BrushNet

This repository contains the implementation of the ECCV2024 paper "BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion"

Keywords: Image Inpainting, Diffusion Models, Image Generation

Xuan Ju¹², Xian Liu¹², Xintao Wang^1*, Yuxuan Bian², Ying Shan¹, Qiang Xu^2*<br> ¹ARC Lab, Tencent PCG ²The Chinese University of Hong Kong ^*Corresponding Author

<p align="center"> <a href="https://tencentarc.github.io/BrushNet/">🌐Project Page</a> | <a href="https://arxiv.org/abs/2403.06976">📜Arxiv</a> | <a href="https://forms.gle/9TgMZ8tm49UYsZ9s5">🗄️Data</a> | <a href="https://drive.google.com/file/d/1IkEBWcd2Fui2WHcckap4QFPcCI0gkHBh/view">📹Video</a> | <a href="https://huggingface.co/spaces/TencentARC/BrushNet">🤗Hugging Face Demo</a> | </p>

📖 Table of Contents

• BrushNet
  • 🔥 Update Log
  • TODO
  • 🛠️ Method Overview
  • 🚀 Getting Started
    • Environment Requirement 🌍
    • Data Download ⬇️
  • 🏃🏼 Running Scripts
    • Training 🤯
    • Inference 📜
    • Evaluation 📏
  • 🤝🏼 Cite Us
  • 💖 Acknowledgement

🔥 Update Log

• [2024/12/17] 📢 📢 BrushEdit are released, an efficient, white-box, free-form image editing tool powered by LLM-agents and an all-in-one inpainting model.
• [2024/12/17] 📢 📢 BrushNetX (Stronger BrushNet) models are released.

TODO

• [x] Release trainig and inference code
• [x] Release checkpoint (sdv1.5)
• [x] Release checkpoint (sdxl). Sadly, I only have V100 for training this checkpoint, which can only train with a batch size of 1 with a slow speed. The current ckpt is only trained for a small step number thus perform not well. But fortunately, yuanhang volunteer to help training a better version. Please stay tuned! Thank yuanhang for his effort!
• [x] Release evluation code
• [x] Release gradio demo
• [x] Release comfyui demo. Thank nullquant (ConfyUI-BrushNet) and kijai (ComfyUI-BrushNet-Wrapper) for helping!
• [x] Release trainig data. Thank random123123 for helping!
• [x] We use BrushNet to participate in CVPR2024 GenAI Media Generation Challenge Workshop and get top prize! The solution is provided in InstructionGuidedEditing
• [x] Release a new version of checkpoint (sdxl).

🛠️ Method Overview

BrushNet is a diffusion-based text-guided image inpainting model that can be plug-and-play into any pre-trained diffusion model. Our architectural design incorporates two key insights: (1) dividing the masked image features and noisy latent reduces the model's learning load, and (2) leveraging dense per-pixel control over the entire pre-trained model enhances its suitability for image inpainting tasks. More analysis can be found in the main paper.

🚀 Getting Started

Environment Requirement 🌍

BrushNet has been implemented and tested on Pytorch 1.12.1 with python 3.9.

Clone the repo:

git clone https://github.com/TencentARC/BrushNet.git

We recommend you first use conda to create virtual environment, and install pytorch following official instructions. For example:

conda create -n diffusers python=3.9 -y
conda activate diffusers
python -m pip install --upgrade pip
pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116

Then, you can install diffusers (implemented in this repo) with:

pip install -e .

After that, you can install required packages thourgh:

cd examples/brushnet/
pip install -r requirements.txt

Data Download ⬇️

Dataset

You can download the BrushData and BrushBench here (as well as the EditBench we re-processed), which are used for training and testing the BrushNet. By downloading the data, you are agreeing to the terms and conditions of the license. The data structure should be like:

|-- data
    |-- BrushData
        |-- 00200.tar
        |-- 00201.tar
        |-- ...
    |-- BrushDench
        |-- images
        |-- mapping_file.json
    |-- EditBench
        |-- images
        |-- mapping_file.json

Noted: We only provide a part of the BrushData in google drive due to the space limit. random123123 has helped upload a full dataset on hugging face here. Thank for his help!

Checkpoints

Checkpoints of BrushNet can be downloaded from here. The ckpt folder contains

• BrushNet pretrained checkpoints for Stable Diffusion v1.5 (segmentation_mask_brushnet_ckpt and random_mask_brushnet_ckpt)
• pretrinaed Stable Diffusion v1.5 checkpoint (e.g., realisticVisionV60B1_v51VAE from Civitai). You can use scripts/convert_original_stable_diffusion_to_diffusers.py to process other models downloaded from Civitai.
• BrushNet pretrained checkpoints for Stable Diffusion XL (segmentation_mask_brushnet_ckpt_sdxl_v1 and random_mask_brushnet_ckpt_sdxl_v0). A better version will be shortly released by yuanhang. Please stay tuned!
• pretrinaed Stable Diffusion XL checkpoint (e.g., juggernautXL_juggernautX from Civitai). You can use StableDiffusionXLPipeline.from_single_file("path of safetensors").save_pretrained("path to save",safe_serialization=False) to process other models downloaded from Civitai.

The data structure should be like:

|-- data
    |-- BrushData
    |-- BrushDench
    |-- EditBench
    |-- ckpt
        |-- realisticVisionV60B1_v51VAE
            |-- model_index.json
            |-- vae
            |-- ...
        |-- segmentation_mask_brushnet_ckpt
        |-- segmentation_mask_brushnet_ckpt_sdxl_v0
        |-- random_mask_brushnet_ckpt
        |-- random_mask_brushnet_ckpt_sdxl_v0
        |-- ...

The checkpoint in segmentation_mask_brushnet_ckpt and segmentation_mask_brushnet_ckpt_sdxl_v0 provide checkpoints trained on BrushData, which has segmentation prior (mask are with the same shape of objects). The random_mask_brushnet_ckpt and random_mask_brushnet_ckpt_sdxl provide a more general ckpt for random mask shape.

🏃🏼 Running Scripts

Training 🤯

You can train with segmentation mask using the script:

# sd v1.5
accelerate launch examples/brushnet/train_brushnet.py \
--pretrained_model_name_or_path runwayml/stable-diffusion-v1-5 \
--output_dir runs/logs/brushnet_segmentationmask \
--train_data_dir data/BrushData \
--resolution 512 \
--learning_rate 1e-5 \
--train_batch_size 2 \
--tracker_project_name brushnet \
--report_to tensorboard \
--resume_from_checkpoint latest \
--validation_steps 300
--checkpointing_steps 10000 

# sdxl
accelerate launch examples/brushnet/train_brushnet_sdxl.py \
--pretrained_model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 \
--output_dir runs/logs/brushnetsdxl_segmentationmask \
--train_data_dir data/BrushData \
--resolution 1024 \
--learning_rate 1e-5 \
--train_batch_size 1 \
--gradient_accumulation_steps 4 \
--tracker_project_name brushnet \
--report_to tensorboard \
--resume_from_checkpoint latest \
--validation_steps 300 \
--checkpointing_steps 10000 

To use custom dataset, you can process your own data to the format of BrushData and revise --train_data_dir.

You can train with random mask using the script (by adding --random_mask):

# sd v1.5
accelerate launch examples/brushnet/train_brushnet.py \
--pretrained_model_name_or_path runwayml/stable-diffusion-v1-5 \
--output_dir runs/logs/brushnet_randommask \
--train_data_dir data/BrushData \
--resolution 512 \
--learning_rate 1e-5 \
--train_batch_size 2 \
--tracker_project_name brushnet \
--report_to tensorboard \
--resume_from_checkpoint latest \
--validation_steps 300 \
--random_mask

# sdxl
accelerate launch examples/brushnet/train_brushnet_sdxl.py \
--pretrained_model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 \
--output_dir runs/logs/brushnetsdxl_randommask \
--train_data_dir data/BrushData \
--resolution 1024 \
--learning_rate 1e-5 \
--train_batch_size 1 \
--gradient_accumulation_steps 4 \
--tracker_project_name brushnet \
--report_to tensorboard \
--resume_from_checkpoint latest \
--validation_steps 300 \
--checkpointing_steps 10000 \
--random_mask

Inference 📜

You can inference with the script:

# sd v1.5
python examples/brushnet/test_brushnet.py
# sdxl
python examples/brushnet/test_brushnet_sdxl.py

Since BrushNet is trained on Laion, it can only guarantee the performance on general scenarios. We recommend you train on your own data (e.g., product exhibition, virtual try-on) if you have high-quality industrial application requirements. We would also be appreciate if you would like to contribute your trained model!

You can also inference through gradio demo:

# sd v1.