AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning (2307.04725v2)

Abstract: With the advance of text-to-image (T2I) diffusion models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. However, adding motion dynamics to existing high-quality personalized T2Is and enabling them to generate animations remains an open challenge. In this paper, we present AnimateDiff, a practical framework for animating personalized T2I models without requiring model-specific tuning. At the core of our framework is a plug-and-play motion module that can be trained once and seamlessly integrated into any personalized T2Is originating from the same base T2I. Through our proposed training strategy, the motion module effectively learns transferable motion priors from real-world videos. Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator. We further propose MotionLoRA, a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost. We evaluate AnimateDiff and MotionLoRA on several public representative personalized T2I models collected from the community. The results demonstrate that our approaches help these models generate temporally smooth animation clips while preserving the visual quality and motion diversity. Codes and pre-trained weights are available at https://github.com/guoyww/AnimateDiff.

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

"AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning" presents a novel framework aimed at animating text-to-image (T2I) diffusion models without requiring model-specific fine-tuning. This work addresses a notable gap in the capabilities of current T2I models by introducing motion dynamics into previously static image generation processes. The core innovation is a plug-and-play motion module, trained once and integratable into any personalized T2I models derived from the same base model, such as Stable Diffusion.

Summary of Contributions

1. Framework for T2I Animation: The primary contribution is AnimateDiff, a framework designed to enhance static T2I diffusion models with animation capabilities without compromising visual quality or requiring extensive computational resources. This is achieved through the integration of a pre-trained motion module.
2. Plug-and-Play Motion Module: The motion module learns transferable motion priors from video datasets. Once trained, it can be seamlessly inserted into various personalized T2I models, thereby converting them into animation generators. This module relies on a temporal Transformer architecture to effectively capture motion dynamics.
3. MotionLoRA for Personalized Motion Patterns: MotionLoRA, a lightweight fine-tuning technique, allows the motion module to adapt to specific motion patterns (e.g., different shot types) using a small number of reference videos. This method leverages Low-Rank Adaptation (LoRA) to enable efficient training and fine-tuning, significantly reducing the computational and data collection costs.
4. Comprehensive Evaluation: The paper evaluates AnimateDiff and MotionLoRA on various publicly available personalized T2I models, demonstrating the ability to generate temporally smooth and visually high-quality animations. Experimental results validate the efficacy of the proposed methods in maintaining domain-specific characteristics and motion diversity.

Methodology

The methodology involves three key stages:

1. Domain Adapter Training: To mitigate the negative impacts of the visual domain gap between high-quality image datasets and lower-quality video datasets, the authors propose a domain adapter. Implemented with LoRA layers, this adapter is trained to align the visual distribution during the learning of motion priors, ensuring the motion module focuses on motion rather than pixel-level details.
2. Motion Module Training: The motion module, designed as a temporal Transformer, is trained to learn motion priors from video data. The authors employ network inflation techniques to adapt the 2D T2I models to handle 3D video inputs, allowing the motion module to process temporal information effectively.
3. MotionLoRA Training: For specific motion adaptations, MotionLoRA fine-tunes the motion module using a minimal dataset. The efficiency of MotionLoRA ensures accessibility for users who may lack the resources for extensive pre-training.

Key Results

• Animation Quality: AnimateDiff successfully animates a wide array of personalized T2I models, generating smooth and coherent animations while preserving domain-specific visual characteristics. The qualitative examples show diverse animations ranging from realistic scenes to artistic renditions.
• User Study and CLIP Metrics: Quantitative evaluation through user studies and CLIP scores demonstrates that AnimateDiff outperforms existing methods like Text2Video-Zero and Tune-a-Video in text alignment, domain similarity, and motion smoothness.
• Ablative Studies: The paper includes ablation studies highlighting the importance of the domain adapter and the superiority of the temporal Transformer architecture over convolutional alternatives in capturing motion dynamics.

Implications

Practical Implications:

• Cost-Efficiency: By eliminating the need for model-specific tuning, AnimateDiff significantly reduces the computational resources required for animating T2I models. This makes high-quality animation generation accessible to a broader audience, including amateur creators and small studios.
• Extensibility: The plug-and-play nature of the motion module allows for easy integration with various personalized T2I models, enhancing their utility in creative fields such as gaming, film production, and digital art.

Theoretical Implications:

• Advancement in Video Synthesis: The successful implementation of a temporal Transformer for motion modeling contributes to the field of video synthesis, providing insights for future research on efficient and scalable video generation techniques.
• Hybrid Models: The combination of image-level and motion-level priors underscores the potential for hybrid models that can leverage strengths from both static and dynamic data.

Future Developments

Future research could explore the following avenues:

• Enhanced Motion Patterns: Extending MotionLoRA to accommodate more complex and nuanced motion patterns, potentially leveraging larger datasets or advanced augmentation techniques.
• Real-Time Animation: Optimizing the pipeline for real-time applications, broadening the scope of practical implementations in interactive domains such as virtual reality and live streaming.
• Cross-Model Integrations: Investigating the integration of AnimateDiff with other generative models, such as those for audio or text, to create multifaceted generative systems capable of producing synchronized multimedia content.

Conclusion

AnimateDiff offers a significant advancement in the capability of T2I diffusion models by enabling high-quality animation generation without the need for extensive fine-tuning. Its practical implications for content creators, combined with its theoretical contributions to video synthesis, highlight its potential as a dynamic tool in the field of generative AI. The proposed framework's ability to maintain visual quality while incorporating motion dynamics opens new possibilities for creative expression and technological innovation.