Pusa V1.0: Unlocking Temporal Control in Pretrained Video Diffusion Models via Vectorized Timestep Adaptation
Authors: Yaofang Liu
Yumeng REN
Aitor Artola
Yuxuan Hu
Xiaodong Cun
Xiaotong Zhao
Alan Zhao
Raymond H. Chan
Suiyun Zhang
Rui Liu
Dandan Tu
Jean-michel Morel
Venue: ICLR 2026 Poster (2026)
Keywords: Vectorized Timesteps
Flow Matching
Temporal Modeling
Video Generation
Primary area: generative models
TL;DR: Pusa proposes a new fintuing paradigm that leverages vectorized timestep adaptation (VTA) to enable fine-grained temporal control within a unified video diffusion framework, achieving SOTA level performance with unprecedented training efficiency

Abstract: The rapid advancement of video diffusion models has been hindered by fundamental limitations in temporal modeling, particularly the rigid synchronization of frame evolution imposed by conventional scalar timestep variables. While task-specific adaptations and autoregressive models have sought to address these challenges, they remain constrained by computational inefficiency, catastrophic forgetting, or narrow applicability. In this work, we present \textbf{Pusa} V1.0, a versatile model that leverages \textbf{vectorized timestep adaptation (VTA)} to enable fine-grained temporal control within a unified video diffusion framework. Note that VTA is a non-destructive adaptation, which means that it fully preserves the capabilities of the base model.
\textbf{Unlike conventional methods like Wan-I2V, which finetune a base text-to-video (T2V) model with abundant resources to do image-to-video (I2V), we achieve comparable results in a zero-shot manner after an ultra-efficient finetuning process based on VTA. Moreover, this method also unlocks many other zero-shot capabilities simultaneously, such as start-end frames and video extension ---all without task-specific training. Meanwhile, it keeps the T2V capability from the base model.} Mechanistic analyses also reveal that our approach preserves the foundation model's generative priors while surgically injecting temporal dynamics, avoiding the combinatorial explosion inherent to the vectorized timestep. This work establishes a scalable, efficient, and versatile paradigm for next-generation video synthesis, democratizing high-fidelity video generation for research and industry alike.

Published as a conference paper at ICLR 2026
PUSA V1.0: UNLOCKING TEMPORAL CONTROL IN PRETRAINED VIDEO DIFFUSION MODELS VIA VEC-
TORIZED TIMESTEP ADAPTATION
Yaofang Liu
Yumeng Ren
Aitor Artola
Yuxuan Hu2.3
Xiaodong Cun
Xiaotong Zhao
Alan Zhao
Raymond H. Chan6.7
Suiyun Zhang
Rui Liu
Dandan Tust
Jean-Michel Morel
City University of Hong Kong "The Chinese University of Hong Kong
HHuawei Research
4Great Bay University AA Technology Center, Tencent PCG Lingnan University "Hong Kong Centre for Cerebro-Cardiovasee Cardiovascular Health Engineering
Correespnndigg authors
ABSTRACT
The rapid advancement of video diffusion models has been hindered by funda- mental limitations in temporal modeling, particularly the rigid synchronization of frame evolution imposed by conventional scalar timestep variables. While task-specific adaptations and autoregressive models have sought to address these challenges, they remain constrained by computational inefficiency, catastrophic forgetting, or narrow upplicability. In this work, we present Pusa' V1.0, a versa- tile model that leverages vectorized timestep adaptation (VTA) to enable fine- grained temporal control within a unified video diffusion framework. Note that VTA is non-destructive adaptation, which means that it fully preserves the ca- pabilities of the base model. Unlike conventional methods like Wan-12V, which finetune a base text-to-video (T2V) model with abundant resources do image- to-video (I2V), we achieve comparable results in a zero-shot manner after an ultra-efficient finetuning process based on VTA. Moreover, this method also un- locks many other zero-shot capabilities simultaneously, such as start-end frames and video extension -all without task-specific training. Meanwhile, it keeps the T2V capability from the base model. Mechanistic analyses also reveal that our approach preserves the foundation model's generative priors while surgically in- jecting temporal dynamics, avoiding the combinatorial explosion inherent to the vectorized timestep. This work establishes a scalable, efficient, and versatile paradigm for next-generation video synthesis, democratizing high-fidelity video generation for research and industry alike.
INTRODUCTION
Diffusion models Song et al. (2020); Ho et al. (2020) have transformed generative modeling, achiev- ing remarkable results in image synthesis. Their extension to video generation Ho et al. (2022); He et al. (2022); Chen et al. (2023); Wang et al. (2023); Ma et al. (2024); OpenAl (2024); Xing et al. (2023b); Liu et al. (2024a) has been a focal point. However, these mainstream video diffusion mod- els (VDMs) still employ a scalar timestep variable following image diffusion models, enforcing uniform temporal evolution across all frames. This approach, while being effective for text-to-video (T2V), struggles with nuanced, lemporal-dependerr tasks like image-to-video (I2V) and video ex- tension Liu et (2024b); Wan al. (2025); Xing et al. (2023a).
'Work partially done during internship Huawei Research.
Pusa (/pu: 'sa:) normally refers to "Thousand Hand Guanyin" in Chinese, reflecting the iconography of many hands to symbolize boundless compassion and ability. We use this name to indicate that our model uses many timestep variables to achieve various video generation capabilities, and we will fully open source it to let the community benefit from this technology.

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Published as a conference paper at ICLR 2026
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(a) Wan-T2V
(b) Wan-12V
(c) Pusa
Figure I: Method comparison. (b) Wan2.1-12V-14P (Wan-12V) and (c) Pusa both support 12V gen- eration and are fine-tuned from (a) Wan2.1-T2V.1 (Wan-T2V), Specifically, Wan-12V represents the mainstream practice for 12V, which modifies the base T2V model with a mask mechanism and an image clip embedding, leading to the destruction of the pretrained priors of the base model. In contrast, Pusa proposes non-destructive VTA approach, which only inflates the model's timestep variable from a scalar to a vector. In this way, Pusa fully utilizes the pretrained priors and uses much less data and computation to achieve comparable I2V results.
Autoregressive alternatives like Diffusion Forcing Chen et al. (2024) and AR-Diffusion Sun et al. (2025) have explored avoiding this rigid synchronization modeling form of conventional VDMs. Nonetheless, their applications for video generation, like start-end frames, remained constrained by the autoregressive design. Despite large-scale deployment such as MAGI-1 Teng et al. (2025) and SkyReels V2 Chen et al. (2025) further advanced scalability, they still face challenges in computa- tional efficiency, bidirectional reasoning, and error accumulation over long sequences.
Concurrently, FVDM Liu et al. (2024b) proposed vectorized timestep to reframe the video diffu- sion paradigm fundamentally. Specifically,, instead of using scalar timestep to control the noise level of the whole video, it allows independent noise evolution per frame by assigning each frame a timestep, together results in a timestep vector for the video. Besides, to address the potential computational explosion inherent with frame-level timesteps during training. FVDM introduces novel probabilistic timestep sampling strategy (PTSS), which not only achieves training efficiency comparable to that of conventional scalar timestep approaches but also unlocks T2V, 12V, and other temporal control tasks simultaneously.
In this work, we extend the paradigm of Frame-Aware Video Diffusion Models (FVDM)Liu et al. (2024b) to an industrial scale by proposing a vectorized timestep adaptation (VTA) strategy, which adapts pretrained large-scale VDMs to support frame-level timesteps (shown in Fig. 1). Besides, to enable fine-grained temporal control with minimal finetuning data and computation, we design the VTA to be non-destructive, which means it does not need any architectural modification to the base model and thus fully preserves the model's 's capabilities. As result, our proposed model, Pusa V1.0, achieves SOTA level performance on the VBench-12V benchmark Huang et al. (2024), comparable with -2n- which is also adapted and fine-tuned from the same base model (Wan-T2V), despite using only 4K samples and 0.5K compute cost (vs. expected 2 10M samples and 2 100K cost Wan et al. (2025)). Besides I2V capability, Pusa also generalizes to tasks such as start-end frames and video extension, all in a zero-shot way without any task specific retraining. This highlights the great potential and versatility of the FVDM/Pusa paradigm at scale.
Our contributions can be summarized as:
Unprecedented Efficiency: base T2V model and an ultra-efficient finetuning strategy, which achieves SOTA I2V per- formance with only 4K samples and 0.5K compute cost.
We present Pusa with a non-destructive VTA method to the
Unified Multi-Task Generalization: Pusa not only preserves T2V capability from the base model, but also generalizes well to many advanced temporal tasks like 12V, start-end frames, video extension, etc., all in zero-shot way.
Underlying Superiority: Mechanistic analyses reveal that our approach preserves the foundation model's generative priors while surgically injecting temporal dynamics, avoid- ing the combinatorial explosion inherent vectorized timesteps. This work marks a critical shift in video generation by combining principled temporal modeling with efficient adap- tation, enabling scalable, high-quality, and general- video synthesis.
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Published as a conference paper at ICLR 2026
2 METHODOLOGY
2.1 PRELIMINARIES: FLOW MATCHING FOR GENERATIVE MODELING
Generative modeling aims to learn model capable of synthesizing samples from target data distributiin (z) over TRD. Continuous Normalizing Flows (CNFs) Chen (2018) achieve this by transforming samples žι from simple base distribution qi (z) (e.g., standard Gaussian (O, I)) to samples Zo that approximate the target distribution go (z). This transformation is defined by an invertible mapping. often conceptualized as an ordinary differential equation (ODE) trajectory.
Specifically, a probability path {z, [o, is defined, connecting Zo at t = 0 to Z1 ~ at t I. The dynamies along this path are described by an ODE:
dz
(z t), t [0, dt where 82 : RR x [0,1] R is a time-dependent vector field.
Flow Matching (FM) Lipman et al. (2022); Liu al. (2022); Tong al. (2023) is simulation- free technique to directly learn this vector field e4 (2),)) by training neural network no(2,, 6) to approximate it. This is achieved by regressing 1h (z, against target vector field ( (o 21)- This target field is defined along specified probability paths pr (zzzz/z. zi) that connect samples Zo ~ qo corresponding samples z Q1-
A common choice for these paths is linear interpolation between a data sample zo and a prior sample Z1:
(1 t)zo + tz
E [0,
(2)
For such paths, the conditional target vector field is the time derivative of z₁:
dz
u4 (1 z1)
21
Zo
(3)
dt
Note that for linear interpolation paths, u, is independent of and z,, depending only on the end- points zn and 21. The (conditional) flow matching objective function train the neural network do is then:
(o) [e 2o
|100 ((1 1)zo (21. () (2) - 20)
(4)
where 00 1] is the uniform distribution over [0, 1] and 11-113 denotes the squared Euclidean norm. Once Up is trained, new samples that approximate go can be generated by first sampling z ~ gi and then solving the ODE de. uo (2(2,, () from 1 down to l = The resulting zo generated sample.
2.2 FRAME-AWARE FLOW MATCHING
Flow matching has become the common choice for SOTA video generation models Wan et al. (2025); Kong et al. (2024); Team (2024). To enable nuanced temporal modeling in existing SOTA models, we first need to extend the FVDM Liu et al. (2024b) paradigm to the flow matching frame- work.
video clip X is represented as sequence of N frames. Each frame xf Rd d-dimensional column vector. The entire video clip can be represented as an N x d matrix X, where the i-th row x" This can be written as X [x! xx ,,,.....,,... NN1, thus X N
In contrast to the single-scalar time variable used in standard flow matching (Eq. 4), we introduce a vectorized timestep variable (VTV) T E [0, 1 defined as:
(5) Here, each component 7' E [0, 1] represents the individual progression parameter of the i-th frame along its respective probability path from the data distribution prior distribution. This vec- torization allows each frame to evolve at potentially different rate or stage within the generative process.
T= --
NNT
Let X. = Rd Similarly, let X, [x]...., , x be video sampled from a simple prior distribution Oprior (e.g., each frame x is drawn independently from NN(0, L2)).
x be a video sampled from the true data distribution Jdat (X), where x E
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For each frame i, we define conditional probability path p(x², |x]], xi) indexed by its individual timestep 7'. Adopting the linear interpolation strategy from Eq. 2 for each frame (which are d- dimensional vectors):
(1
The state of the entire video, corresponding to specific vectorized timestep is then given by the N matrix X,, whose i-th row x
X, =
We aim to learn a single neural network uo(X, (X, 7) that models the joint dynamics of all frames conditioned on their respective timesteps. This network takes the current video state X E Nxx (which is X, during training) and the vectorized timestep field for the entire video, an N x d matrix denoted as 0o [X. v Rdd is the velocity vector for the i-th frame. Thus, vg R xd
[0. 1 as input. It outputs velocity
[v ... N where each [0, 1] - Nadd
The target vector field for the entire video X,, conditioned on the initial video X, and target prior X,, is an N matrix UX X1). Its i-th row is the transpose of the derivative of the i-th frame's path (Eq. 6) with respect to its individual timestep r'. Using the derivative from Eq. 3 for each frame:
dx dr
x - x
(8)
Thus, the target video-level vector field is:
U(Xo. X1) = [[x] xb)
(x)
xx) ) = X, - Xo
(9)
Notably, for the linear interpolation path, this target vector field X Xo is independent of both the current video state X, and the vectorized timestep T itself, simplifying the regression target. The video state X, timestep is constructed via frame-wise linear interpolation:
X+ = (1 - T) c X +
(10)
where denotes dlement-wise multiplication between the timestep vector (2 and each frame
,..,
N
Key Properties:
1. Path Consistency: Each frame evolves linearly: x = (1 -2) + 7'X
2. Vector Field Simplicity: 2r
4x, X Xo (constant for all T)
3. Decoupling: Frame dynamics depend only on their own r', enabling asynchronous evolution.
The parameters of the neural network vo are optimized by minimizing the Frame-Aware Flow Matching (FAFM) objective function:
(0) = Ex X Ka (TTSS(TT) |1000 X,,, T) T) (X, 15,xx, x
(1I)
where X- is the video state constructed according to Eq. 7, 11 denotes the squared Frobenius norm, (T)Ss(T) indicates that the vectorized timestep is sampled according to PTSS in FVDM al. (2024b). This strategy is designed to expose the model to both asynchronous frame evolutions with probability Pasyne E 1J) and synchronized frame evolutions with probability - Pasyne during training.
2.3 VECTORIZED TIMESTEP ADAPTATION
In this work, we aim to adapt a large-scale, pre-trained T2V diffusion models to support the vector- ized timestep Liu al. (2024b). The adaptation, which term Vectorized Timestep Adaptation (VTA), along with a lightweight fine-tuning process, imbues the model with fine-grained temporal control, enabling advanced capabilities such zero-shot I2V.
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2.3.1 IMPLEMENTATION OF VECTORIZED TIMESTEP ADAPTATION
The foundational principle of our implementation is re-engineer the core architecture process a vectorized timestep T instead of scalar timestep t. The architectural adaptation is primarily fo- cused on the model's temporal conditioning mechanism. We introduce two key modifications:
Vectorized Timestep Embedding: The timestep embedding module is modified to process the in- put vector т, generating sequence of frame-specific embeddings E, E R-NIDD, where N1 the number of frames in the video latent sequence, each vector in the sequence corresponds to latent frame's s individual.
These frame-specific embeddings are subsequently projected to produce per-frame modulation parameters (i.e., scale, shift, and gate) within each block of the DiT architec- ture. The operation of DiT Peebles & Xie (2023) block the latent representation z' of the i-th frame is thus conditioned on its individual timestep T', which can be conceptually expressed as:
Per-Frame Modulation:
out DiTBlock ( context, modulate(r"))
Note that this modification is non-destructive, which means it fully preserves the T2V capability of the base model. The adapted model generates the same results by setting all frame timesteps to the same values as the base model.
Table I: Vbench-12V results. Our model demonstrates SOTA-level performance, achieving top- tier rank among open-source models, and is notably comparable with its architectural baseline, Wan- 12V. All scores reported in percentages (%) Higher is better for all metrics. Best score in each column is in bold. Abbreviations: SC: Subject Consistency, BC: Background Consistency, MS: Motion Smoothness, DD: Dynamic Degree, AQ: Aesthetic Quality, IQ: Imaging Quality, 12V-S: 12V Subject Consistency, I2V-B: 12V Background Consistency, CM: Camera Motion.
Overall Scores
Quality Metries
12V Metries
Model
Total
12V
Quality
SC
BC
MS
DD
AQ
IQ
12V-8
12V-B
CM
Closed / Proprictary Models
Gen-4-12V (API)
88.27
95.65
80.89
93.23
96.79
98.99
55.20
61.77
70.41
97.84
97.46
68.26
STIV (Apple)
86.73
93.48
79.98
98.40 98.40
98.39
99.61
15.28
66.00
70.81
98.96
97.35
11.17
Open Source Models
Magi-1
89.28
96.12
82.44
93.96
96.74
98.65 08.68
68.21
64.74
69.71
98.39
99.00
50.85
Step-Video-TE Video-Ti2V
95.50
81.22
96.02
97.06
99.24
48.78
62.29
70.44
97.86
98.63
49.23
SynumiCrafter-511
86.99
93.53
80.46
93.81
96.6 64
96. 84
69.67
60.88
68.
97.21
97.40
31.98
CogVideoX-5b-ll
86.70
94.79
78.61
94,34
96.42
98. 40
33.17
61.87
70.01
97.19
96.74
67.68
Enimate-Anythinn
86.48
94.25
78:71
98.90
98. 19
98.61
2.68
67.12
72.09
98.76
98.58
13.08
SEINE-512x512
85.52
92.67
78.37
95.28
97.12
97.
27.07
64.55
71.39
97.15
20.97
12VGen-XI.
85.28
92.11
78.44
94.18
97.09
98.34
26.10
64.82
69.14
96.48
96.83
18.48
Consist12V
84.07
91.91
76.22
95.27
98. 28
97.38
18.62
59.00
66.92
95.82
95.95
33.92
VidcoCrafter
82.57
86.31
78.84
97.86
98.79
98.00
22.60
60.78
71.68
91.17
91.31
33.60
VigeeX1XX
92.25
$0.90
70.21
97.07
95.50
39.71
SVD-XT-I.1
79.40
95.42
96.77
98.12
43.17
60.23
70.23
97.51
97.62
SVD-XT-LLO
80.11
95.52
96.61
98.09
52.36
60.15
69.80
97.52
97.63
Wan-12V
86.88 86
92.90
80.82
94.86
97.07
97.
51.38
64.75
70.44
96.95
96.44
34.76
Ours
87.32
94.84
79.80
92.27
96.02
98.49
52.60
63.15
68.27
97.64
99.24
29.46
2.3.2 TRAINING PROCEDURE
The optimization follows the FAFM objective defined in Eq. 11. A key advantage of our approach its simplicity: by leveraging the robust generative prior of the base model, we circumvent the need for sampling synchronous timesteps. Instead, we train the model directly with fully randomized vectorized timestep (Pasyne = 1), where each component r' is sampled independently from (0, 1]. This stochastic training regimen compels the model to learn fine-grained temporal control from a maximally diverse distribution of temporal states.
2.3.3 INFERENCE FOR IMAGE-TO- TO-D-OO
Pusa performs zero-shot 12V generation by strategically manipulating the vectorized timestep T during sampling. To condition generation starting image To, for simplicity and fair comparison
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with baselines, clamp its timestep component zero throughout inference (i.e., T! 0 for all steps s). Note that we can also add some noise (e.g., set T.- 0.2 or any level of noise) to the first frame, which may synthesize more coherent videos with slight change the first frame. During sampling, which follows the Fuler method for ODE integration, this ensures the change in the first frame's latent is always zero, effectively fixing it clean condition. This flexible control scheme naturally extends to other complex temporal tasks, such as start-end frames and video extension. The detailed 12V sampling algorithm is outlined Appendix
3
EXPERIMENTS
Our experiments designed rigorously validate the three core contributions of this work: (1) the unprecedented efficiency and SOTA-Ievel performance of our model, Pusa, on the primary task of 12V generation; (2) the underlying mechanism for why Pusa works; and (3) the emergent zero-shot multi-task capabilities of Pusa.
Truining Strutegy Performance Time
Condition
26
Framc
Framl
Tonsl Secee (99 on Vitech-22V
Chrrct ota
(IT)
Oura (FT)
(LoRA)
Qursd
25
150
200
(00)
1350
Tmizing Itecations
Figure 2:
Quantitative compari-
Figure 3: Qualitative comparison of training strategies at
son of training strategies. Both
900 iterations. From top to bottom: Ours (LoRA, a = 1.4),
our LoRA and full fine-tuning (FT)
Ours (FT), and Wan-12V like baseline. Our methods maintain high fidelity to the condition image, whereas the bascline fails preserve subject identity and color under the training bud-
approaches consistently outperform
Wan-12V's fine-tuning method, achieving much superior scores
get.
with unprecedented efficiency.
3.1 SETUP
Our method works for both full fine-tuning and Lora fine-tuning. Towards adaptation with fewer GPUs, perform fine-tuning the SOTA open-source Wan-T2V model using the LoRA (Low- Rank Adaptation) technique Hu et al. (2022) for our final model, which enables parameter-efficient training. The training infrastructure consists of GPUs, each has 80GB of memory and high band- width, with DeepSpeed Zero2 Rajbhandari et al. (2020) for memory optimization, achieving a total batch size 8. The LoRA implementation is based on DiffSyyth-SSudio leveraging its optimized diffusion model training pipeline. Regarding the fine-tuning dataset, we directly utilize T2V samples generated by Wan-T2V Zheng al. (2025); Wan al. (2025). This dataset includes 3,860 high- quality 720p videos and spans diverse visual domains and temporal structures (e.g., natural scenes, human activities, camera motion), ensuring robust model generalization and aligned with Wan's original distribution. For evaluation, we use Vbench-12V Huang et al. (2024) for comprehensive 12V capability evaluation. Note that there is no overlap between the training and evaluation data. For baseline comparison, use the whole testing set, generating 5590 videos. For hyperparameter and ablation studies, we test with a subset 750 videos.
3.2 BASELINE COMPARISON
As shown in Table I, Pusa, with only 10 inference steps, achieves SOTA-level performance among open-source models. This result also comparable to the result direct baseline, Wan-I2V, Note that all the basclines trained with vastly greater resourees. More specifically, our model
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obtains total score of 87.32, outperforming Wan-12V's 86.86. Besides, Pusa demonstrates supe- rior performance in key 12V metrics, such as I2V Background Consistency (99.24 vs. 96.44) and 12V Subject Consistency (97.64 vs. 96.95), indicating more faithful adherence to the input image condition. Furthermore, our model exhibits a higher Dynamic Degree (52.60 vs. 51.38), producing more motion-rich videos while maintaining high Motion Smoothness (98.49 97.90). Hyperpa- rameter studies about Pusa are available in Appendix C.I.
3.3 ABLATION STUDY
We conduct series of ablation studies dissect the core components and verify the effectiveness of our proposed methodology. Due to space constraints, we present summary of our key findings here. Comprehensive results are provided in the Appendix C.2.
Training Strategy. We compare three distinct training strategies: the baseline model trained with the Wan-12V method, our approach with full fine-tuning, and LoRA fine-tuning on the same dataset with 480p resolution. As illustrated in Fig. 2, both our methods converge at very early stage with close results and substantially outperform the baseline across all training steps. This quantitative superiority is mirrored in our qualitative results (Fig. 3). At 900 iterations, both our LoRA and fully fine-tuned models generate videos highly faithful to the conditioning image. In stark contrast, the baseline fails catastrophically under the same training budget, with the generated video completely diverging from the source. This underscores a critical finding: the Wan-12V's method is profoundly inefficient, requiring massive scale of data and compute to reach its official performance Wan et al. (2025). Our method, conversely, delivers superior results with a minuscule fraction these resources, demonstrating paradigm shift in training efficiency.
Timestep Sampling Strategy. Our framework employs a simple yet powerful strategy of sam- pling purely random timesteps for each frame during training. To validate this design, we compare it against a more structured approach, PTSS, where timesteps are preferentially sam- pled from specific range, and an intuitive baseline for 12V with the first frame always be- ing noise-free 71 0 while all other timesteps being of the same value. Table 2 summarizes the performance at 900 steps. The results un- cquivocally demonstrate that our fully random sampling strategy achieves the highest scores, surpassing all baselines. This finding confirms that our training strategy is more effective for learning robust temporal dynamics in our framework.
Table 2: Ablation Timestep Sampling Strat- egy. Comparison of overall scores at 900 train- ing iterations. Our random sampling approach achieves the highest performance.
Sampling Strategy
Total (%)
Quality (%)
12V (%)
Ours
87.69
80.55
94.83
12V
73.27
69.96
76.57
PTSS (p = 0.2)
84.74
77.60
91.88
PTSS (y 0.8)
86.49
79.30
93.69
Table 3: Ablation Study on Base Model.
Base Model
Setting
Overall
Quality Metries
12V Metrics
Tutal
Quality
12V
SC
BC
MS
DD
AQ
1Q
12V-S
12V-B
CM
Wan2.1
80.55
97.78
Wan2.2
high 1.5. low 1.4
87.69
79.89
95.49
91.57
96.83
97.91
63 60
59,30
68.75
96.99
99.18
51.60
Base Model Comparison. We further investigate the impact of different base models on final per- formance. As shown in Table 3, both our Wan2.1-based model and our 2an2.2-basede model achieve an identical overall score 87.69. However, they demonstrate different characteristic strengths: the 1an2.1 variant excels in aesthetic quality and dynamic degree. Conversely, the Wan2.2 variant gen- erates videos with significantly more pronounced camera motion (51.60 vs. 26.40). Since we use the same dataset for finetuning, these differences mainly come from the base models themselves. Overall, this suggests our method works well with different base models, and the choice of base model can be tailored application needs and the model's characteristics.

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Image
AEFT
Zuon AZI' ZURM aur
Figure 4: 12V results, where our model generates a smooth and realistic animation of the first frame while Wan2.1 1-TVV generates completely different frames but aligned with the text prompt, and 1-12V generates frames with noticeable distortions. All videos are generated with the same condition image and text prompt: "a snowboarder is in the air doing trick".
3.4 ANALYSIS OF THE ADAPTATION MECHANISM
We investigate the mechanisms underlying Pusa's efficient adaptation, showing that it performs highly targeted parameter updates that augment rather than overwrite - pretrained knowledge of the foundation model.
12V Qualitative. In Fig. 4, Wan-T2V with VTA uses the same inference method as Pusa to do I2V generation, which is by directly setting the first frame noise-free. Since the model is only trained for the T2V task, the generated frames only align with the text prompt but have no relation to the first frame. Meanwhile, Pusa achieves seamless alignment with both text and input image, outperforming Wan-12V, which exhibits visible distortions and poor character preservation.
Attention Mechanism. Visualization of frame-to-frame, self-attention maps (Fig. reveals critical differences. Specifically, we plot the self-attention maps of queries and keys within the final Trans- former block across 3 different inference steps. i.e., steps 0, 4, and 9 (10 steps in total). Wan-T22 T2V exhibits a diagonal attention pattern, indicating that each frame primarily attends to itself, with little frame correlation. In contrast, both Wan-12V and Pusa show strong attention from all frames the first frame initially, which is essential for maintaining consistency with the input image. However, a key difference emerges: the attention to the first frame in Wan-12V is only strengthened in step O. In Pusa, the attention to the first frame is significantly enhanced across all steps. This exemplifies Pusa's 's surgical injection of temporal dynamics.
Parameter Divergence. This observation is further supported by analysis of parameter changes (Fig. 6). The parameter drift in Wan-I2V substantial and concentrated in modules critical for content generation, such the text encoder and cross-attention blocks. This implies significant al- teration of the model's core generative priors. Pusa, in contrast, exhibits minimal parameter changes, with modifications almost exclusively in the self-attention blocks responsible for temporal dynam- ies. The magnitude of parameter change in Wan-12V is more than an order of magnitude larger than in Pusa. This confirms that our approach constitutes minimal, targeted adaptation, preserving the integrity of the foundation model, and explaining its efficiency.
Vectorized Timestep Adaptation Efficacy. The VTV framework faces combinatorial explosion in temporal composition space (c.g., 48 configurations for 16 frames), making convergence from scratch challenging- Pusa circumvents this by leveraging Wan-T2V's pretrained video generation capabilities, requiring only brief fine-tuning to master temporal control with independent timesteps. The base model's inherent robustness to timestep asynchronization is evidenced by its coherent zero-shot I2V generation (Fig. 4), despite failing image-condition adherence. This stems from Wan- T2V's diagonal self-attention patterns (Fig. 5), indicating frame synthesis independence. Pusa's fine-tuning surgically introduces targeted temporal correlation while preserving the stable genera- tive core, enabling nondestructive adaptation that solves the VTV compositionality problem with unprecedented efficiency.
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Step
Step 4
Step
Quary frame Index
Won2 1-T2V
frome
Query frame
Index
Index
Qusry Frame Indoxx
Frame Inrdxx
Quvery Fiorne Incelss
Won 1-12V
Key Rama
Kay Index
Key Frame
Quary Frame index.
WONII Frame indes
Query Frame Inder
Ours
Key trame Index Figure Visualization of attention maps. Specifically, these maps are of the last DiT block across multiple inference steps. Each value in the attention map represents frame-to-frame cor- relation/attention, a larger value means higher correlation. Zoom in for a better view.
Key frame Index
Key Frome Index
Top Camponent Types Mean Change
Meen Change vs. Depth
TyE
Ours
Top 10 Component Types Mean Relative Change
Moan Belative Cha vs. Block Desth
Wanz. 1-12V
Corpenon Tye
Mear Salatin Change
Block
Figure 6: Analysis on finetuned model's parameter shifts. The columns, from left to right, repre- sent the top 10 components with largest average relative parameter changes and average change by transformer blocks, respectively. Zoom in for better view.

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3.5 ZERO-SHOP MULTI-TASE CAPABILITIES
Our approach demonstrates exceptional generalization across diverse video generation tasks beyond T2V without task-specific training. This capability comes from flexible vectorized time step settings, enabling arbitrary conditioning on any subset of frames with any level of noise. Besides, Pusa retains high T2V generation quality after adaptation from its strong foundation model, confirming that our fine-tuning process avoids catastrophic forgetting of the primary task. More significantly, the proposed method exhibits remarkable zero-shot performance on complex temporal synthesis tasks, including 12V generation, start-end frames, video completion, video extension, and so on. Comprehensive results for these tasks are available in Appendix D.
CONCLUSION
This work introduces Pusa, which achieves SOTA-level 12V performance based Wan-T2V with unprecedented efficiency, requiring only S500 and 4K samples. The key innovation lies in our non- destructive VTA strategy, preserving the foundation model's robust priors while enabling frame- independent evolution. This unlocks many zero-shot generalizations to diverse tasks, a property un- matched by conventional auto-regressive VDMs. Our analysis demonstrates that Pusa's success stems from its minimal, targeted modifications to the base model's temporal attention mechanisms, avoiding the catastrophic forgetting observed task-specific fine-tuning. The implications are pro- found: Pusa redefines the efficiency-quart -qullity tradeoff in video generation, enabling high-fidelity, multi-task generation at fraction of traditional costs.
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APPENDIX
A MORE RELATED WORKS
The field of video generation has rapidly evolved, driven by the success of diffusion models in image synthesis. Our work builds upon and extends several key research threads, positioning itself as a highly efficient and versatile solution for the next paradigm of video diffusion models.
A.1 CONVENTIONAL VIDEO DIFFUSION MODELS
The initial extension of image diffusion models to video generation established a foundational paradigm. Seminal works like VDM Ho al. (2022) and subsequent large-scale models such as Latent Video Diffusion Models (LVDM) He et al. (2022), VideoCrafterl Chen et al. (2023), and others Wang et al. (2023); Ma et al. (2024); Wan et al. (2025); Kong et al. (2024) all adopted the same diffusion framework. A core characteristic of these conventional models is their reliance on scalar timestep variable. This single variable governs the noise level and evolution trajectory uni- formly across all frames of a video clip during the diffusion process. While this synchronized-frann approach proved effective for generating short, self-contained clips, particularly for T2V tasks, imposes rigid temporal structure. The uniform noise schedule inherently limits the model's abil- ity to handle tasks requiring asynchronous frame evolution, such as I2V generation, where the first frame condition image is given, or complex editing tasks like video interpolation. Recognizing the limitations of conventional VDMs in temporal modeling, the research community has developed numerous extensions targeting specific video generation tasks Xing et al. (2023b). These approaches predominantly focus on adapting existing scalar timestep based models through fine-tuning strate- gies zero-shot techniques to handle domain-specific challenges such image-to- -o-geeoo-video generation Xing et al. (2023a); Guo et al. (2023); Zhang et al. (2023); Li et al. (2024); Ni et al. (2024), video interpolation Wang et al. (2024b;a); Wan et al. (2025), and long video synthesis Duan et al. (2024); Henschel et al. (2024); Kim et al. (2024); Lu et al. (2024); Dalal et al. (2025); Zhao et al. (2025) These methods typically involve extensive fine-tuning of large, pre-trained T2V model on task- specific data or employing zero-shot domain transfer techniques.
12V generation has emerged as a particularly active area. For example, the Wan-12V model pre- sented in the Wan paper required fine-tuning Wan T2V model on its T2V pretraining dataset to achieve its SOTA 12V capabilities and can only do 12V after this process Wan et al. (2025). Overall, these extensions reveal fundamental challenges in balancing flexibility, generalization, and the re- tention of original model capabilities. Fine-tuning approaches often suffer from catastrophic forget- ting. where adaptation to specific tasks severely degrades performance on its original capabilityPan et al. (2024); Ramasesh ct al. (2021). Zero-shot methods, exemplified by TI2V-Zero introduces a zero-shot method for conditioning T2V diffusion models on images Ni et al. (2024). However, its generalization and generation quality are limited by potential visual artifacts and reduced robust- ness, as its simple "repeat-and-slide" strategy struggles with diverse input and can produce blurry or flickering videos. The reliance on task-specific architectures and training procedures highlights the need for a more unified and general approach that can handle diverse video generation scenar- ios without requiring extensive finetuning. Our work departs from these limitations by adopting the VTV introduced by FVDM Liu et al. (2024b), enabling fine- -naiirri control over the generative process.
A.2 AUTOREGRESSIVE VIDEO DIFFUSION MODELS
Recently, people have explored autoregressive paradigms for video diffusion models, where frames are generated sequentially rather than simultaneously (Chen et al., 2024; Sun et al., 2025; Teng et al. 2025; Chen et al., 2025; Huang et al., 2025). This direction includes methods like Diffusion Forcing, which trains a causal next-token model to predict one or multiple future tokens without fully diffusing past ones, enabling variable-lengl generation capabilities. Cause Vid Yin et al. (2024) represents another significant advancement in this direction, proposing fast autoregressive video diffusion models that can generate frames on-the-fly with streaming capabilities.
Large-scale autoregressive models such as MAGI-1 Teng et al. (2025) and SkyReels- V2 Chen et al. (2025) have demonstrated the potential for scalable video generation through chunk-by-chunk pro-
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cessing, where each segment is denoised holistically before proceeding to the next. Self-Forcing Huang et al. (2025) addresses the critical issue of exposure bias in autoregressive video diffusion by introducing training paradigm where models condition their previously generated outputs rather than ground-truth frames. This approach enables real-time streaming video generation while maintaining temporal coherence through innovative key-value caching mechanisms.
Despite these advances, autoregressive video diffusion models face inherent limitations that con- strain their applicability. The sequential nature generation restricts these models unidirectional tasks, making them inadequate for many scenarios, such as start-end frames, video transitions, and keyframe interpolation. Moreover, error accumulation and drift issues represent persistent chal- lenges in autoregressive approaches, where small prediction errors compound over time, leading to quality degradation longer sequences Huang al. (2025). Recent theoretical analyses have also identified both error accumulation and memory bottlenecks fundamental phenomena in autore- gressive video diffusion models, revealing a Pareto frontier between these competing constraints Wang et al. (2025).
A.3 FRAME-AWARE VIDEO DIFFUSION MODEL
Frame-award video diffusion model (FVDM) Liu et al. (2024b) is a parallel line of research to re- construct the paradigm for video diffusion models, by enabling independent temporal evolution for each frame. The vectorized timestep approach enables unprecedented flexibility across multiple video generation tasks, including T2V, 12V, start-end frames, video extension, and so on, all within single unified framework. Unlike conventional approaches that require extensive destructive chitectural modifications and retraining, FVDM demonstrates strong zero-shot capabilities across diverse temporal conditioning scenarios with its PTSS training strategy. In this work, Pusa- Pusa-Wan (V1.0) further extends FVDM to an industrial scale and proposes a dedicated VTA strategy and post-training method, achieving remarkable efficiency gains with finetuning costs reduced to mere $500 from above 100K of Wan-12V, while outperforming it on Vbench-12V.
The FVDM/Pusa framework represents fundamental departure from previous temporal modeling approaches, offering solution that can perform both directional generation tasks (like autoregres- sive models) and bidirectional temporal reasoning tasks that autoregressive approaches cannot han- dle. This unified capability, combined with the demonstrated computational efficiency and strong empirical results, positions this approach as a promising direction for next-generation video diffu- sion models.
Algorithm Pusa: Sampling for 12V Generation
Require: Trained model vo. VAE Encoder E and Decoder D, Scheduler. Require: Initial image Ig. text prompt number of frames N1, inference steps S.
1:
Encode prompt: Camb & EncodePrompt(())
2:
Encode image initial latent: 2 E(lo).
Sample noise for remaining frames: [z], 2 zi...., 2 Ni I).
4:
> Initialize video with clean first frame and noisy subsequent frames.
5: Construct initial latent video: Z, + |Z NN1
6: Retrieve scheduler noise levels {a.)5- where 01 > ... 0
7: for f- 1...., S do
8: Let Oeurrent + 0s and Onent + 0++1.
9:
Construct vectorized timestep to freeze the first frame.
10: Set current path parameter T, t o ([0, carrents Jeurrent]
11:
Set current noise levels o. f- 10, Teurrenti Irgrentt
12:
Set next noise levels 0 +- 10, Tnents ..., Unezt]
13:
Predict vector field: U, t- vo [Z, Ts, Cemb).
14:
b Update latents; first frame remains unchanged.
15:
Z.+1 + Z, + Ü. (a -z).
end for
17:
Decode final latent video: X + D(Zs).
return Output video Xout-
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B INFERENCE ALGORITHM FOR 12V GENERATION
Pusa performs zero-shot 12V generation by strategically manipulating the vectorized timestep T during sampling. To condition generation on starting image Jo. for simplicity and fair comparison with baselines, we clamp its timestep component zero throughout inference (i.e., T. 0 for all steps s). Note that we can also add some noise (e.g,, set T = 0.2 any level of noise) to the first frame, which may synthesize more coherent videos with slight change the first frame. During sampling, which follows the Euler method for ODE integration, this ensures the change in the first frame's latent is always zero, effectively fixing naturally extends to other complex temporal tasks, such start-end frames and video extension. The detailed 12V sampling procedure outlined in Algorithm
clean condition. This flexible control scheme
Table Comprehensive studies on key hyperparameters. This table presents detailed analysis of our model's 's performance by varying training iterations, LoRA configurations, and the number of inference steps. All scores are reported percentages (%), with higher values indicating better performance, For each ablation group, the best score per metric is highlighted in bold.
Overall
Quality Metries
12V Metries
Group
Setting
Total
Quality
12V
SC
BC
MS
DD
AQ
IQ
12V-8
12V-B
CM
LoRA Configurations
1.0
79.75
73.25
86.25
84.35
88.20
98.91
28.92
60,90
64.73
91.69
91.18
28,00
256
14
83.12
76.07
97.17
85.65
96.67
98.99
30.06
60.13
67.15
90.33
98.78
23.48
85.86
77.96
93.76
97.18
96.68
99.06
10.40)
63.04
70.70
98.60
98.30
8.40
2.0
84.22
76.30
92.14
96.71
94.63
99.05
6.40
60.34
69.63
97.96
95.77
16.00
1.0
80.17
74.35
85.99
83,16
86.99
98.05
62.40
58.44
62.49
91.23
90.70
34,40
512
14
86.46
79.79
93.13
88.64
93.54
97.68
78.80
61.12
67.47
95.72
97.60
38.40
87.11
80.42
93.80
92.93
95.58
97.72
62.80
70.34
97.30
97,93
29.60
2.0
85.98
79.10
93.87
93.88
93.93
97.96
51.20
60.57
70.38
96.92
97.69
17.60
(b) Inference Steps
2 steps
79.92
67.97
91.87
77.18
92,90
98.18
16.80
$1.69
55,44
94,20
97.55
30.40
5 steps
86.03
77.59
94.48
88.53
95.65
98.25
50.00
60.21
65,96
97.03
99.25
29.20
steps
87.69
80.55
94.83
91.39
96.02
98. 10
62.42
68.57
97.78
99.33
26.40
20 steps
87.84
81.17
94.51
89.54
95.89
98.10
79.88
61.02
68.99
97.07
99.10
31.10
(c) Training Iterations
1.0
78.23
73.36
8
80,29
86.09
98.63
50.80
60.19
63.62
88.67
88.47
34,00
1.4
79.33
73:01
85.65
81.61
86,20
98. 74
18.00
59.32
61.92
90.79
91.11
26.91
150
82.79
74.99
90.60
84.37
89,92
98.84
48.40
60.05
63.25
93.62
95.99
31.60
2.0
84,31
76.25
92.37
87.23
91.86
98.86
53.97
59.03
62.36
95.71
97.04
30.20
1.0
79.27
74,20
84,35
82,20
86.81
98.32
59.20
59.48
62.62
90.32
89.00
33,60
450
a 1.4
85.72
79.41
92.04
88.07
93.34
98.12
73.54
61 .88
66.69
94,94
96.98
32.94
87.37
80.95
93.80
91.67
95.51
97.61
72.54
62.25
69.91
96.87
98.23
30.34
2.0
85.96
79.54
92.39
92.88
94. 12
97.46
60.69
61.04
70.22
96.51
97.52
14.51
1.0
80.17
74.35
85,99
83,16
86.99
98.05
62.40
58.44
62,49
91.23
90.70
34,40
750
0
1.4
86.46
79.79
93.13
88.64
93.54
97.68
78.80
61.12
67.47
95.72
97.60
38.40
1.7
87.11
80.42
93.80
92.93
95.58
97.72
62.80
61.78
70.34
97.30
97.93
29.60
2.0
85,98
79.10
93.87
93.88
93.93
97.96
$1.20
60.57
70.38
96.92
97.69
17.60
1.0
81,78
74,63
88.93
84,23
88, 48
98. .44
60.73
58.56
60.10
93,14
93.73
32,80
900
14
87.69
80.55
94.83
91.39
96.02
98. 10
66.40
62.42
68.57
97.78
99.33
26.40
1.7
86.93
79.19
94.67
94.78
98.22
41,21)
70.14
98.31
99.26
18.00
2.0
85,91
77.96
93.86
95.15
94.26
98.38
32.40
60.57
70.17
98.22
98.95
6.15
1.0
82.08
75.01
89.14
84.81
88.30
98.15
63.20
58.17
61.90
93.02
94,04
34,40
1200
14
87.32
$0.30
94.34
90.72
95.44
97.58
73.20
61.51
68.01
97.19
98.83
29.89
79.70
94,02
933666
95.46
98.02
54.80)
60.77
69.70
97.65
98.
18.80
2.0
86.01
78.32
93.69
94.21
14.16
98.40
41.20
59.51
69.96
97,90
98.74
9.16
C COMPREHENSIVE QUANTITATIVE RESULTS
C.I HYPERPARAMETER STUDY
To validate our hyperparameter choices and understand their impact on performance, we conducted series of studies, summarized in Table 4.
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Published as a conference paper at ICLR 2026
Lora Configurations Lora rank is critical ingredient that influences the fine-tuning performance. As we know, Lora learns much less with small ranks compared to full fine-tuning Biderman et al. (2024), thus, Lora rank should be large enough to have the capacity to learn the new capabilities since tasks like 12V are very general. We investigated the influence of LoRA rank, a proxy for the adaptation's capacity. As shown in Table 4(a), higher rank of 512 consistently outperforms rank of 256 on most metrics, particularly in overall quality. This suggests that a larger adaptation capacity is beneficial in capturing the nuances of temporal dynamics required for 12V tasks. We also find that the LoRA alpha scaling at inference time is critical; an alpha of 1.7 yields the best results for the 750-iteration checkpoint, balancing the influence of the LoRA weights against the pre-trained model.
Inference Steps. detailed in Table 4(b), we analyzed the trade-off between computational cost at inference and generation quality using our best checkpoint with rank 512 and alpha 1.4 with 900 iterations. Performance scales predictably with the number of steps, with significant gains observed up to 10 steps. While 20 steps provide marginal improvement, the results 10 steps nearly identical (87.69 vs. 87.84). Consequently, we adopt 10 inference steps as our default to ensure an optimal balance between quality and generation speed.
Training Progression. We evaluated checkpoints of Lora rank 512 at various stages of training, from 150 1200 iterations, using 10 inference steps. Table 4(c) shows a clear trend of improving performance up to 900 iterations, where we achieved our highest score of 87.69 with an alpha of 1.4. Beyond this point, performance begins to plateau or slightly degrade, indicating that the model has converged. This rapid convergence underscores the data efficiency of our approach. Our final model for comparison in Table uses the 900-iteration checkpoint of rank 512.
C.2 ABLATION STUDIES
We conduct further ablation studies to analyze the core components of our proposed method. We investigate the effectiveness of our training strategy against baseline approach and examine the impact of different timestep sampling strategies during training. The results are summarized in Table 5.
Training Strategy. We compare our training strategy against the baseline method used in Wan- 12V across different training regimes: full model fine-tuning and parameter-efficien LoRA fine- tuning. As shown in Table 5(a), both of our fine-tuning approaches significantly outperform the baseline at every checkpoint. Our full fine-tuning method already achieves a total score of 83.78 in just 150 steps, surpassing the bascline's peak score of 75.01. The CoRA-based approach further accelerates performance gains. The results show that performance consistently improves with more training steps. For instance, with a = 1.7, the score increases from 84.92 at 150 steps to 86.27 at 600 steps. The best LoRA result is 87.09 at 900 steps with a 1.4, which substantially outperforms full fine-tuning at the same iteration (84.06). This highlights not only the effectiveness of our training recipe but also the remarkable efficiency and power of combining with LoRA.
Timestep Sampling Strategy. Our method utilizes a simple yet effective strategy of sampling purely random timesteps for each frame during training. To validate this choice, we compare against an intuitive bascline for 12V with the first frame always being noise-free -1 0 while all other timesteps are of the same value, and a more structured approach, PTSS, where a certain percentage of timesteps are sampled from a specific range. As detailed in Table 5(b), we conduct comprehensive comparison at the 900-iteration mark, testing PTSS with probabilities (p) of 0.2, 0.5, and 0.8 against our fully random approach (equivalent to p 0). The results consistently show that our method outperforms I2V beaseline and all PTSS variants across different LoRA alpha values. Notably, approach with PTSS result (87.36 from p=0.5, a = 1.7). This confirms that a simpler, fully randomized timestep sampling strategy is effective for learning robust temporal dynamics in our framework.
1.4 achieves the highest total score of 87.69, surpassing the best
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Published as a conference paper at ICLR 2026
Table 5: Ablation studies on training methodology. This table presents our analysis of (a) the training strategy, comparing our method (with both Full and LoRA fine-tuning) against the baseline across different training steps, and (b) the timestep sampling strategy. All scores are reported in percentages (%), with higher values being better. The overall best configuration is marked with *
Group
Setting
Overall Quality
Quality Metrics
Metries
Total
12V
SC
BC
MS
DD
AQ
1Q
12V 12V-B
CM
(a) Training Strategy
steps
75.01
77.85
72.16
59.75
63.65
77.43
300 steps
74.02
76.81
71.23
96.93
97.75
99.18
18.30
58.28
63.39
81.58
76.48
30.00
Baseline (Wan-12V)
600 steps
73.29
75.63
70.95
97.45
97.88
99.23
9.60
56.63
62.18
81.47
76.09
30,80
900 steps
73:21
76.02
70.39
96.96
97.65
99.13
17:20
57.00
61.42
80.56
76.15
28.80
1500 steps
73.99
76.36
70.53
97.19
98.00
99.30
11:20
59.34
62.11
80.60
76.13
31.60
150 stops
83.78
76.10
91.46
85.20
91.10
98.79
39.60
63.16
67.95
94.62
96.14
36.00
Ours (Full Finctune)
300
83.35
78.07
68.62
84.86
90.91
98.37
66.40
63.61
67.35
92.77
93.57
32.80
stops
78.51
91.90
85.95
90.20
76.00
61.80
66.69
95.58
95.79
900 steps
84.06
77.67
90.45
85.97
90.67
98.40
66. 801
61,27
66.17
94.27
95.05
33.60
150 steps, steps,
1.4
81.62
74.62
88.6 62.
83.56
88. 10
98, 98.90
45.66
60.52
65.39
92.20
94, 30
29.60
84.92
77.39
92.44
62.73
66.76
Ours (LoRA Finctunc)
300 sleps, a 1.4
82.97
75.39
90.55
86.42
90.99
98.94
40.90
59.90
65.
93.87
95.67
32.00
600 steps, 1.4
85.98
78.60
93.35
89
93.50
98,76
58, 90
60.55
66. 98
96.49
97.88 37.87
30.40
600 steps. 1.7
86.27
78.73
93.81
91.78
94.51
98.40
52.40
60.13
68.37
97.31
98.43
22.80
900 steps,
L.*
87.09
79.77
94.42
62.80 80
61.72
68.20
98.67
31.20
900 steps, a
1.7
87.01
80.02
94.00
93.55
96.01
98.44
51.20
61.74
70.24
97.70
98. 54
20.80
(b) Timestep Sampling Strategy
sleps,
79.27
74.20
59.20
59.18
62.62
450 sleps,
1.4
85.72
79:41
92.04
88.07
93.34
98.12
73.54
61.88
66 69
94.94
96.98
32.94
Ours
450 steps, 1.7
87.37
80.95
93.80
91.67
95.51
97.61
72.54
62.25
69.91
96.87
98.23
30.34
900 steps.
1.0
81.78
74.63
88.93
84.23
88.48
98.44
60.73
58.56
60.10
93.14
93.73
32.80
900 steps, 1.4
87.69
80:55
94.83
91.39
96.02
10
62.42
68.57
97.78
99.33
900 steps,
1.7
86.93
79.19
94.67
94.78
95.80
98.22
41.20
61.84
70.14
98.31
99.26
18.00
450 sleps,
1.0
74.26
71.62
76.91
76.83
84.03
$4.40
58,14
61.92
82.44
84,49
30.80
steps, 450 steps, 1.7
74.03
71.11
76.95
56.
62.20
84.61
29.60
74.73
70.98
78.47
76.63
83.91
98.56
19.60
56.23
62.63
83.65
85,96
29.60
12! - 0)
900 steps,
1.0
73.27
69.96
76.57
75.93
83.37
98, 98.60
43.20
56.57
61.25
82.23
84.15
30.80
900 steps.
1.4
72.54
68.47
76.61
75.11
83.10
98.68
36.40
53.87
60.23
81.61
84.33
36.40
900 steps, 1.7
72.42
67.82
77.02
74.53
82.96
98,72
32.99
53.39
59.66
81 $1.90
85.05
32.00
450 steps,
1.0
77.53
72.33
N2.74
79.62
85. 66
915.61
42. A1l
59, 96
63.85
NN. 18
8%. 80
27.66
450 steps. 1.1
79.67
73.63
65,70
81.07
87.44
98 98.69
46.40
60.23
61.35
90.77
90.77
32.80
PTSS (p=
steps,
82.05
75.32
$8.79
82.47
98.76
62.
64.42
92.70
94.19
28.50
900 steps,
1.0
77.96
72.24
83.68
50.38
85.49
98.40
44.40
58.67
63.72
89.
88.91
31.20
900 steps, 1.4
81.57
74.71
88.43
83.63
88 95
98.51
$2.401
59.57
63.50
92.69
93.51
30.40
900 steps,
1.7
84.74
77.60
91.88
85.97
92.68
98,40
63.20
61.27
64.93
94.68
96.82
34.80
450 steps,
1.0
78.16
72.96
83.36
80.69
86.58
98.42
45.20
59.80
64.21
8R 97
S5.66 64.64
33,60
450 steps, 1.4
82.08
75.76
68.41
84. 34.9%
91.24
98.44
4%, 401
61.64
64.70
91 11.94
94.20
29.60
PISS (.))
450 steps. 1.7
85.52
78.64
92.41
88.57
91.94
98.45
52.40
63.29
67.36
95.06
97.40
33.60
steps, 900 steps, 1.4
79.63
73.51
85.76
64.42
90.80
30.67
86.38
79.51
93.26
90.18
95.0 60
98.26
56.30
63.74
68.01
96.20
98.09
28.75
900
1.7
87.36
80.44
94,28
93.25
96. 63
915007
54. 801
63.58
69.67
97.71
98.36
28.80
steps, 450 sleps, a= 1.4
79.03
73.78
82.13
86.
918.56
65,
89.
34.40
85.59
79:03
92.14
58.10
93.93
98.63
61.60
62.84
67.03
91.78
97.35
32.00
PTSS 0.8)
450 stops, 1.7
87,51
80.84
93.79
91.63
95.9%
98.17
20
63.27
69.41
97.02
98.08
30.40
900 steps. 1.0
80.71
75.17
86.25
82.65
86.73
97.73
69.60
59.37
64,50
91.37
90.96
34.50
steps.
85.98
78.86
93.10
87.10
97.31
86.40
60.79
64.98
13
97.24
900 steps, a 1.7
86.49
79.30
93.69
89.98
92.78
97.11
79.20
60.44
66.67
97.15
97.67
32.40
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Published as a conference paper at ICLR 2026
D QUALITATIVE RESULTS ON MULTI-TASK CAPABILITIES
Our approach demonstrates exceptional generalization across diverse video generation tasks beyond text-to-video (T2V), without task-specific training. This capability comes from flexible vectorized timestep settings, enabling arbitrary conditioning on any subset of frames.
Text-to-Vided Generation. Unlike specialized image-to-video (I2V) models, Pusa preserves the T2V capabilities of its foundation model. As shown in Fig. 14, the qualitative output maintains high quality, demonstrating that our fine-tuning process does not induce catastrophic forgetting of the primary T2V task. This preservation of capabilities establishes Pusa as a truly unified video generation model.
Complex Temporal Tasks. The FVDM framework reveals its true power through zero-shot per- formance on complex temporal synthesis tasks. Additional results demonstrating seamless I2V generation are presented in Fig. 4.
Pusa can perform start-end frames conditioning through various configurations. When conditioning on the first frame and the last frame (encoded a single latent frame similar the first frame), the model generates coherent video sequences, as illustrated in Fig. 8. Alternatively, conditioning on the first frame and the last frames (encoded to single latent frame as default) yields improved results, as shown in Fig. 10. The latter approach addresses the challenge posed by the 4x compression rate for the last frames introduced by the VAE, where conditioning solely on the last frame produces inferior results due to its interpretation as 4 static frames.
Using the unique properties of our framework, we can enhance video coherence by introducing controlled noise to the encoded latents (e.g., setting 7 = 0.3 L and n = 0.7 (). This technique generates appropriate motion and content for the condition frames, resulting in more coherent video synthesis, as demonstrated in Fig. 9.
Furthermore, Figures 11, 12, and 13 showcase Pusa's capabilities for video completion/transition and video extension, seamlessly completing continuing given video sequences. These advanced capabilities emerge inherently from our vectorized timestep adaptation strategy, which does not require task-specific training, and highlight the remarkable versatility and power of our approach.
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Published as a conference paper at ICLR 2026
MAAAA
high wide angle giraffes gently, while space their behind
shape They
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an astronaut riding a horse on the moon amidst moon dust. 1978, Shot in super8
Drone view of waves crashing against the rugged cliffs along Big Sur's garay point beach. The crashing blue waters create white- lipped weves, while the golden light of the setting sun illuminntes the rocky
ZÊN. ZÉN
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Cool objects like hand and jungle blue...
HO00
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Cinematic the small figure giant plastic pink Piggy ocean
Figure 7: More image-to-videe video results. The first frames of each row are the given condition images extracted from Veo2 & Sora demos. Each generated vidco has 81 frames in total.
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Published as a conference paper at ICLR 2026
A high view wide angle shat two rifffos kivinng gentlyy while the space between their necks, heads, and the tromn behtin themn forms the shape of golden
fromt facing wide angle shot of two infarablle dust flonlee They drift toward each other until their beaks moet in gentle kiss and then magically one them cicses its eyes. The cement takes place in queet backyard.
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tracking woman huge library amone ying
Figure 8: Zero-shot results w.r.f. start & end frames to video. The first and last frames are given condition frames extracted from Veo2 & Sora demos. Each generated video has 81 frames total.
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Published as a conference paper at ICLR 2026
continuuus
longboard
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She wears black leather jacket, long red dress.
assses sunglasses
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Figure Zero-shot results w.r.t. & end frames with noise. The first and last frames are given conditions and added 30% and 70% noise during sampling to make the generated video more coherent. Condition frames are extracted from Veo2 & Sora demos. Each generated video has frames in total.
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Published as a conference paper at ICLR 2026
Handheld tourist style video captures slow moving boot glidng down wide jungle river, the camera panning upward to reveal several large alligators allently swimming ng through the air just matera above the surface. bodies move alow, fluid streids
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Figure 10: Zero-shot results w.r.t. start & end frames to video. The first and last 4 frames (encoded to one latent frame) are given condition frames extracted from Veo2 & Sora demos. Each generated video has 81 frames/21 latent frame in total.
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Published as a conference paper at ICLR 2026
First person me into through secret into my magic c Documentary. Soft natural light.
An animated time-lapse of growing agar art inside a small round petri dish, macro lens, showcasing intricate microbial and fung) colonies forming a detailed moving animated moving blinking blinking blinking moving eye. The ...
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Zero-shot results w.r.t. video completion/rransitoon. The first frames and the last 12 frames extracted from Vco2 demos given as conditions and encoded to the first 3 latent frames and the last latent frames, respectively. Each generated video has 81 frames/21 latent frames in total.
Figure 11:
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Published as a conference paper at ICLR 2026
pimet-mounted shot shows golden in her tracks
bike rolling desert dunes. She kicks
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people walking cautiously across narrow suspension bridge that endiessly
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Figure 12: Zero-shot results w.r.t. video extension. The first 13 frames extracted from Veo2 de- mos are given as conditions and encoded to the first 4 latent frames. Each generated video has 81 frames/21 latent frames in total.
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Published as a conference paper at ICLR 2026
cinematic, hyper-realistic video shot 35mm film.A hypar-realistic, image of brutalist spacecraft fleating deep architecture defined immense, structures made textured concrete lkke allays-etched
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fully by torch, shaky
footage
camera,,
pigsy bank surfing tube in teahupo'o o wave dusk light cinemetic shot shot 35mm film
A highly styliced stop motion clay animation scene of cartoon character twerking. The character is sculpted from colorful modeling clay with exeggerates retd features; ultra-long skinny legs that bend and wobele, wide
A highly stylized and exaggerated cartoon character dog caught mid twerk two With stretched-out sausage body, stubny limbs, and enormous cartoon he wears shiny allver tracksult with baby
Figure 13: Zero-shot results w.r.t. video extension. The first 41 frames extracted from Veo2 demos are given as conditions and encoded to the first 11 latent frames. Each generated vidco has 81 frames/21 latent frames in total.
26

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Published as a conference paper at ICLR 2026
changes from galdan white.
dog sofa, dog
A dog on the left of an apple, then the dog runs to the front the apple.
A giraffees is wal king.
A
person eating hot dog.
cating spaghetti with fork.
person sitting they suddenlly get stretching.
A rabbit with the horns of gost, hopping energetically while butting through obstacles.
A whole with the wings of a bai, soaring over the ocean sur lace under the full moon.
woman cutting objects.
wooden toy placed gently the surface small bowl of water.
Figure 14: Text-to-video results. Prompts all from Vbench2.0.
27


Reviews:
[{"review_id": "H1EVWb69X9", "reviewer": "Reviewer_KVjf", "summary": "This paper introduces Pusa V1.0, a novel approach for enhancing temporal control in pretrained video diffusion models through Vectorized Timestep Adaptation (VTA). The authors claim that VTA enables fine-grained, frame-level temporal manipulation by replacing the conventional scalar timestep with a vectorized version, allowing asynchronous frame evolution. This non-destructive adaptation preserves the base model's text-to-video (T2V) capabilities while unlocking zero-shot performance on tasks like image-to-video (I2V), start-end frames, and video extension. The work emphasizes unprecedented efficiency, achieving state-of-the-art (SOTA) I2V results with minimal data (4K samples) and compute cost, compared to resource-intensive baselines like Wan-I2V. By leveraging flow matching and a lightweight fine-tuning strategy, Pusa V1.0 aims to democratize high-fidelity video generation and establish a scalable paradigm for multi-task video generation.", "questions": "1. Could you elaborate on the theoretical justification for why vectorized timesteps avoid combinatorial explosion during training, especially given the high-dimensional temporal composition space?\n2. How does VTA handle varying video lengths or frame rates in zero-shot settings, and are there limitations in temporal consistency for long sequences?\n3. The paper mentions using LoRA for parameter-efficient training; what are the trade-offs between LoRA ranks and adaptation quality, and how was the optimal rank (512) determined?\n4. In the inference algorithm, why is clamping the first frame’s timestep to zero the default choice, and how does adding noise (e.g., τ¹=0.2*s) impact coherence quantitatively?\n5. Could you provide more details on the dataset used for fine-tuning (e.g., diversity, resolution) and how it compares to datasets used by baselines to ensure fair evaluation?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "I would like to highlight the following strong points of the proposed manuscript:\n\n1. Novelty: The vectorized timestep concept, building on FVDM, is a creative extension that addresses key limitations in temporal modeling. The non-destructive VTA strategy is particularly innovative, as it avoids catastrophic forgetting and retains base model priors.\n2. Efficiency: The paper demonstrates remarkable efficiency gains, with SOTA-level performance achieved using only 4K samples and low compute costs, making it accessible for broader research and industry applications.\n3. Experimental results: Comprehensive evaluations on VBench-I2V, detailed ablation studies (e.g., LoRA configurations, inference steps), and mechanistic analyses (e.g., attention maps) provide strong empirical support. The inclusion of zero-shot multi-task results (e.g., start-end frames, video extension) further validates the method's versatility.\n4. Reproducibility: The paper offers clear methodological details, including algorithms, hyperparameters, and training procedures, though reliance on proprietary base models (e.g., Wan-T2V) may pose minor barriers.", "weaknesses": "Among the weak points I would focus on the following ones:\n\n1. Some technical sections, such as the vectorized timestep embedding and per-frame modulation, could be explained more intuitively for readers unfamiliar with DiT architectures. The inference algorithm (Appendix B) lacks thorough discussion of its theoretical underpinnings.\n2. While benchmarks show SOTA performance, comparisons are primarily limited to open-source models; broader evaluation against recent proprietary models (e.g., Sora, Veo) would strengthen claims.\n3. The paper focuses on empirical results but provides limited theoretical analysis of why VTA avoids combinatorial explosion or how it generalizes across tasks.\n4. Claims of \"unprecedented efficiency\" are compelling but could benefit from more context on real-world deployment challenges, such as latency or memory usage during inference.", "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": "82t2hrX3pA", "reviewer": "Reviewer_vHLo", "summary": "The paper introduces Pusa V1.0, a method to adapt pretrained large-scale text-to-video diffusion models (e.g., Wan-T2V) to handle vectorized timesteps instead of a single scalar timestep. This Vectorized Timestep Adaptation (VTA) enables each frame to evolve independently, allowing fine-grained temporal control without destructively retraining or modifying the model. Pusa achieves state-of-the-art image-to-video (I2V) performance on the VBench-I2V benchmark using only 4 K samples and 0.5 K compute, while preserving the base model’s text-to-video ability. It also exhibits zero-shot generalization to other temporal tasks (start–end frames, video extension). Mechanistic analyses and attention visualizations show that Pusa injects temporal dynamics non-destructively, maintaining pretrained priors while improving efficiency", "questions": "Can Pusa be extended to handle long-video generation (>128 frames) without retraining, or is the approach limited by the base model’s context length?\nHow does the method perform on cross-domain generalization, e.g., artistic or high-motion datasets, compared with Wan-I2V?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Turning scalar timesteps into vectorized ones for frame-wise control is conceptually elegant and integrates naturally into the diffusion pipeline.\n2. Comparable or superior I2V results to Wan-I2V at orders-of-magnitude lower data and compute; clear quantitative and qualitative evidence.\n3. Demonstrated versatility across I2V, start–end frames, video completion, and extension, suggesting a broadly useful paradigm.", "weaknesses": "1. Builds heavily on FVDM, the contribution mainly lies in adaptation, rather than introducing new theory.\n2. While the paper emphasizes efficiency (4K samples, 0.5K compute), it does not explore how performance scales with larger datasets or longer fine-tuning. It remains uncertain whether Pusa’s gains plateau quickly due to its limited adaptation space, or if additional data and compute could further improve quality and generalization.", "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": "NEu3TczWgc", "reviewer": "Reviewer_SEmS", "summary": "This paper presents Pusa V1.0, a method that introduces Vectorized Timestep Adaptation to pretrained text-to-video diffusion models. By replacing the traditional scalar timestep with a vectorized one, the model enables each frame to evolve independently, achieving fine-grained temporal control. The approach is non-destructive, preserving the base model’s text-to-video capability while extending it to image-to-video, start-end frame generation, and video extension in a zero-shot manner.", "questions": "1. Could the authors clarify whether the same fine-tuning hyperparameters work across different base models, or if task-specific tuning is needed?\n\n2. How does the approach handle temporal consistency when τ vectors are sampled completely at random during training?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of vectorizing the timestep variable is conceptually clear and provides a unified mechanism for multiple temporal tasks without architectural changes.\n\n2. The proposed non-destructive adaptation preserves the pretrained model’s generative priors, avoiding catastrophic forgetting.\n\n3. The analysis sections, including attention visualization and parameter drift studies, help explain why the approach works.", "weaknesses": "1. The evaluation mainly relies on VBench-I2V and qualitative visualization. Additional comparisons on broader benchmarks or user studies would strengthen the claims of generality and zero-shot capability.\n\n2. Some methodological details, such as how vectorized timestep embeddings are fused with text conditioning or cross-attention, remain underexplained.\n\n3. While the efficiency results are appealing, the paper would benefit from clearer discussions of potential trade-offs, for example, limits of per-frame desynchronization or artifacts under longer sequences.", "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": "wGMJuznhcA", "reviewer": "Reviewer_TcSx", "summary": "The paper proposes Pusa V1.0, a lightweight adaptation for large pretrained text-to-video (T2V) diffusion models that replaces the scalar timestep with a vectorized timestep (one per frame) and learns with a frame-aware flow-matching objective. The key contribution is Vectorized Timestep Adaptation, a non-destructive modification that enhance the base model’s generation ability. With minimal fine-tuning (LoRA or full FT) on ~3.9k Wan-T2V generated clips, Pusa achieves SOTA-level image-to-video (I2V) results on VBench-I2V and further shows zero-shot behavior for other downstream tasks.", "questions": "See the weakness.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Simple, general idea with clear motivation. Moving from a scalar to a vectorized timestep directly attacks the synchronized-frame limitation of standard VDMs; the frame-aware flow-matching formulation is clean and well explained.\n2. High efficiency. Comparable I2V performance to Wan-I2V with a tiny fraction of the compute/data, plus favorable results with only 10 sampling steps. The ablations (LoRA vs full FT, timestep sampling) are helpful.\n3. Unified capability. The same model handles I2V, start–end, and extension without task-specific heads or destructive finetuning.", "weaknesses": "1. Lack of quantitative evaluation for non-I2V tasks. While the author mentions zero-shot, start–end and other applications of its method, it lacks quantitative evaluation for these applications; most quantitative focus is I2V on VBench-I2V. Concrete metrics are missing from the main text. It would be better for the auther to add quantitative results for applications.\n2. Data source and generalization. Fine-tuning uses videos generated by the same base video model. This could limit diversity and introduce bias toward the base model’s distribution; it’s unclear how well Pusa generalizes to challenging real photos as I2V inputs beyond curated benchmarks. Could you provide some details of the ~3,860 Wan-T2V samples? And are there any data filtering you do?", "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)