stable-diffusion-dreambooth.ipynb

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        "id": "XU7NuMAA2drw",
        "outputId": "b97880fd-c0fc-4755-b947-d0870933eecd"
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      "outputs": [],
      "source": [
        "#@markdown Check type of GPU and VRAM available.\n",
        "!nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnTMyW41cC1E"
      },
      "source": [
        "## Install Requirements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "aLWXPZqjsZVV",
        "outputId": "4b4cc553-6ba8-464d-fbf4-8eb49acb003d"
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      "outputs": [],
      "source": [
        "!wget -q https://github.com/ShivamShrirao/diffusers/raw/9db565e15e8e35f1ebb48e1409b8ddb129f6f1e9/examples/dreambooth/train_dreambooth.py\n",
        "%pip install -qq git+https://github.com/ShivamShrirao/diffusers\n",
        "%pip install -q -U --pre triton\n",
        "%pip install -q accelerate==0.12.0 transformers ftfy bitsandbytes gradio"
      ]
    },
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      "source": [
        "#@title Login to HuggingFace 🤗\n",
        "\n",
        "#@markdown You need to accept the model license before downloading or using the Stable Diffusion weights. Please, visit the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4), read the license and tick the checkbox if you agree. You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work.\n",
        "from huggingface_hub import notebook_login\n",
        "!git config --global credential.helper store\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XfTlc8Mqb8iH"
      },
      "source": [
        "### Install xformers from precompiled wheel."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6dcjPnnaiCn",
        "outputId": "01864d24-d96c-4ed5-a972-ad2a3b8b4e85"
      },
      "outputs": [],
      "source": [
        "%pip install --no-deps -q https://github.com/brian6091/xformers-wheels/releases/download/0.0.15.dev0%2B4c06c79/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl\n",
        "# These were compiled on Tesla T4, should also work on P100, thanks to https://github.com/metrolobo\n",
        "\n",
        "# If precompiled wheels don't work, install it with the following command. It will take around 40 minutes to compile.\n",
        "# %pip install git+https://github.com/facebookresearch/xformers@1d31a3a#egg=xformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0NV324ZcL9L"
      },
      "source": [
        "## Settings and run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rxg0y5MBudmd",
        "outputId": "209ca360-3c0b-4bec-fbb6-f207d97cfd8d"
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      "outputs": [],
      "source": [
        "#@markdown Name/Path of the initial model.\n",
        "MODEL_NAME = \"CompVis/stable-diffusion-v1-4\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Path for images of the concept for training.\n",
        "INSTANCE_DIR = \"/content/data/sks\" #@param {type:\"string\"}\n",
        "!mkdir -p $INSTANCE_DIR\n",
        "\n",
        "#@markdown A general name for class like dog for dog images.\n",
        "CLASS_NAME = \"guy\" #@param {type:\"string\"}\n",
        "CLASS_DIR = f\"/content/data/{CLASS_NAME}\"\n",
        "\n",
        "#@markdown A rare token name which will reference the subject.\n",
        "TOKEN_NAME = \"sks\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown If model weights should be saved directly in google drive (takes around 4-5 GB).\n",
        "save_to_gdrive = True #@param {type:\"boolean\"}\n",
        "if save_to_gdrive:\n",
        "    from google.colab import drive\n",
        "    drive.mount('/content/drive')\n",
        "\n",
        "#@markdown Enter the directory name to save model at.\n",
        "\n",
        "OUTPUT_DIR = \"stable_diffusion_weights/sks\" #@param {type:\"string\"}\n",
        "if save_to_gdrive:\n",
        "    OUTPUT_DIR = \"/content/drive/MyDrive/\" + OUTPUT_DIR\n",
        "else:\n",
        "    OUTPUT_DIR = \"/content/\" + OUTPUT_DIR\n",
        "\n",
        "print(f\"[*] Weights will be saved at {OUTPUT_DIR}\")\n",
        "\n",
        "!mkdir -p $OUTPUT_DIR\n",
        "\n",
        "#@markdown sks is a rare identifier, feel free to replace it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fe-GgtnUVO_e"
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      "source": [
        "#@markdown Upload your images by running this cell.\n",
        "\n",
        "#@markdown OR\n",
        "\n",
        "#@markdown You can use the file manager on the left panel to upload (drag and drop) to INSTANCE_DIR (it uploads faster)\n",
        "\n",
        "import os\n",
        "from google.colab import files\n",
        "import shutil\n",
        "\n",
        "uploaded = files.upload()\n",
        "for filename in uploaded.keys():\n",
        "    dst_path = os.path.join(INSTANCE_DIR, filename)\n",
        "    shutil.move(filename, dst_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qn5ILIyDJIcX"
      },
      "source": [
        "# Start Training\n",
        "\n",
        "Use the table below to choose the best flags based on your memory and speed requirements. Tested on Tesla T4 GPU.\n",
        "\n",
        "\n",
        "| `fp16` | `train_batch_size` | `gradient_accumulation_steps` | `gradient_checkpointing` | `use_8bit_adam` | GB VRAM usage | Speed (it/s) |\n",
        "| ---- | ------------------ | ----------------------------- | ----------------------- | --------------- | ---------- | ------------ |\n",
        "| fp16 | 1                  | 1                             | TRUE                    | TRUE            | 9.92       | 0.93         |\n",
        "| no   | 1                  | 1                             | TRUE                    | TRUE            | 10.08      | 0.42         |\n",
        "| fp16 | 2                  | 1                             | TRUE                    | TRUE            | 10.4       | 0.66         |\n",
        "| fp16 | 1                  | 1                             | FALSE                   | TRUE            | 11.17      | 1.14         |\n",
        "| no   | 1                  | 1                             | FALSE                   | TRUE            | 11.17      | 0.49         |\n",
        "| fp16 | 1                  | 2                             | TRUE                    | TRUE            | 11.56      | 1            |\n",
        "| fp16 | 2                  | 1                             | FALSE                   | TRUE            | 13.67      | 0.82         |\n",
        "| fp16 | 1                  | 2                             | FALSE                   | TRUE            | 13.7       | 0.83          |\n",
        "| fp16 | 1                  | 1                             | TRUE                    | FALSE           | 15.79      | 0.77         |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ioxxvHoicPs"
      },
      "source": [
        "Add `--gradient_checkpointing` flag for around 9.92 GB VRAM usage.\n",
        "\n",
        "remove `--use_8bit_adam` flag for full precision. Requires 15.79 GB with `--gradient_checkpointing` else 17.8 GB."
      ]
    },
    {
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      },
      "outputs": [],
      "source": [
        "!accelerate launch train_dreambooth.py \\\n",
        "  --pretrained_model_name_or_path=$MODEL_NAME \\\n",
        "  --instance_data_dir=$INSTANCE_DIR \\\n",
        "  --class_data_dir=$CLASS_DIR \\\n",
        "  --output_dir=$OUTPUT_DIR \\\n",
        "  --with_prior_preservation --prior_loss_weight=1.0 \\\n",
        "  --instance_prompt=\"photo of {TOKEN_NAME} {CLASS_NAME}\" \\\n",
        "  --class_prompt=\"photo of a {CLASS_NAME}\" \\\n",
        "  --seed=1337 \\\n",
        "  --resolution=512 \\\n",
        "  --train_batch_size=1 \\\n",
        "  --mixed_precision=\"fp16\" \\\n",
        "  --use_8bit_adam \\\n",
        "  --gradient_accumulation_steps=1 \\\n",
        "  --learning_rate=5e-6 \\\n",
        "  --lr_scheduler=\"constant\" \\\n",
        "  --lr_warmup_steps=0 \\\n",
        "  --num_class_images=50 \\\n",
        "  --sample_batch_size=4 \\\n",
        "  --max_train_steps=1000\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5V8wgU0HN-Kq"
      },
      "source": [
        "## Convert weights to ckpt to use in web UIs like AUTOMATIC1111."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "baL22PHzOLeP"
      },
      "outputs": [],
      "source": [
        "#@markdown Download script\n",
        "!wget -q https://github.com/ShivamShrirao/diffusers/raw/9db565e15e8e35f1ebb48e1409b8ddb129f6f1e9/scripts/convert_diffusers_to_original_stable_diffusion.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89Az5NUxOWdy",
        "outputId": "12c46dc3-7f23-452e-ca41-e06a3be2c14a"
      },
      "outputs": [],
      "source": [
        "#@markdown Run conversion.\n",
        "ckpt_path = OUTPUT_DIR + \"/model.ckpt\"\n",
        "\n",
        "half_arg = \"\"\n",
        "#@markdown  Whether to convert to fp16, takes half the space (2GB), might loose some quality.\n",
        "fp16 = False #@param {type: \"boolean\"}\n",
        "if fp16:\n",
        "    half_arg = \"--half\"\n",
        "!python convert_diffusers_to_original_stable_diffusion.py --model_path $OUTPUT_DIR  --checkpoint_path $ckpt_path $half_arg\n",
        "print(f\"[*] Converted ckpt saved at {ckpt_path}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ToNG4fd_dTbF"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gW15FjffdTID"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import autocast\n",
        "from diffusers import StableDiffusionPipeline\n",
        "from IPython.display import display\n",
        "\n",
        "model_path = OUTPUT_DIR             # If you want to use previously trained model saved in gdrive, replace this with the full path of model in gdrive\n",
        "\n",
        "pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16).to(\"cuda\")\n",
        "g_cuda = None"
      ]
    },
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        "id": "oIzkltjpVO_f",
        "outputId": "7078b114-461c-4bfe-e7cc-596c3b4c0545"
      },
      "outputs": [],
      "source": [
        "#@markdown Can set random seed here for reproducibility.\n",
        "g_cuda = torch.Generator(device='cuda')\n",
        "seed = 52362 #@param {type:\"number\"}\n",
        "g_cuda.manual_seed(seed)"
      ]
    },
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      "source": [
        "#@title Run for generating images.\n",
        "\n",
        "prompt = \"photo of sks man, anime style\" #@param {type:\"string\"}\n",
        "negative_prompt = \"\" #@param {type:\"string\"}\n",
        "num_samples = 4 #@param {type:\"number\"}\n",
        "guidance_scale = 7.5 #@param {type:\"number\"}\n",
        "num_inference_steps = 50 #@param {type:\"number\"}\n",
        "height = 512 #@param {type:\"number\"}\n",
        "width = 512 #@param {type:\"number\"}\n",
        "\n",
        "with autocast(\"cuda\"), torch.inference_mode():\n",
        "    images = pipe(\n",
        "        prompt,\n",
        "        height=height,\n",
        "        width=width,\n",
        "        negative_prompt=negative_prompt,\n",
        "        num_images_per_prompt=num_samples,\n",
        "        num_inference_steps=num_inference_steps,\n",
        "        guidance_scale=guidance_scale,\n",
        "        generator=g_cuda\n",
        "    ).images\n",
        "\n",
        "for img in images:\n",
        "    display(img)"
      ]
    },
    {
      "cell_type": "code",
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        "id": "WMCqQ5Tcdsm2"
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      "source": [
        "#@markdown Run Gradio UI for generating images.\n",
        "import gradio as gr\n",
        "\n",
        "def inference(prompt, negative_prompt, num_samples, height=512, width=512, num_inference_steps=50, guidance_scale=7.5):\n",
        "    with torch.autocast(\"cuda\"), torch.inference_mode():\n",
        "        return pipe(\n",
        "                prompt, height=int(height), width=int(width),\n",
        "                negative_prompt=negative_prompt,\n",
        "                num_images_per_prompt=int(num_samples),\n",
        "                num_inference_steps=int(num_inference_steps), guidance_scale=guidance_scale,\n",
        "                generator=g_cuda\n",
        "            ).images\n",
        "\n",
        "with gr.Blocks() as demo:\n",
        "    with gr.Row():\n",
        "        with gr.Column():\n",
        "            prompt = gr.Textbox(label=\"Prompt\", value=\"photo of sks guy, digital painting\")\n",
        "            negative_prompt = gr.Textbox(label=\"Negative Prompt\", value=\"\")\n",
        "            run = gr.Button(value=\"Generate\")\n",
        "            with gr.Row():\n",
        "                num_samples = gr.Number(label=\"Number of Samples\", value=4)\n",
        "                guidance_scale = gr.Number(label=\"Guidance Scale\", value=7.5)\n",
        "            with gr.Row():\n",
        "                height = gr.Number(label=\"Height\", value=512)\n",
        "                width = gr.Number(label=\"Width\", value=512)\n",
        "            num_inference_steps = gr.Slider(label=\"Steps\", value=50)\n",
        "        with gr.Column():\n",
        "            gallery = gr.Gallery()\n",
        "\n",
        "    run.click(inference, inputs=[prompt, negative_prompt, num_samples, height, width, num_inference_steps, guidance_scale], outputs=gallery)\n",
        "\n",
        "demo.launch(debug=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
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      "source": [
        "#@title (Optional) Delete diffuser weights and only keep the ckpt to free up drive space (4GB).\n",
        "\n",
        "#@markdown [ ! ] Caution, Only execute if you are sure u want to delete the diffuser format weights and only use the ckpt.\n",
        "import shutil\n",
        "from glob import glob\n",
        "for f in glob(OUTPUT_DIR+\"/*\"):\n",
        "    if not f.endswith(\".ckpt\"):\n",
        "        try:\n",
        "            shutil.rmtree(f)\n",
        "        except NotADirectoryError:\n",
        "            continue\n",
        "        print(\"Deleted\", f)"
      ]
    }
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