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Table of contents
  1. Environment Setup
  2. How to Run
  3. Acknowledgments
  4. Note
  5. License
  6. Contacts

Official PyTorch code of "SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation" (CVPR'24)


teaser

Abstract: Despite their ability to generate high-resolution and diverse images from text prompts, text-to-image diffusion models often suffer from slow iterative sampling processes. Model distillation is one of the most effective directions to accelerate these models. However, previous distillation methods fail to retain the generation quality while requiring a significant amount of images for training, either from real data or synthetically generated by the teacher model. In response to this limitation, we present a novel image-free distillation scheme named SwiftBrush. Drawing inspiration from text-to-3D synthesis, in which a 3D neural radiance field that aligns with the input prompt can be obtained from a 2D text-to-image diffusion prior via a specialized loss without the use of any 3D data ground-truth, our approach re-purposes that same loss for distilling a pretrained multi-step text-to-image model to a student network that can generate high-fidelity images with just a single inference step. In spite of its simplicity, our model stands as one of the first one-step text-to-image generators that can produce images of comparable quality to Stable Diffusion without reliance on any training image data. Remarkably, SwiftBrush achieves an FID score of 16.67 and a CLIP score of 0.29 on the COCO-30K benchmark, achieving competitive results or even substantially surpassing existing state-of-the-art distillation techniques.

TLDR: An image-free distillation method that transform multi-step text-to-image diffusion models into one-step generators.

Details of algorithms and experimental results can be found in our following paper:

@InProceedings{nguyen2024swiftbrush,
  title={SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation},
  author={Thuan Hoang Nguyen and Anh Tran},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}

Please CITE our paper whenever this repository is used to help produce published results or incorporated into other software.

Environment Setup

Before running the scripts, make sure to install the library's training dependencies:

Navigate to the swiftbrush folder and setup the conda environment

cd swiftbrush
conda install -n swiftbrush python=3.10

Then activate the conda environment and install all dependencies

conda activate swiftbrush
pip install -r requirements.txt

(Optional) install xformers using the guide from here

And finally initialize an 🤗Accelerate environment with:

accelerate config

Or for a default accelerate configuration without answering questions about your environment

accelerate config default

How to Run

Training

First prepare your own .txt file containing all the prompts for training and pre-generate the text embeddings to save training time. Running the below command will create a text embeddings folder with the same name as the .txt file

python prepare.py \
  --pretrained_model_name_or_path "stabilityai/stable-diffusion-2-1-base" \
  --prompt_list "/path/to/txt_file" \
  --batch_size 32 \
  --num_processes 16

To train a SwiftBrush model, simply run:

accelerate launch train_swiftbrush.py \
  --pretrained_model_name_or_path "stabilityai/stable-diffusion-2-1-base" \
  --train_data_dir "/path/to/text_embeddings_folder" \
  --resolution 512 \
  --use_ema \
  --validation_prompts "A racoon wearing formal clothes, wearing a tophat. Oil painting in the style of Rembrandt" "a zoomed out DSLR photo of a hippo biting through a watermelon" "a lanky tall alien on a romantic date at italian restaurant with a smiling woman, nice restaurant, photography, bokeh" \
  --validation_steps 500 \
  --train_batch_size 16 \
  --gradient_accumulation_steps 1 \
  --set_grads_to_none \
  --guidance_scale 4.5 \
  --learning_rate 1.e-06 \
  --learning_rate_lora 1.e-03 \
  --lr_scheduler "constant" --lr_warmup_steps 0 \
  --lora_rank 64 --lora_alpha 108 \
  --num_train_epochs 3 \
  --checkpointing_steps 10000

For low-memory GPU, you can add --enable_xformers_memory_efficient_attention (xformers must be installed) and/or --gradient_checkpoint arguments to the above command

Inference

To generate an image, simply run:

python infer.py \
  --pretrained_model_name_or_path "thuanz123/swiftbrush" \
  --prompt "A DSLR photo of a shiba on the beach" \
  --seed 0

Acknowledgments

We give thanks to Uy Dieu Tran for early discussions as well as providing many helpful comments and suggestions throughout the project. Special thanks to Trung Tuan Dao for valuable feedback and support. Last but not least, we thank Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su and Jun Zhu for the work of ProlificDreamer as well as Huggingface team for the diffusers framework.

Note

We have also been developing a superior version, SwiftBrush v2, and a brief introduction of its is available here.

License

Copyright (c) 2024 VinAI
Licensed under the Creative Commons Attribution Non Commercial 4.0 International.
You may obtain a copy of the License at
    https://creativecommons.org/licenses/by-nc/4.0/

Contacts

If you have any questions, please drop an email to [email protected] or open an issue in this repository.