pith. machine review for the scientific record. sign in

arxiv: 2605.10730 · v1 · submitted 2026-05-11 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

Qwen-Image-2.0 Technical Report

Bing Zhao , Chenfei Wu , Deqing Li , Hao Meng , Jiahao Li , Jie Zhang , Jingren Zhou , Junyang Lin
show 67 more authors
Kaiyuan Gao Kuan Cao Kun Yan Liang Peng Lihan Jiang Niantong Li Ningyuan Tang Shengming Yin Tianhe Wu Xiao Xu Xiaoyue Chen Xihua Wang Yan Shu Yanran Zhang Yi Wang Yilei Chen Ying Ba Yixian Xu Yujia Wu Yuxiang Chen Zecheng Tang Zekai Zhang Zhendong Wang Zihao Liu Zikai Zhou An Yang Chen Cheng Chenxu Lv Dayiheng Liu Fan Zhou Hantian Xiong Hongzhu Shi Hu Wei Huihong Zhao Ivy Liu Jianwei Zhang Jiawei Zhang Kai Chen Kang He Levon Xue Lin Qu Linhan Tang Luwen Feng Minggang Wu Minmin Sun Na Ni Rui Men Shuai Bai Sishou Zheng Tao Lan Tianqi Zhang Tingkun Wen Wei Wang Weixu Qiao Weiyi Lu Wenmeng Zhou Xiaodong Deng Xiaoxiao Xu Xinlei Fang Xionghui Chen Yanan Wang Yang Fan Yichang Zhang Yixuan Xu Yu Wu Zhiyuan Ma Zhizhi Cai
Authors on Pith no claims yet

Pith reviewed 2026-05-12 04:16 UTC · model grok-4.3

classification 💻 cs.CV
keywords image generationimage editingdiffusion transformervision-language modeltext renderingphotorealismmultimodal generationinstruction following
0
0 comments X

The pith

Qwen-Image-2.0 unifies high-fidelity image generation and precise editing by coupling Qwen3-VL with a Multimodal Diffusion Transformer.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents Qwen-Image-2.0 as a single model that creates detailed images from text prompts and edits existing images accurately. It targets persistent weaknesses in handling long text strings, multilingual writing, realistic lighting and textures, and complex instructions that earlier image models still mishandle. The approach relies on feeding prompt understanding from Qwen3-VL into a diffusion transformer that models both the prompt and the target image together, backed by curated data and staged training. If this integration works as described, users gain a more dependable tool for producing slides, posters, comics, and photorealistic scenes without separate systems for generation versus editing. Human evaluations are cited as evidence that the new model beats its predecessors on these tasks.

Core claim

Qwen-Image-2.0 addresses limitations in ultra-long text rendering, multilingual typography, high-resolution photorealism, and instruction following by integrating Qwen3-VL as the condition encoder with a Multimodal Diffusion Transformer for joint condition-target modeling, supported by large-scale data curation and a customized multi-stage training pipeline, enabling strong multimodal understanding while preserving flexible generation and editing capabilities, with extensive human evaluations showing substantial outperformance over previous Qwen-Image models.

What carries the argument

Coupling of Qwen3-VL as condition encoder with Multimodal Diffusion Transformer for joint condition-target modeling.

If this is right

  • The model accepts prompts up to 1K tokens and produces coherent text-rich outputs such as slides, posters, infographics, and comics.
  • Multilingual text rendering and typography improve markedly over prior versions.
  • Photorealistic images gain richer details, more accurate textures, and consistent lighting.
  • Complex prompts are followed more reliably across varied artistic styles.
  • Both pure generation and targeted editing tasks show higher success rates in human preference tests.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the joint modeling pattern generalizes, similar encoder-transformer pairings could be tested on video or 3D content creation.
  • The absence of component ablations leaves open the question of how much each part contributes, which future experiments could isolate.
  • Practitioners may find the 1K-token prompt length useful for generating structured documents without post-processing.
  • Scaling the same data-curation and training recipe to larger backbones could further reduce failure modes on edge cases like dense typography.

Load-bearing premise

That the performance gains result specifically from the Qwen3-VL coupling, multimodal diffusion transformer, and multi-stage training rather than from untested factors such as overall data volume or compute.

What would settle it

A controlled comparison in which Qwen-Image-2.0 is retrained without the Qwen3-VL encoder or without the staged training pipeline, followed by the same human evaluations, showing no measurable improvement in text fidelity or editing accuracy.

Figures

Figures reproduced from arXiv: 2605.10730 by An Yang, Bing Zhao, Chen Cheng, Chenfei Wu, Chenxu Lv, Dayiheng Liu, Deqing Li, Fan Zhou, Hantian Xiong, Hao Meng, Hongzhu Shi, Huihong Zhao, Hu Wei, Ivy Liu, Jiahao Li, Jianwei Zhang, Jiawei Zhang, Jie Zhang, Jingren Zhou, Junyang Lin, Kai Chen, Kaiyuan Gao, Kang He, Kuan Cao, Kun Yan, Levon Xue, Liang Peng, Lihan Jiang, Linhan Tang, Lin Qu, Luwen Feng, Minggang Wu, Minmin Sun, Na Ni, Niantong Li, Ningyuan Tang, Rui Men, Shengming Yin, Shuai Bai, Sishou Zheng, Tao Lan, Tianhe Wu, Tianqi Zhang, Tingkun Wen, Wei Wang, Weixu Qiao, Weiyi Lu, Wenmeng Zhou, Xiaodong Deng, Xiaoxiao Xu, Xiao Xu, Xiaoyue Chen, Xihua Wang, Xinlei Fang, Xionghui Chen, Yanan Wang, Yang Fan, Yanran Zhang, Yan Shu, Yichang Zhang, Yilei Chen, Ying Ba, Yi Wang, Yixian Xu, Yixuan Xu, Yujia Wu, Yu Wu, Yuxiang Chen, Zecheng Tang, Zekai Zhang, Zhendong Wang, Zhiyuan Ma, Zhizhi Cai, Zihao Liu, Zikai Zhou.

Figure 1
Figure 1. Figure 1: Qwen-Image-2.0 shows significant improvements across core dimensions, including photoreal [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Photo-realistic image generation showcase with Qwen-Image-2.0. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Complex text rendering showcase with Qwen-Image-2.0. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Image editing showcase with Qwen-Image-2.0. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qwen-Image-2.0 data distribution across image categories. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Overview of the Qwen-Image-2.0 data pipeline. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: An error-attribution-driven closed-loop data flywheel for multi-track targeted optimization. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Overview of the Qwen-Image-2.0 architecture. The model adopts a MMDiT architecture, with [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative comparison of T2I results using the original captions and prompt-enhanced captions. [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative comparison between Qwen-Image-2.0-Base and Qwen-Image-2.0-RL across various [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative comparison between the multi-step teacher and the few-step distilled student. The [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Results from LMArena (accessed April 22, 2026). [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Qualitative comparison of text rendering results. [PITH_FULL_IMAGE:figures/full_fig_p020_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Qualitative comparison of portrait generation results. [PITH_FULL_IMAGE:figures/full_fig_p021_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Qualitative comparison of portrait generation results. [PITH_FULL_IMAGE:figures/full_fig_p022_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Qualitative comparison of complex Chinese text rendering in an image editing task. Qwen [PITH_FULL_IMAGE:figures/full_fig_p023_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Qualitative comparison of identity preservation. In both single-image and multi-image [PITH_FULL_IMAGE:figures/full_fig_p024_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Visualization of multilingual rendering by Qwen-Image-2.0. [PITH_FULL_IMAGE:figures/full_fig_p025_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Visualization of slide generation by Qwen-Image-2.0. [PITH_FULL_IMAGE:figures/full_fig_p026_19.png] view at source ↗
read the original abstract

We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite recent progress, existing models still struggle with ultra-long text rendering, multilingual typography, high-resolution photorealism, robust instruction following, and efficient deployment, especially in text-rich and compositionally complex scenarios. Qwen-Image-2.0 addresses these challenges by coupling Qwen3-VL as the condition encoder with a Multimodal Diffusion Transformer for joint condition-target modeling, supported by large-scale data curation and a customized multi-stage training pipeline. This enables strong multimodal understanding while preserving flexible generation and editing capabilities. The model supports instructions of up to 1K tokens for generating text-rich content such as slides, posters, infographics, and comics, while significantly improving multilingual text fidelity and typography. It also enhances photorealistic generation with richer details, more realistic textures, and coherent lighting, and follows complex prompts more reliably across diverse styles. Extensive human evaluations show that Qwen-Image-2.0 substantially outperforms previous Qwen-Image models in both generation and editing, marking a step toward more general, reliable, and practical image generation foundation models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript presents Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise editing. It couples Qwen3-VL as the condition encoder with a Multimodal Diffusion Transformer for joint condition-target modeling, supported by large-scale data curation and a customized multi-stage training pipeline. The model is claimed to improve ultra-long text rendering, multilingual typography, photorealism, and complex instruction following (up to 1K tokens), with extensive human evaluations showing substantial outperformance over prior Qwen-Image models in both generation and editing.

Significance. If the empirical claims hold, the work advances practical image foundation models by demonstrating a unified architecture for generation and editing that handles text-rich, multilingual, and compositionally complex content more reliably. The emphasis on scalable training and data curation for real-world usability could inform subsequent efforts in multimodal generative systems.

major comments (3)
  1. [Evaluation] Evaluation section: The central claim that 'extensive human evaluations show that Qwen-Image-2.0 substantially outperforms previous Qwen-Image models' is presented without any quantitative metrics, baseline details beyond prior versions, evaluation protocol (e.g., prompt sets, rater instructions, number of comparisons), or statistical significance testing. This leaves the headline empirical result unverifiable and weakens the paper's primary contribution.
  2. [Model Architecture] Model Architecture and Training sections: The improvements are attributed to the specific coupling of Qwen3-VL with the Multimodal Diffusion Transformer plus the multi-stage training and data curation, yet no ablation studies, controlled variants, or component-isolation experiments are reported. Without these, alternative explanations (e.g., scale, data distribution shifts) cannot be ruled out, making the architectural claims load-bearing but unsupported.
  3. [Training Pipeline] Training Pipeline section: The customized multi-stage training is described at a high level without specifics on per-stage objectives, data mixtures, loss formulations, or how each stage targets the listed challenges (ultra-long text, photorealism). This prevents assessment of whether the pipeline is responsible for the reported gains.
minor comments (2)
  1. [Abstract] The abstract states support for 'instructions of up to 1K tokens' but provides no implementation details, context-length handling, or empirical limits on this capability.
  2. Notation for the Multimodal Diffusion Transformer and condition encoding mechanism would benefit from an explicit equation or diagram on first introduction to improve clarity for readers unfamiliar with the exact formulation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important areas for improving the transparency and rigor of our technical report. We address each major comment below and commit to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: The central claim that 'extensive human evaluations show that Qwen-Image-2.0 substantially outperforms previous Qwen-Image models' is presented without any quantitative metrics, baseline details beyond prior versions, evaluation protocol (e.g., prompt sets, rater instructions, number of comparisons), or statistical significance testing. This leaves the headline empirical result unverifiable and weakens the paper's primary contribution.

    Authors: We agree that the current presentation of human evaluation results is insufficiently detailed for full verifiability. In the revised manuscript, we will expand the Evaluation section with a dedicated subsection on the protocol. This will include: the composition and size of the prompt sets (covering text-rich, multilingual, photorealistic, and complex instruction categories), the number of expert raters, the exact instructions and rating scales provided to them, the total number of pairwise comparisons, win-rate percentages with 95% confidence intervals, and results from statistical significance tests (e.g., paired t-tests or McNemar's test). We will also clarify that baselines include prior Qwen-Image versions as well as selected open models where direct comparisons were feasible. revision: yes

  2. Referee: [Model Architecture] Model Architecture and Training sections: The improvements are attributed to the specific coupling of Qwen3-VL with the Multimodal Diffusion Transformer plus the multi-stage training and data curation, yet no ablation studies, controlled variants, or component-isolation experiments are reported. Without these, alternative explanations (e.g., scale, data distribution shifts) cannot be ruled out, making the architectural claims load-bearing but unsupported.

    Authors: We acknowledge that the lack of explicit ablation studies limits the ability to isolate the precise contribution of the Qwen3-VL coupling versus other factors such as scale or data. Full-scale ablations on a model of this size are computationally prohibitive and were not performed. In the revision, we will add a discussion subsection that explains the architectural rationale, drawing on internal smaller-scale experiments and failure-mode analysis from Qwen-Image-1.0. We will also highlight how the joint condition-target modeling in the Multimodal Diffusion Transformer directly enables the observed gains in text rendering and instruction following, as evidenced by the comparative human evaluations against prior versions. revision: partial

  3. Referee: [Training Pipeline] Training Pipeline section: The customized multi-stage training is described at a high level without specifics on per-stage objectives, data mixtures, loss formulations, or how each stage targets the listed challenges (ultra-long text, photorealism). This prevents assessment of whether the pipeline is responsible for the reported gains.

    Authors: We agree that greater specificity on the training pipeline would improve reproducibility and allow readers to better assess the source of improvements. In the revised version, we will expand the Training Pipeline section to describe the objectives of each stage (e.g., initial alignment on broad image-text data, targeted fine-tuning for typography and photorealism), approximate data mixture proportions across stages, the primary loss formulations (including any auxiliary terms for text fidelity), and the progressive targeting of challenges such as ultra-long text rendering and coherent lighting. Certain proprietary details regarding exact data sources will remain summarized at a high level. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance claims rest on human evaluations, not derivations or self-referential fits

full rationale

The paper describes an architecture (Qwen3-VL coupled to Multimodal Diffusion Transformer) and training pipeline, then asserts outperformance via human evaluations. No equations, first-principles derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. The central claim reduces to comparative preference data rather than any quantity defined in terms of itself or forced by prior author work. This is the expected non-finding for an empirical model report without mathematical reduction steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the effectiveness of standard diffusion and vision-language components plus the authors' data and training choices; no new physical or mathematical entities are introduced.

axioms (1)
  • domain assumption A Multimodal Diffusion Transformer can jointly model condition and target for both generation and editing tasks.
    Invoked to justify the unified framework in the abstract.

pith-pipeline@v0.9.0 · 5805 in / 1338 out tokens · 48813 ms · 2026-05-12T04:16:56.019005+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Qwen-Image-VAE-2.0 Technical Report

    cs.CV 2026-05 unverdicted novelty 6.0

    Qwen-Image-VAE-2.0 achieves state-of-the-art high-compression image reconstruction and superior diffusability for diffusion models, with a new text-rich document benchmark.

Reference graph

Works this paper leans on

29 extracted references · 29 canonical work pages · cited by 1 Pith paper · 18 internal anchors

  1. [1]

    Cosmos World Foundation Model Platform for Physical AI

    Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, et al. Cosmos world foundation model platform for physical ai. arXiv preprint arXiv:2501.03575,

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    Qwen3-VL Technical Report

    URLhttps://arena.ai/leaderboard. Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding, Chang Gao, Chunjiang Ge, et al. Qwen3-vl technical report.arXiv preprint arXiv:2511.21631, 2025a. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhon...

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer

    Huanqia Cai, Sihan Cao, Ruoyi Du, Peng Gao, Steven Hoi, Zhaohui Hou, Shijie Huang, Dengyang Jiang, Xin Jin, Liangchen Li, et al. Z-image: An efficient image generation foundation model with single-stream diffusion transformer.arXiv preprint arXiv:2511.22699,

    work page internal anchor Pith review Pith/arXiv arXiv

  4. [4]

    Hunyuanimage 3.0 technical report.arXiv preprint arXiv:2509.23951, 2025

    Siyu Cao, Hangting Chen, Peng Chen, Yiji Cheng, Yutao Cui, Xinchi Deng, Ying Dong, Kipper Gong, Tianpeng Gu, Xiusen Gu, et al. Hunyuanimage 3.0 technical report.arXiv preprint arXiv:2509.23951,

    work page arXiv

  5. [5]

    Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis

    Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis. InInternational conference on learning representations, volume 2024, pp. 57611– 57640,

    work page 2024

  6. [6]

    2Alphabetical order

    1Alphabetical order. 2Alphabetical order. 27 Junyu Chen, Han Cai, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, and Song Han. Deep compression autoencoder for efficient high-resolution diffusion models. InInternational Conference on Learning Representations, volume 2025, pp. 96539–96560,

    work page 2025

  7. [7]

    Seedream 3.0 Technical Report

    Yu Gao, Lixue Gong, Qiushan Guo, Xiaoxia Hou, Zhichao Lai, Fanshi Li, Liang Li, Xiaochen Lian, Chao Liao, Liyang Liu, et al. Seedream 3.0 technical report.arXiv preprint arXiv:2504.11346,

    work page internal anchor Pith review arXiv

  8. [8]

    Mean Flows for One-step Generative Modeling

    Zhengyang Geng, Mingyang Deng, Xingjian Bai, J Zico Kolter, and Kaiming He. Mean flows for one-step generative modeling.arXiv preprint arXiv:2505.13447,

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [9]

    Seedream 2.0: A native chinese-english bilingual image generation foundation model.arXiv preprint arXiv:2503.07703, 2025

    Lixue Gong, Xiaoxia Hou, Fanshi Li, Liang Li, Xiaochen Lian, Fei Liu, Liyang Liu, Wei Liu, Wei Lu, Yichun Shi, et al. Seedream 2.0: A native chinese-english bilingual image generation foundation model. arXiv preprint arXiv:2503.07703,

    work page arXiv

  10. [10]

    Classifier-Free Diffusion Guidance

    Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598,

    work page internal anchor Pith review Pith/arXiv arXiv

  11. [11]

    Auto-Encoding Variational Bayes

    Diederik P Kingma and Max Welling. Auto-encoding variational bayes.arXiv preprint arXiv:1312.6114,

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    HunyuanVideo: A Systematic Framework For Large Video Generative Models

    Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models.arXiv preprint arXiv:2412.03603,

    work page internal anchor Pith review Pith/arXiv arXiv

  13. [13]

    Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, et al. Flux. 1 kontext: Flow matching for in-context image generation and editing in latent space.arXiv preprint arXiv:2506.15742,

    work page internal anchor Pith review Pith/arXiv arXiv

  14. [14]

    net/forum?id=POWv6hDd9XH

    Zhimin Li, Jianwei Zhang, Qin Lin, Jiangfeng Xiong, Yanxin Long, Xinchi Deng, Yingfang Zhang, Xingchao Liu, Minbin Huang, Zedong Xiao, et al. Hunyuan-dit: A powerful multi-resolution diffusion transformer with fine-grained chinese understanding.arXiv preprint arXiv:2405.08748,

    work page arXiv

  15. [15]

    Flow Matching for Generative Modeling

    Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling.arXiv preprint arXiv:2210.02747,

    work page internal anchor Pith review Pith/arXiv arXiv

  16. [16]

    arXiv preprint arXiv:2511.22677 (2025) 4, 5

    Dongyang Liu, Peng Gao, David Liu, Ruoyi Du, Zhen Li, Qilong Wu, Xin Jin, Sihan Cao, Shifeng Zhang, Hongsheng Li, et al. Decoupled DMD: CFG augmentation as the spear, distribution matching as the shield.arXiv preprint arXiv:2511.22677,

    work page arXiv

  17. [17]

    Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

    28 Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow.arXiv preprint arXiv:2209.03003,

    work page internal anchor Pith review Pith/arXiv arXiv

  18. [18]

    Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models

    Cheng Lu and Yang Song. Simplifying, stabilizing and scaling continuous-time consistency models.arXiv preprint arXiv:2410.11081,

    work page internal anchor Pith review arXiv

  19. [19]

    Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model, February 2025

    Guoqing Ma, Haoyang Huang, Kun Yan, Liangyu Chen, Nan Duan, Shengming Yin, Changyi Wan, Ranchen Ming, Xiaoniu Song, Xing Chen, et al. Step-video-t2v technical report: The practice, challenges, and future of video foundation model.arXiv preprint arXiv:2502.10248,

    work page arXiv

  20. [20]

    Sdxl: Improving latent diffusion models for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. In International Conference on Learning Representations, volume 2024, pp. 1862–1874,

    work page 2024

  21. [21]

    Seedream 4.0: Toward Next-generation Multimodal Image Generation

    Team Seedream, Yunpeng Chen, Yu Gao, Lixue Gong, Meng Guo, Qiushan Guo, Zhiyao Guo, Xiaoxia Hou, Weilin Huang, Yixuan Huang, et al. Seedream 4.0: Toward next-generation multimodal image generation.arXiv preprint arXiv:2509.20427,

    work page internal anchor Pith review arXiv

  22. [22]

    DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

    Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models.arXiv preprint arXiv:2402.03300,

    work page internal anchor Pith review Pith/arXiv arXiv

  23. [23]

    Longcat-image technical report

    Mingjie Sun, Xinlei Chen, J Zico Kolter, and Zhuang Liu. Massive activations in large language models. InFirst Conference on Language Modeling, 2024a. Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, and Xinlong Wang. Emu: Generative pretraining in multimodality. InInternational conference ...

    work page arXiv 2024

  24. [24]

    Wan: Open and Advanced Large-Scale Video Generative Models

    29 Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314,

    work page internal anchor Pith review Pith/arXiv arXiv

  25. [25]

    GRPO-Guard: Mitigating implicit over-optimization in flow matching via regulated clipping, 2025

    Jing Wang, Jiajun Liang, Jie Liu, Henglin Liu, Gongye Liu, Jun Zheng, Wanyuan Pang, Ao Ma, Zhenyu Xie, Xintao Wang, et al. Grpo-guard: Mitigating implicit over-optimization in flow matching via regulated clipping.arXiv preprint arXiv:2510.22319,

    work page arXiv

  26. [26]

    Qwen-Image Technical Report

    Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng-ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, et al. Qwen-image technical report.arXiv preprint arXiv:2508.02324,

    work page internal anchor Pith review Pith/arXiv arXiv

  27. [27]

    Diversity-preserved distribution matching distillation for fast visual synthesis.arXiv preprint arXiv:2602.03139, 2026

    Tianhe Wu, Ruibin Li, Lei Zhang, and Kede Ma. Diversity-preserved distribution matching distillation for fast visual synthesis.arXiv preprint arXiv:2602.03139,

    work page arXiv

  28. [28]

    Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

    Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Fredo Durand, and Bill Freeman. Improved distribution matching distillation for fast image synthesis. InAdvances in Neural Information Processing Systems, pp. 47455–47487, 2024a. Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung P...

    work page internal anchor Pith review arXiv

  29. [29]

    DiffusionNFT: Online Diffusion Reinforcement with Forward Process

    Kaiwen Zheng, Huayu Chen, Haotian Ye, Haoxiang Wang, Qinsheng Zhang, Kai Jiang, Hang Su, Stefano Ermon, Jun Zhu, and Ming-Yu Liu. Diffusionnft: Online diffusion reinforcement with forward process. arXiv preprint arXiv:2509.16117,

    work page internal anchor Pith review arXiv