Pith. sign in

REVIEW 1 major objections 1 minor 25 cited by

GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset

T0 review · 1 major / 1 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 By regenerating outputs and rewriting instructions with GPT-4o, this paper builds a 1.5M-pair editing dataset that lifts open-source models to record scores on GEdit-EN, ImgEdit, and Complex-Edit.

desk verdict A genuinely useful 1.5M-triplet dataset with a sensible refinement pipeline, but the abstract overclaims SOTA and the final model's gains are not cleanly attributed to the data. read the letter →

arxiv 2507.21033 v1 pith:K54BE4MW submitted 2025-07-28 cs.CV

Yuhan Wang , Siwei Yang , Bingchen Zhao , Letian Zhang , Qing Liu , Yuyin Zhou , Cihang Xie This is my paper
classification cs.CV
keywords imageeditingdatasetgenerationGPT-4oinstructionfollowingflowmatchingFluxKontextmultimodallearningbenchmarkevaluation
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

This paper claims that a large, publicly released corpus of 1.5M instruction–image–edited-image triplets, produced by using GPT-4o to regenerate and align images from three existing datasets, can close much of the gap between open-source and proprietary image editors. The authors fine-tune FluxKontext, a flow-matching model, on this data and report state-of-the-art open-source scores on GEdit-EN-full (7.24), ImgEdit-Full (3.80), and Complex-Edit (8.78), approaching GPT-4o. The core assertion is that data quality—tight text–image alignment with identity preservation—matters more than instruction complexity alone, and that a GPT-4o-based pipeline can systematically produce such data at scale. If true, the dataset gives open-source researchers a resource to train stronger editors without access to proprietary models or data.

What carries the argument

The paper's mechanism is a multi-step data-curation pipeline: (1) regenerate edited output images with GPT-4o's gpt-image-1 API to improve visual quality and alignment; (2) rewrite about 10% of instructions so they accurately describe the new output; (3) regenerate about 50% of HQ-Edit input images; and (4) add a Complex-Edit subset with instructions composed of three atomic edits. On the model side, the paper fine-tunes FluxKontext, a rectified flow transformer, replacing its text conditioning with concatenated frozen Qwen-VL-7b and T5 embeddings for semantic alignment. Ablations show each curation step contributes positively, and that adding instruction complexity without preserving identity harms performance.

What would settle it

Run a human-rated evaluation on the same edited-image pairs, or use an MLLM judge that is not GPT-4o-based, comparing models fine-tuned on the original versus the GPT-4o-refined datasets. If human raters do not consistently prefer the refined-data model, or if the gap shrinks below significance, the central claim that data refinement drives the reported gains is falsified.

Watch

Extended reading notes

Core claim

The central discovery is that systematically refining existing public editing datasets with GPT-4o's image-generation API is sufficient to produce a training corpus that lifts instruction-following, perceptual quality, and identity preservation in a state-of-the-art open-source editor. The paper demonstrates this through controlled ablations: regenerating only the output image improves GEdit-EN from 4.93 to 5.98 on Flux 1.0 dev with OmniEdit; rewriting instructions to match new outputs adds further gains on ImgEdit (3.24 to 3.40); and full-pair regeneration on HQ-Edit raises GEdit-EN from 5.67 to 5.73. Combined with a Complex-Edit subset of C3-composed instructions and a FluxKontext backbone using concatenated frozen Qwen-VL and T5 encoders, these steps produce a model scoring 7.24 on GEdit-EN-full, 3.80 on ImgEdit-Full, and 8.78 on Complex-Edit, exceeding all previously published open-source methods and narrowing the gap to GPT-4o.

Load-bearing premise

The benchmarks are scored by MLLM judges, and the paper assumes these judges are unbiased about the fact that the training data was generated by GPT-4o; if the judges favor GPT-4o-like outputs, the measured gains would overstate real editing quality.

Editorial extensions

If this is right

  • Fine-tuning open-source models on GPT-IMAGE-EDIT-1.5M yields state-of-the-art open-source scores on GEdit-EN-full (7.24), ImgEdit-Full (3.80), and Complex-Edit (8.78), narrowing the gap to GPT-4o.
  • Each data-refinement step—output regeneration, instruction rewriting, and full-pair regeneration—yields measurable gains in ablations, with output regeneration providing the largest single jump.
  • Training on complex instructions without enforcing identity preservation can hurt performance, so data alignment, not instruction complexity alone, is the key driver of success.
  • The release of the dataset and the fine-tuned model provides a public resource that lets other researchers train capable editors without proprietary data.

Reading between the lines

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

  • A human-rated evaluation would be the natural stress test: if judges unaware of the data provenance do not rank the GPT-4o-refined model above its original-data-trained counterparts on real user edits, the benchmark gains would overstate the true improvement.
  • Because the dataset was produced by a proprietary model, the curation recipe itself is not fully reproducible by the open-source community, though the released 1.5M triplets make the benefit reproducible.
  • The same output-regeneration-plus-instruction-rewriting loop could be applied to future source datasets or other modalities, making data quality a movable target as frontier models improve.
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

1 major / 1 minor

Summary. The paper introduces GPT-IMAGE-EDIT-1.5M, a dataset of over 1.5 million (instruction, source image, edited image) triplets built by using GPT-4o/GPT-Image-1 to refine three existing datasets: OmniEdit, HQ-Edit, and UltraEdit. The refinement pipeline consists of output-image regeneration, selective instruction rewriting, full pair regeneration, and a Complex-Edit-style subset with three atomic edits per instruction. The authors fine-tune FluxKontext, augmented with Qwen-VL-7b and T5 encoders, on the new dataset and report state-of-the-art open-source scores on GEdit-EN-full (7.24), ImgEdit-Full (3.80), and Complex-Edit (8.78), while also reporting results on OmniContext. The central claim is that the GPT-refined data, rather than the architectural upgrades, drives the observed performance gains.

Significance. If the central claim holds, the released dataset is a substantial community resource: it is large, publicly available, and built through a reproducible pipeline that demonstrably improves output quality and instruction alignment on the 100k-scale ablations. The paper ships the dataset, code, and model weights, which supports reproducibility. The consistent gains from output regeneration across two base models (SD3-Medium and Flux 1.0 dev) in Table 5 are genuine evidence that GPT-4o-refined data can improve editing models. However, the paper does not currently isolate the dataset's contribution in the final FluxKontext configuration, and the benchmark evaluation depends entirely on MLLM judges that may be biased toward GPT-4o-like outputs. These gaps affect the strength of the headline claims, so the paper needs revision before the significance can be fully credited.

major comments (1)
  1. [Table 5, Complex-Edit Instruction Ablation row] The 'Complex-Edit Instruction Ablation' row in Table 5 reports only a single condition ('Flux 1.0 dev Complex-Edit 2.89 5.39') with no baseline. The text claims this shows that raw complex instructions are detrimental to identity preservation, but without a comparison to the identical backbone trained on OmniEdit without the complex subset, the effect cannot be attributed to the complex instructions. This row should be replaced with a proper paired ablation (e.g., same data mix with and without the Complex-Edit subset) or removed.
minor comments (1)
  1. [§4.2] The paper does not list training hyperparameters, compute budget, or the exact composition of the final 1.5M dataset (e.g., how many samples from each source after filtering). Including these details in the appendix would aid reproducibility.

Circularity Check

0 steps flagged · score 1.0 of 10

No meaningful circularity; the central claim is an empirical dataset-plus-finetuning result measured on external benchmarks, with only minor self-referential elements that are not load-bearing.

full rationale

The paper makes no theoretical derivation; its central claim is that a GPT-4o-refined dataset, when used to fine-tune FluxKontext with Qwen-VL/T5 encoders, yields strong scores on GEdit-EN-full (7.24), ImgEdit-Full (3.80), Complex-Edit (8.78), and OmniContext. GEdit-EN and ImgEdit are external benchmarks, and the reported gains are measured rather than derived from the dataset construction. The ablations in Table 5 compare baseline versus regenerated data on SD3-Medium and Flux 1.0 dev, and the gains are not equal to the input by construction. Two caveats are self-referential but not circular: (a) Complex-Edit and HQ-Edit come from overlapping author groups (Yang et al., 2025; Hui et al., 2025), and the paper generates 'Complex-Edit style' instructions for part of its dataset and then evaluates on the Complex-Edit benchmark; this is a benchmark-alignment/self-citation concern, not a logical reduction. (b) All benchmarks use MLLM judges while the training data was produced by GPT-4o, and the paper does not test judge bias; this is an external-validity concern, not a circular derivation. The abstract's 'markedly exceed all previously published open-source methods' is also qualified by Table 2 where Ovis-U1 scores 4.00 > 3.80 on ImgEdit-Full; this is a factual accuracy issue, not circularity. The missing ablation of the final architecture on unrefined 1.5M data weakens causal attribution of the dataset, but does not make any claim equivalent to its input. Overall, no circular step can be exhibited with a specific equation or construction, so the paper is not significantly circular.

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

The central claim rests on benchmark validity and the reliability of GPT-4o as a generator. No fitted parameters are used to produce the headline numbers; the hand-set thresholds and cost-limited fractions are dataset construction choices rather than fitted parameters.

free parameters (4)
  • Fraction of OmniEdit instructions rewritten = ~10%
    Chosen under cost limitations, not optimized; affects instruction-image alignment.
  • Fraction of HQ-Edit input images regenerated = ~50%
    Chosen under cost limitations, not optimized; affects input image quality.
  • Complex-Edit instruction complexity level = C3 (3 atomic instructions)
    Chosen to balance complexity and realism; not fitted to benchmark scores.
  • Padding filter threshold = 0.5% uniform border
    Hand-set quality filter to reject residual padding after crop.
assumptions (3)
  • domain assumption Benchmark scores (GEdit-EN, ImgEdit, Complex-Edit, OmniContext) are valid measures of instruction-following, identity preservation, and perceptual quality.
    The central claim rests on these metrics; they are MLLM-based and may be biased by the training-data source.
  • domain assumption GPT-4o/gpt-image-1 can serve as a reliable generator of high-quality editing samples and accurate instruction rewrites.
    The whole dataset depends on the proprietary model's editing and captioning abilities; the paper acknowledges semantic drift and uses rewriting to correct it.
  • domain assumption The source datasets (OmniEdit, HQ-Edit, UltraEdit) contain usable editing pairs, and their redistribution after GPT-4o modification is permissible.
    The dataset is a derivative of these three collections; licensing and terms of use are not discussed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset." pith.science (2026). https://pith.science/paper/K54BE4MW

@misc{pith2026250721033,
  author       = {Pith},
  title        = {Pith review of: GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K54BE4MW}},
  note         = {Machine review of arXiv:2507.21033}
}
read the original abstract

Recent advancements in large multimodal models like GPT-4o have set a new standard for high-fidelity, instruction-guided image editing. However, the proprietary nature of these models and their training data creates a significant barrier for open-source research. To bridge this gap, we introduce GPT-IMAGE-EDIT-1.5M, a publicly available, large-scale image-editing corpus containing more than 1.5 million high-quality triplets (instruction, source image, edited image). We systematically construct this dataset by leveraging the versatile capabilities of GPT-4o to unify and refine three popular image-editing datasets: OmniEdit, HQ-Edit, and UltraEdit. Specifically, our methodology involves 1) regenerating output images to enhance visual quality and instruction alignment, and 2) selectively rewriting prompts to improve semantic clarity. To validate the efficacy of our dataset, we fine-tune advanced open-source models on GPT-IMAGE-EDIT-1.5M. The empirical results are exciting, e.g., the fine-tuned FluxKontext achieves highly competitive performance across a comprehensive suite of benchmarks, including 7.24 on GEdit-EN, 3.80 on ImgEdit-Full, and 8.78 on Complex-Edit, showing stronger instruction following and higher perceptual quality while maintaining identity. These scores markedly exceed all previously published open-source methods and substantially narrow the gap to leading proprietary models. We hope the full release of GPT-IMAGE-EDIT-1.5M can help to catalyze further open research in instruction-guided image editing.

Figures

Figures reproduced from arXiv: 2507.21033 by the authors.

Figure 1
Figure 1. An overview of the GPT-IMAGE-EDIT-1.5M dataset. The figure presents qualitative examples from our dataset, showcasing its ability to handle complex and diverse instruction-guided edits. The bar chart on the right demonstrates the effectiveness of our data; a model fine-tuned on GPT-IMAGE-EDIT-1.5M achieves a new state-of-the-art score of 7.24 on the GEdit-EN-full benchmark, outperforming existing open-source methods… view at source ↗
Figure 2
Figure 2. An overview of GPT-IMAGE-EDIT-1.5M data curation pipeline. We applied mutiple methods to collect high-quality image-editing data. We used GPT-4o to re-write 10% instructions of the original OmniEdit dataset to make them more accurate, and the input images originally generated by DALL-E in HQ-Edit were re-synthesized by GPT-Image-1 for higher alignment. et al., 2025); ImgEdit spans 9 task families (Add, Adjust, Extra… view at source ↗
Figure 3
Figure 3. The qualitative results of our method on G-Edit-Benchmark-EN. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The qualitative results of our method on Img-Edit. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: The qualitative results of our method on OmniContext. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: The qualitative results of our method on Complex-Edit. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 25 Pith papers

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

  1. Editor's Choice: Evaluating Abstract Intent in Image Editing through Atomic Entity Analysis

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    Presents Entity-Rubrics and AbstractEdit benchmark to measure image editing models on abstract intent, finding standard models struggle to balance edit intent with image preservation.

  2. RefineAnything: Multimodal Region-Specific Refinement for Perfect Local Details

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    RefineAnything is a multimodal diffusion model using Focus-and-Refine crop-and-resize with blended paste-back to achieve high-fidelity local image refinement and near-perfect background preservation.

  3. WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing

    cs.CV 2026-03 conditional novelty 7.0 of 10

    WeEdit trains a glyph-guided, RL-optimized image editor on a 330K-pair synthetic multilingual dataset and reports open-source SOTA on its own bilingual and multilingual text-editing benchmarks.

  4. InstructMoLE: Instruction-Guided Mixture of Low-rank Experts for Multi-Conditional Image Generation

    cs.CV 2025-12 unverdicted novelty 7.0 of 10

    InstructMoLE replaces per-token routing with instruction-guided global routing for mixture-of-low-rank-experts in diffusion transformers and adds an output-space orthogonality loss to improve multi-conditional image g...

  5. Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning

    cs.CV 2025-07 unverdicted novelty 7.0 of 10

    Presents Reason50K dataset and ReasonBrain framework for hypothetical instruction-based image editing that requires physical, temporal, causal, and story reasoning.

  6. Illuminating Visual Identity in Universal Multimodal Embeddings

    cs.CV 2026-08 conditional novelty 6.0 of 10

    By adding identity-aware sampling and a contrastive loss on a new 28-dataset benchmark, the authors build multimodal embeddings that are far better at visual identity matching without losing general retrieval accuracy.

  7. FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Video editing can be learned from image-edit pairs that are synthetically warped into videos, plus self-distillation losses that align image and video outputs.

  8. SPAR: Semantic-Pixel Self-Alignment and Adaptive Routing for Unified Multimodal Models

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    SPAR introduces semantic-pixel self-alignment via asymmetric tokenizer and adaptive routing for unified MLLMs that achieve SOTA generation and reconstruction while retaining understanding.

  9. TextSculptor: Training and Benchmarking Scene Text Editing

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    TextSculptor supplies an automated data synthesis pipeline yielding 3.2M samples plus a four-task benchmark that raises open-source scene text editing performance.

  10. MUSE: Resolving Manifold Misalignment in Visual Tokenization via Topological Orthogonality

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    MUSE decouples reconstruction and semantic learning in visual tokenization via topological orthogonality, yielding SOTA generation quality and improved semantic performance over its teacher model.

  11. LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    LIVE achieves state-of-the-art instruction-based video editing by jointly training on image and video data with a frame-wise token noise strategy to bridge domain gaps and a new benchmark of over 60 tasks.

  12. InsEdit: Towards Instruction-based Visual Editing via Data-Efficient Video Diffusion Models Adaptation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    InsEdit adapts a video diffusion backbone for text-instruction video editing via Mutual Context Attention, achieving SOTA open-source results with O(100K) data while also supporting image editing.

  13. SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    SpatialEdit provides a benchmark, large synthetic dataset, and baseline model for precise object and camera spatial manipulations in images, with the model beating priors on spatial editing.

  14. Under One Sun: Multi-Object Generative Perception of Materials and Illumination

    cs.CV 2026-03 conditional novelty 6.0 of 10

    Factorizing video editing into semantic-token anchoring and motion-restoration pre-training produces strong zero-shot and SOTA open-source instruction-guided video edits without heavy external structural priors.

  15. Sparse-LaViDa: Sparse Multimodal Discrete Diffusion Language Models

    cs.CV 2025-12 conditional novelty 6.0 of 10

    Sparse-LaViDa speeds up masked diffusion LLMs by truncating masked tokens, using register tokens and a step-causal attention mask, reaching ~2-2.8x speedup over LaViDa-O with comparable generation quality.

  16. Emu3.5: Native Multimodal Models are World Learners

    cs.CV 2025-10 unverdicted novelty 6.0 of 10

    Emu3.5 is a native multimodal world model pre-trained on over 10 trillion vision-language tokens with next-token prediction, post-trained via reinforcement learning, and accelerated by Discrete Diffusion Adaptation fo...

  17. Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation

    cs.CV 2025-09 conditional novelty 6.0 of 10

    Lavida-O introduces an elastic mixture-of-transformers architecture that brings high-resolution text-to-image generation, object grounding, and image editing into a single masked diffusion model, using planning and se...

  18. Reconstruction Alignment Improves Unified Multimodal Models

    cs.CV 2025-09 conditional novelty 6.0 of 10

    RECA, a self-supervised post-training objective that conditions unified multimodal models on their own visual understanding embeddings to reconstruct input images, improves text-to-image and editing benchmarks across ...

  19. Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A curated GPT-4o synthetic image dataset improves open-source generation models on instruction-following, surreal scenes, and multi-reference synthesis, plus two new benchmarks to measure those skills.

  20. SPAR: Semantic-Pixel Self-Alignment and Adaptive Routing for Unified Multimodal Models

    cs.CV 2026-06 unverdicted novelty 5.0 of 10

    SPAR introduces a semantic-pixel self-alignment tokenizer and dynamic token routing to create a unified multimodal model that performs both understanding and generation at claimed state-of-the-art levels.

  21. Bernini: Latent Semantic Planning for Video Diffusion

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    Bernini is a framework that uses an MLLM planner to output semantic representations for a DiT renderer to generate or edit videos, reporting SOTA benchmark performance.

  22. FineEdit: Fine-Grained Image Edit with Bounding Box Guidance

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    FineEdit adds multi-level bounding box injection to diffusion image editing, releases a 1.2M-pair dataset with box annotations, and shows better instruction following and background consistency than prior open models ...

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

    cs.CV 2025-08 conditional novelty 5.0 of 10

    A 3B model that injects intermediate MLLM query states into matching diffusion transformer layers reports competitive text-to-image and image-editing benchmark scores.

  24. JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation

    cs.GR 2026-05 unverdicted novelty 4.0 of 10

    JoyAI-Image unifies visual understanding and generation via an MLLM-MMDiT architecture with spatial training signals to reach competitive benchmark performance and stronger spatial intelligence.

  25. JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation

    cs.GR 2026-05 unverdicted novelty 4.0 of 10

    JoyAI-Image unifies visual understanding, generation, and editing in one model and claims stronger spatial intelligence through bidirectional perception-generation loops.

Reference graph

Works this paper leans on

15 extracted references · 14 linked inside Pith · cited by 23 Pith papers

  1. [1]

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

    arXiv

  2. [5]

    10 Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al

    URLhttps: //openreview.net/forum?id=mZptYYttFj. 10 Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card.arXiv preprint arXiv:2410.21276,

    arXiv

  3. [7]

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

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

    arXiv

  4. [8]

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

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

    arXiv

  5. [9]

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

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

    arXiv

  6. [10]

    Sdedit: Guided image synthesis and editing with stochastic differential equations.arXiv preprint arXiv:2108.01073,

    Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations.arXiv preprint arXiv:2108.01073,

    arXiv

  7. [12]

    Seededit 3.0: Fast and high-quality generative image editing.arXiv preprint arXiv:2506.05083,

    Peng Wang, Yichun Shi, Xiaochen Lian, Zhonghua Zhai, Xin Xia, Xuefeng Xiao, Weilin Huang, and Jianchao Yang. Seededit 3.0: Fast and high-quality generative image editing.arXiv preprint arXiv:2506.05083,

    arXiv

  8. [13]

    net/forum?id=Hlm0cga0sv

    URLhttps://openreview. net/forum?id=Hlm0cga0sv. Chenyuan Wu, Pengfei Zheng, Ruiran Yan, Shitao Xiao, Xin Luo, Yueze Wang, Wanli Li, Xiyan Jiang, Yexin Liu, Junjie Zhou, et al. Omnigen2: Exploration to advanced multimodal generation. arXiv preprint arXiv:2506.18871,

    arXiv

Show all 15 references
  1. [14]

    Complex- edit: Cot-like instruction generation for complexity-controllable image editing benchmark.arXiv preprint arXiv:2504.13143,

    11 Siwei Yang, Mude Hui, Bingchen Zhao, Yuyin Zhou, Nataniel Ruiz, and Cihang Xie. Complex- edit: Cot-like instruction generation for complexity-controllable image editing benchmark.arXiv preprint arXiv:2504.13143,

    arXiv

  2. [15]

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

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

    arXiv

  3. [2020]

    Sharegpt-4o-image: Aligning multimodal models with gpt-4o-level image generation.arXiv preprint arXiv:2506.18095,

    Junying Chen, Zhenyang Cai, Pengcheng Chen, Shunian Chen, Ke Ji, Xidong Wang, Yunjin Yang, and Benyou Wang. Sharegpt-4o-image: Aligning multimodal models with gpt-4o-level image generation.arXiv preprint arXiv:2506.18095,

    arXiv

  4. [2022]

    Seededit: Align image re-generation to image editing

    Yichun Shi, Peng Wang, and Weilin Huang. Seededit: Align image re-generation to image editing. arXiv preprint arXiv:2411.06686,

    arXiv

  5. [2023]

    Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,

    1901

  6. [2024]

    Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, et al. Flux. 1 kontext: Flow matching for in-context image generation and editing in latent space.arXiv preprint arXiv...

    arXiv

  7. [2025]

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

    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,

    arXiv

Pith tools

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