In-Distribution Forcing for Long Video Generation at Test Time introduces a test‑time framework that aligns KV caching and conditioning with training configurations to prevent out‑of‑distribution key‑value entries during long‑horizon video generation. The core technique, self‑caching, ensures each chunk is cached without attending to prior KV entries, keeping the rolling window exactly in‑distribution. This approach extends short‑horizon autoregressive video diffusion models to minute‑scale generation, outperforming prior methods in mitigating drifting as shown by drift metrics and a user study.
arXiv:2606. 13035v1 Announce Type: cross Abstract: Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generated content.
Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generated content. However, extending these models to minute-level generation remains challenging: the limited KV-cache budget prevents the model from retaining the full history, while repeatedly conditioning on self-generated frames induces a context distribution shift that accumulates over time, leading to visual artifacts, quality degradation, and temporal drift.
The paper introduces Connected Self Forcing, a training framework that reconnects gradient paths across autoregressive video chunks to improve long‑horizon visual quality and temporal consistency. It allows feedback from later predictions to influence earlier context generation, using shortcut gradient replay to keep memory usage manageable. The method is combined with distribution‑matching distillation and shows experimental gains without altering the inference procedure.
arXiv:2609.32540v2 Announce Type: replace-cross Abstract: Few-step autoregressive video diffusion generates a long video by splitting the video into temporal chunks and generating chunk-by-chunk, eac...
arXiv:2609.37925v1 Announce Type: cross Abstract: Autoregressive (AR) video diffusion enables low-latency, streamable video generation, but prediction errors often accumulate over long rollouts. Trai...
arXiv:2610.02914v1 Announce Type: new Abstract: Autoregressive video models can generate minute-long videos in real time, but they produce generic subjects from text rather than specific subjects fro...
arXiv:2609.39096v1 Announce Type: new Abstract: Autoregressive video diffusion supports streaming generation and interactive control, but its KV cache grows with the generated history. Existing compr...
arXiv:2609.35491v2 Announce Type: replace-cross Abstract: Few-step autoregressive video generation commonly relies on Distribution Matching Distillation (DMD), requiring a bidirectional diffusion tea...
arXiv:2606. 14732v1 Announce Type: cross Abstract: Autoregressive video diffusion models enable streaming generation but often degrade over long rollouts: static scene layouts drift, while mechanisms that improve spatial stability tend to suppress motion, causing natural flows such as water, fire, or smoke to stagnate.
arXiv:2607. 20125v1 Announce Type: cross Abstract: Autoregressive (AR) video diffusion models have become a promising paradigm for long and streaming video synthesis, but the continuously growing Key-Value (KV) cache makes attention the dominant inference cost, especially at high resolution where each frame contributes many tokens.
arXiv:2605.25333v3 Announce Type: replace Abstract: Video world models should maintain evolving states when evidence is unobserved, yet current generators often freeze hidden states upon interruption...
DensityKV is a training‑free strategy for managing the historical key‑value (KV) cache in autoregressive video diffusion models. It creates a separate token‑level KV bank for each attention head and uses Soft‑Riesz density to measure and limit local redundancy among post‑RoPE keys, thereby preventing the KV archive from growing indefinitely. Experiments on three video generation backbones demonstrate that, with the same KV capacity limit, DensityKV improves long‑horizon consistency and generation stability while keeping persistent storage bounded regardless of rollout length.