In this paper, we present AnimateDiff, a practical framework for animating personalized T2I models without requiring model-specific tuning, using an innovative plug-and-play motion module.
The core training strategy of our framework allows the motion module to learn transferable motion priors from real-world videos, effectively enhancing the animation capabilities of T2I models.
AnimateDiff not only improves animation quality but also simplifies the integration process, enabling users to generate animations from personalized text-to-image models effortlessly.
Our findings indicate that through the use of MotionLoRA and our motion module, users can create high-quality animations while maintaining the unique characteristics of their personalized T2I outputs.
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