AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning (AI Summary)

Synopsis

AnimateDiff is a method for generating animated images from personalized text-to-image (T2I) models. T2I models have gained attention for their ability to generate high-quality images based on text prompts. However, these models produce static images and lack temporal dynamics. AnimateDiff aims to address this limitation by adding a motion modeling module to personalized T2I models.

The motion modeling module is trained on large-scale video clips to learn motion priors. This module is then inserted into the personalized T2I models, allowing them to generate animated images without additional data collection or customized training. The module is designed as a temporal transformer and operates across frames to capture temporal dependencies and encode motion information.

The authors evaluate AnimateDiff on various representative personalized T2I models, including anime pictures and realistic photographs. They demonstrate that the motion modeling module can successfully animate the personalized models, preserving their visual quality while introducing proper motion. The results show that the module can generate animations with diverse styles and domains.

The article also compares AnimateDiff with a baseline method called Text2Video-Zero, which extends T2I models for video generation through network inflation and latent warping. The comparison shows that AnimateDiff achieves better cross-frame content consistency and temporal smoothness compared to the baseline method.

The authors conduct an ablative study to investigate the impact of different diffusion schedules on the training of the motion modeling module. They find that a slightly modified diffusion schedule from the original pre-training schedule of the T2I model helps improve visual quality and motion smoothness in the generated animations.

Overall, AnimateDiff provides a practical framework for animating personalized T2I models, enabling users to generate animated images with proper motion dynamics. The method shows promising results and has the potential to benefit a wide range of applications in AI-assisted content creation.

Reference: https://arxiv.org/abs/2307.047...