# AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

**Authors:** Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai  
**arXiv:** [2307.04725](https://arxiv.org/abs/2307.04725)  
**Date:** 2023-07-10  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.04725v1/

## Key points

- Advancement of text-to-image models and personalization techniques
- Growing demand for image animation techniques
- Proposal of a practical framework called AnimateDiff
- Core idea behind AnimateDiff: inserting a motion modeling module into a frozen text-to-image model and training it on video clips
- Ability to animate existing personalized text-to-image models without specific tuning for each model
- Evaluation of the framework on public representative personalized text-to-image models across anime pictures and realistic photographs
- Results show that AnimateDiff enables smooth animation clips while preserving domain and diversity of outputs
- Practical solution for animating personalized text-to-image models without extensive tuning
- Potential to enhance creativity in AI-assisted content creation and provide users with more control over generating animated images
- Code and pre-trained weights for AnimateDiff will be publicly available at https://animatediff.github.io/

## Summary

With the advancement of text-to-image models such as Stable Diffusion and corresponding personalization techniques like DreamBooth and LoRA, the ability to transform imagination into high-quality images at an affordable cost has become accessible to everyone. However, there is a growing demand for image animation techniques that can combine static images generated by these models with motion dynamics. In response to this need, the authors propose a practical framework called AnimateDiff that can animate most existing personalized text-to-image models without requiring specific tuning for each model. The core idea behind AnimateDiff is to insert a newly initialized motion modeling module into a frozen text-to-image model and train it on video clips to extract reasonable motion priors. Once trained, this motion modeling module can be injected into all personalized versions derived from the same base text-to-image model. As a result, these personalized models become text-driven and capable of producing diverse and personalized animated images. To evaluate the effectiveness of their framework, the authors conducted experiments on several public representative personalized text-to-image models across anime pictures and realistic photographs. The results demonstrate that AnimateDiff enables these models to generate temporally smooth animation clips while preserving the domain and diversity of their outputs. Overall, AnimateDiff offers a practical solution for animating personalized text-to-image models without extensive model specific tuning. The proposed framework has the potential to enhance creativity in AI assisted content creation and provide users with more control over generating animated images. Code and pre trained weights for AnimateDiff will be made publicly available at https://animatediff.github.io/.