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REVIEW 5 major objections 5 minor 36 references

CharaConsist: Fine-Grained Consistent Character Generation

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

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Pith's one-line read Point tracking pins a character's identity across generated scenes.

desk verdict A well-executed training-free consistency method for DiT models, with a load-bearing point-matching step that needs stronger external validation. read the letter →

arxiv 2507.11533 v1 pith:D6P7JWXM submitted 2025-07-15 cs.CV

Mengyu Wang , Henghui Ding , Jianing Peng , Yao Zhao , Yunpeng Chen , Yunchao Wei This is my paper · ORCID
classification cs.CV
keywords text-to-imagegenerationcharacterconsistencydiffusiontransformertraining-freepoint-trackingattentionadaptivetokenmergelocalitybiasidentity
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 introduces CharaConsist, a training-free method that keeps a character's identity, clothing, and background consistent across a series of text-to-image generations, even when the character moves between shots or scenes. Previous consistency methods built on UNet-based models like SDXL suffer from a locality bias: inter-image attention favors spatially nearby tokens over semantically related ones, so large motion breaks identity and background details. CharaConsist works on the FLUX.1 diffusion transformer and fixes this by first identifying semantic point correspondences between the identity image and each frame, then using those correspondences to re-encode positional embeddings and merge tokens. With a single identity image, it produces fine-grained foreground and background consistency without additional training, and it is the first method of its kind tailored to a DiT backbone.

What carries the argument

The load-bearing mechanism is point-tracking attention combined with adaptive token merge. Point-tracking attention stores the identity image's keys without positional embedding, computes a dense semantic matching map $\mathrm{map}^*(j)=k$ by layer-averaged cosine similarity of attention outputs, and re-encodes each matched key with the query's Rotary Position Embedding, so shared information is transferred to the correct location despite layout changes. Adaptive token merge then blends the frame's attention output with the identity's re-arranged output, using the averaged similarity $S^*_{\max}$ as a confidence weight, and a foreground-background mask $M$ (from text-attention differences) decides which regions share identity versus background content. Each piece counters a specific failure: RoPE re-encoding addresses positional shift, token merge repairs local geometry and imperfect matches, and the mask gives controllable scene persistence.

What would settle it

Run the point-matching module on a public semantic-correspondence benchmark such as SPair-71k using FLUX.1 features at the 11th sampling step; if matching accuracy is near chance despite the claims of stability, or if replacing the learned correspondences with random ones yields equal consistency in the generation pipeline, the central mechanism is falsified.

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Extended reading notes

Core claim

The paper claims that inter-image attention fails at consistent generation because both UNet and DiT models allocate attention by spatial proximity rather than semantic correspondence, a locality bias that grows with positional shift. CharaConsist overcomes this by storing the identity image's keys and attention outputs from FLUX.1, recovering a per-pixel matching map by averaging the cosine similarity of attention outputs across layers at the 11th sampling step, and then re-encoding the matched keys with the query's rotary position embedding during frame generation. An adaptive token merge interpolates the frame's attention output toward the identity's aligned output, weighted by matching confidence, so that low-confidence matches cannot inject wrong features. Decoupled foreground/background masks, derived from attention differences between image tokens and foreground versus background text tokens, allow users to keep a static scene fixed or let it change. The result is claimed to be the largest consistency improvement among training-free methods on consistency metrics, with the method needing only one identity image.

Load-bearing premise

The method rests on the assumption that reliable semantic point correspondences between the frame and identity images can be obtained by averaging cosine similarities of attention outputs at the 11th sampling step; if this matching is inaccurate, the re-encoded attention and token merge both inject mismatched features.

Editorial extensions

If this is right

  • Generating an image series for visual storytelling or keyframe-based video can keep both the character's face, clothing, and scene details stable without retraining, which removes a bottleneck in long-format content production.
  • Because the method needs just one identity image and no parallel batch generation, the extra GPU memory cost over normal generation is small, so it can be used in interactive or resource-limited settings.
  • The point-tracking and mask-extraction controls plug into FLUX.1-style multimodal transformers, meaning they can be ported to other DiT-based editors and generators once the same attention outputs are available.
  • The decoupled foreground/background masks let a user freeze a static scene across shots while characters act, or switch scenes entirely, giving direct control over the trade-off between scene persistence and narrative change.

Reading between the lines

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

  • Inference: the layer-averaged cosine correspondence could be repurposed as a general semantic-correspondence prior for DiT feature spaces, e.g., for image editing and style transfer, independent of character generation.
  • Inference: the 11th-step heuristic suggests attention maps at mid-sampling encode stable semantics; a timestep-adaptive selection or fine-tuning of the matching step could further improve matching on unusual prompts or styles.
  • Inference: since the method declines input identity, combining CharaConsist's point-tracking with a face-reference adapter would likely yield both identity fidelity from the reference and detail/background consistency from the tracking – the paper itself hints at this as future direction.
  • Inference: the locality bias finding is model-agnostic; the same diagnosis likely applies to other generative transformers, so the re-encode-and-merge recipe may transfer to video diffusion or multi-image editing tasks.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper proposes CharaConsist, a training-free consistent text-to-image generation method built on FLUX.1. It introduces three components: point-tracking attention, which establishes semantic correspondences between a generated identity image and each frame image and re-encodes positional embeddings; adaptive token merge, which interpolates attention outputs according to matching confidence; and foreground/background masks, which allow decoupled control of background consistency. The method is evaluated against ConsiStory, StoryDiffusion, IP-Adapter, and PhotoMaker on a GPT-4-generated benchmark, with additional ablations and a user study. The main claims are that CharaConsist achieves fine-grained foreground and background consistency, supports large motion variation, and is the first consistent generation method tailored to a DiT text-to-image model.

Significance. If the claims hold, CharaConsist addresses a real limitation of existing training-free consistency methods: they are built on UNet models and cannot preserve fine-grained background details or handle large pose changes. The proposed mechanism is plausible, memory-efficient (it requires only a single identity image rather than parallel batch generation), and the qualitative results are visually consistent with the claims. The paper also ships source code, which is a positive contribution to reproducibility. The main weakness is that the quantitative evidence is not yet convincing: the evaluation relies on a self-created prompt set and CLIP-based proxy metrics without error bars or significance tests, and the load-bearing point-matching component is validated only on a small, manually annotated set of the method's own outputs rather than on an external correspondence benchmark.

major comments (5)
  1. [§3.2, Eqs. (4)–(5), Appendix A] The point-matching map is the load-bearing component of the method: it is used both to re-encode identity keys with frame positions (Eq. (13)) and to weight the interpolation of identity attention outputs (Eq. (20)). However, its accuracy is validated only on a small, manually annotated set of the method's own generated images (Fig. 7), with no external correspondence benchmark and no breakdown by pose change, occlusion, or texture ambiguity. Because the map is computed from a first frame-image pass generated without any consistency constraint, failures in exactly the large-motion regime the paper targets are plausible. The authors should validate point matching on an external semantic-correspondence benchmark using the same FLUX feature extraction, or at minimum compare with DIFT and a no-matching baseline, and report failure cases.
  2. [Tables 1–3, Table 4] All quantitative tables report point estimates without error bars, confidence intervals, or significance tests. Many differences are small (e.g., CLIP-I-fg 0.883 vs 0.876/0.881/0.882 in Table 1, and increments 0.036 vs 0.041 in Table 2), so the claim that CharaConsist 'significantly outperforms' the baselines is not statistically supported. The user study in Table 4 also lacks inter-annotator agreement or per-item vote counts. The authors should report results over repeated seeds with paired significance tests (e.g., bootstrap or Wilcoxon) for all metrics, and provide more detailed user-study statistics.
  3. [§4.1] The evaluation benchmark is self-created using GPT-4 prompts and is not released, which limits reproducibility and makes the comparison sensitive to prompt-selection bias. The authors should release the full prompt set, evaluation scripts, and generated images, and ideally also evaluate on at least one existing benchmark, such as the ConsiStory evaluation set or a video-frame consistency set, to demonstrate that the improvements are not an artifact of the chosen prompts.
  4. [Table 2] The consistency 'increments' in Table 2 are computed relative to different base models (SDXL, RealVisXL4.0, FLUX.1), so the claim that CharaConsist achieves the largest increment is not well-defined because the baselines and scales differ. To support the conclusion that the improvements are not due to the stronger FLUX.1 base model, the authors should report absolute scores with variance for each base model, and where possible compare all methods on the same base model or provide a normalized increment measure.
  5. [Appendix A, Fig. 7] The critical hyperparameters — the point-matching timestep (step 11), the stopping step for the proposed modules (step 40), the interpolation weight alpha, and the morphological kernel sizes (3x3 erosion, 5x5 dilation) — are selected by manual inspection on a small set of generated outputs, and no sensitivity analysis is reported. The paper should include ablations over these choices (e.g., matching at steps 8/11/15, stopping at steps 35/40/45, different alpha schedules) to show that the method is not brittle to these settings.
minor comments (5)
  1. [Abstract and §4.2] The claim that CharaConsist is 'the first consistent generation method tailored for text-to-image DiT model' is strong; since the related-work section covers only three prior methods, the claim should be softened to 'to the best of our knowledge' and should be checked against more recent DiT-based consistency works.
  2. [Eq. (20)] The interpolation weight alpha is described as decaying with timestep, but the decay schedule is never specified. The paper should give the exact schedule (e.g., linear, cosine, or stepwise) and its hyperparameter values.
  3. [Eq. (6)] The notation Wmm[l:, : lbg + lfg] is unclear: the row index 'l:' should be explicitly defined as the image-token rows of the multimodal attention matrix, and the column range should be described as the text-token columns.
  4. [Appendix A, Fig. 9] The manual evaluation of point matching and masks uses 'manually selected points in the first image,' but the number of annotated points, number of images, and number of annotators are not reported. Please add these details to make the validation reproducible.
  5. [§4.1] For the identity-reference baselines IP-Adapter and PhotoMaker, the comparison may be unfair since these methods optimize for facial identity and are not designed for background or clothing consistency. The paper acknowledges this in the discussion, but the experimental section should more clearly state that these baselines are provided for reference rather than as direct competitors.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the point-tracking attention and adaptive token merge are self-contained modules evaluated against external baselines; the few self-citations appear only in related work and are not load-bearing.

full rationale

CharaConsist's derivation chain is self-contained. Point matching (Eqs. 4-5) is defined directly from averaged cosine similarity of attention outputs; point-tracking attention (Eqs. 12-17) and adaptive token merge (Eqs. 18-20) consume that matching as input rather than fitting a target metric. The consistency improvements are measured with external CLIP, ID-Sim, and user-study metrics against ConsiStory, StoryDiffusion, IP-Adapter, and PhotoMaker, not against quantities derived from the method's own fitted values. Hyperparameters such as the 11th timestep, step 40 cutoff, alpha, and morphological kernel sizes are selected via manual point/mask accuracy checks, which is standard hyperparameter selection, not a fitted parameter renamed as a prediction. The self-citations in the reference list (AnyI2V, a survey, Free-form motion control) are confined to related-work contexts and do not support the central claim. The paper's limitation that it cannot take an input identity as reference is an acknowledged scope restriction, not a circular step. The only notable evidence concern is that point-matching quality is validated on the method's own generated images rather than an external correspondence benchmark; that is a limitation in evaluation robustness, not a circularity, because the matching is not an output that is then fed back as its own validation target. Accordingly, no circular step meeting the required evidentiary standard is present.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claim rests on empirical correspondence quality, mask quality, and several tuning choices. None are formalized, but they are supported by small manual evaluations and ablations.

free parameters (4)
  • Alpha interpolation weight = not fully specified; decays with timestep
    Controls blending of frame and identity attention outputs in Eq. (20); tuned empirically and the exact schedule is not given.
  • Timestep for point matching and mask extraction = 11
    Selected from a plateau in Fig. 7 using manually annotated results generated by the method itself; affects which correspondences are used.
  • Sampling steps for module application = 1 to 40
    Modules are applied from step 1 to step 40 because later steps are claimed to have no significant impact on consistency; justified by qualitative observation.
  • Morphological kernel sizes = 3x3 erosion, 5x5 dilation
    Chosen to clean the raw attention-derived masks; not derived from theory.
assumptions (6)
  • domain assumption Cosine similarity of diffusion attention outputs averaged across layers yields accurate semantic point correspondences between images.
    Used in Eqs. (4)-(5); verified only on a small manually annotated set in Appendix A, Fig. 7.
  • domain assumption Foreground and background can be separated by comparing image-token attention to text tokens, given prompts formatted with background before foreground.
    Eqs. (6)-(9) and the prompt format in Fig. 4; this is a modeling choice that may not transfer to other prompt structures.
  • domain assumption Re-encoding positional embeddings with RoPE for matched keys preserves semantic content while correcting viewpoint.
    Eq. (13); assumes RoPE re-encoding does not break local geometry, which the token merge tries to mitigate but does not prove.
  • domain assumption Inter-image attention in FLUX.1 exhibits a locality bias similar to SDXL.
    Fig. 3 is an empirical demonstration on generated images; it motivates the point tracking module.
  • ad hoc to paper The last 10 sampling steps contribute only low-level texture, not consistency, so modules can be stopped at step 40.
    Appendix A states this without quantitative support; it is a tuning choice rather than a measured fact.
  • domain assumption FLUX.1-dev is a valid base model for training-free consistency and its original CLIP-T domain gap carries over to the method.
    Used in Sec. 4.2 to explain lower CLIP-T scores; not independently benchmarked against other DiT bases.

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Cite this review

Pith. "Pith review of CharaConsist: Fine-Grained Consistent Character Generation." pith.science (2026). https://pith.science/paper/D6P7JWXM

@misc{pith2026250711533,
  author       = {Pith},
  title        = {Pith review of: CharaConsist: Fine-Grained Consistent Character Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D6P7JWXM}},
  note         = {Machine review of arXiv:2507.11533}
}
read the original abstract

In text-to-image generation, producing a series of consistent contents that preserve the same identity is highly valuable for real-world applications. Although a few works have explored training-free methods to enhance the consistency of generated subjects, we observe that they suffer from the following problems. First, they fail to maintain consistent background details, which limits their applicability. Furthermore, when the foreground character undergoes large motion variations, inconsistencies in identity and clothing details become evident. To address these problems, we propose CharaConsist, which employs point-tracking attention and adaptive token merge along with decoupled control of the foreground and background. CharaConsist enables fine-grained consistency for both foreground and background, supporting the generation of one character in continuous shots within a fixed scene or in discrete shots across different scenes. Moreover, CharaConsist is the first consistent generation method tailored for text-to-image DiT model. Its ability to maintain fine-grained consistency, combined with the larger capacity of latest base model, enables it to produce high-quality visual outputs, broadening its applicability to a wider range of real-world scenarios. The source code has been released at https://github.com/Murray-Wang/CharaConsist

Figures

Figures reproduced from arXiv: 2507.11533 by the authors.

Figure 1
Figure 1. CharaConsist achieves fine-grained consistency maintaining. The left part shows that, CharaConsist enables more flexible storytelling, supports controllable scene transitions, and achieves character consistency within a fixed scene, across different scenes, and across resolutions. The right part highlights CharaConsist’s ability to preserve extensive background details with complete consistency. Abstract In text-to-… view at source ↗
Figure 2
Figure 2. Qualitative comparison with StoryDiffusion [36] and ConsiStory [31]. For continuous shots within a fixed scene (the left part), previous methods cannot maintain consistent background and foreground details. For discrete shots across different scenes (the right part), previous methods either exhibit a copy-paste effect or suffer from inconsistency for the characters. In contrast, our CharaConsist maintains fine-grain… view at source ↗
Figure 3
Figure 3. The locality bias in inter-image attention. Both SDXL [19] and FLUX.1 [12] tend to allocate more attention weights to spatially nearby regions instead of more semantically related ones. exhibits a locality bias, tending to allocate more attention weights to spatially nearby regions rather than to more semantically related ones. As a result, the model struggles to maintain consistency across varying layouts, failing … view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: An overview of our CharaConsist. (a) shows an example of the textual prompts. (b) is the overall pipeline of our method, in which we replace the original multimodal attention with our proposed point-tracking attention, and further introduce an adaptive token merge modu…
Figure 5
Figure 5. Figure 5: Qualitative comparisons with previous consistent generation methods and identity-reference methods. Our method demonstrates superior performance in maintain consistency of multiple aspects such as character identity, clothing and background scenes. of the two-types tas…
Figure 6
Figure 6. Figure 6: Point matching and mask extraction results. The points in the first image are manually selected, while those in the subsequent images are determined by the matching relationship. the consistent generation methods, across two evaluation tasks, our CharaConsist achieves …
Figure 7
Figure 7. Figure 7: The accuracy of point matching and mask extraction [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Visualizations of generated images in intermediate timesteps. Compared to the independently generated image, our CharaConsist can progressively align the frame image with the identity image, especially in the early timesteps. Metrics T2I; + (a); + (a, b); + (a, b, c) C…
Figure 9
Figure 9. Figure 9: Qualitative results of point matching and mask extraction. The points in the first image of every pair are manually selected, while those in the second image are determined automatically by the matching relationship [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Qualitative comparisons in the background maintaining task. Comparison methods fail to maintain consistency in the environment and clothing. Furthermore, character’s actions and expressions are overly similar across images, deviating from the unique prompt of each ima…
Figure 11
Figure 11. Figure 11: Qualitative comparisons in the background maintaining task. Our CharaConsist achieves fine-grained consistency in the character identity, clothing, and background environments. Even the subtle details, for example, the writing on the chalkboard in the background, are …
Figure 12
Figure 12. Figure 12: Qualitative comparisons in the background switching task. Our CharaConsist can generate diverse backgrounds and character actions while maintaining consistency in the character’s identity and clothing. While the comparison methods exhibit noticeable inconsistencies in…
Figure 13
Figure 13. Figure 13: Qualitative comparisons in the background switching task. In different scenarios, our CharaConsist effectively maintains consistency in the character’s identity, clothing and the guitar. In contrast, the comparison methods show significant inconsistencies in these asp…

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Reference graph

Works this paper leans on

36 extracted references · 28 canonical work pages

  1. [1]

    Retinaface: Single-shot multi-level face localisation in the wild

    Jiankang Deng, Jia Guo, Evangelos Ververas, Irene Kotsia, and Stefanos Zafeiriou. Retinaface: Single-shot multi-level face localisation in the wild. In CVPR, pages 5203–5212,

    work page

  2. [2]

    Taming transformers for high-resolution image synthesis

    Patrick Esser, Robin Rombach, and Bj ¨orn Ommer. Taming transformers for high-resolution image synthesis. In CVPR, pages 12873–12883. Computer Vision Foundation / IEEE,

    work page

  3. [3]

    Scaling rec- tified flow transformers for high-resolution image synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M ¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, and Robin Rombach. Scaling rec- tified flow transformers for high-resolution image synthesis. In ICML. OpenReview.net, 2024. 1, 2, 3

    work page 2024

  4. [4]

    An image is worth one word: Personalizing text- to-image generation using textual inversion

    Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit Haim Bermano, Gal Chechik, and Daniel Cohen- Or. An image is worth one word: Personalizing text- to-image generation using textual inversion. In ICLR. OpenReview.net, 2023. 4

    work page 2023

  5. [5]

    Generative adversarial networks

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139–144, 2020. 3

    work page 2020

  6. [6]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770–778. IEEE Computer Society, 2016. 3

    work page 2016

  7. [7]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In NeurIPS, 2020. 3

    work page 2020

  8. [8]

    Kingma and Max Welling

    Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. In ICLR, 2014. 3

    work page 2014

Show all 36 references
  1. [9]

    Segment anything

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In ICCV, pages 4015–4026, 2023. 7

    2023

  2. [10]

    Hunyuanvideo: A systematic framework for large video generative models

    Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603, 2024. 3

    2024 arXiv

  3. [11]

    Multi-concept customization of text-to-image diffusion

    Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Multi-concept customization of text-to-image diffusion. In CVPR, pages 1931–1941,

    1931

  4. [12]

    Black Forest Labs. Flux. https://github.com/ black-forest-labs/flux, 2024. 1, 2, 3, 4, 11

    2024

  5. [13]

    Photomaker: Customizing realistic human photos via stacked id embedding

    Zhen Li, Mingdeng Cao, Xintao Wang, Zhongang Qi, Ming- Ming Cheng, and Ying Shan. Photomaker: Customizing realistic human photos via stacked id embedding. In CVPR, pages 8640–8650, 2024. 3, 4, 7, 8, 12

    2024

  6. [14]

    Hunyuan-dit: A powerful multi-resolution diffusion transformer with fine-grained chinese understanding

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

    arXiv

  7. [15]

    AnyI2V: Animating any conditional image with motion control generation

    Ziye Li, Hao Luo, Xincheng Shuai, and Henghui Ding. AnyI2V: Animating any conditional image with motion control generation. In ICCV, 2025. 3

    2025

  8. [16]

    One-prompt-one-story: Free-lunch consistent text- to-image generation using a single prompt

    Tao Liu, Kai Wang, Senmao Li, Joost van de Weijer, Fhad Khan, Shiqi Yang, Yaxing Wang, Jian Yang, and Mingming Cheng. One-prompt-one-story: Free-lunch consistent text- to-image generation using a single prompt. In ICLR, 2025. 2, 3

    2025

  9. [17]

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

    Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In ICLR. OpenReview.net, 2023. 11

    2023

  10. [18]

    Scalable diffusion models with transformers

    William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, pages 4172–4182. IEEE, 2023. 1, 2, 3

    2023

  11. [19]

    SDXL: improving latent diffusion models for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. SDXL: improving latent diffusion models for high-resolution image synthesis. In ICLR. OpenReview.net, 2024. 1, 2, 3, 4

    2024

  12. [20]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pa...

    2021

  13. [21]

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

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67, 2020. 3

    2020

  14. [22]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, pages 10684–10695, 2022. 1, 3

    2022

  15. [23]

    U- net: Convolutional networks for biomedical image segmen- tation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- net: Convolutional networks for biomedical image segmen- tation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, pa...

    2015

  16. [24]

    Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

    Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In CVPR, pages 22500–22510, 2023. 4

    2023

  17. [25]

    Facenet: A unified embedding for face recognition and clustering

    Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In CVPR, pages 815–823, 2015. 7

    2015

  18. [26]

    A survey of multimodal-guided image editing with text-to-image diffu- sion models

    Xincheng Shuai, Henghui Ding, Xingjun Ma, Rongcheng Tu, Yu-Gang Jiang, and Dacheng Tao. A survey of multimodal-guided image editing with text-to-image diffu- sion models. arXiv preprint arXiv:2406.14555, 2024. 3

    2024 arXiv

  19. [27]

    Free-form motion control: A synthetic video generation dataset with controllable camera and object motions

    Xincheng Shuai, Henghui Ding, Zhenyuan Qin, Hao Luo, Xingjun Ma, and Dacheng Tao. Free-form motion control: A synthetic video generation dataset with controllable camera and object motions. In ICCV, 2025. 3

    2025

  20. [28]

    Deep unsupervised learning using nonequilibrium thermodynamics

    Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In ICML, pages 2256–

  21. [29]

    Roformer: Enhanced transformer with rotary position embedding

    Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063,

  22. [30]

    Emergent correspondence from image diffusion

    Luming Tang, Menglin Jia, Qianqian Wang, Cheng Perng Phoo, and Bharath Hariharan. Emergent correspondence from image diffusion. NeurIPS, 36:1363–1389, 2023. 3, 5

    2023

  23. [31]

    Training-free consistent text-to-image generation

    Yoad Tewel, Omri Kaduri, Rinon Gal, Yoni Kasten, Lior Wolf, Gal Chechik, and Yuval Atzmon. Training-free consistent text-to-image generation. ACM TOG, 43(4):1–18,

  24. [32]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3

    2017

  25. [33]

    Q-align: teaching lmms for visual scoring via discrete text-defined levels

    Haoning Wu, Zicheng Zhang, Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, et al. Q-align: teaching lmms for visual scoring via discrete text-defined levels. InProceedings of the 41st International Conference on Machine Learn...

    2024

  26. [34]

    Cogvideox: Text-to-video diffusion models with an expert transformer

    Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072, 2024. 3

    2024 arXiv

  27. [35]

    Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models

    Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models. arXiv preprint arXiv:2308.06721,

    arXiv

  28. [36]

    Storydiffusion: Consistent self- attention for long-range image and video generation

    Yupeng Zhou, Daquan Zhou, Ming-Ming Cheng, Jiashi Feng, and Qibin Hou. Storydiffusion: Consistent self- attention for long-range image and video generation. NeurIPS, 2024. 2, 3, 7, 8, 12 Appendix A. Implementation Details Our CharaConsist is built on the FLUX.1-dev [12] and ma...

    2024

Pith tools

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