VideoGrain is a zero-shot method for class-level, instance-level, and part-level video editing.

• Multi-grained Video Editing
  • class-level: Editing objects within the same class (previous SOTA limited to this level)
  • instance-level: Editing each individual instance to distinct object
  • part-level: Adding new objects or modifying existing attributes at the part-level
• Training-Free
  • Does not require any training/fine-tuning
• One-Prompt Multi-region Control & Deep investigations about cross/self attn
  • modulating cross-attn for multi-regions control (visualizations available)
  • modulating self-attn for feature decoupling (clustering are available)

class level		instance level	part level	animal instances
animal instances		human instances		part-level modification

• [2025/2/25] Our VideoGrain is posted and recommended by Gradio on LinkedIn and Twitter, and recommended by AK.
• [2025/2/25] Our VideoGrain is submited by AK to HuggingFace-daily papers, and rank #1 paper of that day.
• [2025/2/24] We release our paper on arxiv, we also release code and full-data on google drive.
• [2025/1/23] Our paper is accepted to ICLR2025! Welcome to watch 👀 this repository for the latest updates.

Our method is tested using cuda12.1, fp16 of accelerator and xformers on a single L40.

# Step 1: Create and activate Conda environment
conda create -n videograin python==3.10 
conda activate videograin

# Step 2: Install PyTorch, CUDA and Xformers
conda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install --pre -U xformers==0.0.27
# Step 3: Install additional dependencies with pip
pip install -r requirements.txt

xformers is recommended to save memory and running time.

You may download all the base model checkpoints using the following bash command

## download sd 1.5, controlnet depth/pose v10/v11
bash download_all.sh

We have provided all the video data and layout masks in VideoGrain at following link. Please download unzip the data and put them in the `./data' root directory.

gdown https://drive.google.com/file/d/1dzdvLnXWeMFR3CE2Ew0Bs06vyFSvnGXA/view?usp=drive_link
tar -zxvf videograin_data.tar.gz

prepare video to frames If the input video is mp4 file, using the following command to process it to frames:

python image_util/sample_video2frames.py --video_path 'your video path' --output_dir './data/video_name/video_name'

prepare layout masks We segment videos using our ReLER lab's SAM-Track. I suggest using the app.py in SAM-Track for graio mode to manually select which region in the video your want to edit. Here, we also provided an script image_util/process_webui_mask.py to process masks from SAM-Track path to VideoGrain path.

Your can reproduce the instance + part level results in our teaser by running:

bash test.sh 
#or 
CUDA_VISIBLE_DEVICES=0 accelerate launch test.py --config config/part_level/adding_new_object/run_two_man/spider_polar_sunglass.yaml

For other instance/part/class results in VideoGrain project page or teaser, we provide all the data (video frames and layout masks) and corresponding configs to reproduce, check results in 🚀Multi-Grained Video Editing.

result
├── run_two_man
│   ├── control                         # control conditon 
│   ├── infer_samples
│           ├── input                   # the input video frames
│           ├── masked_video.mp4        # check whether edit regions are accuratedly covered
│   ├── sample
│           ├── step_0                  # result image folder
│           ├── step_0.mp4              # result video
│           ├── source_video.mp4        # the input video
│           ├── visualization_denoise   # cross attention weight
│           ├── sd_study                # cluster inversion feature

VideoGrain is a training-free framework. To run VideoGrain on your video, modify ./config/demo_config.yaml based on your needs:

1. Replace your pretrained model path and controlnet path in your config. you can change the control_type to dwpose or depth_zoe or depth(midas).
2. Prepare your video frames and layout masks (edit regions) using SAM-Track or SAM2 in dataset config.
3. Change the prompt, and extract each local prompt in the editing prompts. the local prompt order should be same as layout masks order.
4. Your can change flatten resolution with 1->64, 2->16, 4->8. (commonly, flatten at 64 worked best)
5. To ensure temporal consistency, you can set use_pnp: True and inject_step:5/10. (Note: pnp>10 steps will be bad for multi-regions editing)
6. If you want to visualize the cross attn weight, set vis_cross_attn: True
7. If you want to cluster DDIM Inversion spatial temporal video feature, set cluster_inversion_feature: True

bash test.sh 
#or 
CUDA_VISIBLE_DEVICES=0 accelerate launch test.py --config  /path/to/the/config

You can get multi-grained definition result, using the following command:

CUDA_VISIBLE_DEVICES=0 accelerate launch test.py --config /config/class_level/running_two_man/man2spider.yaml   #class-level
                                                # /config/instance_level/running_two_man/4cls_spider_polar.yaml  #instance-level
                                      #config/part_level/adding_new_object/run_two_man/spider_polar_sunglass.yaml #part-level

source video	class level	instance level	part level

You can get instance-level video editing results, using the following command:

CUDA_VISIBLE_DEVICES=0 accelerate launch test.py --config  config/instance_level/running_two_man/running_3cls_iron_spider.yaml

running_two_man/3cls_iron_spider.yaml	2_monkeys/2cls_teddy_bear_koala.yaml
badminton/2cls_wonder_woman_spiderman.yaml	soap-box/soap-box.yaml
2_cats/4cls_panda_vs_poddle.yaml	2_cars/left_firetruck_right_bus.yaml

You can get part-level video editing results, using the following command:

CUDA_VISIBLE_DEVICES=0 accelerate launch test.py --config config/part_level/modification/man_text_message/blue_shirt.yaml

source video	blue shirt	black suit	source video	ginger head	ginger body
source video	superman	superman + cap	source video	superman	superman + sunglasses

You can get class-level video editing results, using the following command:

CUDA_VISIBLE_DEVICES=0 accelerate launch test.py --config config/class_level/wolf/wolf.yaml

input	pig	husky	bear	tiger
input	iron man	Batman + snow court + iced wall	input	posche

You can get soely video editing results, using the following command:

CUDA_VISIBLE_DEVICES=0 accelerate launch test.py --config config/instance_level/soely_edit/only_left.yaml
                                                #--config config/instance_level/soely_edit/only_right.yaml
                                                #--config config/instance_level/soely_edit/joint_edit.yaml

source video	left→Iron Man	right→Spiderman	joint edit

You can get visulize attention weight editing results, using the following command:

#setting vis_cross_attn: True in your config 
CUDA_VISIBLE_DEVICES=0 accelerate launch test.py --config config/instance_level/running_two_man/3cls_spider_polar_vis_weight.yaml

source video	left→spiderman, right→polar bear, trees→cherry blossoms	spiderman weight	bear weight	cherry weight

If you think this project is helpful, please feel free to leave a star⭐️⭐️⭐️ and cite our paper:

@article{yang2025videograin,
  title={VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing},
  author={Yang, Xiangpeng and Zhu, Linchao and Fan, Hehe and Yang, Yi},
  journal={arXiv preprint arXiv:2502.17258},
  year={2025}
}

Xiangpeng Yang @knightyxp, email: knightyxp@gmail.com/Xiangpeng.Yang@student.uts.edu.au

• This code builds on diffusers, and FateZero. Thanks for open-sourcing!
• We would like to thank AK(@_akhaliq) and Gradio team for recommendation!