyler Edlin,0.44028252,digipa-high-impact -Elwood H. Smith,0.5119027,digipa-med-impact -Ralph Horsley,0.51142794,fineart -Alexander Ivanov,0.4539051,digipa-low-impact -Cedric Peyravernay,0.4200587,digipa-low-impact -Annabel Eyres,0.51136214,digipa-med-impact -Zack Snyder,0.51129746,digipa-med-impact -Gentile Bellini,0.511102,digipa-med-impact -Giovanni Pelliccioli,0.4868688,digipa-med-impact -Fikret Muallâ Saygı,0.510694,digipa-med-impact -Bauhaus,0.43454266,digipa-low-impact -Charles Williams,0.510406,digipa-med-impact -Georg Arnold-Graboné,0.5103381,digipa-med-impact -Fedot Sychkov,0.47935224,digipa-med-impact -Alberto Magnelli,0.5103212,digipa-med-impact -Aloysius O'Kelly,0.5102891,digipa-med-impact -Alexander McQueen,0.5101986,digipa-med-impact -Cam Sykes,0.510071,digipa-med-impact -George Lucas,0.510038,digipa-med-impact -Eglon van der Neer,0.5099339,digipa-med-impact -Christian August Lorentzen,0.50989646,digipa-med-impact -Eleanor Best,0.50966686,digipa-med-impact -Terry Redlin,0.474244,fineart -Ken Kelly,0.4304738,fineart -David 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Kunz,0.4356005,digipa-low-impact -Colin Geller,0.43559563,digipa-low-impact -Allen Tupper True,0.43556124,digipa-low-impact -Jef Wu,0.43555313,digipa-low-impact -Jon McCoy,0.4147122,digipa-low-impact -Cedric Seaut,0.43521535,digipa-low-impact -Emily Shanks,0.43519047,digipa-low-impact -Andrew Whem,0.43512022,digipa-low-impact -Ibrahim Kodra,0.43471518,digipa-low-impact -Harrington Mann,0.4345901,digipa-low-impact -Jerry Siegel,0.43458986,digipa-low-impact -Howard Kanovitz,0.4345178,digipa-low-impact -Cicely Hey,0.43449926,digipa-low-impact -Ben Thompson,0.43436068,digipa-low-impact -Joe Bowler,0.43413073,digipa-low-impact -Lori Earley,0.43389612,digipa-low-impact -Arent Arentsz,0.43373522,digipa-low-impact -David Bailly,0.43371305,digipa-low-impact -Hans Arnold,0.4335214,digipa-low-impact -Constance Copeman,0.4334836,digipa-low-impact -Brent Heighton,0.4333118,fineart -Eric Taylor,0.43312082,digipa-low-impact -Aleksander Gine,0.4326849,digipa-low-impact -Alexander Johnston,0.4326589,digipa-low-impact -David Park,0.43235332,digipa-low-impact -Balázs Diószegi,0.432244,digipa-low-impact -Ed Binkley,0.43222216,digipa-low-impact -Eric Dinyer,0.4321258,digipa-low-impact -Susan Luo,0.43198025,fareast -Cedric Seaut (Keos Masons),0.4317356,digipa-low-impact -Lorena Alvarez Gómez,0.431683,digipa-low-impact -Fred Ludekens,0.431662,digipa-low-impact -David Begbie,0.4316218,digipa-low-impact -Ai Xuan,0.43150818,fareast -Felix-Kelly,0.43132153,digipa-low-impact -Antonín Chittussi,0.431248,digipa-low-impact -Ammi Phillips,0.43095884,digipa-low-impact -Elke Vogelsang,0.43092483,digipa-low-impact -Fathi Hassan,0.43090487,digipa-low-impact -Angela Sung,0.391746,fareast -Clément Serveau,0.43050706,digipa-low-impact -Dong Yuan,0.4303865,fareast -Hew Lorimer,0.43035403,digipa-low-impact -David Finch,0.29487437,digipa-low-impact -Bill Durgin,0.4300932,digipa-low-impact -Alexander Robertson,0.4300743,digipa-low-impact diff --git a/configs/alt-diffusion-inference.yaml b/configs/alt-diffusion-inference.yaml new file mode 100644 index 00000000000..4944ab5c8dc --- /dev/null +++ b/configs/alt-diffusion-inference.yaml @@ -0,0 +1,72 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: modules.xlmr.BertSeriesModelWithTransformation + params: + name: "XLMR-Large" \ No newline at end of file diff --git a/configs/alt-diffusion-m18-inference.yaml b/configs/alt-diffusion-m18-inference.yaml new file mode 100644 index 00000000000..c60dca8c7b3 --- /dev/null +++ b/configs/alt-diffusion-m18-inference.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + use_checkpoint: False + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: modules.xlmr_m18.BertSeriesModelWithTransformation + params: + name: "XLMR-Large" diff --git a/configs/instruct-pix2pix.yaml b/configs/instruct-pix2pix.yaml new file mode 100644 index 00000000000..564e50ae246 --- /dev/null +++ b/configs/instruct-pix2pix.yaml @@ -0,0 +1,98 @@ +# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion). +# See more details in LICENSE. + +model: + base_learning_rate: 1.0e-04 + target: modules.models.diffusion.ddpm_edit.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: edited + cond_stage_key: edit + # image_size: 64 + # image_size: 32 + image_size: 16 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: false + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 0 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + +data: + target: main.DataModuleFromConfig + params: + batch_size: 128 + num_workers: 1 + wrap: false + validation: + target: edit_dataset.EditDataset + params: + path: data/clip-filtered-dataset + cache_dir: data/ + cache_name: data_10k + split: val + min_text_sim: 0.2 + min_image_sim: 0.75 + min_direction_sim: 0.2 + max_samples_per_prompt: 1 + min_resize_res: 512 + max_resize_res: 512 + crop_res: 512 + output_as_edit: False + real_input: True diff --git a/configs/sd3-inference.yaml b/configs/sd3-inference.yaml new file mode 100644 index 00000000000..bccb69d2ea3 --- /dev/null +++ b/configs/sd3-inference.yaml @@ -0,0 +1,5 @@ +model: + target: modules.models.sd3.sd3_model.SD3Inferencer + params: + shift: 3 + state_dict: null diff --git a/configs/sd_xl_inpaint.yaml b/configs/sd_xl_inpaint.yaml new file mode 100644 index 00000000000..f40f45e3316 --- /dev/null +++ b/configs/sd_xl_inpaint.yaml @@ -0,0 +1,98 @@ +model: + target: sgm.models.diffusion.DiffusionEngine + params: + scale_factor: 0.13025 + disable_first_stage_autocast: True + + denoiser_config: + target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser + params: + num_idx: 1000 + + weighting_config: + target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting + scaling_config: + target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling + discretization_config: + target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization + + network_config: + target: sgm.modules.diffusionmodules.openaimodel.UNetModel + params: + adm_in_channels: 2816 + num_classes: sequential + use_checkpoint: False + in_channels: 9 + out_channels: 4 + model_channels: 320 + attention_resolutions: [4, 2] + num_res_blocks: 2 + channel_mult: [1, 2, 4] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: [1, 2, 10] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16 + context_dim: 2048 + spatial_transformer_attn_type: softmax-xformers + legacy: False + + conditioner_config: + target: sgm.modules.GeneralConditioner + params: + emb_models: + # crossattn cond + - is_trainable: False + input_key: txt + target: sgm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: hidden + layer_idx: 11 + # crossattn and vector cond + - is_trainable: False + input_key: txt + target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2 + params: + arch: ViT-bigG-14 + version: laion2b_s39b_b160k + freeze: True + layer: penultimate + always_return_pooled: True + legacy: False + # vector cond + - is_trainable: False + input_key: original_size_as_tuple + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + input_key: crop_coords_top_left + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + input_key: target_size_as_tuple + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + + first_stage_config: + target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + attn_type: vanilla-xformers + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [1, 2, 4, 4] + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity diff --git a/configs/v1-inference.yaml b/configs/v1-inference.yaml new file mode 100644 index 00000000000..25c4d9ed066 --- /dev/null +++ b/configs/v1-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/configs/v1-inpainting-inference.yaml b/configs/v1-inpainting-inference.yaml new file mode 100644 index 00000000000..68c199f99c3 --- /dev/null +++ b/configs/v1-inpainting-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 7.5e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid # important + monitor: val/loss_simple_ema + scale_factor: 0.18215 + finetune_keys: null + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 9 # 4 data + 4 downscaled image + 1 mask + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: False + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/environment-wsl2.yaml b/environment-wsl2.yaml index f8872750783..0c4ae680999 100644 --- a/environment-wsl2.yaml +++ b/environment-wsl2.yaml @@ -4,8 +4,8 @@ channels: - defaults dependencies: - python=3.10 - - pip=22.2.2 - - cudatoolkit=11.3 - - pytorch=1.12.1 - - torchvision=0.13.1 - - numpy=1.23.1 \ No newline at end of file + - pip=23.0 + - cudatoolkit=11.8 + - pytorch=2.0 + - torchvision=0.15 + - numpy=1.23 diff --git a/modules/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py similarity index 77% rename from modules/ldsr_model_arch.py rename to extensions-builtin/LDSR/ldsr_model_arch.py index 14db507668c..7cac36ce55a 100644 --- a/modules/ldsr_model_arch.py +++ b/extensions-builtin/LDSR/ldsr_model_arch.py @@ -1,6 +1,6 @@ +import os import gc import time -import warnings import numpy as np import torch @@ -8,27 +8,47 @@ from PIL import Image from einops import rearrange, repeat from omegaconf import OmegaConf +import safetensors.torch from ldm.models.diffusion.ddim import DDIMSampler from ldm.util import instantiate_from_config, ismap +from modules import shared, sd_hijack, devices -warnings.filterwarnings("ignore", category=UserWarning) +cached_ldsr_model: torch.nn.Module = None # Create LDSR Class class LDSR: def load_model_from_config(self, half_attention): - print(f"Loading model from {self.modelPath}") - pl_sd = torch.load(self.modelPath, map_location="cpu") - sd = pl_sd["state_dict"] - config = OmegaConf.load(self.yamlPath) - model = instantiate_from_config(config.model) - model.load_state_dict(sd, strict=False) - model.cuda() - if half_attention: - model = model.half() - - model.eval() + global cached_ldsr_model + + if shared.opts.ldsr_cached and cached_ldsr_model is not None: + print("Loading model from cache") + model: torch.nn.Module = cached_ldsr_model + else: + print(f"Loading model from {self.modelPath}") + _, extension = os.path.splitext(self.modelPath) + if extension.lower() == ".safetensors": + pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu") + else: + pl_sd = torch.load(self.modelPath, map_location="cpu") + sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd + config = OmegaConf.load(self.yamlPath) + config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1" + model: torch.nn.Module = instantiate_from_config(config.model) + model.load_state_dict(sd, strict=False) + model = model.to(shared.device) + if half_attention: + model = model.half() + if shared.cmd_opts.opt_channelslast: + model = model.to(memory_format=torch.channels_last) + + sd_hijack.model_hijack.hijack(model) # apply optimization + model.eval() + + if shared.opts.ldsr_cached: + cached_ldsr_model = model + return {"model": model} def __init__(self, model_path, yaml_path): @@ -68,7 +88,7 @@ def run(model, selected_path, custom_steps, eta): x_t = None logs = None - for n in range(n_runs): + for _ in range(n_runs): if custom_shape is not None: x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device) x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0]) @@ -90,10 +110,9 @@ def super_resolution(self, image, steps=100, target_scale=2, half_attention=Fals diffusion_steps = int(steps) eta = 1.0 - down_sample_method = 'Lanczos' gc.collect() - torch.cuda.empty_cache() + devices.torch_gc() im_og = image width_og, height_og = im_og.size @@ -101,8 +120,8 @@ def super_resolution(self, image, steps=100, target_scale=2, half_attention=Fals down_sample_rate = target_scale / 4 wd = width_og * down_sample_rate hd = height_og * down_sample_rate - width_downsampled_pre = int(wd) - height_downsampled_pre = int(hd) + width_downsampled_pre = int(np.ceil(wd)) + height_downsampled_pre = int(np.ceil(hd)) if down_sample_rate != 1: print( @@ -110,7 +129,12 @@ def super_resolution(self, image, steps=100, target_scale=2, half_attention=Fals im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS) else: print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)") - logs = self.run(model["model"], im_og, diffusion_steps, eta) + + # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts + pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size + im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge')) + + logs = self.run(model["model"], im_padded, diffusion_steps, eta) sample = logs["sample"] sample = sample.detach().cpu() @@ -120,14 +144,18 @@ def super_resolution(self, image, steps=100, target_scale=2, half_attention=Fals sample = np.transpose(sample, (0, 2, 3, 1)) a = Image.fromarray(sample[0]) + # remove padding + a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4)) + del model gc.collect() - torch.cuda.empty_cache() + devices.torch_gc() + return a def get_cond(selected_path): - example = dict() + example = {} up_f = 4 c = selected_path.convert('RGB') c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0) @@ -137,7 +165,7 @@ def get_cond(selected_path): c = rearrange(c, '1 c h w -> 1 h w c') c = 2. * c - 1. - c = c.to(torch.device("cuda")) + c = c.to(shared.device) example["LR_image"] = c example["image"] = c_up @@ -165,7 +193,7 @@ def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_s @torch.no_grad() def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None, corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False): - log = dict() + log = {} z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key, return_first_stage_outputs=True, @@ -213,7 +241,7 @@ def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True) log["sample_noquant"] = x_sample_noquant log["sample_diff"] = torch.abs(x_sample_noquant - x_sample) - except: + except Exception: pass log["sample"] = x_sample diff --git a/extensions-builtin/LDSR/preload.py b/extensions-builtin/LDSR/preload.py new file mode 100644 index 00000000000..d746007c7cc --- /dev/null +++ b/extensions-builtin/LDSR/preload.py @@ -0,0 +1,6 @@ +import os +from modules import paths + + +def preload(parser): + parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR')) diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py new file mode 100644 index 00000000000..bd78decea1e --- /dev/null +++ b/extensions-builtin/LDSR/scripts/ldsr_model.py @@ -0,0 +1,68 @@ +import os + +from modules.modelloader import load_file_from_url +from modules.upscaler import Upscaler, UpscalerData +from ldsr_model_arch import LDSR +from modules import shared, script_callbacks, errors +import sd_hijack_autoencoder # noqa: F401 +import sd_hijack_ddpm_v1 # noqa: F401 + + +class UpscalerLDSR(Upscaler): + def __init__(self, user_path): + self.name = "LDSR" + self.user_path = user_path + self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1" + self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1" + super().__init__() + scaler_data = UpscalerData("LDSR", None, self) + self.scalers = [scaler_data] + + def load_model(self, path: str): + # Remove incorrect project.yaml file if too big + yaml_path = os.path.join(self.model_path, "project.yaml") + old_model_path = os.path.join(self.model_path, "model.pth") + new_model_path = os.path.join(self.model_path, "model.ckpt") + + local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"]) + local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None) + local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None) + local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None) + + if os.path.exists(yaml_path): + statinfo = os.stat(yaml_path) + if statinfo.st_size >= 10485760: + print("Removing invalid LDSR YAML file.") + os.remove(yaml_path) + + if os.path.exists(old_model_path): + print("Renaming model from model.pth to model.ckpt") + os.rename(old_model_path, new_model_path) + + if local_safetensors_path is not None and os.path.exists(local_safetensors_path): + model = local_safetensors_path + else: + model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt") + + yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml") + + return LDSR(model, yaml) + + def do_upscale(self, img, path): + try: + ldsr = self.load_model(path) + except Exception: + errors.report(f"Failed loading LDSR model {path}", exc_info=True) + return img + ddim_steps = shared.opts.ldsr_steps + return ldsr.super_resolution(img, ddim_steps, self.scale) + + +def on_ui_settings(): + import gradio as gr + + shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling"))) + shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling"))) + + +script_callbacks.on_ui_settings(on_ui_settings) diff --git a/extensions-builtin/LDSR/sd_hijack_autoencoder.py b/extensions-builtin/LDSR/sd_hijack_autoencoder.py new file mode 100644 index 00000000000..c29d274da82 --- /dev/null +++ b/extensions-builtin/LDSR/sd_hijack_autoencoder.py @@ -0,0 +1,293 @@ +# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo +# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo +# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder +import numpy as np +import torch +import pytorch_lightning as pl +import torch.nn.functional as F +from contextlib import contextmanager + +from torch.optim.lr_scheduler import LambdaLR + +from ldm.modules.ema import LitEma +from vqvae_quantize import VectorQuantizer2 as VectorQuantizer +from ldm.modules.diffusionmodules.model import Encoder, Decoder +from ldm.util import instantiate_from_config + +import ldm.models.autoencoder +from packaging import version + +class VQModel(pl.LightningModule): + def __init__(self, + ddconfig, + lossconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=None, + image_key="image", + colorize_nlabels=None, + monitor=None, + batch_resize_range=None, + scheduler_config=None, + lr_g_factor=1.0, + remap=None, + sane_index_shape=False, # tell vector quantizer to return indices as bhw + use_ema=False + ): + super().__init__() + self.embed_dim = embed_dim + self.n_embed = n_embed + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, + remap=remap, + sane_index_shape=sane_index_shape) + self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + self.batch_resize_range = batch_resize_range + if self.batch_resize_range is not None: + print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") + + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or []) + self.scheduler_config = scheduler_config + self.lr_g_factor = lr_g_factor + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=None): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys or []: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if missing: + print(f"Missing Keys: {missing}") + if unexpected: + print(f"Unexpected Keys: {unexpected}") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self) + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info + + def encode_to_prequant(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, quant): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + + def decode_code(self, code_b): + quant_b = self.quantize.embed_code(code_b) + dec = self.decode(quant_b) + return dec + + def forward(self, input, return_pred_indices=False): + quant, diff, (_,_,ind) = self.encode(input) + dec = self.decode(quant) + if return_pred_indices: + return dec, diff, ind + return dec, diff + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + if self.batch_resize_range is not None: + lower_size = self.batch_resize_range[0] + upper_size = self.batch_resize_range[1] + if self.global_step <= 4: + # do the first few batches with max size to avoid later oom + new_resize = upper_size + else: + new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) + if new_resize != x.shape[2]: + x = F.interpolate(x, size=new_resize, mode="bicubic") + x = x.detach() + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + # https://github.com/pytorch/pytorch/issues/37142 + # try not to fool the heuristics + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + + if optimizer_idx == 0: + # autoencode + aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train", + predicted_indices=ind) + + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return aeloss + + if optimizer_idx == 1: + # discriminator + discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) + return discloss + + def validation_step(self, batch, batch_idx): + log_dict = self._validation_step(batch, batch_idx) + with self.ema_scope(): + self._validation_step(batch, batch_idx, suffix="_ema") + return log_dict + + def _validation_step(self, batch, batch_idx, suffix=""): + x = self.get_input(batch, self.image_key) + xrec, qloss, ind = self(x, return_pred_indices=True) + aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, + self.global_step, + last_layer=self.get_last_layer(), + split="val"+suffix, + predicted_indices=ind + ) + + discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, + self.global_step, + last_layer=self.get_last_layer(), + split="val"+suffix, + predicted_indices=ind + ) + rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] + self.log(f"val{suffix}/rec_loss", rec_loss, + prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) + self.log(f"val{suffix}/aeloss", aeloss, + prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) + if version.parse(pl.__version__) >= version.parse('1.4.0'): + del log_dict_ae[f"val{suffix}/rec_loss"] + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr_d = self.learning_rate + lr_g = self.lr_g_factor*self.learning_rate + print("lr_d", lr_d) + print("lr_g", lr_g) + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quantize.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr_g, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr_d, betas=(0.5, 0.9)) + + if self.scheduler_config is not None: + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }, + { + 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }, + ] + return [opt_ae, opt_disc], scheduler + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): + log = {} + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if only_inputs: + log["inputs"] = x + return log + xrec, _ = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["inputs"] = x + log["reconstructions"] = xrec + if plot_ema: + with self.ema_scope(): + xrec_ema, _ = self(x) + if x.shape[1] > 3: + xrec_ema = self.to_rgb(xrec_ema) + log["reconstructions_ema"] = xrec_ema + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +class VQModelInterface(VQModel): + def __init__(self, embed_dim, *args, **kwargs): + super().__init__(*args, embed_dim=embed_dim, **kwargs) + self.embed_dim = embed_dim + + def encode(self, x): + h = self.encoder(x) + h = self.quant_conv(h) + return h + + def decode(self, h, force_not_quantize=False): + # also go through quantization layer + if not force_not_quantize: + quant, emb_loss, info = self.quantize(h) + else: + quant = h + quant = self.post_quant_conv(quant) + dec = self.decoder(quant) + return dec + +ldm.models.autoencoder.VQModel = VQModel +ldm.models.autoencoder.VQModelInterface = VQModelInterface diff --git a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py new file mode 100644 index 00000000000..51ab1821282 --- /dev/null +++ b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py @@ -0,0 +1,1443 @@ +# This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo) +# Original filename: ldm/models/diffusion/ddpm.py +# The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't +# Some models such as LDSR require VQ to work correctly +# The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module + +import torch +import torch.nn as nn +import numpy as np +import pytorch_lightning as pl +from torch.optim.lr_scheduler import LambdaLR +from einops import rearrange, repeat +from contextlib import contextmanager +from functools import partial +from tqdm import tqdm +from torchvision.utils import make_grid +from pytorch_lightning.utilities.distributed import rank_zero_only + +from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config +from ldm.modules.ema import LitEma +from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution +from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL +from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like +from ldm.models.diffusion.ddim import DDIMSampler + +import ldm.models.diffusion.ddpm + +__conditioning_keys__ = {'concat': 'c_concat', + 'crossattn': 'c_crossattn', + 'adm': 'y'} + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +def uniform_on_device(r1, r2, shape, device): + return (r1 - r2) * torch.rand(*shape, device=device) + r2 + + +class DDPMV1(pl.LightningModule): + # classic DDPM with Gaussian diffusion, in image space + def __init__(self, + unet_config, + timesteps=1000, + beta_schedule="linear", + loss_type="l2", + ckpt_path=None, + ignore_keys=None, + load_only_unet=False, + monitor="val/loss", + use_ema=True, + first_stage_key="image", + image_size=256, + channels=3, + log_every_t=100, + clip_denoised=True, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + given_betas=None, + original_elbo_weight=0., + v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta + l_simple_weight=1., + conditioning_key=None, + parameterization="eps", # all assuming fixed variance schedules + scheduler_config=None, + use_positional_encodings=False, + learn_logvar=False, + logvar_init=0., + ): + super().__init__() + assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"' + self.parameterization = parameterization + print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") + self.cond_stage_model = None + self.clip_denoised = clip_denoised + self.log_every_t = log_every_t + self.first_stage_key = first_stage_key + self.image_size = image_size # try conv? + self.channels = channels + self.use_positional_encodings = use_positional_encodings + self.model = DiffusionWrapperV1(unet_config, conditioning_key) + count_params(self.model, verbose=True) + self.use_ema = use_ema + if self.use_ema: + self.model_ema = LitEma(self.model) + print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + self.use_scheduler = scheduler_config is not None + if self.use_scheduler: + self.scheduler_config = scheduler_config + + self.v_posterior = v_posterior + self.original_elbo_weight = original_elbo_weight + self.l_simple_weight = l_simple_weight + + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet) + + self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, + linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + + self.loss_type = loss_type + + self.learn_logvar = learn_logvar + self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) + if self.learn_logvar: + self.logvar = nn.Parameter(self.logvar, requires_grad=True) + + + def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( + 1. - alphas_cumprod) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + if self.parameterization == "eps": + lvlb_weights = self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) + elif self.parameterization == "x0": + lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) + else: + raise NotImplementedError("mu not supported") + # TODO how to choose this term + lvlb_weights[0] = lvlb_weights[1] + self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.model.parameters()) + self.model_ema.copy_to(self.model) + if context is not None: + print(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.model.parameters()) + if context is not None: + print(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=None, only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys or []: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if missing: + print(f"Missing Keys: {missing}") + if unexpected: + print(f"Unexpected Keys: {unexpected}") + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) + variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, clip_denoised: bool): + model_out = self.model(x, t) + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, shape, return_intermediates=False): + device = self.betas.device + b = shape[0] + img = torch.randn(shape, device=device) + intermediates = [img] + for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): + img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), + clip_denoised=self.clip_denoised) + if i % self.log_every_t == 0 or i == self.num_timesteps - 1: + intermediates.append(img) + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, batch_size=16, return_intermediates=False): + image_size = self.image_size + channels = self.channels + return self.p_sample_loop((batch_size, channels, image_size, image_size), + return_intermediates=return_intermediates) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + + def get_loss(self, pred, target, mean=True): + if self.loss_type == 'l1': + loss = (target - pred).abs() + if mean: + loss = loss.mean() + elif self.loss_type == 'l2': + if mean: + loss = torch.nn.functional.mse_loss(target, pred) + else: + loss = torch.nn.functional.mse_loss(target, pred, reduction='none') + else: + raise NotImplementedError("unknown loss type '{loss_type}'") + + return loss + + def p_losses(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_out = self.model(x_noisy, t) + + loss_dict = {} + if self.parameterization == "eps": + target = noise + elif self.parameterization == "x0": + target = x_start + else: + raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported") + + loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3]) + + log_prefix = 'train' if self.training else 'val' + + loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()}) + loss_simple = loss.mean() * self.l_simple_weight + + loss_vlb = (self.lvlb_weights[t] * loss).mean() + loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb}) + + loss = loss_simple + self.original_elbo_weight * loss_vlb + + loss_dict.update({f'{log_prefix}/loss': loss}) + + return loss, loss_dict + + def forward(self, x, *args, **kwargs): + # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size + # assert h == img_size and w == img_size, f'height and width of image must be {img_size}' + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + return self.p_losses(x, t, *args, **kwargs) + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = rearrange(x, 'b h w c -> b c h w') + x = x.to(memory_format=torch.contiguous_format).float() + return x + + def shared_step(self, batch): + x = self.get_input(batch, self.first_stage_key) + loss, loss_dict = self(x) + return loss, loss_dict + + def training_step(self, batch, batch_idx): + loss, loss_dict = self.shared_step(batch) + + self.log_dict(loss_dict, prog_bar=True, + logger=True, on_step=True, on_epoch=True) + + self.log("global_step", self.global_step, + prog_bar=True, logger=True, on_step=True, on_epoch=False) + + if self.use_scheduler: + lr = self.optimizers().param_groups[0]['lr'] + self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False) + + return loss + + @torch.no_grad() + def validation_step(self, batch, batch_idx): + _, loss_dict_no_ema = self.shared_step(batch) + with self.ema_scope(): + _, loss_dict_ema = self.shared_step(batch) + loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema} + self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True) + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self.model) + + def _get_rows_from_list(self, samples): + n_imgs_per_row = len(samples) + denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): + log = {} + x = self.get_input(batch, self.first_stage_key) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + x = x.to(self.device)[:N] + log["inputs"] = x + + # get diffusion row + diffusion_row = [] + x_start = x[:n_row] + + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(x_start) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + diffusion_row.append(x_noisy) + + log["diffusion_row"] = self._get_rows_from_list(diffusion_row) + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) + + log["samples"] = samples + log["denoise_row"] = self._get_rows_from_list(denoise_row) + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.learn_logvar: + params = params + [self.logvar] + opt = torch.optim.AdamW(params, lr=lr) + return opt + + +class LatentDiffusionV1(DDPMV1): + """main class""" + def __init__(self, + first_stage_config, + cond_stage_config, + num_timesteps_cond=None, + cond_stage_key="image", + cond_stage_trainable=False, + concat_mode=True, + cond_stage_forward=None, + conditioning_key=None, + scale_factor=1.0, + scale_by_std=False, + *args, **kwargs): + self.num_timesteps_cond = default(num_timesteps_cond, 1) + self.scale_by_std = scale_by_std + assert self.num_timesteps_cond <= kwargs['timesteps'] + # for backwards compatibility after implementation of DiffusionWrapper + if conditioning_key is None: + conditioning_key = 'concat' if concat_mode else 'crossattn' + if cond_stage_config == '__is_unconditional__': + conditioning_key = None + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + super().__init__(*args, conditioning_key=conditioning_key, **kwargs) + self.concat_mode = concat_mode + self.cond_stage_trainable = cond_stage_trainable + self.cond_stage_key = cond_stage_key + try: + self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 + except Exception: + self.num_downs = 0 + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(scale_factor)) + self.instantiate_first_stage(first_stage_config) + self.instantiate_cond_stage(cond_stage_config) + self.cond_stage_forward = cond_stage_forward + self.clip_denoised = False + self.bbox_tokenizer = None + + self.restarted_from_ckpt = False + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys) + self.restarted_from_ckpt = True + + def make_cond_schedule(self, ): + self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) + ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() + self.cond_ids[:self.num_timesteps_cond] = ids + + @rank_zero_only + @torch.no_grad() + def on_train_batch_start(self, batch, batch_idx, dataloader_idx): + # only for very first batch + if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt: + assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously' + # set rescale weight to 1./std of encodings + print("### USING STD-RESCALING ###") + x = super().get_input(batch, self.first_stage_key) + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + del self.scale_factor + self.register_buffer('scale_factor', 1. / z.flatten().std()) + print(f"setting self.scale_factor to {self.scale_factor}") + print("### USING STD-RESCALING ###") + + def register_schedule(self, + given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s) + + self.shorten_cond_schedule = self.num_timesteps_cond > 1 + if self.shorten_cond_schedule: + self.make_cond_schedule() + + def instantiate_first_stage(self, config): + model = instantiate_from_config(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + if config == "__is_first_stage__": + print("Using first stage also as cond stage.") + self.cond_stage_model = self.first_stage_model + elif config == "__is_unconditional__": + print(f"Training {self.__class__.__name__} as an unconditional model.") + self.cond_stage_model = None + # self.be_unconditional = True + else: + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + assert config != '__is_first_stage__' + assert config != '__is_unconditional__' + model = instantiate_from_config(config) + self.cond_stage_model = model + + def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd.to(self.device), + force_not_quantize=force_no_decoder_quantization)) + n_imgs_per_row = len(denoise_row) + denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + def get_first_stage_encoding(self, encoder_posterior): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + def meshgrid(self, h, w): + y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1) + x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1) + + arr = torch.cat([y, x], dim=-1) + return arr + + def delta_border(self, h, w): + """ + :param h: height + :param w: width + :return: normalized distance to image border, + with min distance = 0 at border and max dist = 0.5 at image center + """ + lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2) + arr = self.meshgrid(h, w) / lower_right_corner + dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0] + dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0] + edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0] + return edge_dist + + def get_weighting(self, h, w, Ly, Lx, device): + weighting = self.delta_border(h, w) + weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"], + self.split_input_params["clip_max_weight"], ) + weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device) + + if self.split_input_params["tie_braker"]: + L_weighting = self.delta_border(Ly, Lx) + L_weighting = torch.clip(L_weighting, + self.split_input_params["clip_min_tie_weight"], + self.split_input_params["clip_max_tie_weight"]) + + L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device) + weighting = weighting * L_weighting + return weighting + + def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code + """ + :param x: img of size (bs, c, h, w) + :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) + """ + bs, nc, h, w = x.shape + + # number of crops in image + Ly = (h - kernel_size[0]) // stride[0] + 1 + Lx = (w - kernel_size[1]) // stride[1] + 1 + + if uf == 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params) + + weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx)) + + elif uf > 1 and df == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf), + dilation=1, padding=0, + stride=(stride[0] * uf, stride[1] * uf)) + fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)) + + elif df > 1 and uf == 1: + fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride) + unfold = torch.nn.Unfold(**fold_params) + + fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df), + dilation=1, padding=0, + stride=(stride[0] // df, stride[1] // df)) + fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2) + + weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype) + normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap + weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)) + + else: + raise NotImplementedError + + return fold, unfold, normalization, weighting + + @torch.no_grad() + def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False, + cond_key=None, return_original_cond=False, bs=None): + x = super().get_input(batch, k) + if bs is not None: + x = x[:bs] + x = x.to(self.device) + encoder_posterior = self.encode_first_stage(x) + z = self.get_first_stage_encoding(encoder_posterior).detach() + + if self.model.conditioning_key is not None: + if cond_key is None: + cond_key = self.cond_stage_key + if cond_key != self.first_stage_key: + if cond_key in ['caption', 'coordinates_bbox']: + xc = batch[cond_key] + elif cond_key == 'class_label': + xc = batch + else: + xc = super().get_input(batch, cond_key).to(self.device) + else: + xc = x + if not self.cond_stage_trainable or force_c_encode: + if isinstance(xc, dict) or isinstance(xc, list): + # import pudb; pudb.set_trace() + c = self.get_learned_conditioning(xc) + else: + c = self.get_learned_conditioning(xc.to(self.device)) + else: + c = xc + if bs is not None: + c = c[:bs] + + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + ckey = __conditioning_keys__[self.model.conditioning_key] + c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y} + + else: + c = None + xc = None + if self.use_positional_encodings: + pos_x, pos_y = self.compute_latent_shifts(batch) + c = {'pos_x': pos_x, 'pos_y': pos_y} + out = [z, c] + if return_first_stage_outputs: + xrec = self.decode_first_stage(z) + out.extend([x, xrec]) + if return_original_cond: + out.append(xc) + return out + + @torch.no_grad() + def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + # same as above but without decorator + def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False): + if predict_cids: + if z.dim() == 4: + z = torch.argmax(z.exp(), dim=1).long() + z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None) + z = rearrange(z, 'b h w c -> b c h w').contiguous() + + z = 1. / self.scale_factor * z + + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + uf = self.split_input_params["vqf"] + bs, nc, h, w = z.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf) + + z = unfold(z) # (bn, nc * prod(**ks), L) + # 1. Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + # 2. apply model loop over last dim + if isinstance(self.first_stage_model, VQModelInterface): + output_list = [self.first_stage_model.decode(z[:, :, :, :, i], + force_not_quantize=predict_cids or force_not_quantize) + for i in range(z.shape[-1])] + else: + + output_list = [self.first_stage_model.decode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L) + o = o * weighting + # Reverse 1. reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization # norm is shape (1, 1, h, w) + return decoded + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + else: + if isinstance(self.first_stage_model, VQModelInterface): + return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize) + else: + return self.first_stage_model.decode(z) + + @torch.no_grad() + def encode_first_stage(self, x): + if hasattr(self, "split_input_params"): + if self.split_input_params["patch_distributed_vq"]: + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + df = self.split_input_params["vqf"] + self.split_input_params['original_image_size'] = x.shape[-2:] + bs, nc, h, w = x.shape + if ks[0] > h or ks[1] > w: + ks = (min(ks[0], h), min(ks[1], w)) + print("reducing Kernel") + + if stride[0] > h or stride[1] > w: + stride = (min(stride[0], h), min(stride[1], w)) + print("reducing stride") + + fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df) + z = unfold(x) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + output_list = [self.first_stage_model.encode(z[:, :, :, :, i]) + for i in range(z.shape[-1])] + + o = torch.stack(output_list, axis=-1) + o = o * weighting + + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + decoded = fold(o) + decoded = decoded / normalization + return decoded + + else: + return self.first_stage_model.encode(x) + else: + return self.first_stage_model.encode(x) + + def shared_step(self, batch, **kwargs): + x, c = self.get_input(batch, self.first_stage_key) + loss = self(x, c) + return loss + + def forward(self, x, c, *args, **kwargs): + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + if self.model.conditioning_key is not None: + assert c is not None + if self.cond_stage_trainable: + c = self.get_learned_conditioning(c) + if self.shorten_cond_schedule: # TODO: drop this option + tc = self.cond_ids[t].to(self.device) + c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float())) + return self.p_losses(x, c, t, *args, **kwargs) + + def apply_model(self, x_noisy, t, cond, return_ids=False): + + if isinstance(cond, dict): + # hybrid case, cond is expected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + + if hasattr(self, "split_input_params"): + assert len(cond) == 1 # todo can only deal with one conditioning atm + assert not return_ids + ks = self.split_input_params["ks"] # eg. (128, 128) + stride = self.split_input_params["stride"] # eg. (64, 64) + + h, w = x_noisy.shape[-2:] + + fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride) + + z = unfold(x_noisy) # (bn, nc * prod(**ks), L) + # Reshape to img shape + z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])] + + if self.cond_stage_key in ["image", "LR_image", "segmentation", + 'bbox_img'] and self.model.conditioning_key: # todo check for completeness + c_key = next(iter(cond.keys())) # get key + c = next(iter(cond.values())) # get value + assert (len(c) == 1) # todo extend to list with more than one elem + c = c[0] # get element + + c = unfold(c) + c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L ) + + cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])] + + elif self.cond_stage_key == 'coordinates_bbox': + assert 'original_image_size' in self.split_input_params, 'BoundingBoxRescaling is missing original_image_size' + + # assuming padding of unfold is always 0 and its dilation is always 1 + n_patches_per_row = int((w - ks[0]) / stride[0] + 1) + full_img_h, full_img_w = self.split_input_params['original_image_size'] + # as we are operating on latents, we need the factor from the original image size to the + # spatial latent size to properly rescale the crops for regenerating the bbox annotations + num_downs = self.first_stage_model.encoder.num_resolutions - 1 + rescale_latent = 2 ** (num_downs) + + # get top left positions of patches as conforming for the bbbox tokenizer, therefore we + # need to rescale the tl patch coordinates to be in between (0,1) + tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w, + rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h) + for patch_nr in range(z.shape[-1])] + + # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w) + patch_limits = [(x_tl, y_tl, + rescale_latent * ks[0] / full_img_w, + rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates] + # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates] + + # tokenize crop coordinates for the bounding boxes of the respective patches + patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device) + for bbox in patch_limits] # list of length l with tensors of shape (1, 2) + print(patch_limits_tknzd[0].shape) + # cut tknzd crop position from conditioning + assert isinstance(cond, dict), 'cond must be dict to be fed into model' + cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device) + print(cut_cond.shape) + + adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]) + adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n') + print(adapted_cond.shape) + adapted_cond = self.get_learned_conditioning(adapted_cond) + print(adapted_cond.shape) + adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1]) + print(adapted_cond.shape) + + cond_list = [{'c_crossattn': [e]} for e in adapted_cond] + + else: + cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient + + # apply model by loop over crops + output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])] + assert not isinstance(output_list[0], + tuple) # todo cant deal with multiple model outputs check this never happens + + o = torch.stack(output_list, axis=-1) + o = o * weighting + # Reverse reshape to img shape + o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L) + # stitch crops together + x_recon = fold(o) / normalization + + else: + x_recon = self.model(x_noisy, t, **cond) + + if isinstance(x_recon, tuple) and not return_ids: + return x_recon[0] + else: + return x_recon + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \ + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + This term can't be optimized, as it only depends on the encoder. + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) + return mean_flat(kl_prior) / np.log(2.0) + + def p_losses(self, x_start, cond, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + model_output = self.apply_model(x_noisy, t, cond) + + loss_dict = {} + prefix = 'train' if self.training else 'val' + + if self.parameterization == "x0": + target = x_start + elif self.parameterization == "eps": + target = noise + else: + raise NotImplementedError() + + loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3]) + loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()}) + + logvar_t = self.logvar[t].to(self.device) + loss = loss_simple / torch.exp(logvar_t) + logvar_t + # loss = loss_simple / torch.exp(self.logvar) + self.logvar + if self.learn_logvar: + loss_dict.update({f'{prefix}/loss_gamma': loss.mean()}) + loss_dict.update({'logvar': self.logvar.data.mean()}) + + loss = self.l_simple_weight * loss.mean() + + loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3)) + loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean() + loss_dict.update({f'{prefix}/loss_vlb': loss_vlb}) + loss += (self.original_elbo_weight * loss_vlb) + loss_dict.update({f'{prefix}/loss': loss}) + + return loss, loss_dict + + def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False, + return_x0=False, score_corrector=None, corrector_kwargs=None): + t_in = t + model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) + + if return_codebook_ids: + model_out, logits = model_out + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1., 1.) + if quantize_denoised: + x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon) + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + if return_codebook_ids: + return model_mean, posterior_variance, posterior_log_variance, logits + elif return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, + return_codebook_ids=False, quantize_denoised=False, return_x0=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, + return_codebook_ids=return_codebook_ids, + quantize_denoised=quantize_denoised, + return_x0=return_x0, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if return_codebook_ids: + raise DeprecationWarning("Support dropped.") + model_mean, _, model_log_variance, logits = outputs + elif return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_codebook_ids: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1) + if return_x0: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False, + img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0., + score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None, + log_every_t=None): + if not log_every_t: + log_every_t = self.log_every_t + timesteps = self.num_timesteps + if batch_size is not None: + b = batch_size if batch_size is not None else shape[0] + shape = [batch_size] + list(shape) + else: + b = batch_size = shape[0] + if x_T is None: + img = torch.randn(shape, device=self.device) + else: + img = x_T + intermediates = [] + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + [x[:batch_size] for x in cond[key]] for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation', + total=timesteps) if verbose else reversed( + range(0, timesteps)) + if type(temperature) == float: + temperature = [temperature] * timesteps + + for i in iterator: + ts = torch.full((b,), i, device=self.device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img, x0_partial = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised, return_x0=True, + temperature=temperature[i], noise_dropout=noise_dropout, + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs) + if mask is not None: + assert x0 is not None + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(x0_partial) + if callback: + callback(i) + if img_callback: + img_callback(img, i) + return img, intermediates + + @torch.no_grad() + def p_sample_loop(self, cond, shape, return_intermediates=False, + x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, start_T=None, + log_every_t=None): + + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + + if start_T is not None: + timesteps = min(timesteps, start_T) + iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed( + range(0, timesteps)) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img = self.p_sample(img, cond, ts, + clip_denoised=self.clip_denoised, + quantize_denoised=quantize_denoised) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: + callback(i) + if img_callback: + img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None, + verbose=True, timesteps=None, quantize_denoised=False, + mask=None, x0=None, shape=None,**kwargs): + if shape is None: + shape = (batch_size, self.channels, self.image_size, self.image_size) + if cond is not None: + if isinstance(cond, dict): + cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else + [x[:batch_size] for x in cond[key]] for key in cond} + else: + cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size] + return self.p_sample_loop(cond, + shape, + return_intermediates=return_intermediates, x_T=x_T, + verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised, + mask=mask, x0=x0) + + @torch.no_grad() + def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): + + if ddim: + ddim_sampler = DDIMSampler(self) + shape = (self.channels, self.image_size, self.image_size) + samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size, + shape,cond,verbose=False,**kwargs) + + else: + samples, intermediates = self.sample(cond=cond, batch_size=batch_size, + return_intermediates=True,**kwargs) + + return samples, intermediates + + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, + quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True, + plot_diffusion_rows=True, **kwargs): + + use_ddim = ddim_steps is not None + + log = {} + z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, + return_first_stage_outputs=True, + force_c_encode=True, + return_original_cond=True, + bs=N) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + log["inputs"] = x + log["reconstruction"] = xrec + if self.model.conditioning_key is not None: + if hasattr(self.cond_stage_model, "decode"): + xc = self.cond_stage_model.decode(c) + log["conditioning"] = xc + elif self.cond_stage_key in ["caption"]: + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"]) + log["conditioning"] = xc + elif self.cond_stage_key == 'class_label': + xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) + log['conditioning'] = xc + elif isimage(xc): + log["conditioning"] = xc + if ismap(xc): + log["original_conditioning"] = self.to_rgb(xc) + + if plot_diffusion_rows: + # get diffusion row + diffusion_row = [] + z_start = z[:n_row] + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(z_start) + z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise) + diffusion_row.append(self.decode_first_stage(z_noisy)) + + diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W + diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w') + diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w') + diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0]) + log["diffusion_row"] = diffusion_grid + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True) + x_samples = self.decode_first_stage(samples) + log["samples"] = x_samples + if plot_denoise_rows: + denoise_grid = self._get_denoise_row_from_list(z_denoise_row) + log["denoise_row"] = denoise_grid + + if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance( + self.first_stage_model, IdentityFirstStage): + # also display when quantizing x0 while sampling + with self.ema_scope("Plotting Quantized Denoised"): + samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, + ddim_steps=ddim_steps,eta=ddim_eta, + quantize_denoised=True) + # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True, + # quantize_denoised=True) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_x0_quantized"] = x_samples + + if inpaint: + # make a simple center square + h, w = z.shape[2], z.shape[3] + mask = torch.ones(N, h, w).to(self.device) + # zeros will be filled in + mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0. + mask = mask[:, None, ...] + with self.ema_scope("Plotting Inpaint"): + + samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_inpainting"] = x_samples + log["mask"] = mask + + # outpaint + with self.ema_scope("Plotting Outpaint"): + samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta, + ddim_steps=ddim_steps, x0=z[:N], mask=mask) + x_samples = self.decode_first_stage(samples.to(self.device)) + log["samples_outpainting"] = x_samples + + if plot_progressive_rows: + with self.ema_scope("Plotting Progressives"): + img, progressives = self.progressive_denoising(c, + shape=(self.channels, self.image_size, self.image_size), + batch_size=N) + prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation") + log["progressive_row"] = prog_row + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + def configure_optimizers(self): + lr = self.learning_rate + params = list(self.model.parameters()) + if self.cond_stage_trainable: + print(f"{self.__class__.__name__}: Also optimizing conditioner params!") + params = params + list(self.cond_stage_model.parameters()) + if self.learn_logvar: + print('Diffusion model optimizing logvar') + params.append(self.logvar) + opt = torch.optim.AdamW(params, lr=lr) + if self.use_scheduler: + assert 'target' in self.scheduler_config + scheduler = instantiate_from_config(self.scheduler_config) + + print("Setting up LambdaLR scheduler...") + scheduler = [ + { + 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule), + 'interval': 'step', + 'frequency': 1 + }] + return [opt], scheduler + return opt + + @torch.no_grad() + def to_rgb(self, x): + x = x.float() + if not hasattr(self, "colorize"): + self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x) + x = nn.functional.conv2d(x, weight=self.colorize) + x = 2. * (x - x.min()) / (x.max() - x.min()) - 1. + return x + + +class DiffusionWrapperV1(pl.LightningModule): + def __init__(self, diff_model_config, conditioning_key): + super().__init__() + self.diffusion_model = instantiate_from_config(diff_model_config) + self.conditioning_key = conditioning_key + assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm'] + + def forward(self, x, t, c_concat: list = None, c_crossattn: list = None): + if self.conditioning_key is None: + out = self.diffusion_model(x, t) + elif self.conditioning_key == 'concat': + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t) + elif self.conditioning_key == 'crossattn': + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc) + elif self.conditioning_key == 'hybrid': + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc) + elif self.conditioning_key == 'adm': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, y=cc) + else: + raise NotImplementedError() + + return out + + +class Layout2ImgDiffusionV1(LatentDiffusionV1): + # TODO: move all layout-specific hacks to this class + def __init__(self, cond_stage_key, *args, **kwargs): + assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"' + super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs) + + def log_images(self, batch, N=8, *args, **kwargs): + logs = super().log_images(*args, batch=batch, N=N, **kwargs) + + key = 'train' if self.training else 'validation' + dset = self.trainer.datamodule.datasets[key] + mapper = dset.conditional_builders[self.cond_stage_key] + + bbox_imgs = [] + map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno)) + for tknzd_bbox in batch[self.cond_stage_key][:N]: + bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256)) + bbox_imgs.append(bboximg) + + cond_img = torch.stack(bbox_imgs, dim=0) + logs['bbox_image'] = cond_img + return logs + +ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1 +ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1 +ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1 +ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1 diff --git a/extensions-builtin/LDSR/vqvae_quantize.py b/extensions-builtin/LDSR/vqvae_quantize.py new file mode 100644 index 00000000000..dd14b8fda5c --- /dev/null +++ b/extensions-builtin/LDSR/vqvae_quantize.py @@ -0,0 +1,147 @@ +# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py, +# where the license is as follows: +# +# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE +# OR OTHER DEALINGS IN THE SOFTWARE./ + +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange + + +class VectorQuantizer2(nn.Module): + """ + Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly + avoids costly matrix multiplications and allows for post-hoc remapping of indices. + """ + + # NOTE: due to a bug the beta term was applied to the wrong term. for + # backwards compatibility we use the buggy version by default, but you can + # specify legacy=False to fix it. + def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", + sane_index_shape=False, legacy=True): + super().__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + self.legacy = legacy + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed + 1 + print(f"Remapping {self.n_e} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_e + + self.sane_index_shape = sane_index_shape + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape) > 1 + inds = inds.reshape(ishape[0], -1) + used = self.used.to(inds) + match = (inds[:, :, None] == used[None, None, ...]).long() + new = match.argmax(-1) + unknown = match.sum(2) < 1 + if self.unknown_index == "random": + new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape) > 1 + inds = inds.reshape(ishape[0], -1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds >= self.used.shape[0]] = 0 # simply set to zero + back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds) + return back.reshape(ishape) + + def forward(self, z, temp=None, rescale_logits=False, return_logits=False): + assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel" + assert rescale_logits is False, "Only for interface compatible with Gumbel" + assert return_logits is False, "Only for interface compatible with Gumbel" + # reshape z -> (batch, height, width, channel) and flatten + z = rearrange(z, 'b c h w -> b h w c').contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \ + torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n')) + + min_encoding_indices = torch.argmin(d, dim=1) + z_q = self.embedding(min_encoding_indices).view(z.shape) + perplexity = None + min_encodings = None + + # compute loss for embedding + if not self.legacy: + loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \ + torch.mean((z_q - z.detach()) ** 2) + else: + loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * \ + torch.mean((z_q - z.detach()) ** 2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # reshape back to match original input shape + z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous() + + if self.remap is not None: + min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis + min_encoding_indices = self.remap_to_used(min_encoding_indices) + min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten + + if self.sane_index_shape: + min_encoding_indices = min_encoding_indices.reshape( + z_q.shape[0], z_q.shape[2], z_q.shape[3]) + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + if self.remap is not None: + indices = indices.reshape(shape[0], -1) # add batch axis + indices = self.unmap_to_all(indices) + indices = indices.reshape(-1) # flatten again + + # get quantized latent vectors + z_q = self.embedding(indices) + + if shape is not None: + z_q = z_q.view(shape) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py new file mode 100644 index 00000000000..17a620f77e3 --- /dev/null +++ b/extensions-builtin/Lora/extra_networks_lora.py @@ -0,0 +1,62 @@ +from modules import extra_networks, shared +import networks + + +class ExtraNetworkLora(extra_networks.ExtraNetwork): + def __init__(self): + super().__init__('lora') + + self.errors = {} + """mapping of network names to the number of errors the network had during operation""" + + remove_symbols = str.maketrans('', '', ":,") + + def activate(self, p, params_list): + additional = shared.opts.sd_lora + + self.errors.clear() + + if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional): + p.all_prompts = [x + f"" for x in p.all_prompts] + params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) + + names = [] + te_multipliers = [] + unet_multipliers = [] + dyn_dims = [] + for params in params_list: + assert params.items + + names.append(params.positional[0]) + + te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0 + te_multiplier = float(params.named.get("te", te_multiplier)) + + unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier + unet_multiplier = float(params.named.get("unet", unet_multiplier)) + + dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None + dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim + + te_multipliers.append(te_multiplier) + unet_multipliers.append(unet_multiplier) + dyn_dims.append(dyn_dim) + + networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims) + + if shared.opts.lora_add_hashes_to_infotext: + if not getattr(p, "is_hr_pass", False) or not hasattr(p, "lora_hashes"): + p.lora_hashes = {} + + for item in networks.loaded_networks: + if item.network_on_disk.shorthash and item.mentioned_name: + p.lora_hashes[item.mentioned_name.translate(self.remove_symbols)] = item.network_on_disk.shorthash + + if p.lora_hashes: + p.extra_generation_params["Lora hashes"] = ', '.join(f'{k}: {v}' for k, v in p.lora_hashes.items()) + + def deactivate(self, p): + if self.errors: + p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items())) + + self.errors.clear() diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py new file mode 100644 index 00000000000..9365aa74b4f --- /dev/null +++ b/extensions-builtin/Lora/lora.py @@ -0,0 +1,9 @@ +import networks + +list_available_loras = networks.list_available_networks + +available_loras = networks.available_networks +available_lora_aliases = networks.available_network_aliases +available_lora_hash_lookup = networks.available_network_hash_lookup +forbidden_lora_aliases = networks.forbidden_network_aliases +loaded_loras = networks.loaded_networks diff --git a/extensions-builtin/Lora/lora_logger.py b/extensions-builtin/Lora/lora_logger.py new file mode 100644 index 00000000000..d51de29704f --- /dev/null +++ b/extensions-builtin/Lora/lora_logger.py @@ -0,0 +1,33 @@ +import sys +import copy +import logging + + +class ColoredFormatter(logging.Formatter): + COLORS = { + "DEBUG": "\033[0;36m", # CYAN + "INFO": "\033[0;32m", # GREEN + "WARNING": "\033[0;33m", # YELLOW + "ERROR": "\033[0;31m", # RED + "CRITICAL": "\033[0;37;41m", # WHITE ON RED + "RESET": "\033[0m", # RESET COLOR + } + + def format(self, record): + colored_record = copy.copy(record) + levelname = colored_record.levelname + seq = self.COLORS.get(levelname, self.COLORS["RESET"]) + colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}" + return super().format(colored_record) + + +logger = logging.getLogger("lora") +logger.propagate = False + + +if not logger.handlers: + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter( + ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s") + ) + logger.addHandler(handler) diff --git a/extensions-builtin/Lora/lora_patches.py b/extensions-builtin/Lora/lora_patches.py new file mode 100644 index 00000000000..b394d8e9ed4 --- /dev/null +++ b/extensions-builtin/Lora/lora_patches.py @@ -0,0 +1,31 @@ +import torch + +import networks +from modules import patches + + +class LoraPatches: + def __init__(self): + self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward) + self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict) + self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward) + self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict) + self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward) + self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict) + self.LayerNorm_forward = patches.patch(__name__, torch.nn.LayerNorm, 'forward', networks.network_LayerNorm_forward) + self.LayerNorm_load_state_dict = patches.patch(__name__, torch.nn.LayerNorm, '_load_from_state_dict', networks.network_LayerNorm_load_state_dict) + self.MultiheadAttention_forward = patches.patch(__name__, torch.nn.MultiheadAttention, 'forward', networks.network_MultiheadAttention_forward) + self.MultiheadAttention_load_state_dict = patches.patch(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict', networks.network_MultiheadAttention_load_state_dict) + + def undo(self): + self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward') + self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict') + self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward') + self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict') + self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward') + self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict') + self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward') + self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict') + self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward') + self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict') + diff --git a/extensions-builtin/Lora/lyco_helpers.py b/extensions-builtin/Lora/lyco_helpers.py new file mode 100644 index 00000000000..6f134d54eb1 --- /dev/null +++ b/extensions-builtin/Lora/lyco_helpers.py @@ -0,0 +1,68 @@ +import torch + + +def make_weight_cp(t, wa, wb): + temp = torch.einsum('i j k l, j r -> i r k l', t, wb) + return torch.einsum('i j k l, i r -> r j k l', temp, wa) + + +def rebuild_conventional(up, down, shape, dyn_dim=None): + up = up.reshape(up.size(0), -1) + down = down.reshape(down.size(0), -1) + if dyn_dim is not None: + up = up[:, :dyn_dim] + down = down[:dyn_dim, :] + return (up @ down).reshape(shape) + + +def rebuild_cp_decomposition(up, down, mid): + up = up.reshape(up.size(0), -1) + down = down.reshape(down.size(0), -1) + return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down) + + +# copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py +def factorization(dimension: int, factor:int=-1) -> tuple[int, int]: + ''' + return a tuple of two value of input dimension decomposed by the number closest to factor + second value is higher or equal than first value. + + In LoRA with Kroneckor Product, first value is a value for weight scale. + secon value is a value for weight. + + Because of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different. + + examples) + factor + -1 2 4 8 16 ... + 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 + 128 -> 8, 16 128 -> 2, 64 128 -> 4, 32 128 -> 8, 16 128 -> 8, 16 + 250 -> 10, 25 250 -> 2, 125 250 -> 2, 125 250 -> 5, 50 250 -> 10, 25 + 360 -> 8, 45 360 -> 2, 180 360 -> 4, 90 360 -> 8, 45 360 -> 12, 30 + 512 -> 16, 32 512 -> 2, 256 512 -> 4, 128 512 -> 8, 64 512 -> 16, 32 + 1024 -> 32, 32 1024 -> 2, 512 1024 -> 4, 256 1024 -> 8, 128 1024 -> 16, 64 + ''' + + if factor > 0 and (dimension % factor) == 0: + m = factor + n = dimension // factor + if m > n: + n, m = m, n + return m, n + if factor < 0: + factor = dimension + m, n = 1, dimension + length = m + n + while m length or new_m>factor: + break + else: + m, n = new_m, new_n + if m > n: + n, m = m, n + return m, n + diff --git a/extensions-builtin/Lora/network.py b/extensions-builtin/Lora/network.py new file mode 100644 index 00000000000..98ff367fd8a --- /dev/null +++ b/extensions-builtin/Lora/network.py @@ -0,0 +1,228 @@ +from __future__ import annotations +import os +from collections import namedtuple +import enum + +import torch.nn as nn +import torch.nn.functional as F + +from modules import sd_models, cache, errors, hashes, shared +import modules.models.sd3.mmdit + +NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module']) + +metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20} + + +class SdVersion(enum.Enum): + Unknown = 1 + SD1 = 2 + SD2 = 3 + SDXL = 4 + + +class NetworkOnDisk: + def __init__(self, name, filename): + self.name = name + self.filename = filename + self.metadata = {} + self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors" + + def read_metadata(): + metadata = sd_models.read_metadata_from_safetensors(filename) + + return metadata + + if self.is_safetensors: + try: + self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata) + except Exception as e: + errors.display(e, f"reading lora {filename}") + + if self.metadata: + m = {} + for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)): + m[k] = v + + self.metadata = m + + self.alias = self.metadata.get('ss_output_name', self.name) + + self.hash = None + self.shorthash = None + self.set_hash( + self.metadata.get('sshs_model_hash') or + hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or + '' + ) + + self.sd_version = self.detect_version() + + def detect_version(self): + if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"): + return SdVersion.SDXL + elif str(self.metadata.get('ss_v2', "")) == "True": + return SdVersion.SD2 + elif len(self.metadata): + return SdVersion.SD1 + + return SdVersion.Unknown + + def set_hash(self, v): + self.hash = v + self.shorthash = self.hash[0:12] + + if self.shorthash: + import networks + networks.available_network_hash_lookup[self.shorthash] = self + + def read_hash(self): + if not self.hash: + self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '') + + def get_alias(self): + import networks + if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases: + return self.name + else: + return self.alias + + +class Network: # LoraModule + def __init__(self, name, network_on_disk: NetworkOnDisk): + self.name = name + self.network_on_disk = network_on_disk + self.te_multiplier = 1.0 + self.unet_multiplier = 1.0 + self.dyn_dim = None + self.modules = {} + self.bundle_embeddings = {} + self.mtime = None + + self.mentioned_name = None + """the text that was used to add the network to prompt - can be either name or an alias""" + + +class ModuleType: + def create_module(self, net: Network, weights: NetworkWeights) -> Network | None: + return None + + +class NetworkModule: + def __init__(self, net: Network, weights: NetworkWeights): + self.network = net + self.network_key = weights.network_key + self.sd_key = weights.sd_key + self.sd_module = weights.sd_module + + if isinstance(self.sd_module, modules.models.sd3.mmdit.QkvLinear): + s = self.sd_module.weight.shape + self.shape = (s[0] // 3, s[1]) + elif hasattr(self.sd_module, 'weight'): + self.shape = self.sd_module.weight.shape + elif isinstance(self.sd_module, nn.MultiheadAttention): + # For now, only self-attn use Pytorch's MHA + # So assume all qkvo proj have same shape + self.shape = self.sd_module.out_proj.weight.shape + else: + self.shape = None + + self.ops = None + self.extra_kwargs = {} + if isinstance(self.sd_module, nn.Conv2d): + self.ops = F.conv2d + self.extra_kwargs = { + 'stride': self.sd_module.stride, + 'padding': self.sd_module.padding + } + elif isinstance(self.sd_module, nn.Linear): + self.ops = F.linear + elif isinstance(self.sd_module, nn.LayerNorm): + self.ops = F.layer_norm + self.extra_kwargs = { + 'normalized_shape': self.sd_module.normalized_shape, + 'eps': self.sd_module.eps + } + elif isinstance(self.sd_module, nn.GroupNorm): + self.ops = F.group_norm + self.extra_kwargs = { + 'num_groups': self.sd_module.num_groups, + 'eps': self.sd_module.eps + } + + self.dim = None + self.bias = weights.w.get("bias") + self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None + self.scale = weights.w["scale"].item() if "scale" in weights.w else None + + self.dora_scale = weights.w.get("dora_scale", None) + self.dora_norm_dims = len(self.shape) - 1 + + def multiplier(self): + if 'transformer' in self.sd_key[:20]: + return self.network.te_multiplier + else: + return self.network.unet_multiplier + + def calc_scale(self): + if self.scale is not None: + return self.scale + if self.dim is not None and self.alpha is not None: + return self.alpha / self.dim + + return 1.0 + + def apply_weight_decompose(self, updown, orig_weight): + # Match the device/dtype + orig_weight = orig_weight.to(updown.dtype) + dora_scale = self.dora_scale.to(device=orig_weight.device, dtype=updown.dtype) + updown = updown.to(orig_weight.device) + + merged_scale1 = updown + orig_weight + merged_scale1_norm = ( + merged_scale1.transpose(0, 1) + .reshape(merged_scale1.shape[1], -1) + .norm(dim=1, keepdim=True) + .reshape(merged_scale1.shape[1], *[1] * self.dora_norm_dims) + .transpose(0, 1) + ) + + dora_merged = ( + merged_scale1 * (dora_scale / merged_scale1_norm) + ) + final_updown = dora_merged - orig_weight + return final_updown + + def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None): + if self.bias is not None: + updown = updown.reshape(self.bias.shape) + updown += self.bias.to(orig_weight.device, dtype=updown.dtype) + updown = updown.reshape(output_shape) + + if len(output_shape) == 4: + updown = updown.reshape(output_shape) + + if orig_weight.size().numel() == updown.size().numel(): + updown = updown.reshape(orig_weight.shape) + + if ex_bias is not None: + ex_bias = ex_bias * self.multiplier() + + updown = updown * self.calc_scale() + + if self.dora_scale is not None: + updown = self.apply_weight_decompose(updown, orig_weight) + + return updown * self.multiplier(), ex_bias + + def calc_updown(self, target): + raise NotImplementedError() + + def forward(self, x, y): + """A general forward implementation for all modules""" + if self.ops is None: + raise NotImplementedError() + else: + updown, ex_bias = self.calc_updown(self.sd_module.weight) + return y + self.ops(x, weight=updown, bias=ex_bias, **self.extra_kwargs) + diff --git a/extensions-builtin/Lora/network_full.py b/extensions-builtin/Lora/network_full.py new file mode 100644 index 00000000000..f221c95f3b5 --- /dev/null +++ b/extensions-builtin/Lora/network_full.py @@ -0,0 +1,27 @@ +import network + + +class ModuleTypeFull(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["diff"]): + return NetworkModuleFull(net, weights) + + return None + + +class NetworkModuleFull(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.weight = weights.w.get("diff") + self.ex_bias = weights.w.get("diff_b") + + def calc_updown(self, orig_weight): + output_shape = self.weight.shape + updown = self.weight.to(orig_weight.device) + if self.ex_bias is not None: + ex_bias = self.ex_bias.to(orig_weight.device) + else: + ex_bias = None + + return self.finalize_updown(updown, orig_weight, output_shape, ex_bias) diff --git a/extensions-builtin/Lora/network_glora.py b/extensions-builtin/Lora/network_glora.py new file mode 100644 index 00000000000..efe5c6814fa --- /dev/null +++ b/extensions-builtin/Lora/network_glora.py @@ -0,0 +1,33 @@ + +import network + +class ModuleTypeGLora(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]): + return NetworkModuleGLora(net, weights) + + return None + +# adapted from https://github.com/KohakuBlueleaf/LyCORIS +class NetworkModuleGLora(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + if hasattr(self.sd_module, 'weight'): + self.shape = self.sd_module.weight.shape + + self.w1a = weights.w["a1.weight"] + self.w1b = weights.w["b1.weight"] + self.w2a = weights.w["a2.weight"] + self.w2b = weights.w["b2.weight"] + + def calc_updown(self, orig_weight): + w1a = self.w1a.to(orig_weight.device) + w1b = self.w1b.to(orig_weight.device) + w2a = self.w2a.to(orig_weight.device) + w2b = self.w2b.to(orig_weight.device) + + output_shape = [w1a.size(0), w1b.size(1)] + updown = ((w2b @ w1b) + ((orig_weight.to(dtype = w1a.dtype) @ w2a) @ w1a)) + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_hada.py b/extensions-builtin/Lora/network_hada.py new file mode 100644 index 00000000000..d95a0fd18e3 --- /dev/null +++ b/extensions-builtin/Lora/network_hada.py @@ -0,0 +1,55 @@ +import lyco_helpers +import network + + +class ModuleTypeHada(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"]): + return NetworkModuleHada(net, weights) + + return None + + +class NetworkModuleHada(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + if hasattr(self.sd_module, 'weight'): + self.shape = self.sd_module.weight.shape + + self.w1a = weights.w["hada_w1_a"] + self.w1b = weights.w["hada_w1_b"] + self.dim = self.w1b.shape[0] + self.w2a = weights.w["hada_w2_a"] + self.w2b = weights.w["hada_w2_b"] + + self.t1 = weights.w.get("hada_t1") + self.t2 = weights.w.get("hada_t2") + + def calc_updown(self, orig_weight): + w1a = self.w1a.to(orig_weight.device) + w1b = self.w1b.to(orig_weight.device) + w2a = self.w2a.to(orig_weight.device) + w2b = self.w2b.to(orig_weight.device) + + output_shape = [w1a.size(0), w1b.size(1)] + + if self.t1 is not None: + output_shape = [w1a.size(1), w1b.size(1)] + t1 = self.t1.to(orig_weight.device) + updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b) + output_shape += t1.shape[2:] + else: + if len(w1b.shape) == 4: + output_shape += w1b.shape[2:] + updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape) + + if self.t2 is not None: + t2 = self.t2.to(orig_weight.device) + updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b) + else: + updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape) + + updown = updown1 * updown2 + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_ia3.py b/extensions-builtin/Lora/network_ia3.py new file mode 100644 index 00000000000..96faeaf3ede --- /dev/null +++ b/extensions-builtin/Lora/network_ia3.py @@ -0,0 +1,30 @@ +import network + + +class ModuleTypeIa3(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["weight"]): + return NetworkModuleIa3(net, weights) + + return None + + +class NetworkModuleIa3(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.w = weights.w["weight"] + self.on_input = weights.w["on_input"].item() + + def calc_updown(self, orig_weight): + w = self.w.to(orig_weight.device) + + output_shape = [w.size(0), orig_weight.size(1)] + if self.on_input: + output_shape.reverse() + else: + w = w.reshape(-1, 1) + + updown = orig_weight * w + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_lokr.py b/extensions-builtin/Lora/network_lokr.py new file mode 100644 index 00000000000..fcdaeafd896 --- /dev/null +++ b/extensions-builtin/Lora/network_lokr.py @@ -0,0 +1,64 @@ +import torch + +import lyco_helpers +import network + + +class ModuleTypeLokr(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + has_1 = "lokr_w1" in weights.w or ("lokr_w1_a" in weights.w and "lokr_w1_b" in weights.w) + has_2 = "lokr_w2" in weights.w or ("lokr_w2_a" in weights.w and "lokr_w2_b" in weights.w) + if has_1 and has_2: + return NetworkModuleLokr(net, weights) + + return None + + +def make_kron(orig_shape, w1, w2): + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + w2 = w2.contiguous() + return torch.kron(w1, w2).reshape(orig_shape) + + +class NetworkModuleLokr(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.w1 = weights.w.get("lokr_w1") + self.w1a = weights.w.get("lokr_w1_a") + self.w1b = weights.w.get("lokr_w1_b") + self.dim = self.w1b.shape[0] if self.w1b is not None else self.dim + self.w2 = weights.w.get("lokr_w2") + self.w2a = weights.w.get("lokr_w2_a") + self.w2b = weights.w.get("lokr_w2_b") + self.dim = self.w2b.shape[0] if self.w2b is not None else self.dim + self.t2 = weights.w.get("lokr_t2") + + def calc_updown(self, orig_weight): + if self.w1 is not None: + w1 = self.w1.to(orig_weight.device) + else: + w1a = self.w1a.to(orig_weight.device) + w1b = self.w1b.to(orig_weight.device) + w1 = w1a @ w1b + + if self.w2 is not None: + w2 = self.w2.to(orig_weight.device) + elif self.t2 is None: + w2a = self.w2a.to(orig_weight.device) + w2b = self.w2b.to(orig_weight.device) + w2 = w2a @ w2b + else: + t2 = self.t2.to(orig_weight.device) + w2a = self.w2a.to(orig_weight.device) + w2b = self.w2b.to(orig_weight.device) + w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b) + + output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)] + if len(orig_weight.shape) == 4: + output_shape = orig_weight.shape + + updown = make_kron(output_shape, w1, w2) + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_lora.py b/extensions-builtin/Lora/network_lora.py new file mode 100644 index 00000000000..a7a088949ea --- /dev/null +++ b/extensions-builtin/Lora/network_lora.py @@ -0,0 +1,94 @@ +import torch + +import lyco_helpers +import modules.models.sd3.mmdit +import network +from modules import devices + + +class ModuleTypeLora(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]): + return NetworkModuleLora(net, weights) + + if all(x in weights.w for x in ["lora_A.weight", "lora_B.weight"]): + w = weights.w.copy() + weights.w.clear() + weights.w.update({"lora_up.weight": w["lora_B.weight"], "lora_down.weight": w["lora_A.weight"]}) + + return NetworkModuleLora(net, weights) + + return None + + +class NetworkModuleLora(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.up_model = self.create_module(weights.w, "lora_up.weight") + self.down_model = self.create_module(weights.w, "lora_down.weight") + self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True) + + self.dim = weights.w["lora_down.weight"].shape[0] + + def create_module(self, weights, key, none_ok=False): + weight = weights.get(key) + + if weight is None and none_ok: + return None + + is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear] + is_conv = type(self.sd_module) in [torch.nn.Conv2d] + + if is_linear: + weight = weight.reshape(weight.shape[0], -1) + module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) + elif is_conv and key == "lora_down.weight" or key == "dyn_up": + if len(weight.shape) == 2: + weight = weight.reshape(weight.shape[0], -1, 1, 1) + + if weight.shape[2] != 1 or weight.shape[3] != 1: + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) + else: + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) + elif is_conv and key == "lora_mid.weight": + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) + elif is_conv and key == "lora_up.weight" or key == "dyn_down": + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) + else: + raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}') + + with torch.no_grad(): + if weight.shape != module.weight.shape: + weight = weight.reshape(module.weight.shape) + module.weight.copy_(weight) + + module.to(device=devices.cpu, dtype=devices.dtype) + module.weight.requires_grad_(False) + + return module + + def calc_updown(self, orig_weight): + up = self.up_model.weight.to(orig_weight.device) + down = self.down_model.weight.to(orig_weight.device) + + output_shape = [up.size(0), down.size(1)] + if self.mid_model is not None: + # cp-decomposition + mid = self.mid_model.weight.to(orig_weight.device) + updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid) + output_shape += mid.shape[2:] + else: + if len(down.shape) == 4: + output_shape += down.shape[2:] + updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim) + + return self.finalize_updown(updown, orig_weight, output_shape) + + def forward(self, x, y): + self.up_model.to(device=devices.device) + self.down_model.to(device=devices.device) + + return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale() + + diff --git a/extensions-builtin/Lora/network_norm.py b/extensions-builtin/Lora/network_norm.py new file mode 100644 index 00000000000..d25afcbb928 --- /dev/null +++ b/extensions-builtin/Lora/network_norm.py @@ -0,0 +1,28 @@ +import network + + +class ModuleTypeNorm(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["w_norm", "b_norm"]): + return NetworkModuleNorm(net, weights) + + return None + + +class NetworkModuleNorm(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.w_norm = weights.w.get("w_norm") + self.b_norm = weights.w.get("b_norm") + + def calc_updown(self, orig_weight): + output_shape = self.w_norm.shape + updown = self.w_norm.to(orig_weight.device) + + if self.b_norm is not None: + ex_bias = self.b_norm.to(orig_weight.device) + else: + ex_bias = None + + return self.finalize_updown(updown, orig_weight, output_shape, ex_bias) diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py new file mode 100644 index 00000000000..1c515ebb739 --- /dev/null +++ b/extensions-builtin/Lora/network_oft.py @@ -0,0 +1,118 @@ +import torch +import network +from einops import rearrange + + +class ModuleTypeOFT(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]): + return NetworkModuleOFT(net, weights) + + return None + +# Supports both kohya-ss' implementation of COFT https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py +# and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py +class NetworkModuleOFT(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + + super().__init__(net, weights) + + self.lin_module = None + self.org_module: list[torch.Module] = [self.sd_module] + + self.scale = 1.0 + self.is_R = False + self.is_boft = False + + # kohya-ss/New LyCORIS OFT/BOFT + if "oft_blocks" in weights.w.keys(): + self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size) + self.alpha = weights.w.get("alpha", None) # alpha is constraint + self.dim = self.oft_blocks.shape[0] # lora dim + # Old LyCORIS OFT + elif "oft_diag" in weights.w.keys(): + self.is_R = True + self.oft_blocks = weights.w["oft_diag"] + # self.alpha is unused + self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size) + + is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear] + is_conv = type(self.sd_module) in [torch.nn.Conv2d] + is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported + + if is_linear: + self.out_dim = self.sd_module.out_features + elif is_conv: + self.out_dim = self.sd_module.out_channels + elif is_other_linear: + self.out_dim = self.sd_module.embed_dim + + # LyCORIS BOFT + if self.oft_blocks.dim() == 4: + self.is_boft = True + self.rescale = weights.w.get('rescale', None) + if self.rescale is not None and not is_other_linear: + self.rescale = self.rescale.reshape(-1, *[1]*(self.org_module[0].weight.dim() - 1)) + + self.num_blocks = self.dim + self.block_size = self.out_dim // self.dim + self.constraint = (0 if self.alpha is None else self.alpha) * self.out_dim + if self.is_R: + self.constraint = None + self.block_size = self.dim + self.num_blocks = self.out_dim // self.dim + elif self.is_boft: + self.boft_m = self.oft_blocks.shape[0] + self.num_blocks = self.oft_blocks.shape[1] + self.block_size = self.oft_blocks.shape[2] + self.boft_b = self.block_size + + def calc_updown(self, orig_weight): + oft_blocks = self.oft_blocks.to(orig_weight.device) + eye = torch.eye(self.block_size, device=oft_blocks.device) + + if not self.is_R: + block_Q = oft_blocks - oft_blocks.transpose(-1, -2) # ensure skew-symmetric orthogonal matrix + if self.constraint != 0: + norm_Q = torch.norm(block_Q.flatten()) + new_norm_Q = torch.clamp(norm_Q, max=self.constraint.to(oft_blocks.device)) + block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8)) + oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse()) + + R = oft_blocks.to(orig_weight.device) + + if not self.is_boft: + # This errors out for MultiheadAttention, might need to be handled up-stream + merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size) + merged_weight = torch.einsum( + 'k n m, k n ... -> k m ...', + R, + merged_weight + ) + merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...') + else: + # TODO: determine correct value for scale + scale = 1.0 + m = self.boft_m + b = self.boft_b + r_b = b // 2 + inp = orig_weight + for i in range(m): + bi = R[i] # b_num, b_size, b_size + if i == 0: + # Apply multiplier/scale and rescale into first weight + bi = bi * scale + (1 - scale) * eye + inp = rearrange(inp, "(c g k) ... -> (c k g) ...", g=2, k=2**i * r_b) + inp = rearrange(inp, "(d b) ... -> d b ...", b=b) + inp = torch.einsum("b i j, b j ... -> b i ...", bi, inp) + inp = rearrange(inp, "d b ... -> (d b) ...") + inp = rearrange(inp, "(c k g) ... -> (c g k) ...", g=2, k=2**i * r_b) + merged_weight = inp + + # Rescale mechanism + if self.rescale is not None: + merged_weight = self.rescale.to(merged_weight) * merged_weight + + updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype) + output_shape = orig_weight.shape + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py new file mode 100644 index 00000000000..67f9abe2a37 --- /dev/null +++ b/extensions-builtin/Lora/networks.py @@ -0,0 +1,737 @@ +from __future__ import annotations +import gradio as gr +import logging +import os +import re + +import lora_patches +import network +import network_lora +import network_glora +import network_hada +import network_ia3 +import network_lokr +import network_full +import network_norm +import network_oft + +import torch +from typing import Union + +from modules import shared, devices, sd_models, errors, scripts, sd_hijack +import modules.textual_inversion.textual_inversion as textual_inversion +import modules.models.sd3.mmdit + +from lora_logger import logger + +module_types = [ + network_lora.ModuleTypeLora(), + network_hada.ModuleTypeHada(), + network_ia3.ModuleTypeIa3(), + network_lokr.ModuleTypeLokr(), + network_full.ModuleTypeFull(), + network_norm.ModuleTypeNorm(), + network_glora.ModuleTypeGLora(), + network_oft.ModuleTypeOFT(), +] + + +re_digits = re.compile(r"\d+") +re_x_proj = re.compile(r"(.*)_([qkv]_proj)$") +re_compiled = {} + +suffix_conversion = { + "attentions": {}, + "resnets": { + "conv1": "in_layers_2", + "conv2": "out_layers_3", + "norm1": "in_layers_0", + "norm2": "out_layers_0", + "time_emb_proj": "emb_layers_1", + "conv_shortcut": "skip_connection", + } +} + + +def convert_diffusers_name_to_compvis(key, is_sd2): + def match(match_list, regex_text): + regex = re_compiled.get(regex_text) + if regex is None: + regex = re.compile(regex_text) + re_compiled[regex_text] = regex + + r = re.match(regex, key) + if not r: + return False + + match_list.clear() + match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()]) + return True + + m = [] + + if match(m, r"lora_unet_conv_in(.*)"): + return f'diffusion_model_input_blocks_0_0{m[0]}' + + if match(m, r"lora_unet_conv_out(.*)"): + return f'diffusion_model_out_2{m[0]}' + + if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"): + return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}" + + if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) + return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" + + if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2]) + return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}" + + if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) + return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" + + if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"): + return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op" + + if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"): + return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv" + + if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"): + if is_sd2: + if 'mlp_fc1' in m[1]: + return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}" + elif 'mlp_fc2' in m[1]: + return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}" + else: + return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}" + + return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}" + + if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"): + if 'mlp_fc1' in m[1]: + return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}" + elif 'mlp_fc2' in m[1]: + return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}" + else: + return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}" + + return key + + +def assign_network_names_to_compvis_modules(sd_model): + network_layer_mapping = {} + + if shared.sd_model.is_sdxl: + for i, embedder in enumerate(shared.sd_model.conditioner.embedders): + if not hasattr(embedder, 'wrapped'): + continue + + for name, module in embedder.wrapped.named_modules(): + network_name = f'{i}_{name.replace(".", "_")}' + network_layer_mapping[network_name] = module + module.network_layer_name = network_name + else: + cond_stage_model = getattr(shared.sd_model.cond_stage_model, 'wrapped', shared.sd_model.cond_stage_model) + + for name, module in cond_stage_model.named_modules(): + network_name = name.replace(".", "_") + network_layer_mapping[network_name] = module + module.network_layer_name = network_name + + for name, module in shared.sd_model.model.named_modules(): + network_name = name.replace(".", "_") + network_layer_mapping[network_name] = module + module.network_layer_name = network_name + + sd_model.network_layer_mapping = network_layer_mapping + + +class BundledTIHash(str): + def __init__(self, hash_str): + self.hash = hash_str + + def __str__(self): + return self.hash if shared.opts.lora_bundled_ti_to_infotext else '' + + +def load_network(name, network_on_disk): + net = network.Network(name, network_on_disk) + net.mtime = os.path.getmtime(network_on_disk.filename) + + sd = sd_models.read_state_dict(network_on_disk.filename) + + # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0 + if not hasattr(shared.sd_model, 'network_layer_mapping'): + assign_network_names_to_compvis_modules(shared.sd_model) + + keys_failed_to_match = {} + is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping + if hasattr(shared.sd_model, 'diffusers_weight_map'): + diffusers_weight_map = shared.sd_model.diffusers_weight_map + elif hasattr(shared.sd_model, 'diffusers_weight_mapping'): + diffusers_weight_map = {} + for k, v in shared.sd_model.diffusers_weight_mapping(): + diffusers_weight_map[k] = v + shared.sd_model.diffusers_weight_map = diffusers_weight_map + else: + diffusers_weight_map = None + + matched_networks = {} + bundle_embeddings = {} + + for key_network, weight in sd.items(): + + if diffusers_weight_map: + key_network_without_network_parts, network_name, network_weight = key_network.rsplit(".", 2) + network_part = network_name + '.' + network_weight + else: + key_network_without_network_parts, _, network_part = key_network.partition(".") + + if key_network_without_network_parts == "bundle_emb": + emb_name, vec_name = network_part.split(".", 1) + emb_dict = bundle_embeddings.get(emb_name, {}) + if vec_name.split('.')[0] == 'string_to_param': + _, k2 = vec_name.split('.', 1) + emb_dict['string_to_param'] = {k2: weight} + else: + emb_dict[vec_name] = weight + bundle_embeddings[emb_name] = emb_dict + + if diffusers_weight_map: + key = diffusers_weight_map.get(key_network_without_network_parts, key_network_without_network_parts) + else: + key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2) + + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + if sd_module is None: + m = re_x_proj.match(key) + if m: + sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None) + + # SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model" + if sd_module is None and "lora_unet" in key_network_without_network_parts: + key = key_network_without_network_parts.replace("lora_unet", "diffusion_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts: + key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + # some SD1 Loras also have correct compvis keys + if sd_module is None: + key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + # kohya_ss OFT module + elif sd_module is None and "oft_unet" in key_network_without_network_parts: + key = key_network_without_network_parts.replace("oft_unet", "diffusion_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + # KohakuBlueLeaf OFT module + if sd_module is None and "oft_diag" in key: + key = key_network_without_network_parts.replace("lora_unet", "diffusion_model") + key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + if sd_module is None: + keys_failed_to_match[key_network] = key + continue + + if key not in matched_networks: + matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module) + + matched_networks[key].w[network_part] = weight + + for key, weights in matched_networks.items(): + net_module = None + for nettype in module_types: + net_module = nettype.create_module(net, weights) + if net_module is not None: + break + + if net_module is None: + raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}") + + net.modules[key] = net_module + + embeddings = {} + for emb_name, data in bundle_embeddings.items(): + embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name) + embedding.loaded = None + embedding.shorthash = BundledTIHash(name) + embeddings[emb_name] = embedding + + net.bundle_embeddings = embeddings + + if keys_failed_to_match: + logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}") + + return net + + +def purge_networks_from_memory(): + while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0: + name = next(iter(networks_in_memory)) + networks_in_memory.pop(name, None) + + devices.torch_gc() + + +def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None): + emb_db = sd_hijack.model_hijack.embedding_db + already_loaded = {} + + for net in loaded_networks: + if net.name in names: + already_loaded[net.name] = net + for emb_name, embedding in net.bundle_embeddings.items(): + if embedding.loaded: + emb_db.register_embedding_by_name(None, shared.sd_model, emb_name) + + loaded_networks.clear() + + unavailable_networks = [] + for name in names: + if name.lower() in forbidden_network_aliases and available_networks.get(name) is None: + unavailable_networks.append(name) + elif available_network_aliases.get(name) is None: + unavailable_networks.append(name) + + if unavailable_networks: + update_available_networks_by_names(unavailable_networks) + + networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names] + if any(x is None for x in networks_on_disk): + list_available_networks() + + networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names] + + failed_to_load_networks = [] + + for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)): + net = already_loaded.get(name, None) + + if network_on_disk is not None: + if net is None: + net = networks_in_memory.get(name) + + if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime: + try: + net = load_network(name, network_on_disk) + + networks_in_memory.pop(name, None) + networks_in_memory[name] = net + except Exception as e: + errors.display(e, f"loading network {network_on_disk.filename}") + continue + + net.mentioned_name = name + + network_on_disk.read_hash() + + if net is None: + failed_to_load_networks.append(name) + logging.info(f"Couldn't find network with name {name}") + continue + + net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0 + net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0 + net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0 + loaded_networks.append(net) + + for emb_name, embedding in net.bundle_embeddings.items(): + if embedding.loaded is None and emb_name in emb_db.word_embeddings: + logger.warning( + f'Skip bundle embedding: "{emb_name}"' + ' as it was already loaded from embeddings folder' + ) + continue + + embedding.loaded = False + if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape: + embedding.loaded = True + emb_db.register_embedding(embedding, shared.sd_model) + else: + emb_db.skipped_embeddings[name] = embedding + + if failed_to_load_networks: + lora_not_found_message = f'Lora not found: {", ".join(failed_to_load_networks)}' + sd_hijack.model_hijack.comments.append(lora_not_found_message) + if shared.opts.lora_not_found_warning_console: + print(f'\n{lora_not_found_message}\n') + if shared.opts.lora_not_found_gradio_warning: + gr.Warning(lora_not_found_message) + + purge_networks_from_memory() + + +def allowed_layer_without_weight(layer): + if isinstance(layer, torch.nn.LayerNorm) and not layer.elementwise_affine: + return True + + return False + + +def store_weights_backup(weight): + if weight is None: + return None + + return weight.to(devices.cpu, copy=True) + + +def restore_weights_backup(obj, field, weight): + if weight is None: + setattr(obj, field, None) + return + + getattr(obj, field).copy_(weight) + + +def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]): + weights_backup = getattr(self, "network_weights_backup", None) + bias_backup = getattr(self, "network_bias_backup", None) + + if weights_backup is None and bias_backup is None: + return + + if weights_backup is not None: + if isinstance(self, torch.nn.MultiheadAttention): + restore_weights_backup(self, 'in_proj_weight', weights_backup[0]) + restore_weights_backup(self.out_proj, 'weight', weights_backup[1]) + else: + restore_weights_backup(self, 'weight', weights_backup) + + if isinstance(self, torch.nn.MultiheadAttention): + restore_weights_backup(self.out_proj, 'bias', bias_backup) + else: + restore_weights_backup(self, 'bias', bias_backup) + + +def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]): + """ + Applies the currently selected set of networks to the weights of torch layer self. + If weights already have this particular set of networks applied, does nothing. + If not, restores original weights from backup and alters weights according to networks. + """ + + network_layer_name = getattr(self, 'network_layer_name', None) + if network_layer_name is None: + return + + current_names = getattr(self, "network_current_names", ()) + wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks) + + weights_backup = getattr(self, "network_weights_backup", None) + if weights_backup is None and wanted_names != (): + if current_names != () and not allowed_layer_without_weight(self): + raise RuntimeError(f"{network_layer_name} - no backup weights found and current weights are not unchanged") + + if isinstance(self, torch.nn.MultiheadAttention): + weights_backup = (store_weights_backup(self.in_proj_weight), store_weights_backup(self.out_proj.weight)) + else: + weights_backup = store_weights_backup(self.weight) + + self.network_weights_backup = weights_backup + + bias_backup = getattr(self, "network_bias_backup", None) + if bias_backup is None and wanted_names != (): + if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None: + bias_backup = store_weights_backup(self.out_proj.bias) + elif getattr(self, 'bias', None) is not None: + bias_backup = store_weights_backup(self.bias) + else: + bias_backup = None + + # Unlike weight which always has value, some modules don't have bias. + # Only report if bias is not None and current bias are not unchanged. + if bias_backup is not None and current_names != (): + raise RuntimeError("no backup bias found and current bias are not unchanged") + + self.network_bias_backup = bias_backup + + if current_names != wanted_names: + network_restore_weights_from_backup(self) + + for net in loaded_networks: + module = net.modules.get(network_layer_name, None) + if module is not None and hasattr(self, 'weight') and not isinstance(module, modules.models.sd3.mmdit.QkvLinear): + try: + with torch.no_grad(): + if getattr(self, 'fp16_weight', None) is None: + weight = self.weight + bias = self.bias + else: + weight = self.fp16_weight.clone().to(self.weight.device) + bias = getattr(self, 'fp16_bias', None) + if bias is not None: + bias = bias.clone().to(self.bias.device) + updown, ex_bias = module.calc_updown(weight) + + if len(weight.shape) == 4 and weight.shape[1] == 9: + # inpainting model. zero pad updown to make channel[1] 4 to 9 + updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5)) + + self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype)) + if ex_bias is not None and hasattr(self, 'bias'): + if self.bias is None: + self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype) + else: + self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype)) + except RuntimeError as e: + logging.debug(f"Network {net.name} layer {network_layer_name}: {e}") + extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 + + continue + + module_q = net.modules.get(network_layer_name + "_q_proj", None) + module_k = net.modules.get(network_layer_name + "_k_proj", None) + module_v = net.modules.get(network_layer_name + "_v_proj", None) + module_out = net.modules.get(network_layer_name + "_out_proj", None) + + if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out: + try: + with torch.no_grad(): + # Send "real" orig_weight into MHA's lora module + qw, kw, vw = self.in_proj_weight.chunk(3, 0) + updown_q, _ = module_q.calc_updown(qw) + updown_k, _ = module_k.calc_updown(kw) + updown_v, _ = module_v.calc_updown(vw) + del qw, kw, vw + updown_qkv = torch.vstack([updown_q, updown_k, updown_v]) + updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight) + + self.in_proj_weight += updown_qkv + self.out_proj.weight += updown_out + if ex_bias is not None: + if self.out_proj.bias is None: + self.out_proj.bias = torch.nn.Parameter(ex_bias) + else: + self.out_proj.bias += ex_bias + + except RuntimeError as e: + logging.debug(f"Network {net.name} layer {network_layer_name}: {e}") + extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 + + continue + + if isinstance(self, modules.models.sd3.mmdit.QkvLinear) and module_q and module_k and module_v: + try: + with torch.no_grad(): + # Send "real" orig_weight into MHA's lora module + qw, kw, vw = self.weight.chunk(3, 0) + updown_q, _ = module_q.calc_updown(qw) + updown_k, _ = module_k.calc_updown(kw) + updown_v, _ = module_v.calc_updown(vw) + del qw, kw, vw + updown_qkv = torch.vstack([updown_q, updown_k, updown_v]) + self.weight += updown_qkv + + except RuntimeError as e: + logging.debug(f"Network {net.name} layer {network_layer_name}: {e}") + extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 + + continue + + if module is None: + continue + + logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation") + extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1 + + self.network_current_names = wanted_names + + +def network_forward(org_module, input, original_forward): + """ + Old way of applying Lora by executing operations during layer's forward. + Stacking many loras this way results in big performance degradation. + """ + + if len(loaded_networks) == 0: + return original_forward(org_module, input) + + input = devices.cond_cast_unet(input) + + network_restore_weights_from_backup(org_module) + network_reset_cached_weight(org_module) + + y = original_forward(org_module, input) + + network_layer_name = getattr(org_module, 'network_layer_name', None) + for lora in loaded_networks: + module = lora.modules.get(network_layer_name, None) + if module is None: + continue + + y = module.forward(input, y) + + return y + + +def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]): + self.network_current_names = () + self.network_weights_backup = None + self.network_bias_backup = None + + +def network_Linear_forward(self, input): + if shared.opts.lora_functional: + return network_forward(self, input, originals.Linear_forward) + + network_apply_weights(self) + + return originals.Linear_forward(self, input) + + +def network_Linear_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return originals.Linear_load_state_dict(self, *args, **kwargs) + + +def network_Conv2d_forward(self, input): + if shared.opts.lora_functional: + return network_forward(self, input, originals.Conv2d_forward) + + network_apply_weights(self) + + return originals.Conv2d_forward(self, input) + + +def network_Conv2d_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return originals.Conv2d_load_state_dict(self, *args, **kwargs) + + +def network_GroupNorm_forward(self, input): + if shared.opts.lora_functional: + return network_forward(self, input, originals.GroupNorm_forward) + + network_apply_weights(self) + + return originals.GroupNorm_forward(self, input) + + +def network_GroupNorm_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return originals.GroupNorm_load_state_dict(self, *args, **kwargs) + + +def network_LayerNorm_forward(self, input): + if shared.opts.lora_functional: + return network_forward(self, input, originals.LayerNorm_forward) + + network_apply_weights(self) + + return originals.LayerNorm_forward(self, input) + + +def network_LayerNorm_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return originals.LayerNorm_load_state_dict(self, *args, **kwargs) + + +def network_MultiheadAttention_forward(self, *args, **kwargs): + network_apply_weights(self) + + return originals.MultiheadAttention_forward(self, *args, **kwargs) + + +def network_MultiheadAttention_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs) + + +def process_network_files(names: list[str] | None = None): + candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"])) + candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"])) + for filename in candidates: + if os.path.isdir(filename): + continue + name = os.path.splitext(os.path.basename(filename))[0] + # if names is provided, only load networks with names in the list + if names and name not in names: + continue + try: + entry = network.NetworkOnDisk(name, filename) + except OSError: # should catch FileNotFoundError and PermissionError etc. + errors.report(f"Failed to load network {name} from {filename}", exc_info=True) + continue + + available_networks[name] = entry + + if entry.alias in available_network_aliases: + forbidden_network_aliases[entry.alias.lower()] = 1 + + available_network_aliases[name] = entry + available_network_aliases[entry.alias] = entry + + +def update_available_networks_by_names(names: list[str]): + process_network_files(names) + + +def list_available_networks(): + available_networks.clear() + available_network_aliases.clear() + forbidden_network_aliases.clear() + available_network_hash_lookup.clear() + forbidden_network_aliases.update({"none": 1, "Addams": 1}) + + os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True) + + process_network_files() + + +re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)") + + +def infotext_pasted(infotext, params): + if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]: + return # if the other extension is active, it will handle those fields, no need to do anything + + added = [] + + for k in params: + if not k.startswith("AddNet Model "): + continue + + num = k[13:] + + if params.get("AddNet Module " + num) != "LoRA": + continue + + name = params.get("AddNet Model " + num) + if name is None: + continue + + m = re_network_name.match(name) + if m: + name = m.group(1) + + multiplier = params.get("AddNet Weight A " + num, "1.0") + + added.append(f"") + + if added: + params["Prompt"] += "\n" + "".join(added) + + +originals: lora_patches.LoraPatches = None + +extra_network_lora = None + +available_networks = {} +available_network_aliases = {} +loaded_networks = [] +loaded_bundle_embeddings = {} +networks_in_memory = {} +available_network_hash_lookup = {} +forbidden_network_aliases = {} + +list_available_networks() diff --git a/extensions-builtin/Lora/preload.py b/extensions-builtin/Lora/preload.py new file mode 100644 index 00000000000..52fab29b08b --- /dev/null +++ b/extensions-builtin/Lora/preload.py @@ -0,0 +1,8 @@ +import os +from modules import paths +from modules.paths_internal import normalized_filepath + + +def preload(parser): + parser.add_argument("--lora-dir", type=normalized_filepath, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora')) + parser.add_argument("--lyco-dir-backcompat", type=normalized_filepath, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS')) diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py new file mode 100644 index 00000000000..d3ea369ae26 --- /dev/null +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -0,0 +1,102 @@ +import re + +import gradio as gr +from fastapi import FastAPI + +import network +import networks +import lora # noqa:F401 +import lora_patches +import extra_networks_lora +import ui_extra_networks_lora +from modules import script_callbacks, ui_extra_networks, extra_networks, shared + + +def unload(): + networks.originals.undo() + + +def before_ui(): + ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora()) + + networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora() + extra_networks.register_extra_network(networks.extra_network_lora) + extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco") + + +networks.originals = lora_patches.LoraPatches() + +script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules) +script_callbacks.on_script_unloaded(unload) +script_callbacks.on_before_ui(before_ui) +script_callbacks.on_infotext_pasted(networks.infotext_pasted) + + +shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), { + "sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks), + "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}), + "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"), + "lora_bundled_ti_to_infotext": shared.OptionInfo(True, "Add Lora name as TI hashes for bundled Textual Inversion").info('"Add Textual Inversion hashes to infotext" needs to be enabled'), + "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"), + "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}), + "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}), + "lora_not_found_warning_console": shared.OptionInfo(False, "Lora not found warning in console"), + "lora_not_found_gradio_warning": shared.OptionInfo(False, "Lora not found warning popup in webui"), +})) + + +shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), { + "lora_functional": shared.OptionInfo(False, "Lora/Networks: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"), +})) + + +def create_lora_json(obj: network.NetworkOnDisk): + return { + "name": obj.name, + "alias": obj.alias, + "path": obj.filename, + "metadata": obj.metadata, + } + + +def api_networks(_: gr.Blocks, app: FastAPI): + @app.get("/sdapi/v1/loras") + async def get_loras(): + return [create_lora_json(obj) for obj in networks.available_networks.values()] + + @app.post("/sdapi/v1/refresh-loras") + async def refresh_loras(): + return networks.list_available_networks() + + +script_callbacks.on_app_started(api_networks) + +re_lora = re.compile("= 16 + + +re_word = re.compile(r"[-_\w']+") +re_comma = re.compile(r" *, *") + + +def build_tags(metadata): + tags = {} + + ss_tag_frequency = metadata.get("ss_tag_frequency", {}) + if ss_tag_frequency is not None and hasattr(ss_tag_frequency, 'items'): + for _, tags_dict in ss_tag_frequency.items(): + for tag, tag_count in tags_dict.items(): + tag = tag.strip() + tags[tag] = tags.get(tag, 0) + int(tag_count) + + if tags and is_non_comma_tagset(tags): + new_tags = {} + + for text, text_count in tags.items(): + for word in re.findall(re_word, text): + if len(word) < 3: + continue + + new_tags[word] = new_tags.get(word, 0) + text_count + + tags = new_tags + + ordered_tags = sorted(tags.keys(), key=tags.get, reverse=True) + + return [(tag, tags[tag]) for tag in ordered_tags] + + +class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor): + def __init__(self, ui, tabname, page): + super().__init__(ui, tabname, page) + + self.select_sd_version = None + + self.taginfo = None + self.edit_activation_text = None + self.slider_preferred_weight = None + self.edit_notes = None + + def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, negative_text, notes): + user_metadata = self.get_user_metadata(name) + user_metadata["description"] = desc + user_metadata["sd version"] = sd_version + user_metadata["activation text"] = activation_text + user_metadata["preferred weight"] = preferred_weight + user_metadata["negative text"] = negative_text + user_metadata["notes"] = notes + + self.write_user_metadata(name, user_metadata) + + def get_metadata_table(self, name): + table = super().get_metadata_table(name) + item = self.page.items.get(name, {}) + metadata = item.get("metadata") or {} + + keys = { + 'ss_output_name': "Output name:", + 'ss_sd_model_name': "Model:", + 'ss_clip_skip': "Clip skip:", + 'ss_network_module': "Kohya module:", + } + + for key, label in keys.items(): + value = metadata.get(key, None) + if value is not None and str(value) != "None": + table.append((label, html.escape(value))) + + ss_training_started_at = metadata.get('ss_training_started_at') + if ss_training_started_at: + table.append(("Date trained:", datetime.datetime.utcfromtimestamp(float(ss_training_started_at)).strftime('%Y-%m-%d %H:%M'))) + + ss_bucket_info = metadata.get("ss_bucket_info") + if ss_bucket_info and "buckets" in ss_bucket_info: + resolutions = {} + for _, bucket in ss_bucket_info["buckets"].items(): + resolution = bucket["resolution"] + resolution = f'{resolution[1]}x{resolution[0]}' + + resolutions[resolution] = resolutions.get(resolution, 0) + int(bucket["count"]) + + resolutions_list = sorted(resolutions.keys(), key=resolutions.get, reverse=True) + resolutions_text = html.escape(", ".join(resolutions_list[0:4])) + if len(resolutions) > 4: + resolutions_text += ", ..." + resolutions_text = f"{resolutions_text}" + + table.append(('Resolutions:' if len(resolutions_list) > 1 else 'Resolution:', resolutions_text)) + + image_count = 0 + for _, params in metadata.get("ss_dataset_dirs", {}).items(): + image_count += int(params.get("img_count", 0)) + + if image_count: + table.append(("Dataset size:", image_count)) + + return table + + def put_values_into_components(self, name): + user_metadata = self.get_user_metadata(name) + values = super().put_values_into_components(name) + + item = self.page.items.get(name, {}) + metadata = item.get("metadata") or {} + + tags = build_tags(metadata) + gradio_tags = [(tag, str(count)) for tag, count in tags[0:24]] + + return [ + *values[0:5], + item.get("sd_version", "Unknown"), + gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False), + user_metadata.get('activation text', ''), + float(user_metadata.get('preferred weight', 0.0)), + user_metadata.get('negative text', ''), + gr.update(visible=True if tags else False), + gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False), + ] + + def generate_random_prompt(self, name): + item = self.page.items.get(name, {}) + metadata = item.get("metadata") or {} + tags = build_tags(metadata) + + return self.generate_random_prompt_from_tags(tags) + + def generate_random_prompt_from_tags(self, tags): + max_count = None + res = [] + for tag, count in tags: + if not max_count: + max_count = count + + v = random.random() * max_count + if count > v: + for x in "({[]})": + tag = tag.replace(x, '\\' + x) + res.append(tag) + + return ", ".join(sorted(res)) + + def create_extra_default_items_in_left_column(self): + + # this would be a lot better as gr.Radio but I can't make it work + self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True) + + def create_editor(self): + self.create_default_editor_elems() + + self.taginfo = gr.HighlightedText(label="Training dataset tags") + self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora") + self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01) + self.edit_negative_text = gr.Text(label='Negative prompt', info="Will be added to negative prompts") + with gr.Row() as row_random_prompt: + with gr.Column(scale=8): + random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False) + + with gr.Column(scale=1, min_width=120): + generate_random_prompt = gr.Button('Generate', size="lg", scale=1) + + self.edit_notes = gr.TextArea(label='Notes', lines=4) + + generate_random_prompt.click(fn=self.generate_random_prompt, inputs=[self.edit_name_input], outputs=[random_prompt], show_progress=False) + + def select_tag(activation_text, evt: gr.SelectData): + tag = evt.value[0] + + words = re.split(re_comma, activation_text) + if tag in words: + words = [x for x in words if x != tag and x.strip()] + return ", ".join(words) + + return activation_text + ", " + tag if activation_text else tag + + self.taginfo.select(fn=select_tag, inputs=[self.edit_activation_text], outputs=[self.edit_activation_text], show_progress=False) + + self.create_default_buttons() + + viewed_components = [ + self.edit_name, + self.edit_description, + self.html_filedata, + self.html_preview, + self.edit_notes, + self.select_sd_version, + self.taginfo, + self.edit_activation_text, + self.slider_preferred_weight, + self.edit_negative_text, + row_random_prompt, + random_prompt, + ] + + self.button_edit\ + .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\ + .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box]) + + edited_components = [ + self.edit_description, + self.select_sd_version, + self.edit_activation_text, + self.slider_preferred_weight, + self.edit_negative_text, + self.edit_notes, + ] + + + self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components) diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py new file mode 100644 index 00000000000..3e34d69dca4 --- /dev/null +++ b/extensions-builtin/Lora/ui_extra_networks_lora.py @@ -0,0 +1,90 @@ +import os + +import network +import networks + +from modules import shared, ui_extra_networks +from modules.ui_extra_networks import quote_js +from ui_edit_user_metadata import LoraUserMetadataEditor + + +class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage): + def __init__(self): + super().__init__('Lora') + + def refresh(self): + networks.list_available_networks() + + def create_item(self, name, index=None, enable_filter=True): + lora_on_disk = networks.available_networks.get(name) + if lora_on_disk is None: + return + + path, ext = os.path.splitext(lora_on_disk.filename) + + alias = lora_on_disk.get_alias() + + search_terms = [self.search_terms_from_path(lora_on_disk.filename)] + if lora_on_disk.hash: + search_terms.append(lora_on_disk.hash) + item = { + "name": name, + "filename": lora_on_disk.filename, + "shorthash": lora_on_disk.shorthash, + "preview": self.find_preview(path) or self.find_embedded_preview(path, name, lora_on_disk.metadata), + "description": self.find_description(path), + "search_terms": search_terms, + "local_preview": f"{path}.{shared.opts.samples_format}", + "metadata": lora_on_disk.metadata, + "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)}, + "sd_version": lora_on_disk.sd_version.name, + } + + self.read_user_metadata(item) + activation_text = item["user_metadata"].get("activation text") + preferred_weight = item["user_metadata"].get("preferred weight", 0.0) + item["prompt"] = quote_js(f"") + + if activation_text: + item["prompt"] += " + " + quote_js(" " + activation_text) + + negative_prompt = item["user_metadata"].get("negative text") + item["negative_prompt"] = quote_js("") + if negative_prompt: + item["negative_prompt"] = quote_js('(' + negative_prompt + ':1)') + + sd_version = item["user_metadata"].get("sd version") + if sd_version in network.SdVersion.__members__: + item["sd_version"] = sd_version + sd_version = network.SdVersion[sd_version] + else: + sd_version = lora_on_disk.sd_version + + if shared.opts.lora_show_all or not enable_filter or not shared.sd_model: + pass + elif sd_version == network.SdVersion.Unknown: + model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1 + if model_version.name in shared.opts.lora_hide_unknown_for_versions: + return None + elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL: + return None + elif shared.sd_model.is_sd2 and sd_version != network.SdVersion.SD2: + return None + elif shared.sd_model.is_sd1 and sd_version != network.SdVersion.SD1: + return None + + return item + + def list_items(self): + # instantiate a list to protect against concurrent modification + names = list(networks.available_networks) + for index, name in enumerate(names): + item = self.create_item(name, index) + if item is not None: + yield item + + def allowed_directories_for_previews(self): + return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat] + + def create_user_metadata_editor(self, ui, tabname): + return LoraUserMetadataEditor(ui, tabname, self) diff --git a/extensions-builtin/ScuNET/preload.py b/extensions-builtin/ScuNET/preload.py new file mode 100644 index 00000000000..f12c5b90ed2 --- /dev/null +++ b/extensions-builtin/ScuNET/preload.py @@ -0,0 +1,6 @@ +import os +from modules import paths + + +def preload(parser): + parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET')) diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py new file mode 100644 index 00000000000..fe5e5a19265 --- /dev/null +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -0,0 +1,74 @@ +import sys + +import PIL.Image + +import modules.upscaler +from modules import devices, errors, modelloader, script_callbacks, shared, upscaler_utils + + +class UpscalerScuNET(modules.upscaler.Upscaler): + def __init__(self, dirname): + self.name = "ScuNET" + self.model_name = "ScuNET GAN" + self.model_name2 = "ScuNET PSNR" + self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth" + self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth" + self.user_path = dirname + super().__init__() + model_paths = self.find_models(ext_filter=[".pth"]) + scalers = [] + add_model2 = True + for file in model_paths: + if file.startswith("http"): + name = self.model_name + else: + name = modelloader.friendly_name(file) + if name == self.model_name2 or file == self.model_url2: + add_model2 = False + try: + scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) + scalers.append(scaler_data) + except Exception: + errors.report(f"Error loading ScuNET model: {file}", exc_info=True) + if add_model2: + scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self) + scalers.append(scaler_data2) + self.scalers = scalers + + def do_upscale(self, img: PIL.Image.Image, selected_file): + devices.torch_gc() + try: + model = self.load_model(selected_file) + except Exception as e: + print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr) + return img + + img = upscaler_utils.upscale_2( + img, + model, + tile_size=shared.opts.SCUNET_tile, + tile_overlap=shared.opts.SCUNET_tile_overlap, + scale=1, # ScuNET is a denoising model, not an upscaler + desc='ScuNET', + ) + devices.torch_gc() + return img + + def load_model(self, path: str): + device = devices.get_device_for('scunet') + if path.startswith("http"): + # TODO: this doesn't use `path` at all? + filename = modelloader.load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth") + else: + filename = path + return modelloader.load_spandrel_model(filename, device=device, expected_architecture='SCUNet') + + +def on_ui_settings(): + import gradio as gr + + shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling")) + shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam")) + + +script_callbacks.on_ui_settings(on_ui_settings) diff --git a/extensions-builtin/SwinIR/preload.py b/extensions-builtin/SwinIR/preload.py new file mode 100644 index 00000000000..567e44bcaa1 --- /dev/null +++ b/extensions-builtin/SwinIR/preload.py @@ -0,0 +1,6 @@ +import os +from modules import paths + + +def preload(parser): + parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR')) diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py new file mode 100644 index 00000000000..16bf9b792fc --- /dev/null +++ b/extensions-builtin/SwinIR/scripts/swinir_model.py @@ -0,0 +1,95 @@ +import logging +import sys + +import torch +from PIL import Image + +from modules import devices, modelloader, script_callbacks, shared, upscaler_utils +from modules.upscaler import Upscaler, UpscalerData + +SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth" + +logger = logging.getLogger(__name__) + + +class UpscalerSwinIR(Upscaler): + def __init__(self, dirname): + self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs + self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings + self.name = "SwinIR" + self.model_url = SWINIR_MODEL_URL + self.model_name = "SwinIR 4x" + self.user_path = dirname + super().__init__() + scalers = [] + model_files = self.find_models(ext_filter=[".pt", ".pth"]) + for model in model_files: + if model.startswith("http"): + name = self.model_name + else: + name = modelloader.friendly_name(model) + model_data = UpscalerData(name, model, self) + scalers.append(model_data) + self.scalers = scalers + + def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image: + current_config = (model_file, shared.opts.SWIN_tile) + + if self._cached_model_config == current_config: + model = self._cached_model + else: + try: + model = self.load_model(model_file) + except Exception as e: + print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr) + return img + self._cached_model = model + self._cached_model_config = current_config + + img = upscaler_utils.upscale_2( + img, + model, + tile_size=shared.opts.SWIN_tile, + tile_overlap=shared.opts.SWIN_tile_overlap, + scale=model.scale, + desc="SwinIR", + ) + devices.torch_gc() + return img + + def load_model(self, path, scale=4): + if path.startswith("http"): + filename = modelloader.load_file_from_url( + url=path, + model_dir=self.model_download_path, + file_name=f"{self.model_name.replace(' ', '_')}.pth", + ) + else: + filename = path + + model_descriptor = modelloader.load_spandrel_model( + filename, + device=self._get_device(), + prefer_half=(devices.dtype == torch.float16), + expected_architecture="SwinIR", + ) + if getattr(shared.opts, 'SWIN_torch_compile', False): + try: + model_descriptor.model.compile() + except Exception: + logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True) + return model_descriptor + + def _get_device(self): + return devices.get_device_for('swinir') + + +def on_ui_settings(): + import gradio as gr + + shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling"))) + shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling"))) + shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run")) + + +script_callbacks.on_ui_settings(on_ui_settings) diff --git a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js new file mode 100644 index 00000000000..7807f7f6185 --- /dev/null +++ b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js @@ -0,0 +1,995 @@ +onUiLoaded(async() => { + const elementIDs = { + img2imgTabs: "#mode_img2img .tab-nav", + inpaint: "#img2maskimg", + inpaintSketch: "#inpaint_sketch", + rangeGroup: "#img2img_column_size", + sketch: "#img2img_sketch" + }; + const tabNameToElementId = { + "Inpaint sketch": elementIDs.inpaintSketch, + "Inpaint": elementIDs.inpaint, + "Sketch": elementIDs.sketch + }; + + + // Helper functions + // Get active tab + + /** + * Waits for an element to be present in the DOM. + */ + const waitForElement = (id) => new Promise(resolve => { + const checkForElement = () => { + const element = document.querySelector(id); + if (element) return resolve(element); + setTimeout(checkForElement, 100); + }; + checkForElement(); + }); + + function getActiveTab(elements, all = false) { + if (!elements.img2imgTabs) return null; + const tabs = elements.img2imgTabs.querySelectorAll("button"); + + if (all) return tabs; + + for (let tab of tabs) { + if (tab.classList.contains("selected")) { + return tab; + } + } + } + + // Get tab ID + function getTabId(elements) { + const activeTab = getActiveTab(elements); + if (!activeTab) return null; + return tabNameToElementId[activeTab.innerText]; + } + + // Wait until opts loaded + async function waitForOpts() { + for (; ;) { + if (window.opts && Object.keys(window.opts).length) { + return window.opts; + } + await new Promise(resolve => setTimeout(resolve, 100)); + } + } + + // Detect whether the element has a horizontal scroll bar + function hasHorizontalScrollbar(element) { + return element.scrollWidth > element.clientWidth; + } + + // Function for defining the "Ctrl", "Shift" and "Alt" keys + function isModifierKey(event, key) { + switch (key) { + case "Ctrl": + return event.ctrlKey; + case "Shift": + return event.shiftKey; + case "Alt": + return event.altKey; + default: + return false; + } + } + + // Check if hotkey is valid + function isValidHotkey(value) { + const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"]; + return ( + (typeof value === "string" && + value.length === 1 && + /[a-z]/i.test(value)) || + specialKeys.includes(value) + ); + } + + // Normalize hotkey + function normalizeHotkey(hotkey) { + return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey; + } + + // Format hotkey for display + function formatHotkeyForDisplay(hotkey) { + return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey; + } + + // Create hotkey configuration with the provided options + function createHotkeyConfig(defaultHotkeysConfig, hotkeysConfigOpts) { + const result = {}; // Resulting hotkey configuration + const usedKeys = new Set(); // Set of used hotkeys + + // Iterate through defaultHotkeysConfig keys + for (const key in defaultHotkeysConfig) { + const userValue = hotkeysConfigOpts[key]; // User-provided hotkey value + const defaultValue = defaultHotkeysConfig[key]; // Default hotkey value + + // Apply appropriate value for undefined, boolean, or object userValue + if ( + userValue === undefined || + typeof userValue === "boolean" || + typeof userValue === "object" || + userValue === "disable" + ) { + result[key] = + userValue === undefined ? defaultValue : userValue; + } else if (isValidHotkey(userValue)) { + const normalizedUserValue = normalizeHotkey(userValue); + + // Check for conflicting hotkeys + if (!usedKeys.has(normalizedUserValue)) { + usedKeys.add(normalizedUserValue); + result[key] = normalizedUserValue; + } else { + console.error( + `Hotkey: ${formatHotkeyForDisplay( + userValue + )} for ${key} is repeated and conflicts with another hotkey. The default hotkey is used: ${formatHotkeyForDisplay( + defaultValue + )}` + ); + result[key] = defaultValue; + } + } else { + console.error( + `Hotkey: ${formatHotkeyForDisplay( + userValue + )} for ${key} is not valid. The default hotkey is used: ${formatHotkeyForDisplay( + defaultValue + )}` + ); + result[key] = defaultValue; + } + } + + return result; + } + + // Disables functions in the config object based on the provided list of function names + function disableFunctions(config, disabledFunctions) { + // Bind the hasOwnProperty method to the functionMap object to avoid errors + const hasOwnProperty = + Object.prototype.hasOwnProperty.bind(functionMap); + + // Loop through the disabledFunctions array and disable the corresponding functions in the config object + disabledFunctions.forEach(funcName => { + if (hasOwnProperty(funcName)) { + const key = functionMap[funcName]; + config[key] = "disable"; + } + }); + + // Return the updated config object + return config; + } + + /** + * The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio. + * If the image display property is set to 'none', the mask breaks. To fix this, the function + * temporarily sets the display property to 'block' and then hides the mask again after 300 milliseconds + * to avoid breaking the canvas. Additionally, the function adjusts the mask to work correctly on + * very long images. + */ + function restoreImgRedMask(elements) { + const mainTabId = getTabId(elements); + + if (!mainTabId) return; + + const mainTab = gradioApp().querySelector(mainTabId); + const img = mainTab.querySelector("img"); + const imageARPreview = gradioApp().querySelector("#imageARPreview"); + + if (!img || !imageARPreview) return; + + imageARPreview.style.transform = ""; + if (parseFloat(mainTab.style.width) > 865) { + const transformString = mainTab.style.transform; + const scaleMatch = transformString.match( + /scale\(([-+]?[0-9]*\.?[0-9]+)\)/ + ); + let zoom = 1; // default zoom + + if (scaleMatch && scaleMatch[1]) { + zoom = Number(scaleMatch[1]); + } + + imageARPreview.style.transformOrigin = "0 0"; + imageARPreview.style.transform = `scale(${zoom})`; + } + + if (img.style.display !== "none") return; + + img.style.display = "block"; + + setTimeout(() => { + img.style.display = "none"; + }, 400); + } + + const hotkeysConfigOpts = await waitForOpts(); + + // Default config + const defaultHotkeysConfig = { + canvas_hotkey_zoom: "Alt", + canvas_hotkey_adjust: "Ctrl", + canvas_hotkey_reset: "KeyR", + canvas_hotkey_fullscreen: "KeyS", + canvas_hotkey_move: "KeyF", + canvas_hotkey_overlap: "KeyO", + canvas_hotkey_shrink_brush: "KeyQ", + canvas_hotkey_grow_brush: "KeyW", + canvas_disabled_functions: [], + canvas_show_tooltip: true, + canvas_auto_expand: true, + canvas_blur_prompt: false, + }; + + const functionMap = { + "Zoom": "canvas_hotkey_zoom", + "Adjust brush size": "canvas_hotkey_adjust", + "Hotkey shrink brush": "canvas_hotkey_shrink_brush", + "Hotkey enlarge brush": "canvas_hotkey_grow_brush", + "Moving canvas": "canvas_hotkey_move", + "Fullscreen": "canvas_hotkey_fullscreen", + "Reset Zoom": "canvas_hotkey_reset", + "Overlap": "canvas_hotkey_overlap" + }; + + // Loading the configuration from opts + const preHotkeysConfig = createHotkeyConfig( + defaultHotkeysConfig, + hotkeysConfigOpts + ); + + // Disable functions that are not needed by the user + const hotkeysConfig = disableFunctions( + preHotkeysConfig, + preHotkeysConfig.canvas_disabled_functions + ); + + let isMoving = false; + let mouseX, mouseY; + let activeElement; + let interactedWithAltKey = false; + + const elements = Object.fromEntries( + Object.keys(elementIDs).map(id => [ + id, + gradioApp().querySelector(elementIDs[id]) + ]) + ); + const elemData = {}; + + // Apply functionality to the range inputs. Restore redmask and correct for long images. + const rangeInputs = elements.rangeGroup ? + Array.from(elements.rangeGroup.querySelectorAll("input")) : + [ + gradioApp().querySelector("#img2img_width input[type='range']"), + gradioApp().querySelector("#img2img_height input[type='range']") + ]; + + for (const input of rangeInputs) { + input?.addEventListener("input", () => restoreImgRedMask(elements)); + } + + function applyZoomAndPan(elemId, isExtension = true) { + const targetElement = gradioApp().querySelector(elemId); + + if (!targetElement) { + console.log("Element not found", elemId); + return; + } + + targetElement.style.transformOrigin = "0 0"; + + elemData[elemId] = { + zoom: 1, + panX: 0, + panY: 0 + }; + let fullScreenMode = false; + + // Create tooltip + function createTooltip() { + const toolTipElement = + targetElement.querySelector(".image-container"); + const tooltip = document.createElement("div"); + tooltip.className = "canvas-tooltip"; + + // Creating an item of information + const info = document.createElement("i"); + info.className = "canvas-tooltip-info"; + info.textContent = ""; + + // Create a container for the contents of the tooltip + const tooltipContent = document.createElement("div"); + tooltipContent.className = "canvas-tooltip-content"; + + // Define an array with hotkey information and their actions + const hotkeysInfo = [ + { + configKey: "canvas_hotkey_zoom", + action: "Zoom canvas", + keySuffix: " + wheel" + }, + { + configKey: "canvas_hotkey_adjust", + action: "Adjust brush size", + keySuffix: " + wheel" + }, + {configKey: "canvas_hotkey_reset", action: "Reset zoom"}, + { + configKey: "canvas_hotkey_fullscreen", + action: "Fullscreen mode" + }, + {configKey: "canvas_hotkey_move", action: "Move canvas"}, + {configKey: "canvas_hotkey_overlap", action: "Overlap"} + ]; + + // Create hotkeys array with disabled property based on the config values + const hotkeys = hotkeysInfo.map(info => { + const configValue = hotkeysConfig[info.configKey]; + const key = info.keySuffix ? + `${configValue}${info.keySuffix}` : + configValue.charAt(configValue.length - 1); + return { + key, + action: info.action, + disabled: configValue === "disable" + }; + }); + + for (const hotkey of hotkeys) { + if (hotkey.disabled) { + continue; + } + + const p = document.createElement("p"); + p.innerHTML = `${hotkey.key} - ${hotkey.action}`; + tooltipContent.appendChild(p); + } + + // Add information and content elements to the tooltip element + tooltip.appendChild(info); + tooltip.appendChild(tooltipContent); + + // Add a hint element to the target element + toolTipElement.appendChild(tooltip); + } + + //Show tool tip if setting enable + if (hotkeysConfig.canvas_show_tooltip) { + createTooltip(); + } + + // In the course of research, it was found that the tag img is very harmful when zooming and creates white canvases. This hack allows you to almost never think about this problem, it has no effect on webui. + function fixCanvas() { + const activeTab = getActiveTab(elements)?.textContent.trim(); + + if (activeTab && activeTab !== "img2img") { + const img = targetElement.querySelector(`${elemId} img`); + + if (img && img.style.display !== "none") { + img.style.display = "none"; + img.style.visibility = "hidden"; + } + } + } + + // Reset the zoom level and pan position of the target element to their initial values + function resetZoom() { + elemData[elemId] = { + zoomLevel: 1, + panX: 0, + panY: 0 + }; + + if (isExtension) { + targetElement.style.overflow = "hidden"; + } + + targetElement.isZoomed = false; + + fixCanvas(); + targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`; + + const canvas = gradioApp().querySelector( + `${elemId} canvas[key="interface"]` + ); + + toggleOverlap("off"); + fullScreenMode = false; + + const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']"); + if (closeBtn) { + closeBtn.addEventListener("click", resetZoom); + } + + if (canvas && isExtension) { + const parentElement = targetElement.closest('[id^="component-"]'); + if ( + canvas && + parseFloat(canvas.style.width) > parentElement.offsetWidth && + parseFloat(targetElement.style.width) > parentElement.offsetWidth + ) { + fitToElement(); + return; + } + + } + + if ( + canvas && + !isExtension && + parseFloat(canvas.style.width) > 865 && + parseFloat(targetElement.style.width) > 865 + ) { + fitToElement(); + return; + } + + targetElement.style.width = ""; + } + + // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements + function toggleOverlap(forced = "") { + const zIndex1 = "0"; + const zIndex2 = "998"; + + targetElement.style.zIndex = + targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1; + + if (forced === "off") { + targetElement.style.zIndex = zIndex1; + } else if (forced === "on") { + targetElement.style.zIndex = zIndex2; + } + } + + // Adjust the brush size based on the deltaY value from a mouse wheel event + function adjustBrushSize( + elemId, + deltaY, + withoutValue = false, + percentage = 5 + ) { + const input = + gradioApp().querySelector( + `${elemId} input[aria-label='Brush radius']` + ) || + gradioApp().querySelector( + `${elemId} button[aria-label="Use brush"]` + ); + + if (input) { + input.click(); + if (!withoutValue) { + const maxValue = + parseFloat(input.getAttribute("max")) || 100; + const changeAmount = maxValue * (percentage / 100); + const newValue = + parseFloat(input.value) + + (deltaY > 0 ? -changeAmount : changeAmount); + input.value = Math.min(Math.max(newValue, 0), maxValue); + input.dispatchEvent(new Event("change")); + } + } + } + + // Reset zoom when uploading a new image + const fileInput = gradioApp().querySelector( + `${elemId} input[type="file"][accept="image/*"].svelte-116rqfv` + ); + fileInput.addEventListener("click", resetZoom); + + // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables + function updateZoom(newZoomLevel, mouseX, mouseY) { + newZoomLevel = Math.max(0.1, Math.min(newZoomLevel, 15)); + + elemData[elemId].panX += + mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel; + elemData[elemId].panY += + mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel; + + targetElement.style.transformOrigin = "0 0"; + targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`; + + toggleOverlap("on"); + if (isExtension) { + targetElement.style.overflow = "visible"; + } + + return newZoomLevel; + } + + // Change the zoom level based on user interaction + function changeZoomLevel(operation, e) { + if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) { + e.preventDefault(); + + if (hotkeysConfig.canvas_hotkey_zoom === "Alt") { + interactedWithAltKey = true; + } + + let zoomPosX, zoomPosY; + let delta = 0.2; + if (elemData[elemId].zoomLevel > 7) { + delta = 0.9; + } else if (elemData[elemId].zoomLevel > 2) { + delta = 0.6; + } + + zoomPosX = e.clientX; + zoomPosY = e.clientY; + + fullScreenMode = false; + elemData[elemId].zoomLevel = updateZoom( + elemData[elemId].zoomLevel + + (operation === "+" ? delta : -delta), + zoomPosX - targetElement.getBoundingClientRect().left, + zoomPosY - targetElement.getBoundingClientRect().top + ); + + targetElement.isZoomed = true; + } + } + + /** + * This function fits the target element to the screen by calculating + * the required scale and offsets. It also updates the global variables + * zoomLevel, panX, and panY to reflect the new state. + */ + + function fitToElement() { + //Reset Zoom + targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`; + + let parentElement; + + if (isExtension) { + parentElement = targetElement.closest('[id^="component-"]'); + } else { + parentElement = targetElement.parentElement; + } + + + // Get element and screen dimensions + const elementWidth = targetElement.offsetWidth; + const elementHeight = targetElement.offsetHeight; + + const screenWidth = parentElement.clientWidth; + const screenHeight = parentElement.clientHeight; + + // Get element's coordinates relative to the parent element + const elementRect = targetElement.getBoundingClientRect(); + const parentRect = parentElement.getBoundingClientRect(); + const elementX = elementRect.x - parentRect.x; + + // Calculate scale and offsets + const scaleX = screenWidth / elementWidth; + const scaleY = screenHeight / elementHeight; + const scale = Math.min(scaleX, scaleY); + + const transformOrigin = + window.getComputedStyle(targetElement).transformOrigin; + const [originX, originY] = transformOrigin.split(" "); + const originXValue = parseFloat(originX); + const originYValue = parseFloat(originY); + + const offsetX = + (screenWidth - elementWidth * scale) / 2 - + originXValue * (1 - scale); + const offsetY = + (screenHeight - elementHeight * scale) / 2.5 - + originYValue * (1 - scale); + + // Apply scale and offsets to the element + targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`; + + // Update global variables + elemData[elemId].zoomLevel = scale; + elemData[elemId].panX = offsetX; + elemData[elemId].panY = offsetY; + + fullScreenMode = false; + toggleOverlap("off"); + } + + /** + * This function fits the target element to the screen by calculating + * the required scale and offsets. It also updates the global variables + * zoomLevel, panX, and panY to reflect the new state. + */ + + // Fullscreen mode + function fitToScreen() { + const canvas = gradioApp().querySelector( + `${elemId} canvas[key="interface"]` + ); + + if (!canvas) return; + + if (canvas.offsetWidth > 862 || isExtension) { + targetElement.style.width = (canvas.offsetWidth + 2) + "px"; + } + + if (isExtension) { + targetElement.style.overflow = "visible"; + } + + if (fullScreenMode) { + resetZoom(); + fullScreenMode = false; + return; + } + + //Reset Zoom + targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`; + + // Get scrollbar width to right-align the image + const scrollbarWidth = + window.innerWidth - document.documentElement.clientWidth; + + // Get element and screen dimensions + const elementWidth = targetElement.offsetWidth; + const elementHeight = targetElement.offsetHeight; + const screenWidth = window.innerWidth - scrollbarWidth; + const screenHeight = window.innerHeight; + + // Get element's coordinates relative to the page + const elementRect = targetElement.getBoundingClientRect(); + const elementY = elementRect.y; + const elementX = elementRect.x; + + // Calculate scale and offsets + const scaleX = screenWidth / elementWidth; + const scaleY = screenHeight / elementHeight; + const scale = Math.min(scaleX, scaleY); + + // Get the current transformOrigin + const computedStyle = window.getComputedStyle(targetElement); + const transformOrigin = computedStyle.transformOrigin; + const [originX, originY] = transformOrigin.split(" "); + const originXValue = parseFloat(originX); + const originYValue = parseFloat(originY); + + // Calculate offsets with respect to the transformOrigin + const offsetX = + (screenWidth - elementWidth * scale) / 2 - + elementX - + originXValue * (1 - scale); + const offsetY = + (screenHeight - elementHeight * scale) / 2 - + elementY - + originYValue * (1 - scale); + + // Apply scale and offsets to the element + targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`; + + // Update global variables + elemData[elemId].zoomLevel = scale; + elemData[elemId].panX = offsetX; + elemData[elemId].panY = offsetY; + + fullScreenMode = true; + toggleOverlap("on"); + } + + // Handle keydown events + function handleKeyDown(event) { + // Disable key locks to make pasting from the buffer work correctly + if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") { + return; + } + + // before activating shortcut, ensure user is not actively typing in an input field + if (!hotkeysConfig.canvas_blur_prompt) { + if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') { + return; + } + } + + + const hotkeyActions = { + [hotkeysConfig.canvas_hotkey_reset]: resetZoom, + [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap, + [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen, + [hotkeysConfig.canvas_hotkey_shrink_brush]: () => adjustBrushSize(elemId, 10), + [hotkeysConfig.canvas_hotkey_grow_brush]: () => adjustBrushSize(elemId, -10) + }; + + const action = hotkeyActions[event.code]; + if (action) { + event.preventDefault(); + action(event); + } + + if ( + isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) || + isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust) + ) { + event.preventDefault(); + } + } + + // Get Mouse position + function getMousePosition(e) { + mouseX = e.offsetX; + mouseY = e.offsetY; + } + + // Simulation of the function to put a long image into the screen. + // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element. + // We hide the image and show it to the user when it is ready. + + targetElement.isExpanded = false; + function autoExpand() { + const canvas = document.querySelector(`${elemId} canvas[key="interface"]`); + if (canvas) { + if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) { + targetElement.style.visibility = "hidden"; + setTimeout(() => { + fitToScreen(); + resetZoom(); + targetElement.style.visibility = "visible"; + targetElement.isExpanded = true; + }, 10); + } + } + } + + targetElement.addEventListener("mousemove", getMousePosition); + + //observers + // Creating an observer with a callback function to handle DOM changes + const observer = new MutationObserver((mutationsList, observer) => { + for (let mutation of mutationsList) { + // If the style attribute of the canvas has changed, by observation it happens only when the picture changes + if (mutation.type === 'attributes' && mutation.attributeName === 'style' && + mutation.target.tagName.toLowerCase() === 'canvas') { + targetElement.isExpanded = false; + setTimeout(resetZoom, 10); + } + } + }); + + // Apply auto expand if enabled + if (hotkeysConfig.canvas_auto_expand) { + targetElement.addEventListener("mousemove", autoExpand); + // Set up an observer to track attribute changes + observer.observe(targetElement, {attributes: true, childList: true, subtree: true}); + } + + // Handle events only inside the targetElement + let isKeyDownHandlerAttached = false; + + function handleMouseMove() { + if (!isKeyDownHandlerAttached) { + document.addEventListener("keydown", handleKeyDown); + isKeyDownHandlerAttached = true; + + activeElement = elemId; + } + } + + function handleMouseLeave() { + if (isKeyDownHandlerAttached) { + document.removeEventListener("keydown", handleKeyDown); + isKeyDownHandlerAttached = false; + + activeElement = null; + } + } + + // Add mouse event handlers + targetElement.addEventListener("mousemove", handleMouseMove); + targetElement.addEventListener("mouseleave", handleMouseLeave); + + // Reset zoom when click on another tab + if (elements.img2imgTabs) { + elements.img2imgTabs.addEventListener("click", resetZoom); + elements.img2imgTabs.addEventListener("click", () => { + // targetElement.style.width = ""; + if (parseInt(targetElement.style.width) > 865) { + setTimeout(fitToElement, 0); + } + }); + } + + targetElement.addEventListener("wheel", e => { + // change zoom level + const operation = (e.deltaY || -e.wheelDelta) > 0 ? "-" : "+"; + changeZoomLevel(operation, e); + + // Handle brush size adjustment with ctrl key pressed + if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) { + e.preventDefault(); + + if (hotkeysConfig.canvas_hotkey_adjust === "Alt") { + interactedWithAltKey = true; + } + + // Increase or decrease brush size based on scroll direction + adjustBrushSize(elemId, e.deltaY); + } + }); + + // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element. + function handleMoveKeyDown(e) { + + // Disable key locks to make pasting from the buffer work correctly + if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") { + return; + } + + // before activating shortcut, ensure user is not actively typing in an input field + if (!hotkeysConfig.canvas_blur_prompt) { + if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') { + return; + } + } + + + if (e.code === hotkeysConfig.canvas_hotkey_move) { + if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) { + e.preventDefault(); + document.activeElement.blur(); + isMoving = true; + } + } + } + + function handleMoveKeyUp(e) { + if (e.code === hotkeysConfig.canvas_hotkey_move) { + isMoving = false; + } + } + + document.addEventListener("keydown", handleMoveKeyDown); + document.addEventListener("keyup", handleMoveKeyUp); + + + // Prevent firefox from opening main menu when alt is used as a hotkey for zoom or brush size + function handleAltKeyUp(e) { + if (e.key !== "Alt" || !interactedWithAltKey) { + return; + } + + e.preventDefault(); + interactedWithAltKey = false; + } + + document.addEventListener("keyup", handleAltKeyUp); + + + // Detect zoom level and update the pan speed. + function updatePanPosition(movementX, movementY) { + let panSpeed = 2; + + if (elemData[elemId].zoomLevel > 8) { + panSpeed = 3.5; + } + + elemData[elemId].panX += movementX * panSpeed; + elemData[elemId].panY += movementY * panSpeed; + + // Delayed redraw of an element + requestAnimationFrame(() => { + targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`; + toggleOverlap("on"); + }); + } + + function handleMoveByKey(e) { + if (isMoving && elemId === activeElement) { + updatePanPosition(e.movementX, e.movementY); + targetElement.style.pointerEvents = "none"; + + if (isExtension) { + targetElement.style.overflow = "visible"; + } + + } else { + targetElement.style.pointerEvents = "auto"; + } + } + + // Prevents sticking to the mouse + window.onblur = function() { + isMoving = false; + }; + + // Checks for extension + function checkForOutBox() { + const parentElement = targetElement.closest('[id^="component-"]'); + if (parentElement.offsetWidth < targetElement.offsetWidth && !targetElement.isExpanded) { + resetZoom(); + targetElement.isExpanded = true; + } + + if (parentElement.offsetWidth < targetElement.offsetWidth && elemData[elemId].zoomLevel == 1) { + resetZoom(); + } + + if (parentElement.offsetWidth < targetElement.offsetWidth && targetElement.offsetWidth * elemData[elemId].zoomLevel > parentElement.offsetWidth && elemData[elemId].zoomLevel < 1 && !targetElement.isZoomed) { + resetZoom(); + } + } + + if (isExtension) { + targetElement.addEventListener("mousemove", checkForOutBox); + } + + + window.addEventListener('resize', (e) => { + resetZoom(); + + if (isExtension) { + targetElement.isExpanded = false; + targetElement.isZoomed = false; + } + }); + + gradioApp().addEventListener("mousemove", handleMoveByKey); + + + } + + applyZoomAndPan(elementIDs.sketch, false); + applyZoomAndPan(elementIDs.inpaint, false); + applyZoomAndPan(elementIDs.inpaintSketch, false); + + // Make the function global so that other extensions can take advantage of this solution + const applyZoomAndPanIntegration = async(id, elementIDs) => { + const mainEl = document.querySelector(id); + if (id.toLocaleLowerCase() === "none") { + for (const elementID of elementIDs) { + const el = await waitForElement(elementID); + if (!el) break; + applyZoomAndPan(elementID); + } + return; + } + + if (!mainEl) return; + mainEl.addEventListener("click", async() => { + for (const elementID of elementIDs) { + const el = await waitForElement(elementID); + if (!el) break; + applyZoomAndPan(elementID); + } + }, {once: true}); + }; + + window.applyZoomAndPan = applyZoomAndPan; // Only 1 elements, argument elementID, for example applyZoomAndPan("#txt2img_controlnet_ControlNet_input_image") + + window.applyZoomAndPanIntegration = applyZoomAndPanIntegration; // for any extension + + /* + The function `applyZoomAndPanIntegration` takes two arguments: + + 1. `id`: A string identifier for the element to which zoom and pan functionality will be applied on click. + If the `id` value is "none", the functionality will be applied to all elements specified in the second argument without a click event. + + 2. `elementIDs`: An array of string identifiers for elements. Zoom and pan functionality will be applied to each of these elements on click of the element specified by the first argument. + If "none" is specified in the first argument, the functionality will be applied to each of these elements without a click event. + + Example usage: + applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]); + In this example, zoom and pan functionality will be applied to the element with the identifier "txt2img_controlnet_ControlNet_input_image" upon clicking the element with the identifier "txt2img_controlnet". + */ + + // More examples + // Add integration with ControlNet txt2img One TAB + // applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]); + + // Add integration with ControlNet txt2img Tabs + // applyZoomAndPanIntegration("#txt2img_controlnet",Array.from({ length: 10 }, (_, i) => `#txt2img_controlnet_ControlNet-${i}_input_image`)); + + // Add integration with Inpaint Anything + // applyZoomAndPanIntegration("None", ["#ia_sam_image", "#ia_sel_mask"]); +}); diff --git a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py new file mode 100644 index 00000000000..17b27b2741b --- /dev/null +++ b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py @@ -0,0 +1,17 @@ +import gradio as gr +from modules import shared + +shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), { + "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"), + "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"), + "canvas_hotkey_shrink_brush": shared.OptionInfo("Q", "Shrink the brush size"), + "canvas_hotkey_grow_brush": shared.OptionInfo("W", "Enlarge the brush size"), + "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"), + "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "), + "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas position"), + "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, needed for testing"), + "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"), + "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"), + "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"), + "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size","Hotkey enlarge brush","Hotkey shrink brush","Moving canvas","Fullscreen","Reset Zoom","Overlap"]}), +})) diff --git a/extensions-builtin/canvas-zoom-and-pan/style.css b/extensions-builtin/canvas-zoom-and-pan/style.css new file mode 100644 index 00000000000..5d8054e6519 --- /dev/null +++ b/extensions-builtin/canvas-zoom-and-pan/style.css @@ -0,0 +1,66 @@ +.canvas-tooltip-info { + position: absolute; + top: 10px; + left: 10px; + cursor: help; + background-color: rgba(0, 0, 0, 0.3); + width: 20px; + height: 20px; + border-radius: 50%; + display: flex; + align-items: center; + justify-content: center; + flex-direction: column; + + z-index: 100; +} + +.canvas-tooltip-info::after { + content: ''; + display: block; + width: 2px; + height: 7px; + background-color: white; + margin-top: 2px; +} + +.canvas-tooltip-info::before { + content: ''; + display: block; + width: 2px; + height: 2px; + background-color: white; +} + +.canvas-tooltip-content { + display: none; + background-color: #f9f9f9; + color: #333; + border: 1px solid #ddd; + padding: 15px; + position: absolute; + top: 40px; + left: 10px; + width: 250px; + font-size: 16px; + opacity: 0; + border-radius: 8px; + box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); + + z-index: 100; +} + +.canvas-tooltip:hover .canvas-tooltip-content { + display: block; + animation: fadeIn 0.5s; + opacity: 1; +} + +@keyframes fadeIn { + from {opacity: 0;} + to {opacity: 1;} +} + +.styler { + overflow:inherit !important; +} \ No newline at end of file diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py new file mode 100644 index 00000000000..a91bea4fa9d --- /dev/null +++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py @@ -0,0 +1,82 @@ +import math + +import gradio as gr +from modules import scripts, shared, ui_components, ui_settings, infotext_utils, errors +from modules.ui_components import FormColumn + + +class ExtraOptionsSection(scripts.Script): + section = "extra_options" + + def __init__(self): + self.comps = None + self.setting_names = None + + def title(self): + return "Extra options" + + def show(self, is_img2img): + return scripts.AlwaysVisible + + def ui(self, is_img2img): + self.comps = [] + self.setting_names = [] + self.infotext_fields = [] + extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img + elem_id_tabname = "extra_options_" + ("img2img" if is_img2img else "txt2img") + + mapping = {k: v for v, k in infotext_utils.infotext_to_setting_name_mapping} + + with gr.Blocks() as interface: + with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname): + + row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols) + + for row in range(row_count): + with gr.Row(): + for col in range(shared.opts.extra_options_cols): + index = row * shared.opts.extra_options_cols + col + if index >= len(extra_options): + break + + setting_name = extra_options[index] + + with FormColumn(): + try: + comp = ui_settings.create_setting_component(setting_name) + except KeyError: + errors.report(f"Can't add extra options for {setting_name} in ui") + continue + + self.comps.append(comp) + self.setting_names.append(setting_name) + + setting_infotext_name = mapping.get(setting_name) + if setting_infotext_name is not None: + self.infotext_fields.append((comp, setting_infotext_name)) + + def get_settings_values(): + res = [ui_settings.get_value_for_setting(key) for key in self.setting_names] + return res[0] if len(res) == 1 else res + + interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False) + + return self.comps + + def before_process(self, p, *args): + for name, value in zip(self.setting_names, args): + if name not in p.override_settings: + p.override_settings[name] = value + + +shared.options_templates.update(shared.options_section(('settings_in_ui', "Settings in UI", "ui"), { + "settings_in_ui": shared.OptionHTML(""" +This page allows you to add some settings to the main interface of txt2img and img2img tabs. +"""), + "extra_options_txt2img": shared.OptionInfo([], "Settings for txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(), + "extra_options_img2img": shared.OptionInfo([], "Settings for img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(), + "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Slider, {"step": 1, "minimum": 1, "maximum": 20}).info("displayed amount will depend on the actual browser window width").needs_reload_ui(), + "extra_options_accordion": shared.OptionInfo(False, "Place added settings into an accordion").needs_reload_ui() +})) + + diff --git a/extensions-builtin/hypertile/hypertile.py b/extensions-builtin/hypertile/hypertile.py new file mode 100644 index 00000000000..0f40e2d3925 --- /dev/null +++ b/extensions-builtin/hypertile/hypertile.py @@ -0,0 +1,351 @@ +""" +Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE +Warn: The patch works well only if the input image has a width and height that are multiples of 128 +Original author: @tfernd Github: https://github.com/tfernd/HyperTile +""" + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Callable + +from functools import wraps, cache + +import math +import torch.nn as nn +import random + +from einops import rearrange + + +@dataclass +class HypertileParams: + depth = 0 + layer_name = "" + tile_size: int = 0 + swap_size: int = 0 + aspect_ratio: float = 1.0 + forward = None + enabled = False + + + +# TODO add SD-XL layers +DEPTH_LAYERS = { + 0: [ + # SD 1.5 U-Net (diffusers) + "down_blocks.0.attentions.0.transformer_blocks.0.attn1", + "down_blocks.0.attentions.1.transformer_blocks.0.attn1", + "up_blocks.3.attentions.0.transformer_blocks.0.attn1", + "up_blocks.3.attentions.1.transformer_blocks.0.attn1", + "up_blocks.3.attentions.2.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "input_blocks.1.1.transformer_blocks.0.attn1", + "input_blocks.2.1.transformer_blocks.0.attn1", + "output_blocks.9.1.transformer_blocks.0.attn1", + "output_blocks.10.1.transformer_blocks.0.attn1", + "output_blocks.11.1.transformer_blocks.0.attn1", + # SD 1.5 VAE + "decoder.mid_block.attentions.0", + "decoder.mid.attn_1", + ], + 1: [ + # SD 1.5 U-Net (diffusers) + "down_blocks.1.attentions.0.transformer_blocks.0.attn1", + "down_blocks.1.attentions.1.transformer_blocks.0.attn1", + "up_blocks.2.attentions.0.transformer_blocks.0.attn1", + "up_blocks.2.attentions.1.transformer_blocks.0.attn1", + "up_blocks.2.attentions.2.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "input_blocks.4.1.transformer_blocks.0.attn1", + "input_blocks.5.1.transformer_blocks.0.attn1", + "output_blocks.6.1.transformer_blocks.0.attn1", + "output_blocks.7.1.transformer_blocks.0.attn1", + "output_blocks.8.1.transformer_blocks.0.attn1", + ], + 2: [ + # SD 1.5 U-Net (diffusers) + "down_blocks.2.attentions.0.transformer_blocks.0.attn1", + "down_blocks.2.attentions.1.transformer_blocks.0.attn1", + "up_blocks.1.attentions.0.transformer_blocks.0.attn1", + "up_blocks.1.attentions.1.transformer_blocks.0.attn1", + "up_blocks.1.attentions.2.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "input_blocks.7.1.transformer_blocks.0.attn1", + "input_blocks.8.1.transformer_blocks.0.attn1", + "output_blocks.3.1.transformer_blocks.0.attn1", + "output_blocks.4.1.transformer_blocks.0.attn1", + "output_blocks.5.1.transformer_blocks.0.attn1", + ], + 3: [ + # SD 1.5 U-Net (diffusers) + "mid_block.attentions.0.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "middle_block.1.transformer_blocks.0.attn1", + ], +} +# XL layers, thanks for GitHub@gel-crabs for the help +DEPTH_LAYERS_XL = { + 0: [ + # SD 1.5 U-Net (diffusers) + "down_blocks.0.attentions.0.transformer_blocks.0.attn1", + "down_blocks.0.attentions.1.transformer_blocks.0.attn1", + "up_blocks.3.attentions.0.transformer_blocks.0.attn1", + "up_blocks.3.attentions.1.transformer_blocks.0.attn1", + "up_blocks.3.attentions.2.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "input_blocks.4.1.transformer_blocks.0.attn1", + "input_blocks.5.1.transformer_blocks.0.attn1", + "output_blocks.3.1.transformer_blocks.0.attn1", + "output_blocks.4.1.transformer_blocks.0.attn1", + "output_blocks.5.1.transformer_blocks.0.attn1", + # SD 1.5 VAE + "decoder.mid_block.attentions.0", + "decoder.mid.attn_1", + ], + 1: [ + # SD 1.5 U-Net (diffusers) + #"down_blocks.1.attentions.0.transformer_blocks.0.attn1", + #"down_blocks.1.attentions.1.transformer_blocks.0.attn1", + #"up_blocks.2.attentions.0.transformer_blocks.0.attn1", + #"up_blocks.2.attentions.1.transformer_blocks.0.attn1", + #"up_blocks.2.attentions.2.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "input_blocks.4.1.transformer_blocks.1.attn1", + "input_blocks.5.1.transformer_blocks.1.attn1", + "output_blocks.3.1.transformer_blocks.1.attn1", + "output_blocks.4.1.transformer_blocks.1.attn1", + "output_blocks.5.1.transformer_blocks.1.attn1", + "input_blocks.7.1.transformer_blocks.0.attn1", + "input_blocks.8.1.transformer_blocks.0.attn1", + "output_blocks.0.1.transformer_blocks.0.attn1", + "output_blocks.1.1.transformer_blocks.0.attn1", + "output_blocks.2.1.transformer_blocks.0.attn1", + "input_blocks.7.1.transformer_blocks.1.attn1", + "input_blocks.8.1.transformer_blocks.1.attn1", + "output_blocks.0.1.transformer_blocks.1.attn1", + "output_blocks.1.1.transformer_blocks.1.attn1", + "output_blocks.2.1.transformer_blocks.1.attn1", + "input_blocks.7.1.transformer_blocks.2.attn1", + "input_blocks.8.1.transformer_blocks.2.attn1", + "output_blocks.0.1.transformer_blocks.2.attn1", + "output_blocks.1.1.transformer_blocks.2.attn1", + "output_blocks.2.1.transformer_blocks.2.attn1", + "input_blocks.7.1.transformer_blocks.3.attn1", + "input_blocks.8.1.transformer_blocks.3.attn1", + "output_blocks.0.1.transformer_blocks.3.attn1", + "output_blocks.1.1.transformer_blocks.3.attn1", + "output_blocks.2.1.transformer_blocks.3.attn1", + "input_blocks.7.1.transformer_blocks.4.attn1", + "input_blocks.8.1.transformer_blocks.4.attn1", + "output_blocks.0.1.transformer_blocks.4.attn1", + "output_blocks.1.1.transformer_blocks.4.attn1", + "output_blocks.2.1.transformer_blocks.4.attn1", + "input_blocks.7.1.transformer_blocks.5.attn1", + "input_blocks.8.1.transformer_blocks.5.attn1", + "output_blocks.0.1.transformer_blocks.5.attn1", + "output_blocks.1.1.transformer_blocks.5.attn1", + "output_blocks.2.1.transformer_blocks.5.attn1", + "input_blocks.7.1.transformer_blocks.6.attn1", + "input_blocks.8.1.transformer_blocks.6.attn1", + "output_blocks.0.1.transformer_blocks.6.attn1", + "output_blocks.1.1.transformer_blocks.6.attn1", + "output_blocks.2.1.transformer_blocks.6.attn1", + "input_blocks.7.1.transformer_blocks.7.attn1", + "input_blocks.8.1.transformer_blocks.7.attn1", + "output_blocks.0.1.transformer_blocks.7.attn1", + "output_blocks.1.1.transformer_blocks.7.attn1", + "output_blocks.2.1.transformer_blocks.7.attn1", + "input_blocks.7.1.transformer_blocks.8.attn1", + "input_blocks.8.1.transformer_blocks.8.attn1", + "output_blocks.0.1.transformer_blocks.8.attn1", + "output_blocks.1.1.transformer_blocks.8.attn1", + "output_blocks.2.1.transformer_blocks.8.attn1", + "input_blocks.7.1.transformer_blocks.9.attn1", + "input_blocks.8.1.transformer_blocks.9.attn1", + "output_blocks.0.1.transformer_blocks.9.attn1", + "output_blocks.1.1.transformer_blocks.9.attn1", + "output_blocks.2.1.transformer_blocks.9.attn1", + ], + 2: [ + # SD 1.5 U-Net (diffusers) + "mid_block.attentions.0.transformer_blocks.0.attn1", + # SD 1.5 U-Net (ldm) + "middle_block.1.transformer_blocks.0.attn1", + "middle_block.1.transformer_blocks.1.attn1", + "middle_block.1.transformer_blocks.2.attn1", + "middle_block.1.transformer_blocks.3.attn1", + "middle_block.1.transformer_blocks.4.attn1", + "middle_block.1.transformer_blocks.5.attn1", + "middle_block.1.transformer_blocks.6.attn1", + "middle_block.1.transformer_blocks.7.attn1", + "middle_block.1.transformer_blocks.8.attn1", + "middle_block.1.transformer_blocks.9.attn1", + ], + 3 : [] # TODO - separate layers for SD-XL +} + + +RNG_INSTANCE = random.Random() + +@cache +def get_divisors(value: int, min_value: int, /, max_options: int = 1) -> list[int]: + """ + Returns divisors of value that + x * min_value <= value + in big -> small order, amount of divisors is limited by max_options + """ + max_options = max(1, max_options) # at least 1 option should be returned + min_value = min(min_value, value) + divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order + ns = [value // i for i in divisors[:max_options]] # has at least 1 element # big -> small order + return ns + + +def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int: + """ + Returns a random divisor of value that + x * min_value <= value + if max_options is 1, the behavior is deterministic + """ + ns = get_divisors(value, min_value, max_options=max_options) # get cached divisors + idx = RNG_INSTANCE.randint(0, len(ns) - 1) + + return ns[idx] + + +def set_hypertile_seed(seed: int) -> None: + RNG_INSTANCE.seed(seed) + + +@cache +def largest_tile_size_available(width: int, height: int) -> int: + """ + Calculates the largest tile size available for a given width and height + Tile size is always a power of 2 + """ + gcd = math.gcd(width, height) + largest_tile_size_available = 1 + while gcd % (largest_tile_size_available * 2) == 0: + largest_tile_size_available *= 2 + return largest_tile_size_available + + +def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]: + """ + Finds h and w such that h*w = hw and h/w = aspect_ratio + We check all possible divisors of hw and return the closest to the aspect ratio + """ + divisors = [i for i in range(2, hw + 1) if hw % i == 0] # all divisors of hw + pairs = [(i, hw // i) for i in divisors] # all pairs of divisors of hw + ratios = [w/h for h, w in pairs] # all ratios of pairs of divisors of hw + closest_ratio = min(ratios, key=lambda x: abs(x - aspect_ratio)) # closest ratio to aspect_ratio + closest_pair = pairs[ratios.index(closest_ratio)] # closest pair of divisors to aspect_ratio + return closest_pair + + +@cache +def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]: + """ + Finds h and w such that h*w = hw and h/w = aspect_ratio + """ + h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) + # find h and w such that h*w = hw and h/w = aspect_ratio + if h * w != hw: + w_candidate = hw / h + # check if w is an integer + if not w_candidate.is_integer(): + h_candidate = hw / w + # check if h is an integer + if not h_candidate.is_integer(): + return iterative_closest_divisors(hw, aspect_ratio) + else: + h = int(h_candidate) + else: + w = int(w_candidate) + return h, w + + +def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable: + + @wraps(params.forward) + def wrapper(*args, **kwargs): + if not params.enabled: + return params.forward(*args, **kwargs) + + latent_tile_size = max(128, params.tile_size) // 8 + x = args[0] + + # VAE + if x.ndim == 4: + b, c, h, w = x.shape + + nh = random_divisor(h, latent_tile_size, params.swap_size) + nw = random_divisor(w, latent_tile_size, params.swap_size) + + if nh * nw > 1: + x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw) # split into nh * nw tiles + + out = params.forward(x, *args[1:], **kwargs) + + if nh * nw > 1: + out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw) + + # U-Net + else: + hw: int = x.size(1) + h, w = find_hw_candidates(hw, params.aspect_ratio) + assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}" + + factor = 2 ** params.depth if scale_depth else 1 + nh = random_divisor(h, latent_tile_size * factor, params.swap_size) + nw = random_divisor(w, latent_tile_size * factor, params.swap_size) + + if nh * nw > 1: + x = rearrange(x, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) + + out = params.forward(x, *args[1:], **kwargs) + + if nh * nw > 1: + out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) + out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw) + + return out + + return wrapper + + +def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False): + hypertile_layers = getattr(model, "__webui_hypertile_layers", None) + if hypertile_layers is None: + if not enable: + return + + hypertile_layers = {} + layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS + + for depth in range(4): + for layer_name, module in model.named_modules(): + if any(layer_name.endswith(try_name) for try_name in layers[depth]): + params = HypertileParams() + module.__webui_hypertile_params = params + params.forward = module.forward + params.depth = depth + params.layer_name = layer_name + module.forward = self_attn_forward(params) + + hypertile_layers[layer_name] = 1 + + model.__webui_hypertile_layers = hypertile_layers + + aspect_ratio = width / height + tile_size = min(largest_tile_size_available(width, height), tile_size_max) + + for layer_name, module in model.named_modules(): + if layer_name in hypertile_layers: + params = module.__webui_hypertile_params + + params.tile_size = tile_size + params.swap_size = swap_size + params.aspect_ratio = aspect_ratio + params.enabled = enable and params.depth <= max_depth diff --git a/extensions-builtin/hypertile/scripts/hypertile_script.py b/extensions-builtin/hypertile/scripts/hypertile_script.py new file mode 100644 index 00000000000..59e7f9907e5 --- /dev/null +++ b/extensions-builtin/hypertile/scripts/hypertile_script.py @@ -0,0 +1,122 @@ +import hypertile +from modules import scripts, script_callbacks, shared + + +class ScriptHypertile(scripts.Script): + name = "Hypertile" + + def title(self): + return self.name + + def show(self, is_img2img): + return scripts.AlwaysVisible + + def process(self, p, *args): + hypertile.set_hypertile_seed(p.all_seeds[0]) + + configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet) + + self.add_infotext(p) + + def before_hr(self, p, *args): + + enable = shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet + + # exclusive hypertile seed for the second pass + if enable: + hypertile.set_hypertile_seed(p.all_seeds[0]) + + configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=enable) + + if enable and not shared.opts.hypertile_enable_unet: + p.extra_generation_params["Hypertile U-Net second pass"] = True + + self.add_infotext(p, add_unet_params=True) + + def add_infotext(self, p, add_unet_params=False): + def option(name): + value = getattr(shared.opts, name) + default_value = shared.opts.get_default(name) + return None if value == default_value else value + + if shared.opts.hypertile_enable_unet: + p.extra_generation_params["Hypertile U-Net"] = True + + if shared.opts.hypertile_enable_unet or add_unet_params: + p.extra_generation_params["Hypertile U-Net max depth"] = option('hypertile_max_depth_unet') + p.extra_generation_params["Hypertile U-Net max tile size"] = option('hypertile_max_tile_unet') + p.extra_generation_params["Hypertile U-Net swap size"] = option('hypertile_swap_size_unet') + + if shared.opts.hypertile_enable_vae: + p.extra_generation_params["Hypertile VAE"] = True + p.extra_generation_params["Hypertile VAE max depth"] = option('hypertile_max_depth_vae') + p.extra_generation_params["Hypertile VAE max tile size"] = option('hypertile_max_tile_vae') + p.extra_generation_params["Hypertile VAE swap size"] = option('hypertile_swap_size_vae') + + +def configure_hypertile(width, height, enable_unet=True): + hypertile.hypertile_hook_model( + shared.sd_model.first_stage_model, + width, + height, + swap_size=shared.opts.hypertile_swap_size_vae, + max_depth=shared.opts.hypertile_max_depth_vae, + tile_size_max=shared.opts.hypertile_max_tile_vae, + enable=shared.opts.hypertile_enable_vae, + ) + + hypertile.hypertile_hook_model( + shared.sd_model.model, + width, + height, + swap_size=shared.opts.hypertile_swap_size_unet, + max_depth=shared.opts.hypertile_max_depth_unet, + tile_size_max=shared.opts.hypertile_max_tile_unet, + enable=enable_unet, + is_sdxl=shared.sd_model.is_sdxl + ) + + +def on_ui_settings(): + import gradio as gr + + options = { + "hypertile_explanation": shared.OptionHTML(""" + Hypertile optimizes the self-attention layer within U-Net and VAE models, + resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the + benefit. + """), + + "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net", infotext="Hypertile U-Net").info("enables hypertile for all modes, including hires fix second pass; noticeable change in details of the generated picture"), + "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass", infotext="Hypertile U-Net second pass").info("enables hypertile just for hires fix second pass - regardless of whether the above setting is enabled"), + "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile U-Net max depth").info("larger = more neural network layers affected; minor effect on performance"), + "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-Net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile U-Net max tile size").info("larger = worse performance"), + "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-Net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile U-Net swap size"), + "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE", infotext="Hypertile VAE").info("minimal change in the generated picture"), + "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile VAE max depth"), + "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile VAE max tile size"), + "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile VAE swap size"), + } + + for name, opt in options.items(): + opt.section = ('hypertile', "Hypertile") + shared.opts.add_option(name, opt) + + +def add_axis_options(): + xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module + xyz_grid.axis_options.extend([ + xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, xyz_grid.apply_override('hypertile_enable_unet', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)), + xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, xyz_grid.apply_override('hypertile_enable_unet_secondpass', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)), + xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, xyz_grid.apply_override("hypertile_max_depth_unet"), confirm=xyz_grid.confirm_range(0, 3, '[Hypertile] Unet Max Depth'), choices=lambda: [str(x) for x in range(4)]), + xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, xyz_grid.apply_override("hypertile_max_tile_unet"), confirm=xyz_grid.confirm_range(0, 512, '[Hypertile] Unet Max Tile Size')), + xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, xyz_grid.apply_override("hypertile_swap_size_unet"), confirm=xyz_grid.confirm_range(0, 64, '[Hypertile] Unet Swap Size')), + xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, xyz_grid.apply_override('hypertile_enable_vae', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)), + xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, xyz_grid.apply_override("hypertile_max_depth_vae"), confirm=xyz_grid.confirm_range(0, 3, '[Hypertile] VAE Max Depth'), choices=lambda: [str(x) for x in range(4)]), + xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, xyz_grid.apply_override("hypertile_max_tile_vae"), confirm=xyz_grid.confirm_range(0, 512, '[Hypertile] VAE Max Tile Size')), + xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, xyz_grid.apply_override("hypertile_swap_size_vae"), confirm=xyz_grid.confirm_range(0, 64, '[Hypertile] VAE Swap Size')), + ]) + + +script_callbacks.on_ui_settings(on_ui_settings) +script_callbacks.on_before_ui(add_axis_options) diff --git a/extensions-builtin/mobile/javascript/mobile.js b/extensions-builtin/mobile/javascript/mobile.js new file mode 100644 index 00000000000..bff1acedff3 --- /dev/null +++ b/extensions-builtin/mobile/javascript/mobile.js @@ -0,0 +1,34 @@ +var isSetupForMobile = false; + +function isMobile() { + for (var tab of ["txt2img", "img2img"]) { + var imageTab = gradioApp().getElementById(tab + '_results'); + if (imageTab && imageTab.offsetParent && imageTab.offsetLeft == 0) { + return true; + } + } + + return false; +} + +function reportWindowSize() { + if (gradioApp().querySelector('.toprow-compact-tools')) return; // not applicable for compact prompt layout + + var currentlyMobile = isMobile(); + if (currentlyMobile == isSetupForMobile) return; + isSetupForMobile = currentlyMobile; + + for (var tab of ["txt2img", "img2img"]) { + var button = gradioApp().getElementById(tab + '_generate_box'); + var target = gradioApp().getElementById(currentlyMobile ? tab + '_results' : tab + '_actions_column'); + target.insertBefore(button, target.firstElementChild); + + gradioApp().getElementById(tab + '_results').classList.toggle('mobile', currentlyMobile); + } +} + +window.addEventListener("resize", reportWindowSize); + +onUiLoaded(function() { + reportWindowSize(); +}); diff --git a/extensions-builtin/postprocessing-for-training/scripts/postprocessing_autosized_crop.py b/extensions-builtin/postprocessing-for-training/scripts/postprocessing_autosized_crop.py new file mode 100644 index 00000000000..7e674989814 --- /dev/null +++ b/extensions-builtin/postprocessing-for-training/scripts/postprocessing_autosized_crop.py @@ -0,0 +1,64 @@ +from PIL import Image + +from modules import scripts_postprocessing, ui_components +import gradio as gr + + +def center_crop(image: Image, w: int, h: int): + iw, ih = image.size + if ih / h < iw / w: + sw = w * ih / h + box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih + else: + sh = h * iw / w + box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2 + return image.resize((w, h), Image.Resampling.LANCZOS, box) + + +def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold): + iw, ih = image.size + err = lambda w, h: 1 - (lambda x: x if x < 1 else 1 / x)(iw / ih / (w / h)) + wh = max(((w, h) for w in range(mindim, maxdim + 1, 64) for h in range(mindim, maxdim + 1, 64) + if minarea <= w * h <= maxarea and err(w, h) <= threshold), + key=lambda wh: (wh[0] * wh[1], -err(*wh))[::1 if objective == 'Maximize area' else -1], + default=None + ) + return wh and center_crop(image, *wh) + + +class ScriptPostprocessingAutosizedCrop(scripts_postprocessing.ScriptPostprocessing): + name = "Auto-sized crop" + order = 4020 + + def ui(self): + with ui_components.InputAccordion(False, label="Auto-sized crop") as enable: + gr.Markdown('Each image is center-cropped with an automatically chosen width and height.') + with gr.Row(): + mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="postprocess_multicrop_mindim") + maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="postprocess_multicrop_maxdim") + with gr.Row(): + minarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area lower bound", value=64 * 64, elem_id="postprocess_multicrop_minarea") + maxarea = gr.Slider(minimum=64 * 64, maximum=2048 * 2048, step=1, label="Area upper bound", value=640 * 640, elem_id="postprocess_multicrop_maxarea") + with gr.Row(): + objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="postprocess_multicrop_objective") + threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="postprocess_multicrop_threshold") + + return { + "enable": enable, + "mindim": mindim, + "maxdim": maxdim, + "minarea": minarea, + "maxarea": maxarea, + "objective": objective, + "threshold": threshold, + } + + def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, mindim, maxdim, minarea, maxarea, objective, threshold): + if not enable: + return + + cropped = multicrop_pic(pp.image, mindim, maxdim, minarea, maxarea, objective, threshold) + if cropped is not None: + pp.image = cropped + else: + print(f"skipped {pp.image.width}x{pp.image.height} image (can't find suitable size within error threshold)") diff --git a/extensions-builtin/postprocessing-for-training/scripts/postprocessing_caption.py b/extensions-builtin/postprocessing-for-training/scripts/postprocessing_caption.py new file mode 100644 index 00000000000..5592a89870e --- /dev/null +++ b/extensions-builtin/postprocessing-for-training/scripts/postprocessing_caption.py @@ -0,0 +1,30 @@ +from modules import scripts_postprocessing, ui_components, deepbooru, shared +import gradio as gr + + +class ScriptPostprocessingCeption(scripts_postprocessing.ScriptPostprocessing): + name = "Caption" + order = 4040 + + def ui(self): + with ui_components.InputAccordion(False, label="Caption") as enable: + option = gr.CheckboxGroup(value=["Deepbooru"], choices=["Deepbooru", "BLIP"], show_label=False) + + return { + "enable": enable, + "option": option, + } + + def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, option): + if not enable: + return + + captions = [pp.caption] + + if "Deepbooru" in option: + captions.append(deepbooru.model.tag(pp.image)) + + if "BLIP" in option: + captions.append(shared.interrogator.interrogate(pp.image.convert("RGB"))) + + pp.caption = ", ".join([x for x in captions if x]) diff --git a/extensions-builtin/postprocessing-for-training/scripts/postprocessing_create_flipped_copies.py b/extensions-builtin/postprocessing-for-training/scripts/postprocessing_create_flipped_copies.py new file mode 100644 index 00000000000..b673003b6ea --- /dev/null +++ b/extensions-builtin/postprocessing-for-training/scripts/postprocessing_create_flipped_copies.py @@ -0,0 +1,32 @@ +from PIL import ImageOps, Image + +from modules import scripts_postprocessing, ui_components +import gradio as gr + + +class ScriptPostprocessingCreateFlippedCopies(scripts_postprocessing.ScriptPostprocessing): + name = "Create flipped copies" + order = 4030 + + def ui(self): + with ui_components.InputAccordion(False, label="Create flipped copies") as enable: + with gr.Row(): + option = gr.CheckboxGroup(value=["Horizontal"], choices=["Horizontal", "Vertical", "Both"], show_label=False) + + return { + "enable": enable, + "option": option, + } + + def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, option): + if not enable: + return + + if "Horizontal" in option: + pp.extra_images.append(ImageOps.mirror(pp.image)) + + if "Vertical" in option: + pp.extra_images.append(pp.image.transpose(Image.Transpose.FLIP_TOP_BOTTOM)) + + if "Both" in option: + pp.extra_images.append(pp.image.transpose(Image.Transpose.FLIP_TOP_BOTTOM).transpose(Image.Transpose.FLIP_LEFT_RIGHT)) diff --git a/extensions-builtin/postprocessing-for-training/scripts/postprocessing_focal_crop.py b/extensions-builtin/postprocessing-for-training/scripts/postprocessing_focal_crop.py new file mode 100644 index 00000000000..cff1dbc5470 --- /dev/null +++ b/extensions-builtin/postprocessing-for-training/scripts/postprocessing_focal_crop.py @@ -0,0 +1,54 @@ + +from modules import scripts_postprocessing, ui_components, errors +import gradio as gr + +from modules.textual_inversion import autocrop + + +class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing): + name = "Auto focal point crop" + order = 4010 + + def ui(self): + with ui_components.InputAccordion(False, label="Auto focal point crop") as enable: + face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_face_weight") + entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_entropy_weight") + edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_focal_crop_edges_weight") + debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug") + + return { + "enable": enable, + "face_weight": face_weight, + "entropy_weight": entropy_weight, + "edges_weight": edges_weight, + "debug": debug, + } + + def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, face_weight, entropy_weight, edges_weight, debug): + if not enable: + return + + if not pp.shared.target_width or not pp.shared.target_height: + return + + dnn_model_path = None + try: + dnn_model_path = autocrop.download_and_cache_models() + except Exception: + errors.report("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", exc_info=True) + + autocrop_settings = autocrop.Settings( + crop_width=pp.shared.target_width, + crop_height=pp.shared.target_height, + face_points_weight=face_weight, + entropy_points_weight=entropy_weight, + corner_points_weight=edges_weight, + annotate_image=debug, + dnn_model_path=dnn_model_path, + ) + + result, *others = autocrop.crop_image(pp.image, autocrop_settings) + + pp.image = result + pp.extra_images = [pp.create_copy(x, nametags=["focal-crop-debug"], disable_processing=True) for x in others] + diff --git a/extensions-builtin/postprocessing-for-training/scripts/postprocessing_split_oversized.py b/extensions-builtin/postprocessing-for-training/scripts/postprocessing_split_oversized.py new file mode 100644 index 00000000000..133e199b838 --- /dev/null +++ b/extensions-builtin/postprocessing-for-training/scripts/postprocessing_split_oversized.py @@ -0,0 +1,71 @@ +import math + +from modules import scripts_postprocessing, ui_components +import gradio as gr + + +def split_pic(image, inverse_xy, width, height, overlap_ratio): + if inverse_xy: + from_w, from_h = image.height, image.width + to_w, to_h = height, width + else: + from_w, from_h = image.width, image.height + to_w, to_h = width, height + h = from_h * to_w // from_w + if inverse_xy: + image = image.resize((h, to_w)) + else: + image = image.resize((to_w, h)) + + split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio))) + y_step = (h - to_h) / (split_count - 1) + for i in range(split_count): + y = int(y_step * i) + if inverse_xy: + splitted = image.crop((y, 0, y + to_h, to_w)) + else: + splitted = image.crop((0, y, to_w, y + to_h)) + yield splitted + + +class ScriptPostprocessingSplitOversized(scripts_postprocessing.ScriptPostprocessing): + name = "Split oversized images" + order = 4000 + + def ui(self): + with ui_components.InputAccordion(False, label="Split oversized images") as enable: + with gr.Row(): + split_threshold = gr.Slider(label='Threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="postprocess_split_threshold") + overlap_ratio = gr.Slider(label='Overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="postprocess_overlap_ratio") + + return { + "enable": enable, + "split_threshold": split_threshold, + "overlap_ratio": overlap_ratio, + } + + def process(self, pp: scripts_postprocessing.PostprocessedImage, enable, split_threshold, overlap_ratio): + if not enable: + return + + width = pp.shared.target_width + height = pp.shared.target_height + + if not width or not height: + return + + if pp.image.height > pp.image.width: + ratio = (pp.image.width * height) / (pp.image.height * width) + inverse_xy = False + else: + ratio = (pp.image.height * width) / (pp.image.width * height) + inverse_xy = True + + if ratio >= 1.0 or ratio > split_threshold: + return + + result, *others = split_pic(pp.image, inverse_xy, width, height, overlap_ratio) + + pp.image = result + pp.extra_images = [pp.create_copy(x) for x in others] + diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js new file mode 100644 index 00000000000..114cf94ccbf --- /dev/null +++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js @@ -0,0 +1,42 @@ +// Stable Diffusion WebUI - Bracket checker +// By Hingashi no Florin/Bwin4L & @akx +// Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs. +// If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong. + +function checkBrackets(textArea, counterElt) { + var counts = {}; + (textArea.value.match(/[(){}[\]]/g) || []).forEach(bracket => { + counts[bracket] = (counts[bracket] || 0) + 1; + }); + var errors = []; + + function checkPair(open, close, kind) { + if (counts[open] !== counts[close]) { + errors.push( + `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.` + ); + } + } + + checkPair('(', ')', 'round brackets'); + checkPair('[', ']', 'square brackets'); + checkPair('{', '}', 'curly brackets'); + counterElt.title = errors.join('\n'); + counterElt.classList.toggle('error', errors.length !== 0); +} + +function setupBracketChecking(id_prompt, id_counter) { + var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); + var counter = gradioApp().getElementById(id_counter); + + if (textarea && counter) { + textarea.addEventListener("input", () => checkBrackets(textarea, counter)); + } +} + +onUiLoaded(function() { + setupBracketChecking('txt2img_prompt', 'txt2img_token_counter'); + setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter'); + setupBracketChecking('img2img_prompt', 'img2img_token_counter'); + setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter'); +}); diff --git a/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py b/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py new file mode 100644 index 00000000000..0e629963af4 --- /dev/null +++ b/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py @@ -0,0 +1,760 @@ +import numpy as np +import gradio as gr +import math +from modules.ui_components import InputAccordion +import modules.scripts as scripts +from modules.torch_utils import float64 + + +class SoftInpaintingSettings: + def __init__(self, + mask_blend_power, + mask_blend_scale, + inpaint_detail_preservation, + composite_mask_influence, + composite_difference_threshold, + composite_difference_contrast): + self.mask_blend_power = mask_blend_power + self.mask_blend_scale = mask_blend_scale + self.inpaint_detail_preservation = inpaint_detail_preservation + self.composite_mask_influence = composite_mask_influence + self.composite_difference_threshold = composite_difference_threshold + self.composite_difference_contrast = composite_difference_contrast + + def add_generation_params(self, dest): + dest[enabled_gen_param_label] = True + dest[gen_param_labels.mask_blend_power] = self.mask_blend_power + dest[gen_param_labels.mask_blend_scale] = self.mask_blend_scale + dest[gen_param_labels.inpaint_detail_preservation] = self.inpaint_detail_preservation + dest[gen_param_labels.composite_mask_influence] = self.composite_mask_influence + dest[gen_param_labels.composite_difference_threshold] = self.composite_difference_threshold + dest[gen_param_labels.composite_difference_contrast] = self.composite_difference_contrast + + +# ------------------- Methods ------------------- + +def processing_uses_inpainting(p): + # TODO: Figure out a better way to determine if inpainting is being used by p + if getattr(p, "image_mask", None) is not None: + return True + + if getattr(p, "mask", None) is not None: + return True + + if getattr(p, "nmask", None) is not None: + return True + + return False + + +def latent_blend(settings, a, b, t): + """ + Interpolates two latent image representations according to the parameter t, + where the interpolated vectors' magnitudes are also interpolated separately. + The "detail_preservation" factor biases the magnitude interpolation towards + the larger of the two magnitudes. + """ + import torch + + # NOTE: We use inplace operations wherever possible. + + if len(t.shape) == 3: + # [4][w][h] to [1][4][w][h] + t2 = t.unsqueeze(0) + # [4][w][h] to [1][1][w][h] - the [4] seem redundant. + t3 = t[0].unsqueeze(0).unsqueeze(0) + else: + t2 = t + t3 = t[:, 0][:, None] + + one_minus_t2 = 1 - t2 + one_minus_t3 = 1 - t3 + + # Linearly interpolate the image vectors. + a_scaled = a * one_minus_t2 + b_scaled = b * t2 + image_interp = a_scaled + image_interp.add_(b_scaled) + result_type = image_interp.dtype + del a_scaled, b_scaled, t2, one_minus_t2 + + # Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.) + # 64-bit operations are used here to allow large exponents. + current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(float64(image_interp)).add_(0.00001) + + # Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1). + a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(float64(a)).pow_(settings.inpaint_detail_preservation) * one_minus_t3 + b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(float64(b)).pow_(settings.inpaint_detail_preservation) * t3 + desired_magnitude = a_magnitude + desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation) + del a_magnitude, b_magnitude, t3, one_minus_t3 + + # Change the linearly interpolated image vectors' magnitudes to the value we want. + # This is the last 64-bit operation. + image_interp_scaling_factor = desired_magnitude + image_interp_scaling_factor.div_(current_magnitude) + image_interp_scaling_factor = image_interp_scaling_factor.to(result_type) + image_interp_scaled = image_interp + image_interp_scaled.mul_(image_interp_scaling_factor) + del current_magnitude + del desired_magnitude + del image_interp + del image_interp_scaling_factor + del result_type + + return image_interp_scaled + + +def get_modified_nmask(settings, nmask, sigma): + """ + Converts a negative mask representing the transparency of the original latent vectors being overlaid + to a mask that is scaled according to the denoising strength for this step. + + Where: + 0 = fully opaque, infinite density, fully masked + 1 = fully transparent, zero density, fully unmasked + + We bring this transparency to a power, as this allows one to simulate N number of blending operations + where N can be any positive real value. Using this one can control the balance of influence between + the denoiser and the original latents according to the sigma value. + + NOTE: "mask" is not used + """ + import torch + return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale) + + +def apply_adaptive_masks( + settings: SoftInpaintingSettings, + nmask, + latent_orig, + latent_processed, + overlay_images, + width, height, + paste_to): + import torch + import modules.processing as proc + import modules.images as images + from PIL import Image, ImageOps, ImageFilter + + # TODO: Bias the blending according to the latent mask, add adjustable parameter for bias control. + if len(nmask.shape) == 3: + latent_mask = nmask[0].float() + else: + latent_mask = nmask[:, 0].float() + # convert the original mask into a form we use to scale distances for thresholding + mask_scalar = 1 - (torch.clamp(latent_mask, min=0, max=1) ** (settings.mask_blend_scale / 2)) + mask_scalar = (0.5 * (1 - settings.composite_mask_influence) + + mask_scalar * settings.composite_mask_influence) + mask_scalar = mask_scalar / (1.00001 - mask_scalar) + mask_scalar = mask_scalar.cpu().numpy() + + latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1) + + kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2) + + masks_for_overlay = [] + + for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)): + converted_mask = distance_map.float().cpu().numpy() + converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center, + percentile_min=0.9, percentile_max=1, min_width=1) + converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center, + percentile_min=0.25, percentile_max=0.75, min_width=1) + + # The distance at which opacity of original decreases to 50% + if len(mask_scalar.shape) == 3: + if mask_scalar.shape[0] > i: + half_weighted_distance = settings.composite_difference_threshold * mask_scalar[i] + else: + half_weighted_distance = settings.composite_difference_threshold * mask_scalar[0] + else: + half_weighted_distance = settings.composite_difference_threshold * mask_scalar + + converted_mask = converted_mask / half_weighted_distance + + converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast) + converted_mask = smootherstep(converted_mask) + converted_mask = 1 - converted_mask + converted_mask = 255. * converted_mask + converted_mask = converted_mask.astype(np.uint8) + converted_mask = Image.fromarray(converted_mask) + converted_mask = images.resize_image(2, converted_mask, width, height) + converted_mask = proc.create_binary_mask(converted_mask, round=False) + + # Remove aliasing artifacts using a gaussian blur. + converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4)) + + # Expand the mask to fit the whole image if needed. + if paste_to is not None: + converted_mask = proc.uncrop(converted_mask, + (overlay_image.width, overlay_image.height), + paste_to) + + masks_for_overlay.append(converted_mask) + + image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height)) + image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"), + mask=ImageOps.invert(converted_mask.convert('L'))) + + overlay_images[i] = image_masked.convert('RGBA') + + return masks_for_overlay + + +def apply_masks( + settings, + nmask, + overlay_images, + width, height, + paste_to): + import torch + import modules.processing as proc + import modules.images as images + from PIL import Image, ImageOps, ImageFilter + + converted_mask = nmask[0].float() + converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2) + converted_mask = 255. * converted_mask + converted_mask = converted_mask.cpu().numpy().astype(np.uint8) + converted_mask = Image.fromarray(converted_mask) + converted_mask = images.resize_image(2, converted_mask, width, height) + converted_mask = proc.create_binary_mask(converted_mask, round=False) + + # Remove aliasing artifacts using a gaussian blur. + converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4)) + + # Expand the mask to fit the whole image if needed. + if paste_to is not None: + converted_mask = proc.uncrop(converted_mask, + (width, height), + paste_to) + + masks_for_overlay = [] + + for i, overlay_image in enumerate(overlay_images): + masks_for_overlay[i] = converted_mask + + image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height)) + image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"), + mask=ImageOps.invert(converted_mask.convert('L'))) + + overlay_images[i] = image_masked.convert('RGBA') + + return masks_for_overlay + + +def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0): + """ + Generalization convolution filter capable of applying + weighted mean, median, maximum, and minimum filters + parametrically using an arbitrary kernel. + + Args: + img (nparray): + The image, a 2-D array of floats, to which the filter is being applied. + kernel (nparray): + The kernel, a 2-D array of floats. + kernel_center (nparray): + The kernel center coordinate, a 1-D array with two elements. + percentile_min (float): + The lower bound of the histogram window used by the filter, + from 0 to 1. + percentile_max (float): + The upper bound of the histogram window used by the filter, + from 0 to 1. + min_width (float): + The minimum size of the histogram window bounds, in weight units. + Must be greater than 0. + + Returns: + (nparray): A filtered copy of the input image "img", a 2-D array of floats. + """ + + # Converts an index tuple into a vector. + def vec(x): + return np.array(x) + + kernel_min = -kernel_center + kernel_max = vec(kernel.shape) - kernel_center + + def weighted_histogram_filter_single(idx): + idx = vec(idx) + min_index = np.maximum(0, idx + kernel_min) + max_index = np.minimum(vec(img.shape), idx + kernel_max) + window_shape = max_index - min_index + + class WeightedElement: + """ + An element of the histogram, its weight + and bounds. + """ + + def __init__(self, value, weight): + self.value: float = value + self.weight: float = weight + self.window_min: float = 0.0 + self.window_max: float = 1.0 + + # Collect the values in the image as WeightedElements, + # weighted by their corresponding kernel values. + values = [] + for window_tup in np.ndindex(tuple(window_shape)): + window_index = vec(window_tup) + image_index = window_index + min_index + centered_kernel_index = image_index - idx + kernel_index = centered_kernel_index + kernel_center + element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)]) + values.append(element) + + def sort_key(x: WeightedElement): + return x.value + + values.sort(key=sort_key) + + # Calculate the height of the stack (sum) + # and each sample's range they occupy in the stack + sum = 0 + for i in range(len(values)): + values[i].window_min = sum + sum += values[i].weight + values[i].window_max = sum + + # Calculate what range of this stack ("window") + # we want to get the weighted average across. + window_min = sum * percentile_min + window_max = sum * percentile_max + window_width = window_max - window_min + + # Ensure the window is within the stack and at least a certain size. + if window_width < min_width: + window_center = (window_min + window_max) / 2 + window_min = window_center - min_width / 2 + window_max = window_center + min_width / 2 + + if window_max > sum: + window_max = sum + window_min = sum - min_width + + if window_min < 0: + window_min = 0 + window_max = min_width + + value = 0 + value_weight = 0 + + # Get the weighted average of all the samples + # that overlap with the window, weighted + # by the size of their overlap. + for i in range(len(values)): + if window_min >= values[i].window_max: + continue + if window_max <= values[i].window_min: + break + + s = max(window_min, values[i].window_min) + e = min(window_max, values[i].window_max) + w = e - s + + value += values[i].value * w + value_weight += w + + return value / value_weight if value_weight != 0 else 0 + + img_out = img.copy() + + # Apply the kernel operation over each pixel. + for index in np.ndindex(img.shape): + img_out[index] = weighted_histogram_filter_single(index) + + return img_out + + +def smoothstep(x): + """ + The smoothstep function, input should be clamped to 0-1 range. + Turns a diagonal line (f(x) = x) into a sigmoid-like curve. + """ + return x * x * (3 - 2 * x) + + +def smootherstep(x): + """ + The smootherstep function, input should be clamped to 0-1 range. + Turns a diagonal line (f(x) = x) into a sigmoid-like curve. + """ + return x * x * x * (x * (6 * x - 15) + 10) + + +def get_gaussian_kernel(stddev_radius=1.0, max_radius=2): + """ + Creates a Gaussian kernel with thresholded edges. + + Args: + stddev_radius (float): + Standard deviation of the gaussian kernel, in pixels. + max_radius (int): + The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2. + The kernel is thresholded so that any values one pixel beyond this radius + is weighted at 0. + + Returns: + (nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2)) + """ + + # Evaluates a 0-1 normalized gaussian function for a given square distance from the mean. + def gaussian(sqr_mag): + return math.exp(-sqr_mag / (stddev_radius * stddev_radius)) + + # Helper function for converting a tuple to an array. + def vec(x): + return np.array(x) + + """ + Since a gaussian is unbounded, we need to limit ourselves + to a finite range. + We taper the ends off at the end of that range so they equal zero + while preserving the maximum value of 1 at the mean. + """ + zero_radius = max_radius + 1.0 + gauss_zero = gaussian(zero_radius * zero_radius) + gauss_kernel_scale = 1 / (1 - gauss_zero) + + def gaussian_kernel_func(coordinate): + x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0 + x = gaussian(x) + x -= gauss_zero + x *= gauss_kernel_scale + x = max(0.0, x) + return x + + size = max_radius * 2 + 1 + kernel_center = max_radius + kernel = np.zeros((size, size)) + + for index in np.ndindex(kernel.shape): + kernel[index] = gaussian_kernel_func(vec(index) - kernel_center) + + return kernel, kernel_center + + +# ------------------- Constants ------------------- + + +default = SoftInpaintingSettings(1, 0.5, 4, 0, 0.5, 2) + +enabled_ui_label = "Soft inpainting" +enabled_gen_param_label = "Soft inpainting enabled" +enabled_el_id = "soft_inpainting_enabled" + +ui_labels = SoftInpaintingSettings( + "Schedule bias", + "Preservation strength", + "Transition contrast boost", + "Mask influence", + "Difference threshold", + "Difference contrast") + +ui_info = SoftInpaintingSettings( + "Shifts when preservation of original content occurs during denoising.", + "How strongly partially masked content should be preserved.", + "Amplifies the contrast that may be lost in partially masked regions.", + "How strongly the original mask should bias the difference threshold.", + "How much an image region can change before the original pixels are not blended in anymore.", + "How sharp the transition should be between blended and not blended.") + +gen_param_labels = SoftInpaintingSettings( + "Soft inpainting schedule bias", + "Soft inpainting preservation strength", + "Soft inpainting transition contrast boost", + "Soft inpainting mask influence", + "Soft inpainting difference threshold", + "Soft inpainting difference contrast") + +el_ids = SoftInpaintingSettings( + "mask_blend_power", + "mask_blend_scale", + "inpaint_detail_preservation", + "composite_mask_influence", + "composite_difference_threshold", + "composite_difference_contrast") + + +# ------------------- Script ------------------- + + +class Script(scripts.Script): + def __init__(self): + self.section = "inpaint" + self.masks_for_overlay = None + self.overlay_images = None + + def title(self): + return "Soft Inpainting" + + def show(self, is_img2img): + return scripts.AlwaysVisible if is_img2img else False + + def ui(self, is_img2img): + if not is_img2img: + return + + with InputAccordion(False, label=enabled_ui_label, elem_id=enabled_el_id) as soft_inpainting_enabled: + with gr.Group(): + gr.Markdown( + """ + Soft inpainting allows you to **seamlessly blend original content with inpainted content** according to the mask opacity. + **High _Mask blur_** values are recommended! + """) + + power = \ + gr.Slider(label=ui_labels.mask_blend_power, + info=ui_info.mask_blend_power, + minimum=0, + maximum=8, + step=0.1, + value=default.mask_blend_power, + elem_id=el_ids.mask_blend_power) + scale = \ + gr.Slider(label=ui_labels.mask_blend_scale, + info=ui_info.mask_blend_scale, + minimum=0, + maximum=8, + step=0.05, + value=default.mask_blend_scale, + elem_id=el_ids.mask_blend_scale) + detail = \ + gr.Slider(label=ui_labels.inpaint_detail_preservation, + info=ui_info.inpaint_detail_preservation, + minimum=1, + maximum=32, + step=0.5, + value=default.inpaint_detail_preservation, + elem_id=el_ids.inpaint_detail_preservation) + + gr.Markdown( + """ + ### Pixel Composite Settings + """) + + mask_inf = \ + gr.Slider(label=ui_labels.composite_mask_influence, + info=ui_info.composite_mask_influence, + minimum=0, + maximum=1, + step=0.05, + value=default.composite_mask_influence, + elem_id=el_ids.composite_mask_influence) + + dif_thresh = \ + gr.Slider(label=ui_labels.composite_difference_threshold, + info=ui_info.composite_difference_threshold, + minimum=0, + maximum=8, + step=0.25, + value=default.composite_difference_threshold, + elem_id=el_ids.composite_difference_threshold) + + dif_contr = \ + gr.Slider(label=ui_labels.composite_difference_contrast, + info=ui_info.composite_difference_contrast, + minimum=0, + maximum=8, + step=0.25, + value=default.composite_difference_contrast, + elem_id=el_ids.composite_difference_contrast) + + with gr.Accordion("Help", open=False): + gr.Markdown( + f""" + ### {ui_labels.mask_blend_power} + + The blending strength of original content is scaled proportionally with the decreasing noise level values at each step (sigmas). + This ensures that the influence of the denoiser and original content preservation is roughly balanced at each step. + This balance can be shifted using this parameter, controlling whether earlier or later steps have stronger preservation. + + - **Below 1**: Stronger preservation near the end (with low sigma) + - **1**: Balanced (proportional to sigma) + - **Above 1**: Stronger preservation in the beginning (with high sigma) + """) + gr.Markdown( + f""" + ### {ui_labels.mask_blend_scale} + + Skews whether partially masked image regions should be more likely to preserve the original content or favor inpainted content. + This may need to be adjusted depending on the {ui_labels.mask_blend_power}, CFG Scale, prompt and Denoising strength. + + - **Low values**: Favors generated content. + - **High values**: Favors original content. + """) + gr.Markdown( + f""" + ### {ui_labels.inpaint_detail_preservation} + + This parameter controls how the original latent vectors and denoised latent vectors are interpolated. + With higher values, the magnitude of the resulting blended vector will be closer to the maximum of the two interpolated vectors. + This can prevent the loss of contrast that occurs with linear interpolation. + + - **Low values**: Softer blending, details may fade. + - **High values**: Stronger contrast, may over-saturate colors. + """) + + gr.Markdown( + """ + ## Pixel Composite Settings + + Masks are generated based on how much a part of the image changed after denoising. + These masks are used to blend the original and final images together. + If the difference is low, the original pixels are used instead of the pixels returned by the inpainting process. + """) + + gr.Markdown( + f""" + ### {ui_labels.composite_mask_influence} + + This parameter controls how much the mask should bias this sensitivity to difference. + + - **0**: Ignore the mask, only consider differences in image content. + - **1**: Follow the mask closely despite image content changes. + """) + + gr.Markdown( + f""" + ### {ui_labels.composite_difference_threshold} + + This value represents the difference at which the original pixels will have less than 50% opacity. + + - **Low values**: Two images patches must be almost the same in order to retain original pixels. + - **High values**: Two images patches can be very different and still retain original pixels. + """) + + gr.Markdown( + f""" + ### {ui_labels.composite_difference_contrast} + + This value represents the contrast between the opacity of the original and inpainted content. + + - **Low values**: The blend will be more gradual and have longer transitions, but may cause ghosting. + - **High values**: Ghosting will be less common, but transitions may be very sudden. + """) + + self.infotext_fields = [(soft_inpainting_enabled, enabled_gen_param_label), + (power, gen_param_labels.mask_blend_power), + (scale, gen_param_labels.mask_blend_scale), + (detail, gen_param_labels.inpaint_detail_preservation), + (mask_inf, gen_param_labels.composite_mask_influence), + (dif_thresh, gen_param_labels.composite_difference_threshold), + (dif_contr, gen_param_labels.composite_difference_contrast)] + + self.paste_field_names = [] + for _, field_name in self.infotext_fields: + self.paste_field_names.append(field_name) + + return [soft_inpainting_enabled, + power, + scale, + detail, + mask_inf, + dif_thresh, + dif_contr] + + def process(self, p, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr): + if not enabled: + return + + if not processing_uses_inpainting(p): + return + + # Shut off the rounding it normally does. + p.mask_round = False + + settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) + + # p.extra_generation_params["Mask rounding"] = False + settings.add_generation_params(p.extra_generation_params) + + def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf, + dif_thresh, dif_contr): + if not enabled: + return + + if not processing_uses_inpainting(p): + return + + if mba.is_final_blend: + mba.blended_latent = mba.current_latent + return + + settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) + + # todo: Why is sigma 2D? Both values are the same. + mba.blended_latent = latent_blend(settings, + mba.init_latent, + mba.current_latent, + get_modified_nmask(settings, mba.nmask, mba.sigma[0])) + + def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf, + dif_thresh, dif_contr): + if not enabled: + return + + if not processing_uses_inpainting(p): + return + + nmask = getattr(p, "nmask", None) + if nmask is None: + return + + from modules import images + from modules.shared import opts + + settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) + + # since the original code puts holes in the existing overlay images, + # we have to rebuild them. + self.overlay_images = [] + for img in p.init_images: + + image = images.flatten(img, opts.img2img_background_color) + + if p.paste_to is None and p.resize_mode != 3: + image = images.resize_image(p.resize_mode, image, p.width, p.height) + + self.overlay_images.append(image.convert('RGBA')) + + if len(p.init_images) == 1: + self.overlay_images = self.overlay_images * p.batch_size + + if getattr(ps.samples, 'already_decoded', False): + self.masks_for_overlay = apply_masks(settings=settings, + nmask=nmask, + overlay_images=self.overlay_images, + width=p.width, + height=p.height, + paste_to=p.paste_to) + else: + self.masks_for_overlay = apply_adaptive_masks(settings=settings, + nmask=nmask, + latent_orig=p.init_latent, + latent_processed=ps.samples, + overlay_images=self.overlay_images, + width=p.width, + height=p.height, + paste_to=p.paste_to) + + def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale, + detail_preservation, mask_inf, dif_thresh, dif_contr): + if not enabled: + return + + if not processing_uses_inpainting(p): + return + + if self.masks_for_overlay is None: + return + + if self.overlay_images is None: + return + + ppmo.mask_for_overlay = self.masks_for_overlay[ppmo.index] + ppmo.overlay_image = self.overlay_images[ppmo.index] diff --git a/extensions/put extensions here.txt b/extensions/put extensions here.txt new file mode 100644 index 00000000000..e69de29bb2d diff --git a/html/card-no-preview.png b/html/card-no-preview.png new file mode 100644 index 00000000000..e2beb269206 Binary files /dev/null and b/html/card-no-preview.png differ diff --git a/html/extra-networks-card.html b/html/extra-networks-card.html new file mode 100644 index 00000000000..f1d959a6733 --- /dev/null +++ b/html/extra-networks-card.html @@ -0,0 +1,9 @@ +
+ {background_image} +
{copy_path_button}{metadata_button}{edit_button}
+
+
{search_terms}
+ {name} + {description} +
+
diff --git a/html/extra-networks-copy-path-button.html b/html/extra-networks-copy-path-button.html new file mode 100644 index 00000000000..50304b42d4b --- /dev/null +++ b/html/extra-networks-copy-path-button.html @@ -0,0 +1,5 @@ +
+
\ No newline at end of file diff --git a/html/extra-networks-edit-item-button.html b/html/extra-networks-edit-item-button.html new file mode 100644 index 00000000000..fd728600f6d --- /dev/null +++ b/html/extra-networks-edit-item-button.html @@ -0,0 +1,4 @@ +
+
\ No newline at end of file diff --git a/html/extra-networks-metadata-button.html b/html/extra-networks-metadata-button.html new file mode 100644 index 00000000000..4ef013bc02a --- /dev/null +++ b/html/extra-networks-metadata-button.html @@ -0,0 +1,4 @@ +
+
\ No newline at end of file diff --git a/html/extra-networks-no-cards.html b/html/extra-networks-no-cards.html new file mode 100644 index 00000000000..389358d6c4b --- /dev/null +++ b/html/extra-networks-no-cards.html @@ -0,0 +1,8 @@ +
+

Nothing here. Add some content to the following directories:

+ +
    +{dirs} +
+
+ diff --git a/html/extra-networks-pane-dirs.html b/html/extra-networks-pane-dirs.html new file mode 100644 index 00000000000..5ce04289a5a --- /dev/null +++ b/html/extra-networks-pane-dirs.html @@ -0,0 +1,8 @@ +
+
+ {dirs_html} +
+
+ {items_html} +
+
diff --git a/html/extra-networks-pane-tree.html b/html/extra-networks-pane-tree.html new file mode 100644 index 00000000000..88561fcdc85 --- /dev/null +++ b/html/extra-networks-pane-tree.html @@ -0,0 +1,8 @@ +
+
+ {tree_html} +
+
+ {items_html} +
+
\ No newline at end of file diff --git a/html/extra-networks-pane.html b/html/extra-networks-pane.html new file mode 100644 index 00000000000..9a67baea90f --- /dev/null +++ b/html/extra-networks-pane.html @@ -0,0 +1,81 @@ +
+ + {pane_content} +
diff --git a/html/extra-networks-tree-button.html b/html/extra-networks-tree-button.html new file mode 100644 index 00000000000..9dc2e2a40c8 --- /dev/null +++ b/html/extra-networks-tree-button.html @@ -0,0 +1,23 @@ + +
+ + {action_list_item_action_leading} + + + {action_list_item_visual_leading} + + + {action_list_item_label} + + + {action_list_item_visual_trailing} + + + {action_list_item_action_trailing} + +
\ No newline at end of file diff --git a/html/footer.html b/html/footer.html new file mode 100644 index 00000000000..69b2372c755 --- /dev/null +++ b/html/footer.html @@ -0,0 +1,15 @@ +
+ API +  •  + Github +  •  + Gradio +  •  + Startup profile +  •  + Reload UI +
+
+
+{versions} +
diff --git a/html/licenses.html b/html/licenses.html new file mode 100644 index 00000000000..9f5d1e9dc5c --- /dev/null +++ b/html/licenses.html @@ -0,0 +1,382 @@ + + +

InvokeAI

+Some code for compatibility with OSX is taken from lstein's repository. +
+MIT License
+
+Copyright (c) 2022 InvokeAI Team
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ +

LDSR

+Code added by contirubtors, most likely copied from this repository. +
+MIT License
+
+Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ +

CLIP Interrogator

+Some small amounts of code borrowed and reworked. +
+MIT License
+
+Copyright (c) 2022 pharmapsychotic
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ +

Memory Efficient Attention

+The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that. +
+MIT License
+
+Copyright (c) 2023 Alex Birch
+Copyright (c) 2023 Amin Rezaei
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+ +

Scaled Dot Product Attention

+Some small amounts of code borrowed and reworked. +
+   Copyright 2023 The HuggingFace Team. All rights reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
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+ +

Curated transformers

+The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers +
+The MIT License (MIT)
+
+Copyright (C) 2021 ExplosionAI GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
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+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+
+ +

TAESD

+Tiny AutoEncoder for Stable Diffusion option for live previews +
+MIT License
+
+Copyright (c) 2023 Ollin Boer Bohan
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
diff --git a/javascript/aspectRatioOverlay.js b/javascript/aspectRatioOverlay.js index 96f1c00d0de..c8751fe494f 100644 --- a/javascript/aspectRatioOverlay.js +++ b/javascript/aspectRatioOverlay.js @@ -1,119 +1,113 @@ - -let currentWidth = null; -let currentHeight = null; -let arFrameTimeout = setTimeout(function(){},0); - -function dimensionChange(e,dimname){ - - if(dimname == 'Width'){ - currentWidth = e.target.value*1.0 - } - if(dimname == 'Height'){ - currentHeight = e.target.value*1.0 - } - - var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200")) - - if(!inImg2img){ - return; - } - - var img2imgMode = gradioApp().querySelector('#mode_img2img.tabs > div > button.rounded-t-lg.border-gray-200') - if(img2imgMode){ - img2imgMode=img2imgMode.innerText - }else{ - return; - } - - var redrawImage = gradioApp().querySelector('div[data-testid=image] img'); - var inpaintImage = gradioApp().querySelector('#img2maskimg div[data-testid=image] img') - - var targetElement = null; - - if(img2imgMode=='img2img' && redrawImage){ - targetElement = redrawImage; - }else if(img2imgMode=='Inpaint' && inpaintImage){ - targetElement = inpaintImage; - } - - if(targetElement){ - - var arPreviewRect = gradioApp().querySelector('#imageARPreview'); - if(!arPreviewRect){ - arPreviewRect = document.createElement('div') - arPreviewRect.id = "imageARPreview"; - gradioApp().getRootNode().appendChild(arPreviewRect) - } - - - - var viewportOffset = targetElement.getBoundingClientRect(); - - viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight ) - - scaledx = targetElement.naturalWidth*viewportscale - scaledy = targetElement.naturalHeight*viewportscale - - cleintRectTop = (viewportOffset.top+window.scrollY) - cleintRectLeft = (viewportOffset.left+window.scrollX) - cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2) - cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2) - - viewRectTop = cleintRectCentreY-(scaledy/2) - viewRectLeft = cleintRectCentreX-(scaledx/2) - arRectWidth = scaledx - arRectHeight = scaledy - - arscale = Math.min( arRectWidth/currentWidth, arRectHeight/currentHeight ) - arscaledx = currentWidth*arscale - arscaledy = currentHeight*arscale - - arRectTop = cleintRectCentreY-(arscaledy/2) - arRectLeft = cleintRectCentreX-(arscaledx/2) - arRectWidth = arscaledx - arRectHeight = arscaledy - - arPreviewRect.style.top = arRectTop+'px'; - arPreviewRect.style.left = arRectLeft+'px'; - arPreviewRect.style.width = arRectWidth+'px'; - arPreviewRect.style.height = arRectHeight+'px'; - - clearTimeout(arFrameTimeout); - arFrameTimeout = setTimeout(function(){ - arPreviewRect.style.display = 'none'; - },2000); - - arPreviewRect.style.display = 'block'; - - } - -} - - -onUiUpdate(function(){ - var arPreviewRect = gradioApp().querySelector('#imageARPreview'); - if(arPreviewRect){ - arPreviewRect.style.display = 'none'; - } - var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200")) - if(inImg2img){ - let inputs = gradioApp().querySelectorAll('input'); - inputs.forEach(function(e){ - let parentLabel = e.parentElement.querySelector('label') - if(parentLabel && parentLabel.innerText){ - if(!e.classList.contains('scrollwatch')){ - if(parentLabel.innerText == 'Width' || parentLabel.innerText == 'Height'){ - e.addEventListener('input', function(e){dimensionChange(e,parentLabel.innerText)} ) - e.classList.add('scrollwatch') - } - if(parentLabel.innerText == 'Width'){ - currentWidth = e.value*1.0 - } - if(parentLabel.innerText == 'Height'){ - currentHeight = e.value*1.0 - } - } - } - }) - } -}); + +let currentWidth = null; +let currentHeight = null; +let arFrameTimeout = setTimeout(function() {}, 0); + +function dimensionChange(e, is_width, is_height) { + + if (is_width) { + currentWidth = e.target.value * 1.0; + } + if (is_height) { + currentHeight = e.target.value * 1.0; + } + + var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block"; + + if (!inImg2img) { + return; + } + + var targetElement = null; + + var tabIndex = get_tab_index('mode_img2img'); + if (tabIndex == 0) { // img2img + targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img'); + } else if (tabIndex == 1) { //Sketch + targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img'); + } else if (tabIndex == 2) { // Inpaint + targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img'); + } else if (tabIndex == 3) { // Inpaint sketch + targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img'); + } + + + if (targetElement) { + + var arPreviewRect = gradioApp().querySelector('#imageARPreview'); + if (!arPreviewRect) { + arPreviewRect = document.createElement('div'); + arPreviewRect.id = "imageARPreview"; + gradioApp().appendChild(arPreviewRect); + } + + + + var viewportOffset = targetElement.getBoundingClientRect(); + + var viewportscale = Math.min(targetElement.clientWidth / targetElement.naturalWidth, targetElement.clientHeight / targetElement.naturalHeight); + + var scaledx = targetElement.naturalWidth * viewportscale; + var scaledy = targetElement.naturalHeight * viewportscale; + + var clientRectTop = (viewportOffset.top + window.scrollY); + var clientRectLeft = (viewportOffset.left + window.scrollX); + var clientRectCentreY = clientRectTop + (targetElement.clientHeight / 2); + var clientRectCentreX = clientRectLeft + (targetElement.clientWidth / 2); + + var arscale = Math.min(scaledx / currentWidth, scaledy / currentHeight); + var arscaledx = currentWidth * arscale; + var arscaledy = currentHeight * arscale; + + var arRectTop = clientRectCentreY - (arscaledy / 2); + var arRectLeft = clientRectCentreX - (arscaledx / 2); + var arRectWidth = arscaledx; + var arRectHeight = arscaledy; + + arPreviewRect.style.top = arRectTop + 'px'; + arPreviewRect.style.left = arRectLeft + 'px'; + arPreviewRect.style.width = arRectWidth + 'px'; + arPreviewRect.style.height = arRectHeight + 'px'; + + clearTimeout(arFrameTimeout); + arFrameTimeout = setTimeout(function() { + arPreviewRect.style.display = 'none'; + }, 2000); + + arPreviewRect.style.display = 'block'; + + } + +} + + +onAfterUiUpdate(function() { + var arPreviewRect = gradioApp().querySelector('#imageARPreview'); + if (arPreviewRect) { + arPreviewRect.style.display = 'none'; + } + var tabImg2img = gradioApp().querySelector("#tab_img2img"); + if (tabImg2img) { + var inImg2img = tabImg2img.style.display == "block"; + if (inImg2img) { + let inputs = gradioApp().querySelectorAll('input'); + inputs.forEach(function(e) { + var is_width = e.parentElement.id == "img2img_width"; + var is_height = e.parentElement.id == "img2img_height"; + + if ((is_width || is_height) && !e.classList.contains('scrollwatch')) { + e.addEventListener('input', function(e) { + dimensionChange(e, is_width, is_height); + }); + e.classList.add('scrollwatch'); + } + if (is_width) { + currentWidth = e.value * 1.0; + } + if (is_height) { + currentHeight = e.value * 1.0; + } + }); + } + } +}); diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js index fe67c42e185..e01fd67e80e 100644 --- a/javascript/contextMenus.js +++ b/javascript/contextMenus.js @@ -1,177 +1,163 @@ - -contextMenuInit = function(){ - let eventListenerApplied=false; - let menuSpecs = new Map(); - - const uid = function(){ - return Date.now().toString(36) + Math.random().toString(36).substr(2); - } - - function showContextMenu(event,element,menuEntries){ - let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft; - let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; - - let oldMenu = gradioApp().querySelector('#context-menu') - if(oldMenu){ - oldMenu.remove() - } - - let tabButton = uiCurrentTab - let baseStyle = window.getComputedStyle(tabButton) - - const contextMenu = document.createElement('nav') - contextMenu.id = "context-menu" - contextMenu.style.background = baseStyle.background - contextMenu.style.color = baseStyle.color - contextMenu.style.fontFamily = baseStyle.fontFamily - contextMenu.style.top = posy+'px' - contextMenu.style.left = posx+'px' - - - - const contextMenuList = document.createElement('ul') - contextMenuList.className = 'context-menu-items'; - contextMenu.append(contextMenuList); - - menuEntries.forEach(function(entry){ - let contextMenuEntry = document.createElement('a') - contextMenuEntry.innerHTML = entry['name'] - contextMenuEntry.addEventListener("click", function(e) { - entry['func'](); - }) - contextMenuList.append(contextMenuEntry); - - }) - - gradioApp().getRootNode().appendChild(contextMenu) - - let menuWidth = contextMenu.offsetWidth + 4; - let menuHeight = contextMenu.offsetHeight + 4; - - let windowWidth = window.innerWidth; - let windowHeight = window.innerHeight; - - if ( (windowWidth - posx) < menuWidth ) { - contextMenu.style.left = windowWidth - menuWidth + "px"; - } - - if ( (windowHeight - posy) < menuHeight ) { - contextMenu.style.top = windowHeight - menuHeight + "px"; - } - - } - - function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){ - - currentItems = menuSpecs.get(targetEmementSelector) - - if(!currentItems){ - currentItems = [] - menuSpecs.set(targetEmementSelector,currentItems); - } - let newItem = {'id':targetEmementSelector+'_'+uid(), - 'name':entryName, - 'func':entryFunction, - 'isNew':true} - - currentItems.push(newItem) - return newItem['id'] - } - - function removeContextMenuOption(uid){ - menuSpecs.forEach(function(v,k) { - let index = -1 - v.forEach(function(e,ei){if(e['id']==uid){index=ei}}) - if(index>=0){ - v.splice(index, 1); - } - }) - } - - function addContextMenuEventListener(){ - if(eventListenerApplied){ - return; - } - gradioApp().addEventListener("click", function(e) { - let source = e.composedPath()[0] - if(source.id && source.id.indexOf('check_progress')>-1){ - return - } - - let oldMenu = gradioApp().querySelector('#context-menu') - if(oldMenu){ - oldMenu.remove() - } - }); - gradioApp().addEventListener("contextmenu", function(e) { - let oldMenu = gradioApp().querySelector('#context-menu') - if(oldMenu){ - oldMenu.remove() - } - menuSpecs.forEach(function(v,k) { - if(e.composedPath()[0].matches(k)){ - showContextMenu(e,e.composedPath()[0],v) - e.preventDefault() - return - } - }) - }); - eventListenerApplied=true - - } - - return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener] -} - -initResponse = contextMenuInit(); -appendContextMenuOption = initResponse[0]; -removeContextMenuOption = initResponse[1]; -addContextMenuEventListener = initResponse[2]; - -(function(){ - //Start example Context Menu Items - let generateOnRepeat = function(genbuttonid,interruptbuttonid){ - let genbutton = gradioApp().querySelector(genbuttonid); - let interruptbutton = gradioApp().querySelector(interruptbuttonid); - if(!interruptbutton.offsetParent){ - genbutton.click(); - } - clearInterval(window.generateOnRepeatInterval) - window.generateOnRepeatInterval = setInterval(function(){ - if(!interruptbutton.offsetParent){ - genbutton.click(); - } - }, - 500) - } - - appendContextMenuOption('#txt2img_generate','Generate forever',function(){ - generateOnRepeat('#txt2img_generate','#txt2img_interrupt'); - }) - appendContextMenuOption('#img2img_generate','Generate forever',function(){ - generateOnRepeat('#img2img_generate','#img2img_interrupt'); - }) - - let cancelGenerateForever = function(){ - clearInterval(window.generateOnRepeatInterval) - } - - appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever) - appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever) - appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever) - appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever) - - appendContextMenuOption('#roll','Roll three', - function(){ - let rollbutton = get_uiCurrentTabContent().querySelector('#roll'); - setTimeout(function(){rollbutton.click()},100) - setTimeout(function(){rollbutton.click()},200) - setTimeout(function(){rollbutton.click()},300) - } - ) -})(); -//End example Context Menu Items - -onUiUpdate(function(){ - addContextMenuEventListener() -}); + +var contextMenuInit = function() { + let eventListenerApplied = false; + let menuSpecs = new Map(); + + const uid = function() { + return Date.now().toString(36) + Math.random().toString(36).substring(2); + }; + + function showContextMenu(event, element, menuEntries) { + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + + let baseStyle = window.getComputedStyle(uiCurrentTab); + + const contextMenu = document.createElement('nav'); + contextMenu.id = "context-menu"; + contextMenu.style.background = baseStyle.background; + contextMenu.style.color = baseStyle.color; + contextMenu.style.fontFamily = baseStyle.fontFamily; + contextMenu.style.top = event.pageY + 'px'; + contextMenu.style.left = event.pageX + 'px'; + + const contextMenuList = document.createElement('ul'); + contextMenuList.className = 'context-menu-items'; + contextMenu.append(contextMenuList); + + menuEntries.forEach(function(entry) { + let contextMenuEntry = document.createElement('a'); + contextMenuEntry.innerHTML = entry['name']; + contextMenuEntry.addEventListener("click", function() { + entry['func'](); + }); + contextMenuList.append(contextMenuEntry); + + }); + + gradioApp().appendChild(contextMenu); + } + + function appendContextMenuOption(targetElementSelector, entryName, entryFunction) { + + var currentItems = menuSpecs.get(targetElementSelector); + + if (!currentItems) { + currentItems = []; + menuSpecs.set(targetElementSelector, currentItems); + } + let newItem = { + id: targetElementSelector + '_' + uid(), + name: entryName, + func: entryFunction, + isNew: true + }; + + currentItems.push(newItem); + return newItem['id']; + } + + function removeContextMenuOption(uid) { + menuSpecs.forEach(function(v) { + let index = -1; + v.forEach(function(e, ei) { + if (e['id'] == uid) { + index = ei; + } + }); + if (index >= 0) { + v.splice(index, 1); + } + }); + } + + function addContextMenuEventListener() { + if (eventListenerApplied) { + return; + } + gradioApp().addEventListener("click", function(e) { + if (!e.isTrusted) { + return; + } + + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + }); + ['contextmenu', 'touchstart'].forEach((eventType) => { + gradioApp().addEventListener(eventType, function(e) { + let ev = e; + if (eventType.startsWith('touch')) { + if (e.touches.length !== 2) return; + ev = e.touches[0]; + } + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + menuSpecs.forEach(function(v, k) { + if (e.composedPath()[0].matches(k)) { + showContextMenu(ev, e.composedPath()[0], v); + e.preventDefault(); + } + }); + }); + }); + eventListenerApplied = true; + + } + + return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]; +}; + +var initResponse = contextMenuInit(); +var appendContextMenuOption = initResponse[0]; +var removeContextMenuOption = initResponse[1]; +var addContextMenuEventListener = initResponse[2]; + +(function() { + //Start example Context Menu Items + let generateOnRepeat = function(genbuttonid, interruptbuttonid) { + let genbutton = gradioApp().querySelector(genbuttonid); + let interruptbutton = gradioApp().querySelector(interruptbuttonid); + if (!interruptbutton.offsetParent) { + genbutton.click(); + } + clearInterval(window.generateOnRepeatInterval); + window.generateOnRepeatInterval = setInterval(function() { + if (!interruptbutton.offsetParent) { + genbutton.click(); + } + }, + 500); + }; + + let generateOnRepeat_txt2img = function() { + generateOnRepeat('#txt2img_generate', '#txt2img_interrupt'); + }; + + let generateOnRepeat_img2img = function() { + generateOnRepeat('#img2img_generate', '#img2img_interrupt'); + }; + + appendContextMenuOption('#txt2img_generate', 'Generate forever', generateOnRepeat_txt2img); + appendContextMenuOption('#txt2img_interrupt', 'Generate forever', generateOnRepeat_txt2img); + appendContextMenuOption('#img2img_generate', 'Generate forever', generateOnRepeat_img2img); + appendContextMenuOption('#img2img_interrupt', 'Generate forever', generateOnRepeat_img2img); + + let cancelGenerateForever = function() { + clearInterval(window.generateOnRepeatInterval); + }; + + appendContextMenuOption('#txt2img_interrupt', 'Cancel generate forever', cancelGenerateForever); + appendContextMenuOption('#txt2img_generate', 'Cancel generate forever', cancelGenerateForever); + appendContextMenuOption('#img2img_interrupt', 'Cancel generate forever', cancelGenerateForever); + appendContextMenuOption('#img2img_generate', 'Cancel generate forever', cancelGenerateForever); + +})(); +//End example Context Menu Items + +onAfterUiUpdate(addContextMenuEventListener); diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js index 070cf255faa..882562d7367 100644 --- a/javascript/dragdrop.js +++ b/javascript/dragdrop.js @@ -1,89 +1,156 @@ // allows drag-dropping files into gradio image elements, and also pasting images from clipboard -function isValidImageList( files ) { +function isValidImageList(files) { return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type); } -function dropReplaceImage( imgWrap, files ) { - if ( ! isValidImageList( files ) ) { +function dropReplaceImage(imgWrap, files) { + if (!isValidImageList(files)) { return; } + const tmpFile = files[0]; + imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click(); const callback = () => { const fileInput = imgWrap.querySelector('input[type="file"]'); - if ( fileInput ) { - fileInput.files = files; - fileInput.dispatchEvent(new Event('change')); + if (fileInput) { + if (files.length === 0) { + files = new DataTransfer(); + files.items.add(tmpFile); + fileInput.files = files.files; + } else { + fileInput.files = files; + } + fileInput.dispatchEvent(new Event('change')); } }; - - if ( imgWrap.closest('#pnginfo_image') ) { + + if (imgWrap.closest('#pnginfo_image')) { // special treatment for PNG Info tab, wait for fetch request to finish const oldFetch = window.fetch; - window.fetch = async (input, options) => { + window.fetch = async(input, options) => { const response = await oldFetch(input, options); - if ( 'api/predict/' === input ) { + if ('api/predict/' === input) { const content = await response.text(); window.fetch = oldFetch; - window.requestAnimationFrame( () => callback() ); + window.requestAnimationFrame(() => callback()); return new Response(content, { status: response.status, statusText: response.statusText, headers: response.headers - }) + }); } return response; - }; + }; } else { - window.requestAnimationFrame( () => callback() ); + window.requestAnimationFrame(() => callback()); } } +function eventHasFiles(e) { + if (!e.dataTransfer || !e.dataTransfer.files) return false; + if (e.dataTransfer.files.length > 0) return true; + if (e.dataTransfer.items.length > 0 && e.dataTransfer.items[0].kind == "file") return true; + + return false; +} + +function isURL(url) { + try { + const _ = new URL(url); + return true; + } catch { + return false; + } +} + +function dragDropTargetIsPrompt(target) { + if (target?.placeholder && target?.placeholder.indexOf("Prompt") >= 0) return true; + if (target?.parentNode?.parentNode?.className?.indexOf("prompt") > 0) return true; + return false; +} + window.document.addEventListener('dragover', e => { const target = e.composedPath()[0]; - const imgWrap = target.closest('[data-testid="image"]'); - if ( !imgWrap && target.placeholder.indexOf("Prompt") == -1) { - return; - } + if (!eventHasFiles(e)) return; + + var targetImage = target.closest('[data-testid="image"]'); + if (!dragDropTargetIsPrompt(target) && !targetImage) return; + e.stopPropagation(); e.preventDefault(); e.dataTransfer.dropEffect = 'copy'; }); -window.document.addEventListener('drop', e => { +window.document.addEventListener('drop', async e => { const target = e.composedPath()[0]; - if (target.placeholder.indexOf("Prompt") == -1) { - return; + const url = e.dataTransfer.getData('text/uri-list') || e.dataTransfer.getData('text/plain'); + if (!eventHasFiles(e) && !isURL(url)) return; + + if (dragDropTargetIsPrompt(target)) { + e.stopPropagation(); + e.preventDefault(); + + const isImg2img = get_tab_index('tabs') == 1; + let prompt_image_target = isImg2img ? "img2img_prompt_image" : "txt2img_prompt_image"; + + const imgParent = gradioApp().getElementById(prompt_image_target); + const files = e.dataTransfer.files; + const fileInput = imgParent.querySelector('input[type="file"]'); + if (eventHasFiles(e) && fileInput) { + fileInput.files = files; + fileInput.dispatchEvent(new Event('change')); + } else if (url) { + try { + const request = await fetch(url); + if (!request.ok) { + console.error('Error fetching URL:', url, request.status); + return; + } + const data = new DataTransfer(); + data.items.add(new File([await request.blob()], 'image.png')); + fileInput.files = data.files; + fileInput.dispatchEvent(new Event('change')); + } catch (error) { + console.error('Error fetching URL:', url, error); + return; + } + } } - const imgWrap = target.closest('[data-testid="image"]'); - if ( !imgWrap ) { + + var targetImage = target.closest('[data-testid="image"]'); + if (targetImage) { + e.stopPropagation(); + e.preventDefault(); + const files = e.dataTransfer.files; + dropReplaceImage(targetImage, files); return; } - e.stopPropagation(); - e.preventDefault(); - const files = e.dataTransfer.files; - dropReplaceImage( imgWrap, files ); }); window.addEventListener('paste', e => { const files = e.clipboardData.files; - if ( ! isValidImageList( files ) ) { + if (!isValidImageList(files)) { return; } const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')] - .filter(el => uiElementIsVisible(el)); - if ( ! visibleImageFields.length ) { + .filter(el => uiElementIsVisible(el)) + .sort((a, b) => uiElementInSight(b) - uiElementInSight(a)); + + + if (!visibleImageFields.length) { return; } - + const firstFreeImageField = visibleImageFields - .filter(el => el.querySelector('input[type=file]'))?.[0]; + .filter(el => !el.querySelector('img'))?.[0]; dropReplaceImage( firstFreeImageField ? - firstFreeImageField : - visibleImageFields[visibleImageFields.length - 1] - , files ); + firstFreeImageField : + visibleImageFields[visibleImageFields.length - 1] + , files + ); }); diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index 67084e7a6ae..b07ba97cb9b 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -1,47 +1,156 @@ -addEventListener('keydown', (event) => { - let target = event.originalTarget || event.composedPath()[0]; - if (!target.hasAttribute("placeholder")) return; - if (!target.placeholder.toLowerCase().includes("prompt")) return; - if (! (event.metaKey || event.ctrlKey)) return; - - - let plus = "ArrowUp" - let minus = "ArrowDown" - if (event.key != plus && event.key != minus) return; - - selectionStart = target.selectionStart; - selectionEnd = target.selectionEnd; - if(selectionStart == selectionEnd) return; - - event.preventDefault(); - - if (selectionStart == 0 || target.value[selectionStart - 1] != "(") { - target.value = target.value.slice(0, selectionStart) + - "(" + target.value.slice(selectionStart, selectionEnd) + ":1.0)" + - target.value.slice(selectionEnd); - - target.focus(); - target.selectionStart = selectionStart + 1; - target.selectionEnd = selectionEnd + 1; - - } else { - end = target.value.slice(selectionEnd + 1).indexOf(")") + 1; - weight = parseFloat(target.value.slice(selectionEnd + 1, selectionEnd + 1 + end)); - if (isNaN(weight)) return; - if (event.key == minus) weight -= 0.1; - if (event.key == plus) weight += 0.1; - - weight = parseFloat(weight.toPrecision(12)); - - target.value = target.value.slice(0, selectionEnd + 1) + - weight + - target.value.slice(selectionEnd + 1 + end - 1); - - target.focus(); - target.selectionStart = selectionStart; - target.selectionEnd = selectionEnd; - } - // Since we've modified a Gradio Textbox component manually, we need to simulate an `input` DOM event to ensure its - // internal Svelte data binding remains in sync. - target.dispatchEvent(new Event("input", { bubbles: true })); -}); +function keyupEditAttention(event) { + let target = event.originalTarget || event.composedPath()[0]; + if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; + if (!(event.metaKey || event.ctrlKey)) return; + + let isPlus = event.key == "ArrowUp"; + let isMinus = event.key == "ArrowDown"; + if (!isPlus && !isMinus) return; + + let selectionStart = target.selectionStart; + let selectionEnd = target.selectionEnd; + let text = target.value; + + function selectCurrentParenthesisBlock(OPEN, CLOSE) { + if (selectionStart !== selectionEnd) return false; + + // Find opening parenthesis around current cursor + const before = text.substring(0, selectionStart); + let beforeParen = before.lastIndexOf(OPEN); + if (beforeParen == -1) return false; + + let beforeClosingParen = before.lastIndexOf(CLOSE); + if (beforeClosingParen != -1 && beforeClosingParen > beforeParen) return false; + + // Find closing parenthesis around current cursor + const after = text.substring(selectionStart); + let afterParen = after.indexOf(CLOSE); + if (afterParen == -1) return false; + + let afterOpeningParen = after.indexOf(OPEN); + if (afterOpeningParen != -1 && afterOpeningParen < afterParen) return false; + + // Set the selection to the text between the parenthesis + const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen); + if (/.*:-?[\d.]+/s.test(parenContent)) { + const lastColon = parenContent.lastIndexOf(":"); + selectionStart = beforeParen + 1; + selectionEnd = selectionStart + lastColon; + } else { + selectionStart = beforeParen + 1; + selectionEnd = selectionStart + parenContent.length; + } + + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + function selectCurrentWord() { + if (selectionStart !== selectionEnd) return false; + const whitespace_delimiters = {"Tab": "\t", "Carriage Return": "\r", "Line Feed": "\n"}; + let delimiters = opts.keyedit_delimiters; + + for (let i of opts.keyedit_delimiters_whitespace) { + delimiters += whitespace_delimiters[i]; + } + + // seek backward to find beginning + while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) { + selectionStart--; + } + + // seek forward to find end + while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) { + selectionEnd++; + } + + // deselect surrounding whitespace + while (text[selectionStart] == " " && selectionStart < selectionEnd) { + selectionStart++; + } + while (text[selectionEnd - 1] == " " && selectionEnd > selectionStart) { + selectionEnd--; + } + + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + // If the user hasn't selected anything, let's select their current parenthesis block or word + if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')') && !selectCurrentParenthesisBlock('[', ']')) { + selectCurrentWord(); + } + + event.preventDefault(); + + var closeCharacter = ')'; + var delta = opts.keyedit_precision_attention; + var start = selectionStart > 0 ? text[selectionStart - 1] : ""; + var end = text[selectionEnd]; + + if (start == '<') { + closeCharacter = '>'; + delta = opts.keyedit_precision_extra; + } else if (start == '(' && end == ')' || start == '[' && end == ']') { // convert old-style (((emphasis))) + let numParen = 0; + + while (text[selectionStart - numParen - 1] == start && text[selectionEnd + numParen] == end) { + numParen++; + } + + if (start == "[") { + weight = (1 / 1.1) ** numParen; + } else { + weight = 1.1 ** numParen; + } + + weight = Math.round(weight / opts.keyedit_precision_attention) * opts.keyedit_precision_attention; + + text = text.slice(0, selectionStart - numParen) + "(" + text.slice(selectionStart, selectionEnd) + ":" + weight + ")" + text.slice(selectionEnd + numParen); + selectionStart -= numParen - 1; + selectionEnd -= numParen - 1; + } else if (start != '(') { + // do not include spaces at the end + while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') { + selectionEnd--; + } + + if (selectionStart == selectionEnd) { + return; + } + + text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd); + + selectionStart++; + selectionEnd++; + } + + if (text[selectionEnd] != ':') return; + var weightLength = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1; + var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + weightLength)); + if (isNaN(weight)) return; + + weight += isPlus ? delta : -delta; + weight = parseFloat(weight.toPrecision(12)); + if (Number.isInteger(weight)) weight += ".0"; + + if (closeCharacter == ')' && weight == 1) { + var endParenPos = text.substring(selectionEnd).indexOf(')'); + text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1); + selectionStart--; + selectionEnd--; + } else { + text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + weightLength); + } + + target.focus(); + target.value = text; + target.selectionStart = selectionStart; + target.selectionEnd = selectionEnd; + + updateInput(target); +} + +addEventListener('keydown', (event) => { + keyupEditAttention(event); +}); diff --git a/javascript/edit-order.js b/javascript/edit-order.js new file mode 100644 index 00000000000..ed4ef9ac399 --- /dev/null +++ b/javascript/edit-order.js @@ -0,0 +1,41 @@ +/* alt+left/right moves text in prompt */ + +function keyupEditOrder(event) { + if (!opts.keyedit_move) return; + + let target = event.originalTarget || event.composedPath()[0]; + if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; + if (!event.altKey) return; + + let isLeft = event.key == "ArrowLeft"; + let isRight = event.key == "ArrowRight"; + if (!isLeft && !isRight) return; + event.preventDefault(); + + let selectionStart = target.selectionStart; + let selectionEnd = target.selectionEnd; + let text = target.value; + let items = text.split(","); + let indexStart = (text.slice(0, selectionStart).match(/,/g) || []).length; + let indexEnd = (text.slice(0, selectionEnd).match(/,/g) || []).length; + let range = indexEnd - indexStart + 1; + + if (isLeft && indexStart > 0) { + items.splice(indexStart - 1, 0, ...items.splice(indexStart, range)); + target.value = items.join(); + target.selectionStart = items.slice(0, indexStart - 1).join().length + (indexStart == 1 ? 0 : 1); + target.selectionEnd = items.slice(0, indexEnd).join().length; + } else if (isRight && indexEnd < items.length - 1) { + items.splice(indexStart + 1, 0, ...items.splice(indexStart, range)); + target.value = items.join(); + target.selectionStart = items.slice(0, indexStart + 1).join().length + 1; + target.selectionEnd = items.slice(0, indexEnd + 2).join().length; + } + + event.preventDefault(); + updateInput(target); +} + +addEventListener('keydown', (event) => { + keyupEditOrder(event); +}); diff --git a/javascript/extensions.js b/javascript/extensions.js new file mode 100644 index 00000000000..cc8ee220b17 --- /dev/null +++ b/javascript/extensions.js @@ -0,0 +1,95 @@ + +function extensions_apply(_disabled_list, _update_list, disable_all) { + var disable = []; + var update = []; + const extensions_input = gradioApp().querySelectorAll('#extensions input[type="checkbox"]'); + if (extensions_input.length == 0) { + throw Error("Extensions page not yet loaded."); + } + extensions_input.forEach(function(x) { + if (x.name.startsWith("enable_") && !x.checked) { + disable.push(x.name.substring(7)); + } + + if (x.name.startsWith("update_") && x.checked) { + update.push(x.name.substring(7)); + } + }); + + restart_reload(); + + return [JSON.stringify(disable), JSON.stringify(update), disable_all]; +} + +function extensions_check() { + var disable = []; + + gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) { + if (x.name.startsWith("enable_") && !x.checked) { + disable.push(x.name.substring(7)); + } + }); + + gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) { + x.innerHTML = "Loading..."; + }); + + + var id = randomId(); + requestProgress(id, gradioApp().getElementById('extensions_installed_html'), null, function() { + + }); + + return [id, JSON.stringify(disable)]; +} + +function install_extension_from_index(button, url) { + button.disabled = "disabled"; + button.value = "Installing..."; + + var textarea = gradioApp().querySelector('#extension_to_install textarea'); + textarea.value = url; + updateInput(textarea); + + gradioApp().querySelector('#install_extension_button').click(); +} + +function config_state_confirm_restore(_, config_state_name, config_restore_type) { + if (config_state_name == "Current") { + return [false, config_state_name, config_restore_type]; + } + let restored = ""; + if (config_restore_type == "extensions") { + restored = "all saved extension versions"; + } else if (config_restore_type == "webui") { + restored = "the webui version"; + } else { + restored = "the webui version and all saved extension versions"; + } + let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + "."); + if (confirmed) { + restart_reload(); + gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) { + x.innerHTML = "Loading..."; + }); + } + return [confirmed, config_state_name, config_restore_type]; +} + +function toggle_all_extensions(event) { + gradioApp().querySelectorAll('#extensions .extension_toggle').forEach(function(checkbox_el) { + checkbox_el.checked = event.target.checked; + }); +} + +function toggle_extension() { + let all_extensions_toggled = true; + for (const checkbox_el of gradioApp().querySelectorAll('#extensions .extension_toggle')) { + if (!checkbox_el.checked) { + all_extensions_toggled = false; + break; + } + } + + gradioApp().querySelector('#extensions .all_extensions_toggle').checked = all_extensions_toggled; +} diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js new file mode 100644 index 00000000000..c5cced97399 --- /dev/null +++ b/javascript/extraNetworks.js @@ -0,0 +1,712 @@ +function toggleCss(key, css, enable) { + var style = document.getElementById(key); + if (enable && !style) { + style = document.createElement('style'); + style.id = key; + style.type = 'text/css'; + document.head.appendChild(style); + } + if (style && !enable) { + document.head.removeChild(style); + } + if (style) { + style.innerHTML == ''; + style.appendChild(document.createTextNode(css)); + } +} + +function setupExtraNetworksForTab(tabname) { + function registerPrompt(tabname, id) { + var textarea = gradioApp().querySelector("#" + id + " > label > textarea"); + + if (!activePromptTextarea[tabname]) { + activePromptTextarea[tabname] = textarea; + } + + textarea.addEventListener("focus", function() { + activePromptTextarea[tabname] = textarea; + }); + } + + var tabnav = gradioApp().querySelector('#' + tabname + '_extra_tabs > div.tab-nav'); + var controlsDiv = document.createElement('DIV'); + controlsDiv.classList.add('extra-networks-controls-div'); + tabnav.appendChild(controlsDiv); + tabnav.insertBefore(controlsDiv, null); + + var this_tab = gradioApp().querySelector('#' + tabname + '_extra_tabs'); + this_tab.querySelectorAll(":scope > [id^='" + tabname + "_']").forEach(function(elem) { + // tabname_full = {tabname}_{extra_networks_tabname} + var tabname_full = elem.id; + var search = gradioApp().querySelector("#" + tabname_full + "_extra_search"); + var sort_dir = gradioApp().querySelector("#" + tabname_full + "_extra_sort_dir"); + var refresh = gradioApp().querySelector("#" + tabname_full + "_extra_refresh"); + var currentSort = ''; + + // If any of the buttons above don't exist, we want to skip this iteration of the loop. + if (!search || !sort_dir || !refresh) { + return; // `return` is equivalent of `continue` but for forEach loops. + } + + var applyFilter = function(force) { + var searchTerm = search.value.toLowerCase(); + gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) { + var searchOnly = elem.querySelector('.search_only'); + var text = Array.prototype.map.call(elem.querySelectorAll('.search_terms, .description'), function(t) { + return t.textContent.toLowerCase(); + }).join(" "); + + var visible = text.indexOf(searchTerm) != -1; + if (searchOnly && searchTerm.length < 4) { + visible = false; + } + if (visible) { + elem.classList.remove("hidden"); + } else { + elem.classList.add("hidden"); + } + }); + + applySort(force); + }; + + var applySort = function(force) { + var cards = gradioApp().querySelectorAll('#' + tabname_full + ' div.card'); + var parent = gradioApp().querySelector('#' + tabname_full + "_cards"); + var reverse = sort_dir.dataset.sortdir == "Descending"; + var activeSearchElem = gradioApp().querySelector('#' + tabname_full + "_controls .extra-network-control--sort.extra-network-control--enabled"); + var sortKey = activeSearchElem ? activeSearchElem.dataset.sortkey : "default"; + var sortKeyDataField = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1); + var sortKeyStore = sortKey + "-" + sort_dir.dataset.sortdir + "-" + cards.length; + + if (sortKeyStore == currentSort && !force) { + return; + } + currentSort = sortKeyStore; + + var sortedCards = Array.from(cards); + sortedCards.sort(function(cardA, cardB) { + var a = cardA.dataset[sortKeyDataField]; + var b = cardB.dataset[sortKeyDataField]; + if (!isNaN(a) && !isNaN(b)) { + return parseInt(a) - parseInt(b); + } + + return (a < b ? -1 : (a > b ? 1 : 0)); + }); + + if (reverse) { + sortedCards.reverse(); + } + + parent.innerHTML = ''; + + var frag = document.createDocumentFragment(); + sortedCards.forEach(function(card) { + frag.appendChild(card); + }); + parent.appendChild(frag); + }; + + search.addEventListener("input", function() { + applyFilter(); + }); + applySort(); + applyFilter(); + extraNetworksApplySort[tabname_full] = applySort; + extraNetworksApplyFilter[tabname_full] = applyFilter; + + var controls = gradioApp().querySelector("#" + tabname_full + "_controls"); + controlsDiv.insertBefore(controls, null); + + if (elem.style.display != "none") { + extraNetworksShowControlsForPage(tabname, tabname_full); + } + }); + + registerPrompt(tabname, tabname + "_prompt"); + registerPrompt(tabname, tabname + "_neg_prompt"); +} + +function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt) { + if (!gradioApp().querySelector('.toprow-compact-tools')) return; // only applicable for compact prompt layout + + var promptContainer = gradioApp().getElementById(tabname + '_prompt_container'); + var prompt = gradioApp().getElementById(tabname + '_prompt_row'); + var negPrompt = gradioApp().getElementById(tabname + '_neg_prompt_row'); + var elem = id ? gradioApp().getElementById(id) : null; + + if (showNegativePrompt && elem) { + elem.insertBefore(negPrompt, elem.firstChild); + } else { + promptContainer.insertBefore(negPrompt, promptContainer.firstChild); + } + + if (showPrompt && elem) { + elem.insertBefore(prompt, elem.firstChild); + } else { + promptContainer.insertBefore(prompt, promptContainer.firstChild); + } + + if (elem) { + elem.classList.toggle('extra-page-prompts-active', showNegativePrompt || showPrompt); + } +} + + +function extraNetworksShowControlsForPage(tabname, tabname_full) { + gradioApp().querySelectorAll('#' + tabname + '_extra_tabs .extra-networks-controls-div > div').forEach(function(elem) { + var targetId = tabname_full + "_controls"; + elem.style.display = elem.id == targetId ? "" : "none"; + }); +} + + +function extraNetworksUnrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate) + extraNetworksMovePromptToTab(tabname, '', false, false); + + extraNetworksShowControlsForPage(tabname, null); +} + +function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt, tabname_full) { // called from python when user selects an extra networks tab + extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt); + + extraNetworksShowControlsForPage(tabname, tabname_full); +} + +function applyExtraNetworkFilter(tabname_full) { + var doFilter = function() { + var applyFunction = extraNetworksApplyFilter[tabname_full]; + + if (applyFunction) { + applyFunction(true); + } + }; + setTimeout(doFilter, 1); +} + +function applyExtraNetworkSort(tabname_full) { + var doSort = function() { + extraNetworksApplySort[tabname_full](true); + }; + setTimeout(doSort, 1); +} + +var extraNetworksApplyFilter = {}; +var extraNetworksApplySort = {}; +var activePromptTextarea = {}; + +function setupExtraNetworks() { + setupExtraNetworksForTab('txt2img'); + setupExtraNetworksForTab('img2img'); +} + +var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/; +var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g; + +var re_extranet_neg = /\(([^:^>]+:[\d.]+)\)/; +var re_extranet_g_neg = /\(([^:^>]+:[\d.]+)\)/g; +function tryToRemoveExtraNetworkFromPrompt(textarea, text, isNeg) { + var m = text.match(isNeg ? re_extranet_neg : re_extranet); + var replaced = false; + var newTextareaText; + var extraTextBeforeNet = opts.extra_networks_add_text_separator; + if (m) { + var extraTextAfterNet = m[2]; + var partToSearch = m[1]; + var foundAtPosition = -1; + newTextareaText = textarea.value.replaceAll(isNeg ? re_extranet_g_neg : re_extranet_g, function(found, net, pos) { + m = found.match(isNeg ? re_extranet_neg : re_extranet); + if (m[1] == partToSearch) { + replaced = true; + foundAtPosition = pos; + return ""; + } + return found; + }); + if (foundAtPosition >= 0) { + if (extraTextAfterNet && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) { + newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length); + } + if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) { + newTextareaText = newTextareaText.substr(0, foundAtPosition - extraTextBeforeNet.length) + newTextareaText.substr(foundAtPosition); + } + } + } else { + newTextareaText = textarea.value.replaceAll(new RegExp(`((?:${extraTextBeforeNet})?${text})`, "g"), ""); + replaced = (newTextareaText != textarea.value); + } + + if (replaced) { + textarea.value = newTextareaText; + return true; + } + + return false; +} + +function updatePromptArea(text, textArea, isNeg) { + if (!tryToRemoveExtraNetworkFromPrompt(textArea, text, isNeg)) { + textArea.value = textArea.value + opts.extra_networks_add_text_separator + text; + } + + updateInput(textArea); +} + +function cardClicked(tabname, textToAdd, textToAddNegative, allowNegativePrompt) { + if (textToAddNegative.length > 0) { + updatePromptArea(textToAdd, gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")); + updatePromptArea(textToAddNegative, gradioApp().querySelector("#" + tabname + "_neg_prompt > label > textarea"), true); + } else { + var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"); + updatePromptArea(textToAdd, textarea); + } +} + +function saveCardPreview(event, tabname, filename) { + var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea'); + var button = gradioApp().getElementById(tabname + '_save_preview'); + + textarea.value = filename; + updateInput(textarea); + + button.click(); + + event.stopPropagation(); + event.preventDefault(); +} + +function extraNetworksSearchButton(tabname, extra_networks_tabname, event) { + var searchTextarea = gradioApp().querySelector("#" + tabname + "_" + extra_networks_tabname + "_extra_search"); + var button = event.target; + var text = button.classList.contains("search-all") ? "" : button.textContent.trim(); + + searchTextarea.value = text; + updateInput(searchTextarea); +} + +function extraNetworksTreeProcessFileClick(event, btn, tabname, extra_networks_tabname) { + /** + * Processes `onclick` events when user clicks on files in tree. + * + * @param event The generated event. + * @param btn The clicked `tree-list-item` button. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + // NOTE: Currently unused. + return; +} + +function extraNetworksTreeProcessDirectoryClick(event, btn, tabname, extra_networks_tabname) { + /** + * Processes `onclick` events when user clicks on directories in tree. + * + * Here is how the tree reacts to clicks for various states: + * unselected unopened directory: Directory is selected and expanded. + * unselected opened directory: Directory is selected. + * selected opened directory: Directory is collapsed and deselected. + * chevron is clicked: Directory is expanded or collapsed. Selected state unchanged. + * + * @param event The generated event. + * @param btn The clicked `tree-list-item` button. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + var ul = btn.nextElementSibling; + // This is the actual target that the user clicked on within the target button. + // We use this to detect if the chevron was clicked. + var true_targ = event.target; + + function _expand_or_collapse(_ul, _btn) { + // Expands
    if it is collapsed, collapses otherwise. Updates button attributes. + if (_ul.hasAttribute("hidden")) { + _ul.removeAttribute("hidden"); + _btn.dataset.expanded = ""; + } else { + _ul.setAttribute("hidden", ""); + delete _btn.dataset.expanded; + } + } + + function _remove_selected_from_all() { + // Removes the `selected` attribute from all buttons. + var sels = document.querySelectorAll("div.tree-list-content"); + [...sels].forEach(el => { + delete el.dataset.selected; + }); + } + + function _select_button(_btn) { + // Removes `data-selected` attribute from all buttons then adds to passed button. + _remove_selected_from_all(); + _btn.dataset.selected = ""; + } + + function _update_search(_tabname, _extra_networks_tabname, _search_text) { + // Update search input with select button's path. + var search_input_elem = gradioApp().querySelector("#" + tabname + "_" + extra_networks_tabname + "_extra_search"); + search_input_elem.value = _search_text; + updateInput(search_input_elem); + } + + + // If user clicks on the chevron, then we do not select the folder. + if (true_targ.matches(".tree-list-item-action--leading, .tree-list-item-action-chevron")) { + _expand_or_collapse(ul, btn); + } else { + // User clicked anywhere else on the button. + if ("selected" in btn.dataset && !(ul.hasAttribute("hidden"))) { + // If folder is select and open, collapse and deselect button. + _expand_or_collapse(ul, btn); + delete btn.dataset.selected; + _update_search(tabname, extra_networks_tabname, ""); + } else if (!(!("selected" in btn.dataset) && !(ul.hasAttribute("hidden")))) { + // If folder is open and not selected, then we don't collapse; just select. + // NOTE: Double inversion sucks but it is the clearest way to show the branching here. + _expand_or_collapse(ul, btn); + _select_button(btn, tabname, extra_networks_tabname); + _update_search(tabname, extra_networks_tabname, btn.dataset.path); + } else { + // All other cases, just select the button. + _select_button(btn, tabname, extra_networks_tabname); + _update_search(tabname, extra_networks_tabname, btn.dataset.path); + } + } +} + +function extraNetworksTreeOnClick(event, tabname, extra_networks_tabname) { + /** + * Handles `onclick` events for buttons within an `extra-network-tree .tree-list--tree`. + * + * Determines whether the clicked button in the tree is for a file entry or a directory + * then calls the appropriate function. + * + * @param event The generated event. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + var btn = event.currentTarget; + var par = btn.parentElement; + if (par.dataset.treeEntryType === "file") { + extraNetworksTreeProcessFileClick(event, btn, tabname, extra_networks_tabname); + } else { + extraNetworksTreeProcessDirectoryClick(event, btn, tabname, extra_networks_tabname); + } +} + +function extraNetworksControlSortOnClick(event, tabname, extra_networks_tabname) { + /** Handles `onclick` events for Sort Mode buttons. */ + + var self = event.currentTarget; + var parent = event.currentTarget.parentElement; + + parent.querySelectorAll('.extra-network-control--sort').forEach(function(x) { + x.classList.remove('extra-network-control--enabled'); + }); + + self.classList.add('extra-network-control--enabled'); + + applyExtraNetworkSort(tabname + "_" + extra_networks_tabname); +} + +function extraNetworksControlSortDirOnClick(event, tabname, extra_networks_tabname) { + /** + * Handles `onclick` events for the Sort Direction button. + * + * Modifies the data attributes of the Sort Direction button to cycle between + * ascending and descending sort directions. + * + * @param event The generated event. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + if (event.currentTarget.dataset.sortdir == "Ascending") { + event.currentTarget.dataset.sortdir = "Descending"; + event.currentTarget.setAttribute("title", "Sort descending"); + } else { + event.currentTarget.dataset.sortdir = "Ascending"; + event.currentTarget.setAttribute("title", "Sort ascending"); + } + applyExtraNetworkSort(tabname + "_" + extra_networks_tabname); +} + +function extraNetworksControlTreeViewOnClick(event, tabname, extra_networks_tabname) { + /** + * Handles `onclick` events for the Tree View button. + * + * Toggles the tree view in the extra networks pane. + * + * @param event The generated event. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + var button = event.currentTarget; + button.classList.toggle("extra-network-control--enabled"); + var show = !button.classList.contains("extra-network-control--enabled"); + + var pane = gradioApp().getElementById(tabname + "_" + extra_networks_tabname + "_pane"); + pane.classList.toggle("extra-network-dirs-hidden", show); +} + +function extraNetworksControlRefreshOnClick(event, tabname, extra_networks_tabname) { + /** + * Handles `onclick` events for the Refresh Page button. + * + * In order to actually call the python functions in `ui_extra_networks.py` + * to refresh the page, we created an empty gradio button in that file with an + * event handler that refreshes the page. So what this function here does + * is it manually raises a `click` event on that button. + * + * @param event The generated event. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + var btn_refresh_internal = gradioApp().getElementById(tabname + "_" + extra_networks_tabname + "_extra_refresh_internal"); + btn_refresh_internal.dispatchEvent(new Event("click")); +} + +var globalPopup = null; +var globalPopupInner = null; + +function closePopup() { + if (!globalPopup) return; + globalPopup.style.display = "none"; +} + +function popup(contents) { + if (!globalPopup) { + globalPopup = document.createElement('div'); + globalPopup.classList.add('global-popup'); + + var close = document.createElement('div'); + close.classList.add('global-popup-close'); + close.addEventListener("click", closePopup); + close.title = "Close"; + globalPopup.appendChild(close); + + globalPopupInner = document.createElement('div'); + globalPopupInner.classList.add('global-popup-inner'); + globalPopup.appendChild(globalPopupInner); + + gradioApp().querySelector('.main').appendChild(globalPopup); + } + + globalPopupInner.innerHTML = ''; + globalPopupInner.appendChild(contents); + + globalPopup.style.display = "flex"; +} + +var storedPopupIds = {}; +function popupId(id) { + if (!storedPopupIds[id]) { + storedPopupIds[id] = gradioApp().getElementById(id); + } + + popup(storedPopupIds[id]); +} + +function extraNetworksFlattenMetadata(obj) { + const result = {}; + + // Convert any stringified JSON objects to actual objects + for (const key of Object.keys(obj)) { + if (typeof obj[key] === 'string') { + try { + const parsed = JSON.parse(obj[key]); + if (parsed && typeof parsed === 'object') { + obj[key] = parsed; + } + } catch (error) { + continue; + } + } + } + + // Flatten the object + for (const key of Object.keys(obj)) { + if (typeof obj[key] === 'object' && obj[key] !== null) { + const nested = extraNetworksFlattenMetadata(obj[key]); + for (const nestedKey of Object.keys(nested)) { + result[`${key}/${nestedKey}`] = nested[nestedKey]; + } + } else { + result[key] = obj[key]; + } + } + + // Special case for handling modelspec keys + for (const key of Object.keys(result)) { + if (key.startsWith("modelspec.")) { + result[key.replaceAll(".", "/")] = result[key]; + delete result[key]; + } + } + + // Add empty keys to designate hierarchy + for (const key of Object.keys(result)) { + const parts = key.split("/"); + for (let i = 1; i < parts.length; i++) { + const parent = parts.slice(0, i).join("/"); + if (!result[parent]) { + result[parent] = ""; + } + } + } + + return result; +} + +function extraNetworksShowMetadata(text) { + try { + let parsed = JSON.parse(text); + if (parsed && typeof parsed === 'object') { + parsed = extraNetworksFlattenMetadata(parsed); + const table = createVisualizationTable(parsed, 0); + popup(table); + return; + } + } catch (error) { + console.error(error); + } + + var elem = document.createElement('pre'); + elem.classList.add('popup-metadata'); + elem.textContent = text; + + popup(elem); + return; +} + +function requestGet(url, data, handler, errorHandler) { + var xhr = new XMLHttpRequest(); + var args = Object.keys(data).map(function(k) { + return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]); + }).join('&'); + xhr.open("GET", url + "?" + args, true); + + xhr.onreadystatechange = function() { + if (xhr.readyState === 4) { + if (xhr.status === 200) { + try { + var js = JSON.parse(xhr.responseText); + handler(js); + } catch (error) { + console.error(error); + errorHandler(); + } + } else { + errorHandler(); + } + } + }; + var js = JSON.stringify(data); + xhr.send(js); +} + +function extraNetworksCopyCardPath(event) { + navigator.clipboard.writeText(event.target.getAttribute("data-clipboard-text")); + event.stopPropagation(); +} + +function extraNetworksRequestMetadata(event, extraPage) { + var showError = function() { + extraNetworksShowMetadata("there was an error getting metadata"); + }; + + var cardName = event.target.parentElement.parentElement.getAttribute("data-name"); + + requestGet("./sd_extra_networks/metadata", {page: extraPage, item: cardName}, function(data) { + if (data && data.metadata) { + extraNetworksShowMetadata(data.metadata); + } else { + showError(); + } + }, showError); + + event.stopPropagation(); +} + +var extraPageUserMetadataEditors = {}; + +function extraNetworksEditUserMetadata(event, tabname, extraPage) { + var id = tabname + '_' + extraPage + '_edit_user_metadata'; + + var editor = extraPageUserMetadataEditors[id]; + if (!editor) { + editor = {}; + editor.page = gradioApp().getElementById(id); + editor.nameTextarea = gradioApp().querySelector("#" + id + "_name" + ' textarea'); + editor.button = gradioApp().querySelector("#" + id + "_button"); + extraPageUserMetadataEditors[id] = editor; + } + + var cardName = event.target.parentElement.parentElement.getAttribute("data-name"); + editor.nameTextarea.value = cardName; + updateInput(editor.nameTextarea); + + editor.button.click(); + + popup(editor.page); + + event.stopPropagation(); +} + +function extraNetworksRefreshSingleCard(page, tabname, name) { + requestGet("./sd_extra_networks/get-single-card", {page: page, tabname: tabname, name: name}, function(data) { + if (data && data.html) { + var card = gradioApp().querySelector(`#${tabname}_${page.replace(" ", "_")}_cards > .card[data-name="${name}"]`); + + var newDiv = document.createElement('DIV'); + newDiv.innerHTML = data.html; + var newCard = newDiv.firstElementChild; + + newCard.style.display = ''; + card.parentElement.insertBefore(newCard, card); + card.parentElement.removeChild(card); + } + }); +} + +window.addEventListener("keydown", function(event) { + if (event.key == "Escape") { + closePopup(); + } +}); + +/** + * Setup custom loading for this script. + * We need to wait for all of our HTML to be generated in the extra networks tabs + * before we can actually run the `setupExtraNetworks` function. + * The `onUiLoaded` function actually runs before all of our extra network tabs are + * finished generating. Thus we needed this new method. + * + */ + +var uiAfterScriptsCallbacks = []; +var uiAfterScriptsTimeout = null; +var executedAfterScripts = false; + +function scheduleAfterScriptsCallbacks() { + clearTimeout(uiAfterScriptsTimeout); + uiAfterScriptsTimeout = setTimeout(function() { + executeCallbacks(uiAfterScriptsCallbacks); + }, 200); +} + +onUiLoaded(function() { + var mutationObserver = new MutationObserver(function(m) { + let existingSearchfields = gradioApp().querySelectorAll("[id$='_extra_search']").length; + let neededSearchfields = gradioApp().querySelectorAll("[id$='_extra_tabs'] > .tab-nav > button").length - 2; + + if (!executedAfterScripts && existingSearchfields >= neededSearchfields) { + mutationObserver.disconnect(); + executedAfterScripts = true; + scheduleAfterScriptsCallbacks(); + } + }); + mutationObserver.observe(gradioApp(), {childList: true, subtree: true}); +}); + +uiAfterScriptsCallbacks.push(setupExtraNetworks); diff --git a/javascript/generationParams.js b/javascript/generationParams.js new file mode 100644 index 00000000000..7c0fd221d63 --- /dev/null +++ b/javascript/generationParams.js @@ -0,0 +1,35 @@ +// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes + +let txt2img_gallery, img2img_gallery, modal = undefined; +onAfterUiUpdate(function() { + if (!txt2img_gallery) { + txt2img_gallery = attachGalleryListeners("txt2img"); + } + if (!img2img_gallery) { + img2img_gallery = attachGalleryListeners("img2img"); + } + if (!modal) { + modal = gradioApp().getElementById('lightboxModal'); + modalObserver.observe(modal, {attributes: true, attributeFilter: ['style']}); + } +}); + +let modalObserver = new MutationObserver(function(mutations) { + mutations.forEach(function(mutationRecord) { + let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText; + if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img')) { + gradioApp().getElementById(selectedTab + "_generation_info_button")?.click(); + } + }); +}); + +function attachGalleryListeners(tab_name) { + var gallery = gradioApp().querySelector('#' + tab_name + '_gallery'); + gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name + "_generation_info_button").click()); + gallery?.addEventListener('keydown', (e) => { + if (e.keyCode == 37 || e.keyCode == 39) { // left or right arrow + gradioApp().getElementById(tab_name + "_generation_info_button").click(); + } + }); + return gallery; +} diff --git a/javascript/hints.js b/javascript/hints.js index b98012f5e9b..6de9372e8ea 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -1,23 +1,29 @@ // mouseover tooltips for various UI elements -titles = { +var titles = { "Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results", "Sampling method": "Which algorithm to use to produce the image", - "GFPGAN": "Restore low quality faces using GFPGAN neural network", - "Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help", - "DDIM": "Denoising Diffusion Implicit Models - best at inpainting", - - "Batch count": "How many batches of images to create", - "Batch size": "How many image to create in a single batch", + "GFPGAN": "Restore low quality faces using GFPGAN neural network", + "Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help", + "DDIM": "Denoising Diffusion Implicit Models - best at inpainting", + "UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models", + "DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution", + + "\u{1F4D0}": "Auto detect size from img2img", + "Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)", + "Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)", "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results", "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result", "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time", - "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed", - "\u{1f3a8}": "Add a random artist to the prompt.", + "\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomized", "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.", "\u{1f4c2}": "Open images output directory", "\u{1f4be}": "Save style", + "\u{1f5d1}\ufe0f": "Clear prompt", "\u{1f4cb}": "Apply selected styles to current prompt", + "\u{1f4d2}": "Paste available values into the field", + "\u{1f3b4}": "Show/hide extra networks", + "\u{1f300}": "Restore progress", "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt", "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back", @@ -35,7 +41,6 @@ titles = { "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image", "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.", - "Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.", "Skip": "Stop processing current image and continue processing.", "Interrupt": "Stop processing images and return any results accumulated so far.", @@ -46,7 +51,7 @@ titles = { "None": "Do not do anything special", "Prompt matrix": "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)", - "X/Y plot": "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows", + "X/Y/Z plot": "Create grid(s) where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows", "Custom code": "Run Python code. Advanced user only. Must run program with --allow-code for this to work", "Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others", @@ -62,27 +67,24 @@ titles = { "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.", - "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.", - "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.", + "Images filename pattern": "Use tags like [seed] and [date] to define how filenames for images are chosen. Leave empty for default.", + "Directory name pattern": "Use tags like [seed] and [date] to define how subdirectories for images and grids are chosen. Leave empty for default.", "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle", - "Loopback": "Process an image, use it as an input, repeat.", - "Loops": "How many times to repeat processing an image and using it as input for the next iteration", + "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.", + "Loops": "How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used.", + "Final denoising strength": "The denoising strength for the final loop of each image in the batch.", + "Denoising strength curve": "The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops.", "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both", "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both", "Apply style": "Insert selected styles into prompt fields", - "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.", + "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style uses that as a placeholder for your prompt when you use the style in the future.", "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.", - - "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).", - - "Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition", - "Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.", + "Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.", "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.", - "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.", "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.", "Filename join string": "This string will be used to join split words into a single line if the option above is enabled.", @@ -91,36 +93,111 @@ titles = { "Weighted sum": "Result = A * (1 - M) + B * M", "Add difference": "Result = A + (B - C) * M", + "No interpolation": "Result = A", + + "Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors", + "Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.", + + "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.", + + "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resolution and lower quality.", + "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resolution and extremely low quality.", + + "Hires. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition", + "Hires steps": "Number of sampling steps for upscaled picture. If 0, uses same as for original.", + "Upscale by": "Adjusts the size of the image by multiplying the original width and height by the selected value. Ignored if either Resize width to or Resize height to are non-zero.", + "Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.", + "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.", + "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.", + "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order listed.", + "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction." +}; + +function updateTooltip(element) { + if (element.title) return; // already has a title + + let text = element.textContent; + let tooltip = localization[titles[text]] || titles[text]; + + if (!tooltip) { + let value = element.value; + if (value) tooltip = localization[titles[value]] || titles[value]; + } + + if (!tooltip) { + // Gradio dropdown options have `data-value`. + let dataValue = element.dataset.value; + if (dataValue) tooltip = localization[titles[dataValue]] || titles[dataValue]; + } + + if (!tooltip) { + for (const c of element.classList) { + if (c in titles) { + tooltip = localization[titles[c]] || titles[c]; + break; + } + } + } + + if (tooltip) { + element.title = tooltip; + } } +// Nodes to check for adding tooltips. +const tooltipCheckNodes = new Set(); +// Timer for debouncing tooltip check. +let tooltipCheckTimer = null; + +function processTooltipCheckNodes() { + for (const node of tooltipCheckNodes) { + updateTooltip(node); + } + tooltipCheckNodes.clear(); +} -onUiUpdate(function(){ - gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){ - tooltip = titles[span.textContent]; - - if(!tooltip){ - tooltip = titles[span.value]; - } - - if(!tooltip){ - for (const c of span.classList) { - if (c in titles) { - tooltip = titles[c]; - break; - } - } - } - - if(tooltip){ - span.title = tooltip; - } - }) - - gradioApp().querySelectorAll('select').forEach(function(select){ - if (select.onchange != null) return; - - select.onchange = function(){ - select.title = titles[select.value] || ""; - } - }) -}) +onUiUpdate(function(mutationRecords) { + for (const record of mutationRecords) { + if (record.type === "childList" && record.target.classList.contains("options")) { + // This smells like a Gradio dropdown menu having changed, + // so let's enqueue an update for the input element that shows the current value. + let wrap = record.target.parentNode; + let input = wrap?.querySelector("input"); + if (input) { + input.title = ""; // So we'll even have a chance to update it. + tooltipCheckNodes.add(input); + } + } + for (const node of record.addedNodes) { + if (node.nodeType === Node.ELEMENT_NODE && !node.classList.contains("hide")) { + if (!node.title) { + if ( + node.tagName === "SPAN" || + node.tagName === "BUTTON" || + node.tagName === "P" || + node.tagName === "INPUT" || + (node.tagName === "LI" && node.classList.contains("item")) // Gradio dropdown item + ) { + tooltipCheckNodes.add(node); + } + } + node.querySelectorAll('span, button, p').forEach(n => tooltipCheckNodes.add(n)); + } + } + } + if (tooltipCheckNodes.size) { + clearTimeout(tooltipCheckTimer); + tooltipCheckTimer = setTimeout(processTooltipCheckNodes, 1000); + } +}); + +onUiLoaded(function() { + for (var comp of window.gradio_config.components) { + if (comp.props.webui_tooltip && comp.props.elem_id) { + var elem = gradioApp().getElementById(comp.props.elem_id); + if (elem) { + elem.title = comp.props.webui_tooltip; + } + } + } +}); diff --git a/javascript/hires_fix.js b/javascript/hires_fix.js new file mode 100644 index 00000000000..0d04ab3b424 --- /dev/null +++ b/javascript/hires_fix.js @@ -0,0 +1,18 @@ + +function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y) { + function setInactive(elem, inactive) { + elem.classList.toggle('inactive', !!inactive); + } + + var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale'); + var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x'); + var hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y'); + + gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""; + + setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0); + setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0); + setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0); + + return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y]; +} diff --git a/javascript/imageMaskFix.js b/javascript/imageMaskFix.js index 3d77bfe9b45..900c56f32fd 100644 --- a/javascript/imageMaskFix.js +++ b/javascript/imageMaskFix.js @@ -2,20 +2,18 @@ * temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668 * @see https://github.com/gradio-app/gradio/issues/1721 */ -window.addEventListener( 'resize', () => imageMaskResize()); function imageMaskResize() { const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas'); - if ( ! canvases.length ) { - canvases_fixed = false; - window.removeEventListener( 'resize', imageMaskResize ); - return; + if (!canvases.length) { + window.removeEventListener('resize', imageMaskResize); + return; } const wrapper = canvases[0].closest('.touch-none'); const previewImage = wrapper.previousElementSibling; - if ( ! previewImage.complete ) { - previewImage.addEventListener( 'load', () => imageMaskResize()); + if (!previewImage.complete) { + previewImage.addEventListener('load', imageMaskResize); return; } @@ -24,22 +22,22 @@ function imageMaskResize() { const nw = previewImage.naturalWidth; const nh = previewImage.naturalHeight; const portrait = nh > nw; - const factor = portrait; - const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw); - const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh); + const wW = Math.min(w, portrait ? h / nh * nw : w / nw * nw); + const wH = Math.min(h, portrait ? h / nh * nh : w / nw * nh); wrapper.style.width = `${wW}px`; wrapper.style.height = `${wH}px`; - wrapper.style.left = `${(w-wW)/2}px`; - wrapper.style.top = `${(h-wH)/2}px`; + wrapper.style.left = `0px`; + wrapper.style.top = `0px`; - canvases.forEach( c => { + canvases.forEach(c => { c.style.width = c.style.height = ''; c.style.maxWidth = '100%'; c.style.maxHeight = '100%'; c.style.objectFit = 'contain'; }); - } - - onUiUpdate(() => imageMaskResize()); \ No newline at end of file +} + +onAfterUiUpdate(imageMaskResize); +window.addEventListener('resize', imageMaskResize); diff --git a/javascript/imageParams.js b/javascript/imageParams.js deleted file mode 100644 index 67404a89ba6..00000000000 --- a/javascript/imageParams.js +++ /dev/null @@ -1,19 +0,0 @@ -window.onload = (function(){ - window.addEventListener('drop', e => { - const target = e.composedPath()[0]; - const idx = selected_gallery_index(); - if (target.placeholder.indexOf("Prompt") == -1) return; - - let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image"; - - e.stopPropagation(); - e.preventDefault(); - const imgParent = gradioApp().getElementById(prompt_target); - const files = e.dataTransfer.files; - const fileInput = imgParent.querySelector('input[type="file"]'); - if ( fileInput ) { - fileInput.files = files; - fileInput.dispatchEvent(new Event('change')); - } - }); -}); diff --git a/javascript/images_history.js b/javascript/images_history.js deleted file mode 100644 index f7d052c3d18..00000000000 --- a/javascript/images_history.js +++ /dev/null @@ -1,206 +0,0 @@ -var images_history_click_image = function(){ - if (!this.classList.contains("transform")){ - var gallery = images_history_get_parent_by_class(this, "images_history_cantainor"); - var buttons = gallery.querySelectorAll(".gallery-item"); - var i = 0; - var hidden_list = []; - buttons.forEach(function(e){ - if (e.style.display == "none"){ - hidden_list.push(i); - } - i += 1; - }) - if (hidden_list.length > 0){ - setTimeout(images_history_hide_buttons, 10, hidden_list, gallery); - } - } - images_history_set_image_info(this); -} - -var images_history_click_tab = function(){ - var tabs_box = gradioApp().getElementById("images_history_tab"); - if (!tabs_box.classList.contains(this.getAttribute("tabname"))) { - gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_renew_page").click(); - tabs_box.classList.add(this.getAttribute("tabname")) - } -} - -function images_history_disabled_del(){ - gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){ - btn.setAttribute('disabled','disabled'); - }); -} - -function images_history_get_parent_by_class(item, class_name){ - var parent = item.parentElement; - while(!parent.classList.contains(class_name)){ - parent = parent.parentElement; - } - return parent; -} - -function images_history_get_parent_by_tagname(item, tagname){ - var parent = item.parentElement; - tagname = tagname.toUpperCase() - while(parent.tagName != tagname){ - console.log(parent.tagName, tagname) - parent = parent.parentElement; - } - return parent; -} - -function images_history_hide_buttons(hidden_list, gallery){ - var buttons = gallery.querySelectorAll(".gallery-item"); - var num = 0; - buttons.forEach(function(e){ - if (e.style.display == "none"){ - num += 1; - } - }); - if (num == hidden_list.length){ - setTimeout(images_history_hide_buttons, 10, hidden_list, gallery); - } - for( i in hidden_list){ - buttons[hidden_list[i]].style.display = "none"; - } -} - -function images_history_set_image_info(button){ - var buttons = images_history_get_parent_by_tagname(button, "DIV").querySelectorAll(".gallery-item"); - var index = -1; - var i = 0; - buttons.forEach(function(e){ - if(e == button){ - index = i; - } - if(e.style.display != "none"){ - i += 1; - } - }); - var gallery = images_history_get_parent_by_class(button, "images_history_cantainor"); - var set_btn = gallery.querySelector(".images_history_set_index"); - var curr_idx = set_btn.getAttribute("img_index", index); - if (curr_idx != index) { - set_btn.setAttribute("img_index", index); - images_history_disabled_del(); - } - set_btn.click(); - -} - -function images_history_get_current_img(tabname, image_path, files){ - return [ - gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"), - image_path, - files - ]; -} - -function images_history_delete(del_num, tabname, img_path, img_file_name, page_index, filenames, image_index){ - image_index = parseInt(image_index); - var tab = gradioApp().getElementById(tabname + '_images_history'); - var set_btn = tab.querySelector(".images_history_set_index"); - var buttons = []; - tab.querySelectorAll(".gallery-item").forEach(function(e){ - if (e.style.display != 'none'){ - buttons.push(e); - } - }); - var img_num = buttons.length / 2; - if (img_num <= del_num){ - setTimeout(function(tabname){ - gradioApp().getElementById(tabname + '_images_history_renew_page').click(); - }, 30, tabname); - } else { - var next_img - for (var i = 0; i < del_num; i++){ - if (image_index + i < image_index + img_num){ - buttons[image_index + i].style.display = 'none'; - buttons[image_index + img_num + 1].style.display = 'none'; - next_img = image_index + i + 1 - } - } - var bnt; - if (next_img >= img_num){ - btn = buttons[image_index - del_num]; - } else { - btn = buttons[next_img]; - } - setTimeout(function(btn){btn.click()}, 30, btn); - } - images_history_disabled_del(); - return [del_num, tabname, img_path, img_file_name, page_index, filenames, image_index]; -} - -function images_history_turnpage(img_path, page_index, image_index, tabname){ - var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item"); - buttons.forEach(function(elem) { - elem.style.display = 'block'; - }) - return [img_path, page_index, image_index, tabname]; -} - -function images_history_enable_del_buttons(){ - gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){ - btn.removeAttribute('disabled'); - }) -} - -function images_history_init(){ - var load_txt2img_button = gradioApp().getElementById('txt2img_images_history_renew_page') - if (load_txt2img_button){ - for (var i in images_history_tab_list ){ - tab = images_history_tab_list[i]; - gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor"); - gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index"); - gradioApp().getElementById(tab + '_images_history_del_button').classList.add("images_history_del_button"); - gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery"); - - } - var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div"); - tabs_box.setAttribute("id", "images_history_tab"); - var tab_btns = tabs_box.querySelectorAll("button"); - for (var i in images_history_tab_list){ - var tabname = images_history_tab_list[i] - tab_btns[i].setAttribute("tabname", tabname); - - // this refreshes history upon tab switch - // until the history is known to work well, which is not the case now, we do not do this at startup - //tab_btns[i].addEventListener('click', images_history_click_tab); - } - tabs_box.classList.add(images_history_tab_list[0]); - - // same as above, at page load - //load_txt2img_button.click(); - } else { - setTimeout(images_history_init, 500); - } -} - -var images_history_tab_list = ["txt2img", "img2img", "extras"]; -setTimeout(images_history_init, 500); -document.addEventListener("DOMContentLoaded", function() { - var mutationObserver = new MutationObserver(function(m){ - for (var i in images_history_tab_list ){ - let tabname = images_history_tab_list[i] - var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item'); - buttons.forEach(function(bnt){ - bnt.addEventListener('click', images_history_click_image, true); - }); - - // same as load_txt2img_button.click() above - /* - var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg"); - if (cls_btn){ - cls_btn.addEventListener('click', function(){ - gradioApp().getElementById(tabname + '_images_history_renew_page').click(); - }, false); - }*/ - - } - }); - mutationObserver.observe( gradioApp(), { childList:true, subtree:true }); - -}); - - diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js index 65a33dd78de..9b23f4700b3 100644 --- a/javascript/imageviewer.js +++ b/javascript/imageviewer.js @@ -5,15 +5,26 @@ function closeModal() { function showModal(event) { const source = event.target || event.srcElement; - const modalImage = gradioApp().getElementById("modalImage") - const lb = gradioApp().getElementById("lightboxModal") - modalImage.src = source.src + const modalImage = gradioApp().getElementById("modalImage"); + const modalToggleLivePreviewBtn = gradioApp().getElementById("modal_toggle_live_preview"); + modalToggleLivePreviewBtn.innerHTML = opts.js_live_preview_in_modal_lightbox ? "🗇" : "🗆"; + const lb = gradioApp().getElementById("lightboxModal"); + modalImage.src = source.src; if (modalImage.style.display === 'none') { lb.style.setProperty('background-image', 'url(' + source.src + ')'); } - lb.style.display = "block"; - lb.focus() - event.stopPropagation() + lb.style.display = "flex"; + lb.focus(); + + const tabTxt2Img = gradioApp().getElementById("tab_txt2img"); + const tabImg2Img = gradioApp().getElementById("tab_img2img"); + // show the save button in modal only on txt2img or img2img tabs + if (tabTxt2Img.style.display != "none" || tabImg2Img.style.display != "none") { + gradioApp().getElementById("modal_save").style.display = "inline"; + } else { + gradioApp().getElementById("modal_save").style.display = "none"; + } + event.stopPropagation(); } function negmod(n, m) { @@ -21,125 +32,133 @@ function negmod(n, m) { } function updateOnBackgroundChange() { - const modalImage = gradioApp().getElementById("modalImage") + const modalImage = gradioApp().getElementById("modalImage"); if (modalImage && modalImage.offsetParent) { - let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") - let currentButton = null - allcurrentButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - currentButton = elem; - } - }) - - if (modalImage.src != currentButton.children[0].src) { + let currentButton = selected_gallery_button(); + let preview = gradioApp().querySelectorAll('.livePreview > img'); + if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) { + // show preview image if available + modalImage.src = preview[preview.length - 1].src; + } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) { modalImage.src = currentButton.children[0].src; if (modalImage.style.display === 'none') { - modal.style.setProperty('background-image', `url(${modalImage.src})`) + const modal = gradioApp().getElementById("lightboxModal"); + modal.style.setProperty('background-image', `url(${modalImage.src})`); } } } } function modalImageSwitch(offset) { - var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all") - var galleryButtons = [] - allgalleryButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - galleryButtons.push(elem); - } - }) + var galleryButtons = all_gallery_buttons(); if (galleryButtons.length > 1) { - var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") - var currentButton = null - allcurrentButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - currentButton = elem; - } - }) - - var result = -1 - galleryButtons.forEach(function(v, i) { - if (v == currentButton) { - result = i - } - }) + var result = selected_gallery_index(); if (result != -1) { - nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)] - nextButton.click() + var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]; + nextButton.click(); const modalImage = gradioApp().getElementById("modalImage"); const modal = gradioApp().getElementById("lightboxModal"); modalImage.src = nextButton.children[0].src; if (modalImage.style.display === 'none') { - modal.style.setProperty('background-image', `url(${modalImage.src})`) + modal.style.setProperty('background-image', `url(${modalImage.src})`); } setTimeout(function() { - modal.focus() - }, 10) + modal.focus(); + }, 10); } } } +function saveImage() { + const tabTxt2Img = gradioApp().getElementById("tab_txt2img"); + const tabImg2Img = gradioApp().getElementById("tab_img2img"); + const saveTxt2Img = "save_txt2img"; + const saveImg2Img = "save_img2img"; + if (tabTxt2Img.style.display != "none") { + gradioApp().getElementById(saveTxt2Img).click(); + } else if (tabImg2Img.style.display != "none") { + gradioApp().getElementById(saveImg2Img).click(); + } else { + console.error("missing implementation for saving modal of this type"); + } +} + +function modalSaveImage(event) { + saveImage(); + event.stopPropagation(); +} + function modalNextImage(event) { - modalImageSwitch(1) - event.stopPropagation() + modalImageSwitch(1); + event.stopPropagation(); } function modalPrevImage(event) { - modalImageSwitch(-1) - event.stopPropagation() + modalImageSwitch(-1); + event.stopPropagation(); } function modalKeyHandler(event) { switch (event.key) { - case "ArrowLeft": - modalPrevImage(event) - break; - case "ArrowRight": - modalNextImage(event) - break; - case "Escape": - closeModal(); - break; + case "s": + saveImage(); + break; + case "ArrowLeft": + modalPrevImage(event); + break; + case "ArrowRight": + modalNextImage(event); + break; + case "Escape": + closeModal(); + break; } } -function showGalleryImage() { - setTimeout(function() { - fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain') - - if (fullImg_preview != null) { - fullImg_preview.forEach(function function_name(e) { - if (e.dataset.modded) - return; - e.dataset.modded = true; - if(e && e.parentElement.tagName == 'DIV'){ - e.style.cursor='pointer' - e.addEventListener('click', function (evt) { - if(!opts.js_modal_lightbox) return; - modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed) - showModal(evt) - }, true); - } - }); +function setupImageForLightbox(e) { + if (e.dataset.modded) { + return; + } + + e.dataset.modded = true; + e.style.cursor = 'pointer'; + e.style.userSelect = 'none'; + + e.addEventListener('mousedown', function(evt) { + if (evt.button == 1) { + open(evt.target.src); + evt.preventDefault(); + return; } + }, true); + + e.addEventListener('click', function(evt) { + if (!opts.js_modal_lightbox || evt.button != 0) return; + + modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed); + evt.preventDefault(); + showModal(evt); + }, true); - }, 100); } function modalZoomSet(modalImage, enable) { - if (enable) { - modalImage.classList.add('modalImageFullscreen'); - } else { - modalImage.classList.remove('modalImageFullscreen'); - } + if (modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable); } function modalZoomToggle(event) { - modalImage = gradioApp().getElementById("modalImage"); - modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen')) - event.stopPropagation() + var modalImage = gradioApp().getElementById("modalImage"); + modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen')); + event.stopPropagation(); +} + +function modalLivePreviewToggle(event) { + const modalToggleLivePreview = gradioApp().getElementById("modal_toggle_live_preview"); + opts.js_live_preview_in_modal_lightbox = !opts.js_live_preview_in_modal_lightbox; + modalToggleLivePreview.innerHTML = opts.js_live_preview_in_modal_lightbox ? "🗇" : "🗆"; + event.stopPropagation(); } function modalTileImageToggle(event) { @@ -148,89 +167,102 @@ function modalTileImageToggle(event) { const isTiling = modalImage.style.display === 'none'; if (isTiling) { modalImage.style.display = 'block'; - modal.style.setProperty('background-image', 'none') + modal.style.setProperty('background-image', 'none'); } else { modalImage.style.display = 'none'; - modal.style.setProperty('background-image', `url(${modalImage.src})`) + modal.style.setProperty('background-image', `url(${modalImage.src})`); } - event.stopPropagation() -} - -function galleryImageHandler(e) { - if (e && e.parentElement.tagName == 'BUTTON') { - e.onclick = showGalleryImage; - } + event.stopPropagation(); } -onUiUpdate(function() { - fullImg_preview = gradioApp().querySelectorAll('img.w-full') +onAfterUiUpdate(function() { + var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img'); if (fullImg_preview != null) { - fullImg_preview.forEach(galleryImageHandler); + fullImg_preview.forEach(setupImageForLightbox); } updateOnBackgroundChange(); -}) +}); document.addEventListener("DOMContentLoaded", function() { - const modalFragment = document.createDocumentFragment(); - const modal = document.createElement('div') + //const modalFragment = document.createDocumentFragment(); + const modal = document.createElement('div'); modal.onclick = closeModal; modal.id = "lightboxModal"; - modal.tabIndex = 0 - modal.addEventListener('keydown', modalKeyHandler, true) + modal.tabIndex = 0; + modal.addEventListener('keydown', modalKeyHandler, true); - const modalControls = document.createElement('div') + const modalControls = document.createElement('div'); modalControls.className = 'modalControls gradio-container'; modal.append(modalControls); - const modalZoom = document.createElement('span') + const modalZoom = document.createElement('span'); modalZoom.className = 'modalZoom cursor'; - modalZoom.innerHTML = '⤡' - modalZoom.addEventListener('click', modalZoomToggle, true) + modalZoom.innerHTML = '⤡'; + modalZoom.addEventListener('click', modalZoomToggle, true); modalZoom.title = "Toggle zoomed view"; - modalControls.appendChild(modalZoom) + modalControls.appendChild(modalZoom); - const modalTileImage = document.createElement('span') + const modalTileImage = document.createElement('span'); modalTileImage.className = 'modalTileImage cursor'; - modalTileImage.innerHTML = '⊞' - modalTileImage.addEventListener('click', modalTileImageToggle, true) + modalTileImage.innerHTML = '⊞'; + modalTileImage.addEventListener('click', modalTileImageToggle, true); modalTileImage.title = "Preview tiling"; - modalControls.appendChild(modalTileImage) - - const modalClose = document.createElement('span') + modalControls.appendChild(modalTileImage); + + const modalSave = document.createElement("span"); + modalSave.className = "modalSave cursor"; + modalSave.id = "modal_save"; + modalSave.innerHTML = "🖫"; + modalSave.addEventListener("click", modalSaveImage, true); + modalSave.title = "Save Image(s)"; + modalControls.appendChild(modalSave); + + const modalToggleLivePreview = document.createElement('span'); + modalToggleLivePreview.className = 'modalToggleLivePreview cursor'; + modalToggleLivePreview.id = "modal_toggle_live_preview"; + modalToggleLivePreview.innerHTML = "🗆"; + modalToggleLivePreview.onclick = modalLivePreviewToggle; + modalToggleLivePreview.title = "Toggle live preview"; + modalControls.appendChild(modalToggleLivePreview); + + const modalClose = document.createElement('span'); modalClose.className = 'modalClose cursor'; - modalClose.innerHTML = '×' + modalClose.innerHTML = '×'; modalClose.onclick = closeModal; modalClose.title = "Close image viewer"; - modalControls.appendChild(modalClose) + modalControls.appendChild(modalClose); - const modalImage = document.createElement('img') + const modalImage = document.createElement('img'); modalImage.id = 'modalImage'; modalImage.onclick = closeModal; - modalImage.tabIndex = 0 - modalImage.addEventListener('keydown', modalKeyHandler, true) - modal.appendChild(modalImage) + modalImage.tabIndex = 0; + modalImage.addEventListener('keydown', modalKeyHandler, true); + modal.appendChild(modalImage); - const modalPrev = document.createElement('a') + const modalPrev = document.createElement('a'); modalPrev.className = 'modalPrev'; - modalPrev.innerHTML = '❮' - modalPrev.tabIndex = 0 + modalPrev.innerHTML = '❮'; + modalPrev.tabIndex = 0; modalPrev.addEventListener('click', modalPrevImage, true); - modalPrev.addEventListener('keydown', modalKeyHandler, true) - modal.appendChild(modalPrev) + modalPrev.addEventListener('keydown', modalKeyHandler, true); + modal.appendChild(modalPrev); - const modalNext = document.createElement('a') + const modalNext = document.createElement('a'); modalNext.className = 'modalNext'; - modalNext.innerHTML = '❯' - modalNext.tabIndex = 0 + modalNext.innerHTML = '❯'; + modalNext.tabIndex = 0; modalNext.addEventListener('click', modalNextImage, true); - modalNext.addEventListener('keydown', modalKeyHandler, true) + modalNext.addEventListener('keydown', modalKeyHandler, true); - modal.appendChild(modalNext) + modal.appendChild(modalNext); + try { + gradioApp().appendChild(modal); + } catch (e) { + gradioApp().body.appendChild(modal); + } - gradioApp().getRootNode().appendChild(modal) - - document.body.appendChild(modalFragment); + document.body.appendChild(modal); }); diff --git a/javascript/imageviewerGamepad.js b/javascript/imageviewerGamepad.js new file mode 100644 index 00000000000..a22c7e6e643 --- /dev/null +++ b/javascript/imageviewerGamepad.js @@ -0,0 +1,63 @@ +let gamepads = []; + +window.addEventListener('gamepadconnected', (e) => { + const index = e.gamepad.index; + let isWaiting = false; + gamepads[index] = setInterval(async() => { + if (!opts.js_modal_lightbox_gamepad || isWaiting) return; + const gamepad = navigator.getGamepads()[index]; + const xValue = gamepad.axes[0]; + if (xValue <= -0.3) { + modalPrevImage(e); + isWaiting = true; + } else if (xValue >= 0.3) { + modalNextImage(e); + isWaiting = true; + } + if (isWaiting) { + await sleepUntil(() => { + const xValue = navigator.getGamepads()[index].axes[0]; + if (xValue < 0.3 && xValue > -0.3) { + return true; + } + }, opts.js_modal_lightbox_gamepad_repeat); + isWaiting = false; + } + }, 10); +}); + +window.addEventListener('gamepaddisconnected', (e) => { + clearInterval(gamepads[e.gamepad.index]); +}); + +/* +Primarily for vr controller type pointer devices. +I use the wheel event because there's currently no way to do it properly with web xr. + */ +let isScrolling = false; +window.addEventListener('wheel', (e) => { + if (!opts.js_modal_lightbox_gamepad || isScrolling) return; + isScrolling = true; + + if (e.deltaX <= -0.6) { + modalPrevImage(e); + } else if (e.deltaX >= 0.6) { + modalNextImage(e); + } + + setTimeout(() => { + isScrolling = false; + }, opts.js_modal_lightbox_gamepad_repeat); +}); + +function sleepUntil(f, timeout) { + return new Promise((resolve) => { + const timeStart = new Date(); + const wait = setInterval(function() { + if (f() || new Date() - timeStart > timeout) { + clearInterval(wait); + resolve(); + } + }, 20); + }); +} diff --git a/javascript/inputAccordion.js b/javascript/inputAccordion.js new file mode 100644 index 00000000000..7570309aa73 --- /dev/null +++ b/javascript/inputAccordion.js @@ -0,0 +1,68 @@ +function inputAccordionChecked(id, checked) { + var accordion = gradioApp().getElementById(id); + accordion.visibleCheckbox.checked = checked; + accordion.onVisibleCheckboxChange(); +} + +function setupAccordion(accordion) { + var labelWrap = accordion.querySelector('.label-wrap'); + var gradioCheckbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input"); + var extra = gradioApp().querySelector('#' + accordion.id + "-extra"); + var span = labelWrap.querySelector('span'); + var linked = true; + + var isOpen = function() { + return labelWrap.classList.contains('open'); + }; + + var observerAccordionOpen = new MutationObserver(function(mutations) { + mutations.forEach(function(mutationRecord) { + accordion.classList.toggle('input-accordion-open', isOpen()); + + if (linked) { + accordion.visibleCheckbox.checked = isOpen(); + accordion.onVisibleCheckboxChange(); + } + }); + }); + observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']}); + + if (extra) { + labelWrap.insertBefore(extra, labelWrap.lastElementChild); + } + + accordion.onChecked = function(checked) { + if (isOpen() != checked) { + labelWrap.click(); + } + }; + + var visibleCheckbox = document.createElement('INPUT'); + visibleCheckbox.type = 'checkbox'; + visibleCheckbox.checked = isOpen(); + visibleCheckbox.id = accordion.id + "-visible-checkbox"; + visibleCheckbox.className = gradioCheckbox.className + " input-accordion-checkbox"; + span.insertBefore(visibleCheckbox, span.firstChild); + + accordion.visibleCheckbox = visibleCheckbox; + accordion.onVisibleCheckboxChange = function() { + if (linked && isOpen() != visibleCheckbox.checked) { + labelWrap.click(); + } + + gradioCheckbox.checked = visibleCheckbox.checked; + updateInput(gradioCheckbox); + }; + + visibleCheckbox.addEventListener('click', function(event) { + linked = false; + event.stopPropagation(); + }); + visibleCheckbox.addEventListener('input', accordion.onVisibleCheckboxChange); +} + +onUiLoaded(function() { + for (var accordion of gradioApp().querySelectorAll('.input-accordion')) { + setupAccordion(accordion); + } +}); diff --git a/javascript/localStorage.js b/javascript/localStorage.js new file mode 100644 index 00000000000..dc1a36c3287 --- /dev/null +++ b/javascript/localStorage.js @@ -0,0 +1,26 @@ + +function localSet(k, v) { + try { + localStorage.setItem(k, v); + } catch (e) { + console.warn(`Failed to save ${k} to localStorage: ${e}`); + } +} + +function localGet(k, def) { + try { + return localStorage.getItem(k); + } catch (e) { + console.warn(`Failed to load ${k} from localStorage: ${e}`); + } + + return def; +} + +function localRemove(k) { + try { + return localStorage.removeItem(k); + } catch (e) { + console.warn(`Failed to remove ${k} from localStorage: ${e}`); + } +} diff --git a/javascript/localization.js b/javascript/localization.js new file mode 100644 index 00000000000..8f00c186860 --- /dev/null +++ b/javascript/localization.js @@ -0,0 +1,205 @@ + +// localization = {} -- the dict with translations is created by the backend + +var ignore_ids_for_localization = { + setting_sd_hypernetwork: 'OPTION', + setting_sd_model_checkpoint: 'OPTION', + modelmerger_primary_model_name: 'OPTION', + modelmerger_secondary_model_name: 'OPTION', + modelmerger_tertiary_model_name: 'OPTION', + train_embedding: 'OPTION', + train_hypernetwork: 'OPTION', + txt2img_styles: 'OPTION', + img2img_styles: 'OPTION', + setting_random_artist_categories: 'OPTION', + setting_face_restoration_model: 'OPTION', + setting_realesrgan_enabled_models: 'OPTION', + extras_upscaler_1: 'OPTION', + extras_upscaler_2: 'OPTION', +}; + +var re_num = /^[.\d]+$/; +var re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u; + +var original_lines = {}; +var translated_lines = {}; + +function hasLocalization() { + return window.localization && Object.keys(window.localization).length > 0; +} + +function textNodesUnder(el) { + var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false); + while ((n = walk.nextNode())) a.push(n); + return a; +} + +function canBeTranslated(node, text) { + if (!text) return false; + if (!node.parentElement) return false; + + var parentType = node.parentElement.nodeName; + if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false; + + if (parentType == 'OPTION' || parentType == 'SPAN') { + var pnode = node; + for (var level = 0; level < 4; level++) { + pnode = pnode.parentElement; + if (!pnode) break; + + if (ignore_ids_for_localization[pnode.id] == parentType) return false; + } + } + + if (re_num.test(text)) return false; + if (re_emoji.test(text)) return false; + return true; +} + +function getTranslation(text) { + if (!text) return undefined; + + if (translated_lines[text] === undefined) { + original_lines[text] = 1; + } + + var tl = localization[text]; + if (tl !== undefined) { + translated_lines[tl] = 1; + } + + return tl; +} + +function processTextNode(node) { + var text = node.textContent.trim(); + + if (!canBeTranslated(node, text)) return; + + var tl = getTranslation(text); + if (tl !== undefined) { + node.textContent = tl; + } +} + +function processNode(node) { + if (node.nodeType == 3) { + processTextNode(node); + return; + } + + if (node.title) { + let tl = getTranslation(node.title); + if (tl !== undefined) { + node.title = tl; + } + } + + if (node.placeholder) { + let tl = getTranslation(node.placeholder); + if (tl !== undefined) { + node.placeholder = tl; + } + } + + textNodesUnder(node).forEach(function(node) { + processTextNode(node); + }); +} + +function localizeWholePage() { + processNode(gradioApp()); + + function elem(comp) { + var elem_id = comp.props.elem_id ? comp.props.elem_id : "component-" + comp.id; + return gradioApp().getElementById(elem_id); + } + + for (var comp of window.gradio_config.components) { + if (comp.props.webui_tooltip) { + let e = elem(comp); + + let tl = e ? getTranslation(e.title) : undefined; + if (tl !== undefined) { + e.title = tl; + } + } + if (comp.props.placeholder) { + let e = elem(comp); + let textbox = e ? e.querySelector('[placeholder]') : null; + + let tl = textbox ? getTranslation(textbox.placeholder) : undefined; + if (tl !== undefined) { + textbox.placeholder = tl; + } + } + } +} + +function dumpTranslations() { + if (!hasLocalization()) { + // If we don't have any localization, + // we will not have traversed the app to find + // original_lines, so do that now. + localizeWholePage(); + } + var dumped = {}; + if (localization.rtl) { + dumped.rtl = true; + } + + for (const text in original_lines) { + if (dumped[text] !== undefined) continue; + dumped[text] = localization[text] || text; + } + + return dumped; +} + +function download_localization() { + var text = JSON.stringify(dumpTranslations(), null, 4); + + var element = document.createElement('a'); + element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text)); + element.setAttribute('download', "localization.json"); + element.style.display = 'none'; + document.body.appendChild(element); + + element.click(); + + document.body.removeChild(element); +} + +document.addEventListener("DOMContentLoaded", function() { + if (!hasLocalization()) { + return; + } + + onUiUpdate(function(m) { + m.forEach(function(mutation) { + mutation.addedNodes.forEach(function(node) { + processNode(node); + }); + }); + }); + + localizeWholePage(); + + if (localization.rtl) { // if the language is from right to left, + (new MutationObserver((mutations, observer) => { // wait for the style to load + mutations.forEach(mutation => { + mutation.addedNodes.forEach(node => { + if (node.tagName === 'STYLE') { + observer.disconnect(); + + for (const x of node.sheet.rules) { // find all rtl media rules + if (Array.from(x.media || []).includes('rtl')) { + x.media.appendMedium('all'); // enable them + } + } + } + }); + }); + })).observe(gradioApp(), {childList: true}); + } +}); diff --git a/javascript/notification.js b/javascript/notification.js index f96de313b6a..3ee972ae166 100644 --- a/javascript/notification.js +++ b/javascript/notification.js @@ -2,20 +2,20 @@ let lastHeadImg = null; -notificationButton = null +let notificationButton = null; -onUiUpdate(function(){ - if(notificationButton == null){ - notificationButton = gradioApp().getElementById('request_notifications') +onAfterUiUpdate(function() { + if (notificationButton == null) { + notificationButton = gradioApp().getElementById('request_notifications'); - if(notificationButton != null){ - notificationButton.addEventListener('click', function (evt) { - Notification.requestPermission(); - },true); + if (notificationButton != null) { + notificationButton.addEventListener('click', () => { + void Notification.requestPermission(); + }, true); } } - const galleryPreviews = gradioApp().querySelectorAll('img.h-full.w-full.overflow-hidden'); + const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"] div[id$="_results"] .thumbnail-item > img'); if (galleryPreviews == null) return; @@ -26,7 +26,11 @@ onUiUpdate(function(){ lastHeadImg = headImg; // play notification sound if available - gradioApp().querySelector('#audio_notification audio')?.play(); + const notificationAudio = gradioApp().querySelector('#audio_notification audio'); + if (notificationAudio) { + notificationAudio.volume = opts.notification_volume / 100.0 || 1.0; + notificationAudio.play(); + } if (document.hasFocus()) return; @@ -42,7 +46,7 @@ onUiUpdate(function(){ } ); - notification.onclick = function(_){ + notification.onclick = function(_) { parent.focus(); this.close(); }; diff --git a/javascript/profilerVisualization.js b/javascript/profilerVisualization.js new file mode 100644 index 00000000000..9822f4b2a2a --- /dev/null +++ b/javascript/profilerVisualization.js @@ -0,0 +1,174 @@ + +function createRow(table, cellName, items) { + var tr = document.createElement('tr'); + var res = []; + + items.forEach(function(x, i) { + if (x === undefined) { + res.push(null); + return; + } + + var td = document.createElement(cellName); + td.textContent = x; + tr.appendChild(td); + res.push(td); + + var colspan = 1; + for (var n = i + 1; n < items.length; n++) { + if (items[n] !== undefined) { + break; + } + + colspan += 1; + } + + if (colspan > 1) { + td.colSpan = colspan; + } + }); + + table.appendChild(tr); + + return res; +} + +function createVisualizationTable(data, cutoff = 0, sort = "") { + var table = document.createElement('table'); + table.className = 'popup-table'; + + var keys = Object.keys(data); + if (sort === "number") { + keys = keys.sort(function(a, b) { + return data[b] - data[a]; + }); + } else { + keys = keys.sort(); + } + var items = keys.map(function(x) { + return {key: x, parts: x.split('/'), value: data[x]}; + }); + var maxLength = items.reduce(function(a, b) { + return Math.max(a, b.parts.length); + }, 0); + + var cols = createRow( + table, + 'th', + [ + cutoff === 0 ? 'key' : 'record', + cutoff === 0 ? 'value' : 'seconds' + ] + ); + cols[0].colSpan = maxLength; + + function arraysEqual(a, b) { + return !(a < b || b < a); + } + + var addLevel = function(level, parent, hide) { + var matching = items.filter(function(x) { + return x.parts[level] && !x.parts[level + 1] && arraysEqual(x.parts.slice(0, level), parent); + }); + if (sort === "number") { + matching = matching.sort(function(a, b) { + return b.value - a.value; + }); + } else { + matching = matching.sort(); + } + var othersTime = 0; + var othersList = []; + var othersRows = []; + var childrenRows = []; + matching.forEach(function(x) { + var visible = (cutoff === 0 && !hide) || (x.value >= cutoff && !hide); + + var cells = []; + for (var i = 0; i < maxLength; i++) { + cells.push(x.parts[i]); + } + cells.push(cutoff === 0 ? x.value : x.value.toFixed(3)); + var cols = createRow(table, 'td', cells); + for (i = 0; i < level; i++) { + cols[i].className = 'muted'; + } + + var tr = cols[0].parentNode; + if (!visible) { + tr.classList.add("hidden"); + } + + if (cutoff === 0 || x.value >= cutoff) { + childrenRows.push(tr); + } else { + othersTime += x.value; + othersList.push(x.parts[level]); + othersRows.push(tr); + } + + var children = addLevel(level + 1, parent.concat([x.parts[level]]), true); + if (children.length > 0) { + var cell = cols[level]; + var onclick = function() { + cell.classList.remove("link"); + cell.removeEventListener("click", onclick); + children.forEach(function(x) { + x.classList.remove("hidden"); + }); + }; + cell.classList.add("link"); + cell.addEventListener("click", onclick); + } + }); + + if (othersTime > 0) { + var cells = []; + for (var i = 0; i < maxLength; i++) { + cells.push(parent[i]); + } + cells.push(othersTime.toFixed(3)); + cells[level] = 'others'; + var cols = createRow(table, 'td', cells); + for (i = 0; i < level; i++) { + cols[i].className = 'muted'; + } + + var cell = cols[level]; + var tr = cell.parentNode; + var onclick = function() { + tr.classList.add("hidden"); + cell.classList.remove("link"); + cell.removeEventListener("click", onclick); + othersRows.forEach(function(x) { + x.classList.remove("hidden"); + }); + }; + + cell.title = othersList.join(", "); + cell.classList.add("link"); + cell.addEventListener("click", onclick); + + if (hide) { + tr.classList.add("hidden"); + } + + childrenRows.push(tr); + } + + return childrenRows; + }; + + addLevel(0, []); + + return table; +} + +function showProfile(path, cutoff = 0.05) { + requestGet(path, {}, function(data) { + data.records['total'] = data.total; + const table = createVisualizationTable(data.records, cutoff, "number"); + popup(table); + }); +} + diff --git a/javascript/progressbar.js b/javascript/progressbar.js index 076f0a9736f..23dea64ceda 100644 --- a/javascript/progressbar.js +++ b/javascript/progressbar.js @@ -1,111 +1,215 @@ // code related to showing and updating progressbar shown as the image is being made -global_progressbars = {} -galleries = {} -galleryObservers = {} - -function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){ - var progressbar = gradioApp().getElementById(id_progressbar) - var skip = id_skip ? gradioApp().getElementById(id_skip) : null - var interrupt = gradioApp().getElementById(id_interrupt) - - if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){ - if(progressbar.innerText){ - let newtitle = 'Stable Diffusion - ' + progressbar.innerText - if(document.title != newtitle){ - document.title = newtitle; - } - }else{ - let newtitle = 'Stable Diffusion' - if(document.title != newtitle){ - document.title = newtitle; + +function rememberGallerySelection() { + +} + +function getGallerySelectedIndex() { + +} + +function request(url, data, handler, errorHandler) { + var xhr = new XMLHttpRequest(); + xhr.open("POST", url, true); + xhr.setRequestHeader("Content-Type", "application/json"); + xhr.onreadystatechange = function() { + if (xhr.readyState === 4) { + if (xhr.status === 200) { + try { + var js = JSON.parse(xhr.responseText); + handler(js); + } catch (error) { + console.error(error); + errorHandler(); + } + } else { + errorHandler(); } } + }; + var js = JSON.stringify(data); + xhr.send(js); +} + +function pad2(x) { + return x < 10 ? '0' + x : x; +} + +function formatTime(secs) { + if (secs > 3600) { + return pad2(Math.floor(secs / 60 / 60)) + ":" + pad2(Math.floor(secs / 60) % 60) + ":" + pad2(Math.floor(secs) % 60); + } else if (secs > 60) { + return pad2(Math.floor(secs / 60)) + ":" + pad2(Math.floor(secs) % 60); + } else { + return Math.floor(secs) + "s"; } - - if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){ - global_progressbars[id_progressbar] = progressbar +} - var mutationObserver = new MutationObserver(function(m){ - preview = gradioApp().getElementById(id_preview) - gallery = gradioApp().getElementById(id_gallery) - if(preview != null && gallery != null){ - preview.style.width = gallery.clientWidth + "px" - preview.style.height = gallery.clientHeight + "px" +var originalAppTitle = undefined; - //only watch gallery if there is a generation process going on - check_gallery(id_gallery); +onUiLoaded(function() { + originalAppTitle = document.title; +}); - var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; - if(!progressDiv){ - if (skip) { - skip.style.display = "none" - } - interrupt.style.display = "none" - - //disconnect observer once generation finished, so user can close selected image if they want - if (galleryObservers[id_gallery]) { - galleryObservers[id_gallery].disconnect(); - galleries[id_gallery] = null; - } - } +function setTitle(progress) { + var title = originalAppTitle; + + if (opts.show_progress_in_title && progress) { + title = '[' + progress.trim() + '] ' + title; + } + if (document.title != title) { + document.title = title; + } +} - } - window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500) - }); - mutationObserver.observe( progressbar, { childList:true, subtree:true }) - } +function randomId() { + return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + ")"; } -function check_gallery(id_gallery){ - let gallery = gradioApp().getElementById(id_gallery) - // if gallery has no change, no need to setting up observer again. - if (gallery && galleries[id_gallery] !== gallery){ - galleries[id_gallery] = gallery; - if(galleryObservers[id_gallery]){ - galleryObservers[id_gallery].disconnect(); +// starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and +// preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd. +// calls onProgress every time there is a progress update +function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress, inactivityTimeout = 40) { + var dateStart = new Date(); + var wasEverActive = false; + var parentProgressbar = progressbarContainer.parentNode; + var wakeLock = null; + + var requestWakeLock = async function() { + if (!opts.prevent_screen_sleep_during_generation || wakeLock) return; + try { + wakeLock = await navigator.wakeLock.request('screen'); + } catch (err) { + console.error('Wake Lock is not supported.'); } - let prevSelectedIndex = selected_gallery_index(); - galleryObservers[id_gallery] = new MutationObserver(function (){ - let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item') - let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2') - if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) { - //automatically re-open previously selected index (if exists) - galleryButtons[prevSelectedIndex].click(); - showGalleryImage(); - } - }) - galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false }) - } -} + }; -onUiUpdate(function(){ - check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery') - check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery') - check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', '', 'ti_interrupt', 'ti_preview', 'ti_gallery') -}) - -function requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt){ - btn = gradioApp().getElementById(id_part+"_check_progress"); - if(btn==null) return; - - btn.click(); - var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; - var skip = id_skip ? gradioApp().getElementById(id_skip) : null - var interrupt = gradioApp().getElementById(id_interrupt) - if(progressDiv && interrupt){ - if (skip) { - skip.style.display = "block" + var releaseWakeLock = async function() { + if (!opts.prevent_screen_sleep_during_generation || !wakeLock) return; + try { + await wakeLock.release(); + wakeLock = null; + } catch (err) { + console.error('Wake Lock release failed', err); } - interrupt.style.display = "block" - } -} + }; + + var divProgress = document.createElement('div'); + divProgress.className = 'progressDiv'; + divProgress.style.display = opts.show_progressbar ? "block" : "none"; + var divInner = document.createElement('div'); + divInner.className = 'progress'; + + divProgress.appendChild(divInner); + parentProgressbar.insertBefore(divProgress, progressbarContainer); + + var livePreview = null; + + var removeProgressBar = function() { + releaseWakeLock(); + if (!divProgress) return; + + setTitle(""); + parentProgressbar.removeChild(divProgress); + if (gallery && livePreview) gallery.removeChild(livePreview); + atEnd(); + + divProgress = null; + }; -function requestProgress(id_part){ - btn = gradioApp().getElementById(id_part+"_check_progress_initial"); - if(btn==null) return; + var funProgress = function(id_task) { + requestWakeLock(); + request("./internal/progress", {id_task: id_task, live_preview: false}, function(res) { + if (res.completed) { + removeProgressBar(); + return; + } + + let progressText = ""; + + divInner.style.width = ((res.progress || 0) * 100.0) + '%'; + divInner.style.background = res.progress ? "" : "transparent"; + + if (res.progress > 0) { + progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%'; + } + + if (res.eta) { + progressText += " ETA: " + formatTime(res.eta); + } + + setTitle(progressText); + + if (res.textinfo && res.textinfo.indexOf("\n") == -1) { + progressText = res.textinfo + " " + progressText; + } + + divInner.textContent = progressText; + + var elapsedFromStart = (new Date() - dateStart) / 1000; + + if (res.active) wasEverActive = true; + + if (!res.active && wasEverActive) { + removeProgressBar(); + return; + } + + if (elapsedFromStart > inactivityTimeout && !res.queued && !res.active) { + removeProgressBar(); + return; + } + + if (onProgress) { + onProgress(res); + } + + setTimeout(() => { + funProgress(id_task, res.id_live_preview); + }, opts.live_preview_refresh_period || 500); + }, function() { + removeProgressBar(); + }); + }; + + var funLivePreview = function(id_task, id_live_preview) { + request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) { + if (!divProgress) { + return; + } + + if (res.live_preview && gallery) { + var img = new Image(); + img.onload = function() { + if (!livePreview) { + livePreview = document.createElement('div'); + livePreview.className = 'livePreview'; + gallery.insertBefore(livePreview, gallery.firstElementChild); + } + + livePreview.appendChild(img); + if (livePreview.childElementCount > 2) { + livePreview.removeChild(livePreview.firstElementChild); + } + }; + img.src = res.live_preview; + } + + setTimeout(() => { + funLivePreview(id_task, res.id_live_preview); + }, opts.live_preview_refresh_period || 500); + }, function() { + removeProgressBar(); + }); + }; + + funProgress(id_task, 0); + + if (gallery) { + funLivePreview(id_task, 0); + } - btn.click(); } diff --git a/javascript/resizeHandle.js b/javascript/resizeHandle.js new file mode 100644 index 00000000000..4aeb14b41f3 --- /dev/null +++ b/javascript/resizeHandle.js @@ -0,0 +1,205 @@ +(function() { + const GRADIO_MIN_WIDTH = 320; + const PAD = 16; + const DEBOUNCE_TIME = 100; + const DOUBLE_TAP_DELAY = 200; //ms + + const R = { + tracking: false, + parent: null, + parentWidth: null, + leftCol: null, + leftColStartWidth: null, + screenX: null, + lastTapTime: null, + }; + + let resizeTimer; + let parents = []; + + function setLeftColGridTemplate(el, width) { + el.style.gridTemplateColumns = `${width}px 16px 1fr`; + } + + function displayResizeHandle(parent) { + if (!parent.needHideOnMoblie) { + return true; + } + if (window.innerWidth < GRADIO_MIN_WIDTH * 2 + PAD * 4) { + parent.style.display = 'flex'; + parent.resizeHandle.style.display = "none"; + return false; + } else { + parent.style.display = 'grid'; + parent.resizeHandle.style.display = "block"; + return true; + } + } + + function afterResize(parent) { + if (displayResizeHandle(parent) && parent.style.gridTemplateColumns != parent.style.originalGridTemplateColumns) { + const oldParentWidth = R.parentWidth; + const newParentWidth = parent.offsetWidth; + const widthL = parseInt(parent.style.gridTemplateColumns.split(' ')[0]); + + const ratio = newParentWidth / oldParentWidth; + + const newWidthL = Math.max(Math.floor(ratio * widthL), parent.minLeftColWidth); + setLeftColGridTemplate(parent, newWidthL); + + R.parentWidth = newParentWidth; + } + } + + function setup(parent) { + + function onDoubleClick(evt) { + evt.preventDefault(); + evt.stopPropagation(); + + parent.style.gridTemplateColumns = parent.style.originalGridTemplateColumns; + } + + const leftCol = parent.firstElementChild; + const rightCol = parent.lastElementChild; + + parents.push(parent); + + parent.style.display = 'grid'; + parent.style.gap = '0'; + let leftColTemplate = ""; + if (parent.children[0].style.flexGrow) { + leftColTemplate = `${parent.children[0].style.flexGrow}fr`; + parent.minLeftColWidth = GRADIO_MIN_WIDTH; + parent.minRightColWidth = GRADIO_MIN_WIDTH; + parent.needHideOnMoblie = true; + } else { + leftColTemplate = parent.children[0].style.flexBasis; + parent.minLeftColWidth = parent.children[0].style.flexBasis.slice(0, -2) / 2; + parent.minRightColWidth = 0; + parent.needHideOnMoblie = false; + } + + if (!leftColTemplate) { + leftColTemplate = '1fr'; + } + + const gridTemplateColumns = `${leftColTemplate} ${PAD}px ${parent.children[1].style.flexGrow}fr`; + parent.style.gridTemplateColumns = gridTemplateColumns; + parent.style.originalGridTemplateColumns = gridTemplateColumns; + + const resizeHandle = document.createElement('div'); + resizeHandle.classList.add('resize-handle'); + parent.insertBefore(resizeHandle, rightCol); + parent.resizeHandle = resizeHandle; + + ['mousedown', 'touchstart'].forEach((eventType) => { + resizeHandle.addEventListener(eventType, (evt) => { + if (eventType.startsWith('mouse')) { + if (evt.button !== 0) return; + } else { + if (evt.changedTouches.length !== 1) return; + + const currentTime = new Date().getTime(); + if (R.lastTapTime && currentTime - R.lastTapTime <= DOUBLE_TAP_DELAY) { + onDoubleClick(evt); + return; + } + + R.lastTapTime = currentTime; + } + + evt.preventDefault(); + evt.stopPropagation(); + + document.body.classList.add('resizing'); + + R.tracking = true; + R.parent = parent; + R.parentWidth = parent.offsetWidth; + R.leftCol = leftCol; + R.leftColStartWidth = leftCol.offsetWidth; + if (eventType.startsWith('mouse')) { + R.screenX = evt.screenX; + } else { + R.screenX = evt.changedTouches[0].screenX; + } + }); + }); + + resizeHandle.addEventListener('dblclick', onDoubleClick); + + afterResize(parent); + } + + ['mousemove', 'touchmove'].forEach((eventType) => { + window.addEventListener(eventType, (evt) => { + if (eventType.startsWith('mouse')) { + if (evt.button !== 0) return; + } else { + if (evt.changedTouches.length !== 1) return; + } + + if (R.tracking) { + if (eventType.startsWith('mouse')) { + evt.preventDefault(); + } + evt.stopPropagation(); + + let delta = 0; + if (eventType.startsWith('mouse')) { + delta = R.screenX - evt.screenX; + } else { + delta = R.screenX - evt.changedTouches[0].screenX; + } + const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - R.parent.minRightColWidth - PAD), R.parent.minLeftColWidth); + setLeftColGridTemplate(R.parent, leftColWidth); + } + }); + }); + + ['mouseup', 'touchend'].forEach((eventType) => { + window.addEventListener(eventType, (evt) => { + if (eventType.startsWith('mouse')) { + if (evt.button !== 0) return; + } else { + if (evt.changedTouches.length !== 1) return; + } + + if (R.tracking) { + evt.preventDefault(); + evt.stopPropagation(); + + R.tracking = false; + + document.body.classList.remove('resizing'); + } + }); + }); + + + window.addEventListener('resize', () => { + clearTimeout(resizeTimer); + + resizeTimer = setTimeout(function() { + for (const parent of parents) { + afterResize(parent); + } + }, DEBOUNCE_TIME); + }); + + setupResizeHandle = setup; +})(); + + +function setupAllResizeHandles() { + for (var elem of gradioApp().querySelectorAll('.resize-handle-row')) { + if (!elem.querySelector('.resize-handle') && !elem.children[0].classList.contains("hidden")) { + setupResizeHandle(elem); + } + } +} + + +onUiLoaded(setupAllResizeHandles); + diff --git a/javascript/settings.js b/javascript/settings.js new file mode 100644 index 00000000000..b2d981c2144 --- /dev/null +++ b/javascript/settings.js @@ -0,0 +1,71 @@ +let settingsExcludeTabsFromShowAll = { + settings_tab_defaults: 1, + settings_tab_sysinfo: 1, + settings_tab_actions: 1, + settings_tab_licenses: 1, +}; + +function settingsShowAllTabs() { + gradioApp().querySelectorAll('#settings > div').forEach(function(elem) { + if (settingsExcludeTabsFromShowAll[elem.id]) return; + + elem.style.display = "block"; + }); +} + +function settingsShowOneTab() { + gradioApp().querySelector('#settings_show_one_page').click(); +} + +onUiLoaded(function() { + var edit = gradioApp().querySelector('#settings_search'); + var editTextarea = gradioApp().querySelector('#settings_search > label > input'); + var buttonShowAllPages = gradioApp().getElementById('settings_show_all_pages'); + var settings_tabs = gradioApp().querySelector('#settings div'); + + onEdit('settingsSearch', editTextarea, 250, function() { + var searchText = (editTextarea.value || "").trim().toLowerCase(); + + gradioApp().querySelectorAll('#settings > div[id^=settings_] div[id^=column_settings_] > *').forEach(function(elem) { + var visible = elem.textContent.trim().toLowerCase().indexOf(searchText) != -1; + elem.style.display = visible ? "" : "none"; + }); + + if (searchText != "") { + settingsShowAllTabs(); + } else { + settingsShowOneTab(); + } + }); + + settings_tabs.insertBefore(edit, settings_tabs.firstChild); + settings_tabs.appendChild(buttonShowAllPages); + + + buttonShowAllPages.addEventListener("click", settingsShowAllTabs); +}); + + +onOptionsChanged(function() { + if (gradioApp().querySelector('#settings .settings-category')) return; + + var sectionMap = {}; + gradioApp().querySelectorAll('#settings > div > button').forEach(function(x) { + sectionMap[x.textContent.trim()] = x; + }); + + opts._categories.forEach(function(x) { + var section = localization[x[0]] ?? x[0]; + var category = localization[x[1]] ?? x[1]; + + var span = document.createElement('SPAN'); + span.textContent = category; + span.className = 'settings-category'; + + var sectionElem = sectionMap[section]; + if (!sectionElem) return; + + sectionElem.parentElement.insertBefore(span, sectionElem); + }); +}); + diff --git a/javascript/textualInversion.js b/javascript/textualInversion.js index 8061be08988..20443fcca01 100644 --- a/javascript/textualInversion.js +++ b/javascript/textualInversion.js @@ -1,8 +1,17 @@ - - -function start_training_textual_inversion(){ - requestProgress('ti') - gradioApp().querySelector('#ti_error').innerHTML='' - - return args_to_array(arguments) -} + + + +function start_training_textual_inversion() { + gradioApp().querySelector('#ti_error').innerHTML = ''; + + var id = randomId(); + requestProgress(id, gradioApp().getElementById('ti_output'), gradioApp().getElementById('ti_gallery'), function() {}, function(progress) { + gradioApp().getElementById('ti_progress').innerHTML = progress.textinfo; + }); + + var res = Array.from(arguments); + + res[0] = id; + + return res; +} diff --git a/javascript/token-counters.js b/javascript/token-counters.js new file mode 100644 index 00000000000..eeea7a5d26c --- /dev/null +++ b/javascript/token-counters.js @@ -0,0 +1,87 @@ +let promptTokenCountUpdateFunctions = {}; + +function update_txt2img_tokens(...args) { + // Called from Gradio + update_token_counter("txt2img_token_button"); + update_token_counter("txt2img_negative_token_button"); + if (args.length == 2) { + return args[0]; + } + return args; +} + +function update_img2img_tokens(...args) { + // Called from Gradio + update_token_counter("img2img_token_button"); + update_token_counter("img2img_negative_token_button"); + if (args.length == 2) { + return args[0]; + } + return args; +} + +function update_token_counter(button_id) { + promptTokenCountUpdateFunctions[button_id]?.(); +} + + +function recalculatePromptTokens(name) { + promptTokenCountUpdateFunctions[name]?.(); +} + +function recalculate_prompts_txt2img() { + // Called from Gradio + recalculatePromptTokens('txt2img_prompt'); + recalculatePromptTokens('txt2img_neg_prompt'); + return Array.from(arguments); +} + +function recalculate_prompts_img2img() { + // Called from Gradio + recalculatePromptTokens('img2img_prompt'); + recalculatePromptTokens('img2img_neg_prompt'); + return Array.from(arguments); +} + +function setupTokenCounting(id, id_counter, id_button) { + var prompt = gradioApp().getElementById(id); + var counter = gradioApp().getElementById(id_counter); + var textarea = gradioApp().querySelector(`#${id} > label > textarea`); + + if (counter.parentElement == prompt.parentElement) { + return; + } + + prompt.parentElement.insertBefore(counter, prompt); + prompt.parentElement.style.position = "relative"; + + var func = onEdit(id, textarea, 800, function() { + if (counter.classList.contains("token-counter-visible")) { + gradioApp().getElementById(id_button)?.click(); + } + }); + promptTokenCountUpdateFunctions[id] = func; + promptTokenCountUpdateFunctions[id_button] = func; +} + +function toggleTokenCountingVisibility(id, id_counter, id_button) { + var counter = gradioApp().getElementById(id_counter); + + counter.style.display = opts.disable_token_counters ? "none" : "block"; + counter.classList.toggle("token-counter-visible", !opts.disable_token_counters); +} + +function runCodeForTokenCounters(fun) { + fun('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button'); + fun('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button'); + fun('img2img_prompt', 'img2img_token_counter', 'img2img_token_button'); + fun('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button'); +} + +onUiLoaded(function() { + runCodeForTokenCounters(setupTokenCounting); +}); + +onOptionsChanged(function() { + runCodeForTokenCounters(toggleTokenCountingVisibility); +}); diff --git a/javascript/ui.js b/javascript/ui.js index 9e1bed4cbd7..20309634fb6 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -1,170 +1,314 @@ -// various functions for interation with ui.py not large enough to warrant putting them in separate files +// various functions for interaction with ui.py not large enough to warrant putting them in separate files -function selected_gallery_index(){ - var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem .gallery-item') - var button = gradioApp().querySelector('[style="display: block;"].tabitem .gallery-item.\\!ring-2') +function set_theme(theme) { + var gradioURL = window.location.href; + if (!gradioURL.includes('?__theme=')) { + window.location.replace(gradioURL + '?__theme=' + theme); + } +} - var result = -1 - buttons.forEach(function(v, i){ if(v==button) { result = i } }) +function all_gallery_buttons() { + var allGalleryButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnails > .thumbnail-item.thumbnail-small'); + var visibleGalleryButtons = []; + allGalleryButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + visibleGalleryButtons.push(elem); + } + }); + return visibleGalleryButtons; +} - return result +function selected_gallery_button() { + return all_gallery_buttons().find(elem => elem.classList.contains('selected')) ?? null; } -function extract_image_from_gallery(gallery){ - if(gallery.length == 1){ - return gallery[0] - } +function selected_gallery_index() { + return all_gallery_buttons().findIndex(elem => elem.classList.contains('selected')); +} + +function gallery_container_buttons(gallery_container) { + return gradioApp().querySelectorAll(`#${gallery_container} .thumbnail-item.thumbnail-small`); +} - index = selected_gallery_index() +function selected_gallery_index_id(gallery_container) { + return Array.from(gallery_container_buttons(gallery_container)).findIndex(elem => elem.classList.contains('selected')); +} - if (index < 0 || index >= gallery.length){ - return [null] +function extract_image_from_gallery(gallery) { + if (gallery.length == 0) { + return [null]; + } + if (gallery.length == 1) { + return [gallery[0]]; } - return gallery[index]; -} + var index = selected_gallery_index(); -function args_to_array(args){ - res = [] - for(var i=0;i= gallery.length) { + // Use the first image in the gallery as the default + index = 0; } - return res + + return [gallery[index]]; } -function switch_to_txt2img(){ +window.args_to_array = Array.from; // Compatibility with e.g. extensions that may expect this to be around + +function switch_to_txt2img() { gradioApp().querySelector('#tabs').querySelectorAll('button')[0].click(); - return args_to_array(arguments); + return Array.from(arguments); } -function switch_to_img2img_img2img(){ +function switch_to_img2img_tab(no) { gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click(); - gradioApp().getElementById('mode_img2img').querySelectorAll('button')[0].click(); + gradioApp().getElementById('mode_img2img').querySelectorAll('button')[no].click(); +} +function switch_to_img2img() { + switch_to_img2img_tab(0); + return Array.from(arguments); +} - return args_to_array(arguments); +function switch_to_sketch() { + switch_to_img2img_tab(1); + return Array.from(arguments); } -function switch_to_img2img_inpaint(){ - gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click(); - gradioApp().getElementById('mode_img2img').querySelectorAll('button')[1].click(); +function switch_to_inpaint() { + switch_to_img2img_tab(2); + return Array.from(arguments); +} - return args_to_array(arguments); +function switch_to_inpaint_sketch() { + switch_to_img2img_tab(3); + return Array.from(arguments); } -function switch_to_extras(){ +function switch_to_extras() { gradioApp().querySelector('#tabs').querySelectorAll('button')[2].click(); - return args_to_array(arguments); + return Array.from(arguments); } -function extract_image_from_gallery_txt2img(gallery){ - switch_to_txt2img() - return extract_image_from_gallery(gallery); +function get_tab_index(tabId) { + let buttons = gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button'); + for (let i = 0; i < buttons.length; i++) { + if (buttons[i].classList.contains('selected')) { + return i; + } + } + return 0; +} + +function create_tab_index_args(tabId, args) { + var res = Array.from(args); + res[0] = get_tab_index(tabId); + return res; } -function extract_image_from_gallery_img2img(gallery){ - switch_to_img2img_img2img() - return extract_image_from_gallery(gallery); +function get_img2img_tab_index() { + let res = Array.from(arguments); + res.splice(-2); + res[0] = get_tab_index('mode_img2img'); + return res; } -function extract_image_from_gallery_inpaint(gallery){ - switch_to_img2img_inpaint() - return extract_image_from_gallery(gallery); +function create_submit_args(args) { + var res = Array.from(args); + + // As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image. + // This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate. + // I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some. + // If gradio at some point stops sending outputs, this may break something + if (Array.isArray(res[res.length - 3])) { + res[res.length - 3] = null; + } + + return res; } -function extract_image_from_gallery_extras(gallery){ - switch_to_extras() - return extract_image_from_gallery(gallery); +function setSubmitButtonsVisibility(tabname, showInterrupt, showSkip, showInterrupting) { + gradioApp().getElementById(tabname + '_interrupt').style.display = showInterrupt ? "block" : "none"; + gradioApp().getElementById(tabname + '_skip').style.display = showSkip ? "block" : "none"; + gradioApp().getElementById(tabname + '_interrupting').style.display = showInterrupting ? "block" : "none"; } -function get_tab_index(tabId){ - var res = 0 +function showSubmitButtons(tabname, show) { + setSubmitButtonsVisibility(tabname, !show, !show, false); +} - gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i){ - if(button.className.indexOf('bg-white') != -1) - res = i - }) +function showSubmitInterruptingPlaceholder(tabname) { + setSubmitButtonsVisibility(tabname, false, true, true); +} - return res +function showRestoreProgressButton(tabname, show) { + var button = gradioApp().getElementById(tabname + "_restore_progress"); + if (!button) return; + button.style.setProperty('display', show ? 'flex' : 'none', 'important'); } -function create_tab_index_args(tabId, args){ - var res = [] - for(var i=0; i label > textarea"); - txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button")); - } - if (!img2img_textarea) { - img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea"); - img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button")); - } -}) +onOptionsChanged(function() { + var elem = gradioApp().getElementById('sd_checkpoint_hash'); + var sd_checkpoint_hash = opts.sd_checkpoint_hash || ""; + var shorthash = sd_checkpoint_hash.substring(0, 10); + + if (elem && elem.textContent != shorthash) { + elem.textContent = shorthash; + elem.title = sd_checkpoint_hash; + elem.href = "https://google.com/search?q=" + sd_checkpoint_hash; + } +}); let txt2img_textarea, img2img_textarea = undefined; -let wait_time = 800 -let token_timeout; -function update_txt2img_tokens(...args) { - update_token_counter("txt2img_token_button") - if (args.length == 2) - return args[0] - return args; +function restart_reload() { + document.body.style.backgroundColor = "var(--background-fill-primary)"; + document.body.innerHTML = '

    Reloading...

    '; + var requestPing = function() { + requestGet("./internal/ping", {}, function(data) { + location.reload(); + }, function() { + setTimeout(requestPing, 500); + }); + }; + + setTimeout(requestPing, 2000); + + return []; +} + +// Simulate an `input` DOM event for Gradio Textbox component. Needed after you edit its contents in javascript, otherwise your edits +// will only visible on web page and not sent to python. +function updateInput(target) { + let e = new Event("input", {bubbles: true}); + Object.defineProperty(e, "target", {value: target}); + target.dispatchEvent(e); +} + + +var desiredCheckpointName = null; +function selectCheckpoint(name) { + desiredCheckpointName = name; + gradioApp().getElementById('change_checkpoint').click(); } -function update_img2img_tokens(...args) { - update_token_counter("img2img_token_button") - if (args.length == 2) - return args[0] - return args; +function currentImg2imgSourceResolution(w, h, scaleBy) { + var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img'); + return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy]; } -function update_token_counter(button_id) { - if (token_timeout) - clearTimeout(token_timeout); - token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time); +function updateImg2imgResizeToTextAfterChangingImage() { + // At the time this is called from gradio, the image has no yet been replaced. + // There may be a better solution, but this is simple and straightforward so I'm going with it. + + setTimeout(function() { + gradioApp().getElementById('img2img_update_resize_to').click(); + }, 500); + + return []; + } -function restart_reload(){ - document.body.innerHTML='

    Reloading...

    '; - setTimeout(function(){location.reload()},2000) + + +function setRandomSeed(elem_id) { + var input = gradioApp().querySelector("#" + elem_id + " input"); + if (!input) return []; + + input.value = "-1"; + updateInput(input); + return []; +} + +function switchWidthHeight(tabname) { + var width = gradioApp().querySelector("#" + tabname + "_width input[type=number]"); + var height = gradioApp().querySelector("#" + tabname + "_height input[type=number]"); + if (!width || !height) return []; + + var tmp = width.value; + width.value = height.value; + height.value = tmp; + + updateInput(width); + updateInput(height); + return []; +} + + +var onEditTimers = {}; + +// calls func after afterMs milliseconds has passed since the input elem has been edited by user +function onEdit(editId, elem, afterMs, func) { + var edited = function() { + var existingTimer = onEditTimers[editId]; + if (existingTimer) clearTimeout(existingTimer); + + onEditTimers[editId] = setTimeout(func, afterMs); + }; + + elem.addEventListener("input", edited); + + return edited; } diff --git a/javascript/ui_settings_hints.js b/javascript/ui_settings_hints.js new file mode 100644 index 00000000000..d088f9494f8 --- /dev/null +++ b/javascript/ui_settings_hints.js @@ -0,0 +1,62 @@ +// various hints and extra info for the settings tab + +var settingsHintsSetup = false; + +onOptionsChanged(function() { + if (settingsHintsSetup) return; + settingsHintsSetup = true; + + gradioApp().querySelectorAll('#settings [id^=setting_]').forEach(function(div) { + var name = div.id.substr(8); + var commentBefore = opts._comments_before[name]; + var commentAfter = opts._comments_after[name]; + + if (!commentBefore && !commentAfter) return; + + var span = null; + if (div.classList.contains('gradio-checkbox')) span = div.querySelector('label span'); + else if (div.classList.contains('gradio-checkboxgroup')) span = div.querySelector('span').firstChild; + else if (div.classList.contains('gradio-radio')) span = div.querySelector('span').firstChild; + else span = div.querySelector('label span').firstChild; + + if (!span) return; + + if (commentBefore) { + var comment = document.createElement('DIV'); + comment.className = 'settings-comment'; + comment.innerHTML = commentBefore; + span.parentElement.insertBefore(document.createTextNode('\xa0'), span); + span.parentElement.insertBefore(comment, span); + span.parentElement.insertBefore(document.createTextNode('\xa0'), span); + } + if (commentAfter) { + comment = document.createElement('DIV'); + comment.className = 'settings-comment'; + comment.innerHTML = commentAfter; + span.parentElement.insertBefore(comment, span.nextSibling); + span.parentElement.insertBefore(document.createTextNode('\xa0'), span.nextSibling); + } + }); +}); + +function settingsHintsShowQuicksettings() { + requestGet("./internal/quicksettings-hint", {}, function(data) { + var table = document.createElement('table'); + table.className = 'popup-table'; + + data.forEach(function(obj) { + var tr = document.createElement('tr'); + var td = document.createElement('td'); + td.textContent = obj.name; + tr.appendChild(td); + + td = document.createElement('td'); + td.textContent = obj.label; + tr.appendChild(td); + + table.appendChild(tr); + }); + + popup(table); + }); +} diff --git a/launch.py b/launch.py index 2e6b3369985..f83820d2534 100644 --- a/launch.py +++ b/launch.py @@ -1,172 +1,48 @@ -# this scripts installs necessary requirements and launches main program in webui.py -import subprocess -import os -import sys -import importlib.util -import shlex -import platform +from modules import launch_utils -dir_repos = "repositories" -python = sys.executable -git = os.environ.get('GIT', "git") -index_url = os.environ.get('INDEX_URL', "") +args = launch_utils.args +python = launch_utils.python +git = launch_utils.git +index_url = launch_utils.index_url +dir_repos = launch_utils.dir_repos +commit_hash = launch_utils.commit_hash +git_tag = launch_utils.git_tag -def extract_arg(args, name): - return [x for x in args if x != name], name in args +run = launch_utils.run +is_installed = launch_utils.is_installed +repo_dir = launch_utils.repo_dir +run_pip = launch_utils.run_pip +check_run_python = launch_utils.check_run_python +git_clone = launch_utils.git_clone +git_pull_recursive = launch_utils.git_pull_recursive +list_extensions = launch_utils.list_extensions +run_extension_installer = launch_utils.run_extension_installer +prepare_environment = launch_utils.prepare_environment +configure_for_tests = launch_utils.configure_for_tests +start = launch_utils.start -def run(command, desc=None, errdesc=None): - if desc is not None: - print(desc) - result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) +def main(): + if args.dump_sysinfo: + filename = launch_utils.dump_sysinfo() - if result.returncode != 0: + print(f"Sysinfo saved as {filename}. Exiting...") - message = f"""{errdesc or 'Error running command'}. -Command: {command} -Error code: {result.returncode} -stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else ''} -stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else ''} -""" - raise RuntimeError(message) - - return result.stdout.decode(encoding="utf8", errors="ignore") - - -def check_run(command): - result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) - return result.returncode == 0 - - -def is_installed(package): - try: - spec = importlib.util.find_spec(package) - except ModuleNotFoundError: - return False - - return spec is not None - - -def repo_dir(name): - return os.path.join(dir_repos, name) - - -def run_python(code, desc=None, errdesc=None): - return run(f'"{python}" -c "{code}"', desc, errdesc) - - -def run_pip(args, desc=None): - index_url_line = f' --index-url {index_url}' if index_url != '' else '' - return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}") - - -def check_run_python(code): - return check_run(f'"{python}" -c "{code}"') - - -def git_clone(url, dir, name, commithash=None): - # TODO clone into temporary dir and move if successful - - if os.path.exists(dir): - if commithash is None: - return - - current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip() - if current_hash == commithash: - return - - run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}") - run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}") - return - - run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}") - - if commithash is not None: - run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}") - - -def prepare_enviroment(): - torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113") - requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt") - commandline_args = os.environ.get('COMMANDLINE_ARGS', "") - - gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379") - clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1") - - stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc") - taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6") - k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878") - codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af") - blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9") - - args = shlex.split(commandline_args) - - args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test') - args, reinstall_xformers = extract_arg(args, '--reinstall-xformers') - xformers = '--xformers' in args - deepdanbooru = '--deepdanbooru' in args - ngrok = '--ngrok' in args - - try: - commit = run(f"{git} rev-parse HEAD").strip() - except Exception: - commit = "" - - print(f"Python {sys.version}") - print(f"Commit hash: {commit}") - - if not is_installed("torch") or not is_installed("torchvision"): - run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch") - - if not skip_torch_cuda_test: - run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'") - - if not is_installed("gfpgan"): - run_pip(f"install {gfpgan_package}", "gfpgan") - - if not is_installed("clip"): - run_pip(f"install {clip_package}", "clip") - - if (not is_installed("xformers") or reinstall_xformers) and xformers and platform.python_version().startswith("3.10"): - if platform.system() == "Windows": - run_pip("install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers") - elif platform.system() == "Linux": - run_pip("install xformers", "xformers") - - if not is_installed("deepdanbooru") and deepdanbooru: - run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru") - - if not is_installed("pyngrok") and ngrok: - run_pip("install pyngrok", "ngrok") - - os.makedirs(dir_repos, exist_ok=True) - - git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash) - git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash) - git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash) - git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash) - git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash) - - if not is_installed("lpips"): - run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer") - - run_pip(f"install -r {requirements_file}", "requirements for Web UI") + exit(0) - sys.argv += args + launch_utils.startup_timer.record("initial startup") - if "--exit" in args: - print("Exiting because of --exit argument") - exit(0) + with launch_utils.startup_timer.subcategory("prepare environment"): + if not args.skip_prepare_environment: + prepare_environment() + if args.test_server: + configure_for_tests() -def start_webui(): - print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}") - import webui - webui.webui() + start() if __name__ == "__main__": - prepare_enviroment() - start_webui() + main() diff --git a/localizations/Put localization files here.txt b/localizations/Put localization files here.txt new file mode 100644 index 00000000000..e69de29bb2d diff --git a/models/VAE-approx/model.pt b/models/VAE-approx/model.pt new file mode 100644 index 00000000000..8bda9d6e952 Binary files /dev/null and b/models/VAE-approx/model.pt differ diff --git a/models/VAE/Put VAE here.txt b/models/VAE/Put VAE here.txt new file mode 100644 index 00000000000..e69de29bb2d diff --git a/models/karlo/ViT-L-14_stats.th b/models/karlo/ViT-L-14_stats.th new file mode 100644 index 00000000000..a6a06e94eca Binary files /dev/null and b/models/karlo/ViT-L-14_stats.th differ diff --git a/modules/Roboto-Regular.ttf b/modules/Roboto-Regular.ttf new file mode 100644 index 00000000000..500b1045b0c Binary files /dev/null and b/modules/Roboto-Regular.ttf differ diff --git a/modules/api/api.py b/modules/api/api.py new file mode 100644 index 00000000000..97ec7514ea1 --- /dev/null +++ b/modules/api/api.py @@ -0,0 +1,928 @@ +import base64 +import io +import os +import time +import datetime +import uvicorn +import ipaddress +import requests +import gradio as gr +from threading import Lock +from io import BytesIO +from fastapi import APIRouter, Depends, FastAPI, Request, Response +from fastapi.security import HTTPBasic, HTTPBasicCredentials +from fastapi.exceptions import HTTPException +from fastapi.responses import JSONResponse +from fastapi.encoders import jsonable_encoder +from secrets import compare_digest + +import modules.shared as shared +from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models, sd_schedulers +from modules.api import models +from modules.shared import opts +from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images +from modules.textual_inversion.textual_inversion import create_embedding, train_embedding +from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork +from PIL import PngImagePlugin +from modules.sd_models_config import find_checkpoint_config_near_filename +from modules.realesrgan_model import get_realesrgan_models +from modules import devices +from typing import Any +import piexif +import piexif.helper +from contextlib import closing +from modules.progress import create_task_id, add_task_to_queue, start_task, finish_task, current_task + +def script_name_to_index(name, scripts): + try: + return [script.title().lower() for script in scripts].index(name.lower()) + except Exception as e: + raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e + + +def validate_sampler_name(name): + config = sd_samplers.all_samplers_map.get(name, None) + if config is None: + raise HTTPException(status_code=400, detail="Sampler not found") + + return name + + +def setUpscalers(req: dict): + reqDict = vars(req) + reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None) + reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None) + return reqDict + + +def verify_url(url): + """Returns True if the url refers to a global resource.""" + + import socket + from urllib.parse import urlparse + try: + parsed_url = urlparse(url) + domain_name = parsed_url.netloc + host = socket.gethostbyname_ex(domain_name) + for ip in host[2]: + ip_addr = ipaddress.ip_address(ip) + if not ip_addr.is_global: + return False + except Exception: + return False + + return True + + +def decode_base64_to_image(encoding): + if encoding.startswith("http://") or encoding.startswith("https://"): + if not opts.api_enable_requests: + raise HTTPException(status_code=500, detail="Requests not allowed") + + if opts.api_forbid_local_requests and not verify_url(encoding): + raise HTTPException(status_code=500, detail="Request to local resource not allowed") + + headers = {'user-agent': opts.api_useragent} if opts.api_useragent else {} + response = requests.get(encoding, timeout=30, headers=headers) + try: + image = images.read(BytesIO(response.content)) + return image + except Exception as e: + raise HTTPException(status_code=500, detail="Invalid image url") from e + + if encoding.startswith("data:image/"): + encoding = encoding.split(";")[1].split(",")[1] + try: + image = images.read(BytesIO(base64.b64decode(encoding))) + return image + except Exception as e: + raise HTTPException(status_code=500, detail="Invalid encoded image") from e + + +def encode_pil_to_base64(image): + with io.BytesIO() as output_bytes: + if isinstance(image, str): + return image + if opts.samples_format.lower() == 'png': + use_metadata = False + metadata = PngImagePlugin.PngInfo() + for key, value in image.info.items(): + if isinstance(key, str) and isinstance(value, str): + metadata.add_text(key, value) + use_metadata = True + image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality) + + elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"): + if image.mode in ("RGBA", "P"): + image = image.convert("RGB") + parameters = image.info.get('parameters', None) + exif_bytes = piexif.dump({ + "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") } + }) + if opts.samples_format.lower() in ("jpg", "jpeg"): + image.save(output_bytes, format="JPEG", exif = exif_bytes, quality=opts.jpeg_quality) + else: + image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality) + + else: + raise HTTPException(status_code=500, detail="Invalid image format") + + bytes_data = output_bytes.getvalue() + + return base64.b64encode(bytes_data) + + +def api_middleware(app: FastAPI): + rich_available = False + try: + if os.environ.get('WEBUI_RICH_EXCEPTIONS', None) is not None: + import anyio # importing just so it can be placed on silent list + import starlette # importing just so it can be placed on silent list + from rich.console import Console + console = Console() + rich_available = True + except Exception: + pass + + @app.middleware("http") + async def log_and_time(req: Request, call_next): + ts = time.time() + res: Response = await call_next(req) + duration = str(round(time.time() - ts, 4)) + res.headers["X-Process-Time"] = duration + endpoint = req.scope.get('path', 'err') + if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'): + print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format( + t=datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"), + code=res.status_code, + ver=req.scope.get('http_version', '0.0'), + cli=req.scope.get('client', ('0:0.0.0', 0))[0], + prot=req.scope.get('scheme', 'err'), + method=req.scope.get('method', 'err'), + endpoint=endpoint, + duration=duration, + )) + return res + + def handle_exception(request: Request, e: Exception): + err = { + "error": type(e).__name__, + "detail": vars(e).get('detail', ''), + "body": vars(e).get('body', ''), + "errors": str(e), + } + if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions + message = f"API error: {request.method}: {request.url} {err}" + if rich_available: + print(message) + console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200])) + else: + errors.report(message, exc_info=True) + return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err)) + + @app.middleware("http") + async def exception_handling(request: Request, call_next): + try: + return await call_next(request) + except Exception as e: + return handle_exception(request, e) + + @app.exception_handler(Exception) + async def fastapi_exception_handler(request: Request, e: Exception): + return handle_exception(request, e) + + @app.exception_handler(HTTPException) + async def http_exception_handler(request: Request, e: HTTPException): + return handle_exception(request, e) + + +class Api: + def __init__(self, app: FastAPI, queue_lock: Lock): + if shared.cmd_opts.api_auth: + self.credentials = {} + for auth in shared.cmd_opts.api_auth.split(","): + user, password = auth.split(":") + self.credentials[user] = password + + self.router = APIRouter() + self.app = app + self.queue_lock = queue_lock + api_middleware(self.app) + self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=models.TextToImageResponse) + self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=models.ImageToImageResponse) + self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse) + self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=models.ExtrasBatchImagesResponse) + self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=models.PNGInfoResponse) + self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=models.ProgressResponse) + self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"]) + self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"]) + self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"]) + self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel) + self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"]) + self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel) + self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem]) + self.add_api_route("/sdapi/v1/schedulers", self.get_schedulers, methods=["GET"], response_model=list[models.SchedulerItem]) + self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem]) + self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem]) + self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem]) + self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=list[models.SDVaeItem]) + self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=list[models.HypernetworkItem]) + self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=list[models.FaceRestorerItem]) + self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem]) + self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem]) + self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse) + self.add_api_route("/sdapi/v1/refresh-embeddings", self.refresh_embeddings, methods=["POST"]) + self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"]) + self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"]) + self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse) + self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=models.CreateResponse) + self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=models.TrainResponse) + self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=models.TrainResponse) + self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=models.MemoryResponse) + self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"]) + self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"]) + self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=models.ScriptsList) + self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=list[models.ScriptInfo]) + self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=list[models.ExtensionItem]) + + if shared.cmd_opts.api_server_stop: + self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"]) + self.add_api_route("/sdapi/v1/server-restart", self.restart_webui, methods=["POST"]) + self.add_api_route("/sdapi/v1/server-stop", self.stop_webui, methods=["POST"]) + + self.default_script_arg_txt2img = [] + self.default_script_arg_img2img = [] + + txt2img_script_runner = scripts.scripts_txt2img + img2img_script_runner = scripts.scripts_img2img + + if not txt2img_script_runner.scripts or not img2img_script_runner.scripts: + ui.create_ui() + + if not txt2img_script_runner.scripts: + txt2img_script_runner.initialize_scripts(False) + if not self.default_script_arg_txt2img: + self.default_script_arg_txt2img = self.init_default_script_args(txt2img_script_runner) + + if not img2img_script_runner.scripts: + img2img_script_runner.initialize_scripts(True) + if not self.default_script_arg_img2img: + self.default_script_arg_img2img = self.init_default_script_args(img2img_script_runner) + + + + def add_api_route(self, path: str, endpoint, **kwargs): + if shared.cmd_opts.api_auth: + return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs) + return self.app.add_api_route(path, endpoint, **kwargs) + + def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())): + if credentials.username in self.credentials: + if compare_digest(credentials.password, self.credentials[credentials.username]): + return True + + raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"}) + + def get_selectable_script(self, script_name, script_runner): + if script_name is None or script_name == "": + return None, None + + script_idx = script_name_to_index(script_name, script_runner.selectable_scripts) + script = script_runner.selectable_scripts[script_idx] + return script, script_idx + + def get_scripts_list(self): + t2ilist = [script.name for script in scripts.scripts_txt2img.scripts if script.name is not None] + i2ilist = [script.name for script in scripts.scripts_img2img.scripts if script.name is not None] + + return models.ScriptsList(txt2img=t2ilist, img2img=i2ilist) + + def get_script_info(self): + res = [] + + for script_list in [scripts.scripts_txt2img.scripts, scripts.scripts_img2img.scripts]: + res += [script.api_info for script in script_list if script.api_info is not None] + + return res + + def get_script(self, script_name, script_runner): + if script_name is None or script_name == "": + return None, None + + script_idx = script_name_to_index(script_name, script_runner.scripts) + return script_runner.scripts[script_idx] + + def init_default_script_args(self, script_runner): + #find max idx from the scripts in runner and generate a none array to init script_args + last_arg_index = 1 + for script in script_runner.scripts: + if last_arg_index < script.args_to: + last_arg_index = script.args_to + # None everywhere except position 0 to initialize script args + script_args = [None]*last_arg_index + script_args[0] = 0 + + # get default values + with gr.Blocks(): # will throw errors calling ui function without this + for script in script_runner.scripts: + if script.ui(script.is_img2img): + ui_default_values = [] + for elem in script.ui(script.is_img2img): + ui_default_values.append(elem.value) + script_args[script.args_from:script.args_to] = ui_default_values + return script_args + + def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner, *, input_script_args=None): + script_args = default_script_args.copy() + + if input_script_args is not None: + for index, value in input_script_args.items(): + script_args[index] = value + + # position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run() + if selectable_scripts: + script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args + script_args[0] = selectable_idx + 1 + + # Now check for always on scripts + if request.alwayson_scripts: + for alwayson_script_name in request.alwayson_scripts.keys(): + alwayson_script = self.get_script(alwayson_script_name, script_runner) + if alwayson_script is None: + raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found") + # Selectable script in always on script param check + if alwayson_script.alwayson is False: + raise HTTPException(status_code=422, detail="Cannot have a selectable script in the always on scripts params") + # always on script with no arg should always run so you don't really need to add them to the requests + if "args" in request.alwayson_scripts[alwayson_script_name]: + # min between arg length in scriptrunner and arg length in the request + for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))): + script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx] + return script_args + + def apply_infotext(self, request, tabname, *, script_runner=None, mentioned_script_args=None): + """Processes `infotext` field from the `request`, and sets other fields of the `request` according to what's in infotext. + + If request already has a field set, and that field is encountered in infotext too, the value from infotext is ignored. + + Additionally, fills `mentioned_script_args` dict with index: value pairs for script arguments read from infotext. + """ + + if not request.infotext: + return {} + + possible_fields = infotext_utils.paste_fields[tabname]["fields"] + set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True) # pydantic v1/v2 have different names for this + params = infotext_utils.parse_generation_parameters(request.infotext) + + def get_field_value(field, params): + value = field.function(params) if field.function else params.get(field.label) + if value is None: + return None + + if field.api in request.__fields__: + target_type = request.__fields__[field.api].type_ + else: + target_type = type(field.component.value) + + if target_type == type(None): + return None + + if isinstance(value, dict) and value.get('__type__') == 'generic_update': # this is a gradio.update rather than a value + value = value.get('value') + + if value is not None and not isinstance(value, target_type): + value = target_type(value) + + return value + + for field in possible_fields: + if not field.api: + continue + + if field.api in set_fields: + continue + + value = get_field_value(field, params) + if value is not None: + setattr(request, field.api, value) + + if request.override_settings is None: + request.override_settings = {} + + overridden_settings = infotext_utils.get_override_settings(params) + for _, setting_name, value in overridden_settings: + if setting_name not in request.override_settings: + request.override_settings[setting_name] = value + + if script_runner is not None and mentioned_script_args is not None: + indexes = {v: i for i, v in enumerate(script_runner.inputs)} + script_fields = ((field, indexes[field.component]) for field in possible_fields if field.component in indexes) + + for field, index in script_fields: + value = get_field_value(field, params) + + if value is None: + continue + + mentioned_script_args[index] = value + + return params + + def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI): + task_id = txt2imgreq.force_task_id or create_task_id("txt2img") + + script_runner = scripts.scripts_txt2img + + infotext_script_args = {} + self.apply_infotext(txt2imgreq, "txt2img", script_runner=script_runner, mentioned_script_args=infotext_script_args) + + selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner) + sampler, scheduler = sd_samplers.get_sampler_and_scheduler(txt2imgreq.sampler_name or txt2imgreq.sampler_index, txt2imgreq.scheduler) + + populate = txt2imgreq.copy(update={ # Override __init__ params + "sampler_name": validate_sampler_name(sampler), + "do_not_save_samples": not txt2imgreq.save_images, + "do_not_save_grid": not txt2imgreq.save_images, + }) + if populate.sampler_name: + populate.sampler_index = None # prevent a warning later on + + if not populate.scheduler and scheduler != "Automatic": + populate.scheduler = scheduler + + args = vars(populate) + args.pop('script_name', None) + args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them + args.pop('alwayson_scripts', None) + args.pop('infotext', None) + + script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner, input_script_args=infotext_script_args) + + send_images = args.pop('send_images', True) + args.pop('save_images', None) + + add_task_to_queue(task_id) + + with self.queue_lock: + with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p: + p.is_api = True + p.scripts = script_runner + p.outpath_grids = opts.outdir_txt2img_grids + p.outpath_samples = opts.outdir_txt2img_samples + + try: + shared.state.begin(job="scripts_txt2img") + start_task(task_id) + if selectable_scripts is not None: + p.script_args = script_args + processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here + else: + p.script_args = tuple(script_args) # Need to pass args as tuple here + processed = process_images(p) + finish_task(task_id) + finally: + shared.state.end() + shared.total_tqdm.clear() + + b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] + + return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js()) + + def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI): + task_id = img2imgreq.force_task_id or create_task_id("img2img") + + init_images = img2imgreq.init_images + if init_images is None: + raise HTTPException(status_code=404, detail="Init image not found") + + mask = img2imgreq.mask + if mask: + mask = decode_base64_to_image(mask) + + script_runner = scripts.scripts_img2img + + infotext_script_args = {} + self.apply_infotext(img2imgreq, "img2img", script_runner=script_runner, mentioned_script_args=infotext_script_args) + + selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner) + sampler, scheduler = sd_samplers.get_sampler_and_scheduler(img2imgreq.sampler_name or img2imgreq.sampler_index, img2imgreq.scheduler) + + populate = img2imgreq.copy(update={ # Override __init__ params + "sampler_name": validate_sampler_name(sampler), + "do_not_save_samples": not img2imgreq.save_images, + "do_not_save_grid": not img2imgreq.save_images, + "mask": mask, + }) + if populate.sampler_name: + populate.sampler_index = None # prevent a warning later on + + if not populate.scheduler and scheduler != "Automatic": + populate.scheduler = scheduler + + args = vars(populate) + args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine. + args.pop('script_name', None) + args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them + args.pop('alwayson_scripts', None) + args.pop('infotext', None) + + script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner, input_script_args=infotext_script_args) + + send_images = args.pop('send_images', True) + args.pop('save_images', None) + + add_task_to_queue(task_id) + + with self.queue_lock: + with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p: + p.init_images = [decode_base64_to_image(x) for x in init_images] + p.is_api = True + p.scripts = script_runner + p.outpath_grids = opts.outdir_img2img_grids + p.outpath_samples = opts.outdir_img2img_samples + + try: + shared.state.begin(job="scripts_img2img") + start_task(task_id) + if selectable_scripts is not None: + p.script_args = script_args + processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here + else: + p.script_args = tuple(script_args) # Need to pass args as tuple here + processed = process_images(p) + finish_task(task_id) + finally: + shared.state.end() + shared.total_tqdm.clear() + + b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] + + if not img2imgreq.include_init_images: + img2imgreq.init_images = None + img2imgreq.mask = None + + return models.ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js()) + + def extras_single_image_api(self, req: models.ExtrasSingleImageRequest): + reqDict = setUpscalers(req) + + reqDict['image'] = decode_base64_to_image(reqDict['image']) + + with self.queue_lock: + result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict) + + return models.ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1]) + + def extras_batch_images_api(self, req: models.ExtrasBatchImagesRequest): + reqDict = setUpscalers(req) + + image_list = reqDict.pop('imageList', []) + image_folder = [decode_base64_to_image(x.data) for x in image_list] + + with self.queue_lock: + result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict) + + return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) + + def pnginfoapi(self, req: models.PNGInfoRequest): + image = decode_base64_to_image(req.image.strip()) + if image is None: + return models.PNGInfoResponse(info="") + + geninfo, items = images.read_info_from_image(image) + if geninfo is None: + geninfo = "" + + params = infotext_utils.parse_generation_parameters(geninfo) + script_callbacks.infotext_pasted_callback(geninfo, params) + + return models.PNGInfoResponse(info=geninfo, items=items, parameters=params) + + def progressapi(self, req: models.ProgressRequest = Depends()): + # copy from check_progress_call of ui.py + + if shared.state.job_count == 0: + return models.ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo) + + # avoid dividing zero + progress = 0.01 + + if shared.state.job_count > 0: + progress += shared.state.job_no / shared.state.job_count + if shared.state.sampling_steps > 0: + progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps + + time_since_start = time.time() - shared.state.time_start + eta = (time_since_start/progress) + eta_relative = eta-time_since_start + + progress = min(progress, 1) + + shared.state.set_current_image() + + current_image = None + if shared.state.current_image and not req.skip_current_image: + current_image = encode_pil_to_base64(shared.state.current_image) + + return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo, current_task=current_task) + + def interrogateapi(self, interrogatereq: models.InterrogateRequest): + image_b64 = interrogatereq.image + if image_b64 is None: + raise HTTPException(status_code=404, detail="Image not found") + + img = decode_base64_to_image(image_b64) + img = img.convert('RGB') + + # Override object param + with self.queue_lock: + if interrogatereq.model == "clip": + processed = shared.interrogator.interrogate(img) + elif interrogatereq.model == "deepdanbooru": + processed = deepbooru.model.tag(img) + else: + raise HTTPException(status_code=404, detail="Model not found") + + return models.InterrogateResponse(caption=processed) + + def interruptapi(self): + shared.state.interrupt() + + return {} + + def unloadapi(self): + sd_models.unload_model_weights() + + return {} + + def reloadapi(self): + sd_models.send_model_to_device(shared.sd_model) + + return {} + + def skip(self): + shared.state.skip() + + def get_config(self): + options = {} + for key in shared.opts.data.keys(): + metadata = shared.opts.data_labels.get(key) + if(metadata is not None): + options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)}) + else: + options.update({key: shared.opts.data.get(key, None)}) + + return options + + def set_config(self, req: dict[str, Any]): + checkpoint_name = req.get("sd_model_checkpoint", None) + if checkpoint_name is not None and checkpoint_name not in sd_models.checkpoint_aliases: + raise RuntimeError(f"model {checkpoint_name!r} not found") + + for k, v in req.items(): + shared.opts.set(k, v, is_api=True) + + shared.opts.save(shared.config_filename) + return + + def get_cmd_flags(self): + return vars(shared.cmd_opts) + + def get_samplers(self): + return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers] + + def get_schedulers(self): + return [ + { + "name": scheduler.name, + "label": scheduler.label, + "aliases": scheduler.aliases, + "default_rho": scheduler.default_rho, + "need_inner_model": scheduler.need_inner_model, + } + for scheduler in sd_schedulers.schedulers] + + def get_upscalers(self): + return [ + { + "name": upscaler.name, + "model_name": upscaler.scaler.model_name, + "model_path": upscaler.data_path, + "model_url": None, + "scale": upscaler.scale, + } + for upscaler in shared.sd_upscalers + ] + + def get_latent_upscale_modes(self): + return [ + { + "name": upscale_mode, + } + for upscale_mode in [*(shared.latent_upscale_modes or {})] + ] + + def get_sd_models(self): + import modules.sd_models as sd_models + return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in sd_models.checkpoints_list.values()] + + def get_sd_vaes(self): + import modules.sd_vae as sd_vae + return [{"model_name": x, "filename": sd_vae.vae_dict[x]} for x in sd_vae.vae_dict.keys()] + + def get_hypernetworks(self): + return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks] + + def get_face_restorers(self): + return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers] + + def get_realesrgan_models(self): + return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)] + + def get_prompt_styles(self): + styleList = [] + for k in shared.prompt_styles.styles: + style = shared.prompt_styles.styles[k] + styleList.append({"name":style[0], "prompt": style[1], "negative_prompt": style[2]}) + + return styleList + + def get_embeddings(self): + db = sd_hijack.model_hijack.embedding_db + + def convert_embedding(embedding): + return { + "step": embedding.step, + "sd_checkpoint": embedding.sd_checkpoint, + "sd_checkpoint_name": embedding.sd_checkpoint_name, + "shape": embedding.shape, + "vectors": embedding.vectors, + } + + def convert_embeddings(embeddings): + return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()} + + return { + "loaded": convert_embeddings(db.word_embeddings), + "skipped": convert_embeddings(db.skipped_embeddings), + } + + def refresh_embeddings(self): + with self.queue_lock: + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) + + def refresh_checkpoints(self): + with self.queue_lock: + shared.refresh_checkpoints() + + def refresh_vae(self): + with self.queue_lock: + shared_items.refresh_vae_list() + + def create_embedding(self, args: dict): + try: + shared.state.begin(job="create_embedding") + filename = create_embedding(**args) # create empty embedding + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used + return models.CreateResponse(info=f"create embedding filename: {filename}") + except AssertionError as e: + return models.TrainResponse(info=f"create embedding error: {e}") + finally: + shared.state.end() + + + def create_hypernetwork(self, args: dict): + try: + shared.state.begin(job="create_hypernetwork") + filename = create_hypernetwork(**args) # create empty embedding + return models.CreateResponse(info=f"create hypernetwork filename: {filename}") + except AssertionError as e: + return models.TrainResponse(info=f"create hypernetwork error: {e}") + finally: + shared.state.end() + + def train_embedding(self, args: dict): + try: + shared.state.begin(job="train_embedding") + apply_optimizations = shared.opts.training_xattention_optimizations + error = None + filename = '' + if not apply_optimizations: + sd_hijack.undo_optimizations() + try: + embedding, filename = train_embedding(**args) # can take a long time to complete + except Exception as e: + error = e + finally: + if not apply_optimizations: + sd_hijack.apply_optimizations() + return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") + except Exception as msg: + return models.TrainResponse(info=f"train embedding error: {msg}") + finally: + shared.state.end() + + def train_hypernetwork(self, args: dict): + try: + shared.state.begin(job="train_hypernetwork") + shared.loaded_hypernetworks = [] + apply_optimizations = shared.opts.training_xattention_optimizations + error = None + filename = '' + if not apply_optimizations: + sd_hijack.undo_optimizations() + try: + hypernetwork, filename = train_hypernetwork(**args) + except Exception as e: + error = e + finally: + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) + if not apply_optimizations: + sd_hijack.apply_optimizations() + shared.state.end() + return models.TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}") + except Exception as exc: + return models.TrainResponse(info=f"train embedding error: {exc}") + finally: + shared.state.end() + + def get_memory(self): + try: + import os + import psutil + process = psutil.Process(os.getpid()) + res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values + ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe + ram = { 'free': ram_total - res.rss, 'used': res.rss, 'total': ram_total } + except Exception as err: + ram = { 'error': f'{err}' } + try: + import torch + if torch.cuda.is_available(): + s = torch.cuda.mem_get_info() + system = { 'free': s[0], 'used': s[1] - s[0], 'total': s[1] } + s = dict(torch.cuda.memory_stats(shared.device)) + allocated = { 'current': s['allocated_bytes.all.current'], 'peak': s['allocated_bytes.all.peak'] } + reserved = { 'current': s['reserved_bytes.all.current'], 'peak': s['reserved_bytes.all.peak'] } + active = { 'current': s['active_bytes.all.current'], 'peak': s['active_bytes.all.peak'] } + inactive = { 'current': s['inactive_split_bytes.all.current'], 'peak': s['inactive_split_bytes.all.peak'] } + warnings = { 'retries': s['num_alloc_retries'], 'oom': s['num_ooms'] } + cuda = { + 'system': system, + 'active': active, + 'allocated': allocated, + 'reserved': reserved, + 'inactive': inactive, + 'events': warnings, + } + else: + cuda = {'error': 'unavailable'} + except Exception as err: + cuda = {'error': f'{err}'} + return models.MemoryResponse(ram=ram, cuda=cuda) + + def get_extensions_list(self): + from modules import extensions + extensions.list_extensions() + ext_list = [] + for ext in extensions.extensions: + ext: extensions.Extension + ext.read_info_from_repo() + if ext.remote is not None: + ext_list.append({ + "name": ext.name, + "remote": ext.remote, + "branch": ext.branch, + "commit_hash":ext.commit_hash, + "commit_date":ext.commit_date, + "version":ext.version, + "enabled":ext.enabled + }) + return ext_list + + def launch(self, server_name, port, root_path): + self.app.include_router(self.router) + uvicorn.run( + self.app, + host=server_name, + port=port, + timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, + root_path=root_path, + ssl_keyfile=shared.cmd_opts.tls_keyfile, + ssl_certfile=shared.cmd_opts.tls_certfile + ) + + def kill_webui(self): + restart.stop_program() + + def restart_webui(self): + if restart.is_restartable(): + restart.restart_program() + return Response(status_code=501) + + def stop_webui(request): + shared.state.server_command = "stop" + return Response("Stopping.") + diff --git a/modules/api/models.py b/modules/api/models.py new file mode 100644 index 00000000000..79c4cbbbbbc --- /dev/null +++ b/modules/api/models.py @@ -0,0 +1,329 @@ +import inspect + +from pydantic import BaseModel, Field, create_model +from typing import Any, Optional, Literal +from inflection import underscore +from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img +from modules.shared import sd_upscalers, opts, parser + +API_NOT_ALLOWED = [ + "self", + "kwargs", + "sd_model", + "outpath_samples", + "outpath_grids", + "sampler_index", + # "do_not_save_samples", + # "do_not_save_grid", + "extra_generation_params", + "overlay_images", + "do_not_reload_embeddings", + "seed_enable_extras", + "prompt_for_display", + "sampler_noise_scheduler_override", + "ddim_discretize" +] + +class ModelDef(BaseModel): + """Assistance Class for Pydantic Dynamic Model Generation""" + + field: str + field_alias: str + field_type: Any + field_value: Any + field_exclude: bool = False + + +class PydanticModelGenerator: + """ + Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about: + source_data is a snapshot of the default values produced by the class + params are the names of the actual keys required by __init__ + """ + + def __init__( + self, + model_name: str = None, + class_instance = None, + additional_fields = None, + ): + def field_type_generator(k, v): + field_type = v.annotation + + if field_type == 'Image': + # images are sent as base64 strings via API + field_type = 'str' + + return Optional[field_type] + + def merge_class_params(class_): + all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_))) + parameters = {} + for classes in all_classes: + parameters = {**parameters, **inspect.signature(classes.__init__).parameters} + return parameters + + self._model_name = model_name + self._class_data = merge_class_params(class_instance) + + self._model_def = [ + ModelDef( + field=underscore(k), + field_alias=k, + field_type=field_type_generator(k, v), + field_value=None if isinstance(v.default, property) else v.default + ) + for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED + ] + + for fields in additional_fields: + self._model_def.append(ModelDef( + field=underscore(fields["key"]), + field_alias=fields["key"], + field_type=fields["type"], + field_value=fields["default"], + field_exclude=fields["exclude"] if "exclude" in fields else False)) + + def generate_model(self): + """ + Creates a pydantic BaseModel + from the json and overrides provided at initialization + """ + fields = { + d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def + } + DynamicModel = create_model(self._model_name, **fields) + DynamicModel.__config__.allow_population_by_field_name = True + DynamicModel.__config__.allow_mutation = True + return DynamicModel + +StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( + "StableDiffusionProcessingTxt2Img", + StableDiffusionProcessingTxt2Img, + [ + {"key": "sampler_index", "type": str, "default": "Euler"}, + {"key": "script_name", "type": str, "default": None}, + {"key": "script_args", "type": list, "default": []}, + {"key": "send_images", "type": bool, "default": True}, + {"key": "save_images", "type": bool, "default": False}, + {"key": "alwayson_scripts", "type": dict, "default": {}}, + {"key": "force_task_id", "type": str, "default": None}, + {"key": "infotext", "type": str, "default": None}, + ] +).generate_model() + +StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( + "StableDiffusionProcessingImg2Img", + StableDiffusionProcessingImg2Img, + [ + {"key": "sampler_index", "type": str, "default": "Euler"}, + {"key": "init_images", "type": list, "default": None}, + {"key": "denoising_strength", "type": float, "default": 0.75}, + {"key": "mask", "type": str, "default": None}, + {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, + {"key": "script_name", "type": str, "default": None}, + {"key": "script_args", "type": list, "default": []}, + {"key": "send_images", "type": bool, "default": True}, + {"key": "save_images", "type": bool, "default": False}, + {"key": "alwayson_scripts", "type": dict, "default": {}}, + {"key": "force_task_id", "type": str, "default": None}, + {"key": "infotext", "type": str, "default": None}, + ] +).generate_model() + +class TextToImageResponse(BaseModel): + images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + parameters: dict + info: str + +class ImageToImageResponse(BaseModel): + images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + parameters: dict + info: str + +class ExtrasBaseRequest(BaseModel): + resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.") + show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?") + gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.") + codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.") + codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.") + upscaling_resize: float = Field(default=2, title="Upscaling Factor", gt=0, description="By how much to upscale the image, only used when resize_mode=0.") + upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.") + upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.") + upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?") + upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") + upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") + extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.") + upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?") + +class ExtraBaseResponse(BaseModel): + html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.") + +class ExtrasSingleImageRequest(ExtrasBaseRequest): + image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.") + +class ExtrasSingleImageResponse(ExtraBaseResponse): + image: str = Field(default=None, title="Image", description="The generated image in base64 format.") + +class FileData(BaseModel): + data: str = Field(title="File data", description="Base64 representation of the file") + name: str = Field(title="File name") + +class ExtrasBatchImagesRequest(ExtrasBaseRequest): + imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings") + +class ExtrasBatchImagesResponse(ExtraBaseResponse): + images: list[str] = Field(title="Images", description="The generated images in base64 format.") + +class PNGInfoRequest(BaseModel): + image: str = Field(title="Image", description="The base64 encoded PNG image") + +class PNGInfoResponse(BaseModel): + info: str = Field(title="Image info", description="A string with the parameters used to generate the image") + items: dict = Field(title="Items", description="A dictionary containing all the other fields the image had") + parameters: dict = Field(title="Parameters", description="A dictionary with parsed generation info fields") + +class ProgressRequest(BaseModel): + skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization") + +class ProgressResponse(BaseModel): + progress: float = Field(title="Progress", description="The progress with a range of 0 to 1") + eta_relative: float = Field(title="ETA in secs") + state: dict = Field(title="State", description="The current state snapshot") + current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.") + textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.") + +class InterrogateRequest(BaseModel): + image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.") + model: str = Field(default="clip", title="Model", description="The interrogate model used.") + +class InterrogateResponse(BaseModel): + caption: str = Field(default=None, title="Caption", description="The generated caption for the image.") + +class TrainResponse(BaseModel): + info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.") + +class CreateResponse(BaseModel): + info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.") + +fields = {} +for key, metadata in opts.data_labels.items(): + value = opts.data.get(key) + optType = opts.typemap.get(type(metadata.default), type(metadata.default)) if metadata.default else Any + + if metadata is not None: + fields.update({key: (Optional[optType], Field(default=metadata.default, description=metadata.label))}) + else: + fields.update({key: (Optional[optType], Field())}) + +OptionsModel = create_model("Options", **fields) + +flags = {} +_options = vars(parser)['_option_string_actions'] +for key in _options: + if(_options[key].dest != 'help'): + flag = _options[key] + _type = str + if _options[key].default is not None: + _type = type(_options[key].default) + flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))}) + +FlagsModel = create_model("Flags", **flags) + +class SamplerItem(BaseModel): + name: str = Field(title="Name") + aliases: list[str] = Field(title="Aliases") + options: dict[str, str] = Field(title="Options") + +class SchedulerItem(BaseModel): + name: str = Field(title="Name") + label: str = Field(title="Label") + aliases: Optional[list[str]] = Field(title="Aliases") + default_rho: Optional[float] = Field(title="Default Rho") + need_inner_model: Optional[bool] = Field(title="Needs Inner Model") + +class UpscalerItem(BaseModel): + name: str = Field(title="Name") + model_name: Optional[str] = Field(title="Model Name") + model_path: Optional[str] = Field(title="Path") + model_url: Optional[str] = Field(title="URL") + scale: Optional[float] = Field(title="Scale") + +class LatentUpscalerModeItem(BaseModel): + name: str = Field(title="Name") + +class SDModelItem(BaseModel): + title: str = Field(title="Title") + model_name: str = Field(title="Model Name") + hash: Optional[str] = Field(title="Short hash") + sha256: Optional[str] = Field(title="sha256 hash") + filename: str = Field(title="Filename") + config: Optional[str] = Field(title="Config file") + +class SDVaeItem(BaseModel): + model_name: str = Field(title="Model Name") + filename: str = Field(title="Filename") + +class HypernetworkItem(BaseModel): + name: str = Field(title="Name") + path: Optional[str] = Field(title="Path") + +class FaceRestorerItem(BaseModel): + name: str = Field(title="Name") + cmd_dir: Optional[str] = Field(title="Path") + +class RealesrganItem(BaseModel): + name: str = Field(title="Name") + path: Optional[str] = Field(title="Path") + scale: Optional[int] = Field(title="Scale") + +class PromptStyleItem(BaseModel): + name: str = Field(title="Name") + prompt: Optional[str] = Field(title="Prompt") + negative_prompt: Optional[str] = Field(title="Negative Prompt") + + +class EmbeddingItem(BaseModel): + step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available") + sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available") + sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead") + shape: int = Field(title="Shape", description="The length of each individual vector in the embedding") + vectors: int = Field(title="Vectors", description="The number of vectors in the embedding") + +class EmbeddingsResponse(BaseModel): + loaded: dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model") + skipped: dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)") + +class MemoryResponse(BaseModel): + ram: dict = Field(title="RAM", description="System memory stats") + cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats") + + +class ScriptsList(BaseModel): + txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)") + img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)") + + +class ScriptArg(BaseModel): + label: str = Field(default=None, title="Label", description="Name of the argument in UI") + value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument") + minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI") + maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI") + step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI") + choices: Optional[list[str]] = Field(default=None, title="Choices", description="Possible values for the argument") + + +class ScriptInfo(BaseModel): + name: str = Field(default=None, title="Name", description="Script name") + is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script") + is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script") + args: list[ScriptArg] = Field(title="Arguments", description="List of script's arguments") + +class ExtensionItem(BaseModel): + name: str = Field(title="Name", description="Extension name") + remote: str = Field(title="Remote", description="Extension Repository URL") + branch: str = Field(title="Branch", description="Extension Repository Branch") + commit_hash: str = Field(title="Commit Hash", description="Extension Repository Commit Hash") + version: str = Field(title="Version", description="Extension Version") + commit_date: str = Field(title="Commit Date", description="Extension Repository Commit Date") + enabled: bool = Field(title="Enabled", description="Flag specifying whether this extension is enabled") diff --git a/modules/artists.py b/modules/artists.py deleted file mode 100644 index 3612758b4ac..00000000000 --- a/modules/artists.py +++ /dev/null @@ -1,25 +0,0 @@ -import os.path -import csv -from collections import namedtuple - -Artist = namedtuple("Artist", ['name', 'weight', 'category']) - - -class ArtistsDatabase: - def __init__(self, filename): - self.cats = set() - self.artists = [] - - if not os.path.exists(filename): - return - - with open(filename, "r", newline='', encoding="utf8") as file: - reader = csv.DictReader(file) - - for row in reader: - artist = Artist(row["artist"], float(row["score"]), row["category"]) - self.artists.append(artist) - self.cats.add(artist.category) - - def categories(self): - return sorted(self.cats) diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py deleted file mode 100644 index 737e1a7613c..00000000000 --- a/modules/bsrgan_model.py +++ /dev/null @@ -1,76 +0,0 @@ -import os.path -import sys -import traceback - -import PIL.Image -import numpy as np -import torch -from basicsr.utils.download_util import load_file_from_url - -import modules.upscaler -from modules import devices, modelloader -from modules.bsrgan_model_arch import RRDBNet - - -class UpscalerBSRGAN(modules.upscaler.Upscaler): - def __init__(self, dirname): - self.name = "BSRGAN" - self.model_name = "BSRGAN 4x" - self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth" - self.user_path = dirname - super().__init__() - model_paths = self.find_models(ext_filter=[".pt", ".pth"]) - scalers = [] - if len(model_paths) == 0: - scaler_data = modules.upscaler.UpscalerData(self.model_name, self.model_url, self, 4) - scalers.append(scaler_data) - for file in model_paths: - if "http" in file: - name = self.model_name - else: - name = modelloader.friendly_name(file) - try: - scaler_data = modules.upscaler.UpscalerData(name, file, self, 4) - scalers.append(scaler_data) - except Exception: - print(f"Error loading BSRGAN model: {file}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - self.scalers = scalers - - def do_upscale(self, img: PIL.Image, selected_file): - torch.cuda.empty_cache() - model = self.load_model(selected_file) - if model is None: - return img - model.to(devices.device_bsrgan) - torch.cuda.empty_cache() - img = np.array(img) - img = img[:, :, ::-1] - img = np.moveaxis(img, 2, 0) / 255 - img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(devices.device_bsrgan) - with torch.no_grad(): - output = model(img) - output = output.squeeze().float().cpu().clamp_(0, 1).numpy() - output = 255. * np.moveaxis(output, 0, 2) - output = output.astype(np.uint8) - output = output[:, :, ::-1] - torch.cuda.empty_cache() - return PIL.Image.fromarray(output, 'RGB') - - def load_model(self, path: str): - if "http" in path: - filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name, - progress=True) - else: - filename = path - if not os.path.exists(filename) or filename is None: - print(f"BSRGAN: Unable to load model from {filename}", file=sys.stderr) - return None - model = RRDBNet(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4) # define network - model.load_state_dict(torch.load(filename), strict=True) - model.eval() - for k, v in model.named_parameters(): - v.requires_grad = False - return model - diff --git a/modules/bsrgan_model_arch.py b/modules/bsrgan_model_arch.py deleted file mode 100644 index cb4d1c133c1..00000000000 --- a/modules/bsrgan_model_arch.py +++ /dev/null @@ -1,102 +0,0 @@ -import functools -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.nn.init as init - - -def initialize_weights(net_l, scale=1): - if not isinstance(net_l, list): - net_l = [net_l] - for net in net_l: - for m in net.modules(): - if isinstance(m, nn.Conv2d): - init.kaiming_normal_(m.weight, a=0, mode='fan_in') - m.weight.data *= scale # for residual block - if m.bias is not None: - m.bias.data.zero_() - elif isinstance(m, nn.Linear): - init.kaiming_normal_(m.weight, a=0, mode='fan_in') - m.weight.data *= scale - if m.bias is not None: - m.bias.data.zero_() - elif isinstance(m, nn.BatchNorm2d): - init.constant_(m.weight, 1) - init.constant_(m.bias.data, 0.0) - - -def make_layer(block, n_layers): - layers = [] - for _ in range(n_layers): - layers.append(block()) - return nn.Sequential(*layers) - - -class ResidualDenseBlock_5C(nn.Module): - def __init__(self, nf=64, gc=32, bias=True): - super(ResidualDenseBlock_5C, self).__init__() - # gc: growth channel, i.e. intermediate channels - self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias) - self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias) - self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias) - self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias) - self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias) - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - # initialization - initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) - - def forward(self, x): - x1 = self.lrelu(self.conv1(x)) - x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) - x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) - x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) - x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) - return x5 * 0.2 + x - - -class RRDB(nn.Module): - '''Residual in Residual Dense Block''' - - def __init__(self, nf, gc=32): - super(RRDB, self).__init__() - self.RDB1 = ResidualDenseBlock_5C(nf, gc) - self.RDB2 = ResidualDenseBlock_5C(nf, gc) - self.RDB3 = ResidualDenseBlock_5C(nf, gc) - - def forward(self, x): - out = self.RDB1(x) - out = self.RDB2(out) - out = self.RDB3(out) - return out * 0.2 + x - - -class RRDBNet(nn.Module): - def __init__(self, in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=4): - super(RRDBNet, self).__init__() - RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) - self.sf = sf - - self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) - self.RRDB_trunk = make_layer(RRDB_block_f, nb) - self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - #### upsampling - self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - if self.sf==4: - self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True) - - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - def forward(self, x): - fea = self.conv_first(x) - trunk = self.trunk_conv(self.RRDB_trunk(fea)) - fea = fea + trunk - - fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest'))) - if self.sf==4: - fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))) - out = self.conv_last(self.lrelu(self.HRconv(fea))) - - return out \ No newline at end of file diff --git a/modules/cache.py b/modules/cache.py new file mode 100644 index 00000000000..f4e5f702b42 --- /dev/null +++ b/modules/cache.py @@ -0,0 +1,123 @@ +import json +import os +import os.path +import threading + +import diskcache +import tqdm + +from modules.paths import data_path, script_path + +cache_filename = os.environ.get('SD_WEBUI_CACHE_FILE', os.path.join(data_path, "cache.json")) +cache_dir = os.environ.get('SD_WEBUI_CACHE_DIR', os.path.join(data_path, "cache")) +caches = {} +cache_lock = threading.Lock() + + +def dump_cache(): + """old function for dumping cache to disk; does nothing since diskcache.""" + + pass + + +def make_cache(subsection: str) -> diskcache.Cache: + return diskcache.Cache( + os.path.join(cache_dir, subsection), + size_limit=2**32, # 4 GB, culling oldest first + disk_min_file_size=2**18, # keep up to 256KB in Sqlite + ) + + +def convert_old_cached_data(): + try: + with open(cache_filename, "r", encoding="utf8") as file: + data = json.load(file) + except FileNotFoundError: + return + except Exception: + os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json")) + print('[ERROR] issue occurred while trying to read cache.json; old cache has been moved to tmp/cache.json') + return + + total_count = sum(len(keyvalues) for keyvalues in data.values()) + + with tqdm.tqdm(total=total_count, desc="converting cache") as progress: + for subsection, keyvalues in data.items(): + cache_obj = caches.get(subsection) + if cache_obj is None: + cache_obj = make_cache(subsection) + caches[subsection] = cache_obj + + for key, value in keyvalues.items(): + cache_obj[key] = value + progress.update(1) + + +def cache(subsection): + """ + Retrieves or initializes a cache for a specific subsection. + + Parameters: + subsection (str): The subsection identifier for the cache. + + Returns: + diskcache.Cache: The cache data for the specified subsection. + """ + + cache_obj = caches.get(subsection) + if not cache_obj: + with cache_lock: + if not os.path.exists(cache_dir) and os.path.isfile(cache_filename): + convert_old_cached_data() + + cache_obj = caches.get(subsection) + if not cache_obj: + cache_obj = make_cache(subsection) + caches[subsection] = cache_obj + + return cache_obj + + +def cached_data_for_file(subsection, title, filename, func): + """ + Retrieves or generates data for a specific file, using a caching mechanism. + + Parameters: + subsection (str): The subsection of the cache to use. + title (str): The title of the data entry in the subsection of the cache. + filename (str): The path to the file to be checked for modifications. + func (callable): A function that generates the data if it is not available in the cache. + + Returns: + dict or None: The cached or generated data, or None if data generation fails. + + The `cached_data_for_file` function implements a caching mechanism for data stored in files. + It checks if the data associated with the given `title` is present in the cache and compares the + modification time of the file with the cached modification time. If the file has been modified, + the cache is considered invalid and the data is regenerated using the provided `func`. + Otherwise, the cached data is returned. + + If the data generation fails, None is returned to indicate the failure. Otherwise, the generated + or cached data is returned as a dictionary. + """ + + existing_cache = cache(subsection) + ondisk_mtime = os.path.getmtime(filename) + + entry = existing_cache.get(title) + if entry: + cached_mtime = entry.get("mtime", 0) + if ondisk_mtime > cached_mtime: + entry = None + + if not entry or 'value' not in entry: + value = func() + if value is None: + return None + + entry = {'mtime': ondisk_mtime, 'value': value} + existing_cache[title] = entry + + dump_cache() + + return entry['value'] diff --git a/modules/call_queue.py b/modules/call_queue.py new file mode 100644 index 00000000000..555c35312dd --- /dev/null +++ b/modules/call_queue.py @@ -0,0 +1,134 @@ +import os.path +from functools import wraps +import html +import time + +from modules import shared, progress, errors, devices, fifo_lock, profiling + +queue_lock = fifo_lock.FIFOLock() + + +def wrap_queued_call(func): + def f(*args, **kwargs): + with queue_lock: + res = func(*args, **kwargs) + + return res + + return f + + +def wrap_gradio_gpu_call(func, extra_outputs=None): + @wraps(func) + def f(*args, **kwargs): + + # if the first argument is a string that says "task(...)", it is treated as a job id + if args and type(args[0]) == str and args[0].startswith("task(") and args[0].endswith(")"): + id_task = args[0] + progress.add_task_to_queue(id_task) + else: + id_task = None + + with queue_lock: + shared.state.begin(job=id_task) + progress.start_task(id_task) + + try: + res = func(*args, **kwargs) + progress.record_results(id_task, res) + finally: + progress.finish_task(id_task) + + shared.state.end() + + return res + + return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True) + + +def wrap_gradio_call(func, extra_outputs=None, add_stats=False): + @wraps(func) + def f(*args, **kwargs): + try: + res = func(*args, **kwargs) + finally: + shared.state.skipped = False + shared.state.interrupted = False + shared.state.stopping_generation = False + shared.state.job_count = 0 + shared.state.job = "" + return res + + return wrap_gradio_call_no_job(f, extra_outputs, add_stats) + + +def wrap_gradio_call_no_job(func, extra_outputs=None, add_stats=False): + @wraps(func) + def f(*args, extra_outputs_array=extra_outputs, **kwargs): + run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats + if run_memmon: + shared.mem_mon.monitor() + t = time.perf_counter() + + try: + res = list(func(*args, **kwargs)) + except Exception as e: + # When printing out our debug argument list, + # do not print out more than a 100 KB of text + max_debug_str_len = 131072 + message = "Error completing request" + arg_str = f"Arguments: {args} {kwargs}"[:max_debug_str_len] + if len(arg_str) > max_debug_str_len: + arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)" + errors.report(f"{message}\n{arg_str}", exc_info=True) + + if extra_outputs_array is None: + extra_outputs_array = [None, ''] + + error_message = f'{type(e).__name__}: {e}' + res = extra_outputs_array + [f"
    {html.escape(error_message)}
    "] + + devices.torch_gc() + + if not add_stats: + return tuple(res) + + elapsed = time.perf_counter() - t + elapsed_m = int(elapsed // 60) + elapsed_s = elapsed % 60 + elapsed_text = f"{elapsed_s:.1f} sec." + if elapsed_m > 0: + elapsed_text = f"{elapsed_m} min. "+elapsed_text + + if run_memmon: + mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()} + active_peak = mem_stats['active_peak'] + reserved_peak = mem_stats['reserved_peak'] + sys_peak = mem_stats['system_peak'] + sys_total = mem_stats['total'] + sys_pct = sys_peak/max(sys_total, 1) * 100 + + toltip_a = "Active: peak amount of video memory used during generation (excluding cached data)" + toltip_r = "Reserved: total amount of video memory allocated by the Torch library " + toltip_sys = "System: peak amount of video memory allocated by all running programs, out of total capacity" + + text_a = f"A: {active_peak/1024:.2f} GB" + text_r = f"R: {reserved_peak/1024:.2f} GB" + text_sys = f"Sys: {sys_peak/1024:.1f}/{sys_total/1024:g} GB ({sys_pct:.1f}%)" + + vram_html = f"

    {text_a}, {text_r}, {text_sys}

    " + else: + vram_html = '' + + if shared.opts.profiling_enable and os.path.exists(shared.opts.profiling_filename): + profiling_html = f"

    [ Profile ]

    " + else: + profiling_html = '' + + # last item is always HTML + res[-1] += f"

    Time taken: {elapsed_text}

    {vram_html}{profiling_html}
    " + + return tuple(res) + + return f + diff --git a/modules/cmd_args.py b/modules/cmd_args.py new file mode 100644 index 00000000000..d71982b2c12 --- /dev/null +++ b/modules/cmd_args.py @@ -0,0 +1,128 @@ +import argparse +import json +import os +from modules.paths_internal import normalized_filepath, models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401 + +parser = argparse.ArgumentParser() + +parser.add_argument("-f", action='store_true', help=argparse.SUPPRESS) # allows running as root; implemented outside of webui +parser.add_argument("--update-all-extensions", action='store_true', help="launch.py argument: download updates for all extensions when starting the program") +parser.add_argument("--skip-python-version-check", action='store_true', help="launch.py argument: do not check python version") +parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly") +parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed") +parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed") +parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup") +parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing") +parser.add_argument("--log-startup", action='store_true', help="launch.py argument: print a detailed log of what's happening at startup") +parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation") +parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages") +parser.add_argument("--dump-sysinfo", action='store_true', help="launch.py argument: dump limited sysinfo file (without information about extensions, options) to disk and quit") +parser.add_argument("--loglevel", type=str, help="log level; one of: CRITICAL, ERROR, WARNING, INFO, DEBUG", default=None) +parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint") +parser.add_argument("--data-dir", type=normalized_filepath, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored") +parser.add_argument("--models-dir", type=normalized_filepath, default=None, help="base path where models are stored; overrides --data-dir") +parser.add_argument("--config", type=normalized_filepath, default=sd_default_config, help="path to config which constructs model",) +parser.add_argument("--ckpt", type=normalized_filepath, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",) +parser.add_argument("--ckpt-dir", type=normalized_filepath, default=None, help="Path to directory with stable diffusion checkpoints") +parser.add_argument("--vae-dir", type=normalized_filepath, default=None, help="Path to directory with VAE files") +parser.add_argument("--gfpgan-dir", type=normalized_filepath, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) +parser.add_argument("--gfpgan-model", type=normalized_filepath, help="GFPGAN model file name", default=None) +parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats") +parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats") +parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") +parser.add_argument("--max-batch-count", type=int, default=16, help="does not do anything") +parser.add_argument("--embeddings-dir", type=normalized_filepath, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") +parser.add_argument("--textual-inversion-templates-dir", type=normalized_filepath, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates") +parser.add_argument("--hypernetwork-dir", type=normalized_filepath, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory") +parser.add_argument("--localizations-dir", type=normalized_filepath, default=os.path.join(script_path, 'localizations'), help="localizations directory") +parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") +parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") +parser.add_argument("--medvram-sdxl", action='store_true', help="enable --medvram optimization just for SDXL models") +parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage") +parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM") +parser.add_argument("--always-batch-cond-uncond", action='store_true', help="does not do anything") +parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.") +parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "half", "autocast"], default="autocast") +parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.") +parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site") +parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None) +parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="") +parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict()) +parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options") +parser.add_argument("--codeformer-models-path", type=normalized_filepath, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer')) +parser.add_argument("--gfpgan-models-path", type=normalized_filepath, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN')) +parser.add_argument("--esrgan-models-path", type=normalized_filepath, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN')) +parser.add_argument("--bsrgan-models-path", type=normalized_filepath, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN')) +parser.add_argument("--realesrgan-models-path", type=normalized_filepath, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN')) +parser.add_argument("--dat-models-path", type=normalized_filepath, help="Path to directory with DAT model file(s).", default=os.path.join(models_path, 'DAT')) +parser.add_argument("--clip-models-path", type=normalized_filepath, help="Path to directory with CLIP model file(s).", default=None) +parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers") +parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work") +parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)") +parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything") +parser.add_argument("--opt-split-attention", action='store_true', help="prefer Doggettx's cross-attention layer optimization for automatic choice of optimization") +parser.add_argument("--opt-sub-quad-attention", action='store_true', help="prefer memory efficient sub-quadratic cross-attention layer optimization for automatic choice of optimization") +parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024) +parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None) +parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None) +parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="prefer InvokeAI's cross-attention layer optimization for automatic choice of optimization") +parser.add_argument("--opt-split-attention-v1", action='store_true', help="prefer older version of split attention optimization for automatic choice of optimization") +parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*") +parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*") +parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization") +parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI") +parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower) +parser.add_argument("--use-ipex", action="store_true", help="use Intel XPU as torch device") +parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model") +parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") +parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) +parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) +parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json')) +parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False) +parser.add_argument("--freeze-settings", action='store_true', help="disable editing of all settings globally", default=False) +parser.add_argument("--freeze-settings-in-sections", type=str, help='disable editing settings in specific sections of the settings page by specifying a comma-delimited list such like "saving-images,upscaling". The list of setting names can be found in the modules/shared_options.py file', default=None) +parser.add_argument("--freeze-specific-settings", type=str, help='disable editing of individual settings by specifying a comma-delimited list like "samples_save,samples_format". The list of setting names can be found in the config.json file', default=None) +parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json')) +parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option") +parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) +parser.add_argument("--gradio-auth-path", type=normalized_filepath, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None) +parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything') +parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything") +parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it", default=[data_path]) +parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last") +parser.add_argument("--styles-file", type=str, action='append', help="path or wildcard path of styles files, allow multiple entries.", default=[]) +parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False) +parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None) +parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False) +parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False) +parser.add_argument("--enable-console-prompts", action='store_true', help="does not do anything", default=False) # Legacy compatibility, use as default value shared.opts.enable_console_prompts +parser.add_argument('--vae-path', type=normalized_filepath, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None) +parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) +parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)") +parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) +parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests") +parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui") +parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI") +parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None) +parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False) +parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None) +parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None) +parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None) +parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None) +parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None) +parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None) +parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True) +parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the default in earlier versions") +parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers") +parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False) +parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False) +parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy') +parser.add_argument('--add-stop-route', action='store_true', help='does not do anything') +parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api') +parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn') +parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False) +parser.add_argument("--disable-extra-extensions", action='store_true', help="prevent all extensions except built-in from running regardless of any other settings", default=False) +parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui") +parser.add_argument("--unix-filenames-sanitization", action='store_true', help="allow any symbols except '/' in filenames. May conflict with your browser and file system") +parser.add_argument("--filenames-max-length", type=int, default=128, help='maximal length of filenames of saved images. If you override it, it can conflict with your file system') +parser.add_argument("--no-prompt-history", action='store_true', help="disable read prompt from last generation feature; settings this argument will not create '--data_path/params.txt' file") diff --git a/modules/codeformer/codeformer_arch.py b/modules/codeformer/codeformer_arch.py deleted file mode 100644 index 11dcc3ee765..00000000000 --- a/modules/codeformer/codeformer_arch.py +++ /dev/null @@ -1,278 +0,0 @@ -# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py - -import math -import numpy as np -import torch -from torch import nn, Tensor -import torch.nn.functional as F -from typing import Optional, List - -from modules.codeformer.vqgan_arch import * -from basicsr.utils import get_root_logger -from basicsr.utils.registry import ARCH_REGISTRY - -def calc_mean_std(feat, eps=1e-5): - """Calculate mean and std for adaptive_instance_normalization. - - Args: - feat (Tensor): 4D tensor. - eps (float): A small value added to the variance to avoid - divide-by-zero. Default: 1e-5. - """ - size = feat.size() - assert len(size) == 4, 'The input feature should be 4D tensor.' - b, c = size[:2] - feat_var = feat.view(b, c, -1).var(dim=2) + eps - feat_std = feat_var.sqrt().view(b, c, 1, 1) - feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) - return feat_mean, feat_std - - -def adaptive_instance_normalization(content_feat, style_feat): - """Adaptive instance normalization. - - Adjust the reference features to have the similar color and illuminations - as those in the degradate features. - - Args: - content_feat (Tensor): The reference feature. - style_feat (Tensor): The degradate features. - """ - size = content_feat.size() - style_mean, style_std = calc_mean_std(style_feat) - content_mean, content_std = calc_mean_std(content_feat) - normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) - return normalized_feat * style_std.expand(size) + style_mean.expand(size) - - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, x, mask=None): - if mask is None: - mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) - not_mask = ~mask - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack( - (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos_y = torch.stack( - (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(F"activation should be relu/gelu, not {activation}.") - - -class TransformerSALayer(nn.Module): - def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"): - super().__init__() - self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) - # Implementation of Feedforward model - MLP - self.linear1 = nn.Linear(embed_dim, dim_mlp) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_mlp, embed_dim) - - self.norm1 = nn.LayerNorm(embed_dim) - self.norm2 = nn.LayerNorm(embed_dim) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward(self, tgt, - tgt_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - - # self attention - tgt2 = self.norm1(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - - # ffn - tgt2 = self.norm2(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout2(tgt2) - return tgt - -class Fuse_sft_block(nn.Module): - def __init__(self, in_ch, out_ch): - super().__init__() - self.encode_enc = ResBlock(2*in_ch, out_ch) - - self.scale = nn.Sequential( - nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) - - self.shift = nn.Sequential( - nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) - - def forward(self, enc_feat, dec_feat, w=1): - enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) - scale = self.scale(enc_feat) - shift = self.shift(enc_feat) - residual = w * (dec_feat * scale + shift) - out = dec_feat + residual - return out - - -@ARCH_REGISTRY.register() -class CodeFormer(VQAutoEncoder): - def __init__(self, dim_embd=512, n_head=8, n_layers=9, - codebook_size=1024, latent_size=256, - connect_list=['32', '64', '128', '256'], - fix_modules=['quantize','generator']): - super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size) - - if fix_modules is not None: - for module in fix_modules: - for param in getattr(self, module).parameters(): - param.requires_grad = False - - self.connect_list = connect_list - self.n_layers = n_layers - self.dim_embd = dim_embd - self.dim_mlp = dim_embd*2 - - self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) - self.feat_emb = nn.Linear(256, self.dim_embd) - - # transformer - self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) - for _ in range(self.n_layers)]) - - # logits_predict head - self.idx_pred_layer = nn.Sequential( - nn.LayerNorm(dim_embd), - nn.Linear(dim_embd, codebook_size, bias=False)) - - self.channels = { - '16': 512, - '32': 256, - '64': 256, - '128': 128, - '256': 128, - '512': 64, - } - - # after second residual block for > 16, before attn layer for ==16 - self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18} - # after first residual block for > 16, before attn layer for ==16 - self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21} - - # fuse_convs_dict - self.fuse_convs_dict = nn.ModuleDict() - for f_size in self.connect_list: - in_ch = self.channels[f_size] - self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) - - def _init_weights(self, module): - if isinstance(module, (nn.Linear, nn.Embedding)): - module.weight.data.normal_(mean=0.0, std=0.02) - if isinstance(module, nn.Linear) and module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - - def forward(self, x, w=0, detach_16=True, code_only=False, adain=False): - # ################### Encoder ##################### - enc_feat_dict = {} - out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] - for i, block in enumerate(self.encoder.blocks): - x = block(x) - if i in out_list: - enc_feat_dict[str(x.shape[-1])] = x.clone() - - lq_feat = x - # ################# Transformer ################### - # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) - pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1) - # BCHW -> BC(HW) -> (HW)BC - feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1)) - query_emb = feat_emb - # Transformer encoder - for layer in self.ft_layers: - query_emb = layer(query_emb, query_pos=pos_emb) - - # output logits - logits = self.idx_pred_layer(query_emb) # (hw)bn - logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n - - if code_only: # for training stage II - # logits doesn't need softmax before cross_entropy loss - return logits, lq_feat - - # ################# Quantization ################### - # if self.training: - # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) - # # b(hw)c -> bc(hw) -> bchw - # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) - # ------------ - soft_one_hot = F.softmax(logits, dim=2) - _, top_idx = torch.topk(soft_one_hot, 1, dim=2) - quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256]) - # preserve gradients - # quant_feat = lq_feat + (quant_feat - lq_feat).detach() - - if detach_16: - quant_feat = quant_feat.detach() # for training stage III - if adain: - quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) - - # ################## Generator #################### - x = quant_feat - fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] - - for i, block in enumerate(self.generator.blocks): - x = block(x) - if i in fuse_list: # fuse after i-th block - f_size = str(x.shape[-1]) - if w>0: - x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w) - out = x - # logits doesn't need softmax before cross_entropy loss - return out, logits, lq_feat \ No newline at end of file diff --git a/modules/codeformer/vqgan_arch.py b/modules/codeformer/vqgan_arch.py deleted file mode 100644 index c06c590ca61..00000000000 --- a/modules/codeformer/vqgan_arch.py +++ /dev/null @@ -1,437 +0,0 @@ -# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py - -''' -VQGAN code, adapted from the original created by the Unleashing Transformers authors: -https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py - -''' -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -import copy -from basicsr.utils import get_root_logger -from basicsr.utils.registry import ARCH_REGISTRY - -def normalize(in_channels): - return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - - -@torch.jit.script -def swish(x): - return x*torch.sigmoid(x) - - -# Define VQVAE classes -class VectorQuantizer(nn.Module): - def __init__(self, codebook_size, emb_dim, beta): - super(VectorQuantizer, self).__init__() - self.codebook_size = codebook_size # number of embeddings - self.emb_dim = emb_dim # dimension of embedding - self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 - self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) - self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size) - - def forward(self, z): - # reshape z -> (batch, height, width, channel) and flatten - z = z.permute(0, 2, 3, 1).contiguous() - z_flattened = z.view(-1, self.emb_dim) - - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \ - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) - - mean_distance = torch.mean(d) - # find closest encodings - # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) - min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False) - # [0-1], higher score, higher confidence - min_encoding_scores = torch.exp(-min_encoding_scores/10) - - min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z) - min_encodings.scatter_(1, min_encoding_indices, 1) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) - # compute loss for embedding - loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2) - # preserve gradients - z_q = z + (z_q - z).detach() - - # perplexity - e_mean = torch.mean(min_encodings, dim=0) - perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q, loss, { - "perplexity": perplexity, - "min_encodings": min_encodings, - "min_encoding_indices": min_encoding_indices, - "min_encoding_scores": min_encoding_scores, - "mean_distance": mean_distance - } - - def get_codebook_feat(self, indices, shape): - # input indices: batch*token_num -> (batch*token_num)*1 - # shape: batch, height, width, channel - indices = indices.view(-1,1) - min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) - min_encodings.scatter_(1, indices, 1) - # get quantized latent vectors - z_q = torch.matmul(min_encodings.float(), self.embedding.weight) - - if shape is not None: # reshape back to match original input shape - z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() - - return z_q - - -class GumbelQuantizer(nn.Module): - def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0): - super().__init__() - self.codebook_size = codebook_size # number of embeddings - self.emb_dim = emb_dim # dimension of embedding - self.straight_through = straight_through - self.temperature = temp_init - self.kl_weight = kl_weight - self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits - self.embed = nn.Embedding(codebook_size, emb_dim) - - def forward(self, z): - hard = self.straight_through if self.training else True - - logits = self.proj(z) - - soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) - - z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) - - # + kl divergence to the prior loss - qy = F.softmax(logits, dim=1) - diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() - min_encoding_indices = soft_one_hot.argmax(dim=1) - - return z_q, diff, { - "min_encoding_indices": min_encoding_indices - } - - -class Downsample(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) - - def forward(self, x): - pad = (0, 1, 0, 1) - x = torch.nn.functional.pad(x, pad, mode="constant", value=0) - x = self.conv(x) - return x - - -class Upsample(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) - - def forward(self, x): - x = F.interpolate(x, scale_factor=2.0, mode="nearest") - x = self.conv(x) - - return x - - -class ResBlock(nn.Module): - def __init__(self, in_channels, out_channels=None): - super(ResBlock, self).__init__() - self.in_channels = in_channels - self.out_channels = in_channels if out_channels is None else out_channels - self.norm1 = normalize(in_channels) - self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) - self.norm2 = normalize(out_channels) - self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) - if self.in_channels != self.out_channels: - self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) - - def forward(self, x_in): - x = x_in - x = self.norm1(x) - x = swish(x) - x = self.conv1(x) - x = self.norm2(x) - x = swish(x) - x = self.conv2(x) - if self.in_channels != self.out_channels: - x_in = self.conv_out(x_in) - - return x + x_in - - -class AttnBlock(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.in_channels = in_channels - - self.norm = normalize(in_channels) - self.q = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.k = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.v = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.proj_out = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - - def forward(self, x): - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - b, c, h, w = q.shape - q = q.reshape(b, c, h*w) - q = q.permute(0, 2, 1) - k = k.reshape(b, c, h*w) - w_ = torch.bmm(q, k) - w_ = w_ * (int(c)**(-0.5)) - w_ = F.softmax(w_, dim=2) - - # attend to values - v = v.reshape(b, c, h*w) - w_ = w_.permute(0, 2, 1) - h_ = torch.bmm(v, w_) - h_ = h_.reshape(b, c, h, w) - - h_ = self.proj_out(h_) - - return x+h_ - - -class Encoder(nn.Module): - def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions): - super().__init__() - self.nf = nf - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.attn_resolutions = attn_resolutions - - curr_res = self.resolution - in_ch_mult = (1,)+tuple(ch_mult) - - blocks = [] - # initial convultion - blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) - - # residual and downsampling blocks, with attention on smaller res (16x16) - for i in range(self.num_resolutions): - block_in_ch = nf * in_ch_mult[i] - block_out_ch = nf * ch_mult[i] - for _ in range(self.num_res_blocks): - blocks.append(ResBlock(block_in_ch, block_out_ch)) - block_in_ch = block_out_ch - if curr_res in attn_resolutions: - blocks.append(AttnBlock(block_in_ch)) - - if i != self.num_resolutions - 1: - blocks.append(Downsample(block_in_ch)) - curr_res = curr_res // 2 - - # non-local attention block - blocks.append(ResBlock(block_in_ch, block_in_ch)) - blocks.append(AttnBlock(block_in_ch)) - blocks.append(ResBlock(block_in_ch, block_in_ch)) - - # normalise and convert to latent size - blocks.append(normalize(block_in_ch)) - blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1)) - self.blocks = nn.ModuleList(blocks) - - def forward(self, x): - for block in self.blocks: - x = block(x) - - return x - - -class Generator(nn.Module): - def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions): - super().__init__() - self.nf = nf - self.ch_mult = ch_mult - self.num_resolutions = len(self.ch_mult) - self.num_res_blocks = res_blocks - self.resolution = img_size - self.attn_resolutions = attn_resolutions - self.in_channels = emb_dim - self.out_channels = 3 - block_in_ch = self.nf * self.ch_mult[-1] - curr_res = self.resolution // 2 ** (self.num_resolutions-1) - - blocks = [] - # initial conv - blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)) - - # non-local attention block - blocks.append(ResBlock(block_in_ch, block_in_ch)) - blocks.append(AttnBlock(block_in_ch)) - blocks.append(ResBlock(block_in_ch, block_in_ch)) - - for i in reversed(range(self.num_resolutions)): - block_out_ch = self.nf * self.ch_mult[i] - - for _ in range(self.num_res_blocks): - blocks.append(ResBlock(block_in_ch, block_out_ch)) - block_in_ch = block_out_ch - - if curr_res in self.attn_resolutions: - blocks.append(AttnBlock(block_in_ch)) - - if i != 0: - blocks.append(Upsample(block_in_ch)) - curr_res = curr_res * 2 - - blocks.append(normalize(block_in_ch)) - blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1)) - - self.blocks = nn.ModuleList(blocks) - - - def forward(self, x): - for block in self.blocks: - x = block(x) - - return x - - -@ARCH_REGISTRY.register() -class VQAutoEncoder(nn.Module): - def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256, - beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None): - super().__init__() - logger = get_root_logger() - self.in_channels = 3 - self.nf = nf - self.n_blocks = res_blocks - self.codebook_size = codebook_size - self.embed_dim = emb_dim - self.ch_mult = ch_mult - self.resolution = img_size - self.attn_resolutions = attn_resolutions - self.quantizer_type = quantizer - self.encoder = Encoder( - self.in_channels, - self.nf, - self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, - self.attn_resolutions - ) - if self.quantizer_type == "nearest": - self.beta = beta #0.25 - self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta) - elif self.quantizer_type == "gumbel": - self.gumbel_num_hiddens = emb_dim - self.straight_through = gumbel_straight_through - self.kl_weight = gumbel_kl_weight - self.quantize = GumbelQuantizer( - self.codebook_size, - self.embed_dim, - self.gumbel_num_hiddens, - self.straight_through, - self.kl_weight - ) - self.generator = Generator( - self.nf, - self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, - self.attn_resolutions - ) - - if model_path is not None: - chkpt = torch.load(model_path, map_location='cpu') - if 'params_ema' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema']) - logger.info(f'vqgan is loaded from: {model_path} [params_ema]') - elif 'params' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) - logger.info(f'vqgan is loaded from: {model_path} [params]') - else: - raise ValueError(f'Wrong params!') - - - def forward(self, x): - x = self.encoder(x) - quant, codebook_loss, quant_stats = self.quantize(x) - x = self.generator(quant) - return x, codebook_loss, quant_stats - - - -# patch based discriminator -@ARCH_REGISTRY.register() -class VQGANDiscriminator(nn.Module): - def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None): - super().__init__() - - layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)] - ndf_mult = 1 - ndf_mult_prev = 1 - for n in range(1, n_layers): # gradually increase the number of filters - ndf_mult_prev = ndf_mult - ndf_mult = min(2 ** n, 8) - layers += [ - nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False), - nn.BatchNorm2d(ndf * ndf_mult), - nn.LeakyReLU(0.2, True) - ] - - ndf_mult_prev = ndf_mult - ndf_mult = min(2 ** n_layers, 8) - - layers += [ - nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False), - nn.BatchNorm2d(ndf * ndf_mult), - nn.LeakyReLU(0.2, True) - ] - - layers += [ - nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map - self.main = nn.Sequential(*layers) - - if model_path is not None: - chkpt = torch.load(model_path, map_location='cpu') - if 'params_d' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d']) - elif 'params' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) - else: - raise ValueError(f'Wrong params!') - - def forward(self, x): - return self.main(x) \ No newline at end of file diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index e6d9fa4f4c3..0b353353be2 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -1,140 +1,64 @@ -import os -import sys -import traceback +from __future__ import annotations -import cv2 -import torch - -import modules.face_restoration -import modules.shared -from modules import shared, devices, modelloader -from modules.paths import script_path, models_path - -# codeformer people made a choice to include modified basicsr library to their project which makes -# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN. -# I am making a choice to include some files from codeformer to work around this issue. -model_dir = "Codeformer" -model_path = os.path.join(models_path, model_dir) -model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth' - -have_codeformer = False -codeformer = None - - -def setup_model(dirname): - global model_path - if not os.path.exists(model_path): - os.makedirs(model_path) - - path = modules.paths.paths.get("CodeFormer", None) - if path is None: - return - - try: - from torchvision.transforms.functional import normalize - from modules.codeformer.codeformer_arch import CodeFormer - from basicsr.utils.download_util import load_file_from_url - from basicsr.utils import imwrite, img2tensor, tensor2img - from facelib.utils.face_restoration_helper import FaceRestoreHelper - from modules.shared import cmd_opts - - net_class = CodeFormer - - class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration): - def name(self): - return "CodeFormer" - - def __init__(self, dirname): - self.net = None - self.face_helper = None - self.cmd_dir = dirname +import logging - def create_models(self): - - if self.net is not None and self.face_helper is not None: - self.net.to(devices.device_codeformer) - return self.net, self.face_helper - model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth') - if len(model_paths) != 0: - ckpt_path = model_paths[0] - else: - print("Unable to load codeformer model.") - return None, None - net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer) - checkpoint = torch.load(ckpt_path)['params_ema'] - net.load_state_dict(checkpoint) - net.eval() - - face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer) - - self.net = net - self.face_helper = face_helper - - return net, face_helper - - def send_model_to(self, device): - self.net.to(device) - self.face_helper.face_det.to(device) - self.face_helper.face_parse.to(device) - - def restore(self, np_image, w=None): - np_image = np_image[:, :, ::-1] - - original_resolution = np_image.shape[0:2] - - self.create_models() - if self.net is None or self.face_helper is None: - return np_image +import torch - self.send_model_to(devices.device_codeformer) +from modules import ( + devices, + errors, + face_restoration, + face_restoration_utils, + modelloader, + shared, +) - self.face_helper.clean_all() - self.face_helper.read_image(np_image) - self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) - self.face_helper.align_warp_face() +logger = logging.getLogger(__name__) - for idx, cropped_face in enumerate(self.face_helper.cropped_faces): - cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) - normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) - cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer) +model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth' +model_download_name = 'codeformer-v0.1.0.pth' - try: - with torch.no_grad(): - output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0] - restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) - del output - torch.cuda.empty_cache() - except Exception as error: - print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr) - restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) +# used by e.g. postprocessing_codeformer.py +codeformer: face_restoration.FaceRestoration | None = None - restored_face = restored_face.astype('uint8') - self.face_helper.add_restored_face(restored_face) - self.face_helper.get_inverse_affine(None) +class FaceRestorerCodeFormer(face_restoration_utils.CommonFaceRestoration): + def name(self): + return "CodeFormer" - restored_img = self.face_helper.paste_faces_to_input_image() - restored_img = restored_img[:, :, ::-1] + def load_net(self) -> torch.Module: + for model_path in modelloader.load_models( + model_path=self.model_path, + model_url=model_url, + command_path=self.model_path, + download_name=model_download_name, + ext_filter=['.pth'], + ): + return modelloader.load_spandrel_model( + model_path, + device=devices.device_codeformer, + expected_architecture='CodeFormer', + ).model + raise ValueError("No codeformer model found") - if original_resolution != restored_img.shape[0:2]: - restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR) + def get_device(self): + return devices.device_codeformer - self.face_helper.clean_all() + def restore(self, np_image, w: float | None = None): + if w is None: + w = getattr(shared.opts, "code_former_weight", 0.5) - if shared.opts.face_restoration_unload: - self.send_model_to(devices.cpu) + def restore_face(cropped_face_t): + assert self.net is not None + return self.net(cropped_face_t, weight=w, adain=True)[0] - return restored_img + return self.restore_with_helper(np_image, restore_face) - global have_codeformer - have_codeformer = True - global codeformer +def setup_model(dirname: str) -> None: + global codeformer + try: codeformer = FaceRestorerCodeFormer(dirname) shared.face_restorers.append(codeformer) - except Exception: - print("Error setting up CodeFormer:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - - # sys.path = stored_sys_path + errors.report("Error setting up CodeFormer", exc_info=True) diff --git a/modules/config_states.py b/modules/config_states.py new file mode 100644 index 00000000000..651793c7f6f --- /dev/null +++ b/modules/config_states.py @@ -0,0 +1,198 @@ +""" +Supports saving and restoring webui and extensions from a known working set of commits +""" + +import os +import json +import tqdm + +from datetime import datetime +import git + +from modules import shared, extensions, errors +from modules.paths_internal import script_path, config_states_dir + +all_config_states = {} + + +def list_config_states(): + global all_config_states + + all_config_states.clear() + os.makedirs(config_states_dir, exist_ok=True) + + config_states = [] + for filename in os.listdir(config_states_dir): + if filename.endswith(".json"): + path = os.path.join(config_states_dir, filename) + try: + with open(path, "r", encoding="utf-8") as f: + j = json.load(f) + assert "created_at" in j, '"created_at" does not exist' + j["filepath"] = path + config_states.append(j) + except Exception as e: + print(f'[ERROR]: Config states {path}, {e}') + + config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True) + + for cs in config_states: + timestamp = datetime.fromtimestamp(cs["created_at"]).strftime('%Y-%m-%d %H:%M:%S') + name = cs.get("name", "Config") + full_name = f"{name}: {timestamp}" + all_config_states[full_name] = cs + + return all_config_states + + +def get_webui_config(): + webui_repo = None + + try: + if os.path.exists(os.path.join(script_path, ".git")): + webui_repo = git.Repo(script_path) + except Exception: + errors.report(f"Error reading webui git info from {script_path}", exc_info=True) + + webui_remote = None + webui_commit_hash = None + webui_commit_date = None + webui_branch = None + if webui_repo and not webui_repo.bare: + try: + webui_remote = next(webui_repo.remote().urls, None) + head = webui_repo.head.commit + webui_commit_date = webui_repo.head.commit.committed_date + webui_commit_hash = head.hexsha + webui_branch = webui_repo.active_branch.name + + except Exception: + webui_remote = None + + return { + "remote": webui_remote, + "commit_hash": webui_commit_hash, + "commit_date": webui_commit_date, + "branch": webui_branch, + } + + +def get_extension_config(): + ext_config = {} + + for ext in extensions.extensions: + ext.read_info_from_repo() + + entry = { + "name": ext.name, + "path": ext.path, + "enabled": ext.enabled, + "is_builtin": ext.is_builtin, + "remote": ext.remote, + "commit_hash": ext.commit_hash, + "commit_date": ext.commit_date, + "branch": ext.branch, + "have_info_from_repo": ext.have_info_from_repo + } + + ext_config[ext.name] = entry + + return ext_config + + +def get_config(): + creation_time = datetime.now().timestamp() + webui_config = get_webui_config() + ext_config = get_extension_config() + + return { + "created_at": creation_time, + "webui": webui_config, + "extensions": ext_config + } + + +def restore_webui_config(config): + print("* Restoring webui state...") + + if "webui" not in config: + print("Error: No webui data saved to config") + return + + webui_config = config["webui"] + + if "commit_hash" not in webui_config: + print("Error: No commit saved to webui config") + return + + webui_commit_hash = webui_config.get("commit_hash", None) + webui_repo = None + + try: + if os.path.exists(os.path.join(script_path, ".git")): + webui_repo = git.Repo(script_path) + except Exception: + errors.report(f"Error reading webui git info from {script_path}", exc_info=True) + return + + try: + webui_repo.git.fetch(all=True) + webui_repo.git.reset(webui_commit_hash, hard=True) + print(f"* Restored webui to commit {webui_commit_hash}.") + except Exception: + errors.report(f"Error restoring webui to commit{webui_commit_hash}") + + +def restore_extension_config(config): + print("* Restoring extension state...") + + if "extensions" not in config: + print("Error: No extension data saved to config") + return + + ext_config = config["extensions"] + + results = [] + disabled = [] + + for ext in tqdm.tqdm(extensions.extensions): + if ext.is_builtin: + continue + + ext.read_info_from_repo() + current_commit = ext.commit_hash + + if ext.name not in ext_config: + ext.disabled = True + disabled.append(ext.name) + results.append((ext, current_commit[:8], False, "Saved extension state not found in config, marking as disabled")) + continue + + entry = ext_config[ext.name] + + if "commit_hash" in entry and entry["commit_hash"]: + try: + ext.fetch_and_reset_hard(entry["commit_hash"]) + ext.read_info_from_repo() + if current_commit != entry["commit_hash"]: + results.append((ext, current_commit[:8], True, entry["commit_hash"][:8])) + except Exception as ex: + results.append((ext, current_commit[:8], False, ex)) + else: + results.append((ext, current_commit[:8], False, "No commit hash found in config")) + + if not entry.get("enabled", False): + ext.disabled = True + disabled.append(ext.name) + else: + ext.disabled = False + + shared.opts.disabled_extensions = disabled + shared.opts.save(shared.config_filename) + + print("* Finished restoring extensions. Results:") + for ext, prev_commit, success, result in results: + if success: + print(f" + {ext.name}: {prev_commit} -> {result}") + else: + print(f" ! {ext.name}: FAILURE ({result})") diff --git a/modules/dat_model.py b/modules/dat_model.py new file mode 100644 index 00000000000..495d5f4937d --- /dev/null +++ b/modules/dat_model.py @@ -0,0 +1,79 @@ +import os + +from modules import modelloader, errors +from modules.shared import cmd_opts, opts +from modules.upscaler import Upscaler, UpscalerData +from modules.upscaler_utils import upscale_with_model + + +class UpscalerDAT(Upscaler): + def __init__(self, user_path): + self.name = "DAT" + self.user_path = user_path + self.scalers = [] + super().__init__() + + for file in self.find_models(ext_filter=[".pt", ".pth"]): + name = modelloader.friendly_name(file) + scaler_data = UpscalerData(name, file, upscaler=self, scale=None) + self.scalers.append(scaler_data) + + for model in get_dat_models(self): + if model.name in opts.dat_enabled_models: + self.scalers.append(model) + + def do_upscale(self, img, path): + try: + info = self.load_model(path) + except Exception: + errors.report(f"Unable to load DAT model {path}", exc_info=True) + return img + + model_descriptor = modelloader.load_spandrel_model( + info.local_data_path, + device=self.device, + prefer_half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling), + expected_architecture="DAT", + ) + return upscale_with_model( + model_descriptor, + img, + tile_size=opts.DAT_tile, + tile_overlap=opts.DAT_tile_overlap, + ) + + def load_model(self, path): + for scaler in self.scalers: + if scaler.data_path == path: + if scaler.local_data_path.startswith("http"): + scaler.local_data_path = modelloader.load_file_from_url( + scaler.data_path, + model_dir=self.model_download_path, + ) + if not os.path.exists(scaler.local_data_path): + raise FileNotFoundError(f"DAT data missing: {scaler.local_data_path}") + return scaler + raise ValueError(f"Unable to find model info: {path}") + + +def get_dat_models(scaler): + return [ + UpscalerData( + name="DAT x2", + path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x2.pth", + scale=2, + upscaler=scaler, + ), + UpscalerData( + name="DAT x3", + path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x3.pth", + scale=3, + upscaler=scaler, + ), + UpscalerData( + name="DAT x4", + path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x4.pth", + scale=4, + upscaler=scaler, + ), + ] diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 4ad334a125e..fb043feb296 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -1,173 +1,98 @@ -import os.path -from concurrent.futures import ProcessPoolExecutor -import multiprocessing -import time +import os import re +import torch +import numpy as np + +from modules import modelloader, paths, deepbooru_model, devices, images, shared + re_special = re.compile(r'([\\()])') -def get_deepbooru_tags(pil_image): - """ - This method is for running only one image at a time for simple use. Used to the img2img interrogate. - """ - from modules import shared # prevents circular reference - - try: - create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts()) - return get_tags_from_process(pil_image) - finally: - release_process() - - -OPT_INCLUDE_RANKS = "include_ranks" -def create_deepbooru_opts(): - from modules import shared - - return { - "use_spaces": shared.opts.deepbooru_use_spaces, - "use_escape": shared.opts.deepbooru_escape, - "alpha_sort": shared.opts.deepbooru_sort_alpha, - OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks, - } - - -def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts): - model, tags = get_deepbooru_tags_model() - while True: # while process is running, keep monitoring queue for new image - pil_image = queue.get() - if pil_image == "QUIT": - break - else: - deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts) - - -def create_deepbooru_process(threshold, deepbooru_opts): - """ - Creates deepbooru process. A queue is created to send images into the process. This enables multiple images - to be processed in a row without reloading the model or creating a new process. To return the data, a shared - dictionary is created to hold the tags created. To wait for tags to be returned, a value of -1 is assigned - to the dictionary and the method adding the image to the queue should wait for this value to be updated with - the tags. - """ - from modules import shared # prevents circular reference - shared.deepbooru_process_manager = multiprocessing.Manager() - shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue() - shared.deepbooru_process_return = shared.deepbooru_process_manager.dict() - shared.deepbooru_process_return["value"] = -1 - shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts)) - shared.deepbooru_process.start() - - -def get_tags_from_process(image): - from modules import shared - - shared.deepbooru_process_return["value"] = -1 - shared.deepbooru_process_queue.put(image) - while shared.deepbooru_process_return["value"] == -1: - time.sleep(0.2) - caption = shared.deepbooru_process_return["value"] - shared.deepbooru_process_return["value"] = -1 - - return caption - - -def release_process(): - """ - Stops the deepbooru process to return used memory - """ - from modules import shared # prevents circular reference - shared.deepbooru_process_queue.put("QUIT") - shared.deepbooru_process.join() - shared.deepbooru_process_queue = None - shared.deepbooru_process = None - shared.deepbooru_process_return = None - shared.deepbooru_process_manager = None - -def get_deepbooru_tags_model(): - import deepdanbooru as dd - import tensorflow as tf - import numpy as np - this_folder = os.path.dirname(__file__) - model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru')) - if not os.path.exists(os.path.join(model_path, 'project.json')): - # there is no point importing these every time - import zipfile - from basicsr.utils.download_util import load_file_from_url - load_file_from_url( - r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip", - model_path) - with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref: - zip_ref.extractall(model_path) - os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip")) - - tags = dd.project.load_tags_from_project(model_path) - model = dd.project.load_model_from_project( - model_path, compile_model=False - ) - return model, tags - - -def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts): - import deepdanbooru as dd - import tensorflow as tf - import numpy as np - - alpha_sort = deepbooru_opts['alpha_sort'] - use_spaces = deepbooru_opts['use_spaces'] - use_escape = deepbooru_opts['use_escape'] - include_ranks = deepbooru_opts['include_ranks'] - - width = model.input_shape[2] - height = model.input_shape[1] - image = np.array(pil_image) - image = tf.image.resize( - image, - size=(height, width), - method=tf.image.ResizeMethod.AREA, - preserve_aspect_ratio=True, - ) - image = image.numpy() # EagerTensor to np.array - image = dd.image.transform_and_pad_image(image, width, height) - image = image / 255.0 - image_shape = image.shape - image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2])) - - y = model.predict(image)[0] - - result_dict = {} - - for i, tag in enumerate(tags): - result_dict[tag] = y[i] - - unsorted_tags_in_theshold = [] - result_tags_print = [] - for tag in tags: - if result_dict[tag] >= threshold: + +class DeepDanbooru: + def __init__(self): + self.model = None + + def load(self): + if self.model is not None: + return + + files = modelloader.load_models( + model_path=os.path.join(paths.models_path, "torch_deepdanbooru"), + model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt', + ext_filter=[".pt"], + download_name='model-resnet_custom_v3.pt', + ) + + self.model = deepbooru_model.DeepDanbooruModel() + self.model.load_state_dict(torch.load(files[0], map_location="cpu")) + + self.model.eval() + self.model.to(devices.cpu, devices.dtype) + + def start(self): + self.load() + self.model.to(devices.device) + + def stop(self): + if not shared.opts.interrogate_keep_models_in_memory: + self.model.to(devices.cpu) + devices.torch_gc() + + def tag(self, pil_image): + self.start() + res = self.tag_multi(pil_image) + self.stop() + + return res + + def tag_multi(self, pil_image, force_disable_ranks=False): + threshold = shared.opts.interrogate_deepbooru_score_threshold + use_spaces = shared.opts.deepbooru_use_spaces + use_escape = shared.opts.deepbooru_escape + alpha_sort = shared.opts.deepbooru_sort_alpha + include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks + + pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512) + a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255 + + with torch.no_grad(), devices.autocast(): + x = torch.from_numpy(a).to(devices.device, devices.dtype) + y = self.model(x)[0].detach().cpu().numpy() + + probability_dict = {} + + for tag, probability in zip(self.model.tags, y): + if probability < threshold: + continue + if tag.startswith("rating:"): continue - unsorted_tags_in_theshold.append((result_dict[tag], tag)) - result_tags_print.append(f'{result_dict[tag]} {tag}') - - # sort tags - result_tags_out = [] - sort_ndx = 0 - if alpha_sort: - sort_ndx = 1 - - # sort by reverse by likelihood and normal for alpha, and format tag text as requested - unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort)) - for weight, tag in unsorted_tags_in_theshold: - # note: tag_outformat will still have a colon if include_ranks is True - tag_outformat = tag.replace(':', ' ') - if use_spaces: - tag_outformat = tag_outformat.replace('_', ' ') - if use_escape: - tag_outformat = re.sub(re_special, r'\\\1', tag_outformat) - if include_ranks: - tag_outformat = f"({tag_outformat}:{weight:.3f})" - - result_tags_out.append(tag_outformat) - - print('\n'.join(sorted(result_tags_print, reverse=True))) - - return ', '.join(result_tags_out) + + probability_dict[tag] = probability + + if alpha_sort: + tags = sorted(probability_dict) + else: + tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])] + + res = [] + + filtertags = {x.strip().replace(' ', '_') for x in shared.opts.deepbooru_filter_tags.split(",")} + + for tag in [x for x in tags if x not in filtertags]: + probability = probability_dict[tag] + tag_outformat = tag + if use_spaces: + tag_outformat = tag_outformat.replace('_', ' ') + if use_escape: + tag_outformat = re.sub(re_special, r'\\\1', tag_outformat) + if include_ranks: + tag_outformat = f"({tag_outformat}:{probability:.3f})" + + res.append(tag_outformat) + + return ", ".join(res) + + +model = DeepDanbooru() diff --git a/modules/deepbooru_model.py b/modules/deepbooru_model.py new file mode 100644 index 00000000000..83d2ff0902f --- /dev/null +++ b/modules/deepbooru_model.py @@ -0,0 +1,678 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from modules import devices + +# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more + + +class DeepDanbooruModel(nn.Module): + def __init__(self): + super(DeepDanbooruModel, self).__init__() + + self.tags = [] + + self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2)) + self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)) + self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) + self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64) + self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64) + self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) + self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64) + self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64) + self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) + self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64) + self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64) + self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256) + self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2)) + self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128) + self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2)) + self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128) + self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128) + self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512) + self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2)) + self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256) + self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2)) + self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2)) + self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2)) + self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256) + self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256) + self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024) + self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2)) + self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512) + self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2)) + self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048) + self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512) + self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512) + self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048) + self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512) + self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512) + self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048) + self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2)) + self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024) + self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2)) + self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096) + self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024) + self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024) + self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096) + self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024) + self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024) + self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096) + self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False) + + def forward(self, *inputs): + t_358, = inputs + t_359 = t_358.permute(*[0, 3, 1, 2]) + t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0) + t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded) + t_361 = F.relu(t_360) + t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf')) + t_362 = self.n_MaxPool_0(t_361) + t_363 = self.n_Conv_1(t_362) + t_364 = self.n_Conv_2(t_362) + t_365 = F.relu(t_364) + t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0) + t_366 = self.n_Conv_3(t_365_padded) + t_367 = F.relu(t_366) + t_368 = self.n_Conv_4(t_367) + t_369 = torch.add(t_368, t_363) + t_370 = F.relu(t_369) + t_371 = self.n_Conv_5(t_370) + t_372 = F.relu(t_371) + t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0) + t_373 = self.n_Conv_6(t_372_padded) + t_374 = F.relu(t_373) + t_375 = self.n_Conv_7(t_374) + t_376 = torch.add(t_375, t_370) + t_377 = F.relu(t_376) + t_378 = self.n_Conv_8(t_377) + t_379 = F.relu(t_378) + t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0) + t_380 = self.n_Conv_9(t_379_padded) + t_381 = F.relu(t_380) + t_382 = self.n_Conv_10(t_381) + t_383 = torch.add(t_382, t_377) + t_384 = F.relu(t_383) + t_385 = self.n_Conv_11(t_384) + t_386 = self.n_Conv_12(t_384) + t_387 = F.relu(t_386) + t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0) + t_388 = self.n_Conv_13(t_387_padded) + t_389 = F.relu(t_388) + t_390 = self.n_Conv_14(t_389) + t_391 = torch.add(t_390, t_385) + t_392 = F.relu(t_391) + t_393 = self.n_Conv_15(t_392) + t_394 = F.relu(t_393) + t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0) + t_395 = self.n_Conv_16(t_394_padded) + t_396 = F.relu(t_395) + t_397 = self.n_Conv_17(t_396) + t_398 = torch.add(t_397, t_392) + t_399 = F.relu(t_398) + t_400 = self.n_Conv_18(t_399) + t_401 = F.relu(t_400) + t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0) + t_402 = self.n_Conv_19(t_401_padded) + t_403 = F.relu(t_402) + t_404 = self.n_Conv_20(t_403) + t_405 = torch.add(t_404, t_399) + t_406 = F.relu(t_405) + t_407 = self.n_Conv_21(t_406) + t_408 = F.relu(t_407) + t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0) + t_409 = self.n_Conv_22(t_408_padded) + t_410 = F.relu(t_409) + t_411 = self.n_Conv_23(t_410) + t_412 = torch.add(t_411, t_406) + t_413 = F.relu(t_412) + t_414 = self.n_Conv_24(t_413) + t_415 = F.relu(t_414) + t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0) + t_416 = self.n_Conv_25(t_415_padded) + t_417 = F.relu(t_416) + t_418 = self.n_Conv_26(t_417) + t_419 = torch.add(t_418, t_413) + t_420 = F.relu(t_419) + t_421 = self.n_Conv_27(t_420) + t_422 = F.relu(t_421) + t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0) + t_423 = self.n_Conv_28(t_422_padded) + t_424 = F.relu(t_423) + t_425 = self.n_Conv_29(t_424) + t_426 = torch.add(t_425, t_420) + t_427 = F.relu(t_426) + t_428 = self.n_Conv_30(t_427) + t_429 = F.relu(t_428) + t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0) + t_430 = self.n_Conv_31(t_429_padded) + t_431 = F.relu(t_430) + t_432 = self.n_Conv_32(t_431) + t_433 = torch.add(t_432, t_427) + t_434 = F.relu(t_433) + t_435 = self.n_Conv_33(t_434) + t_436 = F.relu(t_435) + t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0) + t_437 = self.n_Conv_34(t_436_padded) + t_438 = F.relu(t_437) + t_439 = self.n_Conv_35(t_438) + t_440 = torch.add(t_439, t_434) + t_441 = F.relu(t_440) + t_442 = self.n_Conv_36(t_441) + t_443 = self.n_Conv_37(t_441) + t_444 = F.relu(t_443) + t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0) + t_445 = self.n_Conv_38(t_444_padded) + t_446 = F.relu(t_445) + t_447 = self.n_Conv_39(t_446) + t_448 = torch.add(t_447, t_442) + t_449 = F.relu(t_448) + t_450 = self.n_Conv_40(t_449) + t_451 = F.relu(t_450) + t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0) + t_452 = self.n_Conv_41(t_451_padded) + t_453 = F.relu(t_452) + t_454 = self.n_Conv_42(t_453) + t_455 = torch.add(t_454, t_449) + t_456 = F.relu(t_455) + t_457 = self.n_Conv_43(t_456) + t_458 = F.relu(t_457) + t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0) + t_459 = self.n_Conv_44(t_458_padded) + t_460 = F.relu(t_459) + t_461 = self.n_Conv_45(t_460) + t_462 = torch.add(t_461, t_456) + t_463 = F.relu(t_462) + t_464 = self.n_Conv_46(t_463) + t_465 = F.relu(t_464) + t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0) + t_466 = self.n_Conv_47(t_465_padded) + t_467 = F.relu(t_466) + t_468 = self.n_Conv_48(t_467) + t_469 = torch.add(t_468, t_463) + t_470 = F.relu(t_469) + t_471 = self.n_Conv_49(t_470) + t_472 = F.relu(t_471) + t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0) + t_473 = self.n_Conv_50(t_472_padded) + t_474 = F.relu(t_473) + t_475 = self.n_Conv_51(t_474) + t_476 = torch.add(t_475, t_470) + t_477 = F.relu(t_476) + t_478 = self.n_Conv_52(t_477) + t_479 = F.relu(t_478) + t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0) + t_480 = self.n_Conv_53(t_479_padded) + t_481 = F.relu(t_480) + t_482 = self.n_Conv_54(t_481) + t_483 = torch.add(t_482, t_477) + t_484 = F.relu(t_483) + t_485 = self.n_Conv_55(t_484) + t_486 = F.relu(t_485) + t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0) + t_487 = self.n_Conv_56(t_486_padded) + t_488 = F.relu(t_487) + t_489 = self.n_Conv_57(t_488) + t_490 = torch.add(t_489, t_484) + t_491 = F.relu(t_490) + t_492 = self.n_Conv_58(t_491) + t_493 = F.relu(t_492) + t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0) + t_494 = self.n_Conv_59(t_493_padded) + t_495 = F.relu(t_494) + t_496 = self.n_Conv_60(t_495) + t_497 = torch.add(t_496, t_491) + t_498 = F.relu(t_497) + t_499 = self.n_Conv_61(t_498) + t_500 = F.relu(t_499) + t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0) + t_501 = self.n_Conv_62(t_500_padded) + t_502 = F.relu(t_501) + t_503 = self.n_Conv_63(t_502) + t_504 = torch.add(t_503, t_498) + t_505 = F.relu(t_504) + t_506 = self.n_Conv_64(t_505) + t_507 = F.relu(t_506) + t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0) + t_508 = self.n_Conv_65(t_507_padded) + t_509 = F.relu(t_508) + t_510 = self.n_Conv_66(t_509) + t_511 = torch.add(t_510, t_505) + t_512 = F.relu(t_511) + t_513 = self.n_Conv_67(t_512) + t_514 = F.relu(t_513) + t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0) + t_515 = self.n_Conv_68(t_514_padded) + t_516 = F.relu(t_515) + t_517 = self.n_Conv_69(t_516) + t_518 = torch.add(t_517, t_512) + t_519 = F.relu(t_518) + t_520 = self.n_Conv_70(t_519) + t_521 = F.relu(t_520) + t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0) + t_522 = self.n_Conv_71(t_521_padded) + t_523 = F.relu(t_522) + t_524 = self.n_Conv_72(t_523) + t_525 = torch.add(t_524, t_519) + t_526 = F.relu(t_525) + t_527 = self.n_Conv_73(t_526) + t_528 = F.relu(t_527) + t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0) + t_529 = self.n_Conv_74(t_528_padded) + t_530 = F.relu(t_529) + t_531 = self.n_Conv_75(t_530) + t_532 = torch.add(t_531, t_526) + t_533 = F.relu(t_532) + t_534 = self.n_Conv_76(t_533) + t_535 = F.relu(t_534) + t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0) + t_536 = self.n_Conv_77(t_535_padded) + t_537 = F.relu(t_536) + t_538 = self.n_Conv_78(t_537) + t_539 = torch.add(t_538, t_533) + t_540 = F.relu(t_539) + t_541 = self.n_Conv_79(t_540) + t_542 = F.relu(t_541) + t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0) + t_543 = self.n_Conv_80(t_542_padded) + t_544 = F.relu(t_543) + t_545 = self.n_Conv_81(t_544) + t_546 = torch.add(t_545, t_540) + t_547 = F.relu(t_546) + t_548 = self.n_Conv_82(t_547) + t_549 = F.relu(t_548) + t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0) + t_550 = self.n_Conv_83(t_549_padded) + t_551 = F.relu(t_550) + t_552 = self.n_Conv_84(t_551) + t_553 = torch.add(t_552, t_547) + t_554 = F.relu(t_553) + t_555 = self.n_Conv_85(t_554) + t_556 = F.relu(t_555) + t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0) + t_557 = self.n_Conv_86(t_556_padded) + t_558 = F.relu(t_557) + t_559 = self.n_Conv_87(t_558) + t_560 = torch.add(t_559, t_554) + t_561 = F.relu(t_560) + t_562 = self.n_Conv_88(t_561) + t_563 = F.relu(t_562) + t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0) + t_564 = self.n_Conv_89(t_563_padded) + t_565 = F.relu(t_564) + t_566 = self.n_Conv_90(t_565) + t_567 = torch.add(t_566, t_561) + t_568 = F.relu(t_567) + t_569 = self.n_Conv_91(t_568) + t_570 = F.relu(t_569) + t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0) + t_571 = self.n_Conv_92(t_570_padded) + t_572 = F.relu(t_571) + t_573 = self.n_Conv_93(t_572) + t_574 = torch.add(t_573, t_568) + t_575 = F.relu(t_574) + t_576 = self.n_Conv_94(t_575) + t_577 = F.relu(t_576) + t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0) + t_578 = self.n_Conv_95(t_577_padded) + t_579 = F.relu(t_578) + t_580 = self.n_Conv_96(t_579) + t_581 = torch.add(t_580, t_575) + t_582 = F.relu(t_581) + t_583 = self.n_Conv_97(t_582) + t_584 = F.relu(t_583) + t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0) + t_585 = self.n_Conv_98(t_584_padded) + t_586 = F.relu(t_585) + t_587 = self.n_Conv_99(t_586) + t_588 = self.n_Conv_100(t_582) + t_589 = torch.add(t_587, t_588) + t_590 = F.relu(t_589) + t_591 = self.n_Conv_101(t_590) + t_592 = F.relu(t_591) + t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0) + t_593 = self.n_Conv_102(t_592_padded) + t_594 = F.relu(t_593) + t_595 = self.n_Conv_103(t_594) + t_596 = torch.add(t_595, t_590) + t_597 = F.relu(t_596) + t_598 = self.n_Conv_104(t_597) + t_599 = F.relu(t_598) + t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0) + t_600 = self.n_Conv_105(t_599_padded) + t_601 = F.relu(t_600) + t_602 = self.n_Conv_106(t_601) + t_603 = torch.add(t_602, t_597) + t_604 = F.relu(t_603) + t_605 = self.n_Conv_107(t_604) + t_606 = F.relu(t_605) + t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0) + t_607 = self.n_Conv_108(t_606_padded) + t_608 = F.relu(t_607) + t_609 = self.n_Conv_109(t_608) + t_610 = torch.add(t_609, t_604) + t_611 = F.relu(t_610) + t_612 = self.n_Conv_110(t_611) + t_613 = F.relu(t_612) + t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0) + t_614 = self.n_Conv_111(t_613_padded) + t_615 = F.relu(t_614) + t_616 = self.n_Conv_112(t_615) + t_617 = torch.add(t_616, t_611) + t_618 = F.relu(t_617) + t_619 = self.n_Conv_113(t_618) + t_620 = F.relu(t_619) + t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0) + t_621 = self.n_Conv_114(t_620_padded) + t_622 = F.relu(t_621) + t_623 = self.n_Conv_115(t_622) + t_624 = torch.add(t_623, t_618) + t_625 = F.relu(t_624) + t_626 = self.n_Conv_116(t_625) + t_627 = F.relu(t_626) + t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0) + t_628 = self.n_Conv_117(t_627_padded) + t_629 = F.relu(t_628) + t_630 = self.n_Conv_118(t_629) + t_631 = torch.add(t_630, t_625) + t_632 = F.relu(t_631) + t_633 = self.n_Conv_119(t_632) + t_634 = F.relu(t_633) + t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0) + t_635 = self.n_Conv_120(t_634_padded) + t_636 = F.relu(t_635) + t_637 = self.n_Conv_121(t_636) + t_638 = torch.add(t_637, t_632) + t_639 = F.relu(t_638) + t_640 = self.n_Conv_122(t_639) + t_641 = F.relu(t_640) + t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0) + t_642 = self.n_Conv_123(t_641_padded) + t_643 = F.relu(t_642) + t_644 = self.n_Conv_124(t_643) + t_645 = torch.add(t_644, t_639) + t_646 = F.relu(t_645) + t_647 = self.n_Conv_125(t_646) + t_648 = F.relu(t_647) + t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0) + t_649 = self.n_Conv_126(t_648_padded) + t_650 = F.relu(t_649) + t_651 = self.n_Conv_127(t_650) + t_652 = torch.add(t_651, t_646) + t_653 = F.relu(t_652) + t_654 = self.n_Conv_128(t_653) + t_655 = F.relu(t_654) + t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0) + t_656 = self.n_Conv_129(t_655_padded) + t_657 = F.relu(t_656) + t_658 = self.n_Conv_130(t_657) + t_659 = torch.add(t_658, t_653) + t_660 = F.relu(t_659) + t_661 = self.n_Conv_131(t_660) + t_662 = F.relu(t_661) + t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0) + t_663 = self.n_Conv_132(t_662_padded) + t_664 = F.relu(t_663) + t_665 = self.n_Conv_133(t_664) + t_666 = torch.add(t_665, t_660) + t_667 = F.relu(t_666) + t_668 = self.n_Conv_134(t_667) + t_669 = F.relu(t_668) + t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0) + t_670 = self.n_Conv_135(t_669_padded) + t_671 = F.relu(t_670) + t_672 = self.n_Conv_136(t_671) + t_673 = torch.add(t_672, t_667) + t_674 = F.relu(t_673) + t_675 = self.n_Conv_137(t_674) + t_676 = F.relu(t_675) + t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0) + t_677 = self.n_Conv_138(t_676_padded) + t_678 = F.relu(t_677) + t_679 = self.n_Conv_139(t_678) + t_680 = torch.add(t_679, t_674) + t_681 = F.relu(t_680) + t_682 = self.n_Conv_140(t_681) + t_683 = F.relu(t_682) + t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0) + t_684 = self.n_Conv_141(t_683_padded) + t_685 = F.relu(t_684) + t_686 = self.n_Conv_142(t_685) + t_687 = torch.add(t_686, t_681) + t_688 = F.relu(t_687) + t_689 = self.n_Conv_143(t_688) + t_690 = F.relu(t_689) + t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0) + t_691 = self.n_Conv_144(t_690_padded) + t_692 = F.relu(t_691) + t_693 = self.n_Conv_145(t_692) + t_694 = torch.add(t_693, t_688) + t_695 = F.relu(t_694) + t_696 = self.n_Conv_146(t_695) + t_697 = F.relu(t_696) + t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0) + t_698 = self.n_Conv_147(t_697_padded) + t_699 = F.relu(t_698) + t_700 = self.n_Conv_148(t_699) + t_701 = torch.add(t_700, t_695) + t_702 = F.relu(t_701) + t_703 = self.n_Conv_149(t_702) + t_704 = F.relu(t_703) + t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0) + t_705 = self.n_Conv_150(t_704_padded) + t_706 = F.relu(t_705) + t_707 = self.n_Conv_151(t_706) + t_708 = torch.add(t_707, t_702) + t_709 = F.relu(t_708) + t_710 = self.n_Conv_152(t_709) + t_711 = F.relu(t_710) + t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0) + t_712 = self.n_Conv_153(t_711_padded) + t_713 = F.relu(t_712) + t_714 = self.n_Conv_154(t_713) + t_715 = torch.add(t_714, t_709) + t_716 = F.relu(t_715) + t_717 = self.n_Conv_155(t_716) + t_718 = F.relu(t_717) + t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0) + t_719 = self.n_Conv_156(t_718_padded) + t_720 = F.relu(t_719) + t_721 = self.n_Conv_157(t_720) + t_722 = torch.add(t_721, t_716) + t_723 = F.relu(t_722) + t_724 = self.n_Conv_158(t_723) + t_725 = self.n_Conv_159(t_723) + t_726 = F.relu(t_725) + t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0) + t_727 = self.n_Conv_160(t_726_padded) + t_728 = F.relu(t_727) + t_729 = self.n_Conv_161(t_728) + t_730 = torch.add(t_729, t_724) + t_731 = F.relu(t_730) + t_732 = self.n_Conv_162(t_731) + t_733 = F.relu(t_732) + t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0) + t_734 = self.n_Conv_163(t_733_padded) + t_735 = F.relu(t_734) + t_736 = self.n_Conv_164(t_735) + t_737 = torch.add(t_736, t_731) + t_738 = F.relu(t_737) + t_739 = self.n_Conv_165(t_738) + t_740 = F.relu(t_739) + t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0) + t_741 = self.n_Conv_166(t_740_padded) + t_742 = F.relu(t_741) + t_743 = self.n_Conv_167(t_742) + t_744 = torch.add(t_743, t_738) + t_745 = F.relu(t_744) + t_746 = self.n_Conv_168(t_745) + t_747 = self.n_Conv_169(t_745) + t_748 = F.relu(t_747) + t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0) + t_749 = self.n_Conv_170(t_748_padded) + t_750 = F.relu(t_749) + t_751 = self.n_Conv_171(t_750) + t_752 = torch.add(t_751, t_746) + t_753 = F.relu(t_752) + t_754 = self.n_Conv_172(t_753) + t_755 = F.relu(t_754) + t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0) + t_756 = self.n_Conv_173(t_755_padded) + t_757 = F.relu(t_756) + t_758 = self.n_Conv_174(t_757) + t_759 = torch.add(t_758, t_753) + t_760 = F.relu(t_759) + t_761 = self.n_Conv_175(t_760) + t_762 = F.relu(t_761) + t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0) + t_763 = self.n_Conv_176(t_762_padded) + t_764 = F.relu(t_763) + t_765 = self.n_Conv_177(t_764) + t_766 = torch.add(t_765, t_760) + t_767 = F.relu(t_766) + t_768 = self.n_Conv_178(t_767) + t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:]) + t_770 = torch.squeeze(t_769, 3) + t_770 = torch.squeeze(t_770, 2) + t_771 = torch.sigmoid(t_770) + return t_771 + + def load_state_dict(self, state_dict, **kwargs): + self.tags = state_dict.get('tags', []) + + super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'}) + diff --git a/modules/devices.py b/modules/devices.py index eb422583449..ee679141ad7 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,72 +1,295 @@ +import sys import contextlib +from functools import lru_cache import torch +from modules import errors, shared, npu_specific -from modules import errors +if sys.platform == "darwin": + from modules import mac_specific -# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility -has_mps = getattr(torch, 'has_mps', False) +if shared.cmd_opts.use_ipex: + from modules import xpu_specific -cpu = torch.device("cpu") +def has_xpu() -> bool: + return shared.cmd_opts.use_ipex and xpu_specific.has_xpu -def get_optimal_device(): + +def has_mps() -> bool: + if sys.platform != "darwin": + return False + else: + return mac_specific.has_mps + + +def cuda_no_autocast(device_id=None) -> bool: + if device_id is None: + device_id = get_cuda_device_id() + return ( + torch.cuda.get_device_capability(device_id) == (7, 5) + and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16") + ) + + +def get_cuda_device_id(): + return ( + int(shared.cmd_opts.device_id) + if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() + else 0 + ) or torch.cuda.current_device() + + +def get_cuda_device_string(): + if shared.cmd_opts.device_id is not None: + return f"cuda:{shared.cmd_opts.device_id}" + + return "cuda" + + +def get_optimal_device_name(): if torch.cuda.is_available(): - return torch.device("cuda") + return get_cuda_device_string() + + if has_mps(): + return "mps" + + if has_xpu(): + return xpu_specific.get_xpu_device_string() + + if npu_specific.has_npu: + return npu_specific.get_npu_device_string() + + return "cpu" - if has_mps: - return torch.device("mps") - return cpu +def get_optimal_device(): + return torch.device(get_optimal_device_name()) + + +def get_device_for(task): + if task in shared.cmd_opts.use_cpu or "all" in shared.cmd_opts.use_cpu: + return cpu + + return get_optimal_device() def torch_gc(): + if torch.cuda.is_available(): - torch.cuda.empty_cache() - torch.cuda.ipc_collect() + with torch.cuda.device(get_cuda_device_string()): + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + + if has_mps(): + mac_specific.torch_mps_gc() + + if has_xpu(): + xpu_specific.torch_xpu_gc() + + if npu_specific.has_npu: + torch_npu_set_device() + npu_specific.torch_npu_gc() + + +def torch_npu_set_device(): + # Work around due to bug in torch_npu, revert me after fixed, @see https://gitee.com/ascend/pytorch/issues/I8KECW?from=project-issue + if npu_specific.has_npu: + torch.npu.set_device(0) def enable_tf32(): if torch.cuda.is_available(): + + # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't + # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407 + if cuda_no_autocast(): + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True errors.run(enable_tf32, "Enabling TF32") -device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() -dtype = torch.float16 -dtype_vae = torch.float16 +cpu: torch.device = torch.device("cpu") +fp8: bool = False +# Force fp16 for all models in inference. No casting during inference. +# This flag is controlled by "--precision half" command line arg. +force_fp16: bool = False +device: torch.device = None +device_interrogate: torch.device = None +device_gfpgan: torch.device = None +device_esrgan: torch.device = None +device_codeformer: torch.device = None +dtype: torch.dtype = torch.float16 +dtype_vae: torch.dtype = torch.float16 +dtype_unet: torch.dtype = torch.float16 +dtype_inference: torch.dtype = torch.float16 +unet_needs_upcast = False -def randn(seed, shape): - # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used. - if device.type == 'mps': - generator = torch.Generator(device=cpu) - generator.manual_seed(seed) - noise = torch.randn(shape, generator=generator, device=cpu).to(device) - return noise - torch.manual_seed(seed) - return torch.randn(shape, device=device) +def cond_cast_unet(input): + if force_fp16: + return input.to(torch.float16) + return input.to(dtype_unet) if unet_needs_upcast else input -def randn_without_seed(shape): - # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used. - if device.type == 'mps': - generator = torch.Generator(device=cpu) - noise = torch.randn(shape, generator=generator, device=cpu).to(device) - return noise +def cond_cast_float(input): + return input.float() if unet_needs_upcast else input - return torch.randn(shape, device=device) +nv_rng = None +patch_module_list = [ + torch.nn.Linear, + torch.nn.Conv2d, + torch.nn.MultiheadAttention, + torch.nn.GroupNorm, + torch.nn.LayerNorm, +] -def autocast(disable=False): - from modules import shared +def manual_cast_forward(target_dtype): + def forward_wrapper(self, *args, **kwargs): + if any( + isinstance(arg, torch.Tensor) and arg.dtype != target_dtype + for arg in args + ): + args = [arg.to(target_dtype) if isinstance(arg, torch.Tensor) else arg for arg in args] + kwargs = {k: v.to(target_dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()} + + org_dtype = target_dtype + for param in self.parameters(): + if param.dtype != target_dtype: + org_dtype = param.dtype + break + + if org_dtype != target_dtype: + self.to(target_dtype) + result = self.org_forward(*args, **kwargs) + if org_dtype != target_dtype: + self.to(org_dtype) + + if target_dtype != dtype_inference: + if isinstance(result, tuple): + result = tuple( + i.to(dtype_inference) + if isinstance(i, torch.Tensor) + else i + for i in result + ) + elif isinstance(result, torch.Tensor): + result = result.to(dtype_inference) + return result + return forward_wrapper + + +@contextlib.contextmanager +def manual_cast(target_dtype): + applied = False + for module_type in patch_module_list: + if hasattr(module_type, "org_forward"): + continue + applied = True + org_forward = module_type.forward + if module_type == torch.nn.MultiheadAttention: + module_type.forward = manual_cast_forward(torch.float32) + else: + module_type.forward = manual_cast_forward(target_dtype) + module_type.org_forward = org_forward + try: + yield None + finally: + if applied: + for module_type in patch_module_list: + if hasattr(module_type, "org_forward"): + module_type.forward = module_type.org_forward + delattr(module_type, "org_forward") + + +def autocast(disable=False): if disable: return contextlib.nullcontext() - if dtype == torch.float32 or shared.cmd_opts.precision == "full": + if force_fp16: + # No casting during inference if force_fp16 is enabled. + # All tensor dtype conversion happens before inference. return contextlib.nullcontext() + if fp8 and device==cpu: + return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True) + + if fp8 and dtype_inference == torch.float32: + return manual_cast(dtype) + + if dtype == torch.float32 or dtype_inference == torch.float32: + return contextlib.nullcontext() + + if has_xpu() or has_mps() or cuda_no_autocast(): + return manual_cast(dtype) + return torch.autocast("cuda") + + +def without_autocast(disable=False): + return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext() + + +class NansException(Exception): + pass + + +def test_for_nans(x, where): + if shared.cmd_opts.disable_nan_check: + return + + if not torch.isnan(x[(0, ) * len(x.shape)]): + return + + if where == "unet": + message = "A tensor with NaNs was produced in Unet." + + if not shared.cmd_opts.no_half: + message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this." + + elif where == "vae": + message = "A tensor with NaNs was produced in VAE." + + if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae: + message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this." + else: + message = "A tensor with NaNs was produced." + + message += " Use --disable-nan-check commandline argument to disable this check." + + raise NansException(message) + + +@lru_cache +def first_time_calculation(): + """ + just do any calculation with pytorch layers - the first time this is done it allocates about 700MB of memory and + spends about 2.7 seconds doing that, at least with NVidia. + """ + + x = torch.zeros((1, 1)).to(device, dtype) + linear = torch.nn.Linear(1, 1).to(device, dtype) + linear(x) + + x = torch.zeros((1, 1, 3, 3)).to(device, dtype) + conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype) + conv2d(x) + + +def force_model_fp16(): + """ + ldm and sgm has modules.diffusionmodules.util.GroupNorm32.forward, which + force conversion of input to float32. If force_fp16 is enabled, we need to + prevent this casting. + """ + assert force_fp16 + import sgm.modules.diffusionmodules.util as sgm_util + import ldm.modules.diffusionmodules.util as ldm_util + sgm_util.GroupNorm32 = torch.nn.GroupNorm + ldm_util.GroupNorm32 = torch.nn.GroupNorm + print("ldm/sgm GroupNorm32 replaced with normal torch.nn.GroupNorm due to `--precision half`.") diff --git a/modules/errors.py b/modules/errors.py index 372dc51a0b9..48aa13a1728 100644 --- a/modules/errors.py +++ b/modules/errors.py @@ -1,10 +1,150 @@ import sys +import textwrap import traceback +exception_records = [] + + +def format_traceback(tb): + return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)] + + +def format_exception(e, tb): + return {"exception": str(e), "traceback": format_traceback(tb)} + + +def get_exceptions(): + try: + return list(reversed(exception_records)) + except Exception as e: + return str(e) + + +def record_exception(): + _, e, tb = sys.exc_info() + if e is None: + return + + if exception_records and exception_records[-1] == e: + return + + exception_records.append(format_exception(e, tb)) + + if len(exception_records) > 5: + exception_records.pop(0) + + +def report(message: str, *, exc_info: bool = False) -> None: + """ + Print an error message to stderr, with optional traceback. + """ + + record_exception() + + for line in message.splitlines(): + print("***", line, file=sys.stderr) + if exc_info: + print(textwrap.indent(traceback.format_exc(), " "), file=sys.stderr) + print("---", file=sys.stderr) + + +def print_error_explanation(message): + record_exception() + + lines = message.strip().split("\n") + max_len = max([len(x) for x in lines]) + + print('=' * max_len, file=sys.stderr) + for line in lines: + print(line, file=sys.stderr) + print('=' * max_len, file=sys.stderr) + + +def display(e: Exception, task, *, full_traceback=False): + record_exception() + + print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr) + te = traceback.TracebackException.from_exception(e) + if full_traceback: + # include frames leading up to the try-catch block + te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack) + print(*te.format(), sep="", file=sys.stderr) + + message = str(e) + if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message: + print_error_explanation(""" +The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its config file. +See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this. + """) + + +already_displayed = {} + + +def display_once(e: Exception, task): + record_exception() + + if task in already_displayed: + return + + display(e, task) + + already_displayed[task] = 1 + + def run(code, task): try: code() except Exception as e: - print(f"{task}: {type(e).__name__}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) + display(task, e) + + +def check_versions(): + from packaging import version + from modules import shared + + import torch + import gradio + + expected_torch_version = "2.1.2" + expected_xformers_version = "0.0.23.post1" + expected_gradio_version = "3.41.2" + + if version.parse(torch.__version__) < version.parse(expected_torch_version): + print_error_explanation(f""" +You are running torch {torch.__version__}. +The program is tested to work with torch {expected_torch_version}. +To reinstall the desired version, run with commandline flag --reinstall-torch. +Beware that this will cause a lot of large files to be downloaded, as well as +there are reports of issues with training tab on the latest version. + +Use --skip-version-check commandline argument to disable this check. + """.strip()) + + if shared.xformers_available: + import xformers + + if version.parse(xformers.__version__) < version.parse(expected_xformers_version): + print_error_explanation(f""" +You are running xformers {xformers.__version__}. +The program is tested to work with xformers {expected_xformers_version}. +To reinstall the desired version, run with commandline flag --reinstall-xformers. + +Use --skip-version-check commandline argument to disable this check. + """.strip()) + + if gradio.__version__ != expected_gradio_version: + print_error_explanation(f""" +You are running gradio {gradio.__version__}. +The program is designed to work with gradio {expected_gradio_version}. +Using a different version of gradio is extremely likely to break the program. + +Reasons why you have the mismatched gradio version can be: + - you use --skip-install flag. + - you use webui.py to start the program instead of launch.py. + - an extension installs the incompatible gradio version. + +Use --skip-version-check commandline argument to disable this check. + """.strip()) + diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 46ad0da3cac..70041ab0234 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -1,73 +1,9 @@ -import os - -import numpy as np -import torch -from PIL import Image -from basicsr.utils.download_util import load_file_from_url - -import modules.esrgan_model_arch as arch -from modules import shared, modelloader, images, devices -from modules.upscaler import Upscaler, UpscalerData +from modules import modelloader, devices, errors from modules.shared import opts +from modules.upscaler import Upscaler, UpscalerData +from modules.upscaler_utils import upscale_with_model -def fix_model_layers(crt_model, pretrained_net): - # this code is adapted from https://github.com/xinntao/ESRGAN - if 'conv_first.weight' in pretrained_net: - return pretrained_net - - if 'model.0.weight' not in pretrained_net: - is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"] - if is_realesrgan: - raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.") - else: - raise Exception("The file is not a ESRGAN model.") - - crt_net = crt_model.state_dict() - load_net_clean = {} - for k, v in pretrained_net.items(): - if k.startswith('module.'): - load_net_clean[k[7:]] = v - else: - load_net_clean[k] = v - pretrained_net = load_net_clean - - tbd = [] - for k, v in crt_net.items(): - tbd.append(k) - - # directly copy - for k, v in crt_net.items(): - if k in pretrained_net and pretrained_net[k].size() == v.size(): - crt_net[k] = pretrained_net[k] - tbd.remove(k) - - crt_net['conv_first.weight'] = pretrained_net['model.0.weight'] - crt_net['conv_first.bias'] = pretrained_net['model.0.bias'] - - for k in tbd.copy(): - if 'RDB' in k: - ori_k = k.replace('RRDB_trunk.', 'model.1.sub.') - if '.weight' in k: - ori_k = ori_k.replace('.weight', '.0.weight') - elif '.bias' in k: - ori_k = ori_k.replace('.bias', '.0.bias') - crt_net[k] = pretrained_net[ori_k] - tbd.remove(k) - - crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight'] - crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias'] - crt_net['upconv1.weight'] = pretrained_net['model.3.weight'] - crt_net['upconv1.bias'] = pretrained_net['model.3.bias'] - crt_net['upconv2.weight'] = pretrained_net['model.6.weight'] - crt_net['upconv2.bias'] = pretrained_net['model.6.bias'] - crt_net['HRconv.weight'] = pretrained_net['model.8.weight'] - crt_net['HRconv.bias'] = pretrained_net['model.8.bias'] - crt_net['conv_last.weight'] = pretrained_net['model.10.weight'] - crt_net['conv_last.bias'] = pretrained_net['model.10.bias'] - - return crt_net - class UpscalerESRGAN(Upscaler): def __init__(self, dirname): self.name = "ESRGAN" @@ -82,7 +18,7 @@ def __init__(self, dirname): scaler_data = UpscalerData(self.model_name, self.model_url, self, 4) scalers.append(scaler_data) for file in model_paths: - if "http" in file: + if file.startswith("http"): name = self.model_name else: name = modelloader.friendly_name(file) @@ -91,68 +27,36 @@ def __init__(self, dirname): self.scalers.append(scaler_data) def do_upscale(self, img, selected_model): - model = self.load_model(selected_model) - if model is None: + try: + model = self.load_model(selected_model) + except Exception: + errors.report(f"Unable to load ESRGAN model {selected_model}", exc_info=True) return img model.to(devices.device_esrgan) - img = esrgan_upscale(model, img) - return img + return esrgan_upscale(model, img) def load_model(self, path: str): - if "http" in path: - filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, - file_name="%s.pth" % self.model_name, - progress=True) + if path.startswith("http"): + # TODO: this doesn't use `path` at all? + filename = modelloader.load_file_from_url( + url=self.model_url, + model_dir=self.model_download_path, + file_name=f"{self.model_name}.pth", + ) else: filename = path - if not os.path.exists(filename) or filename is None: - print("Unable to load %s from %s" % (self.model_path, filename)) - return None - - pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None) - crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32) - - pretrained_net = fix_model_layers(crt_model, pretrained_net) - crt_model.load_state_dict(pretrained_net) - crt_model.eval() - - return crt_model - -def upscale_without_tiling(model, img): - img = np.array(img) - img = img[:, :, ::-1] - img = np.moveaxis(img, 2, 0) / 255 - img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(devices.device_esrgan) - with torch.no_grad(): - output = model(img) - output = output.squeeze().float().cpu().clamp_(0, 1).numpy() - output = 255. * np.moveaxis(output, 0, 2) - output = output.astype(np.uint8) - output = output[:, :, ::-1] - return Image.fromarray(output, 'RGB') + return modelloader.load_spandrel_model( + filename, + device=('cpu' if devices.device_esrgan.type == 'mps' else None), + expected_architecture='ESRGAN', + ) def esrgan_upscale(model, img): - if opts.ESRGAN_tile == 0: - return upscale_without_tiling(model, img) - - grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap) - newtiles = [] - scale_factor = 1 - - for y, h, row in grid.tiles: - newrow = [] - for tiledata in row: - x, w, tile = tiledata - - output = upscale_without_tiling(model, tile) - scale_factor = output.width // tile.width - - newrow.append([x * scale_factor, w * scale_factor, output]) - newtiles.append([y * scale_factor, h * scale_factor, newrow]) - - newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor) - output = images.combine_grid(newgrid) - return output + return upscale_with_model( + model, + img, + tile_size=opts.ESRGAN_tile, + tile_overlap=opts.ESRGAN_tile_overlap, + ) diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py deleted file mode 100644 index e413d36ed0c..00000000000 --- a/modules/esrgan_model_arch.py +++ /dev/null @@ -1,80 +0,0 @@ -# this file is taken from https://github.com/xinntao/ESRGAN - -import functools -import torch -import torch.nn as nn -import torch.nn.functional as F - - -def make_layer(block, n_layers): - layers = [] - for _ in range(n_layers): - layers.append(block()) - return nn.Sequential(*layers) - - -class ResidualDenseBlock_5C(nn.Module): - def __init__(self, nf=64, gc=32, bias=True): - super(ResidualDenseBlock_5C, self).__init__() - # gc: growth channel, i.e. intermediate channels - self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias) - self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias) - self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias) - self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias) - self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias) - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - # initialization - # mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) - - def forward(self, x): - x1 = self.lrelu(self.conv1(x)) - x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) - x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) - x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) - x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) - return x5 * 0.2 + x - - -class RRDB(nn.Module): - '''Residual in Residual Dense Block''' - - def __init__(self, nf, gc=32): - super(RRDB, self).__init__() - self.RDB1 = ResidualDenseBlock_5C(nf, gc) - self.RDB2 = ResidualDenseBlock_5C(nf, gc) - self.RDB3 = ResidualDenseBlock_5C(nf, gc) - - def forward(self, x): - out = self.RDB1(x) - out = self.RDB2(out) - out = self.RDB3(out) - return out * 0.2 + x - - -class RRDBNet(nn.Module): - def __init__(self, in_nc, out_nc, nf, nb, gc=32): - super(RRDBNet, self).__init__() - RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc) - - self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) - self.RRDB_trunk = make_layer(RRDB_block_f, nb) - self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - #### upsampling - self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True) - self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True) - - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - - def forward(self, x): - fea = self.conv_first(x) - trunk = self.trunk_conv(self.RRDB_trunk(fea)) - fea = fea + trunk - - fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest'))) - fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))) - out = self.conv_last(self.lrelu(self.HRconv(fea))) - - return out diff --git a/modules/extensions.py b/modules/extensions.py new file mode 100644 index 00000000000..24de766eb90 --- /dev/null +++ b/modules/extensions.py @@ -0,0 +1,299 @@ +from __future__ import annotations + +import configparser +import dataclasses +import os +import threading +import re + +from modules import shared, errors, cache, scripts +from modules.gitpython_hack import Repo +from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401 + +extensions: list[Extension] = [] +extension_paths: dict[str, Extension] = {} +loaded_extensions: dict[str, Exception] = {} + + +os.makedirs(extensions_dir, exist_ok=True) + + +def active(): + if shared.cmd_opts.disable_all_extensions or shared.opts.disable_all_extensions == "all": + return [] + elif shared.cmd_opts.disable_extra_extensions or shared.opts.disable_all_extensions == "extra": + return [x for x in extensions if x.enabled and x.is_builtin] + else: + return [x for x in extensions if x.enabled] + + +@dataclasses.dataclass +class CallbackOrderInfo: + name: str + before: list + after: list + + +class ExtensionMetadata: + filename = "metadata.ini" + config: configparser.ConfigParser + canonical_name: str + requires: list + + def __init__(self, path, canonical_name): + self.config = configparser.ConfigParser() + + filepath = os.path.join(path, self.filename) + # `self.config.read()` will quietly swallow OSErrors (which FileNotFoundError is), + # so no need to check whether the file exists beforehand. + try: + self.config.read(filepath) + except Exception: + errors.report(f"Error reading {self.filename} for extension {canonical_name}.", exc_info=True) + + self.canonical_name = self.config.get("Extension", "Name", fallback=canonical_name) + self.canonical_name = canonical_name.lower().strip() + + self.requires = None + + def get_script_requirements(self, field, section, extra_section=None): + """reads a list of requirements from the config; field is the name of the field in the ini file, + like Requires or Before, and section is the name of the [section] in the ini file; additionally, + reads more requirements from [extra_section] if specified.""" + + x = self.config.get(section, field, fallback='') + + if extra_section: + x = x + ', ' + self.config.get(extra_section, field, fallback='') + + listed_requirements = self.parse_list(x.lower()) + res = [] + + for requirement in listed_requirements: + loaded_requirements = (x for x in requirement.split("|") if x in loaded_extensions) + relevant_requirement = next(loaded_requirements, requirement) + res.append(relevant_requirement) + + return res + + def parse_list(self, text): + """converts a line from config ("ext1 ext2, ext3 ") into a python list (["ext1", "ext2", "ext3"])""" + + if not text: + return [] + + # both "," and " " are accepted as separator + return [x for x in re.split(r"[,\s]+", text.strip()) if x] + + def list_callback_order_instructions(self): + for section in self.config.sections(): + if not section.startswith("callbacks/"): + continue + + callback_name = section[10:] + + if not callback_name.startswith(self.canonical_name): + errors.report(f"Callback order section for extension {self.canonical_name} is referencing the wrong extension: {section}") + continue + + before = self.parse_list(self.config.get(section, 'Before', fallback='')) + after = self.parse_list(self.config.get(section, 'After', fallback='')) + + yield CallbackOrderInfo(callback_name, before, after) + + +class Extension: + lock = threading.Lock() + cached_fields = ['remote', 'commit_date', 'branch', 'commit_hash', 'version'] + metadata: ExtensionMetadata + + def __init__(self, name, path, enabled=True, is_builtin=False, metadata=None): + self.name = name + self.path = path + self.enabled = enabled + self.status = '' + self.can_update = False + self.is_builtin = is_builtin + self.commit_hash = '' + self.commit_date = None + self.version = '' + self.branch = None + self.remote = None + self.have_info_from_repo = False + self.metadata = metadata if metadata else ExtensionMetadata(self.path, name.lower()) + self.canonical_name = metadata.canonical_name + + def to_dict(self): + return {x: getattr(self, x) for x in self.cached_fields} + + def from_dict(self, d): + for field in self.cached_fields: + setattr(self, field, d[field]) + + def read_info_from_repo(self): + if self.is_builtin or self.have_info_from_repo: + return + + def read_from_repo(): + with self.lock: + if self.have_info_from_repo: + return + + self.do_read_info_from_repo() + + return self.to_dict() + + try: + d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo) + self.from_dict(d) + except FileNotFoundError: + pass + self.status = 'unknown' if self.status == '' else self.status + + def do_read_info_from_repo(self): + repo = None + try: + if os.path.exists(os.path.join(self.path, ".git")): + repo = Repo(self.path) + except Exception: + errors.report(f"Error reading github repository info from {self.path}", exc_info=True) + + if repo is None or repo.bare: + self.remote = None + else: + try: + self.remote = next(repo.remote().urls, None) + commit = repo.head.commit + self.commit_date = commit.committed_date + if repo.active_branch: + self.branch = repo.active_branch.name + self.commit_hash = commit.hexsha + self.version = self.commit_hash[:8] + + except Exception: + errors.report(f"Failed reading extension data from Git repository ({self.name})", exc_info=True) + self.remote = None + + self.have_info_from_repo = True + + def list_files(self, subdir, extension): + dirpath = os.path.join(self.path, subdir) + if not os.path.isdir(dirpath): + return [] + + res = [] + for filename in sorted(os.listdir(dirpath)): + res.append(scripts.ScriptFile(self.path, filename, os.path.join(dirpath, filename))) + + res = [x for x in res if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)] + + return res + + def check_updates(self): + repo = Repo(self.path) + branch_name = f'{repo.remote().name}/{self.branch}' + for fetch in repo.remote().fetch(dry_run=True): + if self.branch and fetch.name != branch_name: + continue + if fetch.flags != fetch.HEAD_UPTODATE: + self.can_update = True + self.status = "new commits" + return + + try: + origin = repo.rev_parse(branch_name) + if repo.head.commit != origin: + self.can_update = True + self.status = "behind HEAD" + return + except Exception: + self.can_update = False + self.status = "unknown (remote error)" + return + + self.can_update = False + self.status = "latest" + + def fetch_and_reset_hard(self, commit=None): + repo = Repo(self.path) + if commit is None: + commit = f'{repo.remote().name}/{self.branch}' + # Fix: `error: Your local changes to the following files would be overwritten by merge`, + # because WSL2 Docker set 755 file permissions instead of 644, this results to the error. + repo.git.fetch(all=True) + repo.git.reset(commit, hard=True) + self.have_info_from_repo = False + + +def list_extensions(): + extensions.clear() + extension_paths.clear() + loaded_extensions.clear() + + if shared.cmd_opts.disable_all_extensions: + print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***") + elif shared.opts.disable_all_extensions == "all": + print("*** \"Disable all extensions\" option was set, will not load any extensions ***") + elif shared.cmd_opts.disable_extra_extensions: + print("*** \"--disable-extra-extensions\" arg was used, will only load built-in extensions ***") + elif shared.opts.disable_all_extensions == "extra": + print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***") + + + # scan through extensions directory and load metadata + for dirname in [extensions_builtin_dir, extensions_dir]: + if not os.path.isdir(dirname): + continue + + for extension_dirname in sorted(os.listdir(dirname)): + path = os.path.join(dirname, extension_dirname) + if not os.path.isdir(path): + continue + + canonical_name = extension_dirname + metadata = ExtensionMetadata(path, canonical_name) + + # check for duplicated canonical names + already_loaded_extension = loaded_extensions.get(metadata.canonical_name) + if already_loaded_extension is not None: + errors.report(f'Duplicate canonical name "{canonical_name}" found in extensions "{extension_dirname}" and "{already_loaded_extension.name}". Former will be discarded.', exc_info=False) + continue + + is_builtin = dirname == extensions_builtin_dir + extension = Extension(name=extension_dirname, path=path, enabled=extension_dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin, metadata=metadata) + extensions.append(extension) + extension_paths[extension.path] = extension + loaded_extensions[canonical_name] = extension + + for extension in extensions: + extension.metadata.requires = extension.metadata.get_script_requirements("Requires", "Extension") + + # check for requirements + for extension in extensions: + if not extension.enabled: + continue + + for req in extension.metadata.requires: + required_extension = loaded_extensions.get(req) + if required_extension is None: + errors.report(f'Extension "{extension.name}" requires "{req}" which is not installed.', exc_info=False) + continue + + if not required_extension.enabled: + errors.report(f'Extension "{extension.name}" requires "{required_extension.name}" which is disabled.', exc_info=False) + continue + + +def find_extension(filename): + parentdir = os.path.dirname(os.path.realpath(filename)) + + while parentdir != filename: + extension = extension_paths.get(parentdir) + if extension is not None: + return extension + + filename = parentdir + parentdir = os.path.dirname(filename) + + return None + diff --git a/modules/extra_networks.py b/modules/extra_networks.py new file mode 100644 index 00000000000..ae8d42d9b38 --- /dev/null +++ b/modules/extra_networks.py @@ -0,0 +1,225 @@ +import json +import os +import re +import logging +from collections import defaultdict + +from modules import errors + +extra_network_registry = {} +extra_network_aliases = {} + + +def initialize(): + extra_network_registry.clear() + extra_network_aliases.clear() + + +def register_extra_network(extra_network): + extra_network_registry[extra_network.name] = extra_network + + +def register_extra_network_alias(extra_network, alias): + extra_network_aliases[alias] = extra_network + + +def register_default_extra_networks(): + from modules.extra_networks_hypernet import ExtraNetworkHypernet + register_extra_network(ExtraNetworkHypernet()) + + +class ExtraNetworkParams: + def __init__(self, items=None): + self.items = items or [] + self.positional = [] + self.named = {} + + for item in self.items: + parts = item.split('=', 2) if isinstance(item, str) else [item] + if len(parts) == 2: + self.named[parts[0]] = parts[1] + else: + self.positional.append(item) + + def __eq__(self, other): + return self.items == other.items + + +class ExtraNetwork: + def __init__(self, name): + self.name = name + + def activate(self, p, params_list): + """ + Called by processing on every run. Whatever the extra network is meant to do should be activated here. + Passes arguments related to this extra network in params_list. + User passes arguments by specifying this in his prompt: + + + + Where name matches the name of this ExtraNetwork object, and arg1:arg2:arg3 are any natural number of text arguments + separated by colon. + + Even if the user does not mention this ExtraNetwork in his prompt, the call will still be made, with empty params_list - + in this case, all effects of this extra networks should be disabled. + + Can be called multiple times before deactivate() - each new call should override the previous call completely. + + For example, if this ExtraNetwork's name is 'hypernet' and user's prompt is: + + > "1girl, " + + params_list will be: + + [ + ExtraNetworkParams(items=["agm", "1.1"]), + ExtraNetworkParams(items=["ray"]) + ] + + """ + raise NotImplementedError + + def deactivate(self, p): + """ + Called at the end of processing for housekeeping. No need to do anything here. + """ + + raise NotImplementedError + + +def lookup_extra_networks(extra_network_data): + """returns a dict mapping ExtraNetwork objects to lists of arguments for those extra networks. + + Example input: + { + 'lora': [], + 'lyco': [], + 'hypernet': [] + } + + Example output: + + { + : [, ], + : [] + } + """ + + res = {} + + for extra_network_name, extra_network_args in list(extra_network_data.items()): + extra_network = extra_network_registry.get(extra_network_name, None) + alias = extra_network_aliases.get(extra_network_name, None) + + if alias is not None and extra_network is None: + extra_network = alias + + if extra_network is None: + logging.info(f"Skipping unknown extra network: {extra_network_name}") + continue + + res.setdefault(extra_network, []).extend(extra_network_args) + + return res + + +def activate(p, extra_network_data): + """call activate for extra networks in extra_network_data in specified order, then call + activate for all remaining registered networks with an empty argument list""" + + activated = [] + + for extra_network, extra_network_args in lookup_extra_networks(extra_network_data).items(): + + try: + extra_network.activate(p, extra_network_args) + activated.append(extra_network) + except Exception as e: + errors.display(e, f"activating extra network {extra_network.name} with arguments {extra_network_args}") + + for extra_network_name, extra_network in extra_network_registry.items(): + if extra_network in activated: + continue + + try: + extra_network.activate(p, []) + except Exception as e: + errors.display(e, f"activating extra network {extra_network_name}") + + if p.scripts is not None: + p.scripts.after_extra_networks_activate(p, batch_number=p.iteration, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds, extra_network_data=extra_network_data) + + +def deactivate(p, extra_network_data): + """call deactivate for extra networks in extra_network_data in specified order, then call + deactivate for all remaining registered networks""" + + data = lookup_extra_networks(extra_network_data) + + for extra_network in data: + try: + extra_network.deactivate(p) + except Exception as e: + errors.display(e, f"deactivating extra network {extra_network.name}") + + for extra_network_name, extra_network in extra_network_registry.items(): + if extra_network in data: + continue + + try: + extra_network.deactivate(p) + except Exception as e: + errors.display(e, f"deactivating unmentioned extra network {extra_network_name}") + + +re_extra_net = re.compile(r"<(\w+):([^>]+)>") + + +def parse_prompt(prompt): + res = defaultdict(list) + + def found(m): + name = m.group(1) + args = m.group(2) + + res[name].append(ExtraNetworkParams(items=args.split(":"))) + + return "" + + prompt = re.sub(re_extra_net, found, prompt) + + return prompt, res + + +def parse_prompts(prompts): + res = [] + extra_data = None + + for prompt in prompts: + updated_prompt, parsed_extra_data = parse_prompt(prompt) + + if extra_data is None: + extra_data = parsed_extra_data + + res.append(updated_prompt) + + return res, extra_data + + +def get_user_metadata(filename, lister=None): + if filename is None: + return {} + + basename, ext = os.path.splitext(filename) + metadata_filename = basename + '.json' + + metadata = {} + try: + exists = lister.exists(metadata_filename) if lister else os.path.exists(metadata_filename) + if exists: + with open(metadata_filename, "r", encoding="utf8") as file: + metadata = json.load(file) + except Exception as e: + errors.display(e, f"reading extra network user metadata from {metadata_filename}") + + return metadata diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py new file mode 100644 index 00000000000..b6a6dc0e1d0 --- /dev/null +++ b/modules/extra_networks_hypernet.py @@ -0,0 +1,28 @@ +from modules import extra_networks, shared +from modules.hypernetworks import hypernetwork + + +class ExtraNetworkHypernet(extra_networks.ExtraNetwork): + def __init__(self): + super().__init__('hypernet') + + def activate(self, p, params_list): + additional = shared.opts.sd_hypernetwork + + if additional != "None" and additional in shared.hypernetworks and not any(x for x in params_list if x.items[0] == additional): + hypernet_prompt_text = f"" + p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts] + params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) + + names = [] + multipliers = [] + for params in params_list: + assert params.items + + names.append(params.items[0]) + multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0) + + hypernetwork.load_hypernetworks(names, multipliers) + + def deactivate(self, p): + pass diff --git a/modules/extras.py b/modules/extras.py index f2f5a7b04ba..2a310ae3f25 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -1,226 +1,330 @@ -import math import os +import re +import shutil +import json -import numpy as np -from PIL import Image import torch import tqdm -from modules import processing, shared, images, devices, sd_models -from modules.shared import opts -import modules.gfpgan_model -from modules.ui import plaintext_to_html -import modules.codeformer_model -import piexif -import piexif.helper +from modules import shared, images, sd_models, sd_vae, sd_models_config, errors +from modules.ui_common import plaintext_to_html import gradio as gr +import safetensors.torch -cached_images = {} - - -def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility): - devices.torch_gc() +def run_pnginfo(image): + if image is None: + return '', '', '' - imageArr = [] - # Also keep track of original file names - imageNameArr = [] + geninfo, items = images.read_info_from_image(image) + items = {**{'parameters': geninfo}, **items} - if extras_mode == 1: - #convert file to pillow image - for img in image_folder: - image = Image.open(img) - imageArr.append(image) - imageNameArr.append(os.path.splitext(img.orig_name)[0]) - else: - imageArr.append(image) - imageNameArr.append(None) + info = '' + for key, text in items.items(): + info += f""" +
    +

    {plaintext_to_html(str(key))}

    +

    {plaintext_to_html(str(text))}

    +
    +""".strip()+"\n" - outpath = opts.outdir_samples or opts.outdir_extras_samples + if len(info) == 0: + message = "Nothing found in the image." + info = f"

    {message}

    " - outputs = [] - for image, image_name in zip(imageArr, imageNameArr): - if image is None: - return outputs, "Please select an input image.", '' - existing_pnginfo = image.info or {} + return '', geninfo, info - image = image.convert("RGB") - info = "" - if gfpgan_visibility > 0: - restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8)) - res = Image.fromarray(restored_img) +def create_config(ckpt_result, config_source, a, b, c): + def config(x): + res = sd_models_config.find_checkpoint_config_near_filename(x) if x else None + return res if res != shared.sd_default_config else None - if gfpgan_visibility < 1.0: - res = Image.blend(image, res, gfpgan_visibility) + if config_source == 0: + cfg = config(a) or config(b) or config(c) + elif config_source == 1: + cfg = config(b) + elif config_source == 2: + cfg = config(c) + else: + cfg = None - info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n" - image = res + if cfg is None: + return - if codeformer_visibility > 0: - restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight) - res = Image.fromarray(restored_img) + filename, _ = os.path.splitext(ckpt_result) + checkpoint_filename = filename + ".yaml" - if codeformer_visibility < 1.0: - res = Image.blend(image, res, codeformer_visibility) + print("Copying config:") + print(" from:", cfg) + print(" to:", checkpoint_filename) + shutil.copyfile(cfg, checkpoint_filename) - info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n" - image = res - if resize_mode == 1: - upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height) - crop_info = " (crop)" if upscaling_crop else "" - info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n" +checkpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"] - if upscaling_resize != 1.0: - def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop): - small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10)) - pixels = tuple(np.array(small).flatten().tolist()) - key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels - c = cached_images.get(key) - if c is None: - upscaler = shared.sd_upscalers[scaler_index] - c = upscaler.scaler.upscale(image, resize, upscaler.data_path) - if mode == 1 and crop: - cropped = Image.new("RGB", (resize_w, resize_h)) - cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2)) - c = cropped - cached_images[key] = c +def to_half(tensor, enable): + if enable and tensor.dtype == torch.float: + return tensor.half() - return c + return tensor - info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n" - res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) - if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0: - res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) - info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n" - res = Image.blend(res, res2, extras_upscaler_2_visibility) +def read_metadata(primary_model_name, secondary_model_name, tertiary_model_name): + metadata = {} - image = res + for checkpoint_name in [primary_model_name, secondary_model_name, tertiary_model_name]: + checkpoint_info = sd_models.checkpoints_list.get(checkpoint_name, None) + if checkpoint_info is None: + continue - while len(cached_images) > 2: - del cached_images[next(iter(cached_images.keys()))] + metadata.update(checkpoint_info.metadata) - images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, - forced_filename=image_name if opts.use_original_name_batch else None) + return json.dumps(metadata, indent=4, ensure_ascii=False) - if opts.enable_pnginfo: - image.info = existing_pnginfo - image.info["extras"] = info - outputs.append(image) +def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata, add_merge_recipe, copy_metadata_fields, metadata_json): + shared.state.begin(job="model-merge") - devices.torch_gc() + def fail(message): + shared.state.textinfo = message + shared.state.end() + return [*[gr.update() for _ in range(4)], message] - return outputs, plaintext_to_html(info), '' + def weighted_sum(theta0, theta1, alpha): + return ((1 - alpha) * theta0) + (alpha * theta1) + def get_difference(theta1, theta2): + return theta1 - theta2 -def run_pnginfo(image): - if image is None: - return '', '', '' + def add_difference(theta0, theta1_2_diff, alpha): + return theta0 + (alpha * theta1_2_diff) - items = image.info - geninfo = '' + def filename_weighted_sum(): + a = primary_model_info.model_name + b = secondary_model_info.model_name + Ma = round(1 - multiplier, 2) + Mb = round(multiplier, 2) - if "exif" in image.info: - exif = piexif.load(image.info["exif"]) - exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'') - try: - exif_comment = piexif.helper.UserComment.load(exif_comment) - except ValueError: - exif_comment = exif_comment.decode('utf8', errors="ignore") + return f"{Ma}({a}) + {Mb}({b})" - items['exif comment'] = exif_comment - geninfo = exif_comment + def filename_add_difference(): + a = primary_model_info.model_name + b = secondary_model_info.model_name + c = tertiary_model_info.model_name + M = round(multiplier, 2) - for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif', - 'loop', 'background', 'timestamp', 'duration']: - items.pop(field, None) + return f"{a} + {M}({b} - {c})" - geninfo = items.get('parameters', geninfo) + def filename_nothing(): + return primary_model_info.model_name - info = '' - for key, text in items.items(): - info += f""" -
    -

    {plaintext_to_html(str(key))}

    -

    {plaintext_to_html(str(text))}

    -
    -""".strip()+"\n" - - if len(info) == 0: - message = "Nothing found in the image." - info = f"

    {message}

    " + theta_funcs = { + "Weighted sum": (filename_weighted_sum, None, weighted_sum), + "Add difference": (filename_add_difference, get_difference, add_difference), + "No interpolation": (filename_nothing, None, None), + } + filename_generator, theta_func1, theta_func2 = theta_funcs[interp_method] + shared.state.job_count = (1 if theta_func1 else 0) + (1 if theta_func2 else 0) - return '', geninfo, info + if not primary_model_name: + return fail("Failed: Merging requires a primary model.") + primary_model_info = sd_models.checkpoints_list[primary_model_name] -def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name): - def weighted_sum(theta0, theta1, theta2, alpha): - return ((1 - alpha) * theta0) + (alpha * theta1) + if theta_func2 and not secondary_model_name: + return fail("Failed: Merging requires a secondary model.") - def add_difference(theta0, theta1, theta2, alpha): - return theta0 + (theta1 - theta2) * alpha + secondary_model_info = sd_models.checkpoints_list[secondary_model_name] if theta_func2 else None - primary_model_info = sd_models.checkpoints_list[primary_model_name] - secondary_model_info = sd_models.checkpoints_list[secondary_model_name] - teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None) + if theta_func1 and not tertiary_model_name: + return fail(f"Failed: Interpolation method ({interp_method}) requires a tertiary model.") - print(f"Loading {primary_model_info.filename}...") - primary_model = torch.load(primary_model_info.filename, map_location='cpu') - theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model) + tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None - print(f"Loading {secondary_model_info.filename}...") - secondary_model = torch.load(secondary_model_info.filename, map_location='cpu') - theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model) + result_is_inpainting_model = False + result_is_instruct_pix2pix_model = False - if teritary_model_info is not None: - print(f"Loading {teritary_model_info.filename}...") - teritary_model = torch.load(teritary_model_info.filename, map_location='cpu') - theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model) + if theta_func2: + shared.state.textinfo = "Loading B" + print(f"Loading {secondary_model_info.filename}...") + theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu') else: - theta_2 = None + theta_1 = None - theta_funcs = { - "Weighted sum": weighted_sum, - "Add difference": add_difference, - } - theta_func = theta_funcs[interp_method] + if theta_func1: + shared.state.textinfo = "Loading C" + print(f"Loading {tertiary_model_info.filename}...") + theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu') - print(f"Merging...") + shared.state.textinfo = 'Merging B and C' + shared.state.sampling_steps = len(theta_1.keys()) + for key in tqdm.tqdm(theta_1.keys()): + if key in checkpoint_dict_skip_on_merge: + continue - for key in tqdm.tqdm(theta_0.keys()): - if 'model' in key and key in theta_1: - t2 = (theta_2 or {}).get(key) - if t2 is None: - t2 = torch.zeros_like(theta_0[key]) + if 'model' in key: + if key in theta_2: + t2 = theta_2.get(key, torch.zeros_like(theta_1[key])) + theta_1[key] = theta_func1(theta_1[key], t2) + else: + theta_1[key] = torch.zeros_like(theta_1[key]) - theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier) + shared.state.sampling_step += 1 + del theta_2 - if save_as_half: - theta_0[key] = theta_0[key].half() + shared.state.nextjob() - # I believe this part should be discarded, but I'll leave it for now until I am sure - for key in theta_1.keys(): - if 'model' in key and key not in theta_0: - theta_0[key] = theta_1[key] - if save_as_half: - theta_0[key] = theta_0[key].half() + shared.state.textinfo = f"Loading {primary_model_info.filename}..." + print(f"Loading {primary_model_info.filename}...") + theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu') + + print("Merging...") + shared.state.textinfo = 'Merging A and B' + shared.state.sampling_steps = len(theta_0.keys()) + for key in tqdm.tqdm(theta_0.keys()): + if theta_1 and 'model' in key and key in theta_1: + + if key in checkpoint_dict_skip_on_merge: + continue + + a = theta_0[key] + b = theta_1[key] + + # this enables merging an inpainting model (A) with another one (B); + # where normal model would have 4 channels, for latenst space, inpainting model would + # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9 + if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]: + if a.shape[1] == 4 and b.shape[1] == 9: + raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.") + if a.shape[1] == 4 and b.shape[1] == 8: + raise RuntimeError("When merging instruct-pix2pix model with a normal one, A must be the instruct-pix2pix model.") + + if a.shape[1] == 8 and b.shape[1] == 4:#If we have an Instruct-Pix2Pix model... + theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)#Merge only the vectors the models have in common. Otherwise we get an error due to dimension mismatch. + result_is_instruct_pix2pix_model = True + else: + assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}" + theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier) + result_is_inpainting_model = True + else: + theta_0[key] = theta_func2(a, b, multiplier) + + theta_0[key] = to_half(theta_0[key], save_as_half) + + shared.state.sampling_step += 1 + + del theta_1 + + bake_in_vae_filename = sd_vae.vae_dict.get(bake_in_vae, None) + if bake_in_vae_filename is not None: + print(f"Baking in VAE from {bake_in_vae_filename}") + shared.state.textinfo = 'Baking in VAE' + vae_dict = sd_vae.load_vae_dict(bake_in_vae_filename, map_location='cpu') + + for key in vae_dict.keys(): + theta_0_key = 'first_stage_model.' + key + if theta_0_key in theta_0: + theta_0[theta_0_key] = to_half(vae_dict[key], save_as_half) + + del vae_dict + + if save_as_half and not theta_func2: + for key in theta_0.keys(): + theta_0[key] = to_half(theta_0[key], save_as_half) + + if discard_weights: + regex = re.compile(discard_weights) + for key in list(theta_0): + if re.search(regex, key): + theta_0.pop(key, None) ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path - filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt' - filename = filename if custom_name == '' else (custom_name + '.ckpt') + filename = filename_generator() if custom_name == '' else custom_name + filename += ".inpainting" if result_is_inpainting_model else "" + filename += ".instruct-pix2pix" if result_is_instruct_pix2pix_model else "" + filename += "." + checkpoint_format + output_modelname = os.path.join(ckpt_dir, filename) + shared.state.nextjob() + shared.state.textinfo = "Saving" print(f"Saving to {output_modelname}...") - torch.save(primary_model, output_modelname) + + metadata = {} + + if save_metadata and copy_metadata_fields: + if primary_model_info: + metadata.update(primary_model_info.metadata) + if secondary_model_info: + metadata.update(secondary_model_info.metadata) + if tertiary_model_info: + metadata.update(tertiary_model_info.metadata) + + if save_metadata: + try: + metadata.update(json.loads(metadata_json)) + except Exception as e: + errors.display(e, "readin metadata from json") + + metadata["format"] = "pt" + + if save_metadata and add_merge_recipe: + merge_recipe = { + "type": "webui", # indicate this model was merged with webui's built-in merger + "primary_model_hash": primary_model_info.sha256, + "secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None, + "tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None, + "interp_method": interp_method, + "multiplier": multiplier, + "save_as_half": save_as_half, + "custom_name": custom_name, + "config_source": config_source, + "bake_in_vae": bake_in_vae, + "discard_weights": discard_weights, + "is_inpainting": result_is_inpainting_model, + "is_instruct_pix2pix": result_is_instruct_pix2pix_model + } + + sd_merge_models = {} + + def add_model_metadata(checkpoint_info): + checkpoint_info.calculate_shorthash() + sd_merge_models[checkpoint_info.sha256] = { + "name": checkpoint_info.name, + "legacy_hash": checkpoint_info.hash, + "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None) + } + + sd_merge_models.update(checkpoint_info.metadata.get("sd_merge_models", {})) + + add_model_metadata(primary_model_info) + if secondary_model_info: + add_model_metadata(secondary_model_info) + if tertiary_model_info: + add_model_metadata(tertiary_model_info) + + metadata["sd_merge_recipe"] = json.dumps(merge_recipe) + metadata["sd_merge_models"] = json.dumps(sd_merge_models) + + _, extension = os.path.splitext(output_modelname) + if extension.lower() == ".safetensors": + safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata if len(metadata)>0 else None) + else: + torch.save(theta_0, output_modelname) sd_models.list_models() + created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None) + if created_model: + created_model.calculate_shorthash() + + create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info) + + print(f"Checkpoint saved to {output_modelname}.") + shared.state.textinfo = "Checkpoint saved" + shared.state.end() - print(f"Checkpoint saved.") - return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)] + return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "Checkpoint saved to " + output_modelname] diff --git a/modules/face_restoration_utils.py b/modules/face_restoration_utils.py new file mode 100644 index 00000000000..1cbac236480 --- /dev/null +++ b/modules/face_restoration_utils.py @@ -0,0 +1,180 @@ +from __future__ import annotations + +import logging +import os +from functools import cached_property +from typing import TYPE_CHECKING, Callable + +import cv2 +import numpy as np +import torch + +from modules import devices, errors, face_restoration, shared + +if TYPE_CHECKING: + from facexlib.utils.face_restoration_helper import FaceRestoreHelper + +logger = logging.getLogger(__name__) + + +def bgr_image_to_rgb_tensor(img: np.ndarray) -> torch.Tensor: + """Convert a BGR NumPy image in [0..1] range to a PyTorch RGB float32 tensor.""" + assert img.shape[2] == 3, "image must be RGB" + if img.dtype == "float64": + img = img.astype("float32") + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + return torch.from_numpy(img.transpose(2, 0, 1)).float() + + +def rgb_tensor_to_bgr_image(tensor: torch.Tensor, *, min_max=(0.0, 1.0)) -> np.ndarray: + """ + Convert a PyTorch RGB tensor in range `min_max` to a BGR NumPy image in [0..1] range. + """ + tensor = tensor.squeeze(0).float().detach().cpu().clamp_(*min_max) + tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) + assert tensor.dim() == 3, "tensor must be RGB" + img_np = tensor.numpy().transpose(1, 2, 0) + if img_np.shape[2] == 1: # gray image, no RGB/BGR required + return np.squeeze(img_np, axis=2) + return cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB) + + +def create_face_helper(device) -> FaceRestoreHelper: + from facexlib.detection import retinaface + from facexlib.utils.face_restoration_helper import FaceRestoreHelper + if hasattr(retinaface, 'device'): + retinaface.device = device + return FaceRestoreHelper( + upscale_factor=1, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + save_ext='png', + use_parse=True, + device=device, + ) + + +def restore_with_face_helper( + np_image: np.ndarray, + face_helper: FaceRestoreHelper, + restore_face: Callable[[torch.Tensor], torch.Tensor], +) -> np.ndarray: + """ + Find faces in the image using face_helper, restore them using restore_face, and paste them back into the image. + + `restore_face` should take a cropped face image and return a restored face image. + """ + from torchvision.transforms.functional import normalize + np_image = np_image[:, :, ::-1] + original_resolution = np_image.shape[0:2] + + try: + logger.debug("Detecting faces...") + face_helper.clean_all() + face_helper.read_image(np_image) + face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) + face_helper.align_warp_face() + logger.debug("Found %d faces, restoring", len(face_helper.cropped_faces)) + for cropped_face in face_helper.cropped_faces: + cropped_face_t = bgr_image_to_rgb_tensor(cropped_face / 255.0) + normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) + cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer) + + try: + with torch.no_grad(): + cropped_face_t = restore_face(cropped_face_t) + devices.torch_gc() + except Exception: + errors.report('Failed face-restoration inference', exc_info=True) + + restored_face = rgb_tensor_to_bgr_image(cropped_face_t, min_max=(-1, 1)) + restored_face = (restored_face * 255.0).astype('uint8') + face_helper.add_restored_face(restored_face) + + logger.debug("Merging restored faces into image") + face_helper.get_inverse_affine(None) + img = face_helper.paste_faces_to_input_image() + img = img[:, :, ::-1] + if original_resolution != img.shape[0:2]: + img = cv2.resize( + img, + (0, 0), + fx=original_resolution[1] / img.shape[1], + fy=original_resolution[0] / img.shape[0], + interpolation=cv2.INTER_LINEAR, + ) + logger.debug("Face restoration complete") + finally: + face_helper.clean_all() + return img + + +class CommonFaceRestoration(face_restoration.FaceRestoration): + net: torch.Module | None + model_url: str + model_download_name: str + + def __init__(self, model_path: str): + super().__init__() + self.net = None + self.model_path = model_path + os.makedirs(model_path, exist_ok=True) + + @cached_property + def face_helper(self) -> FaceRestoreHelper: + return create_face_helper(self.get_device()) + + def send_model_to(self, device): + if self.net: + logger.debug("Sending %s to %s", self.net, device) + self.net.to(device) + if self.face_helper: + logger.debug("Sending face helper to %s", device) + self.face_helper.face_det.to(device) + self.face_helper.face_parse.to(device) + + def get_device(self): + raise NotImplementedError("get_device must be implemented by subclasses") + + def load_net(self) -> torch.Module: + raise NotImplementedError("load_net must be implemented by subclasses") + + def restore_with_helper( + self, + np_image: np.ndarray, + restore_face: Callable[[torch.Tensor], torch.Tensor], + ) -> np.ndarray: + try: + if self.net is None: + self.net = self.load_net() + except Exception: + logger.warning("Unable to load face-restoration model", exc_info=True) + return np_image + + try: + self.send_model_to(self.get_device()) + return restore_with_face_helper(np_image, self.face_helper, restore_face) + finally: + if shared.opts.face_restoration_unload: + self.send_model_to(devices.cpu) + + +def patch_facexlib(dirname: str) -> None: + import facexlib.detection + import facexlib.parsing + + det_facex_load_file_from_url = facexlib.detection.load_file_from_url + par_facex_load_file_from_url = facexlib.parsing.load_file_from_url + + def update_kwargs(kwargs): + return dict(kwargs, save_dir=dirname, model_dir=None) + + def facex_load_file_from_url(**kwargs): + return det_facex_load_file_from_url(**update_kwargs(kwargs)) + + def facex_load_file_from_url2(**kwargs): + return par_facex_load_file_from_url(**update_kwargs(kwargs)) + + facexlib.detection.load_file_from_url = facex_load_file_from_url + facexlib.parsing.load_file_from_url = facex_load_file_from_url2 diff --git a/modules/fifo_lock.py b/modules/fifo_lock.py new file mode 100644 index 00000000000..c35b3ae25a3 --- /dev/null +++ b/modules/fifo_lock.py @@ -0,0 +1,37 @@ +import threading +import collections + + +# reference: https://gist.github.com/vitaliyp/6d54dd76ca2c3cdfc1149d33007dc34a +class FIFOLock(object): + def __init__(self): + self._lock = threading.Lock() + self._inner_lock = threading.Lock() + self._pending_threads = collections.deque() + + def acquire(self, blocking=True): + with self._inner_lock: + lock_acquired = self._lock.acquire(False) + if lock_acquired: + return True + elif not blocking: + return False + + release_event = threading.Event() + self._pending_threads.append(release_event) + + release_event.wait() + return self._lock.acquire() + + def release(self): + with self._inner_lock: + if self._pending_threads: + release_event = self._pending_threads.popleft() + release_event.set() + + self._lock.release() + + __enter__ = acquire + + def __exit__(self, t, v, tb): + self.release() diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py deleted file mode 100644 index c27826b6b5a..00000000000 --- a/modules/generation_parameters_copypaste.py +++ /dev/null @@ -1,101 +0,0 @@ -import os -import re -import gradio as gr -from modules.shared import script_path -from modules import shared - -re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)" -re_param = re.compile(re_param_code) -re_params = re.compile(r"^(?:" + re_param_code + "){3,}$") -re_imagesize = re.compile(r"^(\d+)x(\d+)$") -type_of_gr_update = type(gr.update()) - - -def parse_generation_parameters(x: str): - """parses generation parameters string, the one you see in text field under the picture in UI: -``` -girl with an artist's beret, determined, blue eyes, desert scene, computer monitors, heavy makeup, by Alphonse Mucha and Charlie Bowater, ((eyeshadow)), (coquettish), detailed, intricate -Negative prompt: ugly, fat, obese, chubby, (((deformed))), [blurry], bad anatomy, disfigured, poorly drawn face, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), messy drawing -Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model hash: 45dee52b -``` - - returns a dict with field values - """ - - res = {} - - prompt = "" - negative_prompt = "" - - done_with_prompt = False - - *lines, lastline = x.strip().split("\n") - if not re_params.match(lastline): - lines.append(lastline) - lastline = '' - - for i, line in enumerate(lines): - line = line.strip() - if line.startswith("Negative prompt:"): - done_with_prompt = True - line = line[16:].strip() - - if done_with_prompt: - negative_prompt += ("" if negative_prompt == "" else "\n") + line - else: - prompt += ("" if prompt == "" else "\n") + line - - if len(prompt) > 0: - res["Prompt"] = prompt - - if len(negative_prompt) > 0: - res["Negative prompt"] = negative_prompt - - for k, v in re_param.findall(lastline): - m = re_imagesize.match(v) - if m is not None: - res[k+"-1"] = m.group(1) - res[k+"-2"] = m.group(2) - else: - res[k] = v - - return res - - -def connect_paste(button, paste_fields, input_comp, js=None): - def paste_func(prompt): - if not prompt and not shared.cmd_opts.hide_ui_dir_config: - filename = os.path.join(script_path, "params.txt") - if os.path.exists(filename): - with open(filename, "r", encoding="utf8") as file: - prompt = file.read() - - params = parse_generation_parameters(prompt) - res = [] - - for output, key in paste_fields: - if callable(key): - v = key(params) - else: - v = params.get(key, None) - - if v is None: - res.append(gr.update()) - elif isinstance(v, type_of_gr_update): - res.append(v) - else: - try: - valtype = type(output.value) - val = valtype(v) - res.append(gr.update(value=val)) - except Exception: - res.append(gr.update()) - - return res - - button.click( - fn=paste_func, - _js=js, - inputs=[input_comp], - outputs=[x[0] for x in paste_fields], - ) diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index a9452dce5e8..01ef899e4a6 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -1,115 +1,69 @@ +from __future__ import annotations + +import logging import os -import sys -import traceback -import facexlib -import gfpgan +import torch -import modules.face_restoration -from modules import shared, devices, modelloader -from modules.paths import models_path +from modules import ( + devices, + errors, + face_restoration, + face_restoration_utils, + modelloader, + shared, +) -model_dir = "GFPGAN" -user_path = None -model_path = os.path.join(models_path, model_dir) +logger = logging.getLogger(__name__) model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth" -have_gfpgan = False -loaded_gfpgan_model = None - +model_download_name = "GFPGANv1.4.pth" +gfpgan_face_restorer: face_restoration.FaceRestoration | None = None -def gfpgann(): - global loaded_gfpgan_model - global model_path - if loaded_gfpgan_model is not None: - loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan) - return loaded_gfpgan_model - if gfpgan_constructor is None: - return None +class FaceRestorerGFPGAN(face_restoration_utils.CommonFaceRestoration): + def name(self): + return "GFPGAN" - models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN") - if len(models) == 1 and "http" in models[0]: - model_file = models[0] - elif len(models) != 0: - latest_file = max(models, key=os.path.getctime) - model_file = latest_file - else: - print("Unable to load gfpgan model!") - return None - model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None) - loaded_gfpgan_model = model + def get_device(self): + return devices.device_gfpgan - return model + def load_net(self) -> torch.Module: + for model_path in modelloader.load_models( + model_path=self.model_path, + model_url=model_url, + command_path=self.model_path, + download_name=model_download_name, + ext_filter=['.pth'], + ): + if 'GFPGAN' in os.path.basename(model_path): + return modelloader.load_spandrel_model( + model_path, + device=self.get_device(), + expected_architecture='GFPGAN', + ).model + raise ValueError("No GFPGAN model found") + def restore(self, np_image): + def restore_face(cropped_face_t): + assert self.net is not None + return self.net(cropped_face_t, return_rgb=False)[0] -def send_model_to(model, device): - model.gfpgan.to(device) - model.face_helper.face_det.to(device) - model.face_helper.face_parse.to(device) + return self.restore_with_helper(np_image, restore_face) def gfpgan_fix_faces(np_image): - model = gfpgann() - if model is None: - return np_image - - send_model_to(model, devices.device_gfpgan) - - np_image_bgr = np_image[:, :, ::-1] - cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True) - np_image = gfpgan_output_bgr[:, :, ::-1] - - model.face_helper.clean_all() - - if shared.opts.face_restoration_unload: - send_model_to(model, devices.cpu) - + if gfpgan_face_restorer: + return gfpgan_face_restorer.restore(np_image) + logger.warning("GFPGAN face restorer not set up") return np_image -gfpgan_constructor = None - - -def setup_model(dirname): - global model_path - if not os.path.exists(model_path): - os.makedirs(model_path) +def setup_model(dirname: str) -> None: + global gfpgan_face_restorer try: - from gfpgan import GFPGANer - from facexlib import detection, parsing - global user_path - global have_gfpgan - global gfpgan_constructor - - load_file_from_url_orig = gfpgan.utils.load_file_from_url - facex_load_file_from_url_orig = facexlib.detection.load_file_from_url - facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url - - def my_load_file_from_url(**kwargs): - return load_file_from_url_orig(**dict(kwargs, model_dir=model_path)) - - def facex_load_file_from_url(**kwargs): - return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None)) - - def facex_load_file_from_url2(**kwargs): - return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None)) - - gfpgan.utils.load_file_from_url = my_load_file_from_url - facexlib.detection.load_file_from_url = facex_load_file_from_url - facexlib.parsing.load_file_from_url = facex_load_file_from_url2 - user_path = dirname - have_gfpgan = True - gfpgan_constructor = GFPGANer - - class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration): - def name(self): - return "GFPGAN" - - def restore(self, np_image): - return gfpgan_fix_faces(np_image) - - shared.face_restorers.append(FaceRestorerGFPGAN()) + face_restoration_utils.patch_facexlib(dirname) + gfpgan_face_restorer = FaceRestorerGFPGAN(model_path=dirname) + shared.face_restorers.append(gfpgan_face_restorer) except Exception: - print("Error setting up GFPGAN:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) + errors.report("Error setting up GFPGAN", exc_info=True) diff --git a/modules/gitpython_hack.py b/modules/gitpython_hack.py new file mode 100644 index 00000000000..b55f0640e5e --- /dev/null +++ b/modules/gitpython_hack.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +import io +import subprocess + +import git + + +class Git(git.Git): + """ + Git subclassed to never use persistent processes. + """ + + def _get_persistent_cmd(self, attr_name, cmd_name, *args, **kwargs): + raise NotImplementedError(f"Refusing to use persistent process: {attr_name} ({cmd_name} {args} {kwargs})") + + def get_object_header(self, ref: str | bytes) -> tuple[str, str, int]: + ret = subprocess.check_output( + [self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch-check"], + input=self._prepare_ref(ref), + cwd=self._working_dir, + timeout=2, + ) + return self._parse_object_header(ret) + + def stream_object_data(self, ref: str) -> tuple[str, str, int, Git.CatFileContentStream]: + # Not really streaming, per se; this buffers the entire object in memory. + # Shouldn't be a problem for our use case, since we're only using this for + # object headers (commit objects). + ret = subprocess.check_output( + [self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch"], + input=self._prepare_ref(ref), + cwd=self._working_dir, + timeout=30, + ) + bio = io.BytesIO(ret) + hexsha, typename, size = self._parse_object_header(bio.readline()) + return (hexsha, typename, size, self.CatFileContentStream(size, bio)) + + +class Repo(git.Repo): + GitCommandWrapperType = Git diff --git a/modules/gradio_extensons.py b/modules/gradio_extensons.py new file mode 100644 index 00000000000..7d88dc984bb --- /dev/null +++ b/modules/gradio_extensons.py @@ -0,0 +1,83 @@ +import gradio as gr + +from modules import scripts, ui_tempdir, patches + + +def add_classes_to_gradio_component(comp): + """ + this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others + """ + + comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])] + + if getattr(comp, 'multiselect', False): + comp.elem_classes.append('multiselect') + + +def IOComponent_init(self, *args, **kwargs): + self.webui_tooltip = kwargs.pop('tooltip', None) + + if scripts.scripts_current is not None: + scripts.scripts_current.before_component(self, **kwargs) + + scripts.script_callbacks.before_component_callback(self, **kwargs) + + res = original_IOComponent_init(self, *args, **kwargs) + + add_classes_to_gradio_component(self) + + scripts.script_callbacks.after_component_callback(self, **kwargs) + + if scripts.scripts_current is not None: + scripts.scripts_current.after_component(self, **kwargs) + + return res + + +def Block_get_config(self): + config = original_Block_get_config(self) + + webui_tooltip = getattr(self, 'webui_tooltip', None) + if webui_tooltip: + config["webui_tooltip"] = webui_tooltip + + config.pop('example_inputs', None) + + return config + + +def BlockContext_init(self, *args, **kwargs): + if scripts.scripts_current is not None: + scripts.scripts_current.before_component(self, **kwargs) + + scripts.script_callbacks.before_component_callback(self, **kwargs) + + res = original_BlockContext_init(self, *args, **kwargs) + + add_classes_to_gradio_component(self) + + scripts.script_callbacks.after_component_callback(self, **kwargs) + + if scripts.scripts_current is not None: + scripts.scripts_current.after_component(self, **kwargs) + + return res + + +def Blocks_get_config_file(self, *args, **kwargs): + config = original_Blocks_get_config_file(self, *args, **kwargs) + + for comp_config in config["components"]: + if "example_inputs" in comp_config: + comp_config["example_inputs"] = {"serialized": []} + + return config + + +original_IOComponent_init = patches.patch(__name__, obj=gr.components.IOComponent, field="__init__", replacement=IOComponent_init) +original_Block_get_config = patches.patch(__name__, obj=gr.blocks.Block, field="get_config", replacement=Block_get_config) +original_BlockContext_init = patches.patch(__name__, obj=gr.blocks.BlockContext, field="__init__", replacement=BlockContext_init) +original_Blocks_get_config_file = patches.patch(__name__, obj=gr.blocks.Blocks, field="get_config_file", replacement=Blocks_get_config_file) + + +ui_tempdir.install_ui_tempdir_override() diff --git a/modules/hashes.py b/modules/hashes.py new file mode 100644 index 00000000000..d22e5fadc47 --- /dev/null +++ b/modules/hashes.py @@ -0,0 +1,84 @@ +import hashlib +import os.path + +from modules import shared +import modules.cache + +dump_cache = modules.cache.dump_cache +cache = modules.cache.cache + + +def calculate_sha256(filename): + hash_sha256 = hashlib.sha256() + blksize = 1024 * 1024 + + with open(filename, "rb") as f: + for chunk in iter(lambda: f.read(blksize), b""): + hash_sha256.update(chunk) + + return hash_sha256.hexdigest() + + +def sha256_from_cache(filename, title, use_addnet_hash=False): + hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes") + try: + ondisk_mtime = os.path.getmtime(filename) + except FileNotFoundError: + return None + + if title not in hashes: + return None + + cached_sha256 = hashes[title].get("sha256", None) + cached_mtime = hashes[title].get("mtime", 0) + + if ondisk_mtime > cached_mtime or cached_sha256 is None: + return None + + return cached_sha256 + + +def sha256(filename, title, use_addnet_hash=False): + hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes") + + sha256_value = sha256_from_cache(filename, title, use_addnet_hash) + if sha256_value is not None: + return sha256_value + + if shared.cmd_opts.no_hashing: + return None + + print(f"Calculating sha256 for {filename}: ", end='') + if use_addnet_hash: + with open(filename, "rb") as file: + sha256_value = addnet_hash_safetensors(file) + else: + sha256_value = calculate_sha256(filename) + print(f"{sha256_value}") + + hashes[title] = { + "mtime": os.path.getmtime(filename), + "sha256": sha256_value, + } + + dump_cache() + + return sha256_value + + +def addnet_hash_safetensors(b): + """kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py""" + hash_sha256 = hashlib.sha256() + blksize = 1024 * 1024 + + b.seek(0) + header = b.read(8) + n = int.from_bytes(header, "little") + + offset = n + 8 + b.seek(offset) + for chunk in iter(lambda: b.read(blksize), b""): + hash_sha256.update(chunk) + + return hash_sha256.hexdigest() + diff --git a/modules/hat_model.py b/modules/hat_model.py new file mode 100644 index 00000000000..7f2abb41660 --- /dev/null +++ b/modules/hat_model.py @@ -0,0 +1,43 @@ +import os +import sys + +from modules import modelloader, devices +from modules.shared import opts +from modules.upscaler import Upscaler, UpscalerData +from modules.upscaler_utils import upscale_with_model + + +class UpscalerHAT(Upscaler): + def __init__(self, dirname): + self.name = "HAT" + self.scalers = [] + self.user_path = dirname + super().__init__() + for file in self.find_models(ext_filter=[".pt", ".pth"]): + name = modelloader.friendly_name(file) + scale = 4 # TODO: scale might not be 4, but we can't know without loading the model + scaler_data = UpscalerData(name, file, upscaler=self, scale=scale) + self.scalers.append(scaler_data) + + def do_upscale(self, img, selected_model): + try: + model = self.load_model(selected_model) + except Exception as e: + print(f"Unable to load HAT model {selected_model}: {e}", file=sys.stderr) + return img + model.to(devices.device_esrgan) # TODO: should probably be device_hat + return upscale_with_model( + model, + img, + tile_size=opts.ESRGAN_tile, # TODO: should probably be HAT_tile + tile_overlap=opts.ESRGAN_tile_overlap, # TODO: should probably be HAT_tile_overlap + ) + + def load_model(self, path: str): + if not os.path.isfile(path): + raise FileNotFoundError(f"Model file {path} not found") + return modelloader.load_spandrel_model( + path, + device=devices.device_esrgan, # TODO: should probably be device_hat + expected_architecture='HAT', + ) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 4905710e5fa..17454665f28 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -2,88 +2,243 @@ import glob import html import os -import sys -import traceback -import tqdm -import csv - -import torch +import inspect +from contextlib import closing -from ldm.util import default -from modules import devices, shared, processing, sd_models +import modules.textual_inversion.dataset import torch -from torch import einsum +import tqdm from einops import rearrange, repeat -import modules.textual_inversion.dataset -from modules.textual_inversion import textual_inversion +from ldm.util import default +from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors +from modules.textual_inversion import textual_inversion, saving_settings from modules.textual_inversion.learn_schedule import LearnRateScheduler +from torch import einsum +from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_ +from collections import deque +from statistics import stdev, mean -class HypernetworkModule(torch.nn.Module): - multiplier = 1.0 - def __init__(self, dim, state_dict=None): +optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"} + +class HypernetworkModule(torch.nn.Module): + activation_dict = { + "linear": torch.nn.Identity, + "relu": torch.nn.ReLU, + "leakyrelu": torch.nn.LeakyReLU, + "elu": torch.nn.ELU, + "swish": torch.nn.Hardswish, + "tanh": torch.nn.Tanh, + "sigmoid": torch.nn.Sigmoid, + } + activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'}) + + def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', + add_layer_norm=False, activate_output=False, dropout_structure=None): super().__init__() - self.linear1 = torch.nn.Linear(dim, dim * 2) - self.linear2 = torch.nn.Linear(dim * 2, dim) + self.multiplier = 1.0 - if state_dict is not None: - self.load_state_dict(state_dict, strict=True) - else: + assert layer_structure is not None, "layer_structure must not be None" + assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" + assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" + + linears = [] + for i in range(len(layer_structure) - 1): + + # Add a fully-connected layer + linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) + + # Add an activation func except last layer + if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output): + pass + elif activation_func in self.activation_dict: + linears.append(self.activation_dict[activation_func]()) + else: + raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}') + + # Add layer normalization + if add_layer_norm: + linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) - self.linear1.weight.data.normal_(mean=0.0, std=0.01) - self.linear1.bias.data.zero_() - self.linear2.weight.data.normal_(mean=0.0, std=0.01) - self.linear2.bias.data.zero_() + # Everything should be now parsed into dropout structure, and applied here. + # Since we only have dropouts after layers, dropout structure should start with 0 and end with 0. + if dropout_structure is not None and dropout_structure[i+1] > 0: + assert 0 < dropout_structure[i+1] < 1, "Dropout probability should be 0 or float between 0 and 1!" + linears.append(torch.nn.Dropout(p=dropout_structure[i+1])) + # Code explanation : [1, 2, 1] -> dropout is missing when last_layer_dropout is false. [1, 2, 2, 1] -> [0, 0.3, 0, 0], when its True, [0, 0.3, 0.3, 0]. + self.linear = torch.nn.Sequential(*linears) + + if state_dict is not None: + self.fix_old_state_dict(state_dict) + self.load_state_dict(state_dict) + else: + for layer in self.linear: + if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm: + w, b = layer.weight.data, layer.bias.data + if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm: + normal_(w, mean=0.0, std=0.01) + normal_(b, mean=0.0, std=0) + elif weight_init == 'XavierUniform': + xavier_uniform_(w) + zeros_(b) + elif weight_init == 'XavierNormal': + xavier_normal_(w) + zeros_(b) + elif weight_init == 'KaimingUniform': + kaiming_uniform_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu') + zeros_(b) + elif weight_init == 'KaimingNormal': + kaiming_normal_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu') + zeros_(b) + else: + raise KeyError(f"Key {weight_init} is not defined as initialization!") + devices.torch_npu_set_device() self.to(devices.device) - def forward(self, x): - return x + (self.linear2(self.linear1(x))) * self.multiplier + def fix_old_state_dict(self, state_dict): + changes = { + 'linear1.bias': 'linear.0.bias', + 'linear1.weight': 'linear.0.weight', + 'linear2.bias': 'linear.1.bias', + 'linear2.weight': 'linear.1.weight', + } + for fr, to in changes.items(): + x = state_dict.get(fr, None) + if x is None: + continue -def apply_strength(value=None): - HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength + del state_dict[fr] + state_dict[to] = x + + def forward(self, x): + return x + self.linear(x) * (self.multiplier if not self.training else 1) + + def trainables(self): + layer_structure = [] + for layer in self.linear: + if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm: + layer_structure += [layer.weight, layer.bias] + return layer_structure + + +#param layer_structure : sequence used for length, use_dropout : controlling boolean, last_layer_dropout : for compatibility check. +def parse_dropout_structure(layer_structure, use_dropout, last_layer_dropout): + if layer_structure is None: + layer_structure = [1, 2, 1] + if not use_dropout: + return [0] * len(layer_structure) + dropout_values = [0] + dropout_values.extend([0.3] * (len(layer_structure) - 3)) + if last_layer_dropout: + dropout_values.append(0.3) + else: + dropout_values.append(0) + dropout_values.append(0) + return dropout_values class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs): self.filename = None self.name = name self.layers = {} self.step = 0 self.sd_checkpoint = None self.sd_checkpoint_name = None + self.layer_structure = layer_structure + self.activation_func = activation_func + self.weight_init = weight_init + self.add_layer_norm = add_layer_norm + self.use_dropout = use_dropout + self.activate_output = activate_output + self.last_layer_dropout = kwargs.get('last_layer_dropout', True) + self.dropout_structure = kwargs.get('dropout_structure', None) + if self.dropout_structure is None: + self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout) + self.optimizer_name = None + self.optimizer_state_dict = None + self.optional_info = None for size in enable_sizes or []: - self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size)) + self.layers[size] = ( + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.activate_output, dropout_structure=self.dropout_structure), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.activate_output, dropout_structure=self.dropout_structure), + ) + self.eval() def weights(self): res = [] + for layers in self.layers.values(): + for layer in layers: + res += layer.parameters() + return res - for k, layers in self.layers.items(): + def train(self, mode=True): + for layers in self.layers.values(): for layer in layers: - layer.train() - res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias] + layer.train(mode=mode) + for param in layer.parameters(): + param.requires_grad = mode - return res + def to(self, device): + for layers in self.layers.values(): + for layer in layers: + layer.to(device) + + return self + + def set_multiplier(self, multiplier): + for layers in self.layers.values(): + for layer in layers: + layer.multiplier = multiplier + + return self + + def eval(self): + for layers in self.layers.values(): + for layer in layers: + layer.eval() + for param in layer.parameters(): + param.requires_grad = False def save(self, filename): state_dict = {} + optimizer_saved_dict = {} for k, v in self.layers.items(): state_dict[k] = (v[0].state_dict(), v[1].state_dict()) state_dict['step'] = self.step state_dict['name'] = self.name + state_dict['layer_structure'] = self.layer_structure + state_dict['activation_func'] = self.activation_func + state_dict['is_layer_norm'] = self.add_layer_norm + state_dict['weight_initialization'] = self.weight_init state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name + state_dict['activate_output'] = self.activate_output + state_dict['use_dropout'] = self.use_dropout + state_dict['dropout_structure'] = self.dropout_structure + state_dict['last_layer_dropout'] = (self.dropout_structure[-2] != 0) if self.dropout_structure is not None else self.last_layer_dropout + state_dict['optional_info'] = self.optional_info if self.optional_info else None + + if self.optimizer_name is not None: + optimizer_saved_dict['optimizer_name'] = self.optimizer_name torch.save(state_dict, filename) + if shared.opts.save_optimizer_state and self.optimizer_state_dict: + optimizer_saved_dict['hash'] = self.shorthash() + optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict + torch.save(optimizer_saved_dict, filename + '.optim') def load(self, filename): self.filename = filename @@ -92,79 +247,149 @@ def load(self, filename): state_dict = torch.load(filename, map_location='cpu') + self.layer_structure = state_dict.get('layer_structure', [1, 2, 1]) + self.optional_info = state_dict.get('optional_info', None) + self.activation_func = state_dict.get('activation_func', None) + self.weight_init = state_dict.get('weight_initialization', 'Normal') + self.add_layer_norm = state_dict.get('is_layer_norm', False) + self.dropout_structure = state_dict.get('dropout_structure', None) + self.use_dropout = True if self.dropout_structure is not None and any(self.dropout_structure) else state_dict.get('use_dropout', False) + self.activate_output = state_dict.get('activate_output', True) + self.last_layer_dropout = state_dict.get('last_layer_dropout', False) + # Dropout structure should have same length as layer structure, Every digits should be in [0,1), and last digit must be 0. + if self.dropout_structure is None: + self.dropout_structure = parse_dropout_structure(self.layer_structure, self.use_dropout, self.last_layer_dropout) + + if shared.opts.print_hypernet_extra: + if self.optional_info is not None: + print(f" INFO:\n {self.optional_info}\n") + + print(f" Layer structure: {self.layer_structure}") + print(f" Activation function: {self.activation_func}") + print(f" Weight initialization: {self.weight_init}") + print(f" Layer norm: {self.add_layer_norm}") + print(f" Dropout usage: {self.use_dropout}" ) + print(f" Activate last layer: {self.activate_output}") + print(f" Dropout structure: {self.dropout_structure}") + + optimizer_saved_dict = torch.load(self.filename + '.optim', map_location='cpu') if os.path.exists(self.filename + '.optim') else {} + + if self.shorthash() == optimizer_saved_dict.get('hash', None): + self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None) + else: + self.optimizer_state_dict = None + if self.optimizer_state_dict: + self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW') + if shared.opts.print_hypernet_extra: + print("Loaded existing optimizer from checkpoint") + print(f"Optimizer name is {self.optimizer_name}") + else: + self.optimizer_name = "AdamW" + if shared.opts.print_hypernet_extra: + print("No saved optimizer exists in checkpoint") + for size, sd in state_dict.items(): if type(size) == int: - self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1])) + self.layers[size] = ( + HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.activate_output, self.dropout_structure), + HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, + self.add_layer_norm, self.activate_output, self.dropout_structure), + ) self.name = state_dict.get('name', self.name) self.step = state_dict.get('step', 0) self.sd_checkpoint = state_dict.get('sd_checkpoint', None) self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) + self.eval() + + def shorthash(self): + sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}') + + return sha256[0:10] if sha256 else None def list_hypernetworks(path): res = {} - for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True): + for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower): name = os.path.splitext(os.path.basename(filename))[0] - res[name] = filename + # Prevent a hypothetical "None.pt" from being listed. + if name != "None": + res[name] = filename return res -def load_hypernetwork(filename): - path = shared.hypernetworks.get(filename, None) - if path is not None: - print(f"Loading hypernetwork {filename}") - try: - shared.loaded_hypernetwork = Hypernetwork() - shared.loaded_hypernetwork.load(path) +def load_hypernetwork(name): + path = shared.hypernetworks.get(name, None) - except Exception: - print(f"Error loading hypernetwork {path}", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - else: - if shared.loaded_hypernetwork is not None: - print(f"Unloading hypernetwork") + if path is None: + return None + + try: + hypernetwork = Hypernetwork() + hypernetwork.load(path) + return hypernetwork + except Exception: + errors.report(f"Error loading hypernetwork {path}", exc_info=True) + return None - shared.loaded_hypernetwork = None +def load_hypernetworks(names, multipliers=None): + already_loaded = {} -def find_closest_hypernetwork_name(search: str): - if not search: - return None - search = search.lower() - applicable = [name for name in shared.hypernetworks if search in name.lower()] - if not applicable: - return None - applicable = sorted(applicable, key=lambda name: len(name)) - return applicable[0] + for hypernetwork in shared.loaded_hypernetworks: + if hypernetwork.name in names: + already_loaded[hypernetwork.name] = hypernetwork + shared.loaded_hypernetworks.clear() -def apply_hypernetwork(hypernetwork, context, layer=None): - hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None) + for i, name in enumerate(names): + hypernetwork = already_loaded.get(name, None) + if hypernetwork is None: + hypernetwork = load_hypernetwork(name) + + if hypernetwork is None: + continue + + hypernetwork.set_multiplier(multipliers[i] if multipliers else 1.0) + shared.loaded_hypernetworks.append(hypernetwork) + + +def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None): + hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context_k.shape[2], None) if hypernetwork_layers is None: - return context, context + return context_k, context_v if layer is not None: layer.hyper_k = hypernetwork_layers[0] layer.hyper_v = hypernetwork_layers[1] - context_k = hypernetwork_layers[0](context) - context_v = hypernetwork_layers[1](context) + context_k = devices.cond_cast_unet(hypernetwork_layers[0](devices.cond_cast_float(context_k))) + context_v = devices.cond_cast_unet(hypernetwork_layers[1](devices.cond_cast_float(context_v))) return context_k, context_v -def attention_CrossAttention_forward(self, x, context=None, mask=None): +def apply_hypernetworks(hypernetworks, context, layer=None): + context_k = context + context_v = context + for hypernetwork in hypernetworks: + context_k, context_v = apply_single_hypernetwork(hypernetwork, context_k, context_v, layer) + + return context_k, context_v + + +def attention_CrossAttention_forward(self, x, context=None, mask=None, **kwargs): h = self.heads q = self.to_q(x) context = default(context, x) - context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self) + context_k, context_v = apply_hypernetworks(shared.loaded_hypernetworks, context, self) k = self.to_k(context_k) v = self.to_v(context_v) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v)) sim = einsum('b i d, b j d -> b i j', q, k) * self.scale @@ -196,16 +421,73 @@ def stack_conds(conds): return torch.stack(conds) -def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): - assert hypernetwork_name, 'hypernetwork not selected' + +def statistics(data): + if len(data) < 2: + std = 0 + else: + std = stdev(data) + total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})" + recent_data = data[-32:] + if len(recent_data) < 2: + std = 0 + else: + std = stdev(recent_data) + recent_information = f"recent 32 loss:{mean(recent_data):.3f}" + u"\u00B1" + f"({std / (len(recent_data) ** 0.5):.3f})" + return total_information, recent_information + + +def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None): + # Remove illegal characters from name. + name = "".join( x for x in name if (x.isalnum() or x in "._- ")) + assert name, "Name cannot be empty!" + + fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") + if not overwrite_old: + assert not os.path.exists(fn), f"file {fn} already exists" + + if type(layer_structure) == str: + layer_structure = [float(x.strip()) for x in layer_structure.split(",")] + + if use_dropout and dropout_structure and type(dropout_structure) == str: + dropout_structure = [float(x.strip()) for x in dropout_structure.split(",")] + else: + dropout_structure = [0] * len(layer_structure) + + hypernet = modules.hypernetworks.hypernetwork.Hypernetwork( + name=name, + enable_sizes=[int(x) for x in enable_sizes], + layer_structure=layer_structure, + activation_func=activation_func, + weight_init=weight_init, + add_layer_norm=add_layer_norm, + use_dropout=use_dropout, + dropout_structure=dropout_structure + ) + hypernet.save(fn) + + shared.reload_hypernetworks() + + +def train_hypernetwork(id_task, hypernetwork_name: str, learn_rate: float, batch_size: int, gradient_step: int, data_root: str, log_directory: str, training_width: int, training_height: int, varsize: bool, steps: int, clip_grad_mode: str, clip_grad_value: float, shuffle_tags: bool, tag_drop_out: bool, latent_sampling_method: str, use_weight: bool, create_image_every: int, save_hypernetwork_every: int, template_filename: str, preview_from_txt2img: bool, preview_prompt: str, preview_negative_prompt: str, preview_steps: int, preview_sampler_name: str, preview_cfg_scale: float, preview_seed: int, preview_width: int, preview_height: int): + from modules import images, processing + + save_hypernetwork_every = save_hypernetwork_every or 0 + create_image_every = create_image_every or 0 + template_file = textual_inversion.textual_inversion_templates.get(template_filename, None) + textual_inversion.validate_train_inputs(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_hypernetwork_every, create_image_every, log_directory, name="hypernetwork") + template_file = template_file.path path = shared.hypernetworks.get(hypernetwork_name, None) - shared.loaded_hypernetwork = Hypernetwork() - shared.loaded_hypernetwork.load(path) + hypernetwork = Hypernetwork() + hypernetwork.load(path) + shared.loaded_hypernetworks = [hypernetwork] + shared.state.job = "train-hypernetwork" shared.state.textinfo = "Initializing hypernetwork training..." shared.state.job_count = steps + hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0] filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name) @@ -223,127 +505,279 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log else: images_dir = None - shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." - with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size) + checkpoint = sd_models.select_checkpoint() - if unload: - shared.sd_model.cond_stage_model.to(devices.cpu) - shared.sd_model.first_stage_model.to(devices.cpu) + initial_step = hypernetwork.step or 0 + if initial_step >= steps: + shared.state.textinfo = "Model has already been trained beyond specified max steps" + return hypernetwork, filename - hypernetwork = shared.loaded_hypernetwork - weights = hypernetwork.weights() - for weight in weights: - weight.requires_grad = True + scheduler = LearnRateScheduler(learn_rate, steps, initial_step) - losses = torch.zeros((32,)) + clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None + if clip_grad: + clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False) - last_saved_file = "" - last_saved_image = "" + if shared.opts.training_enable_tensorboard: + tensorboard_writer = textual_inversion.tensorboard_setup(log_directory) - ititial_step = hypernetwork.step or 0 - if ititial_step > steps: - return hypernetwork, filename + # dataset loading may take a while, so input validations and early returns should be done before this + shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." - scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) - - pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) - for i, entries in pbar: - hypernetwork.step = i + ititial_step - - scheduler.apply(optimizer, hypernetwork.step) - if scheduler.finished: - break - - if shared.state.interrupted: - break - - with torch.autocast("cuda"): - c = stack_conds([entry.cond for entry in entries]).to(devices.device) -# c = torch.vstack([entry.cond for entry in entries]).to(devices.device) - x = torch.stack([entry.latent for entry in entries]).to(devices.device) - loss = shared.sd_model(x, c)[0] - del x - del c - - losses[hypernetwork.step % losses.shape[0]] = loss.item() - - optimizer.zero_grad() - loss.backward() - optimizer.step() - mean_loss = losses.mean() - if torch.isnan(mean_loss): - raise RuntimeError("Loss diverged.") - pbar.set_description(f"loss: {mean_loss:.7f}") - - if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0: - last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') - hypernetwork.save(last_saved_file) - - textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { - "loss": f"{mean_loss:.7f}", - "learn_rate": scheduler.learn_rate - }) - - if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: - last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') - - optimizer.zero_grad() - shared.sd_model.cond_stage_model.to(devices.device) - shared.sd_model.first_stage_model.to(devices.device) - - p = processing.StableDiffusionProcessingTxt2Img( - sd_model=shared.sd_model, - do_not_save_grid=True, - do_not_save_samples=True, - ) + pin_memory = shared.opts.pin_memory - if preview_from_txt2img: - p.prompt = preview_prompt - p.negative_prompt = preview_negative_prompt - p.steps = preview_steps - p.sampler_index = preview_sampler_index - p.cfg_scale = preview_cfg_scale - p.seed = preview_seed - p.width = preview_width - p.height = preview_height - else: - p.prompt = entries[0].cond_text - p.steps = 20 + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight) - preview_text = p.prompt + if shared.opts.save_training_settings_to_txt: + saved_params = dict( + model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), + **{field: getattr(hypernetwork, field) for field in ['layer_structure', 'activation_func', 'weight_init', 'add_layer_norm', 'use_dropout', ]} + ) + saving_settings.save_settings_to_file(log_directory, {**saved_params, **locals()}) - processed = processing.process_images(p) - image = processed.images[0] if len(processed.images)>0 else None + latent_sampling_method = ds.latent_sampling_method - if unload: - shared.sd_model.cond_stage_model.to(devices.cpu) - shared.sd_model.first_stage_model.to(devices.cpu) + dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory) - if image is not None: - shared.state.current_image = image - image.save(last_saved_image) - last_saved_image += f", prompt: {preview_text}" + old_parallel_processing_allowed = shared.parallel_processing_allowed - shared.state.job_no = hypernetwork.step + if unload: + shared.parallel_processing_allowed = False + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) - shared.state.textinfo = f""" + weights = hypernetwork.weights() + hypernetwork.train() + + # Here we use optimizer from saved HN, or we can specify as UI option. + if hypernetwork.optimizer_name in optimizer_dict: + optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate) + optimizer_name = hypernetwork.optimizer_name + else: + print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!") + optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate) + optimizer_name = 'AdamW' + + if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer. + try: + optimizer.load_state_dict(hypernetwork.optimizer_state_dict) + except RuntimeError as e: + print("Cannot resume from saved optimizer!") + print(e) + + scaler = torch.cuda.amp.GradScaler() + + batch_size = ds.batch_size + gradient_step = ds.gradient_step + # n steps = batch_size * gradient_step * n image processed + steps_per_epoch = len(ds) // batch_size // gradient_step + max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step + loss_step = 0 + _loss_step = 0 #internal + # size = len(ds.indexes) + # loss_dict = defaultdict(lambda : deque(maxlen = 1024)) + loss_logging = deque(maxlen=len(ds) * 3) # this should be configurable parameter, this is 3 * epoch(dataset size) + # losses = torch.zeros((size,)) + # previous_mean_losses = [0] + # previous_mean_loss = 0 + # print("Mean loss of {} elements".format(size)) + + steps_without_grad = 0 + + last_saved_file = "" + last_saved_image = "" + forced_filename = "" + + pbar = tqdm.tqdm(total=steps - initial_step) + try: + sd_hijack_checkpoint.add() + + for _ in range((steps-initial_step) * gradient_step): + if scheduler.finished: + break + if shared.state.interrupted: + break + for j, batch in enumerate(dl): + # works as a drop_last=True for gradient accumulation + if j == max_steps_per_epoch: + break + scheduler.apply(optimizer, hypernetwork.step) + if scheduler.finished: + break + if shared.state.interrupted: + break + + if clip_grad: + clip_grad_sched.step(hypernetwork.step) + + with devices.autocast(): + x = batch.latent_sample.to(devices.device, non_blocking=pin_memory) + if use_weight: + w = batch.weight.to(devices.device, non_blocking=pin_memory) + if tag_drop_out != 0 or shuffle_tags: + shared.sd_model.cond_stage_model.to(devices.device) + c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory) + shared.sd_model.cond_stage_model.to(devices.cpu) + else: + c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory) + if use_weight: + loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step + del w + else: + loss = shared.sd_model.forward(x, c)[0] / gradient_step + del x + del c + + _loss_step += loss.item() + scaler.scale(loss).backward() + + # go back until we reach gradient accumulation steps + if (j + 1) % gradient_step != 0: + continue + loss_logging.append(_loss_step) + if clip_grad: + clip_grad(weights, clip_grad_sched.learn_rate) + + scaler.step(optimizer) + scaler.update() + hypernetwork.step += 1 + pbar.update() + optimizer.zero_grad(set_to_none=True) + loss_step = _loss_step + _loss_step = 0 + + steps_done = hypernetwork.step + 1 + + epoch_num = hypernetwork.step // steps_per_epoch + epoch_step = hypernetwork.step % steps_per_epoch + + description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}" + pbar.set_description(description) + if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0: + # Before saving, change name to match current checkpoint. + hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}' + last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() + save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file) + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. + + + + if shared.opts.training_enable_tensorboard: + epoch_num = hypernetwork.step // len(ds) + epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1 + mean_loss = sum(loss_logging) / len(loss_logging) + textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num) + + textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, { + "loss": f"{loss_step:.7f}", + "learn_rate": scheduler.learn_rate + }) + + if images_dir is not None and steps_done % create_image_every == 0: + forced_filename = f'{hypernetwork_name}-{steps_done}' + last_saved_image = os.path.join(images_dir, forced_filename) + hypernetwork.eval() + rng_state = torch.get_rng_state() + cuda_rng_state = None + if torch.cuda.is_available(): + cuda_rng_state = torch.cuda.get_rng_state_all() + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) + + p = processing.StableDiffusionProcessingTxt2Img( + sd_model=shared.sd_model, + do_not_save_grid=True, + do_not_save_samples=True, + ) + + p.disable_extra_networks = True + + if preview_from_txt2img: + p.prompt = preview_prompt + p.negative_prompt = preview_negative_prompt + p.steps = preview_steps + p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()] + p.cfg_scale = preview_cfg_scale + p.seed = preview_seed + p.width = preview_width + p.height = preview_height + else: + p.prompt = batch.cond_text[0] + p.steps = 20 + p.width = training_width + p.height = training_height + + preview_text = p.prompt + + with closing(p): + processed = processing.process_images(p) + image = processed.images[0] if len(processed.images) > 0 else None + + if unload: + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) + torch.set_rng_state(rng_state) + if torch.cuda.is_available(): + torch.cuda.set_rng_state_all(cuda_rng_state) + hypernetwork.train() + if image is not None: + shared.state.assign_current_image(image) + if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images: + textual_inversion.tensorboard_add_image(tensorboard_writer, + f"Validation at epoch {epoch_num}", image, + hypernetwork.step) + last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False) + last_saved_image += f", prompt: {preview_text}" + + shared.state.job_no = hypernetwork.step + + shared.state.textinfo = f"""

    -Loss: {mean_loss:.7f}
    -Step: {hypernetwork.step}
    -Last prompt: {html.escape(entries[0].cond_text)}
    -Last saved embedding: {html.escape(last_saved_file)}
    +Loss: {loss_step:.7f}
    +Step: {steps_done}
    +Last prompt: {html.escape(batch.cond_text[0])}
    +Last saved hypernetwork: {html.escape(last_saved_file)}
    Last saved image: {html.escape(last_saved_image)}

    """ + except Exception: + errors.report("Exception in training hypernetwork", exc_info=True) + finally: + pbar.leave = False + pbar.close() + hypernetwork.eval() + sd_hijack_checkpoint.remove() - checkpoint = sd_models.select_checkpoint() - hypernetwork.sd_checkpoint = checkpoint.hash - hypernetwork.sd_checkpoint_name = checkpoint.model_name - hypernetwork.save(filename) - return hypernetwork, filename + filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt') + hypernetwork.optimizer_name = optimizer_name + if shared.opts.save_optimizer_state: + hypernetwork.optimizer_state_dict = optimizer.state_dict() + save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename) + + del optimizer + hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory. + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) + shared.parallel_processing_allowed = old_parallel_processing_allowed + return hypernetwork, filename +def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename): + old_hypernetwork_name = hypernetwork.name + old_sd_checkpoint = hypernetwork.sd_checkpoint if hasattr(hypernetwork, "sd_checkpoint") else None + old_sd_checkpoint_name = hypernetwork.sd_checkpoint_name if hasattr(hypernetwork, "sd_checkpoint_name") else None + try: + hypernetwork.sd_checkpoint = checkpoint.shorthash + hypernetwork.sd_checkpoint_name = checkpoint.model_name + hypernetwork.name = hypernetwork_name + hypernetwork.save(filename) + except: + hypernetwork.sd_checkpoint = old_sd_checkpoint + hypernetwork.sd_checkpoint_name = old_sd_checkpoint_name + hypernetwork.name = old_hypernetwork_name + raise diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index dfa599afa42..8b6255e2b67 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -1,29 +1,21 @@ import html -import os import gradio as gr +import modules.hypernetworks.hypernetwork +from modules import devices, sd_hijack, shared -import modules.textual_inversion.textual_inversion -import modules.textual_inversion.preprocess -from modules import sd_hijack, shared, devices -from modules.hypernetworks import hypernetwork +not_available = ["hardswish", "multiheadattention"] +keys = [x for x in modules.hypernetworks.hypernetwork.HypernetworkModule.activation_dict if x not in not_available] -def create_hypernetwork(name, enable_sizes): - fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") - assert not os.path.exists(fn), f"file {fn} already exists" +def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, dropout_structure=None): + filename = modules.hypernetworks.hypernetwork.create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure, activation_func, weight_init, add_layer_norm, use_dropout, dropout_structure) - hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes]) - hypernet.save(fn) - - shared.reload_hypernetworks() - - return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", "" + return gr.Dropdown.update(choices=sorted(shared.hypernetworks)), f"Created: {filename}", "" def train_hypernetwork(*args): - - initial_hypernetwork = shared.loaded_hypernetwork + shared.loaded_hypernetworks = [] assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible' @@ -40,7 +32,6 @@ def train_hypernetwork(*args): except Exception: raise finally: - shared.loaded_hypernetwork = initial_hypernetwork shared.sd_model.cond_stage_model.to(devices.device) shared.sd_model.first_stage_model.to(devices.device) sd_hijack.apply_optimizations() diff --git a/modules/images.py b/modules/images.py index b9589563a20..cfdfb338446 100644 --- a/modules/images.py +++ b/modules/images.py @@ -1,4 +1,8 @@ +from __future__ import annotations + import datetime +import functools +import pytz import io import math import os @@ -8,16 +12,27 @@ import numpy as np import piexif import piexif.helper -from PIL import Image, ImageFont, ImageDraw, PngImagePlugin -from fonts.ttf import Roboto +from PIL import Image, ImageFont, ImageDraw, ImageColor, PngImagePlugin, ImageOps +# pillow_avif needs to be imported somewhere in code for it to work +import pillow_avif # noqa: F401 import string +import json +import hashlib -from modules import sd_samplers, shared -from modules.shared import opts, cmd_opts +from modules import sd_samplers, shared, script_callbacks, errors +from modules.paths_internal import roboto_ttf_file +from modules.shared import opts LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) +def get_font(fontsize: int): + try: + return ImageFont.truetype(opts.font or roboto_ttf_file, fontsize) + except Exception: + return ImageFont.truetype(roboto_ttf_file, fontsize) + + def image_grid(imgs, batch_size=1, rows=None): if rows is None: if opts.n_rows > 0: @@ -31,24 +46,37 @@ def image_grid(imgs, batch_size=1, rows=None): else: rows = math.sqrt(len(imgs)) rows = round(rows) + if rows > len(imgs): + rows = len(imgs) cols = math.ceil(len(imgs) / rows) - w, h = imgs[0].size - grid = Image.new('RGB', size=(cols * w, rows * h), color='black') + params = script_callbacks.ImageGridLoopParams(imgs, cols, rows) + script_callbacks.image_grid_callback(params) + + w, h = map(max, zip(*(img.size for img in imgs))) + grid_background_color = ImageColor.getcolor(opts.grid_background_color, 'RGB') + grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color=grid_background_color) - for i, img in enumerate(imgs): - grid.paste(img, box=(i % cols * w, i // cols * h)) + for i, img in enumerate(params.imgs): + img_w, img_h = img.size + w_offset, h_offset = 0 if img_w == w else (w - img_w) // 2, 0 if img_h == h else (h - img_h) // 2 + grid.paste(img, box=(i % params.cols * w + w_offset, i // params.cols * h + h_offset)) return grid -Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"]) +class Grid(namedtuple("_Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])): + @property + def tile_count(self) -> int: + """ + The total number of tiles in the grid. + """ + return sum(len(row[2]) for row in self.tiles) -def split_grid(image, tile_w=512, tile_h=512, overlap=64): - w = image.width - h = image.height +def split_grid(image: Image.Image, tile_w: int = 512, tile_h: int = 512, overlap: int = 64) -> Grid: + w, h = image.size non_overlap_width = tile_w - overlap non_overlap_height = tile_h - overlap @@ -120,7 +148,12 @@ def __init__(self, text='', is_active=True): self.size = None -def draw_grid_annotations(im, width, height, hor_texts, ver_texts): +def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0): + + color_active = ImageColor.getcolor(opts.grid_text_active_color, 'RGB') + color_inactive = ImageColor.getcolor(opts.grid_text_inactive_color, 'RGB') + color_background = ImageColor.getcolor(opts.grid_background_color, 'RGB') + def wrap(drawing, text, font, line_length): lines = [''] for word in text.split(): @@ -131,8 +164,13 @@ def wrap(drawing, text, font, line_length): lines.append(word) return lines - def draw_texts(drawing, draw_x, draw_y, lines): - for i, line in enumerate(lines): + def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize): + for line in lines: + fnt = initial_fnt + fontsize = initial_fontsize + while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0: + fontsize -= 1 + fnt = get_font(fontsize) drawing.multiline_text((draw_x, draw_y + line.size[1] / 2), line.text, font=fnt, fill=color_active if line.is_active else color_inactive, anchor="mm", align="center") if not line.is_active: @@ -143,13 +181,7 @@ def draw_texts(drawing, draw_x, draw_y, lines): fontsize = (width + height) // 25 line_spacing = fontsize // 2 - try: - fnt = ImageFont.truetype(opts.font or Roboto, fontsize) - except Exception: - fnt = ImageFont.truetype(Roboto, fontsize) - - color_active = (0, 0, 0) - color_inactive = (153, 153, 153) + fnt = get_font(fontsize) pad_left = 0 if sum([sum([len(line.text) for line in lines]) for lines in ver_texts]) == 0 else width * 3 // 4 @@ -159,7 +191,7 @@ def draw_texts(drawing, draw_x, draw_y, lines): assert cols == len(hor_texts), f'bad number of horizontal texts: {len(hor_texts)}; must be {cols}' assert rows == len(ver_texts), f'bad number of vertical texts: {len(ver_texts)}; must be {rows}' - calc_img = Image.new("RGB", (1, 1), "white") + calc_img = Image.new("RGB", (1, 1), color_background) calc_d = ImageDraw.Draw(calc_img) for texts, allowed_width in zip(hor_texts + ver_texts, [width] * len(hor_texts) + [pad_left] * len(ver_texts)): @@ -173,34 +205,38 @@ def draw_texts(drawing, draw_x, draw_y, lines): for line in texts: bbox = calc_d.multiline_textbbox((0, 0), line.text, font=fnt) line.size = (bbox[2] - bbox[0], bbox[3] - bbox[1]) + line.allowed_width = allowed_width hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts] - ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in - ver_texts] + ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts] - pad_top = max(hor_text_heights) + line_spacing * 2 + pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2 - result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white") - result.paste(im, (pad_left, pad_top)) + result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), color_background) + + for row in range(rows): + for col in range(cols): + cell = im.crop((width * col, height * row, width * (col+1), height * (row+1))) + result.paste(cell, (pad_left + (width + margin) * col, pad_top + (height + margin) * row)) d = ImageDraw.Draw(result) for col in range(cols): - x = pad_left + width * col + width / 2 + x = pad_left + (width + margin) * col + width / 2 y = pad_top / 2 - hor_text_heights[col] / 2 - draw_texts(d, x, y, hor_texts[col]) + draw_texts(d, x, y, hor_texts[col], fnt, fontsize) for row in range(rows): x = pad_left / 2 - y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2 + y = pad_top + (height + margin) * row + height / 2 - ver_text_heights[row] / 2 - draw_texts(d, x, y, ver_texts[row]) + draw_texts(d, x, y, ver_texts[row], fnt, fontsize) return result -def draw_prompt_matrix(im, width, height, all_prompts): +def draw_prompt_matrix(im, width, height, all_prompts, margin=0): prompts = all_prompts[1:] boundary = math.ceil(len(prompts) / 2) @@ -210,21 +246,40 @@ def draw_prompt_matrix(im, width, height, all_prompts): hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))] ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))] - return draw_grid_annotations(im, width, height, hor_texts, ver_texts) + return draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin) + + +def resize_image(resize_mode, im, width, height, upscaler_name=None): + """ + Resizes an image with the specified resize_mode, width, and height. + Args: + resize_mode: The mode to use when resizing the image. + 0: Resize the image to the specified width and height. + 1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess. + 2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image. + im: The image to resize. + width: The width to resize the image to. + height: The height to resize the image to. + upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img. + """ + + upscaler_name = upscaler_name or opts.upscaler_for_img2img -def resize_image(resize_mode, im, width, height): def resize(im, w, h): - if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L': + if upscaler_name is None or upscaler_name == "None" or im.mode == 'L': return im.resize((w, h), resample=LANCZOS) scale = max(w / im.width, h / im.height) if scale > 1.0: - upscalers = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img] - assert len(upscalers) > 0, f"could not find upscaler named {opts.upscaler_for_img2img}" + upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name] + if len(upscalers) == 0: + upscaler = shared.sd_upscalers[0] + print(f"could not find upscaler named {upscaler_name or ''}, using {upscaler.name} as a fallback") + else: + upscaler = upscalers[0] - upscaler = upscalers[0] im = upscaler.scaler.upscale(im, scale, upscaler.data_path) if im.width != w or im.height != h: @@ -259,24 +314,35 @@ def resize(im, w, h): if ratio < src_ratio: fill_height = height // 2 - src_h // 2 - res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)) - res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h)) + if fill_height > 0: + res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)) + res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h)) elif ratio > src_ratio: fill_width = width // 2 - src_w // 2 - res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)) - res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0)) + if fill_width > 0: + res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)) + res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0)) return res -invalid_filename_chars = '<>:"/\\|?*\n' +if not shared.cmd_opts.unix_filenames_sanitization: + invalid_filename_chars = '#<>:"/\\|?*\n\r\t' +else: + invalid_filename_chars = '/' invalid_filename_prefix = ' ' invalid_filename_postfix = ' .' re_nonletters = re.compile(r'[\s' + string.punctuation + ']+') -max_filename_part_length = 128 +re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)") +re_pattern_arg = re.compile(r"(.*)<([^>]*)>$") +max_filename_part_length = shared.cmd_opts.filenames_max_length +NOTHING_AND_SKIP_PREVIOUS_TEXT = object() def sanitize_filename_part(text, replace_spaces=True): + if text is None: + return None + if replace_spaces: text = text.replace(' ', '_') @@ -286,49 +352,192 @@ def sanitize_filename_part(text, replace_spaces=True): return text -def apply_filename_pattern(x, p, seed, prompt): - max_prompt_words = opts.directories_max_prompt_words +@functools.cache +def get_scheduler_str(sampler_name, scheduler_name): + """Returns {Scheduler} if the scheduler is applicable to the sampler""" + if scheduler_name == 'Automatic': + config = sd_samplers.find_sampler_config(sampler_name) + scheduler_name = config.options.get('scheduler', 'Automatic') + return scheduler_name.capitalize() + + +@functools.cache +def get_sampler_scheduler_str(sampler_name, scheduler_name): + """Returns the '{Sampler} {Scheduler}' if the scheduler is applicable to the sampler""" + return f'{sampler_name} {get_scheduler_str(sampler_name, scheduler_name)}' - if seed is not None: - x = x.replace("[seed]", str(seed)) - if p is not None: - x = x.replace("[steps]", str(p.steps)) - x = x.replace("[cfg]", str(p.cfg_scale)) - x = x.replace("[width]", str(p.width)) - x = x.replace("[height]", str(p.height)) - x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False)) - x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False)) +def get_sampler_scheduler(p, sampler): + """Returns '{Sampler} {Scheduler}' / '{Scheduler}' / 'NOTHING_AND_SKIP_PREVIOUS_TEXT'""" + if hasattr(p, 'scheduler') and hasattr(p, 'sampler_name'): + if sampler: + sampler_scheduler = get_sampler_scheduler_str(p.sampler_name, p.scheduler) + else: + sampler_scheduler = get_scheduler_str(p.sampler_name, p.scheduler) + return sanitize_filename_part(sampler_scheduler, replace_spaces=False) + return NOTHING_AND_SKIP_PREVIOUS_TEXT + + +class FilenameGenerator: + replacements = { + 'basename': lambda self: self.basename or 'img', + 'seed': lambda self: self.seed if self.seed is not None else '', + 'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0], + 'seed_last': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.all_seeds[-1], + 'steps': lambda self: self.p and self.p.steps, + 'cfg': lambda self: self.p and self.p.cfg_scale, + 'width': lambda self: self.image.width, + 'height': lambda self: self.image.height, + 'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False), + 'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False), + 'sampler_scheduler': lambda self: self.p and get_sampler_scheduler(self.p, True), + 'scheduler': lambda self: self.p and get_sampler_scheduler(self.p, False), + 'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash), + 'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False), + 'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'), + 'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime], [datetime