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VideoPainter

Name: VideoPainter
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VideoPainter

This repository contains the implementation of the paper "VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control"

Keywords: Video Inpainting, Video Editing, Video Generation

Yuxuan Bian¹², Zhaoyang Zhang^1‡, Xuan Ju², Mingdeng Cao³, Liangbin Xie⁴, Ying Shan¹, Qiang Xu^2✉
¹ARC Lab, Tencent PCG ²The Chinese University of Hong Kong ³The University of Tokyo ⁴University of Macau ^‡Project Lead ^✉Corresponding Author

Your star means a lot for us to develop this project! ⭐⭐⭐

VPData and VPBench have been fully uploaded (contain 390K mask sequences and video captions). Welcome to use our biggest video segmentation dataset VPData with video captions! 🔥🔥🔥

📖 Table of Contents

VideoPainter

🔥 Update Log

[2025/3/09] 📢 📢 VideoPainter are released, an efficient, any-length video inpainting & editing framework with plug-and-play context control.
[2025/3/09] 📢 📢 VPData and VPBench are released, the largest video inpainting dataset with precise segmentation masks and dense video captions (>390K clips).
[2025/3/25] 📢 📢 The 390K+ high-quality video segmentation masks of VPData have been fully released.
[2025/3/25] 📢 📢 The raw videos of videovo subset have been uploaded to VPData, to solve the raw video link expiration issue.

TODO

Release trainig and inference code
Release evaluation code
Release VideoPainter checkpoints (based on CogVideoX-5B)
Release VPData and VPBench for large-scale training and evaluation.
Release gradio demo
Data preprocessing code

🛠️ Method Overview

We propose a novel dual-stream paradigm VideoPainter that incorporates an efficient context encoder (comprising only 6% of the backbone parameters) to process masked videos and inject backbone-aware background contextual cues to any pre-trained video DiT, producing semantically consistent content in a plug-and-play manner. This architectural separation significantly reduces the model's learning complexity while enabling nuanced integration of crucial background context. We also introduce a novel target region ID resampling technique that enables any-length video inpainting, greatly enhancing our practical applicability. Additionally, we establish a scalable dataset pipeline leveraging current vision understanding models, contributing VPData and VPBench to facilitate segmentation-based inpainting training and assessment, the largest video inpainting dataset and benchmark to date with over 390K diverse clips. Using inpainting as a pipeline basis, we also explore downstream applications including video editing and video editing pair data generation, demonstrating competitive performance and significant practical potential.

🚀 Getting Started

Environment Requirement 🌍

Clone the repo:

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

We recommend you first use conda to create virtual environment, and install needed libraries. For example:

conda create -n videopainter python=3.10 -y
conda activate videopainter
pip install -r requirements.txt

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

cd ./diffusers
pip install -e .

After that, you can install required ffmpeg thourgh:

conda install -c conda-forge ffmpeg -y

Optional, you can install sam2 for gradio demo thourgh:

cd ./app
pip install -e .

VPBench and VPData Download ⬇️

You can download the VPBench here, and the VPData here (as well as the Davis 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
    |-- davis
        |-- JPEGImages_432_240
        |-- test_masks
        |-- davis_caption
        |-- test.json
        |-- train.json
    |-- videovo/raw_video
        |-- 000005000
            |-- 000005000000.0.mp4
            |-- 000005000001.0.mp4
            |-- ...
        |-- 000005001
        |-- ...
    |-- pexels/pexels/raw_video
        |-- 000000000
            |-- 000000000000_852038.mp4
            |-- 000000000001_852057.mp4
            |-- ...
        |-- 000000001
        |-- ...
    |-- video_inpainting
        |-- videovo
            |-- 000005000000/all_masks.npz
            |-- 000005000001/all_masks.npz
            |-- ...
        |-- pexels
            |-- ...
    |-- pexels_videovo_train_dataset.csv
    |-- pexels_videovo_val_dataset.csv
    |-- pexels_videovo_test_dataset.csv
    |-- our_video_inpaint.csv
    |-- our_video_inpaint_long.csv
    |-- our_video_edit.csv
    |-- our_video_edit_long.csv
    |-- pexels.csv
    |-- videovo.csv

You can download the VPBench, and put the benchmark to the data folder by:

git lfs install
git clone https://huggingface.co/datasets/TencentARC/VPBench
mv VPBench data
cd data
unzip pexels.zip
unzip videovo.zip
unzip davis.zip
unzip video_inpainting.zip

You can download the VPData (only mask and text annotations due to the space limit), and put the dataset to the data folder by:

git lfs install
git clone https://huggingface.co/datasets/TencentARC/VPData
mv VPBench data

# 1. unzip the masks in VPData
python data_utils/unzip_folder.py --source_dir ./data/videovo_masks --target_dir ./data/video_inpainting/videovo
python data_utils/unzip_folder.py --source_dir ./data/pexels_masks --target_dir ./data/video_inpainting/pexels

# 2. unzip the raw videos in Videovo subset in VPData
python data_utils/unzip_folder.py --source_dir ./data/videovo_raw_videos --target_dir ./data/videovo/raw_video

Noted: Due to the space limit, you need to run the following script to download the raw videos of the Pexels subset in VPData. The format should be consistent with VPData/VPBench above (After download the VPData/VPBench, the script will automatically place the raw videos of VPData into the corresponding dataset directories that have been created by VPBench).

cd data_utils
python VPData_download.py

Checkpoints

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

VideoPainter pretrained checkpoints for CogVideoX-5b-I2V
VideoPainter IP Adapter pretrained checkpoints for CogVideoX-5b-I2V
pretrinaed CogVideoX-5b-I2V checkpoint from HuggingFace.

You can download the checkpoints, and put the checkpoints to the ckpt folder by:

git lfs install
git clone https://huggingface.co/TencentARC/VideoPainter
mv VideoPainter ckpt

You also need to download the base model CogVideoX-5B-I2V by:

git lfs install
cd ckpt
git clone https://huggingface.co/THUDM/CogVideoX-5b-I2V

[Optional]You need to download FLUX.1-Fill-dev for first frame inpainting:

git lfs install
cd ckpt
git clone https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev
mv ckpt/FLUX.1-Fill-dev ckpt/flux_inp

[Optional]You need to download SAM2 for video segmentation in gradio demo:

git lfs install
cd ckpt
wget https://huggingface.co/facebook/sam2-hiera-large/resolve/main/sam2_hiera_large.pt

You can also choose the segmentation checkpoints of other sizes to balance efficiency and performance, such as SAM2-Tiny.

The ckpt structure should be like:

|-- ckpt
    |-- VideoPainter/checkpoints
        |-- branch
            |-- config.json
            |-- diffusion_pytorch_model.safetensors
    |-- VideoPainterID/checkpoints
        |-- pytorch_lora_weights.safetensors
    |-- CogVideoX-5b-I2V
        |-- scheduler
        |-- transformer
        |-- vae
        |-- ...
    |-- flux_inp
        |-- scheduler
        |-- transformer
        |-- vae
        |-- ...
    |-- sam2_hiera_large.pt

🏃🏼 Running Scripts

Training 🤯

You can train the VideoPainter using the script:

# cd train
# bash VideoPainter.sh

export MODEL_PATH="../ckpt/CogVideoX-5b-I2V"
export CACHE_PATH="~/.cache"
export DATASET_PATH="../data/videovo/raw_video"
export PROJECT_NAME="pexels_videovo-inpainting"
export RUNS_NAME="VideoPainter"
export OUTPUT_PATH="./${PROJECT_NAME}/${RUNS_NAME}"
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
export TOKENIZERS_PARALLELISM=false
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

accelerate launch --config_file accelerate_config_machine_single_ds.yaml  --machine_rank 0 \
  train_cogvideox_inpainting_i2v_video.py \
  --pretrained_model_name_or_path $MODEL_PATH \
  --cache_dir $CACHE_PATH \
  --meta_file_path ../data/pexels_videovo_train_dataset.csv \
  --val_meta_file_path ../data/pexels_videovo_val_dataset.csv \
  --instance_data_root $DATASET_PATH \
  --dataloader_num_workers 1 \
  --num_validation_videos 1 \
  --validation_epochs 1 \
  --seed 42 \
  --mixed_precision bf16 \
  --output_dir $OUTPUT_PATH \
  --height 480 \
  --width 720 \
  --fps 8 \
  --max_num_frames 49 \
  --video_reshape_mode "resize" \
  --skip_frames_start 0 \
  --skip_frames_end 0 \
  --max_text_seq_length 226 \
  --branch_layer_num 2 \
  --train_batch_size 1 \
  --num_train_epochs 10 \
  --checkpointing_steps 1024 \
  --validating_steps 256 \
  --gradient_accumulation_steps 1 \
  --learning_rate 1e-5 \
  --lr_scheduler cosine_with_restarts \
  --lr_warmup_steps 1000 \
  --lr_num_cycles 1 \
  --enable_slicing \
  --enable_tiling \
  --noised_image_dropout 0.05 \
  --gradient_checkpointing \
  --optimizer AdamW \
  --adam_beta1 0.9 \
  --adam_beta2 0.95 \
  --max_grad_norm 1.0 \
  --allow_tf32 \
  --report_to wandb \
  --tracker_name $PROJECT_NAME \
  --runs_name $RUNS_NAME \
  --inpainting_loss_weight 1.0 \
  --mix_train_ratio 0 \
  --first_frame_gt \
  --mask_add \
  --mask_transform_prob 0.3 \
  --p_brush 0.4 \
  --p_rect 0.1 \
  --p_ellipse 0.1 \
  --p_circle 0.1 \
  --p_random_brush 0.3

# cd train
# bash VideoPainterID.sh
export MODEL_PATH="../ckpt/CogVideoX-5b-I2V"
export BRANCH_MODEL_PATH="../ckpt/VideoPainter/checkpoints/branch"
export CACHE_PATH="~/.cache"
export DATASET_PATH="../data/videovo/raw_video"
export PROJECT_NAME="pexels_videovo-inpainting"
export RUNS_NAME="VideoPainterID"
export OUTPUT_PATH="./${PROJECT_NAME}/${RUNS_NAME}"
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
export TOKENIZERS_PARALLELISM=false
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

accelerate launch --config_file accelerate_config_machine_single_ds_wo_cpu.yaml --machine_rank 0 \
  train_cogvideox_inpainting_i2v_video_resample.py \
  --pretrained_model_name_or_path $MODEL_PATH \
  --cogvideox_branch_name_or_path $BRANCH_MODEL_PATH \
  --cache_dir $CACHE_PATH \
  --meta_file_path ../data/pexels_videovo_train_dataset.csv \
  --val_meta_file_path ../data/pexels_videovo_val_dataset.csv \
  --instance_data_root $DATASET_PATH \
  --dataloader_num_workers 1 \
  --num_validation_videos 1 \
  --validation_epochs 1 \
  --seed 42 \
  --rank 256 \
  --lora_alpha 128 \
  --mixed_precision bf16 \
  --output_dir $OUTPUT_PATH \
  --height 480 \
  --width 720 \
  --fps 8 \
  --max_num_frames 49 \
  --video_reshape_mode "resize" \
  --skip_frames_start 0 \
  --skip_frames_end 0 \
  --max_text_seq_length 226 \
  --branch_layer_num 2 \
  --train_batch_size 1 \
  --num_train_epochs 10 \
  --checkpointing_steps 256 \
  --validating_steps 128 \
  --gradient_accumulation_steps 1 \
  --learning_rate 5e-5 \
  --lr_scheduler cosine_with_restarts \
  --lr_warmup_steps 200 \
  --lr_num_cycles 1 \
  --enable_slicing \
  --enable_tiling \
  --noised_image_dropout 0.05 \
  --gradient_checkpointing \
  --optimizer AdamW \
  --adam_beta1 0.9 \
  --adam_beta2 0.95 \
  --max_grad_norm 1.0 \
  --allow_tf32 \
  --report_to wandb \
  --tracker_name $PROJECT_NAME \
  --runs_name $RUNS_NAME \
  --inpainting_loss_weight 1.0 \
  --mix_train_ratio 0 \
  --first_frame_gt \
  --mask_add \
  --mask_transform_prob 0.3 \
  --p_brush 0.4 \
  --p_rect 0.1 \
  --p_ellipse 0.1 \
  --p_circle 0.1 \
  --p_random_brush 0.3 \
  --id_pool_resample_learnable

Inference 📜

You can inference for the video inpainting or editing with the script:

cd infer
# video inpainting
bash inpaint.sh
# video inpainting with ID resampling
bash inpaint_id_resample.sh
# video editing
bash edit.sh

Our VideoPainter can also function as a video editing pair data generator, you can inference with the script:

bash edit_bench.sh

Since VideoPainter is trained on public Internet videos, it primarily performs well on general scenarios. For high-quality industrial applications (e.g., product exhibitions, virtual try-on), we recommend training the model on your domain-specific data. We welcome and appreciate any contributions of trained models from the community!

Gradio Demo 🖌️

You can also inference through gradio demo:

# cd app
CUDA_VISIBLE_DEVICES=0 python app.py \
    --model_path ../ckpt/CogVideoX-5b-I2V \
    --inpainting_branch ../ckpt/VideoPainter/checkpoints/branch \
    --id_adapter ../ckpt/VideoPainterID/checkpoints \
    --img_inpainting_model ../ckpt/flux_inp

Evaluation 📏

You can evaluate using the script:

cd evaluate
# video inpainting
bash eval_inpainting.sh
# video inpainting with ID resampling
bash eval_inpainting_id_resample.sh
# video editing
bash eval_edit.sh
# video editing with ID resampling
bash eval_editing_id_resample.sh

🤝🏼 Cite Us

@article{bian2025videopainter,
  title={VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control},
  author={Bian, Yuxuan and Zhang, Zhaoyang and Ju, Xuan and Cao, Mingdeng and Xie, Liangbin and Shan, Ying and Xu, Qiang},
  journal={arXiv preprint arXiv:2503.05639},
  year={2025}
}

💖 Acknowledgement

Our code is modified based on diffusers and CogVideoX, thanks to all the contributors!

Jayeep/VideoPainter