Uniform Discrete Diffusion with Metric Path for Video Generation
Authors: Haoge Deng
Ting Pan
Fan Zhang
Yang Liu
Zhuoyan Luo
Yufeng Cui
Wenxuan Wang
Chunhua Shen
Shiguang Shan
Zhaoxiang Zhang
Xinlong Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Text-to-Video Generation
Discrete-valued Space
Primary area: generative models

Abstract: Continuous-space video generation has advanced rapidly, while discrete approaches lag behind due to error accumulation and long-context inconsistency. In this work, we revisit discrete generative modeling and present Uniform discRete diffuSion with metric pAth (URSA), a simple yet powerful framework that bridges the gap with continuous approaches for the scalable video generation. At its core, URSA formulates the video generation task as an iterative global refinement of discrete spatiotemporal tokens. It integrates two key designs: a Linearized Metric Path and a Resolution-dependent Timestep Shifting mechanism. These designs enable URSA to scale efficiently to high-resolution image synthesis and long-duration video generation, while requiring significantly fewer inference steps. Additionally, we introduce an asynchronous temporal fine-tuning strategy that unifies versatile tasks within a single model, including interpolation and image-to-video generation. Extensive experiments on challenging video and image generation benchmarks demonstrate that URSA consistently outperforms existing discrete methods and achieves performance comparable to state-of-the-art continuous diffusion methods. Code and models are available at https://github.com/baaivision/URSA.

Published as a conference paper at ICLR 2026
UNIFORM DISCRETE DIFFUSION WITH METRIC PATH FOR VIDEO GENERATION
Haoge Deng Ting 23n2. Fan Zhang Yang Liu Zhuoyan Luo Yufeng Cui Wenxuan Wang', Chunhua Shen', Shiguang Shan 2:3 Zhaoxiang Zhang! 1,3r Xinlong Wang
1 National Laboratory of Pattern Recognition, CASIA
2 Key Laboratory of Intelligent Information Processing, ICT, CAS
3 University of Chinese Academy of Sciences
ZZeejiang University
Beijing Academy of Artificial Intelligence
text-to-vides
Interplltiion
40s long video
Figure 1: Visualization of URSA across diverse video generation tasks: text-to-video generation, video interpolation, and long video generation. These examples underscore the versatility of URSA.
ABSTRACT
Continuous-space video generation has advanced rapidly, while discrete approaches lag behind due to error accumulation and long-context inconsistency. In this work, we revisit discrete generative modeling and present Uniform discRete diffuSion with metric pAth (URSA), simple yet powerful framework that bridges the gap with continuous approaches for the scalable video generation. At its core, URSA iterative global refinement of discrete
formulates the video generation task spatiotemporal tokens. integrates two key designs: I Linearized Metrie Path and Resolution-depender Timestep Shifting mechanism. These designs enable URSA to scale efficiently to high-resolution image synthesis and long-duration video generation, while requiring significantly fewer inference steps. Additionally, we introduce an asynchronous temporal fine-tuning strategy that unifies versatile tasks within a single model, including interpolation and image-to to-vido generation. Extensive experiments demonstrate that URSA consistently outperforms existing discrete methods and achieves performance comparable to sate-of-thheart continuous diffusion methods. Code and models are available at https: /t/////ttttt /github..bb.g.g.gggg. com/ baai y vis wwsiiinnnssss
challenging video and image generation benchmarks
Equal Contribution. This work done when H.Deng, T.Pan, and Y.L were interns at BAAI. Corresponding Author: wangxinlong @ baai.ac.a... ac.i. zhaoxiang.zharr ia.ac.cn

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Published as a conference paper at ICLR 2026
Astoregresdien Model
Centrmuou Demtrioo Model
Sequential Gemeration by Tokeon Prediction
(b) Continwous Dittasion: Rerative Rednement: in Continuous Space
Staakol Diffuson Model
Unthhem Discrabe Exttutton Medel (3B54)
(o) Masked Diffusion: Paraliel Denoising Masked Tokena
(4) Uniform Diffusion: Tterstive Refinement in Discrete Space
Figure 2: Illustration of different image/video generation paradigms. Discrete-space approaches such AR and MDM adopt non-refinable local generation, where produced tokens are fixed once generated. In contrast, URŠA introduces iterative global refinement, conceptually aligning discrete methods with continuous-space approaches, and substantially narrowing their performance gap.
INTRODUCTION
Continuous-space visual generation has achieved remarkable progress in both image and video synthesis (Batifol et al., 2025; Baldridge al., 2024; Betker et al., 2023; Brooks et al., 2024; Wang et al., 2025a; Gao et al., 2025b; Yang et al., 2025b; Kong et al., 2024). Driven by advances in diffusion model algorithms (Ho et al., 2020; Song al., 2021), these continuous-space methods have demonstrated strong capabilities in producing high-fidelity and visually coherent content, establishing themselves as the dominant paradigm for generative modeling.
In parallel, discrete-space text generation has become the de facto paradigm for large language models (Radford al., 201 8; 2019; Brown et al., 2020). Inspired by the success of LLMs, recent works have extended similar ideas to visual generation through discrete tokenization, using either next-token prediction (Sun et al., 2024a; Wang al., 2024b; Kondratyuk al., 2024) or masked token prediction (Chang et al., 2023; Xie et al., 2025c). However, discrete approaches still lag behind their continuous counterparts, facing challenges such as error accumulation and maintaining long-context consistency, especially in video generation. For instance, even though masked diffusion models employ bidirectional transformers, we still observe low visual quality and unnatural object motions.
In this work, we first revisit discrete generative modeling and introduce URSA, powerful visual generation framework built upon Uniform discRete diffuSion with metric pAth. Our approach is simple: we generate videos and images by iterative refinement over discrete spatiotemporal tokens. As illustrated Fig. 2, unlike classic autoregressive (AR) models and masked diffusion models (MDM) that adopt non-refinable local generation, where produced tokens fixed once generated, URSA emphasizes iterative refinement over global discrete tokens, conceptually aligning discrete methods with continuous counterparts, and substantially narrowing their performance gap. URSA starts from categorical noise, zo ~ Unif([K])) where each of the D-dimensional discrete token is independently sampled from uniform distribution over the vocabulary [K] (1,2,..., K). and iteratively performs global refinement along metric-guiee guriie probability path to obtain on the data manifold, i.e., the target image or video. This iterative process enables URSA capture the hierarchical structure of video data, from global layouts to detailed dynamics, while leveraging temporal redundancy to preserve spatiotemporal coherence.
We propose a novel metric probability path tailored for long visual sequences by incorporating two key components: a linearized metric path and a coooluutin-dependeee timestep shifting mechanism. Collectively, these designs enable precise control over data perturbations, a property essential for effectively learning hierarchical data manifolds. This construction allows URSA to scale efficiently to sengeee tasks, such as high-resolution image synthesis and long video generation, while requiring substantially fewer inference steps. Furthermore, we introduce an asynchronous timestep scheduling strategy, where timesteps independently sampled for each frame. This asynchronous design empowers URSA to generate minute-leve long videos and support a wide range tasks within unified model, including image-to to-ideeo generation, video interpolation, and extrapolation.
URSA achieves a text-to-video score of 82.4 on VBench (Huang et al., 2024a), outperforming discrete and continuous baselines. In -mage-to-video generation tasks, URSA reaches a VBench score of 86.2,
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on par with the state-of-the-arr open-source models. For text-to-image generation, URSA achieves a DPG-Bench (Hu et al., 2024) score of 86.0, exceeding previous discrete approaches. Moreover, URSA exhibits strong zero-shot generalization in variable length contexts, underscoring its versatility.
Our contributions can be summarized as follows: 1) We propose URSA, a simple yet powerful framework that bridges the gap continuous diffusion methods and enables scalable video generation. 2) We highlight two key designs, Linearized Metric Path and Resolution-deeendent Timestep Shifting for long- -eng-eeqennce training. We further propose an asynchronous timestep scheduling strategy that enables multi-task video generation. 3) URSA substantially pushes the envelope of discrete generation, attaining state-of-the -o--t---rttttt performance on VBench, DPG-Bench, and GenEval (Ghosh et al., 2024).
RELATED WORKS
2.1
CONTINUOUS- VISUAL GENERATION
Continuous methods for visual generation have achieved significant progress in recent years, Early endeavors such as variational autoencoders (VAEs) (Kingma Welling, 2014) and flow-based models (Dinh et al., 2014; 2017) exploit continuous latent spaces to model complex images, while GANs (Goodfellow et al., 2014) generate high-resolution images with strong perceptual quality via adversarial training (Brock et al., 2019; Karras et al., 2020). Diffusion models (Ho et al., 2020; Song et al., 2021), I), which learn to recover data by progressively denoising Gaussian noise in a continuous space, demonstrated remarkable performance in both image and video generation (Gao et al., 2025a; Batifol et al., 2025; Baldridge et al., 2024; Betker et al., 2023; Wu et al., 2025a; Brooks et al., 2024; Kong al., 2024; Gao et al., 2025b; Wang et al., 2025a; Kuaishou, 2024; Ma et al., 2025). MAR (Li et al., 2024) employs an autoregressive framework with a diffusion head to produce continuous- valued outputs, and NOVA (Deng al., 2025b) further extends this idea video generation, applying autoregressive modeling to spatiotemporal sequences. URSA shares the same spirit as continuous diffusion models, performing global iterative refinement, but operates over discrete tokens.
2.2 DISCRETE-SPAC VISUAL GENERATION
Discrete visual generation can be broadly categorized into autoregressive and masked diffusion models, both operating on discrete visual tokens such as pixels (Kalchbrenner et al., 2017; Reed et al., 2017) or latent codes (Oord et al., 2017: Esser et al., 2021). Autoregressive models generate discrete visual tokens sequentially, with each prediction conditioned on previously generated context. This approach has been applied to both image (Sun et al., 2024a; Ramesh et al., 2021; Ding al., 2021; Yu et al., 2022; Zhu et al., 2025) and video synthesis (Wang et al., 2024b; Yan et al., 2021; Kondratyuk al., 2024; Wang et al., 2024c). Although simple in concept, this design often has slow inference and significant error accumulation In contrast to autoregressive methods, masked diffusion models (Gat al., 2024: Chang al., 2022: 2023; Yu et al., 2023) introduce the prediction of masked tokens, enabling parallel generation and improved modeling of global context. Despite these ad advantages, it remains challenging to apply these methods to long sequences, e.g. high-fidelity long-form video. FUDOKI (Wang et al., 2025b) investigates the integration of discrete flow matching (Gat et al., 2024) within native multimodal models. In this work, we adopt uniform discrete diffusion approach, which performs iterative global refinement from categorical noise. By addressing key challenges, URSA enables both efficient inference and high-quality long-sequence generation.
METHODOLOGY
We first review the concepts of uniform discrete diffusion discrete flow matching in Sec. 3.1, which provide the theoretical foundation for our framework. In Sec. 3.2.1,,, we introduce URSA, simple yet powerful framework that bridges the gap between discrete and continuous approaches, enabling effective and scalable video generation.
3.1 PRELIMINARY: DISCRETE FLOW MATCHING
Discrete Flow Matching (DFM) (Gat et al., 2024; Shaul et al., 2025) introduces a family of generative models designed to map data from initial distribution (2)),, to final distribution pi (z),, within
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X
X1/2
X,
Sampling Probability
Embedding Space
Embedding Space
Embbediing
Frb
Target
Global refinement via token distance in embedding space. Starting from categorical noise zop (left), framework refines data based token distance get target data ii (right), enabling hierarchical structure generation from global semantics to fine details.
Figure 3:
discrete state space. The model utilizes a time-dependent probability path, Pr(x), which interpolates between these two distributions over the interval E [0. 1]. The key idea behind DFM is define velocity field, ug, which drives the evolution of this probability path, enabling the model to simulate Markov process and generate new data samples.
Probability paths. We consider the probability path pe(z)), where / E [0, 1] indexes time-dependent probability distribution between source distribution (o())) and a target distribution p1 (x) over t. Given data distribution ((x)
over x (x ..... [K],, the probability path is defined as
pc(x) 4 Σ pe(x(a))((11), where ((())))) & II ((('la))
RLES
Pe(a:" (x])) denotes conditional forward probability path, characterizing the evolution of the state x' given the initial state x
Probability velocities. To generate the predefined probability path Pr()), consider Continuous- Time Markov Chain (CTMC), modeled as a stochastic process. The dynamics of this CTMC are governed by a probability velocity we, also known as the transition rate. The transition rate models how the current state x, evolves toward the target state Ii over time. Within this framework, each token is updated independently according to the following transition rule:
F4th 6xg(+) (-) In ( ao x1),
where ( wt, . represents velocity field, conditional rate function that governs the flow of probability from the current state xi to the target state æf over time. Equation (2) can be interpreted small perturbation of the point mass 6gg, scaled by the step size h, effectively modeling discrete state transitions as continuous-time stochastic process. This velocity field is central to DFM, it characterizes the dynamics the probability path and is the primary quantity learned during training.
3.2 UNIFORM DISCRETE DIFFUSION WITH METRIC PATH
We present URSA, novel framework built on uniform discrete diffusion with a metric path for image and video generation. In this section, we first introduce three key innovations: (1) a Linearized Metric- Path for structured and tractable trajectory design, (2) Resolltionn-dependent Timestep Shifting mechanism to improve training stability and representation learning for long video sequences, and (3) Frame-wiin wise Independent Perturbation Scheduling strategy for unified long-video generation and multitask learning. After introducing these core components, we further provide the training procedure and sampling algorithm.
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3.2.1 METRIC PROBABILITY PATH FOR LONG SEQUENCE DATA
For data with varying sequence lengths, the degree of perturbation should be adapted during training. This requires probability path to effectively handle sequences of different lengths, such as high- resolution images or videos. In this section, we introduce two key techniques, linearized metric path and resolution-dependent timestep shifting, address this challenge, ensuring that the perturbation process is appropriately adjusted based on the sequence length.
Linearized metric path. Inspired by (Shaul al., 2025), We introduce the linearized metric path, a novel probability path derived from token embedding distances. Formally, we define the distance function d x T + 120, which measures the discrepancy between the codebook embeddings of generated token x and the target tokens 21. The distance satisfies the property d(z,, z + x 11, ensuring well-defined metric structure. Based on this, the probability path
is defined as
Pe ((( softmax (~B,d(2, o1))
Il softmax (-B,d(a',
where 3r 10 + Rz is monotonic scheduler function with boundary conditions 8o 0, 31 = oc. The core of linearized path lies in the functional form of Be, which parameterized as
& = ( -
10 t
where c >> and a > O are hyperparameters that control the relationship between the sampling distance d(zt, 21) and time t. Specifically, the forward process samples I, ~ Ptj1 (- 21), with boundary conditions yielding a uniform distribution over codebook embeddings at = 0 and a deterministic sample at 21 when
1, illustrated in Figure 3. When is between 0 and 1, our objective is to find an appropriate set of values for c and a that preserve the linear relationship between and d(z,, 21). This linearity provides finer control of perturbations over the probability path, as described next. Additional experiments and discussions on the impact of linearized metric path model convergence speed and overall performance are provided in Sec. 4.3.
Resolution-dependent timestep shifting. Intuitively, since higher resolutions contain more pixels, more perturbation is needed to alter the signal. To address this, introduce time shift parameter which adjusts the timestep based on the resolution. For any given t, we define the shifted timestep E
(5)
1+ 1+111-1
Because our proposed linearized metric path enforces linear relationship between and d(æ,, x we modulate this path using À to accommodate varying data resolutions. For higher resolutions, we set >> to create convex relationship between i and d(xr,; æ1) that introduces stronger perturbations. For lower resolutions, we set À < 1, yielding concave relationship with more gradual perturbations.
3.2.2 ASYNCHRONOUS TIMESTEP SCHEDULING
Due to their complex spatiotemporal dynamics and broad applicability across downstream tasks, prior video generation methods render task-specific modeling both inefficient and resource-intensive Motivated by diffusion forcing (Chen et al., 2024a), we propose an asynchronous timestep scheduling strategy tailored for multi-task training and sampling. Rather than applying the same noise level across all frames in video sequence (i.e., synchronous timestep scheduling), we assign uniform noise levels independently to each frame. Formally, given clean video sequence F = (f(1), f2)).... f(²) with n frames, we assign each frame a continuous time L ~ (() 1), forming the timestep schedule
T = (41, where the metric-induced probability path in Eq. is applied frame-wise according to its assigned f. This strategy enables fine-grained temporal modeling over the timestep schedule via decoupling noise levels across frames. As a result, the training progress adaptively balances local frame reconstruction with global temporal coherence, facilitating versatile generation objectives, such as text-to text-to-video, amage-to-vides video extrapolation, and start-end frame control within a unified model architecture. Additional visualizations for these advanced generation tasks are provided in Appendix C & D.
}. f. The corresponding noisy sequence is denoted as F (f((1) _ ..... -.,.,, f(²)),
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3.2.3 TRAINING AND SAMPLING
Training. We first encode video clips into discrete token sequences using a pre-trained tokenizer, }. where n denotes the number of video
resulting in a clean video sequence #1 = (5l" __(",,.___ ""... J("),, frames and f denotes the i-th frame tokens. At each training step, we uniformly sample timesteps t E [0, 1] for each frame g(0 in the sequence and obtain a perturbed sequence Ie ~ Pr(- z1) via the proposed metric probability path. The backbone, implemented with the LLM architecture, takes as input the concatenation of text tokens e and noisy tokens It, and produces logits over the token vocabulary predict the original sequence 21- The training objective is formulated as the expected cross-entropy between the ground-trute visual tokens and the model's 's predicted distribution:
L = 124-2((011) x1. [- Poje (#r1 [22,, [x,,e]] e)]]
(6)
Sampling. We follow Gat et al. (2024); Shaul et al. (2025) and employ the Euler solver for efficient and high-quality generation. Specifically, we first uniformly sample zg from the full vision vocabulary and feed it into the model obtain the prediction d1. Following Shaul et al. (2025), the velocity field ( 2t, .1). We then iteratively refine x; using ur, where each iteration updates the sample si along the estimated direction. Once the T refinement steps have been completed, the sampling process returns a clean image or video. Additional details are provided in Appendix A.
compute
4 EXPERIMENT
4.1 EXPERIMENT SETUP
Datasets. We leverage a curated selection of high-quality datasets to effectively train URSA models. For text-to-image training, we collect 16M image-text pairs sourced from Unsplash (Unsplash, 2020),
DataComp (Gadre et al., 2024), COYO (Byeon et al., 2022), and Journey (Sun et al., 2023).
These pairs are filtered by image resolution and aesthetic score, and further supplemented with 14M Al-generated image samples using the FLUX.1 model (Batifol al., 2025). For text-to-video training, we select 12M video-text pairs from the highest scoring subset of Koala-36M (Wang et al., 2025c) and complement them with 12M internal video-text pairs. The internal videos are captioned using the Emu2-17B model (Sun et al., 2024b) in conjunction with the captioning engine (Diao et al., 2024). We uniformly sample short and long captions during training, with a maximum length of 320 tokens.
Architectures. We initialize our visual generation model with weights from a pre-trained LLM. Specifically, we adopt the Qwen3 LLM architecture (Yang et al., 2025a), which natively incorporates QK-Norm (Dehghani et al., 2023) layer to stabilize the multimodal training. To better capture the spatiotemporal structure inherent in videos, we introduce an enhanced M-RoPE (Wang et al., 2024a) that allocates interleaved frequency components across temporal, height, and width dimensions, following the approach of Mogao (Liao et al., 2025). Crucially, unlike Liao et al. (2025), our 3D- RoPE assigns identical positions for texts, ensuring equivalence with the ID-RoPE (Su et al., 2024). We use the Cosmos (Agarwal et al., 2025) tokenizer to extract image and video tokens, achieving 4x temporal and 8x8 spatial compression through 64K FSQ (Mentzer al., 2024) codebook. Furthermore, we train an IBQ (Shi et al.. 2025) tokenizer for high-resolution image generation, facilitating efficient 16x 16 spatial compression via a 156-dimensional codebook with 131K entries.
Diffusion schedulers. We adopt the Kinetic Optimal Scheduler (Shaul et al., 2025), equipped with a metric-induced probability path specifically designed for the embedding space of vision tokenizers. Following Shaul et al. (2025), we perform a grid search over the path hyperparameters a and c, visually inspecting the reconstructed samples for each (a, c) that fully exploit the time interval [o. 1]. Eventually, we select (a, c) to (1.0, 5) for the Cosmos tokenizer and (0.5, 6) for our IBQ tokenizer. For standard uniform diffusion, we use the mixture probability path proposed by Gat al. (2024). In contrast, for masked diffusion, we adopt the MaskGIT (Chang al., 2022) scheduler, which has been empirically shown to achieve state-of-the-ar performance in both image and video generation models (Kondratyuk et al., 2024; Bai et al., 2025). Following established practice in continuous diffusion models, we default to 25 inference steps for image generation and 50 for video generation.
Training details. URSA is trained on 128 A100 (40GB) GPUs. In all experiments, we use the Adam W optimizer (Loshchilov & Hutter, 2019) with 8 = 0.9, B2 = 0.95, weight decay of 0.05, and an initial learning rate of le-4. The learning rate employs cosine decay (Loshchilov & Hutter, 2017).

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We first pre-train text-to-image models and leverage their weights to initialize text-to-video models. Subsequently, following Chen et al. (2025a), we adapt full-sequence video diffusion models to diffusion forcing architectures by applying frame-wise noise schedules for autoregressive generation.
Evaluation. We evaluate -atatt aligmment using benchmarks DPG-Bench (Hu et al., 2024) and GenEval (Ghosh al., 2024). Each image is generated from original rewritten text prompts, with resolution determined by model type: x024x 1024 for image generation models to support high fidelity, and 512x320 for video generation models effectively measure cross-modal generalization. We access text-to-video generation using VBench (Huang et al., 2024a) and image-to-vider generation with VBench++ (Huang et al., 2024b), its comprehensive successor tailored for real-world scenarios. The videos, sized x9xx12x322 are generated from rewritten prompts for text-to-video evaluation, and from original text prompts with official cropped first-frame images for amage-to-vided evaluation. We apply classifier-free guidance (Ho & Salimans, 2022) with scale value of 7.0 in all evaluations.
4.2
MAIN RESULTS
URSA rivals Sora-like text-to-video generation models despite using discrete video tokenizer.
Current discrete video tokenizers offer limited spatiotemporal compression and reconstruction quality, posing significant challenges to bidirectional diffusion transformers. However, URSA excels in generating video clips from text, achieving strong performance on the VBench, as shown Table 1. Compared to Sora-like diffusion models: Vchitect (Fan et al., 2025), Pyramid Flow (Jin et al., 2025), Lumina Video (Liu et al., 2025a), OpenSora (Peng et al., 2025) and OpenSoraPlan (Lin et al., 2024), LudinaVideo
URSA matches exceeds their performance, particularly in the semantic field. These results further underscore the need for a tokenizer that satisfies the imaging quality of state-of-the-art continuous models (Kong al., 2024; Teng et al., 2025; Ma et al., 2025; Yang et al., 2025b; Wang et al., 2025a).
Table 1: Text-to-video evaluation on VBench. For clarity and to better highlight distinctions between models, we report only the most relevant metrics across the quality and semantic dimensions.
Model
#params
Pvideus
Total Score
Quality Score
Semantic Score
Dynımic Degree
Aesthriic Quality
Imaging Quality
Object Class
Multiple Objects
Spatial Relationship
Coker
Scene
modelss
NOVA
0.60
20M
80.1
80.4
79.1
20.1
59.4
59.4
92.0
77.5
77.5
$7.7
54.1
Vehitect-2.0
28
134M
81.6
82.5
77.8
58.3
61.5
65.6
87.8
69.4
54.6
36.9
57.5
Pyrumid Flow
2B
10M
81.7
84.7
69.6
64.6
63.3
65.0
$6.7
50.7
59.5
$2.9
43.2
Viden
83.0
83.9
79.3
67.1
91.0
67.3
90.1
OpenSoraPaan v1.5
8B
40M
83.0
84.2
78.2
64.4
66.9
68.5
91.9
70.7
90.1
81.8
52.1
OpenSora 2.0
11D
95M
83.6
85.4
80.3
56.4
65.3
65.7
78.0
76.8
86.3
53.1
MAGI-1
24B
81.8
84.7
7044
72.5
59.3
73.0
57.5
289
Step-Vidon
30R
81.8
84.5
71.3
$5.1
61.2
7006
9016
50.6
71.5
$8.1
24.4
Cog VideoX11
5B
82.0
82.7
79.2
56.2
62.1
65.3
83.4
65.3
79.4
$5.4
53.3
VudyuaVsseen
13B
70.8
$3.0
Wan1.1
140
83.7
85.6
76.1
65.5
66.1
89.4
86.3
69.5
75.4
$8.6
45.8
Discrete models
368
79.5
Eam3
84.1
79.3
68.7
58.3
37.1
URSA
1.78
24M
82.4
85.4
78.5
81.4
63.1
62.2
93.4
70.6
62.1
90.7
52.3
URSA emerges frame-conditioned video generation by accurately modeling the future motion. Prior methods typically adapt text-to-image (Ren et al., 2024; Chen et al., 2024b; Xing et al., 2024) or text-to-video models with clean first frame for image-to-video generation. In contrast, URSA seamlessly integrates asynchronous frame conditions, enabling zero-shot generalization for this task. As depicted in Table URSA excels camera control and subject movement versus specialized frame-conditonn conditiondd models (A (Agarwal et al., 2025; Yu et al., 2025; Wang et al., 2025a; Liu et al., 2025b). Our results demonstrate that diffusion forcing effectively generalizes image-to-videe -o-iieo generation, pushing the boundaries of autoregressive discrete video generation models without causal attention.
URSA performs on par with the state-of-the-art models in generating high-resolution images. We compare URSA against continuous models in Table 3, encompassing specialist architectures: SDXL (Podell et al., 2024), SD3 (Esser et al., 2024), FLUX (Batifol et al., 2025), SANA (Xie et al., 2025a) and NOVA (Deng et al., 2025b), as well as unified architectures: Mogao (Liao et al., 2025), Bagel (Deng et al., 2025a), 0mniGen2 (Wu et al., 2025b) and Show-o2 (Xie et al., 2025d). Through joint modeling of discrete text and visual tokens, URSA demonstrates strong text-image alignment. For example, on the DPG-Bench, URSA reaches a leading overall score with dense text prompts. This strong performance is consistently sustained on the GenEval when using the rewritten prompts.
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Table Image-to-video evaluation on VBench++. To evaluate temporal consistency, we focus on amage-to-vider (12V) metrics of visual similarity between each generated frame and reference image.
Total
Quality
12V
Dynamic Degree
Acsthetic Quality
Imaging Quality
Camera Motion
12V Subject Consistency
12V Background Consistency
Aparams
@vidoas
Contimoous models
Consist12y
10M
84.1
91.9
59.0
66.9
33.9
95.8
96.0
IZVGxa-XL
2B
35M
85.3
78.4
92.1
26.1
64.8
69.1
18.5
96.5
96.8
SEINE
3B
25M
85.5
78.4
92.7
27.1
64.6
71.4
21.0
97.2
97.0
DynamiCraßter
2B
10M
86.9
80.5
93.5
69.7
60.9
68.6
31.2
97.2
97.4
Cosmos
138
100M
84.2
75.8
92.6
18.7
55.8
59.9
25.4
96.0
97.4
VideoMAR
98.4
VigVedexX
5B
86.7
78.6
94,8
33.2
61.9
70.0
67.7
97.2
96.7
HunyuanViden
13H
86.8
95.1
62.6
70.1
49.9
98.5
97.4
Wan2.1
148
86.9
80.8
92.9
51.4
64.8
70.4
34,8
97.0
96.4
Pusa
14B
$7.3
79.8
94.8
52.6
63.2
68.3
29.5
97.6
99.2
Step-Vice Video
30B
88.4
81.2
95.5
62.3
70.4
49.2
97.9
98.5
MAGL-
24H
89.3
82.4
96.1
68.2
64.7
69.7
50.9
98.4
99.0
Discreie
Lumos-1
3.6B
10M
81.7
76.1
93.3
69.2
97.4
97.4
92.6
96.1
96.5
At high resolutions, URSA surpasses the autoregressive (Wang et al., 2024b; Han et al., 2025; Chen al., 2025b) and masked diffusion (Bai et al., 2025; Yuan al., 2025) approaches in efficiency, effectively reducing inference steps through iterative refinement while preserving fine-grained detail.
Table 3: Text-to-image evaluation on DPG-Bench and GenEval. We prefer the DPG-Bench metrics mitigate potential prompt template leakage concerns (Xie et al., 2025b) associated with GenEval. i refers to the methods using rewritten GenEval prompts for clearer position and attribute guidance.
Mpde
DPGGBenchh
GenExal
Model
#purams
Aimages
Overall
Emity
Attribute
Relatio
Ovenall
Single
Two
Counting
Coker
Position
ColorAir
Contimouu
SDXL.
2668
74.7
82.4
80.9
86.8
0.55
0.98
0.44
0.30
0.85
0.15
0.23
SD3
2B
84,1
91.0
88.8
80.7
0.62
0.98
0.74
0.63
0.67
0.34
0.36
FLUX.1
0.79
0.21
NOVA
1.48
600M
83.0
88.7
86.4
91.9
0.71
0.99
0.91
8:62
0.85
0.33
0.56
SANA-1.5
4.8B
50M
34.7
0.81
0.99
0.93
0.86
0.84
0.59
0.65
OnuniGee2
7B
88.8
0.95
0.55
Megaa!
7B
84.3
90.0
88.1
93.2
0.89
1.00
0.97
0.83
0.93
0.84
0.80
Bogel
34B
85.1
90.4
91.3
90.8
0.82
0.99
0.94
0.81
0.88
0.64
0.63
Show-a2
7B
66M
91.8
90.0
0.87
Divente modeds
Shuw-o
1.3B
2B
67.3
75.4
78.0
84.5
0.68
0.98
0.80
0.66
0.84
0.31
0.50
Eauu3
8B
87.2
86.3
0.49
FUDORT
1.58
13M
83.6
89.7
88.1
93.7
0.77
0.96
0.85
0.56
0.88
0.68
0.67
Janus-Pro Pro
7B
72M
84.2
$3.9
89.4
89.3
0.50
0.99
0.89
0.59
0,90
0.79
0.66
Meissonic
210M
0.54
0.66
0.10
Lamos11
3,6
60M
0.66
0.95
0.80
0:46
0.81
0.48
0:48
Infinky'
2B
83.5
90.8
0.73
0.99
0.85
0.84
0.19
0.57
URSA (212>3310)
1.78
30M
88.3
86.4
92.9
0.99
0.83
0.83
URSA (1024x 1024)
1.78
30M
36.0
91.5
89.6
94.7
0.68
0.99
0.92
0.63
0.86
0.25
0:40
URSA" (1024x1024)
1.7B
30M
0.92
0.89
0.67
4.3 ABLATION STUDY
Effectiveness of iterative refinement for visual generation. Discrete diffusion models inherently incur elevated sampling errors, exhibited in prior studies (Tang al., 2022; Feng al., 2025). To systematically investigate this issue in image and video generation, we train three variants of the discrete diffusion model, assessing performance across insufficient and excessive sampling regimes. Figure 4 compares key performance metries of text-to-image models on GenFivl and text-to-video models on VBench, with all models evaluated after being trained for an identical number of iterations. In the image generation task, which is characterized by low structural redundancy, all three models can generate feasible images within the conventional 25 inference steps. Without iterative refinement, reducing the number of steps substantially decreases the GenEval score in masked diffusion sampling. As we progress into video generation, a task rich in contextual redundancy, it becomes essential to correct sampling errors at each step, ensuring temporal coherence and visual fidelity across frames.
Effectiveness of path linearity for uniform diffusion. As shown in Figure 5, the left plot shows the average Fuclidean distance between the embedding of noisy images and the clean image using
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Published as a conference paper at ICLR 2026
masked diffusion
uniform diffusion w/ mixture path
uniform diffusion w/ metric path
VBeach Imseging Quality
Gealvvl. Score
Vhench Sonn
78
500
45
72-
Inference Steps
Inference Steps
Inference Steps
(a) Text-lo-inage performance on GenEval.
(b) Texi-to-video performance on VBench.
Figure 4: Sampling performance across inference steps. Using the Cosmos tokenizer, evaluate
image samples x56 (~IK tokens) and the video samples at x84 (~10K tokens).
10K images sampled from the training set. We compute the Pearson correlation coefficients between the Euclidean distance and the timestep, which are -0.995, -0.921, -0.997, and -0,949, We find that the choice of the probability path is significantly influenced by the values of and which has substantial impact on the model performance. To determine the optimal values for e and a, we draw inspiration from the continuous diffusion model SD3 (Esser et al., 2024), where the relationship between and d(,,, 31) demonstrates a strong linear correlation. This insight guides our approach to calibrating and a to effectively reach the limits of model performance for different vision tokenizers.
VAE (SD3)
FSQ (a=1.0,c=1)
FSQ (a=1.0,c=5)
FSQ (a=005.c=5)
1.4
1.2
0.6
122-Dist: (K. x))
1.0
GonEval Scoro
0.5
0.8
0.4
0.6
0.4
0.3
0.2
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
10k
20k
30k
40k
50k
60k
Timestop t
Training Iterations
Figure 5: Sampling performance of different paths. We evaluate the image samples at 256x256.
Effectiveness of model size for uniform diff'usion. To study the scaling properties of URSA models, we train three variants that are initialized from Qwen3 models with 0.6B, 1.7B, and 4B parameters. Figure compares the performance of different model sizes DPG-Bench, GenEval, and VBench, with all models trained for the same epoch count as in Sec. 4.2. We find that increasing model size considerably enhances semantic performance across both text-to-image and text-to-video evaluations but does not significantly improve generation quality. This suggests that while larger models better capture high-level semantics and align more accurately with text prompts, the fidelity the generated outputs may ultimately be constrained by the representation capacity of the discrete vision tokenizer.
Effectiveness of timestep shifting for video generation. As outlined Section 4. our probability path is designed to maximize the time interval. In line with continuous models (Kong et al., 2024; Wang et al., 2025a; Liu et al., 2025a), the optimal SNR schedule should tailored with video size. To study the impact of the SNR schedule video generation, with divergent timestep shifting and evaluate their performance using the respective value on VBench. Figure presents shifting schedules, accompanied by their evaluation metrics and visualizations.
train four text-to- text-to-video models

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Published as a conference paper at ICLR 2026
Text-to- Im-ge-mageee Performance
Text to Video Porformance
80.3
00.3
L7
78.1
76.6
VBench Quality
DPG-Bench GenEval
VBench-Semantic
VBBnch-Overte
0.6B
1.7B
4B
0,6B
1.7B
4B
Model Size
Model Size
Figure 6: Sampling performance of different model sizes. All models are trained for the same epoch count in the main experiments and evaluated 256x images and 25 x x videos.
shilll
shi02
shin3
shill
Imaging Quality
S Semantic
Overall
1.00
81.2
VBench Score
Timestep
0.75
37.2
29.1
0.50
76.8
36.1
0.25
0.00
0.0
0.2
0.4
0.6
0.8
1.0
1.0
2.0
3.0
4.0
Timestep
Shift A
t.00
0.25
1=0.50
1=0.75
t=1.00
256x160
Shift1
384x240
Shift1
384x240
Shift3
Figure 7: Timestep shifting across SNR schedules. We sample 385x384x2222 videos for evaluation.
Surprisingly, the shifting strategy proposed by Esser et al. (2024) demonstrates strong effectiveness for uniform diffusion, empowering URSA to match the performance of its continuous counterparts.
CONCLUSION
In this work, we revisited discrete generative modeling for video synthesis and introduced URSA, uniform diffusion framework with metric path that bridges discrete and continuous paradigms. URSA employs two key innovations, linearized metric path and desplution-depeedeet timestep shifting strategy, to provide fine-grained control over perturbations for long sequences. On top of this, our asynchronous temporal scheduling strategy enables multi-task video generation in one model. Extensive experiments show that URSA not only consistently outperforms existing discrete methods but also achieves highly competitive results compared to ofate-of-the-eee continuous diffusion models. We contend that this work represents significant step toward unifying discrete and continuous paradigms and provides a promising direction for scalable, versatile, and efficient video generation.
Acknowledgements. This work supported by the NSFC (No. 62320106010. U21B2042). We thank Jin Wang, Jiahao Wang, Zhengxiong Luo, Yuanzhi Zhu, Shilin Lu, Honghao Chen, Jinming Wu, Chengyuan Wang, and colleagues BAAI for their valuable discussions and feedback on implementation and manuscript preparation.
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Published as a conference paper at ICLR 2026
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16


Reviews:
[{"review_id": "XRrd9g0C3O", "reviewer": "Reviewer_22K9", "summary": "This paper proposes UDM, a uniform discrete diffusion framework with a metric path for image and video generation. Its key designs include a linearized metric path for probability trajectory control and a resolution-dependent timestep shifting mechanism for high resolutions. An asynchronous timestep scheduling strategy is also adopted to unify multiple generation tasks within a single model. The paper demonstrates competitive performance against state-of-the-art continuous methods and improved performance over previous discrete methods on standard benchmarks.", "questions": "1.  Could the authors please elaborate on the motivation and precise meaning behind the claim of preserving a a \"linear relationship between $t$ and $d(x_t, x_1)$\" in line 229-230? A more detailed theoretical explanation or an empirical plot showing this relationship would be helpful.\n\n2.  Figure 4 compares uniform diffusion with and without the metric path. To better validate the core innovation of the linearized path (Eq. 4), could you provide an ablation comparing performance *with the metric path* but *with and without* the specific parameterization given by Eq. 4?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "This work represents a solid application of discrete diffusion models to video generation. The experiments comprehensively show UDM's advantages on multiple generation benchmarks, achieving competitive results with both discrete and continuous baselines. The proposed framework demonstrates the potential for scalable and unified visual generation.", "weaknesses": "The paper's claimed key innovations lack sufficient novelty and rigorous justification.\n\n*   **Metric Path Novelty:** The core concept of a metric path has been previously proposed and applied to multimodal tasks[1,2]. While cited, the discussion of the relationship to these works is inadequate. The primary novelty lies in the \"linear relationship\" established by Eq. 4. However, the motivation and precise meaning of preserving a \"linear relationship between $t$ and $d(x_t, x_1)$\" in line 229-230 are unclear and not rigorously derived.\n\n*   **Timestep Shifting Novelty:** The resolution-dependent timestep shifting is a common technique in continuous diffusion/rectified flow models [3,4]. Notably, Eq. 5 in this paper is functionally equivalent to Eq. 23 in [3] under simple variable substitutions: $t_n \\to 1 - t,t_m \\to 1 - \\tilde{t},\\sqrt{\\frac{m}{n}} \\to \\lambda$. While its validation in the discrete setting is valuable, the connection to prior art is under-discussed, mentioned only briefly in the experiments.\n\n*   **Typo:** There is a likely typo in Eq. 6, where `p_{t|1}` should probably be `p_{1|t}`.\n\n[1] Shaul, Neta, et al. Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective. ICLR 2025.\n\n[2] Jin Wang, Yao Lai, et al. Fudoki: Discrete flow-based unified understanding and generation via kinetic-optimal velocities. NeurIPS 2025.\n\n[3] Esser, Patrick, et al. Scaling rectified flow transformers for high-resolution image synthesis. ICLR 2024.\n\n[4] Hoogeboom, Emiel, Jonathan Heek, and Tim Salimans. simple diffusion: End-to-end diffusion for high resolution images. ICML 2023.", "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": "SxZJGgmK5z", "reviewer": "Reviewer_4TJR", "summary": "The paper introduces a Uniform Discrete Diffusion approach that brings iterative, continuous-style refinement to discrete tokens for image/video generation. Core ideas include a metric-induced probability path over token embeddings, a resolution-dependent timestep shift to align effective SNR with sequence length, and frame-level (asynchronous) timesteps to unify T2V, I2V, interpolation, and extrapolation within one model. Training uses a cross-entropy objective; sampling integrates the learned probability velocity with few steps. Experiments report competitive image (GenEval/DPG) and video (VBench) quality and consistency, suggesting discrete diffusion can match continuous counterparts while remaining tokenizer/LLM-friendly.", "questions": "1. Differentiate from SkyReels-V2/FVDM&Pusa (especially): What is *new* about your frame-level timestep usage (objective, schedule, conditioning, or analysis)? \n2. Asynchronous impact: Quantify incremental gains over a global-t schedule for T2V at fixed steps/compute.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Clarity & focus: Clean objective and sampling recipe; equations and schedules are easy to implement.\n* Unification: One model handles multiple video tasks via per-frame timesteps.\n* Practicality: Simple schedules; no exotic losses or architectures required.\n* Results: Broad benchmarks indicate strong quality and temporal stability with modest steps.\n* Positioning: Bridges discrete tokenization with diffusion-style global refinement, relevant for LLM-aligned video generation.", "weaknesses": "* Novelty overlap: The *frame-level/asynchronous timestep* contribution is close to prior video schedulers (e.g., SkyReels-V2) and essentially the same idea as Pusa & FVDM; the paper should clarify what is new beyond this reuse.\n* Relation to DFM/schedulers: The metric path and schedule resemble discrete flow matching/kinetic schedules; limited theory for superiority beyond heuristic tuning.\n* Ablation depth: Missing systematic sweeps for the shift parameter \\lambda; marginal gains of asynchronous vs. synchronous timesteps are not isolated.", "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": "6AsopsoBVr", "reviewer": "Reviewer_efUW", "summary": "This paper introduces Uniform Discrete diffusion with Metric path (UDM), a framework designed to improve discrete generative modeling for video synthesis by incorporating iterative refinement over spatio-temporal tokens. The method's key contributions are a linearized metric-path and resolution-dependent timestep shifting, which aim to stabilize training and enable scaling to long-sequence data. Furthermore, the authors propose an asynchronous timestep scheduling strategy to unify multiple video tasks, claiming that this approach closes the performance gap and achieves results comparable to state-of-the-art continuous diffusion models.", "questions": "1. What is the average inference time for the proposed model? Comparison with previous discrete and continuous diffusion methods could be helpful.\n\n2. How will the model degrade when generating longer video sequences?\n\n3. It seems that the final model performance is not that sensitive to variations in the hyperparameters (α and c) that control the relationship between the sampling distance d(xt, x1) and time t from Appendix Figure 3. What is the rationale to choose the best path for different paths?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The linearized metric-path and timestep shifting mechanism looks sound to me. The motivation is clear and the theoretical explanation is reasonable. I think the authors find an effective manner to make the discrete diffusion model more powerful.\n\n2. Experienmental analysis are comprehensive. The authors compare many state-of-the-art advances on three tasks: text-to-image generation, text-to-video generation, and image-to-video generation on several benchmarks. The results show that the proposed discrete diffusion model can outperform many continuous diffusion baseline methods.\n\n3. The authors also conduct a series of ablation studies to fully probe the role of each modules. The performance across different inference steps and training iterations are also well ablated.", "weaknesses": "1. The scalability of the proposed uniform discrete diffusion method seems to be unclear. As the authors claim that scalability is one of the key highlight factors of the proposed method, it is crucial to conduct experiments on different model sizes to probe its scalability. This part of the experiment is completely missing both in the main manuscript and the attachment.\n\n2. Some model details are missing. What is the size of the tokenizer and the Qwen LLM? What is the rationale for choosing Qwen instead of other open-source LLMs for training? Is there any ablation?\n\n3. The authors do not provide any qualitative demos in the supplementary materials. It is always hard to distinguish the model performance from the concatenated screenshots.", "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": "D6AZxFGFQG", "reviewer": "Reviewer_PjBQ", "summary": "The paper proposes a generative model for image and video generation using discrete rectified flows. To achieve this, it introduces a new metric path to measure the token embedding distances from a pre-trained token cookbook. This, combined with other training stability tricks such as resolution-dependent timestep shifting and randomised frame-level timestep sampling, enables the proposed method to achieve comparable text-to-image and text-to-video generation performance on standard benchmarks to continuous generative methods. Ablation studies demonstrate the effectiveness of its design components.", "questions": "- Provide the generation examples of text-to-video, video interpolation, as well as long-video generations.\n- If time allowed, please compare with self-forcing / diffusion forcing to validate the claim on long video generation.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The current video generation methods predominantly rely on continuous representation via latent diffusion or flow matching. This paper explores the potential of using discrete representation combined with flow matching for video generation. The paper is well-written, and the parameterisation of the sampling ratio via timestep is both interesting and intuitive. The results on the standard benchmarking are strong.", "weaknesses": "- I feel like the paper touches on a wider range of topics. While the title and method section focus on video generation, the experiment also briefly touches on image generation, which I think is unnecessary.\n\n- The paper introduces several potential applications, such as long video generation and interpolation through diffusion-forcing training strategies. However, it lacks qualitative and quantitative results. For example, the paper could easily compare self-forcing, vanilla diffusion-forcing, on long video generation using the same VBench scores, or attach some generated videos. For a video generation paper, it’s a bit unacceptable not to include any generation examples, as the figures provided in the paper simply couldn’t reflect the generation qualities.\n- Resolution-dependent timestep shifting is introduced and well-validated in SD3, so it shouldn’t be considered a proper technical contribution. However, the paper only acknowledges this in the experiment section, which seems suspicious.\n- Asynchronous timestep scheduling is introduced in diffusion forcing, which the paper has acknowledged in the main paper. However, no visualisation or quantitative evaluation of the long/autoregressive video generation performance is discussed in the main paper.", "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)