
自定义文本到视频 模型的动作生成
MotionDirector是一款文本到视频扩散模型定制工具,可根据视频样本学习特定动作概念并应用于视频生成。该工具支持单个或多个参考视频,能准确捕捉动作特征,实现外观和动作的同步定制。此外,MotionDirector还具备图像动画和电影镜头效果功能,为AI视频创作提供更多可能性。
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.
| Type | Training Data | Descriptions | Link |
|---|---|---|---|
| MotionDirector for Sports | Multiple videos for each model. | Learn motion concepts of sports, i.e. lifting weights, riding horse, palying golf, etc. | Link |
| MotionDirector for Cinematic Shots | A single video for each model. | Learn motion concepts of cinematic shots, i.e. dolly zoom, zoom in, zoom out, etc. | Link |
| MotionDirector for Image Animation | A single image for spatial path. And a single video or multiple videos for temporal path. | Animate the given image with learned motions. | Link |
| MotionDirector with Customized Appearance | A single image or multiple images for spatial path. And a single video or multiple videos for temporal path. | Customize both appearance and motion in video generation. | Link |
# create virtual environment conda create -n motiondirector python=3.8 conda activate motiondirector # install packages pip install -r requirements.txt
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/
# 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.
python MotionDirector_train.py --config ./configs/config_multi_videos.yaml
python MotionDirector_train.py --config ./configs/config_single_video.yaml
Note:
config_multi_videos.yaml or config_single_video.yaml.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.n_sample_frames if your GPU memory is limited.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:
/path/to/the/foundation/model with your own path to the foundation model, like ZeroScope.checkpoint_index means the checkpoint saved at which the training step is selected.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.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.
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:
/path/to/the/ZeroScope with your own path to the foundation model, i.e. the ZeroScope.prompt to generate different videos.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:
<table class="center"> <tr> <td style="text-align:center;"><b>Reference Videos</b></td> <td style="text-align:center;" colspan="3"><b>Videos Generated by MotionDirector</b></td> </tr> <tr> <td><img src=assets/multi_videos_results/reference_videos.gif></td> <td><img src=assets/multi_videos_results/A_person_is_riding_a_bicycle_past_the_Eiffel_Tower_7192280.gif></td> <td><img src=assets/multi_videos_results/A_panda_is_riding_a_bicycle_in_a_garden_2178639.gif></td> <td><img src=assets/multi_videos_results/An_alien_is_riding_a_bicycle_on_Mars_2390886.gif></td> </tr> <tr> <td width=25% style="text-align:center;color:gray;">"A person is riding a bicycle."</td> <td width=25% style="text-align:center;">"A person is riding a bicycle past the Eiffel Tower.” </br> seed: 7192280</td> <td width=25% style="text-align:center;">"A panda is riding a bicycle in a garden." </br> seed: <s>2178639</s> </td> <td width=25% style="text-align:center;">"An alien is riding a bicycle on Mars." </br> seed: 2390886</td> </table>16 frames:
<table class="center"> <tr> <td style="text-align:center;"><b>Reference Video</b></td> <td style="text-align:center;" colspan="3"><b>Videos Generated by MotionDirector</b></td> </tr> <tr> <td><img src=assets/single_video_results/reference_video.gif></td> <td><img src=assets/single_video_results/A_tank_is_running_on_the_moon_8551187.gif></td> <td><img src=assets/single_video_results/A_lion_is_running_past_the_pyramids_431554.gif></td> <td><img src=assets/single_video_results/A_spaceship_is_flying_past_Mars_8808231.gif></td> </tr> <tr> <td width=25% style="text-align:center;color:gray;">"A car is running on the road."</td> <td width=25% style="text-align:center;">"A tank is running on the moon.” </br> seed: 8551187</td> <td width=25% style="text-align:center;">"A lion is running past the pyramids." </br> seed: 431554</td> <td width=25% style="text-align:center;">"A spaceship is flying past Mars." </br> seed: 8808231</td> </tr> </table>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:
<table class="center"> <tr> <td style="text-align:center;"><b>Reference Video</b></td> <td style="text-align:center;" colspan="3"><b>Videos Generated by MotionDirector</b></td> </tr> <tr> <td><img src=assets/single_video_results/24_frames/reference_video.gif></td> <td><img src=assets/single_video_results/24_frames/A_truck_is_running_past_the_Arc_de_Triomphe_34543.gif></td> <td><img src=assets/single_video_results/24_frames/An_elephant_is_running_in_a_forest_2171736.gif></td> </tr> <tr> <td width=25% style="text-align:center;color:gray;">"A car is running on the road."</td> <td width=25% style="text-align:center;">"A truck is running past the Arc de Triomphe.” </br> seed: 34543</td> <td width=25% style="text-align:center;">"An elephant is running in a forest." </br> seed: 2171736</td> </tr> <tr> <td><img src=assets/single_video_results/24_frames/reference_video.gif></td> <td><img src=assets/single_video_results/24_frames/A_person_on_a_camel_is_running_past_the_pyramids_4904126.gif></td> <td><imgpython 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


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