In our experiments, we observed that adjusting the scaler in the domain adapter during inference significantly enhances visual quality, demonstrating its essential role in the AnimateDiff framework. By decreasing the scaler from 1 to 0, we not only improved the overall visual output but also reduced the visual content distribution learned from training datasets, such as WebVid. This insight reflects the adapter’s ability to effectively bridge the visual quality gap in video generation.
The comparative analysis of motion module designs revealed that our temporal Transformer outperforms the standard convolution approach in generating coherent video sequences. While convolutional methods are often used, the ability of temporal Transformers to better capture and adapt to motion patterns plays a crucial role in generating higher-quality video outputs. This innovation is central to the performance of AnimateDiff, highlighting the importance of motion representation in video generation.
#video-generation #machine-learning #domain-adaptation #motion-representation #artificial-intelligence
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