In-Distribution Forcing for Long Video Generation at Test Time

Jeongwoo Shin; Youngyoon Choi; Sangwoo Jo; Hyunmog Kim; Sungjoon Choi; Joonseok Lee; Jaewoong Choi; Jaemoo Choi

cs.CVOct 2, 2026

In-Distribution Forcing for Long Video Generation at Test Time

Authors: Jeongwoo Shin, Youngyoon Choi, Sangwoo Jo, Hyunmog Kim, Sungjoon Choi, Joonseok Lee, Jaewoong Choi, Jaemoo Choi

Organizations: Seoul National University · Korea University · Sungkyunkwan University · Georgia Institute of Technology

Abstract

Modern autoregressive (AR) video diffusion models excel at short-horizon video generation, yet generating long videos remains challenging due to drifting, where colors and textures shift, and motion dynamics decay. Existing works primarily rely on KV conditioning, which selects or modifies cached key-value (KV) entries to mitigate drifting. However, we observe that KV conditioning alone is insufficient as it assumes cached KV entries remain in-distribution. This assumption fails beyond the training horizon: nothing constrains the construction of KV entries during rollout, giving rise to the KV-provenance problem where cached entries themselves become out-of-distribution (OOD). To address this, we propose In-Distribution Forcing (ID-Forcing), a test-time framework that aligns both KV caching and KV conditioning with training configurations. Its key mechanism, self-caching, prevents OOD KV entries at their source. Each chunk is cached without attending to prior KV entry, keeping the rolling window exactly in-distribution. Consequently, ID-Forcing seamlessly extends short-horizon models to minute-scale video generation. Extensive evaluations show that our method remains competitive on standard video generation benchmark while substantially outperforming prior work in mitigating drifting, as validated by both our drift metrics and a user study.

Figures & tables

Figure 1: ID-Forcing extends autoregressive video generation far beyond the training horizon. In-distribution KV operations suppress color and motion drift, enabling stable minute-scale generation.

Figure 2: Same conditioning, different provenance. We generate the next chunk $x_{7}$ from the same conditioning window $\kappa_{1:6}$ , varying only how the last chunk of the horizon, $x_{6}$ , is cached into $\kappa_{6}$ . Case 1 (self-caching): $x_{6}$ attends to no prior entry, and $x_{7}$ shows no drifting. Case 2 (self-forcing caching): $x_{6}$ attends to a window never seen in training, and $x_{7}$ degrades. Case 3 (sink caching): keeping the first chunk does not help, as the window is still unseen in training. Thus, how a chunk is cached alone can determine whether the next chunk drifts.

Figure 3: KV operations comparison. Both illustrations show the caching window of each KV entry and the conditioning window of size

L

used to generate chunk

x_{i}

. Left : Self-Forcing caches every entry autoregressively, so entries beyond the horizon are cached under windows unseen in training. Right : ID-Forcing caches the earlier entries with self-caching and the rest autoregressively on top of them, so every entry is cached under a window seen in training.

Method	Aesthetic Quality $\uparrow$	Background Consistency $\uparrow$	Imaging Quality $\uparrow$	Motion Smoothness $\uparrow$	Subject Consistency $\uparrow$	Dynamic Degree $\uparrow$	Color Drift $\uparrow$	Motion Drift $\uparrow$
120 seconds
Results on Self-Forcing
Self-Forcing	49.95	96.17	61.60	98.26	96.20	30.04	25.54	46.88
Deep Forcing	57.26	96.28	65.94	98.21	97.22	45.49	59.65	71.88
$\infty$ -RoPE	55.54	95.58	67.42	97.64	96.18	62.30	54.15	85.16
MemRoPE	55.45	95.78	67.70	97.69	96.32	62.90	59.25	82.81

Table 1: Quantitative evaluation on long video generation. We report VBench-Long ( Huang et al., 2024 ) metrics and drift metrics (color drift and motion drift) on 120- and 240-second videos, with methods grouped by base model.

Figure 4: Qualitative comparison on 2-minute videos. Full videos are available at this link .

Method	Color Cons.	Bg. Cons.	Subj. Cons.	Dyn. Deg.	Temp. Flick.	Over. Pref.
Self-Forcing	100.0	99.0	96.0	93.0	98.0	100.0
Deep Forcing	90.0	84.0	85.0	81.0	97.0	95.0
$\infty$ -RoPE	91.0	90.0	87.0	75.0	91.0	93.0
MemRoPE	88.0	82.0	78.0	59.0	89.0	86.0

Table 2: User study. Preference rate (%) of ID-Forcing over each baseline in pairwise comparisons (Baseline vs. Ours). Each value denotes the percentage of participants who preferred ours (

\uparrow

Figure 5: Qualitative ablation on 2-minute video. Components are added from top to bottom. Without self-caching, colors and textures still drift even with the sink and re-rotation, whereas the full ID-Forcing preserves the scene and color throughout the 2-minute video.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Method	Aesthetic Quality $\uparrow$	Background Consistency $\uparrow$	Imaging Quality $\uparrow$	Motion Smoothness $\uparrow$	Subject Consistency $\uparrow$	Dynamic Degree $\uparrow$	Color Drift $\uparrow$	Motion Drift $\uparrow$
30 seconds
Results on Self-Forcing
Self-Forcing	56.85	96.06	67.48	98.37	96.33	44.58	37.45	60.94
Deep Forcing	57.79	96.05	67.00	98.13	96.79	59.22	63.61	82.03
$\infty$ -RoPE	57.38	95.87	67.84	97.80	96.65	64.79	56.77	85.94
MemRoPE	57.32	96.08	67.73	98.05	96.78	66.35	65.61	89.84

Appendix

Table 3: Quantitative evaluation on VBench-Long ( Huang et al., 2024 ) (30s and 60s).

Method	Aesthetic Quality $\uparrow$	Background Consistency $\uparrow$	Imaging Quality $\uparrow$	Motion Smoothness $\uparrow$	Subject Consistency $\uparrow$	Dynamic Degree $\uparrow$	Color Drift $\uparrow$	Motion Drift $\uparrow$
Self-Forcing	49.95	96.17	61.60	98.26	96.20	30.04	25.54	46.88
+ Sink	55.23	95.59	64.68	96.88	95.70	46.90	48.65	67.19
+ Re-rotation	54.14	95.68	66.89	97.36	96.08	65.44	60.66	87.50
+ Self-Caching (ID-Forcing)	58.23	96.13	68.20	97.77	96.76	65.65	72.37	89.06

Appendix

Table 4: Quantitative results of ablation study on VBench-Long ( Huang et al., 2024 ) (120s).

Figure 6: Additional Qualitative results on 2-minute videos.

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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. In this paper, we propose TetherCache, a training-free and plug-and-play cache management strategy for drift-resistant long video generation. TetherCache organizes the cache into sink, memory, and recent regions, and introduces two complementary mechanisms. First, GRAB (Gated Recall with Attention-Diversity Balancing) selects long-range memory frames using a gated score that combines attention-based relevance with temporal diversity, preserving informative yet diverse historical context under a fixed cache budget. Second, TAME (Trusted Alignment via Memory Editing) lightly edits newly recalled memory tokens by aligning their statistics to a trusted context distribution, reducing the pollution caused by drifted historical features. Built on Self-Forcing, TetherCache consistently improves long-video generation quality on VBench-Long across 30s, 60s, and 240s settings. In particular, for 240s generation, it substantially improves overall and semantic scores while reducing quality drift from 7.84 to 1.33, demonstrating its effectiveness for stable long-horizon autoregressive video diffusion.

Abstract

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