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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[SIGGRAPH ASIA 2024 TCS] AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data
| Date | Stars |
|---|---|
| 2026-07-24 | 659 |
| 2026-07-25 | 659 |
| 2026-07-28 | 659 |
| 2026-07-30 | 659 |
| 2026-08-06 | 659 |
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<div align="center">
## ⚡️AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data
[[Paper]](https://arxiv.org/abs/2402.00769) [[Project Page ✨]](https://animatelcm.github.io/) [[Demo in 🤗Hugging Face]](https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD) [[Pre-trained Models]](https://huggingface.co/wangfuyun/AnimateLCM) [[Civitai]](https://civitai.com/models/290375/animatelcm-fast-video-generation) 
by *[Fu-Yun Wang](https://g-u-n.github.io), Zhaoyang Huang📮, Weikang Bian, Xiaoyu Shi, Keqiang Sun, Guanglu Song, Yu Liu, Hongsheng Li📮*
</div>
| Example 1 | Example 2 | Example 3 |
|-----------------|-----------------|-----------------|
|  |  |  |
If you use any components of our work, please cite it.
```
@article{wang2024animatelcm,
title={AnimateLCM: Accelerating the Animation of Personalized Diffusion Models and Adapters with Decoupled Consistency Learning},
author={Wang, Fu-Yun and Huang, Zhaoyang and Shi, Xiaoyu and Bian, Weikang and Song, Guanglu and Liu, Yu and Li, Hongsheng},
journal={arXiv preprint arXiv:2402.00769},
year={2024}
}
```
### News
- [2024.05]: 🔥🔥🔥 We release the [training script](https://github.com/G-U-N/AnimateLCM/blob/master/animatelcm_svd/train_svd_lcm.py) for accelerating Stable Video Diffusion.
- [2024.03]: 😆😆😆 We release the AnimateLCM-I2V and AnimateLCM-SVD for fast image animation.
- [2024.02]: 🤗🤗🤗 Release pretrained model weights and Huggingface Demo.
- [2024.02]: 💡💡💡 Technical report is available on arXiv.
Here is a screen recording of usage. Prompt:"river reflecting mountain"
### Introduction
Animate-LCM is **a pioneer work** and exploratory on fast animation generation following the consistency models, being able to generate animations in good quality with 4 inference steps.
It relies on the **decoupled** learning paradigm, firstly learning image generation prior and then learning the temporal generation prior for fast sampling, greatly boosting the training efficiency.
The High-level workflow of AnimateLCM can be
<div align="center">
<img src="__assets__/imgs/demo_figure.png" alt="comparison" style="zoom:80%;" />
</div>
### Demos
We have **launched lots of demo videos generated by Animate-LCM on the [Project Page](https://animatelcm.github.io/)**. Generally speaking, AnimateLCM works for fast, text-to-video, control-to-video, image-to-video, video-to-video stylization, and longer video generation.
<div align="center">
<img src="__assets__/imgs/examples.png" alt="comparison" style="zoom:80%;" />
</div>
### Models
So far, we have released three models for usage
- [Animate-LCM-T2V](https://huggingface.co/wangfuyun/AnimateLCM): A spatial LoRA weight and a motion module for personalized video generation. Some trying from the community point out that the motion module is also compatible with many personalized models tuned for LCM, for example [Dreamshaper-LCM](https://civitai.com/models/4384?modelVersionId=252914).
- [AnimateLCM-SVD-xt](https://huggingface.co/wangfuyun/AnimateLCM-SVD-xt). I provide AnimateLCM-SVD-xt and AnimateLCM-SVD-xt 1.1, which are tuned from [SVD-xt](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt) and [SVD-xt 1.1](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt-1-1) respectively. They work for high-resolution image animation with 25 frames with 1~8 steps. You can try it with the Hugging Face [Demo](https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD). Thanks to the Hugging Face team for providing the GPU grants.
- [AnimateLCM-I2V](https://huggingface.co/wangfuyun/AnimateLCM-I2V). A spatial LoRA weight and a motion module with an additional iExcerpt of 7,842 characters
Read on GitHub
82
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c2251dbc97d7f407, topic:video-generation, desc:video generation, readme:video generation
matched fp:c2251dbc97d7f407, topic:deep-learning