Pith. sign in

REVIEW 4 major objections 5 minor 2 cited by

Demystifying Flux Architecture

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read FLUX.1's architecture is laid bare from its public code.

desk verdict A useful first map of FLUX.1's architecture, but the block counts and other specifics are claims anchored to a community reimplementation, not verified with a pinned commit or code excerpts. read the letter →

arxiv 2507.09595 v1 pith:SRPENPEE submitted 2025-07-13 cs.CV

Or Greenberg This is my paper · ORCID
classification cs.CV
keywords FLUX.1text-to-imagegenerationdiffusiontransformerrectifiedflowlatentarchitecturereverse-engineeringadaptivelayernormalizationrotarypositionembedding
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

The paper is an unofficial technical report that reverse-engineers the architecture of FLUX.1, a state-of-the-art text-to-image diffusion model, from its publicly available inference code. It claims to document the full inference pipeline: dual text encoders, a 16-channel latent autoencoder, a rectified-flow transformer with 19 double-stream blocks followed by single-stream blocks, adaptive layer normalization, and rotary positional embeddings. The purpose is to provide a faithful technical reference so researchers can adopt FLUX as a backbone for further work, given that the developers released no official architecture documentation. The report also compares FLUX's qualitative performance against Stable Diffusion 2.1 in automotive scenarios. If accurate, it fills the documentation gap for one of the strongest open text-to-image models.

What carries the argument

The central object is the transformer-based velocity predictor $v_\theta$ of the rectified flow, specifically its hybrid double-stream/single-stream block design. The blocks process concatenated text and image tokens through joint multi-modal attention, with AdaLN (adaptive layer normalization) computing modulation parameters from timestep, guidance, and pooled prompt, and RoPE (rotary position embedding) applied to queries and keys based on 3D token grid ids. This block structure and its data flow carry the argument, since the paper's claim is essentially a precise description of this machinery.

What would settle it

Load the released open-weights model and verify that the actual state-dict tensor names and shapes match the documented layout of 19 double-stream blocks, a 16-channel VAE, and the described attention/MLP ordering; a mismatch, or the release of official documentation specifying different block counts or components, would falsify the report's central claim.

Watch

Extended reading notes

Core claim

The central claim is that the architecture and inference scheme of FLUX.1 can be fully recovered from the public inference code, and the paper provides that recovery. It identifies the model as a rectified-flow transformer in latent space: text is encoded by two pretrained encoders — CLIP for a pooled prompt embedding and T5 for per-token dense embeddings — while the image latent is handled by a 16-channel VAE. The denoising transformer applies a series of 19 double-stream blocks with separate weights for text and image tokens, followed by single-stream blocks with shared weights and parallel attention/MLP computation. Conditioning uses adaptive layer normalization driven by timestep, guidance scale, and the pooled CLIP embedding, and positional information comes from rotary embeddings computed from 3D token grid indices. The paper presents this as a faithful reference, with the caveat that training details and the hosted-only Pro variant's internals are not verified.

Load-bearing premise

The report assumes the public inference code and open weights faithfully represent the architecture of every FLUX.1 variant, including the hosted-only Pro model, and trusts the developers' statement that FLUX was trained with rectified flow, since training details are undisclosed.

Editorial extensions

If this is right

  • Researchers can treat the documented architecture as a reliable reference for fine-tuning, distillation, or using FLUX as a backbone, without waiting for official documentation.
  • The 19 double-stream plus single-stream hybrid design offers a concrete template for building rectified-flow text-to-image transformers, with the single-stream blocks adding capacity efficiently.
  • The documented dual-encoder setup (CLIP pooled plus T5 dense) and 16-channel VAE provide concrete design choices that can be replicated or ablated in new models.
  • The clarified sampling pipeline, using Euler discretization of the learned velocity field with guidance and timestep conditioning, enables accurate reproduction of FLUX's inference behavior in other codebases.
  • If the hosted Pro variant shares the described architecture, the report serves as a bridge for understanding its behavior through the open-weight siblings.

Reading between the lines

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

  • Because the report derives everything from inference code, it cannot say why the specific block counts or the double-stream/single-stream split were chosen; a natural testable extension is to ablate the single-stream blocks on a smaller rectified-flow model to estimate their contribution to prompt alignment versus parameter cost.
  • The reverse-engineering method generalizes: the same approach of reconstructing architecture from public inference code could be applied to other closed-documentation models, making the lack of official papers less of a barrier to adoption.
  • The qualitative evaluation focuses on automotive domains; a broader benchmark across diverse prompts and the Pro variant would be needed to confirm that the documented architecture indeed explains the advertised state-of-the-art performance.
  • If official documentation or weight inspection later reveals deviations, the report's value would shift from a precise blueprint to a historical snapshot of the open-weights version.
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

4 major / 5 minor

Summary. This paper is an unofficial technical report that claims to reverse-engineer the architecture and inference scheme of Black Forest Labs' FLUX.1 text-to-image model from publicly available inference code. It describes the sampling pipeline, dual CLIP/T5 text encoders, a 16-channel latent VAE, a rectified-flow transformer consisting of 19 double-stream blocks followed by single-stream blocks with AdaLN conditioning and RoPE positional embeddings, and the three released model variants. The report explicitly limits its scope to inference and acknowledges that training details are undisclosed and that FLUX.1[pro] is hosted-only.

Significance. If verified, this document would be a useful technical reference for researchers who want to adopt FLUX as a backbone, since the official documentation is incomplete. The report has clear diagrams and a sensible top-down organization, and it honestly flags key epistemic limitations (undisclosed training details, hosted-only pro weights). Its principal weakness is that the evidence for the architectural claims is not contained in the paper: there are no code excerpts, config parameters, commit hashes, or reproducibility checks against released checkpoints. The paper also omits at least one essential architectural parameter (the number of single-stream blocks) and inconsistently mixes diffusion-style and flow-style latent notation. These issues are fixable, so I view the contribution as potentially valuable but not yet at archival reliability.

major comments (4)
  1. [§2.1, §2.2, §2.3.1] The paper's central claim is that FLUX.1's architecture and inference scheme 'can be reverse-engineered from the publicly available inference code' (§2.1), but the paper never shows that code. It cites the black-forest-labs/flux repository [7] and the diffusers library [19] without providing a commit hash, file paths, code excerpts, or the transformer config JSON. Because the diffusers implementation is a third-party reimplementation rather than the vendor's own inference code, calling it the 'official implementation' (§2.2, §2.3.1) is misleading. As a result, the asserted values (19 double-stream blocks, 3072-dimensional tokens, summed temb, two text encoders) cannot be checked from the manuscript. Please pin the exact code and configuration version and include the relevant excerpts or a versioned configuration listing.
  2. [§2.3.3, Table 1] The report never states how many single-stream blocks follow the 19 double-stream blocks. Section 2.3.2 gives the double-stream depth, but Section 2.3.3 and Table 1 describe only the functional differences of the single-stream block. Since the block count is an essential parameter for adopting FLUX as a research backbone, this omission makes the architectural inventory incomplete. Add the number of single-stream layers (and related config values such as attention heads and head dimension) to Section 2.3.3 or Table 1, and verify the values against a released checkpoint.
  3. [§3.1] The statement that all three variants 'share the same architecture of 12B parameters' is attributed to the vendor announcement [18], but FLUX.1[pro] is hosted-only and its weights are not released, as the paper itself notes. The architecture documentation can therefore be checked directly only for the open-weight dev and schnell checkpoints; the pro variant's identity is an unverifiable vendor claim. Please state this limitation explicitly in Section 2.1 and Section 3.1, and label the 12B/shared-architecture claim as vendor-reported rather than reverse-engineered.
  4. [§2.2.2, §2.3.1, Figure 5] The latent notation is internally inconsistent. Figure 5 says the final refined latent z0 is decoded to RGB, while Section 2.2.2 says the final clean latent z1 is decoded. Section 2.2.2 says timesteps range from 0 to 1 with z1 the clean image, but Section 2.3.1 says the timestep is iterated from 1 to 0. Since the report's purpose is to demystify the inference scheme, these conflicting conventions should be reconciled in one place, with the Euler step Δt (including its sign) defined explicitly.
minor comments (5)
  1. [§1.4] The statement that 'FLUX was trained using this method' is made without the qualifier that appears later in §2.1 ('according to the developers statement'). Since training details are undisclosed, add the qualifier in §1.4 as well.
  2. [§2.2.1] The text-encoder description should name the concrete pretrained models and sizes (e.g., the exact CLIP and T5 variants used by the released checkpoints), since these are needed by anyone adopting the model.
  3. [§2.3.2, Eq. (3)] Equation (3) has a typo: the V matrix appears inside the softmax. It should read Attention(Q,K,V) = softmax(QK^T / sqrt(d_k)) V.
  4. [Appendix A] The qualitative comparison is anecdotal: it uses a single SD version, one resolution, and no seeds or quantitative metrics, and the claims of state-of-the-art performance in §2.1 are taken from vendor figures [18]. Please label the evaluation as illustrative and vendor-reported, or add a minimal quantitative protocol.
  5. [Throughout] The manuscript contains numerous typos and grammatical slips (e.g., 'prompmts', 'handels', 'aformentioned', 'pronaunced', 'voucbulary', and 'Multi-Layer Processor' for 'Multi-Layer Perceptron'). A careful proofread is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the report is a descriptive reverse-engineering write-up with no fitted parameters, predictions, or self-citation chain.

full rationale

The paper's load-bearing claim is that FLUX.1's architecture and inference scheme can be reverse-engineered from publicly available inference code, and the report then describes that code. This is description of an external artifact, not a derivation. No equation in the paper takes a fitted parameter and renames it as a prediction: Eq. (1) and Eq. (2) are background definitions of diffusion and rectified-flow objectives, copied from the cited literature, and Eq. (3) and Eq. (4) are standard attention and Euler sampling formulas. The report does not fit FLUX's architecture to any data, so there is nothing for a 'prediction' to reduce to by construction. The author is not affiliated with Black Forest Labs, and the manuscript includes no self-citations: the closest load-bearing references are the diffusers implementation [19] and the developers' announcement [18], both external. The manuscript explicitly acknowledges its sourcing limits: training details 'have not been publicly disclosed' (Section 2.1), the reverse-engineering relies on 'the official implementation of FLUX.1 pipeline in diffusers [19]' (Section 2.2), and FLUX.1[pro] 'weights are not publicly released' (Section 3.1). These are evidence-quality and reproducibility limitations, not circular reasoning. If the diffusers reimplementation deviates from the shipped weights, the report may be inaccurate, but inaccuracy is not circularity. Accordingly, no circular step can be exhibited, and the honest finding is score 0.

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

The report introduces no fitted constants or invented entities; its central claim depends on the faithfulness of public code as a proxy for the real model and on vendor statements about training and quality. The qualitative evaluation uses chosen sampling settings (50 steps, guidance 2.5 for FLUX and 7.5 for SD2.1), but these are experimental settings, not fitted parameters.

assumptions (4)
  • domain assumption FLUX.1 was trained using the Rectified Flow paradigm as stated by the developers.
    Sections 1.4 and 2.1 rely on the developers' statement rather than published training details; the training setup is undisclosed.
  • domain assumption The public inference code (diffusers and the FLUX repository) faithfully reflects the architecture of all FLUX variants, including the hosted-only pro model.
    Section 2.1 says the architecture can be reverse-engineered from public inference code, but Section 3.1 notes that pro weights are not public, so this cannot be checked for pro.
  • domain assumption The ELO scores reproduced from Black Forest Labs' announcement [18] are a valid basis for the state-of-the-art claim.
    Section 2.1 and Figures 2 and 3 adopt the vendor's ELO evaluation without independent benchmarking.
  • domain assumption The qualitative 512x512 automotive comparisons in Appendix A are representative of general text-to-image capability.
    Appendix A generalizes a state-of-the-art conclusion from a small, single-domain, qualitative comparison with no quantitative metrics.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Demystifying Flux Architecture." pith.science (2026). https://pith.science/paper/SRPENPEE

@misc{pith2026250709595,
  author       = {Pith},
  title        = {Pith review of: Demystifying Flux Architecture},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SRPENPEE}},
  note         = {Machine review of arXiv:2507.09595}
}
read the original abstract

FLUX.1 is a diffusion-based text-to-image generation model developed by Black Forest Labs, designed to achieve faithful text-image alignment while maintaining high image quality and diversity. FLUX is considered state-of-the-art in text-to-image generation, outperforming popular models such as Midjourney, DALL-E 3, Stable Diffusion 3 (SD3), and SDXL. Although publicly available as open source, the authors have not released official technical documentation detailing the model's architecture or training setup. This report summarizes an extensive reverse-engineering effort aimed at demystifying FLUX's architecture directly from its source code, to support its adoption as a backbone for future research and development. This document is an unofficial technical report and is not published or endorsed by the original developers or their affiliated institutions.

Figures

Figures reproduced from arXiv: 2507.09595 by the authors.

Figure 1
Figure 1. Teaser image. Abstract FLUX.1 is a diffusion-based text-to-image generation model developed by Black Forest Labs, designed to achieve faithful text-image alignment while maintaining high image quality and diversity. FLUX is considered state-of-the-art in text-to-image generation, outperforming popular models such as Midjour￾ney, DALL·E 3, Stable Diffusion 3 (SD3), and SDXL. Although publicly available as open source… view at source ↗
Figure 2
Figure 2. FLUX.1 defines a new state-of-the-art in image detail, prompt adherence, style diversity and scene [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. ELO scores for different aspects: Prompt Following, Size/Aspect Variability, Typography, Output [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: High-level overview of the FLUX.1 architecture. Text embeddings and latent image embeddings [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Flux.1 sampling pipeline. Just like in Diffusion Models, after pre-processing the noisy latent [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Flux Transformer: high-level overview 2.3 Transformer The core component of FLUX.1’s synthesis pipeline is the velocity predictor vθ, which is optimized to es￾timate the velocity vector along the sampling trajectory (see Section 1.4). Similar to SD3 [8], FLUX.1 replace…
Figure 7
Figure 7. Figure 7: Flux Transformer: per-iteration inputs pre-process [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Double-Stream Block [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: AdaLN layer, where MSA (Multi-head Self Attention) and MLP (Multi-Layer Processor) modula [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Single-Stream Block. 2.3.3 Single-Stream Transformer Block After the series of Double-Stream blocks is applied, the processed latent and prompt embeddings are con￾catenated and fed through a series of Single-Stream blocks. While the Double-Stream blocks apply differen…
Figure 1
Figure 1. Figure 1: Overall image quality comparison. FLUX.1 generates high quality automotive scenes with different [PITH_FULL_IMAGE:figures/full_fig_p018_1.png]
Figure 2
Figure 2. Figure 2: FLUX.1 is sensitive to subtle differences in textual guidance- for example, distinguishing between [PITH_FULL_IMAGE:figures/full_fig_p018_2.png]
Figure 3
Figure 3. Figure 3: FLUX.1 is capable of interpreting textual guidance that is minimal or loosely defined, such as [PITH_FULL_IMAGE:figures/full_fig_p019_3.png]
Figure 4
Figure 4. Figure 4: FLUX.1’s rich vocabulary and extensive pre-training enable it to handle rare concepts such as [PITH_FULL_IMAGE:figures/full_fig_p019_4.png]
Figure 5
Figure 5. Figure 5: The dense prompt embeddings produced by the T5 text encoder enable FLUX.1 to adhere to [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]
Figure 6
Figure 6. Figure 6: While existing U-Net–based diffusion models are typically limited to a specific or fixed set of [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. ToolArtist: Tool-Using Unified Multimodal Models for Agentic Image Generation

    cs.CV 2026-08 conditional novelty 6.0 of 10

    ToolArtist trains a unified multimodal model to reason, search the web, and generate images as one policy, improving scores on WISE and WorldGenBench-Humanities.

  2. Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models

    cs.CV 2026-06 conditional novelty 5.0 of 10

    High-noise flow-matching training makes one-step VLA action decoding competitive with multi-step decoding because actions are compact targets under rich observations.

Reference graph

Works this paper leans on

28 extracted references · 15 canonical work pages · cited by 2 Pith papers

  1. [7]

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

    2024

  2. [19]

    Diffusers: State-of-the-art diffusion models,

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

    work page 2022

  3. [18]

    Black-forest-labs official flux.1 announcement,

    “Black-forest-labs official flux.1 announcement,” https://bfl.ai/announcements/24-08-01-bfl

    work page

  4. [1]

    Auto-encoding variational bayes,

    D. P. Kingma, M. Welling et al. , “Auto-encoding variational bayes,” 2013

    2013

  5. [2]

    Generative adversarial networks,

    I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Ben- gio, “Generative adversarial networks,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020

    work page 2020

  6. [3]

    Normalizing flows for probabilistic modeling and inference,

    G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, “Normalizing flows for probabilistic modeling and inference,” Journal of Machine Learning Research , vol. 22, no. 57, pp. 1–64, 2021

    2021

  7. [4]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural informa- tion processing systems, vol. 33, pp. 6840–6851, 2020

    work page 2020

  8. [5]

    Zero-shot text-to-image generation,

    A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International conference on machine learning. Pmlr, 2021, pp. 8821–8831. 14

    work page 2021

Show all 28 references
  1. [6]

    High-resolution image synthesis with latent diffusion models,

    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, 2022, pp. 10 684–10 695

    2022

  2. [8]

    Scaling rectified flow transformers for high-resolution image synthesis,

    P. Esser, S. Kulal, A. Blattmann, R. Entezari, J. M¨ uller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel et al. , “Scaling rectified flow transformers for high-resolution image synthesis,” in Forty-first international conference on machine learning , 2024

    2024

  3. [9]

    Learning transferable visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” inInternational conference on machine learning . PmLR, 2021, pp. 8748–8763

    2021

  4. [10]

    LAION-5b: An open large-scale dataset for training next generation image-text models,

    C. Schuhmann, R. Beaumont, R. Vencu, C. W. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, P. Schramowski, S. R. Kundurthy, K. Crowson, L. Schmidt, R. Kaczmarczyk, and J. Jitsev, “LAION-5b: An open large-scale dataset for training next generation ...

    2022

  5. [11]

    Openclip,

    G. Ilharco, M. Wortsman, R. Wightman, C. Gordon, N. Carlini, R. Taori, A. Dave, V. Shankar, H. Namkoong, J. Miller, H. Hajishirzi, A. Farhadi, and L. Schmidt, “Openclip,” Jul. 2021. [Online]. Available: https://doi.org/10.5281/zenodo.5143773

    2021 doi

  6. [12]

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

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

    2023 arXiv

  7. [13]

    Adversarial diffusion distillation,

    A. Sauer, D. Lorenz, A. Blattmann, and R. Rombach, “Adversarial diffusion distillation,” in European Conference on Computer Vision . Springer, 2024, pp. 87–103

    2024

  8. [14]

    Flow straight and fast: Learning to generate and transfer data with rectified flow,

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

    2022 arXiv

  9. [15]

    Flow matching for generative modeling,

    Y. Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le, “Flow matching for generative modeling,” arXiv preprint arXiv:2210.02747 , 2022

    2022 arXiv

  10. [16]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017

    2017

  11. [17]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Min- derer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020

    2010 arXiv

  12. [20]

    Exploring the limits of transfer learning with a unified text-to-text transformer,

    C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of machine learning research, vol. 21, no. 140, pp. 1–67, 2020

    2020

  13. [21]

    Vision transformers inference acceleration based on adaptive layer normalization,

    F. E. Keddous, A. Llanza, N. Shvai, and A. Nakib, “Vision transformers inference acceleration based on adaptive layer normalization,” Neurocomputing, vol. 610, p. 128524, 2024

    2024

  14. [22]

    Glide: Towards photorealistic image generation and editing with text-guided diffusion models,

    A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” arXiv preprint arXiv:2112.10741, 2021. 15

    2021 arXiv

  15. [23]

    Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis,

    A. Sauer, T. Karras, S. Laine, A. Geiger, and T. Aila, “Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis,” in International conference on machine learning . PMLR, 2023, pp. 30 105–30 118

    2023

  16. [24]

    Arbitrary style transfer in real-time with adaptive instance normalization,

    X. Huang and S. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 1501–1510

    2017

  17. [25]

    Roformer: Enhanced transformer with rotary position embedding,

    J. Su, M. Ahmed, Y. Lu, S. Pan, W. Bo, and Y. Liu, “Roformer: Enhanced transformer with rotary position embedding,” Neurocomputing, vol. 568, p. 127063, 2024

    2024

  18. [26]

    On distillation of guided diffusion models,

    C. Meng, R. Rombach, R. Gao, D. Kingma, S. Ermon, J. Ho, and T. Salimans, “On distillation of guided diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 14 297–14 306

    2023

  19. [27]

    Fast high-resolution image synthesis with latent adversarial diffusion distillation,

    A. Sauer, F. Boesel, T. Dockhorn, A. Blattmann, P. Esser, and R. Rombach, “Fast high-resolution image synthesis with latent adversarial diffusion distillation,” in SIGGRAPH Asia 2024 Conference Papers, 2024, pp. 1–11

    2024

  20. [28]

    Sigmoid loss for language image pre-training,

    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 , 2023, pp. 11 975–11 986. 16 Appendices A Qualitative Evaluation In this section, we provide a qualitat...

    2023

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.