Developed the AutoencoderVidTok feature for the huggingface/diffusers repository, focusing on enhanced video processing capabilities. The work centered on building an autoencoder model with improved encoding and decoding for video data, while ensuring compatibility with BFloat16 on CPU hardware. Using Python and PyTorch, the implementation included code refactoring to align with diffusers’ standards, integration of dummy objects for future extensibility, and targeted fixes for avg_pool3d operations. Adjustments to continuous integration tests were made by skipping non-critical checks, maintaining CI stability. This contribution expanded the repository’s support for deep learning-based video processing and improved hardware compatibility in production environments.