Stable Video Infinity: Infinite-Length Video Generation with Error
  Recycling
We propose Stable Video Infinity (SVI) that is able to generate
infinite-length videos with high temporal consistency, plausible scene
transitions, and controllable streaming storylines. While existing long-video
methods attempt to mitigate accumulated errors via handcrafted anti-drifting
(e.g., modified noise scheduler, frame anchoring), they remain limited to
single-prompt extrapolation, producing homogeneous scenes with repetitive
motions. We identify that the fundamental challenge extends beyond error
accumulation to a critical discrepancy between the training assumption (seeing
clean data) and the test-time autoregressive reality (conditioning on
self-generated, error-prone outputs). To bridge this hypothesis gap, SVI
incorporates Error-Recycling Fine-Tuning, a new type of efficient training that
recycles the Diffusion Transformer (DiT)'s self-generated errors into
supervisory prompts, thereby encouraging DiT to actively identify and correct
its own errors. This is achieved by injecting, collecting, and banking errors
through closed-loop recycling, autoregressively learning from error-injected
feedback. Specifically, we (i) inject historical errors made by DiT to
intervene on clean inputs, simulating error-accumulated trajectories in flow
matching; (ii) efficiently approximate predictions with one-step bidirectional
integration and calculate errors with residuals; (iii) dynamically bank errors
into replay memory across discretized timesteps, which are resampled for new
input. SVI is able to scale videos from seconds to infinite durations with no
additional inference cost, while remaining compatible with diverse conditions
(e.g., audio, skeleton, and text streams). We evaluate SVI on three benchmarks,
including consistent, creative, and conditional settings, thoroughly verifying
its versatility and state-of-the-art role.
68edc0efde1fee572713a88a
Wuyang Li
claimed_verified
2025-10-14T07:30:28.090Z
62b705a6dd998a8b1e422936
/avatars/ab307b6548c8c627d640635f3316c5ad.svg
wuyang li
user
wymanCV
68edc0efde1fee572713a88b
Wentao Pan
68edc0efde1fee572713a88c
Po-Chien Luan
68edc0efde1fee572713a88d
Yang Gao
68edc0efde1fee572713a88e
Alexandre Alahi
Stable Video Infinity: Infinite-Length Video Generation with Error
  Recycling
We propose Stable Video Infinity (SVI) that is able to generate
infinite-length videos with high temporal consistency, plausible scene
transitions, and controllable streaming storylines. While existing long-video
methods attempt to mitigate accumulated errors via handcrafted anti-drifting
(e.g., modified noise scheduler, frame anchoring), they remain limited to
single-prompt extrapolation, producing homogeneous scenes with repetitive
motions. We identify that the fundamental challenge extends beyond error
accumulation to a critical discrepancy between the training assumption (seeing
clean data) and the test-time autoregressive reality (conditioning on
self-generated, error-prone outputs). To bridge this hypothesis gap, SVI
incorporates Error-Recycling Fine-Tuning, a new type of efficient training that
recycles the Diffusion Transformer (DiT)'s self-generated errors into
supervisory prompts, thereby encouraging DiT to actively identify and correct
its own errors. This is achieved by injecting, collecting, and banking errors
through closed-loop recycling, autoregressively learning from error-injected
feedback. Specifically, we (i) inject historical errors made by DiT to
intervene on clean inputs, simulating error-accumulated trajectories in flow
matching; (ii) efficiently approximate predictions with one-step bidirectional
integration and calculate errors with residuals; (iii) dynamically bank errors
into replay memory across discretized timesteps, which are resampled for new
input. SVI is able to scale videos from seconds to infinite durations with no
additional inference cost, while remaining compatible with diverse conditions
(e.g., audio, skeleton, and text streams). We evaluate SVI on three benchmarks,
including consistent, creative, and conditional settings, thoroughly verifying
its versatility and state-of-the-art role.
Stable Video Infinity generates infinite-length videos with high temporal consistency and controllable storylines by using Error-Recycling Fine-Tuning on the Diffusion Transformer.
Error-Recycling Fine-Tuning Diffusion Transformer DiT flow matching one-step bidirectional integration replay memory temporal consistency scene transitions autoregressive learning error injection error accumulation