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REVIEW 3 major objections 5 minor 2 cited by

Image Editing with Diffusion Models: A Survey

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This survey argues that all diffusion-based image editing can be organized by one taxonomy: visual content versus visual expression, four operations, and three method families.

desk verdict A useful but over-claimed taxonomy survey: broad, readable, and worth engaging, but the three-way method split is cleaner on paper than on the evidence. read the letter →

arxiv 2504.13226 v1 pith:YBTSNVFV submitted 2025-04-17 cs.GR

Jia Wang , Jie Hu , Xiaoqi Ma , Hanghang Ma , Xiaoming Wei , Enhua Wu This is my paper · ORCID
classification cs.GR
keywords diffusionmodelsimageeditingtaxonomyinversion-basedmethodsfine-tuning-basedadapter-basedbenchmarksdatasets
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 sets out to make the rapidly growing field of diffusion-model image editing navigable by giving it a single organizational structure. It argues that behind the variety of effects and implementations there is a stable split: each edit targets either visual content or visual expression; each user operation is an add, delete, change, or combine; and each method alters the base model in one of three ways—by inversion without parameter changes, by fine-tuning, or by training a separate adapter module. The survey also organizes evaluation metrics, benchmarks, and dataset-construction pipelines along the same lines. A sympathetic reader would care because, if the structure holds, researchers gain a common vocabulary for comparing methods and building new ones, rather than facing an unorganized list of papers.

What carries the argument

The load-bearing device is the taxonomy itself: a decomposition of an image into visual content and visual expression, a four-way action set of add, delete, change, and combine, and a three-way method split determined by whether the base model is untouched, fine-tuned, or extended with an adapter (a trained add-on module that injects processed prompts into a frozen model). The taxonomy does the work of assigning every task a coordinate, every method a bucket, and every benchmark or dataset a rationale; all four survey sections are organized around it.

What would settle it

Take the methods from a recent major computer-vision venue and try to place each one into exactly one of the three categories. A single method whose contribution depends equally on fine-tuning base-model parameters and training an adapter—with ablations showing both are necessary—would disprove exclusivity, and a recognized editing task that changes neither visual content nor visual expression and is not an add, delete, change, or combine would disprove completeness.

Watch

Extended reading notes

Core claim

The paper's central claim is organizational: image editing can be defined as modifying an existing image to satisfy a user intention, and a systematic taxonomy falls out of that definition. Images are first separated into feature images (keypoints, edges, depth) and natural images; natural images are then divided into visual content (objects, backgrounds) and visual expression (structure, style, light, texture). User operations reduce to four actions—add, delete, change, combine—and instructions can arrive as text, as a feature image, or as in-context image pairs. Methods are grouped by how much of the base model they modify: inversion-based methods steer noise latents or attention maps without touching parameters, fine-tuning-based methods retrain all or part of the network at training or test time, and adapter-based methods keep the base model frozen and inject processed multimodal prompts through trained adapters. The survey claims this classification is clearer and simpler than earlier schemes, and it uses the same distinctions to organize evaluation, benchmarks, and dataset construction.

Load-bearing premise

The load-bearing premise is that every editing task fits the content/expression grid with the four actions and every method fits exactly one of the three buckets by its dominant mechanism; the paper itself notes that some methods combine adapter training with base-model fine-tuning, so the exclusivity of the buckets is the assumption most likely to give way.

Editorial extensions

If this is right

  • A new editing method can be located by asking three questions: which image component it targets, which of the four actions it performs, and whether it leaves the base model untouched, fine-tunes it, or trains an adapter.
  • The task grid gives benchmark builders a checklist of task cells to cover; the survey reviews EditBench, EditVal, TEdBench, I2EBench, and EditEval as variants of one pipeline.
  • Dataset construction splits into two pipelines—extraction-based (feature maps, subject sets, video frames) and generation-based (LLM-written instructions plus consistent image pairs)—so data builders can choose by task type.
  • The survey's challenges follow from the taxonomy: inversion methods lose detail to stochasticity, fine-tuning needs data and compute, adapters handle multi-image inputs poorly, and current metrics miss semantic coherence; the stated future directions are unified, multimodal, multi-turn editing models.

Reading between the lines

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

  • An implication the authors leave implicit is that the task grid is generative: crossing every content/expression component with every action defines a design space, and cells such as 'combine applied to lighting' or 'delete applied to texture' remain largely unexplored.
  • Because the paper concedes that some methods train adapters while also fine-tuning base parameters, the three method families are best read as labels for the dominant mechanism rather than exclusive classes; marking hybrid methods explicitly would make the taxonomy more robust.
  • If MLLM scoring plus human validation continues to displace pure compute metrics, editing evaluation will increasingly need to measure instruction-intent understanding, not just fidelity—a direction the survey signals but does not develop.
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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

3 major / 5 minor

Summary. This manuscript is a survey of image editing with diffusion models. It proposes a task taxonomy in which an image is decomposed into visual content and visual expression, editing operations are characterized as add/delete/change/combine, and instructions are classified as text, feature-image, or in-context. It then organizes editing methods into inversion-based, fine-tuning-based, and adapter-based categories, and reviews evaluation metrics, benchmarks, and dataset construction approaches. The final sections discuss open challenges and future directions. The paper states that its main distinction from prior surveys is a clearer and simpler classification.

Significance. The survey's four-part organization (tasks, methods, evaluation, datasets) is sensible, and the detailed account of dataset construction pipelines in Section 5, including generation-based construction and data filtering, is a practical contribution that goes beyond many earlier surveys. The paper covers a broad range of recent methods and provides helpful illustrative figures. The main caveat is that the central method taxonomy is not yet internally consistent: the manuscript itself contains examples that do not fit the trichotomy and it resolves a hybrid case with a subjective tie-breaker. This weakens the survey's central claim, but the issue is addressable by revision. The contribution is organizational; the paper provides no code or machine-checked artifacts.

major comments (3)
  1. [Section 3, Section 3.3.2] The central claim that every editing method falls into exactly one of the inversion-based, fine-tuning-based, or adapter-based categories is not supported by the manuscript's own text. Section 3 defines the categories by 'the extent of modifications applied to the base model' and states that adapter-based methods require no modification of base-model parameters. Section 3.3.2 then says that 'some recent methods simultaneously use adapters to process multimodal prompts and fine-tune the original model parameters,' and assigns these methods to the adapter category because their 'main innovation and contribution' lies in prompt processing. This is a subjective priority rule rather than a classification criterion, so the proposed categories are not mutually exclusive as claimed. I recommend either defining an explicit primary-modification rule or presenting the three categories as overlapping families and discussing hybrid methods.
  2. [Section 3.2.2] Textual Inversion is described as a testing-time fine-tuning method, but under the definitions in Section 3 it does not modify base-model parameters, and it does not manipulate noise latents or attention maps in the way the inversion-based category is described. It optimizes a learnable text embedding from a few images. This is an internal counterexample to the claimed partition: the method belongs to none of the three categories as defined. The taxonomy needs either a fourth category, such as embedding or prompt optimization, or a broader definition of fine-tuning that is stated explicitly and applied consistently.
  3. [Section 5.1.1] The dataset MultiGen-20M is attributed to 'UniControl (Zhao et al., 2024a),' but MultiGen-20M was introduced by Qin et al. (2023) with UniControl, while Zhao et al. (2024a) is Uni-ControlNet. The inconsistency is evident because Section 3.3.1 correctly cites UniControl as Qin et al. (2023). This attribution error should be corrected, since Section 5 is one of the survey's main contributions and accuracy of dataset provenance is essential in a survey.
minor comments (5)
  1. [Section 5.2] The paragraph introducing generation-based dataset construction contains a duplicated fragment: '...the strategies they employ to ensure the quality and consistency of the generated datasets. have approached these steps and the strategies they employ...' The second fragment should be deleted.
  2. [Section 3.2.2] The description of Imagic says that it 'fixes the network parameters' and then states that 'the fine-tuning process involves adjusting the entire denoising network.' Please clarify that these statements refer to the two stages of Imagic, because as written the description is self-contradictory.
  3. [Section 4.2.1] The text cites 'Reason-Edit (Huang et al., 2024b),' but the bibliography entry for Huang et al. (2024b) is titled SmartEdit; please align the in-text name with the cited work.
  4. [Sections 3.2.1, 5.2.1] The paper uses inconsistent spelling for OmniControl: 'Omini-control' in Section 3.2.1 and 'OminiControl' in Section 5.2.1 versus 'OmniControl' elsewhere.
  5. [Figure 5, Section 4.2.1] There are several typos: 'beah' in the Fig. 5 caption should be 'beach'; 'reasonina' in Section 4.2.1 should be 'reasoning'; and 'OMniEdit-Filtered-1.2M' in Section 5.2.3 has unusual capitalization.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's taxonomy and summaries describe external literature rather than deriving predictions from fitted inputs.

full rationale

This is a survey paper whose proposed contributions are organizational: a task taxonomy (visual content vs. visual expression; add/delete/change/combine), a method trichotomy (inversion-based, fine-tuning-based, adapter-based), a metrics summary, and a dataset-construction overview. None of these are derived from a fitted parameter that is then renamed as a prediction, and no equation or theorem in the paper is shown to reduce to its own inputs. The method classification is justified by the stated criterion of 'the extent of modifications applied to the base model' (Section 3), with each category illustrated by representative external methods; this is an interpretive taxonomy, not a self-referential derivation. The paper explicitly acknowledges that some recent methods combine adapter processing with fine-tuning (Section 3.3.2), and the placement of borderline methods such as Textual Inversion under testing-time fine-tuning is a classification judgment, not a circular argument. The stated distinctiveness relative to prior surveys is 'clearer, simpler classifications,' which is a claim about expository utility rather than an empirically derived result. No load-bearing self-citations, uniqueness theorems, or fitted-input-called-prediction steps are present. Any weakness about taxonomy completeness or overlap is a correctness/evidence concern, not circularity. The survey is therefore self-contained as a literature organization, and the honest finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

A survey introduces no fitted parameters or new entities; it relies on the reliability of its reading of prior work and on the representativeness of its selection.

assumptions (2)
  • domain assumption The selected references are representative of the image editing field and are described accurately.
    The survey's taxonomy and overview are only as valid as its summarization of each cited method; any misreading or selective omission distorts the map.
  • ad hoc to paper Each editing method can be assigned to exactly one of the three categories: inversion-based, fine-tuning-based, or adapter-based.
    The paper asserts this partition at the start of Section 3 without a systematic audit showing all surveyed methods fit exactly one category.

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Cite this review

Pith. "Pith review of Image Editing with Diffusion Models: A Survey." pith.science (2026). https://pith.science/paper/YBTSNVFV

@misc{pith2026250413226,
  author       = {Pith},
  title        = {Pith review of: Image Editing with Diffusion Models: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YBTSNVFV}},
  note         = {Machine review of arXiv:2504.13226}
}
read the original abstract

With deeper exploration of diffusion model, developments in the field of image generation have triggered a boom in image creation. As the quality of base-model generated images continues to improve, so does the demand for further application like image editing. In recent years, many remarkable works are realizing a wide variety of editing effects. However, the wide variety of editing types and diverse editing approaches have made it difficult for researchers to establish a comprehensive view of the development of this field. In this survey, we summarize the image editing field from four aspects: tasks definition, methods classification, results evaluation and editing datasets. First, we provide a definition of image editing, which in turn leads to a variety of editing task forms from the perspective of operation parts and manipulation actions. Subsequently, we categorize and summary methods for implementing editing into three categories: inversion-based, fine-tuning-based and adapter-based. In addition, we organize the currently used metrics, available datasets and corresponding construction methods. At the end, we present some visions for the future development of the image editing field based on the previous summaries.

Figures

Figures reproduced from arXiv: 2504.13226 by the authors.

Figure 1
Figure 1. An overview of our survey, which includes four main parts: editing tasks, methods classification, results [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Partition of images and corresponding editing tasks. An image can be divided into two primary components: [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Examples of feature images and natural images. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Different instruction types. Text instructions are [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Examples of different editing tasks. From the perspective of simplicity, we standardized the source images [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: An overview of inversion-based methods. We select four representative methods: SDEdit ( [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Sematic vector alignment in text and image [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Three representative adapter structure: Control [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Original image and multimodal prompts pro [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Performance assessment pipeline. Evaluation [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: General pipeline of editing benchmarks. Despite their distinct focuses, these benchmarks fol￾low a general evaluation pipeline as summarized in [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]
Figure 12
Figure 12. Figure 12: Three types of extraction-based dataset construction methods. [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: Generation-based dataset construction pipeline. The “guidance” in image generation part includes mask, [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. Appearance Pointers -- Multimodal Region Control of Diffusion Transformers

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Appearance pointers are compact tokens that let a diffusion transformer apply text, image, or combined prompts to specific image regions in a single pass.

  2. Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations

    cs.MM 2025-06 conditional novelty 5.0 of 10

    MultiFakeVerse provides 845,286 person-centric images edited through VLM-generated instructions; state-of-the-art deepfake detectors and human observers misclassify a large fraction of them.

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.