Pith. sign in

REVIEW 3 major objections 4 minor 2 cited by

TBAC-UniImage: Unified Understanding and Generation by Ladder-Side Diffusion Tuning

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read One frozen MLLM feeds every layer of a diffusion model, unifying image understanding and generation in a single trainable pass.

desk verdict A credible unified MLLM+DiT system with a genuinely new multi-layer conditioning mechanism, but the causal role of the ladder is untested because no ablation isolates it. read the letter →

arxiv 2508.08098 v2 pith:NO74TOPD submitted 2025-08-11 cs.CV

Junzhe Xu , Yuyang Yin , Xi Chen This is my paper
classification cs.CV
keywords unifiedmultimodalmodeldiffusiontransformerladder-sidetuninglayer-wiseconditioningtext-to-imagegenerationimageeditingfrozenMLLMflowmatching
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

TBAC-UniImage claims that deep unification of multimodal understanding and generation can be achieved by feeding hidden states from multiple layers of a frozen multimodal large language model (MLLM) into corresponding layers of a pre-trained diffusion transformer (DiT), instead of using only the MLLM's final output. By treating the DiT as a ladder that receives guidance from every depth of the MLLM's representation, the model learns to generate images that respect fine-grained semantic and relational details in the prompt. The paper reports strong results on GenEval, DPG-Bench, TIIF-Bench, and ImgEdit, matching or exceeding larger unified models while keeping the MLLM frozen and training only a small set of queries and the DiT. The central claim is that layer-wise conditioning creates a better synergy between understanding and generation than shallow, single-output conditioning or expensive from-scratch pretraining.

What carries the argument

Ladder-Side Diffusion Tuning: for an m-layer MLLM and an n-layer DiT (m ≥ n), the hidden states of learnable query tokens from MLLM layer m−n+i are passed through a 10M-parameter connector and used as the conditioning input for the i-th DiT layer. This mechanism is what creates the claimed deep, hierarchical bridge between understanding and generation; it is the paper's core architectural contribution.

What would settle it

Run the same training recipe with the layer-mapping indices shuffled (e.g., feed layer m−n+i to DiT layer n−i+1) or with a single final-state condition repeated across all DiT layers; if GenEval and DPG-Bench scores stay the same (within noise), the ladder-depth claim is false.

Watch

Extended reading notes

Core claim

TBAC-UniImage establishes a layer-wise interface between an m-layer MLLM and an n-layer DiT. Learnable query tokens are inserted into the MLLM input; after passing through the MLLM, the hidden states of these queries at layer m−n+i are projected by a lightweight two-layer connector and fed as the conditioning input to the i-th DiT layer. This 'Ladder-Side Diffusion Tuning' lets the diffusion model draw on hierarchical representations from all depths of the MLLM, rather than only the final hidden state. The MLLM stays frozen; only the queries and the DiT are trained with a flow-matching objective. The paper reports that this design achieves a GenEval score of 0.87, 80.97 on DPG-Bench, and 62.

Load-bearing premise

The load-bearing assumption is that the specific layer pairing (MLLM layer m−n+i paired with DiT layer i) is the right semantic correspondence; if this alignment is arbitrary or suboptimal, the ladder mechanism may not be what drives the reported gains.

Editorial extensions

If this is right

  • If the layer-wise conditioning works as claimed, diffusion models can be upgraded with fine-grained semantic understanding without retraining the language backbone, drastically lowering the compute barrier for unified multimodal models.
  • The approach offers a drop-in alternative to final-state conditioning (e.g., MetaQuery-style) and to fully unified from-scratch training, potentially becoming a standard recipe for attaching a generator to an existing MLLM.
  • Strong performance on advanced instruction following (TIIF-Bench Advanced Following) suggests that deeper conditioning helps especially with dense, attribute-heavy prompts, not just basic text-to-image alignment.
  • Image editing without image VAE features (relying only on MLLM query semantics) indicates that the ladder mechanism may transfer to other generation tasks such as editing, inpainting, or controllable synthesis.
  • The small connector size (10M parameters) means the paradigm can be adapted to other MLLM/DiT pairs with minimal training cost.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not ablate the layer-alignment choice (m−n+i vs a random permutation or vs always using the final layer); a concrete test would be to shuffle or replace the layer mapping and measure whether benchmark scores drop, which would confirm that the depth-wise correspondence itself carries the benefit.
  • The ladder mechanism might be seen as a form of progressive feature alignment, where early DiT layers receive low-level grounding and later layers receive high-level semantics; this intuition could generalize to other generative backbones (e.g., auto-regressive image models) if similar layer-wise injection is applied.
  • Because the MLLM is frozen, the model cannot adapt its understanding during generation; a possible extension is to unfreeze the last few MLLM layers during fine-tuning, which could further improve editing consistency but would break the parameter-efficient claim.
  • The reported gains on GenEval and DPG-Bench are modest against the strongest specialized models, so the practical advantage may be largest for resource-constrained settings where a single frozen MLLM must serve both understanding and generation.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes TBAC-UniImage, a unified multimodal understanding and generation model that couples a frozen Qwen2.5-VL-3B MLLM with a tunable SANA-1600M DiT. Instead of conditioning the DiT only on the MLLM's final hidden state, the method injects learnable query hidden states from MLLM layers m-n+i into DiT layer i (ladder-side diffusion tuning). Training proceeds in three stages (text-to-image pretraining, image-text-to-image pretraining, and instruction fine-tuning) using flow matching. The model reports 0.87 on GenEval, 80.97 on DPG-Bench, 62.37/61.18 on TIIF-Bench overall-short/long, and 3.25 on ImgEdit, alongside qualitative samples.

Significance. If the mechanism claim is valid, the paper offers a training-friendly and parameter-efficient way to fuse a pre-trained MLLM with a pre-trained DiT, without from-scratch unified pretraining. The model and code are released, and the evaluations use external, widely used benchmarks rather than fitted internal targets. These are real strengths. However, the paper's headline contribution—that multi-layer 'ladder' conditioning yields deeper and finer unification—is not directly supported by the experiments as reported: all benchmark results compare complete systems that differ in multiple dimensions, and no ablation isolates the ladder mechanism.

major comments (3)
  1. [§2.1, §3, Tables 2–5] The central causal claim in the abstract—'Consequently, TBAC-UniImage achieves a much deeper and more fine-grained unification'—is not tested. Every reported result compares the complete TBAC-UniImage-3B system against models with different backbones, datasets, and training budgets. There is no matched baseline using the same Qwen2.5-VL-3B + SANA-1600M + training recipe but conditioning the DiT only on the final MLLM hidden state (the MetaQuery-style setup the paper argues against). The gains in GenEval, DPG-Bench, TIIF-Bench, and ImgEdit could be explained by the learnable queries, the connector, DiT fine-tuning, or the GPT-Image-Edit-1.5M fine-tuning data—none of which require the ladder. Please add a final-hidden-state control under the identical recipe.
  2. [§2.1] The layer-index mapping 'MLLM layer m-n+i feeds DiT layer i' is asserted without motivation or sensitivity analysis. There is no discussion of why this particular alignment is semantically appropriate, and no experiments with alternatives (e.g., reversed alignment, uniform sampling of layers, or all-to-all conditioning). If the mapping is arbitrary, the ladder could be adding noise rather than depth, and the 'deeper unification' claim would not follow from the benchmark scores. Please justify the mapping or provide a sensitivity study.
  3. [Conclusion] The limitations paragraph lists three avenues for future work—dense-prompt comprehension, editing consistency, and in-image text rendering—but does not acknowledge the absence of a mechanism ablation for the central ladder claim. Since the paper's main contribution is architectural, the missing control is a significant unsupported step that should be explicitly disclosed, not omitted.
minor comments (4)
  1. [Table 2] The OpenUni-B-512 row appears to have only six numeric entries while the table has seven columns; the Overall value is missing or misaligned. Please verify.
  2. [§2.1] Typographical issues: 'paramters' should be 'parameters'; 'fig. 2' should be 'Fig. 2' with a capital F for consistency with other figure references.
  3. [§2.2, Table 1] The text says the fine-tuning stage uses 'GPT Image 1 distilled data,' but Table 1 lists GPT-Image-Edit-1.5M and BLIP3o-60k as the FT datasets. Please clarify the provenance of the 'GPT Image 1 distilled' data and how it relates to the GPT-Image-Edit-1.5M dataset, especially because the paper compares against GPT Image 1 in evaluation tables.
  4. [§3] Benchmark scores are reported without error bars or repeated trials. Since image generation evaluation has stochasticity, a single run may not be stable; please state the number of runs or seeds, or add a brief note on variance.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the architecture is defined independently of the benchmark results, and no prediction reduces to a fitted input or self-citation.

full rationale

The paper's central claim is architectural: §2.1 defines Ladder-Side Diffusion Tuning, in which hidden states from MLLM layers m−n+i are fed through a 10M-parameter connector into DiT layer i. This design is stated directly and is not derived from, or defined in terms of, any benchmark score. The quantitative evidence in Tables 2–5 comes from external benchmarks (GenEval, DPG-Bench, TIIF-Bench, ImgEdit) with fixed protocols; the model is trained with flow matching on public datasets (BLIP3o, GPT-Image-Edit-1.5M, ShareGPT-4o-Image) and no benchmark target is used as a training signal or fitted parameter. The comparison against GPT Image 1 is not circular in the reasoning sense: training on GPT-Image-1-distilled data may raise data-contamination or distributional concerns, but the benchmark numbers are not constructed from the model's own outputs or from the claimed ladder mechanism. The absence of an ablation isolating multi-layer conditioning from a final-hidden-state baseline is an evidential gap, not a circularity: the headline 'deeper unification' could be false without being definitionally forced. The paper contains no load-bearing self-citation; references to MetaQuery, BLIP-3o, Qwen, and SANA are prior external work, and no uniqueness theorem or prior result by the present authors is invoked to rule out alternatives. The 'ladder' terminology is borrowed analogically from Ladder Side Tuning, but the diffusion-side conditioning architecture is a new construction, not a renaming of a known empirical result. Accordingly, no step in the derivation chain reduces to its own inputs.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The ledger is light on invented mathematical entities because this is an empirical ML paper. The central free parameters are the trained query vectors and connector, plus the hand-chosen layer offset. The key assumptions are semantic: MLLM intermediate layers encode useful generative conditions, and layer i of the MLLM aligns with layer i of the DiT.

free parameters (3)
  • Learnable query tokens Q (N=64) = trained, values not reported
    Injected into the MLLM and used as the sole source of generative conditions; trained in every stage.
  • Two-layer connector f (~10M parameters) = trained, architecture not fully specified
    Maps query hidden states Q^(m-n+i) to the input of DiT layer i; central to the ladder mechanism.
  • Layer offset m-n = not reported; depends on Qwen2.5-VL and SANA layer counts
    Hand-chosen alignment of MLLM layer indices to DiT layer indices; no tuning or ablation is reported.
assumptions (4)
  • standard math Flow Matching objective (Lipman et al., 2022) is a valid optimization target for the conditional DiT.
    Adopted as background without modification; standard generative modeling objective.
  • domain assumption Qwen2.5-VL intermediate hidden states carry hierarchical semantic information that is useful for diffusion conditioning.
    Load-bearing for the entire ladder idea; the paper does not measure or analyze what each MLLM layer contributes.
  • ad hoc to paper MLLM layer index m-n+i aligns semantically with DiT layer index i.
    The specific layer-wise correspondence in section 2.1 is asserted without derivation, justification, or sensitivity analysis.
  • domain assumption Frozen MLLM weights preserve understanding while extra query tokens are optimized for generation.
    The paper does not report any understanding-only benchmark on the final model to confirm the frozen MLLM is not degraded by the added queries.

how reviews work

0 comments
Cite this review

Pith. "Pith review of TBAC-UniImage: Unified Understanding and Generation by Ladder-Side Diffusion Tuning." pith.science (2026). https://pith.science/paper/NO74TOPD

@misc{pith2026250808098,
  author       = {Pith},
  title        = {Pith review of: TBAC-UniImage: Unified Understanding and Generation by Ladder-Side Diffusion Tuning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NO74TOPD}},
  note         = {Machine review of arXiv:2508.08098}
}
read the original abstract

This paper introduces TBAC-UniImage, a novel unified model for multimodal understanding and generation. We achieve this by deeply integrating a pre-trained Diffusion Model, acting as a generative ladder, with a Multimodal Large Language Model (MLLM). Previous diffusion-based unified models face two primary limitations. One approach uses only the MLLM's final hidden state as the generative condition. This creates a shallow connection, as the generator is isolated from the rich, hierarchical representations within the MLLM's intermediate layers. The other approach, pretraining a unified generative architecture from scratch, is computationally expensive and prohibitive for many researchers. To overcome these issues, our work explores a new paradigm. Instead of relying on a single output, we use representations from multiple, diverse layers of the MLLM as generative conditions for the diffusion model. This method treats the pre-trained generator as a ladder, receiving guidance from various depths of the MLLM's understanding process. Consequently, TBAC-UniImage achieves a much deeper and more fine-grained unification of understanding and generation.

Figures

Figures reproduced from arXiv: 2508.08098 by the authors.

Figure 1
Figure 1. Text-to-image generation results of TBAC-UniImage [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The overview of TBAC-UniImage, where the MLLM parameters are frozen, learnable queries [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Text-to-image generation samples generated by TBAC-UniImage. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Image Editing samples generated by TBAC-UniImage. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

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

  1. Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes

    cs.CV 2026-08 conditional novelty 6.0 of 10

    Multimodal pretraining transfers asymmetrically: language boosts vision, understanding boosts generation, generation is mostly neutral, and early unified training prevents vision laziness.

  2. Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    By requiring and using highly discriminative LLM text features, the work enables the first effective one-step text-conditioned image generation with MeanFlow.

Reference graph

Works this paper leans on

21 extracted references · 21 linked inside Pith · cited by 2 Pith papers

  1. [1]

    Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report.arXiv preprint arXiv:2502.13923,

    arXiv

  2. [5]

    Making llama see and draw with seed tokenizer.arXiv preprint arXiv:2310.01218,

    Yuying Ge, Sijie Zhao, Ziyun Zeng, Yixiao Ge, Chen Li, Xintao Wang, and Ying Shan. Making llama see and draw with seed tokenizer.arXiv preprint arXiv:2310.01218,

    arXiv

  3. [6]

    Vision as a dialect: Unifying visual understanding and generation via text-aligned represen- tations.arXiv preprint arXiv:2506.18898, 2025a

    7 Jiaming Han, Hao Chen, Yang Zhao, Hanyu Wang, Qi Zhao, Ziyan Yang, Hao He, Xiangyu Yue, and Lu Jiang. Vision as a dialect: Unifying visual understanding and generation via text-aligned represen- tations.arXiv preprint arXiv:2506.18898, 2025a. Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, and Xiaobing Liu. Infinity: Scal...

    arXiv

  4. [7]

    Playground v2

    Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2. 5: Three insights towards enhancing aesthetic quality in text-to-image generation.arXiv preprint arXiv:2402.17245, 2024a. Zhimin Li, Jianwei Zhang, Qin Lin, Jiangfeng Xiong, Yanxin Long, Xinchi Deng, Yingfang Zhang, Xingchao Liu, Minbin Huang, Zedong Xiao, Dayo...

    arXiv

  5. [8]

    Mogao: An omni foundation model for interleaved multi-modal generation

    Chao Liao, Liyang Liu, Xun Wang, Zhengxiong Luo, Xinyu Zhang, Wenliang Zhao, Jie Wu, Liang Li, Zhi Tian, and Weilin Huang. Mogao: An omni foundation model for interleaved multi-modal generation. arXiv preprint arXiv:2505.05472,

    arXiv

  6. [9]

    Uniworld-v1: High-resolution semantic encoders for unified visual understanding and generation.arXiv preprint arXiv:2506.03147,

    Bin Lin, Zongjian Li, Xinhua Cheng, Yuwei Niu, Yang Ye, Xianyi He, Shenghai Yuan, Wangbo Yu, Shaodong Wang, Yunyang Ge, et al. Uniworld-v1: High-resolution semantic encoders for unified visual understanding and generation.arXiv preprint arXiv:2506.03147,

    arXiv

  7. [10]

    Flow matching for generative modeling.arXiv preprint arXiv:2210.02747,

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

    arXiv

  8. [11]

    Step1x-edit: A practical framework for general image editing.arXiv preprint arXiv:2504.17761,

    Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang, Honghao Fu, Chunrui Han, et al. Step1x-edit: A practical framework for general image editing.arXiv preprint arXiv:2504.17761,

    arXiv

Show all 21 references
  1. [12]

    Xichen Pan, Satya Narayan Shukla, Aashu Singh, Zhuokai Zhao, Shlok Kumar Mishra, Jialiang Wang, Zhiyang Xu, Jiuhai Chen, Kunpeng Li, Felix Juefei-Xu, et al

    URLhttps://openai.com/index/introducing-4o-image-generation/. Xichen Pan, Satya Narayan Shukla, Aashu Singh, Zhuokai Zhao, Shlok Kumar Mishra, Jialiang Wang, Zhiyang Xu, Jiuhai Chen, Kunpeng Li, Felix Juefei-Xu, et al. Transfer between modalities with metaqueries.arXiv preprin...

    arXiv

  2. [13]

    Lumina-image 2.0: A unified and efficient image generative framework.arXiv preprint arXiv:2503.21758,

    Qi Qin, Le Zhuo, Yi Xin, Ruoyi Du, Zhen Li, Bin Fu, Yiting Lu, Jiakang Yuan, Xinyue Li, Dongyang Liu, et al. Lumina-image 2.0: A unified and efficient image generative framework.arXiv preprint arXiv:2503.21758,

    arXiv

  3. [14]

    Autore- gressive model beats diffusion: Llama for scalable image generation.arXiv preprint arXiv:2406.06525,

    Peize Sun, Yi Jiang, Shoufa Chen, Shilong Zhang, Bingyue Peng, Ping Luo, and Zehuan Yuan. Autore- gressive model beats diffusion: Llama for scalable image generation.arXiv preprint arXiv:2406.06525,

    arXiv

  4. [16]

    Simplear: Pushing the frontier of autoregressive visual generation through pretraining, sft, and rl.arXiv preprint arXiv:2504.11455, 2025a

    Junke Wang, Zhi Tian, Xun Wang, Xinyu Zhang, Weilin Huang, Zuxuan Wu, and Yu-Gang Jiang. Simplear: Pushing the frontier of autoregressive visual generation through pretraining, sft, and rl.arXiv preprint arXiv:2504.11455, 2025a. Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Qu...

    arXiv

  5. [17]

    Gpt- image-edit-1.5 m: A million-scale, gpt-generated image dataset.arXiv preprint arXiv:2507.21033, 2025b

    Yuhan Wang, Siwei Yang, Bingchen Zhao, Letian Zhang, Qing Liu, Yuyin Zhou, and Cihang Xie. Gpt- image-edit-1.5 m: A million-scale, gpt-generated image dataset.arXiv preprint arXiv:2507.21033, 2025b. Xinyu Wei, Jinrui Zhang, Zeqing Wang, Hongyang Wei, Zhen Guo, and Lei Zhang. T...

    arXiv

  6. [18]

    Qwen-image technical report.arXiv preprint arXiv:2508.02324, 2025a

    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, 2025a. Chenyuan Wu, Pengfei Zheng, Ruiran Yan, Shitao Xiao, Xin Luo, Yueze Wang, Wanli Li...

    arXiv

  7. [19]

    Sana: Efficient high-resolution image synthesis with linear diffusion transformers.arXiv preprint arXiv:2410.10629, 2024a

    Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, et al. Sana: Efficient high-resolution image synthesis with linear diffusion transformers.arXiv preprint arXiv:2410.10629, 2024a. Enze Xie, Junsong Chen, Yuyang Z...

    arXiv

  8. [20]

    Imgedit: A unified image editing dataset and benchmark.arXiv preprint arXiv:2505.20275,

    Yang Ye, Xianyi He, Zongjian Li, Bin Lin, Shenghai Yuan, Zhiyuan Yan, Bohan Hou, and Li Yuan. Imgedit: A unified image editing dataset and benchmark.arXiv preprint arXiv:2505.20275,

    arXiv

  9. [21]

    In-context edit: Enabling instructional image editing with in-context generation in large scale diffusion transformer.arXiv preprint arXiv:2504.20690,

    9 Zechuan Zhang, Ji Xie, Yu Lu, Zongxin Yang, and Yi Yang. In-context edit: Enabling instructional image editing with in-context generation in large scale diffusion transformer.arXiv preprint arXiv:2504.20690,

    arXiv

  10. [2022]

    Metamorph: Multimodal understanding and generation via instruction tuning.arXiv preprint arXiv:2412.14164,

    Shengbang Tong, David Fan, Jiachen Zhu, Yunyang Xiong, Xinlei Chen, Koustuv Sinha, Michael Rabbat, Yann LeCun, Saining Xie, and Zhuang Liu. Metamorph: Multimodal understanding and generation via instruction tuning.arXiv preprint arXiv:2412.14164,

    arXiv

  11. [2023]

    Hidream-i1: A high-efficient image generative foundation model with sparse diffusion transformer.arXiv preprint arXiv:2505.22705,

    Qi Cai, Jingwen Chen, Yang Chen, Yehao Li, Fuchen Long, Yingwei Pan, Zhaofan Qiu, Yiheng Zhang, Fengbin Gao, Peihan Xu, et al. Hidream-i1: A high-efficient image generative foundation model with sparse diffusion transformer.arXiv preprint arXiv:2505.22705,

    arXiv

  12. [2024]

    Seedream 3.0 technical report.arXiv preprint arXiv:2504.11346,

    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,

    arXiv

  13. [2025]

    Blip3-o: A family of fully open unified multimodal models- architecture, training and dataset.arXiv preprint arXiv:2505.09568, 2025a

    Jiuhai Chen, Zhiyang Xu, Xichen Pan, Yushi Hu, Can Qin, Tom Goldstein, Lifu Huang, Tianyi Zhou, Saining Xie, Silvio Savarese, et al. Blip3-o: A family of fully open unified multimodal models- architecture, training and dataset.arXiv preprint arXiv:2505.09568, 2025a. Junsong Ch...

    arXiv

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.