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REVIEW 4 major objections 9 minor 2 cited by

Multi-turn Consistent Image Editing

T0 review · 4 major / 9 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A control loop with two anchors keeps an edited image stable across many edit rounds while keeping edits localized.

desk verdict A workable multi-turn editing recipe with a promising dual-objective LQR, but the attention-mask localization is under-validated and the headline claims overstate the numbers. read the letter →

arxiv 2505.04320 v1 pith:Q5VY6QLM submitted 2025-05-07 cs.CV

Zijun Zhou , Yingying Deng , Xiangyu He , Weiming Dong , Fan Tang This is my paper · ORCID
classification cs.CV
keywords multi-turnimageeditingflowmatchinginversiondual-objectiveLQRadaptiveattentionguidancerectifiedFLUXDiTPIE-Benchconsistency
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 argues that single-step image editing methods fail when users want to refine an image over multiple rounds: each round accumulates errors, drifts from the original, and eventually produces artifacts. The authors propose a multi-turn editing framework that anchors every edit to both the original image and the previous edit result using a dual-objective LQR controller in the flow-matching latent space, and then uses a mask built from mid-level attention maps to keep each edit focused on the intended object. They report that at the fourth editing turn their method achieves the best FID (5.553) among all compared methods while keeping CLIP-I and CLIP-T scores balanced, meaning the image stays both faithful to the source and aligned with the prompt. A sympathetic reader would care because interactive tools for design, retouching, and content creation need this kind of iterative, stable editing.

What carries the argument

The central object is the dual-objective LQR update for sampling: at edit turn $k$, the guidance target is $X_{\text{dual}} = X_{0,0} + \lambda(X_{k-1,0} - X_{0,0})$, where $X_{0,0}$ is the original image and $X_{k-1,0}$ is the previous edit. This target is plugged into the LQR-derived conditional velocity $v_t(x_t \mid X_{\text{dual}}) = (X_{\text{dual}} - x_t)/(1-t)$, which steers the flow back toward the original while allowing the previous edit to influence the change. The second component is the adaptive attention mask $M_k$, computed by sorting the 19 double-block self-attention maps by their activation sums, averaging maps 10 through 14 (the medium-low activation ones), and thresholding the average to produce a binary mask. This mask is applied element-wise to the attention matrix at the next step, localizing the edit. Together they balance the tension between preservation and editability that limits single-anchor LQR methods.

What would settle it

Run the method on the full PIE-Bench multi-turn dataset and compare each generated editing mask against the ground-truth edited-region masks; if the average intersection-over-union of the mask with the true edit region is near zero, or if the FID at turn four does not beat the baselines when more than one seed is used, the attention-localization premise is not supported. Also rerun with the mask modules removed after using the attention mask and vice versa.

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Extended reading notes

Core claim

The central claim is that multi-turn image editing can be made stable by combining three components: second-order flow-matching inversion for accurate image-to-noise mapping, a dual-objective linear quadratic regulator (LQR) that guides sampling toward a weighted blend of the original image and the previous edit result, and an adaptive attention mask that reweights only the image regions the edit should affect. The dual-objective LQR is derived by showing that the multi-target optimal control problem reduces to a single-target LQR whose effective target is the weighted average of the individual targets. The attention mask is formed by taking the sum of selected medium-low activation self-attention maps from the FLUX double blocks, averaging them, and thresholding, then element-wise multiplying this mask into the next attention computation. The paper reports that this combination prevents error accumulation across edit turns, preserves the original image's structure and distribution, and still performs the requested edits, with the best FID at turn four.

Load-bearing premise

The assumption that the sum of a FLUX double-block self-attention map reliably indicates where the edit should happen, so that averaging the medium-low activated maps and thresholding gives a correct editing mask.

Editorial extensions

If this is right

  • If the method works as claimed, interactive image editors can support multi-turn refinement without users having to restart from scratch after each failed edit.
  • The dual-objective LQR formulation gives a principled way to anchor generation to both a fixed reference and a drifting target, which should reduce error accumulation over long sequences.
  • The finding that medium-low activation self-attention maps are the ones that localize fine details suggests that layer-wise attention analysis can guide where edits are applied in rectified flow transformers.
  • The new multi-turn extension of PIE-Bench provides a benchmark for future work to measure drift, editability, and quality over successive editing rounds.
  • The method needs only 15 steps for inversion and sampling, comparable to FireFlow's 8 steps and far fewer than the 28-50 steps used by several baselines.

Reading between the lines

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

  • The dual-anchor idea is likely transferable to video editing: treating the first frame as the fixed anchor and the previous edited frame as the drifting target could reduce temporal drift across many frames.
  • The hand-picked layer range (10 to 14) for the attention mask could instead be learned from ground-truth edit masks, which would likely make the method work across more diverse edit types and prompts.
  • Because the LQR guidance alone can over-regularize, the authors' attention mask is what controls editability; a user-defined or prompt-derived mask might allow even more precise control in mixed edits such as changing one region's color while keeping another unchanged.
  • A fair test of the mask quality would be to measure the intersection over union between the generated mask and the human-annotated edit region in PIE-Bench; the authors only show visual examples.
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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 / 9 minor

Summary. The paper proposes a multi-turn image editing framework built on the FLUX.1-dev rectified flow model. The two stated technical contributions are (i) a dual-objective Linear Quadratic Regulator (LQR) guidance that conditions sampling on both the original image and the previous turn's result, with the goal of preventing error accumulation across editing rounds, and (ii) an adaptive attention highlighting mechanism that selects 'medium-low' activated self-attention maps from the FLUX double blocks to build a spatial mask that localizes edits to salient regions while preserving the rest of the image. The method is evaluated on a GPT-4-Turbo-extended version of PIE-Bench, with quantitative metrics (FID, CLIP-T, CLIP-I, CLIP-Edit, structure distance) and qualitative examples. The central claim is that the framework 'significantly improves edit success rates and visual fidelity compared to existing methods' for iterative, multi-turn editing.

Significance. If the claims are substantiated, the paper would provide a useful tool for interactive, iterative image editing, an area that is underexplored relative to single-turn editing. The dual-objective LQR is a natural and technically sound extension of the single-objective LQR in RF-Inversion, and the idea of exploiting layer-wise activation levels in DiT self-attention to localize edits is interesting and potentially generalizable. The paper also ships a multi-turn benchmark extension that could support future work. However, the significance is currently limited by the partial support for the headline claims: the best-FID result at the fourth turn is offset by non-best CLIP-T and CLIP-I scores, the attention-mask mechanism is not quantitatively validated against ground-truth edit regions, and a number of technical details in the equations need correction. The framework is defensible, but the current evidence does not yet establish a consistent multi-turn advantage.

major comments (4)
  1. [Sec. 5.2, Eqs. (12)-(15)] The mask construction as written is mathematically inconsistent. Eq. (12) sorts the scalar activation values a_{k,l} = sum(s_{k,l}), so the sorted quantities a'_{k,l} are scalars, not 2D attention maps. Eq. (13) then averages these scalars, producing a scalar \bar{v}_{i:j}; thresholding a scalar in Eq. (14) cannot yield a spatial mask M_k as used in Eq. (15). To implement the method described in the text, Eq. (13) must average the 2D attention maps s'_{k,l} associated with the selected ranks, not their scalar activation sums. This is a load-bearing error: without a spatial mask, the adaptive attention guidance cannot localize edits, and the claimed editability/consistency trade-off is undefined.
  2. [Sec. 5.2 and Sec. 6.3] The central novelty, the adaptive attention mask, is never validated against ground truth. PIE-Bench provides edit-region masks, and the supplement even uses them to compute CLIP-Edit (Sec. D.1), yet the paper reports no IoU or region-overlap between M_k and these masks, and no per-edit-type breakdown. The ten PIE-Bench categories include global edits such as background and style changes, for which a localized 'medium-low' activation mask is conceptually mismatched. The block range i=10, j=14, hfactor, rfactor, and tau are all hand-set without sensitivity analysis or a held-out validation set. The evidence for the mask's localization quality is limited to two visual examples in Fig. 5. Without quantitative localization validation, the claimed balance between editability and multi-turn coherence is unsupported.
  3. [Abstract, Sec. 6.3, Table 1, and Supplement Table 3] The claim that the framework 'significantly improves edit success rates and visual fidelity compared to existing methods' is overstated relative to the reported numbers. At the fourth editing turn, the proposed method achieves the best FID (5.553) but not the best CLIP-T (FireFlow-v: 28.281 vs 26.831) or the best CLIP-I (RF-Solver: 0.906 vs 0.894). Supplement Table 3 shows that in rounds 1-3 the method has higher FID than RF-Inv. (e.g., round 1: 2.554 vs 1.854) and lower CLIP-T than FireFlow, and Supplement C.2 concedes that first-round editing is suboptimal. All results are point estimates without error bars or significance tests, and Table 1 contains two rows labeled 'Ours' (15 and 8 steps) without explaining which configuration corresponds to the method described in Sec. 5. A claim of 'significantly improves' requires either stronger quantitative support or a more carefully qualified presentation.
  4. [Sec. 5.1, Eqs. (8)-(9)] The guidance terms in Eqs. (8) and (9) appear to have a sign error. In Eq. (5) and in the original RF-Inversion formulation, the LQR guidance is \eta(v_t(X_t|X1) - v_t(X_t)), with a minus sign between the conditional and unconditional velocity fields. In Eqs. (8) and (9), the corresponding terms are written with a plus sign: v_t(X_t|X0) + v_t(X_t) and v_t(X_t|X_dual) + v_t(X_t). If taken literally, this would push the trajectory away from the conditioning image rather than toward it, which contradicts the stated goal of anchoring sampling to the original image and the previous-turn result. Please clarify whether this is a typesetting error and correct the equations; the ablation and Fig. 3 depend on this guidance mechanism.
minor comments (9)
  1. [Abstract] The abstract contains a grammatical error: 'a adaptive attention highlighting method' should read 'an adaptive attention highlighting method'.
  2. [Sec. 5.1, Eqs. (6)-(9)] The notation v_t(X_t|x') for the conditional velocity field is not explicitly defined. State whether v_t(X_t|X_dual) is the analytic field (X_dual - X_t)/(1-t) from Proposition 1 or a network-conditioned variant; the sign analysis in the paper depends on this definition.
  3. [Sec. 5.2, Eq. (10)] The index k is overloaded: it denotes the denoising step in Eq. (10)-(15) but also refers to the editing turn in Eqs. (8)-(9) (e.g., 'the k-th editing step'). Please use distinct symbols to avoid confusion.
  4. [Sec. 5.2, Eq. (13)] The symbol \bar{v}_{i:j} is easily confused with the velocity field v; rename the averaged attention map, for example to \bar{A}_{i:j} or \bar{s}_{i:j}, especially given that scalar activation sums are also denoted a'.
  5. [Sec. 6.4] The ablation terminology is confusing: 'low' activation is defined as blocks 12-17, 'medium' as blocks 6-10, and 'high' as the top 5, but Eq. (12) sorts blocks in ascending order of activation, so blocks 12-17 are actually high-activation blocks in that ordering. Clarify the relationship between the ablation definitions and the main 'medium-low' selection (i=10, j=14).
  6. [Fig. 12 and Supplement Figs. 11, 13, 14] The captions of several qualitative figures say 'Quantitative Results' (e.g., Fig. 12, 'Quantitative Results on Natural Animals'); these should say 'Qualitative Results'.
  7. [References [51]] The FID metric is cited to reference [51], which is the LPIPS paper (Zhang et al., 'The unreasonable effectiveness of deep features as a perceptual metric'). FID should be cited to Heusel et al., 2017; please correct the reference.
  8. [Supplement Sec. C.1] The paper acknowledges that evaluation is limited to four editing turns because of instruction-generation errors. The abstract and introduction claim 'multi-turn' and 'iterative refinement' more generally; it would be helpful to state this four-turn limit explicitly in the main text so readers calibrate the scope of the claims.
  9. [Table 1] The two rows labeled 'Ours' (one with 15 steps and one with 8 steps) are not explained in the table or in the surrounding text. Please label them clearly (e.g., 'Ours (15 steps)' and 'Ours (8 steps)') and specify which configuration is reported in the ablation study.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the LQR derivation reduces to standard single-target control, and the attention-mask heuristic is an empirical choice rather than a fitted prediction.

full rationale

The derivation chain is self-contained and not circular. The dual-objective LQR guidance is not a fitted prediction: Proposition 1 explicitly reduces the multi-target LQR to a single-target LQR with target X_hat = (sum lambda_i X_i)/(sum lambda_i), and Supplement B.2 proves the optimal controller is c*_t = (X_hat - z_t)/(1-t), which is the standard rectified-flow conditional velocity; no evaluation metric is manufactured from the method's own outputs. The adaptive attention mask (Eqs. 10-15) is an empirical heuristic: it selects FLUX double-block attention maps by activation sum, thresholds their average, and modulates the next step's attention. The choices i=10, j=14, hfactor, rfactor, and tau are hand-set rather than derived from the evaluation metrics, so the later FID/CLIP numbers are not equal to these choices by construction. The method is evaluated against external baselines on PIE-Bench with a GPT-4-extended multi-turn protocol, and the ablations in Table 2 test components independently. The only self-citation that touches the method is Xu et al. [47] (co-authored by Fan Tang) for the FLUX attention-quadrant property; the paper supplements it with its own visualizations in Fig. 5, so the citation is not load-bearing in the sense of forcing the result. Supplement C.2's admission that first-round editing is suboptimal is a stated limitation, not evidence of circularity. The absence of held-out validation for the mask hyperparameters is a correctness or robustness concern, not a circularity.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The method introduces no new physical entities. It relies on established flow matching, a second-order solver from the authors' prior FireFlow work, an LQR framework from RF-Inversion, and an attention-mask assumption that is justified mainly by visual inspection. The free parameters are numerous and hand-tuned on the evaluation metric, with tau left unspecified.

free parameters (5)
  • eta (LQR guidance strength) = 0.9
    Controls influence of the LQR guidance term in both inversion (Eq. 8) and sampling (Eq. 9). Chosen by hand, no sensitivity analysis.
  • lambda (dual-objective weight) = 0.7 (for initial 4 sampling steps)
    Weights the previous edit relative to the original image in X_dual (Eq. 9). Applied only for the first 4 sampling steps by hand.
  • mask index range i, j = i=10, j=14
    Selects the 'medium-low' activated attention maps among the 19 FLUX double blocks (Eq. 12-13). Chosen from visual inspection of Fig. 5, not from a quantitative study.
  • hfactor and rfactor = 2.0 and 0.8
    Amplify or reduce attention values in the mask (Eq. 14). Hand-tuned.
  • tau (mask threshold) = not reported
    Threshold in Eq. 14 for binarizing the averaged attention mask. The value is never given in Sec. 6.1, which is a reproducibility gap.
assumptions (5)
  • standard math Rectified flow ODE (dxt/dt = x1 - x0) with a neural velocity field is a valid generative model.
    Adopted from Liu et al. [30] and used throughout Sec. 3 as the foundation of inversion and sampling.
  • domain assumption The midpoint (second-order) ODE solver reduces single-step truncation error and keeps a comparable distribution.
    Borrowed from RF-Solver [44] and FireFlow [11]; used in Eq. 4 and Eq. 8 without new proof.
  • domain assumption FLUX lower-left self-attention quadrant encodes text-to-image spatial influence, and individual columns correspond to text tokens.
    Taken as given from Xu et al. [47] (HeadRouter) in Sec. 5.2; the paper builds its attention analysis on this unverified premise.
  • ad hoc to paper The sum-activation level of a self-attention map correlates with global versus local editing behavior.
    Introduced in Sec. 5.2 from observing only two qualitative examples (Fig. 5); no quantitative support that 'medium-low' activation reliably marks the edit target.
  • standard math Optimal control via the minimum principle gives the LQR solution used to define conditional vector fields.
    The proof in Sec. B.2 follows the classical Hamiltonian minimum principle; the math is standard.

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Cite this review

Pith. "Pith review of Multi-turn Consistent Image Editing." pith.science (2026). https://pith.science/paper/Q5VY6QLM

@misc{pith2026250504320,
  author       = {Pith},
  title        = {Pith review of: Multi-turn Consistent Image Editing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q5VY6QLM}},
  note         = {Machine review of arXiv:2505.04320}
}
read the original abstract

Many real-world applications, such as interactive photo retouching, artistic content creation, and product design, require flexible and iterative image editing. However, existing image editing methods primarily focus on achieving the desired modifications in a single step, which often struggles with ambiguous user intent, complex transformations, or the need for progressive refinements. As a result, these methods frequently produce inconsistent outcomes or fail to meet user expectations. To address these challenges, we propose a multi-turn image editing framework that enables users to iteratively refine their edits, progressively achieving more satisfactory results. Our approach leverages flow matching for accurate image inversion and a dual-objective Linear Quadratic Regulators (LQR) for stable sampling, effectively mitigating error accumulation. Additionally, by analyzing the layer-wise roles of transformers, we introduce a adaptive attention highlighting method that enhances editability while preserving multi-turn coherence. Extensive experiments demonstrate that our framework significantly improves edit success rates and visual fidelity compared to existing methods.

Figures

Figures reproduced from arXiv: 2505.04320 by the authors.

Figure 1
Figure 1. Our method efficiently preserves the original image’s features during multi-turn image reconstruction. Additionally, it enables [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Multi-turn Reconstruction Results. This figure compares image reconstructions using our method and baseline methods across 1, 2, 4, and 8 reconstruction iterations. Our method effectively preserves color, background, structure, and semantic consistency across multiple reconstruction rounds, outperforming the baseline methods. trol over the final result by allowing continuous adjustments based on intermediate outcome… view at source ↗
Figure 3
Figure 3. We visualize the differences in single-step and multi-round accumulative errors during inversion ( [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Multi-turn editing pipeline. In each editing iteration, a high-accuracy rectified flow inversion maps the image back to the Gaussian noise space, followed by sampling to generate the edited images. To better constrain the distribution of edits across multi￾ple turns, t…
Figure 5
Figure 5. Figure 5: Self-attention map visualizations from selected FLUX double blocks (19 total) illustrate layer-specific roles in the editing process (e.g., global, local, details). Top row: attention maps corresponding to the “monkey” text token. Bottom row: maps for the “moon” token.…
Figure 6
Figure 6. Figure 6: Qualitative comparison of multi-turn editing results against baseline methods. Note that our method effectively preserves the original image structure while achieving high-quality edits. provides images paired with editing instructions. To extend this resource for mult…
Figure 7
Figure 7. Figure 7: Ablation study of single-objective LQR guidance. Guidance based solely on the source image limits editability, while relying only on the previous step leads to accumulated error and artifacts. man → superhero source w/o attn low medium high [PITH_FULL_IMAGE:figures/fu…
Figure 8
Figure 8. Figure 8: Ablation study of adaptive attention guidance. Re￾sults demonstrate that editing without attention guidance struggles to affect salient areas, while increasing attention map activation leads to structural damage overly aggressive edits. Solver Edit [44], and StableFlow…
Figure 9
Figure 9. Figure 9: Prompts for GPT4-Turbo genrating multi-turn editing [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: We rank the performance of our method compared to [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: Our method consistently follows the color tone of the original image while achieving the desired editing. The second prompt is [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]
Figure 12
Figure 12. Figure 12: Quantitative Results on Natural Animals. Our method successfully performs edits without introducing artifacts. [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: Quantitative results on artificial images show that our method successfully preserves the background while performing the [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: Quantitative results on artificial images show that our method successfully preserves the background while performing the [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]

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Forward citations

Cited by 2 Pith papers

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  2. EditVerse3D: High-Quality 3D Object Editing with Region-Aware Learning

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    An end-to-end 3D editing framework achieves high-fidelity local edits from coarse bounding boxes and 2D image prompts using region-aware loss reweighting and a large-scale parts-derived training dataset.

Reference graph

Works this paper leans on

65 extracted references · 41 canonical work pages · cited by 2 Pith papers

  1. [1]

    Albergo and Eric Vanden-Eijnden

    Michael S. Albergo and Eric Vanden-Eijnden. Building Nor- malizing Flows with Stochastic Interpolants, 2023. 2, 3

    work page 2023

  2. [2]

    Stable flow: Vital layers for training-free image editing

    Omri Avrahami, Or Patashnik, Ohad Fried, Egor Nemchi- nov, Kfir Aberman, Dani Lischinski, and Daniel Cohen- Or. Stable flow: Vital layers for training-free image editing. arXiv preprint arXiv:2411.14430, 2024. 2, 3, 6, 7, 8

    arXiv 2024

  3. [3]

    Masactrl: Tuning-free mu- tual self-attention control for consistent image synthesis and editing

    Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xi- aohu Qie, and Yinqiang Zheng. Masactrl: Tuning-free mu- tual self-attention control for consistent image synthesis and editing. In Proceedings of the IEEE/CVF international con- ference on computer vision, pages 22560–22570, 2023. 2, 3, 5, 6, 7, 8

    work page 2023

  4. [4]

    Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models

    Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or. Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models. ACM transactions on Graphics (TOG), 42(4):1–10, 2023. 2

    work page 2023

  5. [5]

    Training-free layout control with cross-attention guidance

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

    work page 2024

  6. [6]

    Style injec- tion in diffusion: A training-free approach for adapting large- scale diffusion models for style transfer

    Jiwoo Chung, Sangeek Hyun, and Jae-Pil Heo. Style injec- tion in diffusion: A training-free approach for adapting large- scale diffusion models for style transfer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8795–8805, 2024. 3

    work page 2024

  7. [7]

    Diffedit: Diffusion-based seman- tic image editing with mask guidance

    Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. Diffedit: Diffusion-based seman- tic image editing with mask guidance. arXiv preprint arXiv:2210.11427, 2022. 2, 3

    arXiv 2022

  8. [8]

    CHATEDIT: Towards Multi- turn Interactive Facial Image Editing via Dialogue

    Xing Cui, Zekun Li, Peipei Li, Yibo Hu, Hailin Shi, and Zhaofeng He. CHATEDIT: Towards Multi- turn Interactive Facial Image Editing via Dialogue. https://arxiv.org/abs/2303.11108v3, 2023. 3

    arXiv 2023

Show all 65 references
  1. [9]

    Fluxs- pace: Disentangled semantic editing in rectified flow trans- formers

    Yusuf Dalva, Kavana Venkatesh, and Pinar Yanardag. Fluxs- pace: Disentangled semantic editing in rectified flow trans- formers. arXiv preprint arXiv:2412.09611, 2024. 6

    2024 arXiv

  2. [10]

    Z∗: Zero-shot style transfer via attention rearrangement

    Yingying Deng, Xiangyu He, Fan Tang, and Weiming Dong. Z∗: Zero-shot style transfer via attention rearrangement. arXiv preprint arXiv:2311.16491, 2023. 2, 3

    2023 arXiv

  3. [11]

    FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing, 2024

    Yingying Deng, Xiangyu He, Changwang Mei, Peisong Wang, and Fan Tang. FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing, 2024. 3, 4, 5, 6, 7, 8

    2024

  4. [12]

    Diffusion self-guidance for control- lable image generation

    Dave Epstein, Allan Jabri, Ben Poole, Alexei Efros, and Aleksander Holynski. Diffusion self-guidance for control- lable image generation. Advances in Neural Information Processing Systems, 36:16222–16239, 2023. 6

    2023

  5. [13]

    Scaling Rectified Flow Trans- formers for High-Resolution Image Synthesis, 2024

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M ¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach. Scaling Rectified Flow Tran...

    2024

  6. [14]

    Swapanything: Enabling arbitrary ob- ject swapping in personalized image editing

    Jing Gu, Nanxuan Zhao, Wei Xiong, Qing Liu, Zhifei Zhang, He Zhang, Jianming Zhang, HyunJoon Jung, Yilin Wang, and Xin Eric Wang. Swapanything: Enabling arbitrary ob- ject swapping in personalized image editing. In European Conference on Computer Vision , pages 402–418. Springer,

  7. [15]

    Focus on your instruction: Fine-grained and multi-instruction image editing by atten- tion modulation

    Qin Guo and Tianwei Lin. Focus on your instruction: Fine-grained and multi-instruction image editing by atten- tion modulation. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition , pages 6986–6996, 2024. 3

    2024

  8. [16]

    Prompt-to-prompt im- age editing with cross attention control

    Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt im- age editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022. 2, 3, 5

    2022 arXiv

  9. [17]

    Style aligned image generation via shared atten- tion

    Amir Hertz, Andrey V oynov, Shlomi Fruchter, and Daniel Cohen-Or. Style aligned image generation via shared atten- tion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 4775–4785,

  10. [18]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 2, 3

    2020

  11. [19]

    Region-aware diffusion for zero-shot text-driven image editing

    Nisha Huang, Fan Tang, Weiming Dong, Tong-Yee Lee, and Changsheng Xu. Region-aware diffusion for zero-shot text-driven image editing. arXiv preprint arXiv:2302.11797,

    arXiv

  12. [20]

    Diff- styler: Controllable dual diffusion for text-driven image styl- ization

    Nisha Huang, Yuxin Zhang, Fan Tang, Chongyang Ma, Haibin Huang, Weiming Dong, and Changsheng Xu. Diff- styler: Controllable dual diffusion for text-driven image styl- ization. IEEE Transactions on Neural Networks and Learn- ing Systems, 2024. 2

    2024

  13. [21]

    Iterative multi-granular image editing using diffusion models

    KJ Joseph, Prateksha Udhayanan, Tripti Shukla, Aishwarya Agarwal, Srikrishna Karanam, Koustava Goswami, and Bal- aji Vasan Srinivasan. Iterative multi-granular image editing using diffusion models. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vi...

    2024

  14. [22]

    Direct inversion: Boosting diffusion-based edit- ing with 3 lines of code

    Xuan Ju, Ailing Zeng, Yuxuan Bian, Shaoteng Liu, and Qiang Xu. Direct inversion: Boosting diffusion-based edit- ing with 3 lines of code. arXiv preprint arXiv:2310.01506,

    arXiv

  15. [23]

    Diffu- sionclip: Text-guided diffusion models for robust image ma- nipulation

    Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye. Diffu- sionclip: Text-guided diffusion models for robust image ma- nipulation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2426–2435,

  16. [24]

    Flowedit: Inversion-free text-based editing using pre-trained flow models

    Vladimir Kulikov, Matan Kleiner, Inbar Huberman- Spiegelglas, and Tomer Michaeli. Flowedit: Inversion-free text-based editing using pre-trained flow models. arXiv preprint arXiv:2412.08629, 2024. 6, 7, 8

    2024 arXiv

  17. [25]

    Multi-concept customization of text-to-image diffusion

    Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Multi-concept customization of text-to-image diffusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 1931–1941, 2023. 2, 3, 5

    1931

  18. [26]

    Flux.1 [dev] is an open-weight, guidance- distilled model for non-commercial applications, 2024

    Black Forest Labs. Flux.1 [dev] is an open-weight, guidance- distilled model for non-commercial applications, 2024. 2

    2024

  19. [27]

    TextBind: Multi-turn Interleaved Multimodal Instruction- following in the Wild, 2024

    Huayang Li, Siheng Li, Deng Cai, Longyue Wang, Lemao Liu, Taro Watanabe, Yujiu Yang, and Shuming Shi. TextBind: Multi-turn Interleaved Multimodal Instruction- following in the Wild, 2024. 3

    2024

  20. [28]

    Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maxi- milian Nickel, and Matt Le. Flow Matching for Generative Modeling, 2023. 2, 3

    2023

  21. [29]

    Towards understanding cross and self-attention in stable diffusion for text-guided image editing

    Bingyan Liu, Chengyu Wang, Tingfeng Cao, Kui Jia, and Jun Huang. Towards understanding cross and self-attention in stable diffusion for text-guided image editing. InProceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7817–7826, 2024. 3

    2024

  22. [30]

    Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow, 2022

    Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow, 2022. 2, 3

    2022

  23. [31]

    Text-guided mask-free local im- age retouching

    Zerun Liu, Fan Zhang, Jingxuan He, Jin Wang, Zhangye Wang, and Lechao Cheng. Text-guided mask-free local im- age retouching. In 2023 IEEE International Conference on Multimedia and Expo (ICME) , pages 2783–2788. IEEE,

    2023

  24. [32]

    Negative-prompt inversion: Fast image inversion for editing with text-guided diffusion models

    Daiki Miyake, Akihiro Iohara, Yu Saito, and Toshiyuki Tanaka. Negative-prompt inversion: Fast image inversion for editing with text-guided diffusion models. arXiv preprint arXiv:2305.16807, 2023. 2, 3

    2023 arXiv

  25. [33]

    Null-text inversion for editing real im- ages using guided diffusion models

    Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Null-text inversion for editing real im- ages using guided diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6038–6047, 2023. 2, 3

    2023

  26. [34]

    Dreammatcher: appearance matching self-attention for semantically-consistent text-to- image personalization

    Jisu Nam, Heesu Kim, DongJae Lee, Siyoon Jin, Seungry- ong Kim, and Seunggyu Chang. Dreammatcher: appearance matching self-attention for semantically-consistent text-to- image personalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition...

    2024

  27. [35]

    Improved denoising diffusion probabilistic models

    Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International conference on machine learning, pages 8162–8171. PMLR,

  28. [36]

    Gpt-4v(ision) system card, 2023

    OpenAI. Gpt-4v(ision) system card, 2023. 3

    2023

  29. [37]

    Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 5

    2023 arXiv

  30. [38]

    Learning transferable visual models from natural language supervi- sion

    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 supervi- sion. In International conference on machine learning, ...

    2021

  31. [39]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 5 10

    2022

  32. [40]

    Semantic Im- age Inversion and Editing using Rectified Stochastic Differ- ential Equations, 2024

    Litu Rout, Yujia Chen, Nataniel Ruiz, Constantine Carama- nis, Sanjay Shakkottai, and Wen-Sheng Chu. Semantic Im- age Inversion and Editing using Rectified Stochastic Differ- ential Equations, 2024. 3, 4, 6, 8

    2024

  33. [41]

    Denoising diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 2, 3

    2010 arXiv

  34. [42]

    Ctrloralter: Con- ditional loradapter for efficient 0-shot control and altering of t2i models

    Nick Stracke, Stefan Andreas Baumann, Joshua Susskind, Miguel Angel Bautista, and Bj¨orn Ommer. Ctrloralter: Con- ditional loradapter for efficient 0-shot control and altering of t2i models. In European Conference on Computer Vision , pages 87–103. Springer, 2024. 3

    2024

  35. [43]

    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. 2, 3, 6, 7, 8

    1921

  36. [44]

    Taming Rectified Flow for Inversion and Editing, 2024

    Jiangshan Wang, Junfu Pu, Zhongang Qi, Jiayi Guo, Yue Ma, Nisha Huang, Yuxin Chen, Xiu Li, and Ying Shan. Taming Rectified Flow for Inversion and Editing, 2024. 3, 4, 6, 7, 8

    2024

  37. [45]

    Compositional text-to-image synthe- sis with attention map control of diffusion models

    Ruichen Wang, Zekang Chen, Chen Chen, Jian Ma, Haonan Lu, and Xiaodong Lin. Compositional text-to-image synthe- sis with attention map control of diffusion models. In Pro- ceedings of the AAAI Conference on Artificial Intelligence , pages 5544–5552, 2024. 3

    2024

  38. [46]

    Stylediffusion: Controllable disentangled style transfer via diffusion models

    Zhizhong Wang, Lei Zhao, and Wei Xing. Stylediffusion: Controllable disentangled style transfer via diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7677–7689, 2023. 2

    2023

  39. [47]

    Head- router: A training-free image editing framework for mm- dits by adaptively routing attention heads

    Yu Xu, Fan Tang, Juan Cao, Yuxin Zhang, Xiaoyu Kong, Jintao Li, Oliver Deussen, and Tong-Yee Lee. Head- router: A training-free image editing framework for mm- dits by adaptively routing attention heads. arXiv preprint arXiv:2411.15034, 2024. 5

    2024 arXiv

  40. [48]

    Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation, 2023

    Zhengyuan Yang, Jianfeng Wang, Linjie Li, Kevin Lin, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang. Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation, 2023. 3

    2023

  41. [49]

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

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

    arXiv

  42. [50]

    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. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3836–3847, 2023. 3

    2023

  43. [51]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 586–595, 2018. 7

    2018

  44. [52]

    Attention calibration for disentangled text-to-image person- alization

    Yanbing Zhang, Mengping Yang, Qin Zhou, and Zhe Wang. Attention calibration for disentangled text-to-image person- alization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4764– 4774, 2024. 3 11 Multi-turn Consistent Image Editing...

    2024

  45. [53]

    Dual-objective LQR Guidance

    Method 5 5.1. Dual-objective LQR Guidance . . . . . . . . 5 5.2. Adaptive Attention Guidance . . . . . . . . 5

  46. [54]

    Implementation Details

    Experiment 6 6.1. Implementation Details . . . . . . . . . . . . 6 6.2. Multi-turn Reconstruction . . . . . . . . . . 8 6.3. Multi-turn Editing . . . . . . . . . . . . . . 8 6.4. Ablation Study . . . . . . . . . . . . . . . . 8

  47. [55]

    Datasets 12 B

    Conclusion 9 A . Datasets 12 B . Technical Proofs 12 B.1. Proof of Proposition 1 . . . . . . . . . . . . 12 B.2. Solution to LQR Problem . . . . . . . . . . 13 C . Limitations 14 C.1. Editing Iterations . . . . . . . . . . . . . . . 14 C.2. First Round Editing . . . . . . . . ...

  48. [56]

    Random editing: Random prompt written by volunteers or examples in previous research

  49. [57]

    Change object: Change an object to another, e.g., dog to cat

  50. [58]

    Add object: add an object, e.g., add flowers

  51. [59]

    Delete object: delete an object, e.g., delete the clouds in the image

  52. [60]

    Change sth’s content: dhange the content of sth, e.g., change a smiling man to an angry man by editing his facial expression

  53. [61]

    Change sth’s pose: dhange the pose of sth, e.g., change a standing dog to a running dog

  54. [62]

    Change sth’s color: change the color of sth, e.g., change a red heart to a pink heart

  55. [63]

    Change sth’s material: change the material of sth, e.g., change a wooden table to a glass table

  56. [64]

    Change image background: change the image back- ground, e.g., change white background to grasses

  57. [65]

    sitting on a pink flower

    Change image style: change the image style, e.g., change a photo to watercolor. PIE-Bench is a dataset designed for single-turn editing, where each image is paired with an original prompt and an editing instruction. To extend it for multi-turn editing, we utilize OpenAI’s GPT-...

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.