In practice, we can also inject the domain adapter into the personalized T2I model during inference, adjusting its contribution through a scaler parameter, α.
Our approach emphasizes the separation of trainable and frozen parameters, allowing the model to efficiently adapt without global retraining during motion generation.
The integration of the motion module and optional MotionLoRA enhances the common workflow of how animations are generated, improving personalization in animated content.
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