Any Forcing: Any-Step Generality and One-Step Stability for Autoregressive Video Generation

Jiahua Xu; Yijie Fang; Nannan Wang; Xunhao Lin; Li Cao; Lei Hao; Mingrui Zhu; Fei Gao

cs.CVOct 30, 2026

Any Forcing: Any-Step Generality and One-Step Stability for Autoregressive Video Generation

Authors: Jiahua Xu, Yijie Fang, Nannan Wang, Xunhao Lin, Li Cao, Lei Hao, Mingrui Zhu, Fei Gao,

Organizations: State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China · Technology Planning Dept of the Computing Technology Research Institute, Huawei, Shanghai, China · Huawei Noah’s Ark Institute, Huawei, Shenzhen, China

Abstract

Autoregressive video generation enables efficient inference through streaming prediction. However, existing methods are typically restricted to fixed sampling steps, limiting the flexibility to balance generation quality and efficiency, while one-step generation suffers from degraded visual quality and error accumulation over long sequences, leading to content drift and generation collapse. To address these issues, we propose Any Forcing, a unified framework for achieving Any-Step Generality and One-Step Stability. Specifically, we introduce a unified adversarial discriminator that jointly performs real-distribution alignment, inter-step consistency alignment, and first-frame quality enhancement, enabling flexible sampling while preserving one-step generation quality. Furthermore, we propose a training-free adaptive long-term memory selection mechanism that selects historical states based on global semantic relevance and local value contribution, while remapping their temporal positions to preserve temporal relationships within the memory cache, thereby enabling stable one-step long-video generation. Experiments demonstrate that Any Forcing achieves high-quality and stable one-step autoregressive video generation, achieving 20.9 FPS and 24.7 FPS for short and long video generation on a single H100 GPU, respectively.

Figures & tables

Figure 1: Visual comparison of short- and long-video generation. Existing methods exhibit degraded visual quality and suffer from long-horizon error accumulation under one-step sampling. Any Forcing achieves stable one-step generation and long-horizon consistency.

Figure 2: Motivation of Any Forcing. Existing methods struggle to balance sampling-step generality and one-step quality, while insufficient global relevance or local contribution undermines one-step stability in long-video generation. Any Forcing addresses these challenges through a unified adversarial discriminator and adaptive long-term memory selection mechanism.

Figure 3: Overview of Any Forcing. Left: A unified discriminator jointly performs real-distribution alignment, inter-step consistency alignment, and first-frame enhancement, complemented by teacher ODE endpoint supervision during early training. Right: Adaptive long-term memory selection combines a global sink, a dynamic memory pool, and recent local context. Global semantic relevance and local value contribution determine memory admission before generating the next frame, followed by position mapping within the trained RoPE horizon. For better visualization, we use video frames as an example to show the mechanism, where $F_{j}$ represents the $j$ -th frame.

Method	NFE	Total $\uparrow$	Quality $\uparrow$	Sem. $\uparrow$	IQ $\uparrow$	AQ $\uparrow$	OC $\uparrow$	NFE	Total $\uparrow$	Quality $\uparrow$	Sem. $\uparrow$	IQ $\uparrow$	AQ $\uparrow$	OC $\uparrow$
Base model
Wan 2.1-1.3B [ 24 ]	50	83.99	84.82	80.66	67.52	65.70	94.56	20	83.63	84.52	80.06	65.71	65.19	93.81
Few steps
CausVid [ 34 ]	4	83.05	84.14	78.70	68.44	66.04	93.35	2	81.03	82.26	76.11	63.25	62.55	90.22
rCM [ 40 ]	4	83.11	84.48	77.65	66.30	63.33	92.96	2	81.06	83.23	72.34	59.84	62.04	90.35
Self Forcing [ 8 ]	4	84.20	84.88	81.48	69.44	66.68	94.37	2	81.79	83.13	76.40	66.02	63.39	90.40

Table 1: Comparison on VBench for 5-second video generation at 480p resolution. For the few-step setting, the left and right groups use four and two denoising steps, respectively. For the one-step setting,

1^{*}

and

1^{\dagger}

use four and two denoising steps for the first latent frame, respectively, followed by one step for subsequent frames. Best and second-best results within each sampling group are bold and underlined , respectively. Sem.: Semantic. We report three representative quality and semantic metrics here: IQ: Imaging Quality; AQ: Aesthetic Quality; OC: Object Class. Detailed results can be found in Supplementary Material E .

Method	30-second video generation							60-second video generation
	NFE	Total $\uparrow$	Quality $\uparrow$	Sem. $\uparrow$	IQ $\uparrow$	AQ $\uparrow$	OC $\uparrow$	NFE	Total $\uparrow$	Quality $\uparrow$	Sem. $\uparrow$	IQ $\uparrow$	AQ $\uparrow$	OC $\uparrow$
Few steps
CausVid [ 34 ]	4	78.54	81.08	68.38	60.30	55.42	81.05	4	74.92	79.29	57.42	56.80	50.78	65.50
LongLive [ 28 ]	4	82.81	83.34	80.69	68.73	63.90	95.12	4	82.73	83.24	80.69	68.57	64.16	94.43
One step
Infinity RoPE [ 30 ]	$1^{*}$	84.51	85.83	79.22	70.52	62.77	94.88	$1^{*}$	84.12	85.57	78.31	70.80	61.46	93.41

Table 2: Comparison on VBench for 30- and 60-second video generation at 480p resolution.

1^{*}

and

1^{\dagger}

use four and two sampling steps for the first latent frame, respectively, followed by one step for subsequent frames. Best and second-best results within each one-step sampling group and video length are bold and underlined , respectively.

Figure 4: Visual comparisons of one-step short-video (top) and long-video (bottom) generation. Any Forcing achieves high-quality video generation while preserving stable subject appearance and scene structure over time. All methods adopt the $1^{*}$ sampling configuration. More visualization results are available in Supplementary Material E .

Figure 5: Human preference results. The corresponding sampling settings are indicated to the right of the bars. We report the win rate of Any Forcing against others in blind pairwise comparisons.

Figure 6: Performance analysis of short (left) and long (right) video generation on a single H100 GPU.

1^{*}

and

1^{\dagger}

use four and two first-frame steps, respectively, followed by one step per frame. The bubble size indicates the number of sampling steps (left) and the historical-frame budget

H

, excluding the current frame (right).

Method	Total	Quality	Semantic	Total	Quality	Semantic
Any Forcing	85.06	86.19	80.54	84.79	85.83	80.64
w/o CA	84.66	85.67	80.62	84.14	85.11	80.25
w/o RA	83.78	85.19	78.15	83.86	85.12	78.85
w/o FE	83.52	84.86	78.15	81.86	83.22	76.45
w/o ODE	84.67	85.83	80.00	83.10	84.17	78.84

Table 3: Ablation study of the proposed components on VBench. The left and right groups correspond to

1^{*}

and

1^{\dagger}

sampling, respectively. Best and second-best results within each sampling group are bold and underlined , respectively.

Method	IQ	AQ	OC	MO	SR	TF	Scene	TS
Any Forcing	71.71	59.38	93.50	82.08	89.14	99.34	59.70	23.28
w/o GSR	71.68	59.33	92.67	81.55	89.55	99.37	59.36	23.20
w/o LVC	71.69	59.35	93.27	81.72	89.73	99.33	59.15	23.09
w/o PM	70.90	58.99	92.45	81.44	89.51	99.27	59.05	22.95

Table 4: Ablation study of adaptive long-term memory selection on VBench. GSR: Global Semantic Relevance; LVC: Local Value Contribution; PM: Position Mapping. Best and second-best results are bold and underlined , respectively.

$\lambda_{\mathrm{FE}}$	Total	Quality	Semantic	Total	Quality	Semantic
0.01	83.46	84.95	77.49	81.59	82.94	76.23
0.05	85.06	86.19	80.54	84.79	85.83	80.64
0.10	83.54	84.88	78.16	81.64	82.78	77.08

Table 5: Sensitivity analysis of the first-frame enhancement weight

\lambda_{\mathrm{FE}}

on VBench. The left and right groups correspond to

1^{*}

and

1^{\dagger}

sampling, respectively. Best and second-best results within each sampling group are bold and underlined , respectively.

Figure 7: Sensitivity analysis of recent-context length (left) and long-term memory capacity (right). Recent frames denote the number of retained local-context frames after candidate removal.

Appendix figures & tables10 assets

Supplementary material from the paper’s appendix.

Appendix

Hyperparameter	Setting
Generator initialization	ODE-initialization causal model from Causal Forcing
Generator / real score / fake score	Wan2.1-T2V-1.3B / Wan2.1-T2V-14B / Wan2.1-T2V-1.3B
Objective	Flow denoising for fake score; DMD + multi-task GAN + ODE endpoint supervision for generator
Training frames	21 latent frames; $16\times 60\times 104$ per frame
Frames per autoregressive block	1
Training sampling steps	Randomly selected from $\{1,2,3,4\}$

Appendix

Table 6: Implementation settings of Any Forcing.

Figure 8: Interface for the blind pairwise user study. Participants compare videos generated from the same prompt and select their preferred result based on visual quality and semantic alignment.

Figure 9: Adversarial training dynamics with and without ODE endpoint warm-up over the first 300 training steps. Columns show real-distribution alignment, inter-step consistency, and first-frame enhancement; rows show generator and discriminator losses, respectively. The shaded region indicates the 200-step warm-up stage.

Setting	IQ	AQ	OC	MO	SR	TF	Scene	TS
Any Forcing	71.71	59.38	93.50	82.08	89.14	99.34	59.70	23.28
w/ noisy query	71.74	59.36	93.31	81.84	89.77	99.34	59.88	23.10

Appendix

Table 7: Effect of including the current noisy query in local value evaluation.

Figure 10: Qualitative examples of generation with the current noisy query included in local value evaluation. Red arrows indicate regions of content degradation, including duplicated sheep heads.

Setting	IQ	AQ	OC	MO	SR	TF	Scene	TS
Any Forcing	71.71	59.38	93.50	82.08	89.14	99.34	59.70	23.28
w/o collisions	71.65	59.34	92.90	82.09	89.03	99.31	58.95	23.13

Appendix

Table 8: Effect of preventing positional collisions.

Figure 11: Comparison across all 16 VBench dimensions. Top: 5s generation with uniform four-step and two-step sampling, followed by one-step generation with four and two first-frame steps. Bottom: corresponding one-step comparisons for 30s (left two panels) and 60s (right two panels). Any Forcing is highlighted in red. The radar plots use axis-specific display ranges shared by all methods within each panel; outermost labels denote axis limits rather than method scores. SC: Subject Consistency; BC: Background Consistency; TF: Temporal Flickering; MS: Motion Smoothness; DD: Dynamic Degree; AQ: Aesthetic Quality; IQ: Imaging Quality; OC: Object Class; MO: Multiple Objects; HA: Human Action; SR: Spatial Relationship; AS: Appearance Style; TS: Temporal Style; Overall: Overall Consistency.

Figure 12: Comparisons of 60-second video generation on a Tokyo street scene (top) and a coastal landscape (bottom). All methods adopt the $1^{*}$ sampling configuration.

Figure 13: Comparisons of Any Forcing under different sampling schedules. The top, middle, and bottom groups show 5-, 30-, and 60-second generation, respectively.

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Recent advances in autoregressive diffusion-based video generation have substantially improved the quality of real-time video synthesis. However, most existing methods still require multiple denoising steps, while reducing sampling to a single step often leads to severe quality degradation: trajectory-based consistency distillation methods often produce videos with weak dynamics, whereas DMD-based methods, such as Self-Forcing, tend to generate blurry frames. We attribute this limitation to the teacher trajectories exhibiting highly concentrated curvature near the high-noise endpoint, which poses a fundamental geometric challenge for one-step distillation under the consistency distillation framework. To address these limitations, we propose One-Forcing, a simple yet effective approach that augments the DMD objective with an auxiliary GAN loss for high-quality and efficient one-step video generation. We further find that framewise autoregression stabilizes adversarial training, enabling higher-quality generation with substantially fewer training iterations than chunkwise autoregression. Experiments on VBench show that One-Forcing achieves a total score of 83.76, establishing state-of-the-art performance among one-step causal video generation methods while remaining competitive with strong multi-step approaches.

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