FIFO-Diffusion: Generating Infinite Videos from Text without Training

Jihwan Kim, Junoh Kang, Jinyoung Choi, Bohyung Han

FIFO-Diffusion: Generating Infinite Videos from Text without Training: 55 upvotes on Hugging Face Daily Papers, #1 of 8 papers on 2024-05-21. Day-by-day upvote history.

We propose a novel inference technique based on a pretrained diffusion model for text-conditional video generation. Our approach, called FIFO-Diffusion, is conceptually capable of generating infinitely long videos without training. This is achieved by iteratively performing diagonal denoising, which concurrently processes a series of consecutive frames with increasing noise levels in a queue; our method dequeues a fully denoised frame at the head while enqueuing a new random noise frame at the tail. However, diagonal denoising is a double-edged sword as the frames near the tail can take advantage of cleaner ones by forward reference but such a strategy induces the discrepancy between training and inference. Hence, we introduce latent partitioning to reduce the training-inference gap and lookahead denoising to leverage the benefit of forward referencing. We have demonstrated the promising results and effectiveness of the proposed methods on existing text-to-video generation baselines.

Paper page on Hugging Face · arXiv

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