Stable Video Infinity: Infinite-Length Video Generation with Error
  Recycling
Authors: name: Wuyang Li; name: Wentao Pan; name: Po-Chien Luan; name: Yang Gao; name: Alexandre Alahi
Published: 2025-10-10 09:45:46

Abstract: 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.