Add model card for FlashMotion

Hi! I'm Niels from the Hugging Face community science team. I noticed this repository was missing a model card, so I've opened this PR to add one.

This model card includes:
- A link to the research paper on Hugging Face.
- Links to the project page and GitHub repository.
- A summary of the FlashMotion framework.
- Installation and inference instructions based on the repository documentation.
- The appropriate pipeline tag for better discoverability on the Hub.

Documentation like this helps other researchers and developers understand and build upon your work.

README.md

@@ -0,0 +1,56 @@
+---
+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.
+
+[**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)
+
+## 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
+
+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
+```
+
+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}, 
+}
+```