This repository contains the implementation of the 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 Wang1*, Yuxuan Bian2, Ying Shan1, Qiang Xu2*
1ARC Lab, Tencent PCG 2The Chinese University of Hong Kong *Corresponding Author
๐Project Page | ๐Arxiv | ๐๏ธData | ๐นVideo | ๐คHugging Face Demo |
๐ Table of Contents
- Release trainig and inference code
- Release checkpoint (sdv1.5)
- 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!
- Release evluation code
- Release gradio demo
- Release comfyui demo. Thank nullquant (ConfyUI-BrushNet) and kijai (ComfyUI-BrushNet-Wrapper) for helping!
- Release trainig data. Thank random123123 for helping!
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.
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
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_ckptandrandom_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.pyto process other models downloaded from Civitai. - BrushNet pretrained checkpoints for Stable Diffusion XL (
segmentation_mask_brushnet_ckpt_sdxl_v0andrandom_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.
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
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 v1.5
python examples/brushnet/app_brushnet.py
You can evaluate using the script:
python examples/brushnet/evaluate_brushnet.py \
--brushnet_ckpt_path data/ckpt/segmentation_mask_brushnet_ckpt \
--image_save_path runs/evaluation_result/BrushBench/brushnet_segmask/inside \
--mapping_file data/BrushBench/mapping_file.json \
--base_dir data/BrushBench \
--mask_key inpainting_mask
The --mask_key indicates which kind of mask to use, inpainting_mask for inside inpainting and outpainting_mask for outside inpainting. The evaluation results (images and metrics) will be saved in --image_save_path.
Noted that you need to ignore the nsfw detector in src/diffusers/pipelines/brushnet/pipeline_brushnet.py#1261 to get the correct evaluation results. Moreover, we find different machine may generate different images, thus providing the results on our machine here.
@misc{ju2024brushnet,
title={BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion},
author={Xuan Ju and Xian Liu and Xintao Wang and Yuxuan Bian and Ying Shan and Qiang Xu},
year={2024},
eprint={2403.06976},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Our code is modified based on diffusers, thanks to all the contributors!
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