Table of Links

Abstract and 1 Introduction

2 Work Related

3 Preliminary

4. AnimateDiff

4.1 Alleviate Negative Effects from Training Data with Domain Adapter

4.2 Learn Motion Priors with Motion Module

4.3 Adapt to New Motion Patterns with MotionLora

4.4 AnimateDiff in Practice

5 Experiments and 5.1 Qualitative Results

5.2 Qualitative Comparison

5.3 Ablative Study

5.4 Controllable Generation

6 Conclusion

7 Ethics Statement

8 Reproducibility Statement, Acknowledgement and References

6 CONCLUSION

In this paper, we present AnimateDiff, a practical pipeline directly turning personalized text-toimage (T2I) models for animation generation once and for all, without compromising quality or losing pre-learned domain knowledge. To accomplish this, we design three component modules in AnimateDiff to learn meaningful motion priors while alleviating visual quality degradation and enabling motion personalization with a lightweight fine-tuning technique named MotionLoRA. Once trained, our motion module can be integrated into other personalized T2Is to generate animated images with natural and coherent motions while remaining faithful to the personalized domain. Extensive evaluation with various personalized T2I models also validates the effectiveness and generalizability of our AnimateDiff and MotionLoRA. Furthermore, we demonstrate the compatibility of our method with existing content-controlling approaches, enabling controllable generation without incurring additional training costs. Overall, AnimateDiff provides an effective baseline for personalized animation and holds significant potential for a wide range of applications.