mlsanityData to 7 Oct 2026

Stale Diffusion: Hyper-realistic 5D Movie Generation Using Old-school Methods

arXiv 2404.01079 ↗First posted 1 Apr 2024cs.CVcited by 0

On the map: Diffusion models → Video generation

Abstract (arXiv)

Two years ago, Stable Diffusion achieved super-human performance at generating images with super-human numbers of fingers. Following the steady decline of its technical novelty, we propose Stale Diffusion, a method that solidifies and ossifies Stable Diffusion in a maximum-entropy state. Stable Diffusion works analogously to a barn (the Stable) from which an infinite set of horses have escaped (the Diffusion). As the horses have long left the barn, our proposal may be seen as antiquated and irrelevant. Nevertheless, we vigorously defend our claim of novelty by identifying as early adopters of the Slow Science Movement, which will produce extremely important pearls of wisdom in the future. Our speed of contributions can also be seen as a quasi-static implementation of the recent call to pause AI experiments, which we wholeheartedly support. As a result of a careful archaeological expedition to 18-months-old Git commit histories, we found that naturally-accumulating errors have produced a novel entropy-maximising Stale Diffusion method, that can produce sleep-inducing hyper-realistic 5D video that is as good as one's imagination.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 5 of its 12 references that are in the map. First posted on arXiv 1 Apr 2024.

Builds on

5 of 12 references.

References · 5

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2018Substitute Teacher Networks: Learning with Almost No Supervisioncited by 2
  3. 2017Stopping GAN Violence: Generative Unadversarial Networkscited by 1
  4. 2020State-of-Art-Reviewing: A Radical Proposal to Improve Scientific Publicationcited by 1
  5. 2021On the Origin of Species of Self-Supervised Learningcited by 1

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  3. 2026Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them
  4. 2025Image Diffusion Preview with Consistency Solver
  5. 2025Survey of Video Diffusion Models: Foundations, Implementations, and Applications
  6. 2026Steady-Forcing: Balancing Spatial Persistence and Motion Continuity in Long-Horizon Nature Video Diffusion
  7. 2026Broken Memories: Detecting and Mitigating Memorization in Diffusion Models with Degraded Generations
  8. 2022Efficient Diffusion Models for Vision: A Survey