| --- |
| pipeline_tag: image-to-video |
| license: other |
| --- |
| |
| # FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance |
|
|
| FlashMotion is a novel training framework designed for few-step trajectory-controllable video generation. It enables precise motion control along predefined trajectories while significantly reducing the computational overhead and time redundancy typically associated with multi-step denoising processes. |
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|
| [**Project Page**](https://quanhaol.github.io/flashmotion-site/) | [**Paper**](https://huggingface.co/papers/2603.12146) | [**GitHub**](https://github.com/quanhaol/FlashMotion) | [**FlashBench**](https://huggingface.co/datasets/quanhaol/FlashBench) |
|
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| ## Abstract |
| Recent advances in trajectory-controllable video generation have achieved remarkable progress. However, existing methods rely on multi-step denoising, leading to substantial computational overhead. FlashMotion bridges this gap by introducing a three-stage training framework: training a trajectory adapter on a multi-step generator, distilling the generator into a few-step version (FastGenerator), and finally aligning the adapter with the few-step generator using a hybrid diffusion and adversarial objective. |
|
|
| ## Installation |
|
|
| ```bash |
| # Clone this repository. |
| git clone https://github.com/quanhaol/FlashMotion |
| cd FlashMotion |
| |
| # Install requirements |
| conda create -n flashmotion python=3.10 -y |
| conda activate flashmotion |
| pip install -r requirements.txt |
| pip install flash-attn --no-build-isolation |
| python setup.py develop |
| ``` |
|
|
| ## Sample Usage |
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| FlashMotion supports trajectory-controllable video generation using two types of adapters: ResNet and ControlNet. You can run the provided demo scripts for inference: |
|
|
| ```bash |
| # Inference using the ControlNet FastAdapter |
| bash running_scripts/inference/i2v_control_fewstep_controlnet.sh |
| |
| # Inference using the ResNet FastAdapter |
| bash running_scripts/inference/i2v_control_fewstep_resnet.sh |
| ``` |
|
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| You can customize the generation by modifying the `--prompt`, `--image`, and `--trajectory` arguments within the scripts. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{li2026flashmotionfewstepcontrollablevideo, |
| title={FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance}, |
| author={Quanhao Li and Zhen Xing and Rui Wang and Haidong Cao and Qi Dai and Daoguo Dong and Zuxuan Wu}, |
| year={2026}, |
| eprint={2603.12146}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2603.12146}, |
| } |
| ``` |
