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arxiv: 2501.17811 · v1 · submitted 2025-01-29 · 💻 cs.AI · cs.CL· cs.CV

Recognition: 2 theorem links

· Lean Theorem

Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

Xiaokang Chen , Zhiyu Wu , Xingchao Liu , Zizheng Pan , Wen Liu , Zhenda Xie , Xingkai Yu , Chong Ruan

Authors on Pith no claims yet

Pith reviewed 2026-05-11 08:09 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.CV
keywords multimodal understandingtext-to-image generationmodel scalingdata scalingunified multimodal modelsinstruction followingtraining optimization
0
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The pith

Janus-Pro improves multimodal understanding and text-to-image instruction following by optimizing training, expanding data, and scaling model size.

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

This paper introduces Janus-Pro as an updated version of the prior Janus model for handling both understanding of image-text inputs and generation of images from text. It applies three changes—an optimized training strategy, larger volumes of training data, and a bigger overall model—to produce stronger results on understanding benchmarks and on tasks where generated images must match detailed instructions. The work also reports more consistent image outputs without as many artifacts or variations. A reader would care because the improvements come from straightforward extensions rather than new architectural inventions, showing a direct path to stronger single-model systems that both interpret and create visual content.

Core claim

Janus-Pro incorporates an optimized training strategy, expanded training data, and scaling to larger model size. With these improvements, Janus-Pro achieves significant advancements in both multimodal understanding and text-to-image instruction-following capabilities, while also enhancing the stability of text-to-image generation.

What carries the argument

The unified Janus-Pro architecture that performs both multimodal understanding and text-to-image generation within one model, advanced through optimized training, data expansion, and increased scale.

If this is right

  • Unified models can reach higher capability on both comprehension and generation tasks without separate specialized systems.
  • Training data volume and model size continue to drive gains even in architectures that already combine vision and language.
  • More stable text-to-image outputs reduce the need for post-processing or multiple sampling attempts.
  • Public release of code and models allows direct testing and extension by others.

Where Pith is reading between the lines

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

  • The pattern suggests scaling laws observed in language models may transfer to joint understanding-plus-generation systems.
  • Similar gains could appear if the same three changes were applied to other base multimodal models.
  • Longer-term, this points toward simpler AI pipelines where one model handles visual input and output without task-specific retraining.

Load-bearing premise

The reported performance gains come from the three specific changes of optimized training, expanded data, and larger model size rather than from differences in evaluation protocols, data details, or other unmentioned choices.

What would settle it

A controlled experiment that applies the three changes one at a time to the original Janus model and finds no meaningful gains on the same benchmarks would show the combined improvements are not responsible for the results.

read the original abstract

In this work, we introduce Janus-Pro, an advanced version of the previous work Janus. Specifically, Janus-Pro incorporates (1) an optimized training strategy, (2) expanded training data, and (3) scaling to larger model size. With these improvements, Janus-Pro achieves significant advancements in both multimodal understanding and text-to-image instruction-following capabilities, while also enhancing the stability of text-to-image generation. We hope this work will inspire further exploration in the field. Code and models are publicly available.

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

2 major / 1 minor

Summary. The paper introduces Janus-Pro as an advancement over the prior Janus model by incorporating three changes: an optimized training strategy, expanded training data, and scaling to larger model size. It claims these yield significant improvements in multimodal understanding, text-to-image instruction following, and generation stability, with code and models released publicly.

Significance. If the gains are causally attributable to the three factors, the work provides empirical support for scaling benefits in unified multimodal models handling both understanding and generation. The public code release is a notable strength enabling reproducibility and community verification.

major comments (2)
  1. [Abstract] Abstract: The central claim attributes performance advancements directly to the three listed changes (optimized training, expanded data, larger model), yet no controlled ablations are described that isolate each factor while holding the others and the evaluation protocol fixed. This undermines causal attribution, as differences in data curation, prompt formatting, or inference details could account for the deltas instead.
  2. [Experiments] Experiments section (inferred from standard structure and abstract claims): Without within-paper ablation tables or results showing incremental gains from each change individually (e.g., base model with only expanded data), the magnitude of reported improvements cannot be confidently linked to the stated scaling factors rather than unmentioned implementation choices.
minor comments (1)
  1. Ensure all reported benchmark results include standard deviations or multiple-run statistics to support the 'significant advancements' claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on clarifying the attribution of improvements in Janus-Pro. We address the major comments point by point below, with planned revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim attributes performance advancements directly to the three listed changes (optimized training, expanded data, larger model), yet no controlled ablations are described that isolate each factor while holding the others and the evaluation protocol fixed. This undermines causal attribution, as differences in data curation, prompt formatting, or inference details could account for the deltas instead.

    Authors: We agree that the abstract phrasing could be interpreted as implying direct causal effects for each factor individually. The manuscript presents Janus-Pro as the result of applying all three changes together and reports performance relative to the original Janus and other baselines. No isolated ablations holding all other variables fixed are included. In revision we will rephrase the abstract to describe the improvements as resulting from the collective incorporation of the three changes, and we will add a brief discussion of this limitation in the Experiments section. revision: yes

  2. Referee: [Experiments] Experiments section (inferred from standard structure and abstract claims): Without within-paper ablation tables or results showing incremental gains from each change individually (e.g., base model with only expanded data), the magnitude of reported improvements cannot be confidently linked to the stated scaling factors rather than unmentioned implementation choices.

    Authors: The current Experiments section focuses on the final Janus-Pro model and its comparisons to prior work rather than incremental ablations of each scaling factor. We acknowledge that this leaves open the possibility that unmentioned implementation details contribute to the observed gains. We will expand the Experiments section with additional discussion of the cumulative nature of the changes and the practical constraints on running fully controlled large-scale ablations. We will also note that the public code and model release enables the community to perform further targeted experiments. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical gains rest on external benchmarks

full rationale

The paper presents an empirical scaling study: it applies three engineering changes (optimized training, more data, larger model) to a prior architecture and measures performance on public multimodal understanding and generation benchmarks. No equations, first-principles derivations, or internal predictions are defined; the reported deltas are direct comparisons against external test sets and prior models. No self-definitional loops, fitted-input-as-prediction, or load-bearing self-citations appear. The argument is therefore self-contained against reproducible external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The work relies on standard machine-learning scaling practices and publicly available benchmarks; no new free parameters, axioms, or invented entities are introduced beyond the model architecture inherited from prior work.

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discussion (0)

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Reference graph

Works this paper leans on

57 extracted references · 57 canonical work pages · cited by 59 Pith papers · 19 internal anchors

  1. [1]

    J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P . Wang, J. Lin, C. Zhou, and J. Zhou. Qwen-vl: A fron- tier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  2. [2]

    Betker, G

    J. Betker, G. Goh, L. Jing, T. Brooks, J. Wang, L. Li, L. Ouyang, J. Zhuang, J. Lee, Y. Guo, et al. Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf, 2(3):8, 2023

    work page 2023

  3. [3]

    X. Bi, D. Chen, G. Chen, S. Chen, D. Dai, C. Deng, H. Ding, K. Dong, Q. Du, Z. Fu, et al. Deepseek llm: Scaling open-source language models with longtermism. arXiv preprint arXiv:2401.02954, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  4. [4]

    J. Chen, J. Yu, C. Ge, L. Yao, E. Xie, Y. Wu, Z. Wang, J. Kwok, P . Luo, H. Lu, et al. Pixart- 𝑎𝑙 𝑝ℎ𝑎: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426, 2023

    work page internal anchor Pith review arXiv 2023

  5. [5]

    J. Chen, C. Ge, E. Xie, Y. Wu, L. Yao, X. Ren, Z. Wang, P . Luo, H. Lu, and Z. Li. PixArt-Sigma: Weak-to-strong training of diffusion transformer for 4K text-to-image generation. arXiv preprint arXiv:2403.04692, 2024

    work page arXiv 2024

  6. [6]

    X. Chu, L. Qiao, X. Lin, S. Xu, Y. Yang, Y. Hu, F. Wei, X. Zhang, B. Zhang, X. Wei, et al. Mobilevlm: A fast, reproducible and strong vision language assistant for mobile devices. arXiv preprint arXiv:2312.16886, 2023

    work page arXiv 2023

  7. [7]

    X. Chu, L. Qiao, X. Zhang, S. Xu, F. Wei, Y. Yang, X. Sun, Y. Hu, X. Lin, B. Zhang, et al. Mobilevlm v2: Faster and stronger baseline for vision language model. arXiv preprint arXiv:2402.03766, 2024

    work page arXiv 2024

  8. [8]

    W. Dai, J. Li, D. Li, A. M. H. Tiong, J. Zhao, W. Wang, B. Li, P . Fung, and S. Hoi. Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023

    work page 2023

  9. [9]

    J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchi- cal image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009

    work page 2009

  10. [10]

    R. Dong, C. Han, Y. Peng, Z. Qi, Z. Ge, J. Yang, L. Zhao, J. Sun, H. Zhou, H. Wei, et al. Dream- llm: Synergistic multimodal comprehension and creation. arXiv preprint arXiv:2309.11499, 2023. 9

    work page arXiv 2023

  11. [11]

    Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

    P . Esser, S. Kulal, A. Blattmann, R. Entezari, J. Müller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel, D. Podell, T. Dockhorn, Z. English, K. Lacey, A. Goodwin, Y. Marek, and R. Rom- bach. Scaling rectified flow transformers for high-resolution image synthesis, 2024. URL https://arxiv.org/abs/2403.03206

    work page internal anchor Pith review arXiv 2024

  12. [12]

    C. Fu, P . Chen, Y. Shen, Y. Qin, M. Zhang, X. Lin, J. Yang, X. Zheng, K. Li, X. Sun, et al. Mme: A comprehensive evaluation benchmark for multimodal large language models. arXiv preprint arXiv:2306.13394, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  13. [13]

    Y. Ge, S. Zhao, J. Zhu, Y. Ge, K. Yi, L. Song, C. Li, X. Ding, and Y. Shan. Seed-x: Multimodal models with unified multi-granularity comprehension and generation. arXiv preprint arXiv:2404.14396, 2024

    work page arXiv 2024

  14. [14]

    Ghosh, H

    D. Ghosh, H. Hajishirzi, and L. Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. Advances in Neural Information Processing Systems, 36, 2024

    work page 2024

  15. [15]

    Hai-llm: Efficient and lightweight training tool for large models, 2023

    High-flyer. Hai-llm: Efficient and lightweight training tool for large models, 2023. URL https://www.high-flyer.cn/en/blog/hai-llm

    work page 2023

  16. [16]

    X. Hu, R. Wang, Y. Fang, B. Fu, P . Cheng, and G. Yu. Ella: Equip diffusion models with llm for enhanced semantic alignment. arXiv preprint arXiv:2403.05135, 2024

    work page internal anchor Pith review arXiv 2024

  17. [17]

    D. A. Hudson and C. D. Manning. Gqa: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6700–6709, 2019

    work page 2019

  18. [18]

    Y. Jin, K. Xu, L. Chen, C. Liao, J. Tan, B. Chen, C. Lei, A. Liu, C. Song, X. Lei, et al. Unified language-vision pretraining with dynamic discrete visual tokenization. arXiv preprint arXiv:2309.04669, 2023

    work page arXiv 2023

  19. [19]

    Laurençon, D

    H. Laurençon, D. van Strien, S. Bekman, L. Tronchon, L. Saulnier, T. Wang, S. Karamcheti, A. Singh, G. Pistilli, Y. Jernite, and et al. Introducing idefics: An open reproduction of state-of-the-art visual language model, 2023. URL https://huggingface.co/blog/id efics

    work page 2023

  20. [20]

    Laurençon, A

    H. Laurençon, A. Marafioti, V . Sanh, and L. Tronchon. Building and better understanding vision-language models: insights and future directions., 2024

    work page 2024

  21. [21]

    B. Li, R. Wang, G. Wang, Y. Ge, Y. Ge, and Y. Shan. Seed-bench: Benchmarking multimodal llms with generative comprehension. arXiv preprint arXiv:2307.16125, 2023

    work page internal anchor Pith review arXiv 2023

  22. [22]

    D. Li, A. Kamko, E. Akhgari, A. Sabet, L. Xu, and S. Doshi. Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation. arXiv preprint arXiv:2402.17245, 2024

    work page arXiv 2024

  23. [23]

    Y. Li, Y. Du, K. Zhou, J. Wang, W. X. Zhao, and J.-R. Wen. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:2305.10355, 2023

    work page internal anchor Pith review arXiv 2023

  24. [24]

    Z. Li, H. Li, Y. Shi, A. B. Farimani, Y. Kluger, L. Yang, and P . Wang. Dual diffusion for unified image generation and understanding. arXiv preprint arXiv:2501.00289, 2024

    work page arXiv 2024

  25. [25]

    Z. Li, J. Zhang, Q. Lin, J. Xiong, Y. Long, X. Deng, Y. Zhang, X. Liu, M. Huang, Z. Xiao, et al. Hunyuan-DiT: A powerful multi-resolution diffusion transformer with fine-grained chinese understanding. arXiv preprint arXiv:2405.08748, 2024. 10

    work page arXiv 2024

  26. [26]

    H. Liu, C. Li, Y. Li, and Y. J. Lee. Improved baselines with visual instruction tuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296–26306, 2024

    work page 2024

  27. [27]

    H. Liu, C. Li, Q. Wu, and Y. J. Lee. Visual instruction tuning. Advances in neural information processing systems, 36, 2024

    work page 2024

  28. [28]

    H. Liu, W. Yan, M. Zaharia, and P . Abbeel. World model on million-length video and language with ringattention. arXiv preprint arXiv:2402.08268, 2024

    work page arXiv 2024

  29. [29]

    Y. Liu, H. Duan, Y. Zhang, B. Li, S. Zhang, W. Zhao, Y. Yuan, J. Wang, C. He, Z. Liu, et al. Mm- bench: Is your multi-modal model an all-around player? arXiv preprint arXiv:2307.06281, 2023

    work page internal anchor Pith review arXiv 2023

  30. [30]

    Y. Ma, X. Liu, X. Chen, W. Liu, C. Wu, Z. Wu, Z. Pan, Z. Xie, H. Zhang, X. yu, L. Zhao, Y. Wang, J. Liu, and C. Ruan. Janusflow: Harmonizing autoregression and rectified flow for unified multimodal understanding and generation, 2024

    work page 2024

  31. [31]

    Yfcc-huggingface

    mehdidc. Yfcc-huggingface. https://huggingface.co/datasets/mehdidc/yfcc15 m, 2024

    work page 2024

  32. [32]

    SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

    D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rom- bach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  33. [33]

    Podell, Z

    D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rom- bach. SDXL: Improving latent diffusion models for high-resolution image synthesis. 2024

    work page 2024

  34. [34]

    L. Qu, H. Zhang, Y. Liu, X. Wang, Y. Jiang, Y. Gao, H. Ye, D. K. Du, Z. Yuan, and X. Wu. Tokenflow: Unified image tokenizer for multimodal understanding and generation. arXiv preprint arXiv:2412.03069, 2024

    work page arXiv 2024

  35. [35]

    Hierarchical Text-Conditional Image Generation with CLIP Latents

    A. Ramesh, P . Dhariwal, A. Nichol, C. Chu, and M. Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  36. [36]

    Rombach, A

    R. Rombach, A. Blattmann, D. Lorenz, P . Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. 2022

    work page 2022

  37. [37]

    Rombach, A

    R. Rombach, A. Blattmann, D. Lorenz, P . Esser, and B. Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022

    work page 2022

  38. [38]

    P . Sun, Y. Jiang, S. Chen, S. Zhang, B. Peng, P . Luo, and Z. Yuan. Autoregressive model beats diffusion: Llama for scalable image generation. arXiv preprint arXiv:2406.06525, 2024

    work page internal anchor Pith review arXiv 2024

  39. [39]

    Q. Sun, Q. Yu, Y. Cui, F. Zhang, X. Zhang, Y. Wang, H. Gao, J. Liu, T. Huang, and X. Wang. Generative pretraining in multimodality. arXiv preprint arXiv:2307.05222, 2023

    work page arXiv 2023

  40. [40]

    C. Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.09818, 2024

    work page internal anchor Pith review arXiv 2024

  41. [41]

    G. Team, R. Anil, S. Borgeaud, Y. Wu, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023. 11

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  42. [42]

    S. Tong, D. Fan, J. Zhu, Y. Xiong, X. Chen, K. Sinha, M. Rabbat, Y. LeCun, S. Xie, and Z. Liu. Metamorph: Multimodal understanding and generation via instruction tuning. arXiv preprint arXiv:2412.14164, 2024

    work page arXiv 2024

  43. [43]

    Midjourney prompts dataset

    Vivym. Midjourney prompts dataset. https://huggingface.co/datasets/vivym/ midjourney-prompts, 2023. Accessed: [Insert Date of Access, e.g., 2023-10-15]

    work page 2023

  44. [44]

    C. Wang, G. Lu, J. Yang, R. Huang, J. Han, L. Hou, W. Zhang, and H. Xu. Illume: Il- luminating your llms to see, draw, and self-enhance. arXiv preprint arXiv:2412.06673, 2024

    work page arXiv 2024

  45. [45]

    X. Wang, X. Zhang, Z. Luo, Q. Sun, Y. Cui, J. Wang, F. Zhang, Y. Wang, Z. Li, Q. Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024

    work page internal anchor Pith review arXiv 2024

  46. [46]

    C. Wu, X. Chen, Z. Wu, Y. Ma, X. Liu, Z. Pan, W. Liu, Z. Xie, X. Yu, C. Ruan, et al. Janus: Decoupling visual encoding for unified multimodal understanding and generation. arXiv preprint arXiv:2410.13848, 2024

    work page arXiv 2024

  47. [47]

    S. Wu, H. Fei, L. Qu, W. Ji, and T.-S. Chua. Next-gpt: Any-to-any multimodal llm. arXiv preprint arXiv:2309.05519, 2023

    work page arXiv 2023

  48. [48]

    Y. Wu, Z. Zhang, J. Chen, H. Tang, D. Li, Y. Fang, L. Zhu, E. Xie, H. Yin, L. Yi, et al. Vila-u: a unified foundation model integrating visual understanding and generation. arXiv preprint arXiv:2409.04429, 2024

    work page arXiv 2024

  49. [49]

    Z. Wu, X. Chen, Z. Pan, X. Liu, W. Liu, D. Dai, H. Gao, Y. Ma, C. Wu, B. Wang, et al. Deepseek-vl2: Mixture-of-experts vision-language models for advanced multimodal un- derstanding. arXiv preprint arXiv:2412.10302, 2024

    work page internal anchor Pith review arXiv 2024

  50. [50]

    J. Xie, W. Mao, Z. Bai, D. J. Zhang, W. Wang, K. Q. Lin, Y. Gu, Z. Chen, Z. Yang, and M. Z. Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408.12528, 2024

    work page internal anchor Pith review arXiv 2024

  51. [51]

    W. Yu, Z. Yang, L. Li, J. Wang, K. Lin, Z. Liu, X. Wang, and L. Wang. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490, 2023

    work page internal anchor Pith review arXiv 2023

  52. [52]

    X. Yue, Y. Ni, K. Zhang, T. Zheng, R. Liu, G. Zhang, S. Stevens, D. Jiang, W. Ren, Y. Sun, et al. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9556–9567, 2024

    work page 2024

  53. [53]

    X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer. Sigmoid loss for language image pre- training. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11975–11986, 2023

    work page 2023

  54. [54]

    C. Zhao, Y. Song, W. Wang, H. Feng, E. Ding, Y. Sun, X. Xiao, and J. Wang. Monoformer: One transformer for both diffusion and autoregression. arXiv preprint arXiv:2409.16280, 2024

    work page arXiv 2024

  55. [55]

    C. Zhou, L. Yu, A. Babu, K. Tirumala, M. Yasunaga, L. Shamis, J. Kahn, X. Ma, L. Zettle- moyer, and O. Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model. arXiv preprint arXiv:2408.11039, 2024

    work page internal anchor Pith review arXiv 2024

  56. [56]

    Y. Zhu, M. Zhu, N. Liu, Z. Ou, X. Mou, and J. Tang. Llava-phi: Efficient multi-modal assistant with small language model. arXiv preprint arXiv:2401.02330, 2024. 12

    work page arXiv 2024

  57. [57]

    L. Zhuo, R. Du, H. Xiao, Y. Li, D. Liu, R. Huang, W. Liu, L. Zhao, F.-Y. Wang, Z. Ma, et al. Lumina-Next: Making Lumina-T2X stronger and faster with Next-DiT. arXiv preprint arXiv:2406.18583, 2024. 13

    work page arXiv 2024