FIFO-Diffusion: Generating Infinite Videos from Text without Training
Authors: name: Jihwan Kim, name: Junoh Kang, name: Jinyoung Choi, name: Bohyung Han

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.