Haonan Qiu, Zhaoxi Chen, Zhouxia Wang, Yingqing He, Menghan Xia*, and Ziwei Liu*
(* corresponding author)
🤗🤗🤗 FreeTraj is a tuning-free method for trajectory-controllable video generation based on pre-trained video diffusion models.
"A chihuahua in an astronaut suit floating in the universe, cinematic lighting, glow effect." | "A swan floating gracefully on a lake." | "A corgi running on the grassland on the grassland." |
"A barrel floating in a river." | "A dog running across the garden, photorealistic, 4k." | "A helicopter hovering above a cityscape." |
- [2024.07.04]: 🔥🔥 Release the FreeTraj, trajectory controllable video generation!
- [2024.07.09]: 🔥🔥 Release a user-friendly interface.
- TODO: 1. a powerful mode for better control.
Model | Resolution | Checkpoint | Description |
---|---|---|---|
VideoCrafter2 (Text2Video) | 320x512 | Hugging Face |
conda create -n freetraj python=3.8.5
conda activate freetraj
pip install -r requirements.txt
🤗 Quick start with Gradio
gradio app/app.py
- Download pretrained T2V models via Hugging Face, and put the
model.ckpt
incheckpoints/base_512_v2/model.ckpt
. - Input the following commands in terminal.
sh scripts/run_text2video_freetraj_512.sh
- Write new trajectory files, the format should be
frame index, h start, h end, w start, w end
. In the current version, the bbox size should be the same. Please refer toprompts/freetraj/traj_l.txt
. - Modify
scripts/run_text2video_freetraj_512.sh
and set$traj_file
. - Slightly increase
$ddim_edit
to enhance the control ability, but may reduce the video quality.
@misc{qiu2024freetraj,
title={FreeTraj: Tuning-Free Trajectory Control in Video Diffusion Models},
author={Haonan Qiu and Zhaoxi Chen and Zhouxia Wang and Yingqing He and Menghan Xia and Ziwei Liu},
year={2024},
eprint={2406.16863},
archivePrefix={arXiv}
}
We develop this repository for RESEARCH purposes, so it can only be used for personal/research/non-commercial purposes. The success rate is not guaranteed due to the variety of generative video prior.