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REVIEW 4 major objections 5 minor 68 references

MIND-Edit: MLLM Insight-Driven Editing via Language-Vision Projection

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read MIND-Edit claims image editing improves when a multimodal language model predicts a visual embedding of the desired result and injects it into a diffusion model through an image adapter.

desk verdict Plausible method, but Table 1 undercuts the SOTA claim and CLIP-I is circular; needs serious revision, not acceptance as is. read the letter →

arxiv 2505.19149 v1 pith:HZRHEAAE submitted 2025-05-25 cs.CV

Shuyu Wang , Weiqi Li , Qian Wang , Shijie Zhao , Jian Zhang This is my paper
classification cs.CV
keywords instruction-basedimageeditingmultimodallargelanguagemodelvisualrepresentationgenerationdiffusionIP-AdapterjointtrainingCLIPembeddingcomplexmulti-step
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

Image editing systems that accept natural-language instructions often fail when the instruction is vague, because they treat the user's words as the complete specification of intent. MIND-Edit argues that a multimodal large language model's visual understanding should be used directly, not only its text output. The paper proposes two complementary mechanisms: the MLLM rewrites ambiguous instructions into clearer directives, and, more centrally, it predicts a visual embedding of the desired edited image that is fed into the diffusion model through an existing image adapter. Both outputs are trained jointly so that text and visual reasoning reinforce each other. The paper claims this beats current instruction-based editing methods on standard and complex multi-step benchmarks, with particular gains in semantic alignment.

What carries the argument

The load-bearing mechanism is the language-vision projection: a lightweight vision head reading the MLLM's $m$-th hidden layer and outputting an embedding whose dimension matches the image adapter's expected clip_embedding input. The adapter then runs a second cross-attention branch parallel to the text-conditioned one, so the visual embedding $v_{\mathrm{insight}}$ steers the UNet without retraining the diffusion model. The joint loss $\ell_{\text{total}} = \ell_{\text{text}} + \lambda \ell_{\text{embed}}$ ties the text and visual heads together.

What would settle it

Build a test set of edits whose target images have near-identical CLIP embeddings to their sources, such as changing a small object's color or removing one element, and measure whether MIND-Edit still reproduces the edit. If the predicted visual embedding is nearly the source embedding and the edit fails, the assumption that CLIP embeddings encode the needed editing intent is the weak link.

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Extended reading notes

Core claim

The central claim is that editing intent can be carried by a learned visual representation $v_{\mathrm{insight}}$, computed as $v_{\mathrm{insight}} = F_v(h_m)$ from the MLLM's intermediate hidden states, and that injecting this representation via an image adapter's decoupled cross-attention gives the diffusion model guidance that pure text prompting misses. The vision head $F_v$ is trained against the CLIP image embedding of the ground-truth edited image with a cosine-similarity loss, while the text head is trained with cross-entropy to produce an optimized instruction. A weighted sum of the two losses is trained jointly. The paper reports that ablations removing either strategy degrade performance and that independent training of the two heads is worse than joint training, supporting the claim that the two modalities reinforce each other.

Load-bearing premise

The whole pipeline leans on the assumption that a single embedding of the target edited image, drawn from a standard image-text model, is a faithful and complete summary of what the edit should look like, and that the frozen image adapter can turn that summary back into pixels.

Editorial extensions

If this is right

  • Editorial intent can be transferred as an embedding rather than only as text, which gives diffusion models a direct visual target to reconstruct.
  • The joint text-plus-embedding training means an editing model can improve both its instruction rewriting and its visual guidance with one objective, rather than maintaining two separate models.
  • Because the visual representation plugs into an existing image adapter, the approach can be retrofitted to other pretrained diffusion backbones without retraining the adapter.
  • The reported gains on the complex multi-step dataset suggest the method is most valuable where instructions require inference about layout, object state, or style, not just vocabulary matching.

Reading between the lines

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

  • A ceiling implied by the design, though not stated in the paper, is that the visual guidance is only as informative as the CLIP embedding space used as supervision; edits that change pixels but not CLIP semantics are likely to be missed, and replacing the regression target with patch-level or dense features would test this directly.
  • The joint-training result hints at a shared reasoning pathway between the text head and the vision head; one could probe this by ablating the intermediate layer index $m$ and measuring whether the optimal layer shifts with instruction complexity.
  • A natural extension is to apply the same insight-driven embedding to video editing, where per-frame consistent edits need the same visual intent carried across time.
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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

4 major / 5 minor

Summary. The paper proposes MIND-Edit, an image-editing framework that couples a multimodal large language model (LLaVA-OneVision-7B) with a frozen Stable Diffusion backbone. The MLLM both rewrites ambiguous user instructions into clearer text and produces a visual embedding v_insight from an intermediate hidden layer; this embedding is injected into the diffusion model through a pretrained IP-Adapter. The two outputs are trained jointly with a cross-entropy text loss and a cosine-similarity loss between v_insight and the CLIP image embedding of the target edited image. Quantitative results are reported on HumanEdit and ComplexMultistepImageEditing against SmartEdit, BrushNet, and MagicQuill, together with ablations that separate instruction optimization, visual guidance, and joint versus independent training.

Significance. The core idea is plausible and potentially useful: instead of relying on the MLLM's text output alone, the paper attempts to exploit the MLLM's internal visual representations to guide a diffusion model, and the plug-and-play use of IP-Adapter is practically attractive. The ablation design is thoughtful in isolating the contributions of the two strategies and of joint training, and the paper includes an explicit Limitations section as well as concrete training details. However, the significance is severely undercut because the paper's headline empirical claim is contradicted by its own quantitative table, and because the training objective in Eq. (5) directly targets the same CLIP embedding space used by the CLIP-I evaluation metric. As presented, the evidence does not support the claimed state-of-the-art performance.

major comments (4)
  1. [§4.2, Table 1] The central claim in the Abstract, §4.2, and Conclusion that MIND-Edit outperforms state-of-the-art methods is contradicted by Table 1. Against MagicQuill, the strongest baseline, MIND-Edit loses CLIP-I on both datasets (0.9310 vs 0.9381 on HumanEdit; 0.6340 vs 0.6996 on ComplexMultistepImageEditing), loses LPIPS and SSIM on HumanEdit (0.1245 vs 0.1162 and 0.8517 vs 0.8981, respectively), and wins only PSNR on HumanEdit by 0.033 dB. On ComplexMultistepImageEditing, MIND-Edit wins LPIPS, PSNR, and SSIM, but by small margins (0.0074, 0.1087 dB, and 0.0252, respectively). The text in §4.2 itself concedes that MIND-Edit "performs comparably to MagicQuill" in simple scenarios, which is incompatible with the abstract's "outperforms" claim. The paper's own numbers therefore do not support the main conclusion.
  2. [§3.5, Eq. (5); §4.1 evaluation metrics] The training objective ℓ_embed in Eq. (5) maximizes cosine similarity between v_insight and E_CLIP(I_gt), the CLIP image embedding of the target edited image. The evaluation metric CLIP-I in Table 1 is, in standard practice, computed with the same CLIP image encoder, making CLIP-I the metric most directly aligned with the training objective. This creates a circularity concern: CLIP-I cannot serve as an independent measure of semantic alignment for this method. The fact that MIND-Edit still loses CLIP-I to MagicQuill on both datasets is therefore especially damaging to the claim that the visual-representation mechanism provides an advantage. The authors should specify the exact CLIP-I computation (model variant, prompt template, and whether the source or target image is used) and add at least one evaluation that does not rely on the same embedding space, such as a DINO-based metric, a user study, or a different CLIP checkpoint.
  3. [Table 2, rows #1 and #6] The ablation study does not fully support the statement in §4.3 that both instruction optimization and visual representation guidance contribute to improved editing performance. In Table 2, row #1 (instruction optimization only, without v_insight) has higher PSNR than the full method in row #6 (22.4354 vs 22.2714), and row #5 (independent text head, jointly trained visual head) has higher PSNR and SSIM than row #6 (22.3914 and 0.8524 vs 22.2714 and 0.8517). Thus the claim of "consistent improvements across all quantitative metrics" from joint training is not supported by the reported values. The authors should correct the interpretation, report error bars, or provide significance tests before drawing these conclusions.
  4. [§4.1, datasets and metrics] Most of the reported differences are very small (e.g., 0.003 in CLIP-I, 0.03 dB in PSNR) and are based on 500 and 120 test images, respectively, with no confidence intervals or significance tests. As reported, the quantitative comparisons cannot reliably distinguish methods from noise, especially for the claim of superiority. The paper should report confidence intervals or paired significance tests, and should clarify whether the baseline numbers are taken from the original papers or re-run under identical experimental protocols.
minor comments (5)
  1. [§3.4 and §4.1] The choice of hidden layer m=21 and the balancing weight λ=2 are not justified and are not varied in any sensitivity analysis; a short study of these choices would make the method more robust.
  2. [Throughout] The model name is written inconsistently as "LLaV A-OneVision" and "LLaV A-OneVision-7B"; the spacing should be corrected to "LLaVA-OneVision".
  3. [Table 1 caption and §4.2] The caption says "Our method demonstrates competitive performance," which is weaker than and inconsistent with the abstract's "outperforms state-of-the-art" claim; the claims should be aligned with what the numbers actually show.
  4. [Eq. (1)] The notation z0=E(y) is used but the diffusion loss refers to noisy latents zt; please clarify the conditioning convention and whether c_T is the raw instruction or the optimized instruction during training.
  5. [Figures 3, 4, and A] The qualitative comparisons would be easier to assess if they included side-by-side crops and a clear statement of which input mask, if any, is used for each baseline, since mask versus mask-free evaluation affects fairness.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Eq. (5)-CLIP-I overlap is metric alignment, not a by-construction reduction; Table 1 contradiction is an empirical correctness issue.

full rationale

The only candidate circular step is the relation between Eq. (5) and the CLIP-I metric. Eq. (5) trains v_insight to maximize cosine similarity with E_CLIP(I_gt), and CLIP-I also uses a CLIP image encoder, so the training signal is aligned with the semantic-similarity metric. But v_insight is not the final edited image: it is injected into a frozen IP-Adapter, and CLIP-I is computed on the rendered output pixels. The paper gives no equation setting E_CLIP(output) equal to v_insight, and the IP-Adapter decoding is a nontrivial pretrained component, so the final metric is not forced by construction. The instruction-optimization component is supervised with GPT-4o-generated target texts rather than with the downstream editing metrics, and the joint-training loss is a weighted sum of text cross-entropy and the CLIP-embedding cosine loss; neither reduces to the Table 1 numbers. Self-citations ([27], [28], [29], [55]) appear only as related-work examples and are not load-bearing; the MetaMorph/MetaQueries motivation is external prior work. Finally, the discrepancy between the abstract's 'outperforms state-of-the-art' claim and Table 1, where MagicQuill wins several metrics, is a correctness/empirical-evidence problem, not a circular-derivation problem. Under the strict 'exhibit Eq. X = Eq. Y' rule, no circular step is established.

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

The framework introduces no new physical entities or new conserved quantities; the only new component is the trainable vision head Fv, which is a standard neural network module. The central assumptions are about the sufficiency of CLIP embeddings as an editing-intent representation and the transferability of IP-Adapter conditioning, both of which are empirical premises rather than derived results.

free parameters (3)
  • lambda (balancing weight in Eq. 6) = 2
    Chosen by hand to balance text and visual embedding losses; no sensitivity analysis is provided.
  • hidden layer index m for vision head (Eq. 2) = 21 (out of 28)
    Selected without ablation or justification; the choice of which layer's hidden states to use is a free design parameter that likely affects the quality of v_insight.
  • training hyperparameters (learning rate, epochs, batch size) = 1e-5, 5 epochs, batch size 8
    Standard training defaults; no exploration or sensitivity study is reported, but they influence the final model.
assumptions (4)
  • domain assumption Pretrained LLaVA-OneVision-7B has sufficient visual understanding to infer editing intent from an image and a text instruction.
    The entire method depends on the MLLM's hidden states encoding the semantic content needed for the edit. This is assumed from prior success of LLaVA and not independently validated in this paper.
  • domain assumption The frozen IP-Adapter can translate a CLIP image embedding into effective conditioning for Stable Diffusion to produce the desired edit.
    The paper relies on IP-Adapter's pretrained capability to use image embeddings as prompts. If IP-Adapter cannot faithfully render the predicted embedding, the visual guidance strategy fails.
  • domain assumption The CLIP image embedding of the ground-truth edited image is a valid and sufficient supervisor for learning editing intent.
    Eq. (5) assumes that maximizing cosine similarity to E_CLIP(I_gt) captures the essence of the edit. CLIP embeddings are known to be lossy and may not encode fine-grained spatial or attribute changes.
  • ad hoc to paper Joint training of text and visual heads mutually enhances both modalities.
    The paper hypothesizes this based on MetaMorph, but provides no mechanism or analysis beyond an ablation that shows some improvement. It is a postulate specific to this design.

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Cite this review

Pith. "Pith review of MIND-Edit: MLLM Insight-Driven Editing via Language-Vision Projection." pith.science (2026). https://pith.science/paper/HZRHEAAE

@misc{pith2026250519149,
  author       = {Pith},
  title        = {Pith review of: MIND-Edit: MLLM Insight-Driven Editing via Language-Vision Projection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HZRHEAAE}},
  note         = {Machine review of arXiv:2505.19149}
}
read the original abstract

Recent advances in AI-generated content (AIGC) have significantly accelerated image editing techniques, driving increasing demand for diverse and fine-grained edits. Despite these advances, existing image editing methods still face challenges in achieving high precision and semantic accuracy in complex scenarios. Recent studies address this issue by incorporating multimodal large language models (MLLMs) into image editing pipelines. However, current MLLM-based methods mainly rely on interpreting textual instructions, leaving the intrinsic visual understanding of large models largely unexplored, thus resulting in insufficient alignment between textual semantics and visual outcomes. To overcome these limitations, we propose MIND-Edit, an end-to-end image-editing framework integrating pretrained diffusion model with MLLM. MIND-Edit introduces two complementary strategies: (1) a text instruction optimization strategy that clarifies ambiguous user instructions based on semantic reasoning from the MLLM, and (2) an MLLM insight-driven editing strategy that explicitly leverages the intrinsic visual understanding capability of the MLLM to infer editing intent and guide the diffusion process via generated visual embeddings. Furthermore, we propose a joint training approach to effectively integrate both strategies, allowing them to reinforce each other for more accurate instruction interpretation and visually coherent edits aligned with user intent. Extensive experiments demonstrate that MIND-Edit outperforms state-of-the-art image editing methods in both quantitative metrics and visual quality, particularly under complex and challenging scenarios.

Figures

Figures reproduced from arXiv: 2505.19149 by the authors.

Figure 1
Figure 1. Overview of the proposed MIND-Edit framework. MIND-Edit takes text instructions, original images, and optional editing masks as inputs. It integrates an instruction optimization strategy and an MLLM insight-driven image editing strategy, jointly optimizing instructions and generating visual representations to guide the diffusion model in creating semantically accurate edited images. zt , conditioned on both the imag… view at source ↗
Figure 2
Figure 2. Illustration of the text instruction optimization strategy. A prompt informs the MLLM about the upcoming instruction optimization task. Given an image and an instruction from the user, the MLLM refines the instruction by resolving ambiguities based on visual and textual context. to the diffusion model. Besides, motivated by recent findings [52], we hypothesize that jointly optimizing the textual and visual outputs o… view at source ↗
Figure 3
Figure 3. Qualitative comparisons on the HumanEdit dataset [2]. A mask is provided for each sample. MIND-Edit achieves superior instruction alignment and visual quality, surpassing or matching other methods even though MagicQuill’s generation branch alone contains twice the parameters of our method [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Qualitative comparisons on the ComplexMultistepImageEditing dataset [11], where no mask is provided. MIND-Edit achieves precise semantic alignment in complex editing scenarios. and layout of the acorns were largely consistent with the original image. Across both cases,…
Figure 5
Figure 5. Figure 5: Qualitative results of the ablation study. With the proposed instruction optimization, visual representation generation strategies, and joint training approach, MIND-Edit achieves improved instruction-aligned details, textures, and overall visual consistency compared t…

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Reference graph

Works this paper leans on

68 extracted references · 17 canonical work pages

  1. [1]

    Blended diffusion for text-driven editing of natural images

    Omri Avrahami, Dani Lischinski, and Ohad Fried. Blended diffusion for text-driven editing of natural images. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18208–18218, 2022

    2022

  2. [2]

    Humanedit: A high-quality human-rewarded dataset for instruction-based image editing

    Jinbin Bai, Wei Chow, Ling Yang, Xiangtai Li, Juncheng Li, Hanwang Zhang, and Shuicheng Yan. Humanedit: A high-quality human-rewarded dataset for instruction-based image editing. arXiv preprint arXiv:2412.04280, 2024

    arXiv 2024

  3. [3]

    Instructpix2pix: Learning to follow image editing instructions

    Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18392–18402, 2023

    2023

  4. [4]

    Training-free layout control with cross-attention guidance

    Minghao Chen, Iro Laina, and Andrea Vedaldi. Training-free layout control with cross-attention guidance. InProceedings of the IEEE/CVF winter conference on applications of computer vision, pages 5343–5353, 2024

    2024

  5. [5]

    Zero-shot Image Editing with Reference Imitation

    Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang, Shilong Zhang, Yu Liu, Yujun Shen, and Hengshuang Zhao. Zero-shot image editing with reference imitation.arXiv preprint arXiv:2406.07547, 2024

    work page Pith review arXiv 2024

  6. [6]

    Anydoor: Zero-shot object-level image customization

    Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao. Anydoor: Zero-shot object-level image customization. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6593–6602, 2024

    2024

  7. [7]

    Region-aware text-to-image generation via hard binding and soft refinement

    Zhennan Chen, Yajie Li, Haofan Wang, Zhibo Chen, Zhengkai Jiang, Jun Li, Qian Wang, Jian Yang, and Ying Tai. Region-aware text-to-image generation via hard binding and soft refinement. arXiv preprint arXiv:2411.06558, 2024

    arXiv 2024

  8. [8]

    Swiftbrush v2: Make your one-step diffusion model better than its teacher

    Trung Dao, Thuan Hoang Nguyen, Thanh Le, Duc Vu, Khoi Nguyen, Cuong Pham, and Anh Tran. Swiftbrush v2: Make your one-step diffusion model better than its teacher. InEuropean Conference on Computer Vision, pages 176–192. Springer, 2024

    work page 2024

Show all 68 references
  1. [9]

    Turboedit: Text- based image editing using few-step diffusion models

    Gilad Deutch, Rinon Gal, Daniel Garibi, Or Patashnik, and Daniel Cohen-Or. Turboedit: Text- based image editing using few-step diffusion models. InSIGGRAPH Asia 2024 Conference Papers, pages 1–12, 2024

    2024

  2. [10]

    Textcrafter: Accurately rendering multiple texts in complex visual scenes.arXiv preprint arXiv:2503.23461, 2025

    Nikai Du, Zhennan Chen, Zhizhou Chen, Shan Gao, Xi Chen, Zhengkai Jiang, Jian Yang, and Ying Tai. Textcrafter: Accurately rendering multiple texts in complex visual scenes.arXiv preprint arXiv:2503.23461, 2025

    2025

  3. [11]

    Complex multistep image-editing dataset, 2025

    Nilan Ekanayake. Complex multistep image-editing dataset, 2025

    2025

  4. [12]

    Guid- ing instruction-based image editing via multimodal large language models.arXiv preprint arXiv:2309.17102, 2023

    Tsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang, Yinfei Yang, and Zhe Gan. Guid- ing instruction-based image editing via multimodal large language models.arXiv preprint arXiv:2309.17102, 2023

    2023 arXiv

  5. [13]

    Renoise: Real image inversion through iterative noising

    Daniel Garibi, Or Patashnik, Andrey V oynov, Hadar Averbuch-Elor, and Daniel Cohen-Or. Renoise: Real image inversion through iterative noising. InEuropean Conference on Computer Vision, pages 395–413. Springer, 2024

    2024

  6. [14]

    Generative adversarial nets.Advances in neural information processing systems, 27, 2014

    Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets.Advances in neural information processing systems, 27, 2014

    2014

  7. [15]

    Multi- reward as condition for instruction-based image editing.arXiv preprint arXiv:2411.04713, 2024

    Xin Gu, Ming Li, Libo Zhang, Fan Chen, Longyin Wen, Tiejian Luo, and Sijie Zhu. Multi- reward as condition for instruction-based image editing.arXiv preprint arXiv:2411.04713, 2024

    2024 arXiv

  8. [16]

    Freeedit: Mask-free reference-based image editing with multi-modal instruction.arXiv preprint arXiv:2409.18071, 2024

    Runze He, Kai Ma, Linjiang Huang, Shaofei Huang, Jialin Gao, Xiaoming Wei, Jiao Dai, Jizhong Han, and Si Liu. Freeedit: Mask-free reference-based image editing with multi-modal instruction.arXiv preprint arXiv:2409.18071, 2024

    2024 arXiv

  9. [17]

    Prompt-to-prompt image editing with cross attention control.arXiv preprint arXiv:2208.01626, 2022

    Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control.arXiv preprint arXiv:2208.01626, 2022. 10

    2022 arXiv

  10. [18]

    Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

    2020

  11. [19]

    Smartedit: Exploring complex instruction- based image editing with multimodal large language models

    Yuzhou Huang, Liangbin Xie, Xintao Wang, Ziyang Yuan, Xiaodong Cun, Yixiao Ge, Jiantao Zhou, Chao Dong, Rui Huang, Ruimao Zhang, et al. Smartedit: Exploring complex instruction- based image editing with multimodal large language models. InProceedings of the IEEE/CVF Conference...

    2024

  12. [20]

    Brushnet: A plug-and-play image inpainting model with decomposed dual-branch diffusion

    Xuan Ju, Xian Liu, Xintao Wang, Yuxuan Bian, Ying Shan, and Qiang Xu. Brushnet: A plug-and-play image inpainting model with decomposed dual-branch diffusion. InEuropean Conference on Computer Vision, pages 150–168. Springer, 2024

    2024

  13. [21]

    Image inpainting models are effective tools for instruction-guided image editing.arXiv preprint arXiv:2407.13139, 2024

    Xuan Ju, Junhao Zhuang, Zhaoyang Zhang, Yuxuan Bian, Qiang Xu, and Ying Shan. Image inpainting models are effective tools for instruction-guided image editing.arXiv preprint arXiv:2407.13139, 2024

    2024 arXiv

  14. [22]

    A style-based generator architecture for generative adversarial networks

    Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401–4410, 2019

    2019

  15. [23]

    Analyzing and improving the image quality of stylegan

    Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110–8119, 2020

    2020

  16. [24]

    Auto-encoding variational bayes, 2013

    Diederik P Kingma, Max Welling, et al. Auto-encoding variational bayes, 2013

    2013

  17. [25]

    Generating images with multimodal language models.Advances in Neural Information Processing Systems, 36:21487–21506, 2023

    Jing Yu Koh, Daniel Fried, and Russ R Salakhutdinov. Generating images with multimodal language models.Advances in Neural Information Processing Systems, 36:21487–21506, 2023

    2023

  18. [26]

    Llava-onevision: Easy visual task transfer.arXiv preprint arXiv:2408.03326, 2024

    Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, et al. Llava-onevision: Easy visual task transfer.arXiv preprint arXiv:2408.03326, 2024

    2024 arXiv

  19. [27]

    Q-insight: Understanding image quality via visual reinforcement learning.arXiv preprint arXiv:2503.22679, 2025

    Weiqi Li, Xuanyu Zhang, Shijie Zhao, Yabin Zhang, Junlin Li, Li Zhang, and Jian Zhang. Q-insight: Understanding image quality via visual reinforcement learning.arXiv preprint arXiv:2503.22679, 2025

    2025 arXiv

  20. [28]

    Resvr: Joint rescaling and viewport rendering of omnidirectional images

    Weiqi Li, Shijie Zhao, Bin Chen, Xinhua Cheng, Junlin Li, Li Zhang, and Jian Zhang. Resvr: Joint rescaling and viewport rendering of omnidirectional images. InProceedings of the 32nd ACM International Conference on Multimedia, pages 78–87, 2024

    2024

  21. [29]

    Omnidrag: Enabling motion control for omnidirectional image-to- video generation.arXiv preprint arXiv:2412.09623, 2024

    Weiqi Li, Shijie Zhao, Chong Mou, Xuhan Sheng, Zhenyu Zhang, Qian Wang, Junlin Li, Li Zhang, and Jian Zhang. Omnidrag: Enabling motion control for omnidirectional image-to- video generation.arXiv preprint arXiv:2412.09623, 2024

    2024 arXiv

  22. [30]

    Brushedit: All-in-one image inpainting and editing.arXiv preprint arXiv:2412.10316, 2024

    Yaowei Li, Yuxuan Bian, Xuan Ju, Zhaoyang Zhang, Ying Shan, Yuexian Zou, and Qiang Xu. Brushedit: All-in-one image inpainting and editing.arXiv preprint arXiv:2412.10316, 2024

    2024 arXiv

  23. [31]

    Adversarial supervision makes layout-to-image diffusion models thrive

    Yumeng Li, Margret Keuper, Dan Zhang, and Anna Khoreva. Adversarial supervision makes layout-to-image diffusion models thrive. InThe Twelfth International Conference on Learning Representations, 2024

    2024

  24. [32]

    Drag your noise: Interactive point-based editing via diffusion semantic propagation

    Haofeng Liu, Chenshu Xu, Yifei Yang, Lihua Zeng, and Shengfeng He. Drag your noise: Interactive point-based editing via diffusion semantic propagation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6743–6752, 2024

    2024

  25. [33]

    Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

    2023

  26. [34]

    Step1x-edit: A practical framework for general image editing.arXiv preprint arXiv:2504.17761, 2025

    Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang, Honghao Fu, Chunrui Han, Guopeng Li, Yuang Peng, Quan Sun, Jingwei Wu, Yan Cai, Zheng Ge, Ranchen Ming, Lei Xia, Xianfang Zeng, Yibo Zhu, Binxing Jiao, Xiangyu Zhang, Gang Yu, and Dax...

    2025 arXiv

  27. [35]

    Magicquill: An intelligent interactive image editing system

    Zichen Liu, Yue Yu, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Wen Wang, Zhiheng Liu, Qifeng Chen, and Yujun Shen. Magicquill: An intelligent interactive image editing system. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 11

    2025

  28. [36]

    Diffeditor: Boosting accuracy and flexibility on diffusion-based image editing

    Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang. Diffeditor: Boosting accuracy and flexibility on diffusion-based image editing. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8488–8497, 2024

    2024

  29. [37]

    Dragondiffusion: Enabling drag-style manipulation on diffusion models

    Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang. Dragondiffusion: Enabling drag-style manipulation on diffusion models. InThe Twelfth International Conference on Learning Representations, 2024

    2024

  30. [38]

    T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

    Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, and Ying Shan. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. InProceedings of the AAAI conference on artificial intelligence, volume 38, pages 429...

    2024

  31. [39]

    Handiffuser: Text-to-image generation with realistic hand appearances

    Supreeth Narasimhaswamy, Uttaran Bhattacharya, Xiang Chen, Ishita Dasgupta, Saayan Mitra, and Minh Hoai. Handiffuser: Text-to-image generation with realistic hand appearances. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2468–2479, 2024

    2024

  32. [40]

    Transfer between modalities with metaqueries.arXiv preprint arXiv:2504.06256, 2025

    Xichen Pan, Satya Narayan Shukla, Aashu Singh, Zhuokai Zhao, Shlok Kumar Mishra, Jialiang Wang, Zhiyang Xu, Jiuhai Chen, Kunpeng Li, Felix Juefei-Xu, et al. Transfer between modalities with metaqueries.arXiv preprint arXiv:2504.06256, 2025

    2025 arXiv

  33. [41]

    Drag your gan: Interactive point-based manipulation on the generative image manifold

    Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, and Christian Theobalt. Drag your gan: Interactive point-based manipulation on the generative image manifold. InACM SIGGRAPH 2023 conference proceedings, pages 1–11, 2023

    2023

  34. [42]

    Sdxl: Improving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952, 2023

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952, 2023

    2023 arXiv

  35. [43]

    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. InInternational conference on machine learning, pag...

    2021

  36. [44]

    High- resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10684–10695, June 2022

    2022

  37. [45]

    Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information ...

    2022

  38. [46]

    Emu edit: Precise image editing via recognition and generation tasks

    Shelly Sheynin, Adam Polyak, Uriel Singer, Yuval Kirstain, Amit Zohar, Oron Ashual, Devi Parikh, and Yaniv Taigman. Emu edit: Precise image editing via recognition and generation tasks. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),...

    2024

  39. [47]

    Dragdiffusion: Harnessing diffusion models for interactive point-based image editing

    Yujun Shi, Chuhui Xue, Jun Hao Liew, Jiachun Pan, Hanshu Yan, Wenqing Zhang, Vincent YF Tan, and Song Bai. Dragdiffusion: Harnessing diffusion models for interactive point-based image editing. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,...

    2024

  40. [48]

    Insert anything: Image insertion via in-context editing in dit.arXiv preprint arXiv:2504.15009, 2025

    Wensong Song, Hong Jiang, Zongxing Yang, Ruijie Quan, and Yi Yang. Insert anything: Image insertion via in-context editing in dit.arXiv preprint arXiv:2504.15009, 2025

    2025 arXiv

  41. [49]

    Invert- ible consistency distillation for text-guided image editing in around 7 steps.arXiv preprint arXiv:2406.14539, 2024

    Nikita Starodubcev, Mikhail Khoroshikh, Artem Babenko, and Dmitry Baranchuk. Invert- ible consistency distillation for text-guided image editing in around 7 steps.arXiv preprint arXiv:2406.14539, 2024

    2024 arXiv

  42. [50]

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

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

    2024 arXiv

  43. [51]

    Gpt-4o system card, 2024

    OpenAI Team. Gpt-4o system card, 2024

    2024

  44. [52]

    Metamorph: Multimodal under- standing and generation via instruction tuning.arXiv preprint arXiv:2412.14164, 2024

    Shengbang Tong, David Fan, Jiachen Zhu, Yunyang Xiong, Xinlei Chen, Koustuv Sinha, Michael Rabbat, Yann LeCun, Saining Xie, and Zhuang Liu. Metamorph: Multimodal under- standing and generation via instruction tuning.arXiv preprint arXiv:2412.14164, 2024. 12

    2024 arXiv

  45. [53]

    Plug-and-play diffusion features for text-driven image-to-image translation

    Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel. Plug-and-play diffusion features for text-driven image-to-image translation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1921–1930, 2023

    1921

  46. [54]

    Flexedit: Marrying free-shape masks to vllm for flexible image editing

    Jue Wang, Yuxiang Lin, Tianshuo Yuan, Zhi-Qi Cheng, Xiaolong Wang, Jiao GH, Wei Chen, and Xiaojiang Peng. Flexedit: Marrying free-shape masks to vllm for flexible image editing. arXiv preprint arXiv:2408.12429, 2024

    2024 arXiv

  47. [55]

    360dvd: Controllable panorama video generation with 360-degree video diffusion model

    Qian Wang, Weiqi Li, Chong Mou, Xinhua Cheng, and Jian Zhang. 360dvd: Controllable panorama video generation with 360-degree video diffusion model. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6913–6923, 2024

    2024

  48. [56]

    Emu3: Next-token prediction is all you need

    Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409.18869, 2024

    2024 arXiv

  49. [57]

    In- stancediffusion: Instance-level control for image generation

    Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, and Ishan Misra. In- stancediffusion: Instance-level control for image generation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6232–6242, 2024

    2024

  50. [58]

    Genartist: Multimodal llm as an agent for unified image generation and editing.Advances in Neural Information Processing Systems, 37:128374–128395, 2024

    Zhenyu Wang, Aoxue Li, Zhenguo Li, and Xihui Liu. Genartist: Multimodal llm as an agent for unified image generation and editing.Advances in Neural Information Processing Systems, 37:128374–128395, 2024

    2024

  51. [59]

    Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600– 612, 2004

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600– 612, 2004

    2004

  52. [60]

    Visionllm v2: An end-to-end generalist multimodal large language model for hundreds of vision-language tasks.Advances in Neural Information Processing Systems, 37:69925–69975, 2024

    Jiannan Wu, Muyan Zhong, Sen Xing, Zeqiang Lai, Zhaoyang Liu, Zhe Chen, Wenhai Wang, Xizhou Zhu, Lewei Lu, Tong Lu, et al. Visionllm v2: An end-to-end generalist multimodal large language model for hundreds of vision-language tasks.Advances in Neural Information Processing Sys...

    2024

  53. [61]

    Show-o: One single trans- former to unify multimodal understanding and generation.arXiv preprint arXiv:2408.12528, 2024

    Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single trans- former to unify multimodal understanding and generation.arXiv preprint arXiv:2408.12528, 2024

    2024 arXiv

  54. [62]

    Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models.arXiv preprint arXiv:2308.06721, 2023

    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, 2023

    2023 arXiv

  55. [63]

    Anyedit: Mastering unified high-quality image editing for any idea.arXiv preprint arXiv:2411.15738, 2024

    Qifan Yu, Wei Chow, Zhongqi Yue, Kaihang Pan, Yang Wu, Xiaoyang Wan, Juncheng Li, Siliang Tang, Hanwang Zhang, and Yueting Zhuang. Anyedit: Mastering unified high-quality image editing for any idea.arXiv preprint arXiv:2411.15738, 2024

    2024 arXiv

  56. [64]

    Magicbrush: A manually annotated dataset for instruction-guided image editing.Advances in Neural Information Processing Systems, 36:31428–31449, 2023

    Kai Zhang, Lingbo Mo, Wenhu Chen, Huan Sun, and Yu Su. Magicbrush: A manually annotated dataset for instruction-guided image editing.Advances in Neural Information Processing Systems, 36:31428–31449, 2023

    2023

  57. [65]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. InProceedings of IEEE/CVF International Conference on Computer Vision (ICCV), 2023

    2023

  58. [66]

    The unrea- sonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unrea- sonable effectiveness of deep features as a perceptual metric. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 586–595, 2018

    2018

  59. [67]

    Gooddrag: Towards good practices for drag editing with diffusion models.arXiv preprint arXiv:2404.07206, 2024

    Zewei Zhang, Huan Liu, Jun Chen, and Xiangyu Xu. Gooddrag: Towards good practices for drag editing with diffusion models.arXiv preprint arXiv:2404.07206, 2024

    2024 arXiv

  60. [68]

    Ultraedit: Instruction-based fine-grained image editing at scale.Advances in Neural Information Processing Systems, 37:3058–3093, 2024

    Haozhe Zhao, Xiaojian Shawn Ma, Liang Chen, Shuzheng Si, Rujie Wu, Kaikai An, Peiyu Yu, Minjia Zhang, Qing Li, and Baobao Chang. Ultraedit: Instruction-based fine-grained image editing at scale.Advances in Neural Information Processing Systems, 37:3058–3093, 2024. 13 A More Re...

    2024

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.