Truncate Diffusion: Efficient Video Editing With Low-Rank Truncate

Diffusion models have demonstrated remarkable capabilities for text-to-video (T2V) editing tasks, relying on fine-tuning for pretrained text-to-image (T2I) diffusion models with only one video-prompt pair. However, conventional fine-tuning approaches require tuning and storing numerous parameters for each video, leading to substantial parameter and memory costs. To mitigate these issues, we propose Truncating Diffusion, an efficient fine-tuning method for video editing that optimizes both parameter and memory usage. Specifically, we propose the Truncating Diffusion module, which is designed with a focus on module architecture and initialization, specifically targeting optimization with a small training set, such as a single video-prompt pair. Theoretical analysis using the Johnson-Lindenstrauss lemma and the Eckart-Young-Mirsky theorem shows that Truncating Diffusion can achieve a minimal Frobenius norm distance to the original attention algorithm with appropriate initialization, which enhances the ease of optimization and improves video editing performance. During fine-tuning, the Truncating Diffusion module integrates seamlessly with the original diffusion model. We freeze the weights of the denoising network within the original pretrained diffusion model, updating only the introduced low-rank alternatives to ensure parameter and memory efficiency. Additionally, we propose Latent Flow Loss and Bidirectional Inter-Frame Attention (BIFA) to improve temporal consistency in synthesized videos. The Latent Flow Loss leverages global temporal information from the input video during training, while BIFA utilizes local temporal information from adjacent frames during inference. These enhancements do not incur additional memory or parameter costs during fine-tuning. Comparisons with state-of-the-art approaches demonstrate that Truncating Diffusion provides superior text alignment, video quality, and inter-frame temporal consistency in video editing. Importantly, Truncating Diffusion requires fine-tuning only 3.2% of the parameters and uses just 62% of the memory compared to the baseline model.