Awesome

Task Definition

Motion Customization of Text-to-Video Diffusion Models: </br> Given a set of video clips of the same motion concept, the task of Motion Customization is to adapt existing text-to-video diffusion models to generate diverse videos with this motion.

Demos

Demo Video:

Customize both Appearance and Motion: <a name="Customize_both_Appearance_and_Motion"></a>

News

• [2024.02.03] MotionDirector for AnimateDiff is available. Thanks to ExponentialML.
• [2023.12.27] MotionDirector with Customized Appearance released. Now, you can customize both appearance and motion in video generation.
• [2023.12.27] MotionDirector for Image Animation released.
• [2023.12.23] MotionDirector has been featured in Hugging Face's 'Spaces of the Week 🔥' trending list!
• [2023.12.13] Online gradio demo released @ Hugging Face Spaces! Welcome to try it.
• [2023.12.06] MotionDirector for Sports released! Lifting weights, riding horse, palying golf, etc.
• [2023.12.05] Colab demo is available. Thanks to Camenduru.
• [2023.12.04] MotionDirector for Cinematic Shots released. Now, you can make AI films with professional cinematic shots!
• [2023.12.02] Code and model weights released!
• [2023.10.12] Paper and project page released.

ToDo

• Gradio Demo
• More trained weights of MotionDirector

Model List

Setup

Requirements

# create virtual environment
conda create -n motiondirector python=3.8
conda activate motiondirector
# install packages
pip install -r requirements.txt

Weights of Foundation Models

git lfs install
## You can choose the ModelScopeT2V or ZeroScope, etc., as the foundation model.
## ZeroScope
git clone https://huggingface.co/cerspense/zeroscope_v2_576w ./models/zeroscope_v2_576w/
## ModelScopeT2V
git clone https://huggingface.co/damo-vilab/text-to-video-ms-1.7b ./models/model_scope/

Weights of trained MotionDirector <a name="download_weights"></a>

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/ruizhaocv/MotionDirector_weights ./outputs

# More and better trained MotionDirector are released at a new repo:
git clone https://huggingface.co/ruizhaocv/MotionDirector ./outputs
# The usage is slightly different, which will be updated later.

Usage

Training

Train MotionDirector on multiple videos:

python MotionDirector_train.py --config ./configs/config_multi_videos.yaml

Train MotionDirector on a single video:

python MotionDirector_train.py --config ./configs/config_single_video.yaml

Note:

• Before running the above command, make sure you replace the path to foundational model weights and training data with your own in the config files config_multi_videos.yaml or config_single_video.yaml.
• Generally, training on multiple 16-frame videos usually takes 300~500 steps, about 9~16 minutes using one A5000 GPU. Training on a single video takes 50~150 steps, about 1.5~4.5 minutes using one A5000 GPU. The required VRAM for training is around 14GB.
• Reduce n_sample_frames if your GPU memory is limited.
• Reduce the learning rate and increase the training steps for better performance.

Inference

python MotionDirector_inference.py --model /path/to/the/foundation/model  --prompt "Your prompt" --checkpoint_folder /path/to/the/trained/MotionDirector --checkpoint_index 300 --noise_prior 0.

Note:

• Replace /path/to/the/foundation/model with your own path to the foundation model, like ZeroScope.
• The value of checkpoint_index means the checkpoint saved at which the training step is selected.
• The value of noise_prior indicates how much the inversion noise of the reference video affects the generation. We recommend setting it to 0 for MotionDirector trained on multiple videos to achieve the highest diverse generation, while setting it to 0.1~0.5 for MotionDirector trained on a single video for faster convergence and better alignment with the reference video.

Inference with pre-trained MotionDirector

All available weights are at official Huggingface Repo. Run the download command, the weights will be downloaded to the folder outputs, then run the following inference command to generate videos.

MotionDirector trained on multiple videos:

python MotionDirector_inference.py --model /path/to/the/ZeroScope  --prompt "A person is riding a bicycle past the Eiffel Tower." --checkpoint_folder ./outputs/train/riding_bicycle/ --checkpoint_index 300 --noise_prior 0. --seed 7192280

Note:

• Replace /path/to/the/ZeroScope with your own path to the foundation model, i.e. the ZeroScope.
• Change the prompt to generate different videos.
• The seed is set to a random value by default. Set it to a specific value will obtain certain results, as provided in the table below.

Results:

MotionDirector trained on a single video:

16 frames:

python MotionDirector_inference.py --model /path/to/the/ZeroScope  --prompt "A tank is running on the moon." --checkpoint_folder ./outputs/train/car_16/ --checkpoint_index 150 --noise_prior 0.5 --seed 8551187

24 frames:

python MotionDirector_inference.py --model /path/to/the/ZeroScope  --prompt "A truck is running past the Arc de Triomphe." --checkpoint_folder ./outputs/train/car_24/ --checkpoint_index 150 --noise_prior 0.5 --width 576 --height 320 --num-frames 24 --seed 34543

MotionDirector for Sports <a name="MotionDirector_for_Sports"></a>

python MotionDirector_inference.py --model /path/to/the/ZeroScope  --prompt "A panda is lifting weights in a garden." --checkpoint_folder ./outputs/train/lifting_weights/ --checkpoint_index 300 --noise_prior 0. --seed 9365597

More sports, to be continued ...

MotionDirector for Cinematic Shots <a name="MotionDirector_for_Cinematic_Shots"></a>

1. Zoom

1.1 Dolly Zoom (Hitchcockian Zoom)

python MotionDirector_inference.py --model /path/to/the/ZeroScope  --prompt "A firefighter standing in front of a burning forest captured with a dolly zoom." --checkpoint_folder ./outputs/train/dolly_zoom/ --checkpoint_index 150 --noise_prior 0.5 --seed 9365597

1.2 Zoom In

The reference video is shot with my own water cup. You can also pick up your cup or any other object to practice camera movements and turn it into imaginative videos. Create your AI films with customized camera movements!

python MotionDirector_inference.py --model /path/to/the/ZeroScope  --prompt "A firefighter standing in front of a burning forest captured with a zoom in." --checkpoint_folder ./outputs/train/zoom_in/ --checkpoint_index 150 --noise_prior 0.3 --seed 1429227

1.3 Zoom Out

python MotionDirector_inference.py --model /path/to/the/ZeroScope  --prompt "A firefighter standing in front of a burning forest captured with a zoom out." --checkpoint_folder ./outputs/train/zoom_out/ --checkpoint_index 150 --noise_prior 0.3 --seed 4971910

2. Advanced Cinematic Shots

More Cinematic Shots, to be continued ....

MotionDirector for Image Animation <a name="MotionDirector_for_Image_Animation"></a>

Train

Train the spatial path with reference image.

python MotionDirector_train.py --config ./configs/config_single_image.yaml

Then train the temporal path to learn the motion in reference video.

python MotionDirector_train.py --config ./configs/config_single_video.yaml

Inference

Inference with spatial path learned from reference image and temporal path learned form reference video.

python MotionDirector_inference_multi.py --model /path/to/the/foundation/model  --prompt "Your prompt" --spatial_path_folder /path/to/the/trained/MotionDirector/spatial/lora/ --temporal_path_folder /path/to/the/trained/MotionDirector/temporal/lora/ --noise_prior 0.

Example

Download the pre-trained weights.

git clone https://huggingface.co/ruizhaocv/MotionDirector ./outputs

Run the following command.

python MotionDirector_inference_multi.py --model /path/to/the/ZeroScope  --prompt "A car is running on the road." --spatial_path_folder ./outputs/train/image_animation/train_2023-12-26T14-37-16/checkpoint-300/spatial/lora/ --temporal_path_folder ./outputs/train/image_animation/train_2023-12-26T13-08-20/checkpoint-300/temporal/lora/ --noise_prior 0.5 --seed 5057764

MotionDirector with Customized Appearance <a name="MotionDirector_with_Customized_Appearance"></a>

Train

Train the spatial path with reference images.

python MotionDirector_train.py --config ./configs/config_multi_images.yaml

Then train the temporal path to learn the motions in reference videos.

python MotionDirector_train.py --config ./configs/config_multi_videos.yaml

Inference

Inference with spatial path learned from reference images and temporal path learned form reference videos.

python MotionDirector_inference_multi.py --model /path/to/the/foundation/model  --prompt "Your prompt" --spatial_path_folder /path/to/the/trained/MotionDirector/spatial/lora/ --temporal_path_folder /path/to/the/trained/MotionDirector/temporal/lora/ --noise_prior 0.

Example

Download the pre-trained weights.

git clone https://huggingface.co/ruizhaocv/MotionDirector ./outputs

Run the following command.

python MotionDirector_inference_multi.py --model /path/to/the/ZeroScope  --prompt "A Terracotta Warrior is riding a horse through an ancient battlefield." --spatial_path_folder ./outputs/train/customized_appearance/terracotta_warrior/checkpoint-default/spatial/lora --temporal_path_folder ./outputs/train/riding_horse/checkpoint-default/temporal/lora/ --noise_prior 0. --seed 1455028

Results are shown in the table.

More results

If you have a more impressive MotionDirector or generated videos, please feel free to open an issue and share them with us. We would greatly appreciate it. Improvements to the code are also highly welcome.

Please refer to Project Page for more results.

Astronaut's daily life on Mars:

Citation

@article{zhao2023motiondirector,
  title={MotionDirector: Motion Customization of Text-to-Video Diffusion Models},
  author={Zhao, Rui and Gu, Yuchao and Wu, Jay Zhangjie and Zhang, David Junhao and Liu, Jiawei and Wu, Weijia and Keppo, Jussi and Shou, Mike Zheng},
  journal={arXiv preprint arXiv:2310.08465},
  year={2023}
}

Shoutouts

• This code builds on diffusers, Tune-a-video and Text-To-Video-Finetuning. Thanks for open-sourcing!
• Thanks to camenduru for the colab demo.
• Thanks to yhyu13 for the Huggingface Repo.
• We would like to thank AK(@_akhaliq) and huggingface team for the help of setting up oneline gradio demo.
• Thanks to MagicAnimate for the gradio demo template.
• Thanks to deepbeepmeep, and XiaominLi for improving the code.