Pith. sign in

REVIEW 3 major objections 5 minor 48 references

The abstract claims CTA-Flux gives Flux culturally authentic Chinese text-to-image generation with LoRA, IP-Adapter, and ControlNet compatibility intact; the submitted full text is a different LLM paper and contains none of it.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

The paper claims a Chinese-prompt adapter for the Flux text-to-image model, but the manuscript body is an unrelated LLM self-recognition paper, leaving the claim unsupported.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection Abstract promises a Chinese-to-image adapter for Flux; the full text is a different paper on LLM self-recognition, so the submission is fundamentally incoherent. the 3 major comments →

arxiv 2508.14405 v1 pith:KNPOAP2F submitted 2025-08-20 cs.CV

CTA-Flux: Integrating Chinese Cultural Semantics into High-Quality English Text-to-Image Communities

Yue Gong , Shanyuan Liu , Liuzhuozheng Li , Jian Zhu , Bo Cheng , Liebucha Wu , Xiaoyu Wu , Yuhang Ma
show 2 more authors
Dawei Leng Yuhui Yin
This is my paper
classification cs.CV
keywords CTA-FluxFluxChinese semanticstext-to-image adaptationMMDiTimplicit self-recognitioncognitive surgerysubspace representation editing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The abstract claims a parameter-efficient bridge between Chinese prompts and Flux, an English-centric text-to-image model: a MultiModal Diffusion Transformer (MMDiT) adapter sits on the Flux backbone, learns Chinese cultural semantics without full retraining, and leaves the plugin ecosystem intact. The claimed payoff is that Chinese and English prompts both produce generation with higher quality and cultural authenticity. The manuscript body does not describe this system. Instead it contains a separate study of implicit self-recognition in large language models, proposing cognitive surgery (CoSur), which reports over 97% accuracy in the individual-presentation paradigm. A reader therefore can assess the abstract's claim only as a claim; the submitted text offers no architecture, training procedure, dataset, or evaluation for CTA-Flux.

Core claim

The claimed discovery, read from the abstract alone, is that English-centric text-to-image models can be made to understand Chinese cultural prompts with a comparatively small adapter: a MultiModal Diffusion Transformer (MMDiT) module steers the Flux backbone directly, so the process avoids full retraining and keeps existing plugins working. The accompanying empirical claim is that the adapter improves generation quality and cultural authenticity on Chinese and English prompts. The body of the manuscript does not describe or evaluate this system; instead it presents cognitive surgery (CoSur), a method that improves LLM self-recognition by SVD-based subspace projection and representation edit

What carries the argument

The abstract's named machinery is MMDiT, a MultiModal Diffusion Transformer inserted as a controller over the Flux backbone; the claimed work is that this controller compresses the parameter cost of bilingual adaptation and preserves compatibility with LoRA, IP-Adapter, and ControlNet. The machinery actually present in the manuscript is CoSur's: per-class SVD subspaces built from last-token hidden states, projection-energy scoring to assign authorship, and steering of the hidden state toward the target token's output weight vector.

Load-bearing premise

The load-bearing premise is that the manuscript actually contains the CTA-Flux system and its evaluation; the provided full text does not, which leaves the abstract's empirical claims unsupported.

What would settle it

Search the manuscript body for the CTA-Flux modules named in the abstract—MMDiT, Flux, LoRA, IP-Adapter, ControlNet, Chinese prompt benchmark, or any training or evaluation section; none of these appears. This absence is directly observable and settles that the abstract's claim is not supported by the submitted file.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If the abstract's claim held, Chinese prompts could be sent to Flux without a translate-to-English preprocessing step, removing a lossy bottleneck.
  • A working CTA-Flux would let users stack LoRA, IP-Adapter, and ControlNet on the culturally adapted model, because the adapter does not alter the backbone.
  • Parameter efficiency would make bilingual cultural adaptation feasible on a single consumer GPU rather than requiring full fine-tuning of Flux.
  • The claimed evaluation would give a measurable target: CTA-Flux outputs should beat both translated prompts and bilingual fine-tunes on Chinese cultural fidelity.
  • Bilingual support means the same checkpoint could serve English and Chinese prompts, broadening deployment of existing English-centric text-to-image communities.

Where Pith is reading between the lines

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

  • A reader should not infer that any CTA-Flux result exists from the abstract alone; the manuscript body's CoSur content gives no information about the adapter, its training, or its evaluation.
  • If such an adapter is later described, the most direct test is plugin preservation: fine-tune a LoRA on the adapted model and verify that it still trains and generates as it does on the original Flux.
  • The cultural-authenticity claim depends on how the evaluation operationalizes 'Chinese semantics'; without specifying the prompt set, the metric, and the human judging protocol, the claim is untestable.
  • A concrete repair would be to supply the missing architecture, dataset, and results, or to withdraw the abstract; the current file cannot support a public claim of improved Chinese cultural generation.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The manuscript's abstract and metadata describe CTA-Flux, a Chinese-semantics text-adapter for the Flux text-to-image model, claiming a MultiModal Diffusion Transformer (MMDiT) approach that reduces parameters, preserves compatibility with LoRA/IP-Adapter/ControlNet, and achieves superior generation quality and cultural authenticity. The full text, however, is an entirely different paper, 'From Implicit to Explicit: Enhancing Self-Recognition in Large Language Models', which proposes the CoSur method for LLM self-recognition under the individual presentation paradigm (IPP). None of the 13 pages contains CTA-Flux, MMDiT, Flux, Chinese prompts, cultural semantics, or any related architecture, dataset, training procedure, or evaluation. The abstract's central claim therefore has no support in the manuscript body. The body's CoSur content is internally coherent but irrelevant to the claimed contribution.

Significance. If the claimed CTA-Flux result were true and properly validated, it would be a practically valuable contribution: a parameter-efficient way to give an English-centric text-to-image model faithful Chinese cultural semantics while retaining the existing plugin ecosystem. However, this manuscript provides zero evidence for that claim. No method description, no equations, no architecture diagram, no dataset, and no evaluation for CTA-Flux appear anywhere. The CoSur material, taken on its own, addresses a different problem (LLM self-recognition) and contains some interesting empirical observations, such as the linear separability of self/other representations under IPP and a subspace-editing intervention. But it cannot serve as a substitute for the claimed contribution, and the manuscript does not even frame it as background. The paper also ships no code and no machine-checked proofs; the only empirical content is in the unrelated CoSur experiments.

major comments (3)
  1. [Abstract vs. full text] The abstract and metadata describe CTA-Flux, a Chinese-semantics adapter for the Flux TTI model using MMDiT, with claims of parameter reduction, plugin compatibility, and empirical gains in image quality and cultural authenticity. The full text is an unrelated cs.CL paper on LLM self-recognition (CoSur). No section, equation, table, or figure in the manuscript describes CTA-Flux, MMDiT, Flux, Chinese semantics, or any text-to-image evaluation. This is not a technical flaw in a derivation; the central claim simply has no derivation or evidence in the submitted manuscript.
  2. [§5.6, Figures 7–8] Even if the body's CoSur content were the intended paper, the two core hyperparameters (subspace dimension k and editing strength alpha) are selected by inspecting accuracy on the evaluation data in §5.6. Table 3 then reports the resulting accuracies (97–99%) as CoSur's performance. This constitutes selection on the test set; no independent validation or corrected evaluation is provided. The claim that CoSur reliably improves self-recognition is therefore not established by the reported numbers.
  3. [§3–§4, Eqs. (3)–(8)] The CoSur method assumes that projecting last-token hidden states onto SVD-derived subspaces (Eqs. 3–5) reliably separates authorship, and that steering the hidden state toward the target token's output weight vector (Eqs. 6–8) converts that separation into correct output behavior without disrupting generation. The paper provides no formal argument or robustness analysis for either assumption beyond the specific datasets in Table 3. Section 7 explicitly acknowledges that the properties of the extracted subspace are not analyzed. Again, this is secondary to the fact that none of this bears on the abstract's CTA-Flux claim.
minor comments (5)
  1. [Figure 4] The caption reads 'Average accuracy variation with different editing Strength', but the surrounding text describes time efficiency. The figure/caption/content mismatch should be corrected.
  2. [Table 7] The caption says 'using Qwen', but the rows list Llama and Deepseek. This appears to be a copy-paste error.
  3. [Equations throughout] Many equations are rendered as missing images or garbled Unicode (e.g., Eq. 1, Eq. 3, Eq. 6, and others), making the mathematical content partially unreadable. A clean typeset version is needed.
  4. [References] Several references contain corrupted markup and missing venue/title information (e.g., the Panickssery et al. and Ackerman and Panickssery entries). The bibliography needs a full cleanup.
  5. [Appendix G] The final sentence of Appendix G reads 'achieving an average accuracy of demonstrating its significant ability...' with no number provided. This is incomplete.

Circularity Check

1 steps flagged

CoSur hyperparameters are selected on the evaluation metric later reported; the CTA-Flux abstract has no derivation in the body (a completeness failure, not circularity).

specific steps
  1. fitted input called prediction [Section 5.6 (Hyperparameter Analysis) and Section 5.3 (Experimental Setting); Figures 7-8]
    "When �� � , the average recognition accuracy reaches 99%. Hence, we adopt�� �in our experiments."

    Section 5.3 fixes subspace dimension k and editing strength alpha for the final experiments. Section 5.6 selects these values by inspecting average recognition-accuracy curves on Qwen (Figures 7-8) and adopts the value that 'reaches 99%'. The final Table 3 reports the same IPP average accuracy for Qwen with those adopted values. The paper nowhere states that the hyperparameter search was confined to an independent validation fold, so the reported accuracy is the very quantity maximized during selection. Hence the 'prediction' of high accuracy is partly a fitted outcome rather than an independent evaluation of CoSur.

full rationale

The submitted full text is a different paper (CoSur, arXiv:2508.14408v2) and contains no CTA-Flux, MMDiT, Flux, Chinese-prompt, or plugin-compatibility content. That is a fatal completeness/derivation failure for the abstract's central claim, but it is not a circular reduction. Within CoSur, the derivation is mostly constructive: SVD-based subspace construction (Eq. 3), projection-energy authorship discrimination (Eqs. 4-5), and linear cognitive editing (Eqs. 6-8) are all well-defined operations evaluated on a 6:2:2 train/validation/test split. The one identifiable circular step is the hyperparameter selection: Section 5.6 picks k and alpha by plotting average recognition accuracy on Qwen and adopts them for the final experiments without stating that this selection used the validation split. The reported Table 3 accuracy for Qwen is therefore the same objective used to choose the hyperparameters, making the result partly fitted. This is a mild fitting rather than a self-citation chain or a definitional equivalence; the method retains independent content for other models and the framework is not reduced to its inputs. The abstract/body mismatch is weighed here as an unsupported claim, not as circularity.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 2 invented entities

Because the abstract's CTA-Flux claim contains no method or derivation, the ledger below is audited from the actual body content (the CoSur LLM self-recognition paper), which is the only substance in the manuscript. For CTA-Flux itself, nothing can be listed because nothing is present: no free parameters are reported, no assumptions are stated, no entities are introduced. The entries above therefore document the body, not the claimed contribution.

free parameters (3)
  • Subspace dimension k = value at which accuracy reaches ~99% on Qwen (Section 5.6, Figure 7)
    Number of top right-singular vectors kept for each subspace; chosen by inspecting the accuracy curve on the evaluation data.
  • Editing strength alpha = threshold where accuracy saturates, alpha > 50 (Section 5.6, Figure 8)
    Scaling factor for the steering direction; selected where recognition accuracy plateaus on the same evaluation data.
  • Number of attention heads amplified = top 15 heads (Section 5.5)
    In the head-intervention validation, the top 15 heads by contribution score are scaled; the choice is empirical.
axioms (5)
  • domain assumption Last-token, final-layer hidden representations carry sufficient authorship information and are linearly separable (linear probe >90% on training representations).
    Section 3, Figure 1. The ISR diagnosis and the subspace construction both presuppose this separability.
  • domain assumption The softmax output head acts as an information bottleneck filtering self-recognition signals (Markov chain h -> p -> y, Data Processing Inequality bound).
    Section 3, Eq. 1. Offered as the explanation for ISR; asserted, not derived from the model's training objective.
  • domain assumption Top-k right singular vectors of each class's representation matrix form subspaces whose projection-energy difference is a valid authorship discriminator.
    Section 4.2-4.3, Eqs. 3-5. No argument that per-class SVD yields discriminative subspaces beyond the empirical validation.
  • domain assumption Steering the hidden representation toward the target token's output weight vector changes decoded behavior as intended (linear intervention transfers to the output distribution).
    Section 4.4, Eqs. 6-8. The editing direction is normalized token weight vectors; transfer to correct output is assumed and validated empirically.
  • standard math Singular value decomposition and the Data Processing Inequality hold as standard mathematical results.
    Used in Eqs. 1-3 and the Appendix C metrics; standard, unproved background.
invented entities (2)
  • Self-recognition and other-recognition subspaces no independent evidence
    purpose: Bases for authorship discrimination via projection energy (Eqs. 4-5)
    Constructed from the paper's own training representations; methodological constructs with no falsifiable handle outside the paper's evaluation.
  • Cognitive editing target direction no independent evidence
    purpose: Steering hidden states toward the desired answer token (Eqs. 6-8)
    Defined entirely inside the paper's framework from the model's own output weights; no external evidence.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of CTA-Flux: Integrating Chinese Cultural Semantics into High-Quality English Text-to-Image Communities." pith.science (2026). https://pith.science/paper/KNPOAP2F

@misc{pith2026250814405,
  author       = {Pith},
  title        = {Pith review of: CTA-Flux: Integrating Chinese Cultural Semantics into High-Quality English Text-to-Image Communities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KNPOAP2F}},
  note         = {Machine review of arXiv:2508.14405}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

We proposed the Chinese Text Adapter-Flux (CTA-Flux). An adaptation method fits the Chinese text inputs to Flux, a powerful text-to-image (TTI) generative model initially trained on the English corpus. Despite the notable image generation ability conditioned on English text inputs, Flux performs poorly when processing non-English prompts, particularly due to linguistic and cultural biases inherent in predominantly English-centric training datasets. Existing approaches, such as translating non-English prompts into English or finetuning models for bilingual mappings, inadequately address culturally specific semantics, compromising image authenticity and quality. To address this issue, we introduce a novel method to bridge Chinese semantic understanding with compatibility in English-centric TTI model communities. Existing approaches relying on ControlNet-like architectures typically require a massive parameter scale and lack direct control over Chinese semantics. In comparison, CTA-flux leverages MultiModal Diffusion Transformer (MMDiT) to control the Flux backbone directly, significantly reducing the number of parameters while enhancing the model's understanding of Chinese semantics. This integration significantly improves the generation quality and cultural authenticity without extensive retraining of the entire model, thus maintaining compatibility with existing text-to-image plugins such as LoRA, IP-Adapter, and ControlNet. Empirical evaluations demonstrate that CTA-flux supports Chinese and English prompts and achieves superior image generation quality, visual realism, and faithful depiction of Chinese semantics.

discussion (0)

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

Reference graph

Works this paper leans on

48 extracted references · 13 canonical work pages · 2 internal anchors

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    Bai, S.; Chen, K.; Liu, X.; Wang, J.; Ge, W.; Song, S.; Dang, K.; Wang, P.; Wang, S.; Tang, J.; Zhong, H.; Zhu, Y.; Yang, M.; Li, Z.; Wan, J.; Wang, P.; Ding, W.; Fu, Z.; Xu, Y.; Ye, J.; Zhang, X.; Xie, T.; Cheng, Z.; Zhang, H.; Yang, Z.; Xu, H.; and Lin, J. 2025. Qwen2.5-VL Technical Report. arXiv:2502.13923

    Pith/arXiv arXiv 2025

  4. [4]

    Chen, Z.; Liu, G.; Zhang, B.-W.; Ye, F.; Yang, Q.; and Wu, L. 2022. Altclip: Altering the language encoder in clip for extended language capabilities. arXiv preprint arXiv:2211.06679

    Pith/arXiv arXiv 2022

  5. [5]

    Dhariwal, P.; and Nichol, A. 2021. Diffusion Models Beat GANs on Image Synthesis. Advances in Neural Information Processing Systems

    work page 2021

  6. [6]

    Esser, P.; Kulal, S.; Blattmann, A.; Entezari, R.; M \"u ller, J.; Saini, H.; Levi, Y.; Lorenz, D.; Sauer, A.; Boesel, F.; et al. 2024. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference on machine learning

    2024

  7. [7]

    Evans, L. C. 2010. Partial Differential Equations. Providence, RI: American Mathematical Society

    work page 2010

  8. [8]

    J.; Guan, X.; and Wang, X

    Fan, Y.; He, X.; Yang, D.; Zheng, K.; Kuo, C.-C.; Zheng, Y.; Narayanaraju, S. J.; Guan, X.; and Wang, X. E. 2025. GRIT: Teaching MLLMs to Think with Images. arXiv:2505.15879

    Pith/arXiv arXiv 2025

  9. [9]

    Feng, Z.; Zhang, Z.; Yu, X.; Fang, Y.; Li, L.; Chen, X.; Lu, Y.; Liu, J.; Yin, W.; Feng, S.; Sun, Y.; Chen, L.; Tian, H.; Wu, H.; and Wang, H. 2023 a . ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts. arXiv:2210.15257

    Pith/arXiv arXiv 2023

  10. [10]

    Feng, Z.; Zhang, Z.; Yu, X.; Fang, Y.; Li, L.; Chen, X.; Lu, Y.; Liu, J.; Yin, W.; Feng, S.; et al. 2023 b . Ernie-vilg 2.0: Improving text-to-image diffusion model with knowledge-enhanced mixture-of-denoising-experts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10135--10145

    work page 2023

  11. [11]

    H.; Chechik, G.; and Cohen-Or, D

    Gal, R.; Alaluf, Y.; Atzmon, Y.; Patashnik, O.; Bermano, A. H.; Chechik, G.; and Cohen-Or, D. 2022. An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion. arXiv:2208.01618

    Pith/arXiv arXiv 2022

  12. [12]

    Gao, Y.; Gong, L.; Guo, Q.; Hou, X.; Lai, Z.; Li, F.; Li, L.; Lian, X.; Liao, C.; Liu, L.; Liu, W.; Shi, Y.; Sun, S.; Tian, Y.; Tian, Z.; Wang, P.; Wang, R.; Wang, X.; Wang, X.; Wang, Y.; Wu, G.; Wu, J.; Xia, X.; Xiao, X.; Zhai, Z.; Zhang, X.; Zhang, Q.; Zhang, Y.; Zhao, S.; Yang, J.; and Huang, W. 2025. Seedream 3.0 Technical Report. arXiv:2504.11346

    Pith/arXiv arXiv 2025

  13. [13]

    Gu, J.; Meng, X.; Lu, G.; Hou, L.; Minzhe, N.; Liang, X.; Yao, L.; Huang, R.; Zhang, W.; Jiang, X.; et al. 2022. Wukong: A 100 million large-scale chinese cross-modal pre-training benchmark. Advances in Neural Information Processing Systems, 35: 26418--26431

    work page 2022

  14. [14]

    Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2018. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. arXiv:1706.08500

    Pith/arXiv arXiv 2018

  15. [15]

    Ho, J.; Jain, A.; and Abbeel, P. 2020. Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems

    work page 2020

  16. [16]

    Hong, W.; Wang, W.; Ding, M.; Yu, W.; Lv, Q.; Wang, Y.; Cheng, Y.; Huang, S.; Ji, J.; Xue, Z.; Zhao, L.; Yang, Z.; Gu, X.; Zhang, X.; Feng, G.; Yin, D.; Wang, Z.; Qi, J.; Song, X.; Zhang, P.; Liu, D.; Xu, B.; Li, J.; Dong, Y.; and Tang, J. 2024. CogVLM2: Visual Language Models for Image and Video Understanding. arXiv:2408.16500

    Pith/arXiv arXiv 2024

  17. [17]

    J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W.; et al

    Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; Chen, W.; et al. 2022. Lora: Low-rank adaptation of large language models. ICLR, 1(2): 3

    2022

  18. [18]

    Jiang, D.; He, X.; Zeng, H.; Wei, C.; Ku, M.; Liu, Q.; and Chen, W. 2024. MANTIS: Interleaved Multi-Image Instruction Tuning. arXiv:2405.01483

    Pith/arXiv arXiv 2024

  19. [19]

    P.; and Ba, J

    Kingma, D. P.; and Ba, J. 2017. Adam: A Method for Stochastic Optimization. arXiv:1412.6980

    Pith/arXiv arXiv 2017

  20. [20]

    Labs, B. F. 2024. FLUX. https://github.com/black-forest-labs/flux

    work page 2024

  21. [21]

    F.; Batifol, S.; Blattmann, A.; Boesel, F.; Consul, S.; Diagne, C.; Dockhorn, T.; English, J.; English, Z.; Esser, P.; et al

    Labs, B. F.; Batifol, S.; Blattmann, A.; Boesel, F.; Consul, S.; Diagne, C.; Dockhorn, T.; English, J.; English, Z.; Esser, P.; et al. 2025. FLUX. 1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space. arXiv preprint arXiv:2506.15742

    Pith/arXiv arXiv 2025

  22. [22]

    Li, X.; Tu, H.; Hui, M.; Wang, Z.; Zhao, B.; Xiao, J.; Ren, S.; Mei, J.; Liu, Q.; Zheng, H.; Zhou, Y.; and Xie, C. 2024. What If We Recaption Billions of Web Images with LLaMA-3? arXiv preprint arXiv:2406.12345

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  23. [23]

    Li, Y.; Chang, C.-Y.; Rawls, S.; Vulić, I.; and Korhonen, A. 2023. Translation-Enhanced Multilingual Text-to-Image Generation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 9174–9193. Association for Computational Linguistics

    work page 2023

  24. [24]

    L.; and Dollár, P

    Lin, T.-Y.; Maire, M.; Belongie, S.; Bourdev, L.; Girshick, R.; Hays, J.; Perona, P.; Ramanan, D.; Zitnick, C. L.; and Dollár, P. 2015. Microsoft COCO: Common Objects in Context. arXiv:1405.0312

    Pith/arXiv arXiv 2015

  25. [25]

    Lipman, Y.; Chen, R. T. Q.; Ben-Hamu, H.; Nickel, M.; and Le, M. 2023. Flow Matching for Generative Modeling. In The Eleventh International Conference on Learning Representations

    work page 2023

  26. [26]

    Liu, S.; Cheng, B.; Ma, Y.; Wu, L.; Ma, A.; Wu, X.; Leng, D.; and Yin, Y. 2025. Bridge Diffusion Model: Bridge Chinese Text-to-Image Diffusion Model with English Communities. Proceedings of the AAAI Conference on Artificial Intelligence, 39(5): 5541--5549

    work page 2025

  27. [27]

    Liu, X.; Gong, C.; and Liu, Q. 2022. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003

    Pith/arXiv arXiv 2022

  28. [28]

    Loshchilov, I.; and Hutter, F. 2019. Decoupled Weight Decay Regularization. arXiv:1711.05101

    Pith/arXiv arXiv 2019

  29. [29]

    H.; Constant, N.; Ma, J.; Hall, K

    Ni, J.; Ábrego, G. H.; Constant, N.; Ma, J.; Hall, K. B.; Cer, D.; and Yang, Y. 2021. Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. arXiv:2108.08877

    Pith/arXiv arXiv 2021

  30. [30]

    Podell, D.; English, Z.; Lacey, K.; Blattmann, A.; Dockhorn, T.; Müller, J.; Penna, J.; and Rombach, R. 2023. SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis. arXiv:2307.01952

    Pith/arXiv arXiv 2023

  31. [31]

    W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; Krueger, G.; and Sutskever, I

    Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; Krueger, G.; and Sutskever, I. 2021. Learning Transferable Visual Models From Natural Language Supervision. arXiv:2103.00020

    Pith/arXiv arXiv 2021

  32. [32]

    Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; and Sutskever, I. 2021. Zero-shot text-to-image generation. In International conference on machine learning, 8821--8831. Pmlr

    2021

  33. [33]

    Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022. High-Resolution Image Synthesis with Latent Diffusion Models. arXiv preprint arXiv:2112.10752

    Pith/arXiv arXiv 2022

  34. [34]

    L.; Ghasemipour, K.; Gontijo Lopes, R.; Karagol Ayan, B.; Salimans, T.; et al

    Saharia, C.; Chan, W.; Saxena, S.; Li, L.; Whang, J.; Denton, E. L.; Ghasemipour, K.; Gontijo Lopes, R.; Karagol Ayan, B.; Salimans, T.; et al. 2022. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information processing systems, 35: 36479--36494

    2022

  35. [35]

    Schuhmann, C.; Beaumont, R.; Vencu, R.; Gordon, C.; Wightman, R.; Cherti, M.; Coombes, T.; Katta, A.; Mullis, C.; Wortsman, M.; Schramowski, P.; Kundurthy, S.; Crowson, K.; Schmidt, L.; Kaczmarczyk, R.; and Jitsev, J. 2022. LAION-5B: An open large-scale dataset for training next generation image-text models. arXiv:2210.08402

    Pith/arXiv arXiv 2022

  36. [36]

    A.; Maheswaranathan, N.; and Ganguli, S

    Sohl-Dickstein, J.; Weiss, E. A.; Maheswaranathan, N.; and Ganguli, S. 2015. Deep Unsupervised Learning using Nonequilibrium Thermodynamics. arXiv preprint arXiv:1503.03585

    Pith/arXiv arXiv 2015

  37. [37]

    Song, J.; Meng, C.; and Ermon, S. 2020. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502

    Pith/arXiv arXiv 2020

  38. [38]

    Song, Y.; and Ermon, S. 2019. Generative Modeling by Estimating Gradients of the Data Distribution. Advances in Neural Information Processing Systems

    work page 2019

  39. [39]

    Song, Y.; Meng, C.; and Ermon, S. 2023. Consistency Models. arXiv preprint arXiv:2201.00367

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  40. [40]

    P.; Kumar, A.; Ermon, S.; and Poole, B

    Song, Y.; Sohl-Dickstein, J.; Kingma, D. P.; Kumar, A.; Ermon, S.; and Poole, B. 2020. Score-Based Generative Modeling through Stochastic Differential Equations. arXiv preprint arXiv:2011.13456

    Pith/arXiv arXiv 2020

  41. [41]

    N.; Kaiser, L.; and Polosukhin, I

    Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2023. Attention Is All You Need. arXiv:1706.03762

    Pith/arXiv arXiv 2023

  42. [42]

    von Platen, P.; Patil, S.; Lozhkov, A.; Cuenca, P.; Lambert, N.; Rasul, K.; Davaadorj, M.; Nair, D.; Paul, S.; Berman, W.; Xu, Y.; Liu, S.; and Wolf, T. 2022. Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers

    work page 2022

  43. [43]

    Yang, A.; Li, A.; Yang, B.; Zhang, B.; Hui, B.; Zheng, B.; Yu, B.; Gao, C.; Huang, C.; Lv, C.; et al. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388

    Pith/arXiv arXiv 2025

  44. [44]

    Ye, H.; Zhang, J.; Liu, S.; Han, X.; and Yang, W. 2023. IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models. arXiv:2308.06721

    Pith/arXiv arXiv 2023

  45. [45]

    T.; and Park, T

    Yin, T.; Gharbi, M.; Zhang, R.; Shechtman, E.; Durand, F.; Freeman, W. T.; and Park, T. 2024. One-step diffusion with distribution matching distillation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 6613--6623

    2024

  46. [46]

    Y.; Luong, T.; Baid, G.; Wang, Z.; Vasudevan, V.; Ku, A.; Yang, Y.; Ayan, B

    Yu, J.; Xu, Y.; Koh, J. Y.; Luong, T.; Baid, G.; Wang, Z.; Vasudevan, V.; Ku, A.; Yang, Y.; Ayan, B. K.; et al. 2022. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2(3): 5

    Pith/arXiv arXiv 2022

  47. [47]

    Zhang, J.; Gan, R.; Wang, J.; Zhang, Y.; Zhang, L.; Yang, P.; Gao, X.; Wu, Z.; Dong, X.; He, J.; Zhuo, J.; Yang, Q.; Huang, Y.; Li, X.; Wu, Y.; Lu, J.; Zhu, X.; Chen, W.; Han, T.; Pan, K.; Wang, R.; Wang, H.; Wu, X.; Zeng, Z.; and Chen, C. 2022. Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence. CoRR, abs/2209.02970

    Pith/arXiv arXiv 2022

  48. [48]

    Zhang, L.; Rao, A.; and Agrawala, M. 2023. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision, 3836--3847

    2023

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.