Streaming Autoregressive Video Generation via Diagonal Distillation
Authors: Jinxiu Liu
Xuanming Liu
Kangfu Mei
Yandong Wen
Ming-Hsuan Yang
Weiyang Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Video Generation
Diffusion Models
Primary area: generative models
TL;DR: We propose ​Diagonal Distillation, a new method for making high-quality video generation much faster. Current methods are either too slow or create videos with poor motion and errors over time.

Abstract: Large pretrained diffusion models have significantly enhanced the quality of generated videos, and yet their use in real-time streaming remains limited. Autoregressive models offer a natural framework for sequential frame synthesis but require heavy computation to achieve high fidelity. Diffusion distillation can compress these models into efficient few-step variants, but existing video distillation approaches largely adapt image-specific methods that neglect temporal dependencies. These techniques often excel in image generation but underperform in video synthesis, exhibiting reduced motion coherence, error accumulation over long sequences, and a latency-quality trade-off. We identify two factors that result in these limitations: insufficient utilization of temporal context during step reduction and implicit prediction of subsequent noise levels in next-chunk prediction (i.e., exposure bias). To address these issues, we propose Diagonal Distillation, which operates orthogonally to existing approaches and better exploits temporal information across both video chunks and denoising steps. Central to our approach is an asymmetric generation strategy: more steps early, fewer steps later. This design allows later chunks to inherit rich appearance information from thoroughly processed early chunks, while using partially denoised chunks as conditional inputs for subsequent synthesis. By aligning the implicit prediction of subsequent noise levels during chunk generation with the actual inference conditions, our approach mitigates error propagation and reduces oversaturation in long-range sequences. We further incorporate implicit optical flow modeling to preserve motion quality under strict step constraints. Our method generates a 5-second video in 2.61 seconds (up to 31 FPS), achieving a 277.3× speedup over the undistilled model.

Published as a conference paper at ICLR 2026
STREAMING AUTOREGRESSIVE VIDEO GENERATION VIA DIAGONAL DISTILLATION
Jinxiu Liu
Xuanming Liu
Kangfu Mei
Yandong Wen
Ming-Hsuan Yang'
Weiyang Liu
'outh China University of Technology
"Westlake University
' Johns Hopkins University
Universityy of California, Merced
'The Chinese University of Hong Kong
SphereLab.. wwtpreeaa.aaddiidddiii ai/diagdistill
ABSTRACT
Large pretrained diffusion models have significantly enhanced the quality of gen- crated videos, and yet their use in real-time streaming remains limited. Autore- gressive models offer natural framework for sequential frame synthesis but re- quire heavy computation to achieve high fidelity. Diffusion distillation can com- press these models into efficient few-step variants, but existing video distillation approaches largely adapt image-specific methods that neglect temporal dependen- cies. These techniques often excel in image generation but underperform in video synthesis, exhibiting reduced motion coherence, error accumulation over long se- quences, and a latency-quality trade-off. We identify two factors that result these limitations: insufficient utilization of temporal context during step reduction and implicit prediction of subsequent noise levels in next-chunk prediction (i.e., exposure bias). To address these issues, we propose Diagonal Distillation, which operates orthogonally to existing approaches and better exploits temporal infor- mation across both video chunks and denoising steps. Central to our approach is an asymmetric generation strategy: more steps early, fewer steps later. This design allows later chunks inherit rich appearance information from thoroughly pro- cessed early chunks, while using partially denoised chunks as conditional inputs for subsequent synthesis. By aligning the implicit prediction of subsequent noise levels during chunk generation with the actual inference conditions, our approach mitigates error propagation and reduces oversaturation in long-range sequences. We further incorporate implicit optical flow modeling to preserve motion qual- ity under strict step constraints. Our method generates a 5-second video in 2.61 seconds (up to 31 FPS), achieving a 277.3x speedup over the undistilled model.
INTRODUCTION
Recent years have witnessed the rapid progress of diffusion models in video generation. A ma- jor enabler of such progress has been Diffusion Transformer architectures (Peebles & Xie, 2023), which leverage bidirectional attention to denoise all video frames simultaneously (Blattmann al., 2023a;b; Brooks et al., 2024; Kong et al., 2024; Polyak et al., 2024; Villegas et al., 2022; Wan al., 2025; Yang et al., 2024). While effective for offline generation, this design requires the entire video to be generated at once, as each frame can attend to all others, including future ones. As a result, such models face fundamental limitations in real-time applications. including game simula- tion (Deng et al., 2024; Peebles & Xie, 2023; Song et al., 2023; Vondrick et al., 2016) and robot learning (Ge et al., 2022;
2018; Wang al., 2023), where future frames are unavailable when generating the current frame.
Autoregressive (AR) models are well-suited for streaming video generation, as their chunk- chunk-by-chunn by-chunk synthesis naturally aligns with real-time constraints (Bruce et al., 2024; Kondratyuk et al., 2023; Ren al., 2025; Wang et al., 2024; Weissenborn et al., 2019; Yan et al., 2021). However, traditional GPT- style models (Wang et al., 2024; Yan et al., 2021) often suffer from limited visual quality (Gao al., 2024a). To address this, recent works (Jin et al., 2024; Weng et al., 2024; Teng et al., 2025) integrate diffusion processes into AR generation. Yet these methods still require multiple denoising steps per
Equul contribution with alphahetical order.

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3s
Clussiid 4.91s
11.88 Faster
Durs
Figure I: Diagonal Distillation achieves comparable quality to the full-step model while significantly reducing latency. The method yields 1.88x speedup 5-second short video generation on single H110 GPU.
chunk, which hinders real-time deployment. To reduce inference latency, step distillation (Yin et al., 2025; Huang et al., 2025; Yin et al., 2024b) has been introduced to distill multi-step diffusion models into efficient few-step sampling AR model. Recent training methods (Chen et al., 2024; Gao al., 2024b; Gu et al., 2025: Hu et al., 2024; Li et al.. 2024b; Liu et al., 2024b; Weng et al., 2024; Yin al., 2025; Zhang et al., 2025a;b) have further improved stability and efficiency, making interactive applications increasingly feasible (Arriola et al., 2025; Liu et al., 2024c).
Despite the encouraging advancement, existing video distillation methods are largely adapted from image generation, and their direct extension to video often yields suboptimal results. This limita- tion arises from insufficient consideration of the temporal dimension and the neglect of inter-frame consistency. As a result, multi-step sampling remains essential for maintaining high-quality video generation. For example, while autoregressive frameworks such as Causvid (Yin et al., 2025) and Self-Forcing (Huang et al., 2025) can reduce latency, they still require multiple steps per segment, and compressing them to fewer steps leads to noticeable performance degradation.
Our guiding insight is that, in autoregressive video generation, predicting the next chunk inherently requires predicting the next noise level (see Figure 2). This implicit prediction, however, introduces two critical challenges. First, autoregressive video models often suffer from exposure bias. When predicting the next chunk conditioned on previously generated clean frames, the model must implic- itly predict the next noise level for subsequent frames. This can lead to progressive visual quality degradation, such as over-saturation in later frames, as errors in noise-level prediction can accu- mulate over time. Although techniques like Self-Forcing (Huang et al., 2025) have been proposed to mitigate exposure bias by using model-generated content during training, they still struggle to maintain visual quality over long sequences. Second, the same phenomenon implies that if struc- tural priors are captured in early chunks, later chunks can generate relatively clear frames even with fewer denoising steps. However, existing distillation approaches often discard valuable temporal context accumulated across denoising steps in video generation models, which is essential for pre- serving coherence and detail when reducing the sampling steps.
Motivated by these observations, we introduce a flow-aware diagonal distillation framework, termed DiagDistill that redefines the temporal context incorporation by leveraging information across both time and denoising steps. Different from common practices that process chunks in isolation, our method employs a novel diagonal attention mechanism operating jointly across time and denois- ing steps. This results in diagonal denoising trajectory wherein earlier chunks are denoised with more steps, while later chunks use progressively fewer steps. This strategy improves computational efficiency by using less denoising steps in total and allows each chunk to inherit denoising trajec- tories from prior chunks as contextual priors, which leads to training paradigm we term Diagonal Forcing. By explicitly simulating diagonal denoising paths during training through controlled noise injection, Diagonal Forcing enhances self-conditioned generation and mitigates error accumulation in long videos. However, we empirically observe that employing very few steps in later chunks can attenuate motion amplitude. To address this, we introduce Flow Distribution Matching, which integrates explicit temporal modeling into the distillation loss. This approach preserves dynamic consistency by ensuring the predicted motion distributions align with those of the full-step model, thus ensuring that the student model not only matches the teacher in image quality but also faithfully preserves motion characteristics. We summarize the major contributions of this work below:
We propose Diagonal Distillation, a method for efficient autoregressive video generation. Instead of assigning a fixed number of denoising steps to all chunks, it allocates more steps to earlier
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Figure 2: When the training data uses explicit noise frames as conditions in Causvid (Yin et al., 2025), the next chunk prediction essentially functions as an implicit next noise level prediction. We observe that even with single-step prediction, the image progressively becomes clearer.
chunks and progressively fewer to later ones. This strategy leverages contextual structural priors in autoregressive video generation, achieving a good balance between quality and efficiency.
We introduce Diagonal Forcing, built upon diagonal distillation, unified method operating across both temporal and denoising-step dimensions. It leverages trajectories from preceding chunks as contextual priors and explicitly simulates diagonal denoising paths during training via controlled noise injection, effectively mitigating long-term error accumulation.
To address motion degradation and amplitude attenuation in later chunks, we propose Flow Dis- tribution Matching that works alongside diagonal distillation. By incorporating explicit temporal modeling into the distillation loss, we find that this approach effectively enhances dynamic con- sistency and ensures smooth motion transitions.
Our method achieves state-of of-th----r performance on video generation. It generates a 5-second video in 2.61 seconds (up to 31 FPS), reaching a 277.3x speedup over the undistilled model.
2 RELATED WORK
Diffusion Distillation. Diffusion distillation accelerates sampling via deterministic or distributional approaches. Deterministic methods (e.g.. progressive distillation (Salimans Ho, 2022), consis- tency distillation (Li et al., 2023; Song et al., 2023), rectified flow (Lamb et al., 2016)) regress noise-to-sample mappings but often yield blurry outputs with few steps due to optimization chal- lenges (Kingma et al., 2021), typically requiring multiple steps for acceptable quality (Li et al., 2023; 2024a). Distributional methods approximate the teacher's 's distribution using adversarial train- ing (Brooks al., 2024; Ho et al., 2022), score distillation (Li et al., 2022; Luo et al., 2024), or hybrid objectives. Recent hybrids combine both paradigms but still suffer from one-step artifacts and commonly need multi-step sampling. Representative works include LADD (Sauer al., 2024a), which relies on expensive pre- ge-generrtee teacher targets; Lightning (Lin et al., 2024) and Hyper (Ren al., 2024). which require intermediate timestep supervision; and DMD/DMD2 (Yin al., 2024b;a) and ADD (Sauer et al., 2024b). which integrate adversarial and score-matching losses. While these distillation methods have shown impressive results in image generation, their direct application to video often yields suboptimal results due to insufficient consideration of the temporal dimension and anter-frame consistency. Our work addresses this problem by proposing a flow-aware diagonal distillation framework specifically designed for video generation, which leverages temporal context across both time and denoising steps to maintain coherence while reducing sampling steps.
Autoregressive, Diffusion, and Hybrid Video Generation. Modern video generation is dominated by scalable diffusion and autoregressive (AR) models. Video diffusion models use bidirectional at- tention to denoise all frames concurrently (Blattmann et al., 2023a;b; Brooks et al., 2024; Deng al., 2024; Kong al.. 2024; Polyak al., 2024; Villegas et al., 2022; Wan et al., 2025; Yang et al., 2024), while AR models generate spatiotemporal tokens sequentially via next-token predic- tion (Bruce et al., 2024; Kondratyuk al., 2023; Ren et al., 2025: Wang al., 2024; Weissenborn et al., 2019; Yan et al., 2021; Liu et al., 2025). Hybrid models that merge these two paradigms have recently emerged as a promising direction (Chen et al., 2024; Gao al., 2024b; Gu al., 2025; Hu et al., 2024; Jin et al., 2024; Li et al., 2024b; Liu et al., 2024a;b; Weng et al., 2024; Yin et al., 2025; Zhang al., 2025abb), also in other sequence domains (Arriola et al., 2025; Liu et al., 2024c). These hybrids typically integrate diffusion into AR generation to boost visual quality, but they still require multiple denoising steps per chunk, hindering real-time deployment. Our work builds on these hy- brids, drawing inspiration from Yin al. (2025) and Huang et al. (2025) mitigate exposure bias. However, these methods still face challenges with long-term error accumulation and motion degra- dation when compressed to fewer steps. Our diagonal distillation framework addresses these chal-
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Chunk
Chunk
Chunk 3
Chink 4
Chunk 5
Chunk 6
Chunk 7
Few-Stop Model Conditioned Frames Different Noise Levels and Temporal Positions
Mulni Step Warm Up
KV cache
Denoising
video
5-nep Distiled Medel
Astepe Disilled Modd
Jistope Disilled Madel
2.scps Distiled
2-stepe Destiled
2-9000 Disiiled
2-steps Dasillee Model
Model
Model
Model
Figure 3: Diagonal Denoising with Diagonal Forcing and Progressive Step Reduction. We give an illustration of our method by starting with five denoising steps for the first chunk and gradually reducing them to two steps by Chunk 7. For chunks with & 2 4. we use fixed two-step denoising process, reusing the Sey-Value (KV) cache from the final noisy frame of the preceding chunk. This design preserves temporal coherence while minimizing latency, and the corresponding pseudo-code is provided the appendix.
lenges through a novel diagonal attention mechanism that operates jointly across time and denoising steps, achieving efficient computation while maintaining temporal coherence. The diagonal forcing training paradigm simulates diagonal denoising paths to improve self-conditioned generation, while flow distribution matching enforces motion consistency with fewer denoising steps.
THE DIAGONAL DISTILLATION FRAMEWORK
3.1 PRELIMINARY AND FRAMEWORK OVERVIEW
Diffusion Models generate data through iterative denoising process. The forward diffusion pro- cess progressively corrupts sample ~ Preat over T steps, such that at timestep t. the diffused sample follows p are determined by the noise schedule. The model learns to reverse this process by predicting a denoised estimate p( t). The score function of the diffused distribution
Preat (x)) (r)),r, with ge (x( ((aa, of where ou, ar
Ir Ct Preat t)
(re, t) Vx, log Preal,
(I)
of
Sampling typically requires many iterative steps. Distribution Matching Distillation (DMD) distills multi-step diffusion model (teacher) into a one-step generator G by minimizing the KL divergence between the diffused real and generated distributions, Preal,t and Piake,t- The gradient of this loss is:
(/
dGo(z) dz
VLDMD =E, (Vo KL (Ilraak -E,, (Feaa Spake (FGG
d0
where 2 ~ (O, 1), F is the forward diffusion process, and Sreal- Stake are scores from models trained on real and generated data, An additional regression loss is often used for regularization:
Creg = (G)).))
(3)
where y is an image generated by the teacher from Directly applying DMD to video genera- tion faces a significant challenge: the regression loss Lae primarily ensures per-frame quality but fails to explicitly capture the underlying temporal coherence and long-range dependeneies between frames, which are critical for video quality. This often results in degraded fluidity and consistency. To overcome this, we extend the DMD framework with two core innovations: noising with Diagonal Forcing strategy that manages oong-eequence generation and reduces error accumulation (Section 3.2); 2) a novel Flow Distribution Matching objective that explicitly aligns the temporal dynamics of the student and teacher models (Section 3.3).
Diagonal De-
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3.2 DIAGONAL DENOISING WITH DIAGONAL FORCING
Building upon the DMD foundation, we present diagonal distillation, framework for efficient video generation. As illustrated in Figure 3, our approach introduces a Diagonal Denoising strategy that progressively reduces denoising steps across video chunks, combined with a novel Diagonal Forcing mechanism to maintain temporal coherence and mitigate error accumulation.
Diagonal Denoising: Progressive Step Reduction Strategy. Our core innovation diagonal denoising strategy that allocates computation based on temporal importance. The method assigns more denoising steps to earlier chunks and progressively fewer to later ones, rather than maintaining a constant number of steps across all chunks. This approach achieves an improved trade-off between quality and efficiency by leveraging contextual structured priors in autoregressive video generation. For the first three chunks (k 1,2,3), we use distilled models with decreasing steps ($ 5,4,3)
X Day, (Ze|Xet),
(4)
where X3 is the k-th chunk output, Za \o I) is Gaussian noise, and Xe contains previously noised chunks. For k 2 4, we employ efficient two-step denoising:
C. = T((,))).
D2 (Z, (C))
where Cg is the conditioning signal derived from previous chunks, T denotes the conditioning module, and Di, D2 represent the first and second denoising steps respectively.
Diagonal Forcing: Contextual Prior Propagation. The core innovation of Diagonal Forcing lies in its explicit modeling of diagonal denoising trajectories during training through controlled noise injection. This approach ensures temporal coherence across chunks while minimizing error accumu- lation by conditioning each new chunk on the final noised state from the previous chunk's diffusion process. Specifically, the conditioning input for chunk & is derived from the clean output X of chunk h through a noise injection operation:
- Vok-i - 06-116 (~
where a--1 controls the noise schedule along the diagonal path and is standard Gaussian noise. This formulation explicitly maintains the diagonal denoising trajectory X + X + X where X serves the KV cache input for chunk k. By propagating these noised representa- tions across chunks, the method effectively leverages denoising trajectories from prior chunks as contextual priors. The diagonal alignment of these trajectories ensures that error accumulation is minimized while preserving long-range coherence in the generated output.
3.3 FLOW DISTRIBUTION MATCHING
Motion attemuation in few-step denoising stems from truncated noise estimation paths. We quantify the temporal distribution mismatch through flow-based divergence:
Emotion
DRL. (Preaheer (F())(xx) Patueem (F(x)
where F(x) represents the motion flow field extracted from video sequence x. This measures the distributional divergence between teacher and student in the temporal dimension.
The standard Distribution Matching Distillation (DMD) framework minimizes spatial divergence through reverse KL minimization. We extend this to the temporal domain by defining flow distribu- tion matching:
(8) where Po ((F(x) (xx is the smoothed flow distribution from real data, and Pyen,foo.t flow. = (xx(x) v (E), t)) (e),t) is the generator's 's flow distribution. The gradient approximation
V DMD - E. (V (Pgen Illaataa (oodaat,Bow..)) Row.))
for flow distribution matching follows the DMD framework:
V DMD & Ee
[f
flow (< (V(Go(e),)).)
flow Sgen, (V(Go(e),)).)) dGa(c) de
(9)
dø
where .low and & flow g are the flow score functions defined as:
Bow (x,, t) Vxi log (ogp((FF (F())))))
(10)
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5
Ours
Self Forcing
Causvic vid
Ours
Self Forcing
Causvid
Ours
Self Forcing
Causvid
Figure 4: Comparing the results from three different models. For more results, please refer to the Appendices.
To operationalize this framework, we employ flow regression loss for feature alignment:
Cllow
Er,e [[IF (e,t)) F(G (c,
(II)
where Gøle, t) denotes the generator output at timestep Our method uses lightweight, self- contained motion feature extraction module F(.) that operates directly on latent representations, avoiding dependencies externl pre-trained optical flow estimators. Specifically, F(.) as a learnable representation with convolution on latent difference: first computes the dif- ference between consecutive latent frames, then applies convolutional layers extract local motion patterns, followed by an ML.P for feature adaptation. The student version is trainable with gradient flow, while the teacher components are updated via EMA, ensuring stable and efficient motion repre- sentation learning. The overall objective combines both spatial and temporal distribution matching:
implement
Toaal Aspatial LDMD + freg + + (Nooow floow
DMD
flow
(12)
where 7 weights the temporal terms. We set 4 and >now= This framework jointly minimizes motion distribution divergence while maintaining spatial fidelity in the distilled video model.
4 EXPERIMENTS AND RESULTS
4.1 IMPLEMENTATION DETAILS
Training Details. We implement DiagDistill using W (Wan et al., 2025), a model based on Flow Matching (Lipman et al., 2022) that capable of generating 5 videos 16 FPS with

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100
80
60
60
40
20-
5W0
400
3100
200
100
0.00
0.25
0.50
0.75
1,00
1.25
1.50
1.73
2.00
Tempuri Quality
Frimz Quality
Text Alignment
Temporal Frame Quality
Text Aligmment
Average Quality over Time (a)
Flow Loss Weight Evaluation (b)
Figure 5: Ablation study results. (a) Performance evaluation across different diagonal forcing timesteps, demonstrating optimal outcomes at 100 steps (1000 steps correspond to complete noise addition, while 0 steps represent the clean frame) of motion loss weight on model performance.
a resolution of 832 x 480. For both ODE initialization and Diagonal Distillation training, we sample text prompts from filtered and LLM-extended version of VidProM (Wang & Yang, 2024).
Inference Details. To assess real-time applicability, we measured both throughput (frames per second) and first-frame latency, acknowledging that true real-time performance requires exceeding video playback rates while maintaining imperceptible delay. All speed tests were conducted on single NVIDIA H100 GPU with tiny VAE (Boer Bohan, 2025). The core component is the rolling KV cache mechanism following Self-Forcing (Huang et al., 2025), which operates with a chunk size of 3 frames. Our buffering strategy is implemented using fixed-size KV cache that maintains context from the most recent chunks, resulting in consistent memory footprint of 17.5 GB. For detailed ablation analysis please refer to the Appendices.
Evaluation Details. We evaluated visual quality and semantic consistency using VBench (Huang et al., 2024). Temporal Quality is the average of Subject Consistency, Background Consistency, Temporal Flickering, Motion Smoothness, and Dynamic Degree. Frame Quality is the average of Aesthetic Quality and Imaging Quality. Text Alignment is the average of Object Class, Multiple Objects, Human Action, Color, Spatial Relationship, Scene, Appearance, Style, and Temporal Style. The aggregation method for each score is simple arithmetic mean of the normalized scores from its constituent sub-dimensions. This evaluation approach is consistent with prior works like Causvid and Self-Forcing for fair comparison.
4.2 COMPARISON WITH STATE-OF-THE- OF-THE-ART METHODS
We start by evaluating Di- agDistill against five state- of-the-art video generation methods: Wan2.1 (Wan al., 2025), SkyReels-V V2 (Chen et al., 2025), MAGI-1 (Teng al., 2025), Causvid (Yin et al., 2025). and Self- Forcing (Huang et al., 2025).
Frame
Model
Throughputt
Speedup Totalt Quality? Semantict
Latency 4
Wan2,1 ( (Winetal, al., 2025)
0.78
103
1.0x
84.26
85.30
80.09
SkyReels V2 (Chen
2025)
0.49
112
0.91
82.67
84,70
74.53
MAGI-1 (Teng et al., 2025)
0.19
282
0.36x
79.18
82.04
67.74
Cansvid (Yin et al, 2015)
17.0
0.69
149.3x
81.20
84.05
69.80
Self-Forcing Forcing (Hunng et al., 2025)
17.0
0.69
149.3x
84.31
85.07
81.28
DiagDistill (Ours)
31.0
0.37
277.3x
84.48
85.26
81.73
Table Comprehensive comparison of video generation methods
As shown in Table 1, our method achieves 277.3x speedup over the Wan2.1 1 baseline while main- taining competitive visual quality (85.26, vs. 85.3). This represents 1 1.53x improvement in latency over the previous fastest method, Self-Forcing (149.3 x)), alongside superior overall performance and semantic consistency. Qualitative results in Figure 4 further demonstrate advantages temporal consistency, with smoother frame transitions and fewer dynamic artifacts. Visual fidelity improve- ments are most apparent in complex motions and textures, where baseline methods exhibit blurring or distortion. These findings collectively show that DiagDistill effectively balances the traditional trade-off between generation quality and computational efficiency.
4.3 ABLATION STUDIES
Key Components. Diagonal Denoising assigns more denoising steps to early video chunks to es- tablish high-quality foundation and progressively reduces denoising steps for subsequent chunks, whereas without it, the same number steps is applied uniformly across all chunks. Diagonal Forcing refers to using noisy frames instead of clean frames the Key-Value (KV) cache in toregressive generation. Our ablation study shows that removing either flow distribution matching loss or Diagonal Forcing significantly degrades video quality across all metrics (Table 2). Without
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(a) w/o flow loss
(b) with flow loss
Figure 6: Visual comparison of motion effects. (a) Without motion loss shows minimal motion amplitude with only slight object movement; (b) With motion loss demonstrates significantly increased motion amplitude throughout the entire frame, validating our method's effectiveness.
15c
30+
Clurs
Self Forcing
Causvid
Self Forving
Causvid
Clur'
Self Forcing
Cansvill
Figure 7: Qualitative comparison of long video generation (45s) with Self-Force Forcing and Causvid. The visual re- sults show that other methods suffer from noticeable saturation distortion and quality decay over time, whereas approach preserves detail and consistency. Additional results are provided in the supplementary material.
Diagonal Denoising (this corresponds the inference cost of Self-Forcing in Table 1), we observe that the model achieves performance comparable to ours, while our method attains a 1 x.53x speedup. Notably, we find that the flow distribution matching loss primarily benefits the few-step denoising regime by aligning its performance with the many-step denoising baseline (i.e., without Diagonal Denoising), while offering limited gains when applied in a many-step denoising setting.
Diagonal Forcing Timesteps. Moreover, we systematically evaluated diagonal forcing using met- rics across different noise levels of timesteps for the kv cache. As Figure 5(a) shows, 100 timesteps achieved optimal scores across all evaluation dimensions. including temporal quality, frame quality, and text alignment. The performance peaks at this specific noise level before degrading as timesteps approach complete noise addition (1000 steps) clean frames steps). This be attributed to
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Temporal
Frame
Text
Total
Temporal Quality "Qualiy A'llggmmet
Frame
Text
In-Flight Latency (S)
Thooughpul
Ablation Variant
Steps
Quality 1Quality T'llgnment SSore f
(FPS)
Wilhout Diagonal Forcing
92.1
60.1
26.9
83.55
1322222
91.9
63.4
28.9
0:23
0.02
31.0
5433133
95.1
63.2
29.3
48
0.34
+ 0.02
23.3
Without Flow
92.5
27.8
5492222
94.8
29.0
0.23
29.7
Without Diagonal Denoising
95.1
63.2
28.6
84.46
5333333
95.0
63.9
29.1
0.34
22.5
4333333
95.0
63.7
28.5
44
0.34
0.02
23.5
Full Method (Ours)
91.9
63.4
28,9
84,48
4222222
93.4
62.3
27.8
0.23
4 0.02
32.0
Table 2: Ablation Study on Key Components of DiagDistill.
Table 3: Evaluation of denoising step configurations.
100
Skyleck-V2
0.8
Averygo Quality Time
Preferoce Ittee
75
33.9%
37.3%
42.1%
45.8%
40.7%
0.7
0.6
-0-
66.1%
62.7%
57.9%
54.2%
39.3%
0.4
0.3
5
10
15
20
25 Cauevid
30
35 Pynaming Flow
40
45
Ours
Ours
Ours
Ours
Ours
Ours
Self
FIFO
Streming T2V
Figure 8: Quantitative evaluation long video generation. The plot compares human preference scores and quality consistency over time for different methods under identical conditions. The results show that our ap- proach maintains a stable quality throughout extended sequences, achieving scores above 50%, and attains significant reduction in inference latency.
the fact that excessive noise (high timesteps) blurs the structural priors in the video context, leading to reduced motion magnitude. This also explains why our method generates larger motion ampli- tudes compared to MAGI (Teng et al., 2025). Conversely, insufficient noise (low timesteps) causes the next chunk prediction to implicitly perform next noise level prediction, which can result in over- denoising of subsequent chunks and ultimately lead to over-saturated outputs.
Flow Loss Weight. We conducted comprehensive ablation study across eight motion loss weight configurations. Figure 5(b) reveals the crucial balance between motion guidance (via Flow Distribu- tion Matching) and the DMD learning objectives, with optimal performance observed at weight of 1.0. This balanced weighting scheme ensures the harmonious optimization of temporal consistency, frame quality, and textual alignment metrics.
Denoising Configurationss We evaluated six denoising configurations (represented by 7-digit se- quences specifying steps per chunk 5 seconds video have chunks in our setting) across quality and computational metrics. As shown in Table 3, these configurations exhibit trade-offs between generation quality and efficiency. Among them, configuration 5333333 achieves the highest quality, while 4222222 offers the maximum throughput. To balance video quality and real-time perfor- mance, we selected configuration 4322222, as it has the second-lowest number of NFEs and deliv- performance comparable to configurations with significantly higher latency and throughput, with only marginal differences.
4.4 LONG VIDEO GENERATION EVALUATION
We evaluated our long video generation framework using both simple and complex prompts. As shown in Figure 8, our model maintains consistent perceptual quality over time, whereas bascline methods suffer from rapid quality decay due to error accumulation. participants, 1.50 comparisons per model pair) the first 50 prompts from MovieGenBench further validated our method's 's superiority in overall visual quality, text faithfulness, and long-term con- sistency. User study results, consistent with the qualitative comparison in Figure 7, confirm that baseline methods suffer from degradation issues such as saturation distortion, whereas our approach consistently maintains high visual quality. A key feature of our framework is its support for dynamic prompting (Figure 9), which enables users to introduce new text descriptions at any point along the timeline to create complex narratives with evolving scenes and actions.
large-scale user study (93
5 CONCLUDING REMARKS
In this work, present Diagonal Distillation, novel framework for efficient autoregressive video generation that explicitly accounts for the temporal structure of the denoising process. Our approach exploits dependencies across both video chunks and denoising steps through asymmetric denois-

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Figure 9: Illustration of long video generation with dynamic prompting. This feature allows for the integration new prompts arbitrary time points, facilitating the creation of coherent long videos with changing narra- tives. The specific prompts used for each segment are detailed in the appendix.
ing strategy, in which more steps are allocated to early chunks and progressively fewer to later ones. This design reflects the observation that early chunks play motion and appearance, allowing us to substantially reduce the overall number of denoising steps without degrading motion coherence or visual fidelity.
critical role in establishing global
To further improve temporal stability, Diagonal Forcing explicitly models the denoising trajectory along the temporal dimension, mitigating error accumulation across chunks and narrowing the mis- match between training and inference dynamics. This alignment leads to more stable long-range synthesis and reduces drift in extended video generation. In addition, Flow Distribution Matching enforces dynamic consistency under strict step constraints by aligning the optical flow distribu- tions of generated and real videos, thereby preserving realistic motion patterns even in low-step regimes. Extensive experiments demonstrate that our method consistently achieves a superior trade- off between computational efficiency and generation quality, enabling scalable long- mong-horizon video synthesis while maintaining strong temporal coherence.
ACKNOWLEDGEMENT
The authors would like to thank all the anonymous reviewers for providing helpful suggestions to improve this paper. Authors with equal contributions are listed in alphabetical order and allowed to change their orders freely their resume and website.
ETHICS STATEMENT
While the real-time video generation technology presented in this study significantly improves gen- eration efficiency (achieving 277.3x speedup compared to the baseline model), we are fully aware of its dual-use nature. This technology could potentially be misused to create misleading content or deepfake videos. To mitigate this risk, we commit to embedding usage guidelines and restrictions when open-sourcing the code and models, and we advocate for the adoption of traceability tech- nologies such as digital watermarks and content authentication. Concurrently, this technology holds significant positive potential in fields such as education, the creative industries, and assistive tools. We aim to maximize its societal benefits and minimize potential barms through ongoing discussions on technology ethics and responsible release practices.
REPRODUCIBILITY STATEMENT
For detailed reproducibility information, including full implementation details, training configura- tions, hyperparameters, and evaluation protocols, please refer the appendix sections. All source code, trained model weights, and configuration files will be released to ensure the full reproducibility of our results.
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Appendix
Table of Contents
A Use of LLMs
16
B Noise Scheduling and Model Parameterization
C Comparison of Temporal Training Strategies and Model Architecture Details
16
D Pscudo-code for Diagonal Denoising with Noisy KV Cache
17
E Detailed Analysis of Acceleration and Step Allocation
17
E.1 Acceleration Analysis
17
Step Allocation Strategy Analysis
F Technical details of the stream protocol
20
G Detailed Ablation Study Motion Flow Field Representation
21
H
User Study
22
H.1
Experimental Sctup
22
H.2
Evaluation Methodology
22
User Study Results
23
H.4
Questionnaire Design and Implementation
23
H.5
Detailed Questionnaire Example
23
H.6
Statistical Analysis and Interpretation
24
Societal Impact Considerations
25
Prompts
25
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A USE OF LLMS
In this study, Large Language Models (LLMs) were used exclusively for the purpose of checking and correcting grammatical errors in the manuscript.
B NOISE SCHEDULING AND MODEL PARAMETERIZATION
Following the design principles of the Wan 2.1 series, we adopt Flow Matching framework, utiliz- ing a time step offset defined as:
(k: t/1000)
tk, + (A- ()(1/0000
1000
with an offiset factor ht = 5.0. The forward process is defined as:
l'
(1
L'
x
1000
x x e ((01)
1000
where the time step & [0, 1000]. The data prediction model is expressed as:
Go 1, c) Oskip E Caut
(o
Xt, Cuoise (t'),
We maintain the preconditioning coefficients identical to the base model configuration, i.e., Cskip
Gin = Cout and Cnoise (4) =
Based on our actual configuration, DMD (Distribution Matching Distillation) training employs Diagonal Denoising mode with time step list of [1000, 100]. The enabling time step wrapping is controlled by the warp_dersoising__stee parameter.
C COMPARISON OF TEMPORAL TRAINING STRATEGIES AND MODEL ARCHITECTURE DETAILS
This appendix explains Figure 10, which compares temporal training strategies for autoregressive video generation, highlighting our Diagonal Forcing approach. The figure shows how a Causal DiT model generates new frames conditioned on previous ones, with strategy differences affecting robustness and performance.
The standard baseline approaches include Teacher Forcing, Diffusion Forcing, and Self Forcing training strategies, as shown in Figure 10. Teacher Forcing conditions the model exclusively on ground-truth previous frames during training, providing a clean learning signal but creating a train- inference discrepancy that leads to eиror compounding during long sequences. Diffusion Forcing addresses this by exposing the model to noisy latents from the diffusion process, enhancing robust- ness but introducing mismatch since the model learns to denoise based on noisy context unlike the clean context used at inference. Self Forcing directly mimics inference by conditioning on the model's own previous predictions, but this approach trains the model its own early, often low- quality predictions, which can hinder learning and lead to suboptimal convergence.
Our proposed Diagonal Forcing method, illustrated in Figure 10(d), combines the advantages of these strategies while mitigating their weaknesses. The core innovation conditions the denoising of the current frame on a mixture of clean ground-truth and previously generated frames, arranged in diagonal pattern across the temporal dimension. For given target frame, the model is conditioned on previous frames where the most recent ones are its own predictions from previous autoregres- sive steps, while frames further in the past are drawn from ground-truth data. This pattern shifts diagonally for each subsequent target frame, providing two crucial benefits: it gradually exposes the model to its own prediction errors to improve long-term generation robustness, while simulta- neously anchoring the process on distant, clean ground-trute frames to prevent semantic drift. Most importantly, this conditioning pattern closely approximates the inference-tima scenario of our Diag- onal Denoising algorithm, directly aligning training and inference distributions for generating long, coherent videos.
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Causal DiT
Causal DiT
Teacher Foncing: Training
Diffusion Forcing Traising
Causal DiT
Causal DiT
(e) Self Forcing Training
Diegonal Forcing Training(Our))
Figure 10: Comparative visualization of temporal training strategies for autoregressive video generation using Causal DIT. Four panels illustrate: (a) Teacher Forcing (green boxes for ground-truth frames). (b) Diffusion Forcing (red boxes for noisy latents), (c) Self Forcing (red boxes for model's own predictions), and (d) Di- agonal Forcing (Ours) (mixed green/red boxes in diagonal patterns). Each row represents sequential frame generation, with arrows indicating causal dependencics. The diagonal pattem in (d) highlights the core inno- vation blending clean past frames with recent model-generated ones align training/infernnee distributions. This visual comparison underscores how Diagonal Forcing bridges gaps in robustness and coherence seen in baseline methods.
D PSEUDO-CODE FOR DIAGONAL DENOISING WITH NOISY KV CACHE
This appendix provides the pseudo-code for Algorithm 1, which implements the Diagonal Denois- ing with Noisy KV Cache strategy introduced Section 3.2 and Figure 3. The algorithm enables efficient long video generation through progressive step reduction and KV cache reuse. The algo- rithm processes video chunks sequentially. During the Base Phase (k S 4), each chunk is denoised for s(k] steps (progressively reducing from to 2). At the penultimate step, the intermediate latent is processed by CACHENOISY
which adds noise to create iinterim and caches its KV embed- dings. This noisy caching simulates the teacher's 's denoising trajectory, providing temporal context for subsequent chunks.
In the Extension Phase (k: > 4), generation uses only 2 steps. Chunk is conditioned on the previ- ous chunk's s output (Xt-1), and the first step's sep's result is similarly cached. This approach maintains coherence while significantly improving efficiency, balancing quality and computational cost for long video generation.
E DETAILED ANALYSIS OF ACCELERATION AND STEP ALLOCATTON
This section provides a comprehensive analysis of the acceleration mechanisms in Diagonal Forcing and explores various denoising step allocation strategies.
E. ACCELERATION ANALYSIS
The acceleration Diagonal Forcing stems from four synergistic optimizations that collectively achieve superior performance compared to Self-Foreing
1. Reduction in Denoising Steps: fewer total Noise Function Evaluations (NFEs). While our method naturally reduces the number
Diagonal Forcing achieves comparable or better quality with
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Algorithm Diagonal Denoising with Noisy KV Cache 2, (0,,0),
Require: M,
Ensure: ,I....,
1: X 0.C C-Cecg.
for
do
3:
<~ (0,
4:
if S then
b Base phase
5:
x ~ ~
6:
for
[ = to s|k do
7:
X - (x (1))
Xen, C; (x(H))
8:
if = s(kj s
then
9:
CACHENOISYRESULTTXXX
D. Cache penultimate step
end if for
12:
X5 t Mix(X c))
13:
else
D. Extension phase (2-step generation)
14:
+ ((Xim)
15:
X
Cx,
16:
CACHENOISYRESSUITTC.."
17:
X1 (X C:O2))
18: end if
19: X
(-uU(X
20:
end for
21:
return X
22:
procedure CACHENOISYRESCLIIY
23:
Xrimtesim + V VT- agE
24:
C [Compuuuk (Auserim)
25:
end procedure
of required denoising steps, we conducted controlled experiments under identical NFE budgets to isolate the contributions of other optimizations. As shown in Table 5, even with the same number of NFEs, Diagonal Forcing consistently outperforms Self-Forcing across all quality metrics while delivering lower latency and higher throughput.
Efficient KV Cache Mechanism: Another architectural advantage lies in our integrated KV cache strategy. Self-Foreing requires separate KV cache computations on clean frames without per- forming denoising, creating redundant operations. In contrast, our method performs KV caching di- rectly the noisy latent shown in Figure 10 and apply tiny vae
tokenization (Boer Bohan, 2025), which simultaneously serves two purposes: providing conditioning for subsequent chunks and progressing the denoising process yield clean latents. This elimination of redundant compu- tations directly translates to reduced both denoising and decoding latency and increased throughput.
3. Optimized Attention Window Size: Our rolling KV cache mechanism, inspired by Self-Forcing. employs a more efficient context window strategy. While maintaining seamless rolling forward to avoid hiding-windoww overhead, reduce the KV cache size from chunks (used in original Self- Forcing) to 4 chunks. This optimization is supported by our scaling analysis in Table 4, which shows that performance plateaus around window sizes of 12-27, with smaller windows providing better latency-ment memory trade-offs. The reduced window size contributes significantly to lower memory usage and faster inference and lower in-flight latency without compromising long-video generation quality.
Efficient Tokenization Mechanism: The tokenization process operates directly on the latent representations from the Tiny VAE (Boer Bohan, 2025), which encodes frames into tokens. As shown in Table 8, the Tiny VAE achieves substantially faster decoding with significantly fewer parameters than the Full VAE. For streaming applications, we further optimize the pipeline using an efficient Tiny VAE. By eliminating the VAE bottleneck, the Tiny VAE reduces decoding time by more than 10x.
The combination of these four optimizations creates a compound effect: the reduced denoising steps decrease computational load, the efficient KV cache mechanism eliminates redundant operations,
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Total Score
In-Fight Latency (6)
Memory (GB)
Window
80.9
0.37 1: 0.01
14.9
81.3
0.38 + 0.01
15.8
82.3
0,40 + 0.01
16.6
84.3
0.46 : 0.02
17.5
84.2
0.51 = 0.02
18.4
84.4
0.54 0.02
19.2
21
84.5
0.59 = 0.02
20.1
24
84.3
0.61 0.02
20.9
27
84.5
0.68 + 0.02
21.8
Table 4: KV cache scaling analysis
Steps Allocation
Temporal Quality
Quality
Text Aligament
In-Flight Latency (6)
Throughput (FPS)
Diagonal
Self
4322222
93.5
25.9
40
Diagonal
5432222
94.3
63.1
29.0
0.23 :: 0.02
29.7
Self
5430222
93.9
62.3
28.
0.43 - 0.02
23.8
Diagonal
4333335
28.5
0.34
23.5
44
Self
4333333
94.2
62.8
27.8
0.56 = 0.02
21.5
Diagonal 5335335
95.0
63.9
29.1
0.34 = 0.02
22.5
19.8
Diagonal
5433333
95.1
63.2
20.3
0.34 = 0.02
23.3
48
Self
5433333
94.3
62.5
28.4
0.56 = 0.02
18.6
Diagonal
0.46 = 0.02
Setf
4444442
94.3
17.0
Table 5: Comparative evaluation under identical NFE budgets demonstrates Diagonal Forcing's superior ef- ficiency. Our method achieves better quality metrics with significantly lower latency and higher throughput across all configurations.
the optimized window size minimizes memory overhead and the tiny vae reduce the extra decod- ing time. This holistic approach enables Diagonal Forcing to achieve the performance advantages demonstrated in our comparative evaluations, making it particularly suitable for long-video genera- tion scenarios where both quality and efficiency are critical.
E.2 STEP ALLOCATION STRATEGY ANALYSIS
Schedule
Temporal Quality 1
Frame Quality 1 Text Aligmuenn
NFEs
In-Flight Latency (6)
Throughput (FPS)
Baseline Strutegies (From Paper)
5333333
95.0
63.9
29.1
46
0.34 1: 0.02
22.5
4333333
95.0
28.5
0.31
23,5
5433333
95.1
29.3
0.31
23.3
5432222
94.8
40
29.7
4322222
94.9
63.4
28.9
34
0.23 + 0.02
31.0
4222222
93.4
62.3
27.8
32
0.23 + 0.02
32.0
Exploration: Emphasicing Early Frames
5422222
95.1
64.1
29.2
38
0.23 0.02
28.5
5522222
95.3
63.8
29.4
40
0.23 1 0.02
27.1
4432222
94.9
63.5
28.8
38
0.23 1 0.02
30.2
Exploration: Non-monotonic Dynamic Allo Allocation
4343232
95.0
63.6
28.4
42
0.34 0.02*
21.0
4532232
95.0
28.6
42
0.34 0.02*
20.5
3331533
93.5
0.37 0.02*
22.8
3233343
93.6
0.34 0.02*
23.3
Table 6: Quantitative comparison of different denoising step allocation strategics. Strategies are grouped by type: Baseline Strategies (evaluated the main paper), Exploration: Emphasizing Early Frames, and Explo- ration: Non-monotonic Dynamic Allocation. NFEs denotes the total number of function evaluations, Best results are in bold.
To thoroughly investigate the impact of denoising step allocation on the performance of our distilled video diffusion model, extend the analysis beyond the baseline strategies presented in the main paper. We systematically explore a wider range of allocation schedules, which can be broadly cate- gorized into two groups: strategies that emphasize early frames and those employing non-monotonic
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or dynamic allocations. The quantitative comparison of these strategies is summarized in Table 6. For non non-monttonccaly decreasing allocations, we report the average latency, which is provided for reference only and is marked with asterisk (+).
Performance Upper Bound:
We confirm that using 4 steps for the initial chunk (400000)
essentially reaches the performance upper bound for our distillation framework. Strategies like 5422222 show marginally better metrics than 4322222, but the improvement is min- imal and comes with latency cost. This suggests that for our DMD framework, using steps for the initial generation is often sufficient to approach the performance upper bound. Further increasing steps (e.g., to 5) provides diminishing returns.
Monotonic Decrease is Optimal: The exploration of non-monotonic schedules (e.g., 4343232) reveals that they do not provide consistent or significant advantage. More importantly, they can harm motion quality. In our autoregressive framework, subsequent chunks are generated conditioned on the motion characteristics of prior chunks. If the ini- tial chunks have strong motion (established by sufficient denoising steps), later chunks can maintain good motion even with fewer steps. Introducing more steps later does not effec- tively enhance motion and can disrupt this inherent consistency. Therefore, monotonically decreasing schedule is the most sensible approach.
Limited Search Space: The solution space for effective schedules is relatively small. The principle of "more steps early, fewer steps later" robust. For practical application, manu- ally selecting a schedule like 4322222 (for 5s) and cyclically extending it for longer videos (43222224322222 for 10s) robust, effective, and recommended practice. Users can adjust the initial step count (3, 4, or 5) based on their specific latency- requirements.
In conclusion, while algorithmic schedule selection confirms the robustness of our manually tuned strategy, it ultimately reinforces that a simple, monotonically decreasing allocation cyclically ex- tended for longer videos is the most effective and principled rule.
F TECHNICAL DETAILS OF THE STREAM PROTOCOL
In this section, we provide our streaming protocol detailed as follows:
(1) Chunk Size The core component is rolling KV cache mechanism following the Self-Forcing approach (Huang et al., 2025), operating with a chunk size of 3 frames. This chunk size was em- pirically selected based on teacher model's VAE latent space and provides the optimal trade-off. Comparative analysis in Table 7 shows that the chunk-level variant (size=3) achieves a total score of 84.48, while the frame-level variant (size=1) scores 84.29. The chunk-leve variant provides higher throughput (31 FPS vs. 16.5 FPS) at the cost of slightly higher latency (0.37s vs. 0.25s). This obser-
vation aligns with Self-Foreing [1], where frame-level configurations more suitable for real-time applications on constrained devices but result in inferior video modeling performance.
Type
Throughput (FPS)
Lateney (5) 4 Speedup f
Total Score
Quality Semantics
DiagDistil (Chunk-level, chunk size=3)
31.0
0.37
277.3x
84.48
85.26
81.73
DingDistill (Frame-level, chunk size=1)
16.5
0.25
412.0x
84.29
85.41
80.40
Table 7: Performance comparison of chunk-level (size=3) vs. frame-level (size=1) streaming variants
(2) Overlap, Buffering, and KV Cache Size Our buffering strategy employs fixed-size KV cache that maintains context from the most recent 4 chunks, resulting in a consistent memory foot- print of 17.5GB. This represents significant improvement over the original Self-Forcing approach, which requires 19. 19.2GB (with 18-frame KV cache reuse equivalent to 6 chunks overlap). The latent space overlap is frames. We use the denoising state immediately preceding the final clean step as conditioning for the KV cache. This approach completes the denoising process while preserving the noise-conditioned frames for injection into the KV cache sequence. Each chunk is generated using denoising steps, with total scores evaluated on VBench-Long. As shown in Table 4 from our paper, the KV cache size scaling analysis reveals critical performance trade-offs associated with attention window size. As the window increases from 3 to 27 frames, the total score improves significantly from 80.9 to 84.5, but diminishing returns are observed beyond 12 frames. This performance im- provement comes cost: latency increases linearly from 0.37s to 0.68s due the larger context
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that must be processed, and memory footprint grows substantially from 14.9GB to 21.8GB. The optimal operating point is therefore determined to be 12 frames-our our chosen configuration which effectively balances high total score of 84.3, reasonable latency of 0.46s, and manageable memory usage of 17.5GB.
(3) Tokenization The tokenization process operates directly on the tiny VAE latent space rep- resentations (Boer Bohan, 2025), where frames are encoded as tokens. As shown in Table 8, Tiny VAE achieves substantially faster decoding with significantly fewer parameters compared the Full VAE. For streaming demos, we further optimize the pipeline with an efficient Tiny VAE. Tiny VAE removes the VAE bottleneck by reducing the decoding time over 10x.
Model
Decoder Parums Compression Decoding Timets) x832x480)
Full VAE (Wan 2.1)
73.3M
8x8x4
1.67
Thny VAE (Wan 1) (Boer Bohan, 2025)
9.84M
8x8x4
0.12
Table 8: Comparison Causal VAE Models.
(4) Pre-fill Cost The reported "first-frame latency" includes the complete multi-step denoising process for the initial chunk. For all subsequent chunks, the pre-fill cost is effectively eliminated as the model leverages the rolling cache, with computation focused solely on denoising new frames. This architecture, with its favorable O(TL.) time complexity, has been extensively validated on long sequences (exceeding 45 seconds) with no measurable performance degradation, confirming its ro- bustness for real-time deployment.
G DETAILED ABLATION STUDY ON MOTION FLOW FIELD REPRESENTATION
Motivated by MCM (Zhai et al., 2024) and as detailed in our paper, our method eschews reliance on external, pre-trdi optical flow estimators (e.g., RAFT (Teed Deng, 2020)). Instead, we introduce lightweight, learnable motion feature extraction module, F(.). that operates directly on the latent space of the video diffusion model. A key component of our implementation is an Exponential Moving Average (EMA) framework. Within this framework, the student component of F(-) is trained with gradient backpropagation, enabling end-to-end motion representation learning. The teacher component is updated via EMA of the student's parameters. We apply standard feature normalization to the latent features, and this suf--supee distillation framework inherently avoids biases that could be introduced by errors from external flow estimators. We investigated several design variants for the motion feature extractor F(.):
1. Latent Difference: Computes Frame-wise differences between consecutive latent repre- sentations: Fan X) Xx+1 X,
2. Latent Correlation: Constructs 4D correlation volumes via pairwise dot-products: Pave(() (X, Xx+1) (Shi et al., 2023)
3. Latent Frequency Components: Extracts low-frequency components (Fiow) and high- frequency components (Frigh) using DCT transformations (Wu et al., 2024).
4. Learnable Representation (MLP): Applies a two-layer MLP to adaptively extract motion features. This MLP contains no temporal layers, thus preserving motion information while avoiding direct application of appearance loss to the latent representation. The target MLP head is updated via exponential moving average (EMA) and is discarded during inference. This design analogous to the projection heads used in self-supervised learning (Chen et al., 2020).
5. Learnable Representation with Convolution Latent Difference: First computes la- tent differences between consecutive frames, then applies convolutional layers to extract local motion patterns. This hierarchical design captures both explicit motion cues from frame differences and local spatiotemporal correlations through convolutional processing, effectively implementing the motion flow field extraction function F(x) described in our flow distribution matching framework.
For the learnable representations (variants 4 and 5), we employ an EMA-based framework to ensure training stability:
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Student Network: Parameters 0 are updated via gradient descent.
Target Network: Parameters 8- are updated via EMA: f <- 1..00 0 + (1 - p.. .
The teacher's output is processed by the target MLP and the student's output by the student MLP. EMA ensures stable target evolution, improved feature quality, and prevents model collapse.
The computational overhead of our flow module is minimal. It consists of very light Conv-MLP with only two convolutional layers, making computational cost almost negligible. This es- pecially true when compared to the alternative approach of decoding frames with VAE and then applying a separate optical flow estimator, which would incur significant memory overhead.
The regression loss term is critical for stable optimization. Without it, training is highly prone to collapse. The regression loss provides a crucial grounding signal that constrains the optimization trajectory. This is corroborated by our ablation study the flow loss weight (see Figure 5b), which shows that performance degrades if the weight set excessively high. This indicates that an overly strong flow constraint is detrimental. comprehensive ablation over eight configurations confirmed that a weight of 1.0 achieves the optimal balance, ensuring harmonious optimization of temporal consistency, frame quality, and textual alignment.
Flow Representation Variant
Temporal Quality
Frame Quality
Text Alignment
Raw Laleni
92.5
60.8
27.8
Latent Difference
93.8
61.5
28.2
Correlation
94.2
62.1
Frequency Components
93.5
61.8
High Frequency Components
62.3
Learnable MLP
94.6
63.2
28.7
Learnable + Cony on Difl'
94,9
63.4
28.9
Table Performance comparison different F()) implementations.
H
USER STUDY
H.1 EXPERIMENTAL SETUP
To comprehensively evaluate the quality and consistency of long video generation methods, we ducted a large-scale double-blind user study involving 93 participants. Each participant completed 150 comparisons per model pair, resulting in statistically significant findings. The evaluation was performed on the first 50 prompts from MovieGenBench to ensure comprehensive assessment across diverse scenarios.
H.2 EVALUATION METHODOLOGGY
EVALUATION CRITERIA
Participants compared different methods based on three key aspects:
Overall Visual Quality: Subjective assessment of visual fidelity, naturalness, and absence of visual artifacts
Text Faithfulness: Accuracy in matching the input text description and maintaining mantic alignment
Long-term Consistency: Quality maintenance across extended sequences (up to 45 sec- onds)
COMPARATIVE FRAMEWORK
The study employed a paired comparison design where participants evaluated:
bide comparisons our method against five baseline approaches
Videos generated from identical prompts under controlled conditions
Both simple and complex prompt scenarios to test generalization capability
Dynamic prompting scenarios with multiple scene transitions
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H.3 USER STUDY RESULTS
The quantitative results from the user study demonstrate clear superiority of our method, as show in our paper. The preference rates are summarized as follows:
vs. Causvid: 66. % preference for our method (33.9% for baseline)
vs. WAN2.1: 62.7% preference for our method (37.3% for baseline)
vs. SkyReels- V2: 57.9% preference for method (42. 12.1% for baseline)
vs. MAGI-1: 54.2% preference for our method (45.8% for baseline)
vs. Self-Forcing: 59.3% preference for our method (40.7% for baseline)
Statistical analysis using paired t-tests confirmed that all preference rates are statistically significant (p < 0.01), indicating robust user preference for our approach across all baseline comparisons.
H.4 QUESTIONNAIRE DESIGN AND IMPLEMENTATION
PARTICIPANT DEMOGRAPHICS
The study involved 93 participants with diverse backgrounds:
Background distribution: Computer Science (42%), AI/ML (28%), Visual Arts (18%),
Other (12%)
Experience levels: Beginner (15%), Intermediate (45%), Expert (40%)
Familiarity with video generation: Average rating of 3.8/5.0
QUESTIONNAIRE STRUCTURE
1. Demographic Information Collection
Professional background and expertise level assessment
Prior experience with video generation technologies
2. Individual Video Quality Assessment
5-point Likert scale evaluation (I=Poor, 5=Excellent)
Assessment of visual realism, color consistency, motion naturalness
Artifact detection and severity rating
3. Comparative Evaluation Tasks
bide-b method comparisons with forced-choice preference
Specific aspect comparisons (text alignment, temporal consistency)
Quality assessment at multiple time points (15s, 30s, 45s)
4. Dynamic Prompting Evaluation
Transition smoothness assessment for multi-scent narratives
Narrative coherence evaluation across prompt changes
Visual consistency maintenance during scene transitions
5. Qualitative Feedback Collection
Open-ended comments observed artifacts
Identification of temporal consistency issues
Suggestions for methodological improvements
H.5 DRTAILED QUESTIONNAIRE EXAMPLE
Part 1: Demographic Information
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Item
Options
Background
Computer Science
AI/ML
Visual
Arts
Other:
Experience level
Beginner Intermediate O
Expert
Familiarity with video generation
2 R
4 5 D (1=Not familiar, 5=Very familiar)
Part 2: Video Quality Assessment (Prompt: "A sunset over ocean waves")
Evaluation Metric
Poor (1) Below Avg (2)
Average (3)
Good (4)
Excellent (5)
Visual realism
Color consistency
Motion naturalness
Artifact absence
Part 3: Comparative Evaluation
Prompt: "A bird flying through forest, then landing on branch"
Overall preference: D Strongly prefer D Slightly prefer D Neutral Slightly prefer
B C Strongly prefer B
Specific aspects:
Text aligmment: O A better D Equal D B better
Temporal consistency: D A better Equal D B better
45-second quality: D A better D Equal O B better
Part 4: Long-term Consistency Assessment
Time Segment
Consistent
Minor Decay Significant Decay
0-15 seconds
15-30 seconds
30-45 seconds
Part Dynamic Prompting Evaluation
Prompt: "A car driving on highway - entering tunnel + emerging in mountains"
Transition smoothness:
2 D D O D (1=Abrupt, 5=Seamless)
Narrative coherence:
C
2
D 3 D 4 D
(1 Poor. 5=Excellent)
Visual consistency:
3 D
(I=Poor, 5=Excellent)
Part 6: Qualitative Feedback
Notable artifacts observed:
Temporal consistency issues:
Suggestions for improvement:
H.6
STATISTICAL ANALYSIS AND INTERPRETATION
The comprehensive user study design enabled rigorous statistical analysis through:
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Preference rate calculation with confidence intervals
Inter-rater reliability assessment using Cohen's kappa (s 0.78)
Statistical significance testing via paired t-tests and ANOVA
Qualitative data analysis using thematic coding approaches
The results consistently demonstrate our method's superiority in maintaining visual quality and temporal consistency, particularly long video generation scenarios. The highest preference rate against Causvid (66. 1%) highlights our approach's 's effectiveness in addressing enror accumulation issues that plague baseline methods.
I SOCIETAL IMPACT CONSIDERATIONS
Generative modeling technology, particularly video generation, holds significant application poten- tial but also carries risks of potential misuse. Deepfake technology could be exploited disseminate misinformation, which is particularly critical in the current information age. Our research on real- time video generation, by lowering the computational cost barrier, might increase the risk of tech- nological misuse. However, this technology can also foster positive applications such creative content production, educational tool development, and assistive technology innovation. Recogniz- ing the dual-use nature of this technology, strongly advocate for the continued development of corresponding detection technologies, digital watermarking techniques, and policy frameworks to maximize the positive impact of the technology while minimizing potential harms.
PROMPTS
The prompt of the first case (left-1) in figure 4:
A red-haired woman, dressed in a cobalt blue top, immersed in reading book. She was in dream scene like Van Gogh's "The Starry Night" : the background was huge, swirling nebulae and the moon the night sky, and her brushstrokes were full of dynamism. The warm glow of the desk lamp illuminated her focused face and the pages of the book. The entire picture was enveloped in serene yet vibrant atmosphere, featuring thick, flowing colors and sharp brushstrokes.
The prompt of the first case (left-2) in figure 4:
In the center of the picture, there is a golden sandy beach and crystal-clear green sea water. Under the sapphire-like sky, a few seagulls glided leisurely, adding a dynamic touch to the serene scene. In the background, a thick forest frames the entire coastline in deep green color, adding touch of mystery and depth.
The prompt of the first case (left-3) figure 4:
The handheld camera shook violently, and the muddy, earth-yellow flood rushed into the village like a wall. The water current, carrying broken wood and debris, raised muddy waves over one meter high, violently crashing and submerging low houses. The sky was overcast, the rain was slanting, and people were running in panic in the distance, filled with sense of urgency as if sudden disaster had struck.
The prompt of the first case (right-1) in figure 4:
The expressway is congested at night. An endless dragon composed of red taillights contrasts with the cold blue road sign lights. The main camera moves forward slowly. Occasionally, vehicle roars past the emergency lane the left, with a blurry high beam track, breaking the frozen rhythm. The air was filled with a tense atmosphere.
The prompt of the first case (right-2) in figure 4:
Low-angle follow-up shooting. person in red trousers is cycling, with the camera focusing on the rapidly pedaling pedals and the rolling wheels. Sunlight filtered through the trees on both sides of the road, casting flowing shadows on his trouser legs. We could only see his lower body and bicycle. The background was the rapidly receding green bushes, full of mystery and dynamism.
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The prompt of the first case (right-3) in figure 4:
A smooth blue robot is walking cautiously on a vast and crested glacier. marches with steady and precise steps, and its streamlined helmet-like head is slowly and systematically turning left and right, as if scanning the environment to collect data. The background is a huge, deep blue ice wall, and the air is filled with cold mist. The camera advances slowly, focusing on the faint light emitted by the robot's optical sensor.
The prompt of the first case (left-1) in figure 7:
1. "iid establishing shot of a perfectly calm ocean at dusk. The water is like glass, reflecting the soft pastel colors of the sunset sky. A solitary lighthouse stands silent on distant rocky outerop. The atmosphere is one of profound peace. Cinematic, serene, ultra-realistic, 4K."
2. Low-angle shot focusing the lighthouse structure. It is classic, white-and-red striped tower. The light is inactive. The only movement is from few seagulls floating on the gentle air currents around it. The scene is quiet and still. Photorealistic, detailed, calm."
3. 3."xxtree close-up on the ocean's surface. The water is incredibly clear and motionless, acting as a perfect mirror for the fading light. A subtle, almost imperceptible ripple suddenly distorts the reflection, hinting at a change. Macro detail, reflective, serene, ominous."
"Dynamic time-lapse shot. Dark, ominous clouds swiftly invade the sky, blotting out the sun. The wind begins to whip across the water, transforming the calm surface into chaotic pattern of whitecaps. The camera shakes slightly with the growing gusts. Handheld feel, dramatic transition."
5. "Powerful wide-angle shot as the storm hits. The sea erupts into fury of massive, crashing waves. The color palette shifts to menacing greys and deep greens. The camera is positioned low to the water, emphasizing the immense scale and raw power of the raging ocean. Stormy, turbulent, VFX, high dynamic range."
6. "Dramatic shot of the lighthouse at the storm's peak. Its powerful beacon SUDDENLY ignites, cutting a brilliant, rotating beam through the darkness and driving rain. Volumetric light effects create god rays that illuminate the dense spray and water particles in the air. A beacon of hope in the chaos." "
7. "Shot from the lighthouse's perspective (POV). The rotating beam rhythmically scans the tumul- tuous sea. Each pass briefly illuminates the raging waves below, creating a strobe-like effect that reveals the terrifying scale of the storm. Night vision, gritty, immersive, cyclical illumination."
8."A new element emerges. Through the sheets of rain and spray, a cluster of stable, bright lights becomes visible on the horizon. It is large ship, holding its course against the enormous swells. The reveal is slow and cinematic, building anticipation. Long lens, shot through a rain-soaked atmosphere."
9. ".'Cosrr, dynamic shot of the large container ship battling the elements. It pitches and rolls dra- matically, its bow crashing into a massive wave and sending wall of spray over the deck. The lighthouse beam sweeps across the ship's hull, highlighting its struggle against the sea. Action, scale, powerful movement."
10. "Finl resolving shot. The ship, now clearly guided by the steadfast lighthouse beam, navigates steadily through the storm. The waves, while still large. are now framed as an obstacle being over- come. The composition includes both the resilient ship and the unwavering lighthouse, symbolizing safe passage. Heroic, resolved, symbolic, hopeful ending." "
The prompt of the first case (left-2) in figure 7:
1."A wide, dynamic shot of a sun-drenched racetrack. A blur of red, a low-slung racing car. domi- nates the straight, kicking up slight haze from its tires. The camera tracks smoothly, emphasizing immense speed against the backdrop of grandstands and clear sky."
2. "A dramatic fode-follow shot, low to the ground. The red racing car fills the frame, its tires gripping the tarmac. The powerful engine roar is visceral. It rockets past the camera, the focus pin-sharp on its livery, creating a powerful sense of explosive motion."
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3. 3."An extreme low-angle shot, almost on the asphalt. The screams over the camera lens, flash of red focusing on its undercarriage and a blur of spinning wheels. The distorted perspective amplifies the raw speed and ground-shakinngg power."
4."A tense medium shot from the side. The car suddenly veers, its left-side tires violently hitting the grass verge. Dirt and turf spray into the air the chassis jolts, fighting for stability while maintaining its forward surge. The camera shakes slightly."
5." "A shaky, immersive driver's seiver's-eye s-eye view. The steering wheel is jerked sharply. The view lurches vi- olently, switching from clean track to a chaotic blur of green grass filling the windshield, simulating the driver's corrective input."
6."A A high, wide aerial panorama. The red car carves a sharp, deliberate are across the green grass. fully sideways in a controlled slide, tires scouring clear marks into the turf, capturing the precise moment of correction."
7."A tight, detailed close-up on the front wheel and suspension. The components work violently over the uneven grass, absorbing impacts and adjusting the steering angle. Every movement of the mechanical parts is clear and powerful."
8."A crisp medium shot from behind. The car masterfully completes its correction, sliding back onto the track. The front wheels grip the pavement as the rear follows, trailing final cloud of dust from the grass."
.."A chasing shot from directly behind the car, now fully on track. It accelerates hard into the distance, the engine screaming. The camera tracks its movement as it shrinks down the long straight, a streak of red against the asphalt."
10."A A final, wide, static shot. The red car is a distant speck accelerating towards far corner, quickly disappearing. The scene is quiet, showing the empty track and the solitary tire marks scarring the grass, telling the story of the incident."
The prompt of the first case (left-3) in figure 7:
1."A bustling, vibrant street market in Southeast Asia, shot with wide lens. Vendors under colorful awnings sell fruits and textiles. white delivery truck is momentarily stopped, partially blocking the narrow street. The atmosphere is lively and crowded."
2. close-up, dynamic shot focusing on the rear of the white truck. A small tear in the tarpaulin cover is subtly visible. Sand begins to trickle out in thin stream, creating a small pile on the cobblestones below. The sound of the market drowns out the faint sound of the leaking sand."
3. 3."A medium shot from the side of the truck. As the truck's 's suspension creaks, the leak suddenly widens. A much larger cascade of golden sand pours onto the street, creating a growing mound that starts spill onto the pathway. The truck driver is unaware, visible in the side mirror."
4." "A low-angle shot from the ground level. The stream of sand continues to flow, spreading across the uneven street. The focus is the growing pile of sand, with the market-goers' legs and the truck's wheels blurred in the background."
5. "The first pedestrian, woman in sandals, steps into the edge of the spilled sand. She looks down with mild annoyance, shaking her foot to dislodge the grains. She then continues walking, leaving the first set of footprints in the pile."
A wider shot showing the traffic of people. More pedestrians now have to walk through the spreading sand. Some look down curiously, others step carefully avoid slipping. The sand is beginning to be tracked further down the street."
7."A time-lapse effect over 10 seconds. The scene speeds up to show a continuous flow of people walking through the sand, which is now thoroughly scattered across the width of the street. The once-distinct pile flattened and spread into wide, messy patch." "
8."A close-up on the feet of various pedestrians. Different types of footwear- Sondwaa--sandass, sneakers, boots tread through the sand, kicking up small clouds of dust. The sand sticks to wet soles and is carried away."
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9. nigh-angle shot from a nearby balcony. This clearly shows the broad, sandy path the pedestrians have created through the heart of the market, leading away from the now-departed white truck. The scene returns to its normal bustling state, but with the sandy evidence remaining."
10."A final static shot of the now-deserted spot where the leak occurred. The ground is covered in a wide, uneven layer of trampled sand, crisscrossed with footprints. The white truck is gone, and the market activity continues around the sandy patch." "
The prompt of the first case (right-1) in figure 7:
1."A wide establishing shot of a rugged mountain range under a crisp blue sky. A lone figure, a man in dark green jacket with hiking backpack, is a small speck midway up steep, rocky slope. Patches of snow cling to the shaded crevices. The camera slowly pulls back, emphasizing the vast scale of the wilderness."
2. "A medium shot from the side as the man hikes steadily up narrow, winding dirt path. His breath visible in the cold air. He adjusts the straps of his large backpack, determined. The trail is flanked by scattered snowdrifts and low, hardy alpine vegetation."
3."A low-angle shot looking up at the man from the ground level, making him appear tall against the sky. He steps over a large, weather-smoother rock on the path. The focus is on his sturdy hiking boots crunching on the gravel, with melting snowbank in the foreground." "
4." "A panoramic shot from behind the man, following him as he walks. We see his backpack and his gaze scanning the path ahead. The view overlooks a vast, dramatic valley, with the next mountain peak, dusted with snow, visible in the distance."
"A close-up on the man's face, showing concentration and slight fatigue. Beads of sweat are on his temple despite the cold. He pauses for a moment, looking towards his destination, the wind gently ruffling his hair and jacket collar."
6. A dynamic drone shot circling the man as he traverses a high, exposed ridge. The camera reveals the steep drop-offs on either side and the stunning panoramic view of interconnected peaks, some crowned with pure white snow."
7."A detail shot of the man's hands adjusting the compass on his wrist. He then takes a sip of water from bottle attached to his backpack strap. The dark green fabric of his jacket and the worn straps of his pack are clearly visible."
8. "he man reaches the summit of the first hill. A wide shot shows him standing there, catching his breath, looking down into the saddle leading to the next, higher mountain. The path continues clearly ahead, snaking through rocky terrain and snow patches."
9."A long-lens shot from the next mountain, capturing the man as a small figure beginning his descent into the col. He moves carefully down the scree slope, maintaining his balance under the weight of his pack, heading towards the camera's 's position."
10."A final wide shot from an elevated vantage point. The man is now a tiny, dark green speck on the flank of the adjacent mountain, steadily progressing upward. The scene captures the immense journey undertaken, with the first mountain now behind him."
The prompt of the first case (right-2) in figure 7:
1."A wide, establishing shot of winding two-lane highway cutting through lush, dark green moun- tains under a brilliant, sun-drenched sky. The asphalt shimmers with heat. A lone cyclist is a small dot in the distance, approaching the camera."
2. "A lowlangle side shot, the camera tracking alongside the cyclist. A man in a crisp white cycling helmet and mirrored sunglasses leans forward over his road bike. His legs pump rhythmically, the chain and gears whirring softly. Sunlight glints off his helmet and frame."
3."A point-of-view shot from the cyclist's perspective. The handlebars and the rider's hands are in the foreground. The road stretches ahead, black ribbon winding between the deep green mountain slopes. The view is bright and slightly overexposed from the intense sun."
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4."A dynamic shot from behind the cyclist, following closely. The focus is on the rider's back and the white helmet, with the road unfurling behind him. The dark green mountain vistas blur on either side, creating a strong sense of forward motion and speed."
5."A close-up on the cyclist's face in profile, His determined expression is visible behind the sleek sunglasses. Beads of sweat trickle down his temple. The bright sunlight creates sharp contrasts on his skin and the white helmet."
6." A detail shot of the cyclist's s legs and the bike's drivetrain. The muscles in his calves contract with each powerful pedal stroke. The chain moves smoothly over the gears, capturing the mechanical efficiency and physical exertion."
7."A high-angle drone shot looking down on the cyclist from above. He is a solitary figure on the empty road, which snakes like river through the vast, dark green mountainous landscape. The shadows are sharp and defined under the high sun."
8."A shot from the opposite side of the road, capturing the cyclist as he passes by the camera. The whoosh of air is audible as he speeds past. The focus pulls from the sharp, fast-moving cyclist to the blurred, static mountain background."
9." 9."An extreme wide shot from a mountain ledge opposite the road. The cyclist is a tiny, moving speck of white and color on the distant highway, emphasizing the grand scale of the environment and the solitude of the journey."
10." "A final shot from behind as the eyclist crests a hill and begins descending. The road dips down into a valley framed by majestic green peaks. The bright sun creates lens flare, and the rider picks up speed, disappearing around a bend."
The prompt of the first case (right-1) in figure 7:
1."A breathtaking wide shot of a vibrant coral reef at the bottom of the clear ocean. Sunlight filters down from the surface, creating shimmering shafts of light. The water is incredibly transparent, revealing a colorful landscape of branching and brain corals stunning detail." "
2."A slow, upward tilt of the camera from the seabed. The view focuses on the mesmerizing caustic canterns-shimmering wavy lines of fight-danccin on the sandy patches between coral formations, created by the sun's 's rays refracting through the water's surface."
"A"An ultra-wide angle lens capturing the scale of the reef. The camera looks up towards the distant, glittering water surface, which looks like a moving mirror. Schools of small, silvery fish are visible as dark specks far above, near the sparkling light."
4."A close-up shot of particularly beautiful staghorn coral colony. The crystal-clear water allows for perfect visibility of every polyp. The shimmering light from above dances across the coral's textured surface, highlighting its vivid colors."
5. "he camera drifts slowly over the reef, following a gentle current. The focus is on the incredible clarity of the water, with particles floating in the water column. The sparkling surface reflection is visible across the entire top of the frame."
6." dramatic low-angle shot from behind large coral head. The view is framed by the coral, looking out into the open, blue water where the sunlight is strongest. The water surface shimmers intensely, creating bright, inviting portal."
7." A subtle movement catches the eye. From the right edge of the frame, a solitary, brightly colored angelfish elegantly swims into view. It glides effortlessly over the coral, its fins moving gracefully in the clear water."
8."A wider shot now reveals small school of striped clownfish emerging from the protective tenta- cles of a sea anemone. They dart playfully through the water, their orange bodies contrasting sharply with the blue water and the dancing light from above."
9."A graceful tracking shot follows a small group of blue tangs as they swim in loose formation. They move between coral structures, their path taking them through the dappled, shimmering light that penetrates from the world above."
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Published as a conference paper at ICLR 2026
10."A final, serene wide shot. The fish continue their journey and swim out of frame. The scene returns to the peaceful, majestic beauty of the untouched coral reef under the sparkling ceiling of water, showcasing the perfect tranquility of the underwater world."
The prompt of the video in the first line of Figure 9:
1. "O'O golden waterfront promenade at sunset the person strides confidently in fitted t-shirt, jeans and sneakers, their determined expression lit by warm side light as a medium-low drdium-low-annge low-engle tracking shot follows them past glinting water and squawking gulls."
2. "Under neon signs and rain- ain-slicked streets at night the same individual marches forward a fitted I-shirt, jeans and sneakers, puddles throwing back kaleidoscopic reflections while a slightly elevated medium shot captures the rim-lit silhouette and blurred taxis behind them."
3. "On bustling subway platform fluorescent lights stutter above and train's 's rumble fills the air as the person in fitted t-shirt, jeans and sneakers keeps a purposeful stride, captured in a medium shot from platform level with commuters streaking past in motion blur."
4." Atoo a windswept rooftop helipad at dawn the skyline looms behind them while their fitted t- shirt, jeans and sneakers catch the cool backlight, medium shot from a slightly elevated angle emphasizing the wind-swept hair and unwavering determination."
5. "Inside cavernous, sun-beamed industrial warehouse dust motes hang in shafts of light as the person in a fitted t-shirt, jeans and sneakers marches across cracked concrete, a medium shot from high angle accentuating the gritty textures and their steady forward motion."
6." AAross quiet, newly fallen snow in an urban park the person in a fitted t-shirt, jeans and sneakers strides with visible breath and a frost-bitten glow on the path, recorded in medium shot with soft overcast light that heightens their resolute expression."
7." 7."Along a boardwalk sunrise waves crash and lens flares burst around the figure in a fitted t-shirt, jeans and sneakers as they march with purpose past sleeping vendors, the scene captured in slightly elevated medium shot that catches the pastel sky and sparkling sea."
8. "h"Through a crowded open-air market bathed in golden afternoon light the person in a fitted t-shirt, jeans and sneakers weaves past colorful stalls and gesturing vendors, the camera holding medium shot that tracks their purposeful stride amid a whirl of activity."
9." AArros a foggy, moonlit bridge the city lights smear into halos while the person in a fitted t-shirt, jeans and sneakers marches with head held high, low-angle medium shot capturing the cool blue tint and the dramatic silhouettes of suspension cables behind them." "
"Insiee glossy, futuristic glass corridor lit by linear LED strips the person in fitted t-shirt, jeans and sneakers moves with measured strides past reflective surfaces, the symmetrical medium shot emphasizing their determined expression and the sterile, neon ambience."
The prompt of the video in the second line of Figure 9:
1. "In room with afternoon sunlight slanting in, dust motes dance in the beams, and a pile of debris awaits cleaning in the corner."
2. A close-up shot of hand gripping the broom handle, picking up the tool to begin sweeping."
3. A medium shot shows the rhythmic sweeping motion, gathering the dust together."
4." A low-angle shot captures the broom passing through a sunbeam, dust swirling vividly within the light, textures clear and distinct."
5. "An overhead view shows the debris being gradually swept into pile, the process orderly and satisfying." "
6. "Sweeping pauses briefly; the character rests, showing moment of contentment."
dustpan enters the frame, precisely catching the garbage one clean, efficient motion." "
8. "A close-up the freshly swept clean floor, wood grain distinct, pure light and shadow."
"Tools are returned to their place; the broom leans against the wall, completing the ritual."
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Published as a conference paper at ICLR 2026
10."A fixed camera shot reveals the tidy corner, the sunlight warmer, the space quiet and satisfied." "
The prompt of the video in the third line of Figure 9:
1."At dawn, thick fog envelops the coast, where a solitary red telephone booth stands amidst the gentle lapping of waves."
2. "he camera pushes in for close-up water droplets condensed on the glass and the blurred outline the telephone inside, creating a hazy and mysterious atmosphere."
3. "A seagull briefly alights the roof of the booth, preens its feathers, and then flies away, adding a touch of life to the scene."
4. A breeze temporarily thins the fog, revealing faint, blurred outlines in the distance, hinting the vast scale of the environment."
5. "The sound of distant bird calls is abruptly pierced by single, sharp ring from the telephone inside the booth, breaking the silence before it returns." "
6. "The perspective shifts to subjective view from inside the booth; the world outside is distorted through the glass, breath forms mist in the cold air, and a dial tone drones from the receiver."
7."A medium shot re-establishes the telephone booth the pebble beach, emphasizing its isolated position." "
8. 8."A subtle shift in lighting suggests the passage of time to midday, with the fog taking warmer hue."
9." AA brief, faint beam of light precisely illuminates the phone booth, bestowing momentary sense of sanctity before vanishing."
10. The camera slowly pulls back, the phone booth gradually being swallowed by the thickening fog until it finally disappears into nothingness." "
The prompt of the video in the fourth line of Figure 9:
1."A damp backstreet under neon lights, where holographic billboards blaze with color and a crowd moves about." "
2."A close-up on a customized motorcycle, its dashboard glowing, as hand grips the handlebar, ready start."
3. The ignition button is pressed; the vehicle roars to life with a growl, its headlights blazing as energy erupts."
4."A first-person view from inside the helmet, the HUD displaying data as the cityscape speeds by outside the visor."
5. "A third-person tracking shot follows the motorcycle weaving through traffic, kicking up spray and leaving dazzling light trails."
6. "A slow-motion close-up of the wheel cutting through a puddle, the water bursting into a crown- like spray that refracts the neon like jewels." "
7." "An extreme low-angle shot looks up as the motorcycle races past, forming a powerful, imposing silhouette against the sky."
8." god's-eye view looks down on the city grid, where the motorcycle moves like a single point of light across circuit board."
9. "It enters a dimly lit tunnel, the engine's 's roar echoing, focus fixed on the distant pinprick of light at the exit."
10."It stops on a high platform, the engine cutting to silence as the rider gazes down upon the sleepless city below, detached and pensive."
31


Reviews:
[{"review_id": "iUxJycRb1g", "reviewer": "Reviewer_zYkc", "summary": "This paper introduces Diagonal Distillation, a novel framework for efficient streaming autoregressive video generation. The method addresses the limitations of existing diffusion-based autoregressive models, i.e., the insufficient utilization of temporal context during step reduction and implicit prediction of subsequent noisel evels in next-chunk prediction (exposure bias). The core idea is to allocate more denoising steps to early chunks and fewer to later ones, forming a diagonal denoising trajectory. To support this, the authors propose two key techniques: Diagonal Forcing which explicitly simulating diagonal denoising paths through controlled noise injection, allowing the model to leverage partially denoised previous chunks as contextual priors. Flow Distribution Matching (FDM) that aligns optical flow distributions between teacher and student models to preserve motion consistency during step reduction.\n\nExperimental results show substantial gains: the method achieves 1.53× speedup over Self Forcing, with comparable visual quality and temporal coherence. Ablation studies validate the gain of each proposed component, and qualitative examples demonstrate stability in long (45s) video generation and dynamic prompting.", "questions": "1. Can you provide the total scores in ablation study (Table 2)? \n2. The generated videos in the demo have some scene cut and flickering, what is the reason behind that? Is the proposed method tends to cause more artifacts compared to the original Self Forcing? \n3. Can you provide a breakdown of the speedup?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The proposed design is novel and well-motivated. The diagonal denoising idea is conceptually elegant, aligning the temporal and diffusion-step dimensions in a unified framework. This bridges autoregressive conditioning with step-efficient diffusion distillation.\n\n2. This work addresses critical problems in streaming generation: insufficient utilization of temporal context during step reduction and implicit prediction of subsequent noise levels in next-chunk prediction (exposure bias).\n\n3. The authors demonstrate strong empirical results, including both quantitative and qualitative evaluations across different benchmarks, and also include a thorough user study in the appendix. The reported 1.53× speedup with minimal quality drop is impressive and relevant for real-time applications.\n\n4. The ablation study is solid. Table 2, 3; Figure 5 convincingly show how Diagonal Forcing and Flow Distribution Matching contribute to temporal coherence and motion fidelity.", "weaknesses": "1. Limited novelty in the distillation objective. While the diagonal scheduling and forcing mechanism are new, the underlying distillation objective remains close to prior work Self Forcing with Distribution Matching Distillation. The conceptual leap may be seen as an engineering refinement rather than a fundamentally new learning principle.\n\n2. Some format flaws, e.g., order of Table 1, 2 and 3. Caption font is too small. The resolution seems to be very different in Figure 7.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "COrczWwNYR", "reviewer": "Reviewer_i62A", "summary": "The paper proposes Diagonal Distillation for streaming autoregressive video generation distilled from a diffusion teacher. The core idea is a diagonal denoising schedule—early chunks use more steps, later chunks fewer—so later chunks inherit stronger priors while keeping latency low. Two components support this: Diagonal Forcing (training on noisy previous-chunk states/KV cache to better match inference and reduce exposure bias) and Flow Distribution Matching (a temporal loss to preserve motion amplitude otherwise damped by few-step distillation). Experiments on a modern T2V backbone show substantially lower first-frame latency and higher throughput with comparable or slightly better quality on standard video benchmarks, plus qualitative improvements on long-horizon and dynamic-prompting scenarios.", "questions": "Please see Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Timely problem: addresses online/streaming latency, not just offline T2V.\n* Coherent design: diagonal schedule + noisy conditioning + flow loss form a simple, compatible recipe.\n* Strong practicality: low first-frame latency, high FPS, and straightforward cache reuse.\n* Empirical support: consistent speedups with minimal quality loss; informative ablations on step allocation and losses.\n* Clarity: figures and narrative make the training–inference mismatch and diagonal rationale intuitive.", "weaknesses": "* Longer Videos Test: While 45 seconds is impressive, many streaming use cases (e.g., live streams) require minutes of content. Does error accumulation reemerge for 1–5 minute videos, and if so, can the diagonal strategy be extended (e.g., adaptive step resets)?\n* Insufficient Analysis of Step Allocation Heuristics: A quantitative comparison of more step sequences (beyond the 6 evaluated) would clarify how step allocation impacts the quality-efficiency frontier. For example, does 5422222 yield better early-frame quality at the cost of marginal latency? Besides, the paper assumes step reduction is monotonic (fewer steps over time), but dynamic allocation (e.g., more steps for high-motion chunks) could further optimize performance. No analysis of non-monotonic strategies is provided.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4MSMnaeGE6", "reviewer": "Reviewer_UG2y", "summary": "The paper proposes Diagonal Distillation (DiaDistill) for real-time, streaming autoregressive (AR) video generation. The key idea is an asymmetric, chunk-wise denoising schedule, more steps for early chunks, fewer for later ones, combined with Diagonal Forcing, which conditions each new chunk on a noised version of the previous chunk (reusing the KV cache) to align training and inference and reduce exposure bias. A complementary Flow Distribution Matching loss is introduced to preserve motion amplitude when using very few denoising steps. Empirically, the method is evaluated on a Wan2.1-based text-to-video system and VBench metrics; it reports 31 FPS, 0.37 s first-frame latency, and a 277.3× speedup over an undistilled baseline while retaining visual quality.", "questions": "1. Is there a true attention change? Please clarify whether “diagonal attention mechanism” denotes a new attention pattern (e.g., attention over (time, step) diagonals) or simply the training/inference schedule + KV-cache conditioning. If it is architectural, provide equations, masks, and complexity; if not, please remove or rephrase to avoid confusion.\n2. Which optical-flow network is used? Is it frozen? Do you backprop through it? How are flow distributions parameterized for KL (bins, continuous density, or feature-space scores)?\n3. What is the runtime overhead of computing flows during training, and how stable is optimization without the regression term?\n4. What chunk size and overlap were used, and how do they affect latency and quality? What is the KV-cache memory footprint over time, and how does cache reuse interact with the noised conditioning frames? Any degradation when streaming beyond 45 s or at higher frame rates/resolutions?\n5. The paper highlights schedules like 4322222 for 7 chunks (5s). How is the schedule chosen for other durations (e.g., 10s, 45s)? Is there a principled rule (e.g., warm-up length, geometric decay) or an auto-tuner? Please include results for algorithmic schedule selection (not hand-tuned) and its effect on quality/latency.\n6. Can you provide a direct measurement that the method reduces exposure bias (e.g., error growth curves, saturation shift statistics across time, calibration of the “implicit next-noise prediction” claim), rather than only qualitative frames?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The diagonal allocation of denoising effort across time (many steps early->few later) is a simple but appealing scheduling concept for AR diffusion models, explicitly exploiting temporal priors accumulated early. The Diagonal Forcing mechanism, feeding noised previous-chunk states (rather than clean frames) as the KV cache, targets exposure bias in a way that is tailored to AR diffusion, not borrowed wholesale from image distillation. \n2. Adding Flow Distribution Matching to align motion distributions (rather than only framewise fidelity) is a sensible, task-appropriate extension to DMD.\n3. Streaming, low-latency T2V is important for interactive and real-time applications; a method that roughly doubles acceleration vs. prior AR-diffusion baselines (e.g., vs. “Self Forcing”) while keeping quality could materially impact practice.", "weaknesses": "1. Early sections mention a “diagonal attention mechanism operating jointly across time and denoising steps,” but the implementation centers on scheduling (step counts per chunk) plus conditioning with a noised KV cache. It’s unclear whether there is any architectural change to attention patterns (e.g., block-sparse or strided attention over (time × step) axes) beyond cache reuse. If there is special attention, the paper needs explicit architecture diagrams and tensor shapes; if not, the phrase “diagonal attention mechanism” is misleading.\n2. The paper defines a KL over optical-flow distributions and shows a regression term, but it does not specify: which flow estimator is used (RAFT? GMFlow? in-house?), whether it is frozen, if gradients flow through it, how flow fields are normalized, or how flow-estimation errors bias training. Without these details, the flow objective’s reproducibility and reliability are uncertain.\n3. VBench categories and the exact metric subsets used (“Temporal Quality,” “Frame Quality,” “Text Alignment”) are referenced, but the paper doesn’t enumerate which VBench dimensions or how they’re aggregated; this matters because VBench includes many sub-metrics with different sensitivities.\n4. First-frame latency and throughput are reported, but the streaming protocol (chunk size, overlap, buffering, tokenization, KV cache size, prefill costs) isn’t fully specified. For real deployments, memory footprint from KV-cache reuse and any stall during step-count transitions matter; these are not reported. \n5. The paper claims to mitigate over-saturation and motion attenuation, but does not quantify residual failure rates, sensitivity to prompt types (e.g., rapid scene changes), camera motion vs. object motion, or scaling to higher resolutions and frame rates beyond the default (832×480 @ 16 FPS generation).\n6. The quality of the figures provided in the paper appears to be relatively low.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "i7qnGGyAId", "reviewer": "Reviewer_uXB6", "summary": "This paper presents a new method for distilling diffusion models into autoregresive models. It includes three designs: (1) To mitigate the exposure bias in autoregressive generation, a diagonal distillation strategy is proposed that allocates more inference steps in early chunks and fewer steps in later chunks. (2) A diagonal forcing method is introduced that simulates the same denoising strategy during training to further reduce error accumulation. (3) The authors propose flow distribution matching that performs DMD on motion flow field. Experiments based on Wan2.1 1.3B shows the model achieves real-time performance without much quality degradation.", "questions": "1. How are the training loss coefficients $\\lambda_{\\textrm{spatial}}$, $\\lambda_{\\textrm{flow}}$, $\\gamma$ determined, and how sensitive is the model's performance to these training hyperparameters?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 1, "contribution": 2, "strengths": "1. The proposed designs (diagonal sampling trajectory and DMD loss for motion field) are well motivated and effectively address exposure bias while enhancing motion quality.\n2. Experimental results demonstrate that the proposed strategy preserves performance with fewer number of steps, thereby improving throughput and reducing latency.", "weaknesses": "1. Missing critical details. The proposed flow distribution matching method (Section 3.3) depends on estimating a \"motion flow field\" using $\\mathcal{F}$. However, the definition of this flow field is unclear, does it refer to optical flow, motion vector, or motion feature? Furthermore, the paper has not discussed the design of the motion estimator, making it difficult to assess its reliability, especially given that it operates on potentially noisy latents.\n2. Insufficient comparisons. Since the proposed diagonal distillation method is essentially re-allocating the number of inference steps, it is important to compare against baselines (e.g. self-forcing) under comparable number of steps. For example, it would be better to evaluate (1) both self-forcing and diagonal forcing at 2 NFEs per later chunk, (2) both at 4NFEs per latent chunk.\n3. Lack of generalization study. As the inference schedule is largely hand-crafted, it would be better to test the proposed strategy on alternative models (e.g. Wan2.1 14B) to evaluate its transferability without inference hyperparameter tuning.\n4. Incomplete related work. The proposed flow distribution matching is related to many motion-based video modeling methods such as VideoJAM [Chefer'25], Video-LaVIT [Jin'24], MicroCinema [Wang'24]. A comprehensive discussion about prior work would help clarify the paper's contribution and improve its clarity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)