title: FIFO-Diffusion: Generating Infinite Videos from Text without Training
summary: 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_authors._id: 664c08072f72dbb18fbcd9c9
paper_authors.name: Jihwan Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-21T07:23:41.782Z
paper_authors._id: 651f62b9b54adab2d4454490
paper_authors.avatarUrl: /avatars/ecfaf6283c4961278cfee5e9df18fccf.svg
paper_authors.fullname: Jihwan Kim
paper_authors.type: user
paper_authors.user: jjihwannn
paper_authors._id: 664c08072f72dbb18fbcd9ca
paper_authors.name: Junoh Kang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-21T08:10:32.535Z
paper_authors._id: 664c41d2533c5d0c9c826734
paper_authors.avatarUrl: /avatars/3c1c89790cc61ca0ef29b7c8e24867d5.svg
paper_authors.fullname: Junoh Kang
paper_authors.type: user
paper_authors.user: Junoh
paper_authors._id: 664c08072f72dbb18fbcd9cb
paper_authors.name: Jinyoung Choi
paper_authors._id: 664c08072f72dbb18fbcd9cc
paper_authors.name: Bohyung Han
paper_title: FIFO-Diffusion: Generating Infinite Videos from Text without Training
paper_summary: 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_ai_keywords: diffusion model FIFO-Diffusion text-conditional video generation diagonal denoising latent queue forward reference latent training lookahead denoising