The implementation of AnimateDiff with Stable Diffusion V1.5 demonstrates significant improvements in motion generation, addressing limitations posed by training data and enhancing control over animation outcomes.
By integrating a motion module with AnimateDiff, we achieve a unique ability to adapt to new motion patterns, ultimately yielding more personalized and diverse animation outputs across multiple domains.
Our experiments with community models uncovered that AnimateDiff, supported by a robust dataset like WebVid10M, offers superior qualitative results compared to existing methods in personalized T2I animation.
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