Meet AnimateDiff: An Effective AI Framework For Extending Personalized Text-to-Image (T2I) Models Into An Animation Generator Without Model-Specific Tuning

Text-to-image (T2I) generative models have attracted unheard-of attention from both within and outside the research community, serving as a low-barrier entry point for non-researcher users like artists and amateurs to engage in AI-assisted content creation. Several lightweight personalization techniques, such as DreamBooth and LoRA, are suggested to enable customized fine-tuning of these models on small datasets with a consumer-grade device like a laptop with an RTX3080, after which these models can produce customized content with noticeably improved quality. These techniques aim further to encourage the creativity of existing T2I generative models. 

This enables users to quickly and affordably add fresh ideas or aesthetics to a pre-trained T2I model, which has led to the proliferation of customized models created by professionals and amateurs on model-sharing websites like CivitAI and Huggingface. Although customized text-to-image models developed using DreamBooth or LoRA have earned admiration for their exceptional visual quality, they only produce static pictures. The lack of a temporal degree of flexibility is the main issue. They want to know if they can convert most of the current customized T2I models into models that create animated pictures while maintaining the original visual quality in light of the diverse uses of animation. 

Incorporating temporal modeling into the initial T2I models and fine-tuning the models using the video datasets are two recent generic text-to-video generating techniques’ recommendations. But for customized T2I models, it becomes difficult since consumers often need help to afford the delicate hyperparameter tweaking, customized video collecting, and demanding computing resources. In this work, researchers from Shanghai AI Laboratory, The Chinese University of Hong Kong, and Stanford University describe a generic technique called AnimateDiff that enables the creation of animated pictures for any customized T2I model without the need for model-specific tweaking and with aesthetically pleasing content consistency across time. 

Given that the majority of customized T2I models are derived from the same base model (such as stable diffusion) and that gathering the corresponding videos for each customized domain is not feasible, they turn to design a motion modeling module that could finally animate the majority of customized T2I models. To put it more specifically, a motion modeling module is added to a base T2I model and refined on big video clips, learning the appropriate motion priors. It is important to note that the underlying model’s parameters are unaltered. After some fine-tuning, they show that the personalized T2I that was created might also profit from the well-learned motion priors, creating attractive and fluid animations. 

The motion modeling module may animate all relevant, personalized T2I models without needing extra data collection or tailored training. They test their AnimateDiff on various typical DreamBooth and LoRA models that include realistic and anime images. Most customized T2I models might be directly animated by installing the skilled motion modeling module without special adjustment. Additionally, they discovered in practice that the motion modeling module could acquire the correct motion priors with only plain vanilla attention along the temporal dimension. They also show how motion priors may be used in domains like 2D anime and 3D animation. To do this, their AnimateDiff might result in a straightforward yet efficient baseline for customized energy, allowing consumers to easily acquire bespoke animations for the little fee of customizing the picture models. Code is available on GitHub.

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