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A lightweight projector into the modulation space of a flow-based editing model lets one slider continuously control the strength of text-driven image edits, from none to full, without per-attribute training.

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Kontinuous Kontext adds continuous edit-strength control to instruction-based image editing by projecting a scalar strength and text embedding into the modulation space of a Flux Kontext diffusion editor.

T0 review reviewed 2026-08-04 challenge →

load-bearing objection Solid, practically useful extension with a credible modulation-space projector, but the 'strength' semantics are defined by the training morphs and never independently validated. the 3 major comments →

arxiv 2510.08532 v2 pith:DAXCM4FP submitted 2025-10-09 cs.CV cs.AI

Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing

Rishubh Parihar , Or Patashnik , Daniil Ostashev , R. Venkatesh Babu , Daniel Cohen-Or , Kuan-Chieh Wang This is my paper
classification cs.CV cs.AI
keywords instruction-based image editingedit strength controlcontinuous controlmodulation spaceflow matchingdiffusion morphingdataset synthesisimage editing sliders
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 tries to establish that edit intensity is not something a text prompt can convey precisely, but it is already encoded in the modulation parameters of a flow-based instruction-driven editing model. The authors show that a small learned projector, mapping a scalar strength and the instruction's text embedding to offsets in those modulation parameters, turns any instruction into a smooth continuous trajectory of images. They prove this by building a synthetic training set of image-instruction-edit-strength quadruplets, using a vision-language model to write diverse instructions, a full-strength editor to produce endpoints, and a diffusion morphing method to create intermediate frames, filtered for uniformity. If true, users get a single dial that works across stylization, attribute, material, background, and shape edits, eliminating the need to train separate sliders per attribute. A sympathetic reader should care because continuous strength control is the missing axis between language and precise image manipulation.

Core claim

On its own terms, the paper claims that a scalar can be injected into a flow-based instruction-driven image editing model through its modulation space, and that this yields calibrated, monotone, smooth strength control. The authors start from a state-of-the-art editor that already maps an image and a text instruction to an edit; they freeze most of it, add a tiny MLP that consumes the scalar strength and the pooled text embedding, and produce additive offsets to the text-token modulation parameters (shift and scale). Training uses synthetic quadruplets where intermediate strengths come from a diffusion morphing model interpolating between the source and a full edit, heavily filtered for unif

What carries the argument

The central object is the strength projector, a small MLP that maps an edit-strength scalar (positionally encoded) concatenated with the pooled text embedding of the instruction into offsets (Δshift, Δscale) for the modulation parameters of the text tokens in a flow-based DiT image editor. It carries the argument by showing that edit intensity lives in these modulation coefficients; adjusting them with learned, instruction-aware offsets is what produces smooth and calibrated trajectories, in contrast to text-space conditioning or per-attribute adapters.

Load-bearing premise

The load-bearing premise is that the filtered intermediate images generated by the diffusion morphing model genuinely represent the intended edit strengths; if those morphs are not semantically monotone, the model's learned strength semantics will be wrong.

What would settle it

Take a held-out set of instructions and generate edits at multiple slider values; ask human raters to sort them by perceived intensity. If a substantial fraction of sequences are not monotone with the slider value, the central claim that the projector controls strength fails.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If the claim is right, a single learned slider will replace per-attribute adapter training for continuous control in instruction-driven editing.
  • The method indicates that other continuous conditioning signals (spatial masks, time-varying intensity) could be injected through the same modulation pathway.
  • The synthetic data pipeline shows that morphing-based interpolation plus aggressive filtering is sufficient supervision to learn strength semantics, making the approach practical without real labeled data.
  • The released dataset and model could form the basis for downstream tasks like interactive editing, animation, and controllable generation.
  • Limitations acknowledged by the paper: discrete edits (insert/remove objects) and extrapolation beyond full strength are not handled smoothly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The strength semantics are defined by the morphing model's notion of interpolation, not by any ground-truth attribute intensity; if a different morphing model or human annotation were used, the learned slider would arguably match that definition.
  • Because the projector is instruction-aware via the pooled text embedding, it should, in principle, generalize to zero-shot edit categories provided the base editor can perform the edit; a direct test is to prompt the model with novel attribute pairs and check monotonicity.
  • A natural extension is a 'strength-plus-location' control, where the same modulation-offset idea is applied per spatial region or per token, enabling local strength control without new architecture.
  • One could also invert the projector to find the strength value that best matches a given pair of images, making it a tool for edit-strength estimation rather than just generation.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper introduces Kontinuous Kontext, an extension of Flux Kontext that accepts a scalar edit strength alongside an instruction. It trains a lightweight projector that maps the strength and pooled CLIP text embedding to offsets of the text-token modulation parameters, with a rank-4 LoRA. Training data are synthetic quadruplets: source images from Subject200K, instructions from Qwen-VLM, full edits from Flux Kontext, and intermediate edits from Freemorph morphs, filtered for inversion quality and trajectory uniformity. Evaluation on PIEBench compares smoothness (second-order triangle deficit with DreamSim) and CLIP directional similarity against interpolation baselines, domain-specific methods (ConceptSliders, MARBLE), and inference-time controls, plus a user study. The central claim is that edit intensity is naturally encoded in modulation space and that a single model generalizes across edit categories.

Significance. If the central claim holds, the paper offers a simple, unified mechanism for continuous edit strength, avoiding per-attribute training. The architecture is straightforward, ablations are informative, and the release of code, models, and filtered dataset supports reproducibility. The modulation-space observation is supported by a clean scaling experiment (Fig. 14). However, the evaluation as presented does not directly verify that the scalar s is a calibrated, monotone strength control; the primary metrics measure smoothness and instruction following, not strength semantics. Given that training supervision derives from Freemorph interpolations, this is the main risk to the paper's contribution.

major comments (3)
  1. [§3.1, §4, Fig. 17] The paper's central quantity is edit strength, but no experiment validates that the scalar s orders outputs by edit magnitude or that s=0.25 corresponds to a quarter-scale edit. Training labels are Freemorph morphs (Sec. 3.1), and filtering only enforces inversion quality and trajectory uniformity; a sequence can be smooth in LPIPS/DreamSim while changing attributes non-monotonically or in the wrong order. The quantitative metrics (δ_smooth, CLIP-dir, and Fig. 17's CLIP image similarity) do not measure monotonicity toward the full edit. The user study (Sec. 4.3) asks about smoothness, realism, editing capability, and overall quality, not strength ordering. I recommend adding a direct perceptual study (e.g., pairwise 'which image is a stronger edit?' or rank ordering) and/or per-attribute quantitative measures that track target-attribute progression as s increases, comparing against a shu
  2. [§4.1, Tables 1a/1b] All quantitative claims of superiority over baselines rest on point estimates without error bars or significance tests. The PIEBench evaluation is 540 images, but the domain-specific comparisons use small, bespoke subsets: 40 images for MARBLE and 44 generated images for ConceptSliders; Table 1b reports a second 'Ours' row without stating whether the same images were used for each comparison. The user study has 20 participants and 40 pairs for metric validation, also without confidence intervals. Please report per-sample standard deviations/standard errors, paired significance tests, and matched subsets for every comparison. The per-category breakdown would also strengthen the generalization claim.
  3. [§4, PIEBench categories] The claim of generalization to unseen edits is not quantified. Training categories include stylization, appearance, material, attribute, environment, and shape morphing; PIEBench includes change pose and object change, which are not in that list. Reporting only aggregate δ_smooth and CLIP-dir over all 540 images does not show whether the method generalizes to these unseen categories or just performs well on categories seen at training. Please provide a per-category breakdown (especially pose/object changes) and, if possible, qualitative/quantitative results on truly unseen edit types.
minor comments (6)
  1. [§3.1, A.2] Dataset count inconsistency: Sec. 3.1 states 110,147 images filtered to 64,613; Appendix A.2 says '66K' trajectories. Align the numbers.
  2. [§3.1, A.2] Strength grid inconsistency: Sec. 3.1 says N=6 (as provided by Freemorph); Appendix A.2 says N=5 intermediate morphs. Clarify which is used.
  3. [Table 1b] The repeated 'Ours' rows should be labeled (e.g., 'Ours (ConceptSliders subset)' and 'Ours (MARBLE subset)') and the subset sizes stated in the caption.
  4. [Fig. 15] Report the number of participants and 95% confidence intervals for the agreement fractions; 0.686 vs 0.599 may not be significant with 40 pairs.
  5. [A.3, various] Typographical issues: 'modualtion' (A.3), 'W AN' spacing (Tables/Figs), and inconsistent use of 'δ' vs 'δ_smooth'.
  6. [A.6.2] The aggregated normalized CLIP-dir formula divides by s_i; for s_i close to 0 this can amplify noise. Consider a robust aggregation or report per-strength values.

Circularity Check

0 steps flagged

No significant circularity: the strength-control claim is an empirical supervised-learning result with external evaluation, not a derivation that reduces to its inputs.

full rationale

Reviewing the claimed derivation chain: (1) The scalar edit strength is operationalized by Freemorph interpolation in Sec. 3.1 ('we use off-the-shelf diffusion based image morphing method Freemorph to generate the intermediate images y_{s_i}, which we treat as edits at the corresponding strengths s_i'). This is a training-label construction, not a predicted result: the paper does not claim to infer Freemorph's outputs, and the learned model is evaluated on PIEBench images/instructions not used in training. (2) The central modulation-space claim (Sec. 5) is not imported from a self-citation. It is motivated by an independent inference-time experiment (Sec. A.4, Fig. 14) and by external prior work [9,16], then tested through training and generalization. (3) The smoothness metric and the data filter both measure trajectory regularity, so the evaluation is correlated with the training-data construction; this raises a construct-validity concern about whether the slider is semantically monotonic, but it is not a formal circularity: Eq. (1) is a flow-matching loss on synthetic targets, not an optimization of the reported metric, and the central generalization claim is quantified on unseen data against external baselines. (4) No uniqueness theorem, forcing argument, or load-bearing self-citation is used; self-citations appear only in related-work or metric contexts and do not support the core result. The paper is therefore self-contained against external benchmarks for its stated claims.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 0 invented entities

The central claim rests on unverified domain assumptions about the smoothness of the modulation space, the validity of Freemorph morphs as strength-ordered ground truths, and transfer from synthetic data to real edits. The free parameters are data-curation thresholds chosen without sensitivity analysis. No new physical or theoretical entities are introduced; the projector is a learned network component.

free parameters (3)
  • KL divergence threshold for trajectory uniformity filtering = 0.15
    Hand-set filtering threshold; discards roughly 41% of trajectories; no sensitivity analysis is reported (Sec. 3.1).
  • LPIPS thresholds for inversion quality and weak-edit filtering = not reported
    Used to filter poor inversions and Flux Kontext failures; exact threshold values are not given, making the curation process partly underspecified (Sec. 3.1).
  • Strength grid size N = 6 (main text) / 5 (appendix)
    Controls the discrete resolution of strength labels; the paper is internally inconsistent about whether N=6 or N=5 intermediate morphs are generated (Sec. 3.1 vs Appendix A.2).
axioms (4)
  • domain assumption Flux Kontext is a capable instruction-editing base, and its modulation parameters can be perturbed to vary edit strength without destroying image identity.
    The whole method conditions on this; supported by the paper's own scaling experiment (Fig. 6/14) and prior work [9,16], but with no formal guarantee that the perturbation direction corresponds to perceptual strength.
  • domain assumption Freemorph's attention-space interpolation yields intermediate images that are valid, smoothly ordered edits at the assigned strengths.
    Central to data creation; the paper acknowledges Freemorph produces artifacts and filters them, but still treats the surviving morphs as ground-truth strength supervision (Sec. 3.1).
  • domain assumption Strength labels derived from Freemorph morphs transfer to real images and unseen instructions in PIEBench.
    Evaluation assumes that training on synthetic morphs teaches a general notion of edit strength; no independent calibration of strength is provided.
  • domain assumption Pooled CLIP text embedding is sufficient for the projector to calibrate per-instruction strength offsets.
    Ablation shows removing it hurts, but there is no proof that the pooled embedding captures all edit structure relevant for strength calibration.

reviewed 2026-08-04 · how reviews work

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Cite this review

Pith. "Pith review of Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing." pith.science (2026). https://pith.science/paper/DAXCM4FP

@misc{pith2026251008532,
  author       = {Pith},
  title        = {Pith review of: Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DAXCM4FP}},
  note         = {Machine review of arXiv:2510.08532}
}
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read the original abstract

Instruction-based image editing offers a powerful and intuitive way to manipulate images through natural language. Yet, relying solely on text instructions limits fine-grained control over the extent of edits. We introduce Kontinuous Kontext, an instruction-driven editing model that provides a new dimension of control over edit strength, enabling users to adjust edits gradually from no change to a fully realized result in a smooth and continuous manner. Kontinuous Kontext extends a state-of-the-art image editing model to accept an additional input, a scalar edit strength which is then paired with the edit instruction, enabling explicit control over the extent of the edit. To inject this scalar information, we train a lightweight projector network that maps the input scalar and the edit instruction to coefficients in the model's modulation space. For training our model, we synthesize a diverse dataset of image-edit-instruction-strength quadruplets using existing generative models, followed by a filtering stage to ensure quality and consistency. Kontinuous Kontext provides a unified approach for fine-grained control over edit strength for instruction driven editing from subtle to strong across diverse operations such as stylization, attribute, material, background, and shape changes, without requiring attribute-specific training.

Figures

Figures reproduced from arXiv: 2510.08532 by Daniel Cohen-Or, Daniil Ostashev, Kuan-Chieh Wang, Or Patashnik, Rishubh Parihar, R. Venkatesh Babu.

Figure 1
Figure 1. Figure 1: Kontinuous Kontext produces smooth edit trajectories across diverse attributes given an image, instruction, and an edit scalar strength. Unlike prior methods that require attribute-specific training, ours is a unified approach to enable fine-grained control. Abstract Instruction-based image editing offers a powerful and intu￾itive way to manipulate images through natural language. Yet, relying solely on te… view at source ↗
Figure 2
Figure 2. Figure 2: Overview Edits with intermediate strengths Strength s 1 Strength s2 Strength sk Strength 1 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Data generation. Our pipeline consists of three steps: (a) We generate an edit instruction for each source image using a pretrained VLM, then apply Flux Kontext, an instruction-driven editing model, to produce a full-strength edit. (b) We synthesize intermediate-strength edits using a diffusion-based morphing method [6], which inverts both the source and edited images into the diffusion latent space and in… view at source ↗
Figure 4
Figure 4. Figure 4: Samples from diverse image editing categories in our synthesized dataset. We cover a wide range of global edits, including [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Generating intermediate images with Freemorph can in [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Model architecture. (a) In a simple experiment, we scale the text-token modulation parameters in Flux Kontext with a scalar to generate edit variations. This perturbation produces edits of varying strengths, revealing that modulation parameters can govern edit strength. (b) Building on this insight, we design a lightweight projector network that maps a scalar edit strength s to offsets of the text modulati… view at source ↗
Figure 7
Figure 7. Figure 7: Adding text embeddings into the slider projector im [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Our method enables continuous control for challenging geometric edits, including smooth transformations between animal shapes [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visual Comparison. We evaluate against (a) image interpolation methods, where we first generate a full strength edit with Flux-Kontext and interpolate to obtain intermediate edits, and (b) domain-specific methods, which train separate LoRAs/Adapters for each attribute. Our generalized method achieves superior slider control with consistent image identity and smooth edit transitions. In comparison to Concep… view at source ↗
Figure 10
Figure 10. Figure 10: User study win-rates (%) of our method against base [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Kontinuous Kontext can enable fine-grained control over the edit strength for diverse instruction-driven image editing operations [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Samples for generated edit instructions and the generated edits from Flux Kontext [PITH_FULL_IMAGE:figures/full_fig_p016_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Samples trajectories from our synthesized dataset [PITH_FULL_IMAGE:figures/full_fig_p017_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Inference time control in modulation space. We conducted a simple experiment by scaling the text modulation parameters with values of v ∈ (0.5, 1.3) to generate multiple edits. While these edits varied across different scales, the variations did not consistently correlate with the intended edit strength. This highlights the need for a dedicated learning module that can translate such variations into user-… view at source ↗
Figure 15
Figure 15. Figure 15: We performed one user study where we compute the [PITH_FULL_IMAGE:figures/full_fig_p018_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Qualitative interpretation for first order and second order smoothness. For slider-based image editing, second-order smoothness is more important than first-order smoothness, as it captures the local consistency needed for gradual, nuanced changes with slider controls. Methods δsmooth ↓ CLIP-dir ↑ CFG-scale 152.205 0.242 Attention-weighing 120.760 0.237 Ours 0.329 0.241 [PITH_FULL_IMAGE:figures/full_fig_… view at source ↗
Figure 17
Figure 17. Figure 17: Comparison for identity preservation of our method against baselines. Our method smoothly transforms the image into target edit over different edit strengths, resulting in close to linear decay in identity change and preserving identity well in lower strengths. In contrast, baselines change the identity of the subject significantly even with small edit strengths and don’t change the image for stronger edi… view at source ↗
Figure 18
Figure 18. Figure 18: Ablation over architecture of Kontinuous Kontext [PITH_FULL_IMAGE:figures/full_fig_p020_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Comparison with interpolation baselines. Morphing-based methods generate smooth transitions; however, they often intro￾duce artifacts in the intermediate images or omit details such as leaves. Similarly, the video inbetweening model WAN produces strong artifacts in intermediate frames, as these appearance transitions are out of domain for an inbetweening model trained only on real data [PITH_FULL_IMAGE:f… view at source ↗
Figure 20
Figure 20. Figure 20: Comparison with interpolation baselines. DiffMorpher and FreeMorph remove objects in the intermediate edits of the first examples. Moreover, DiffMorpher produces blurred outputs even for simple stylization transitions. The WAN inbetweening model generates transitions with abrupt jumps in both examples. In contrast, our method produces smooth transitions while preserving image identity [PITH_FULL_IMAGE:fi… view at source ↗
Figure 21
Figure 21. Figure 21: Comparison with MARBLE for material control [PITH_FULL_IMAGE:figures/full_fig_p023_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Comparison with Concept Sliders for diverse attribute editing. [PITH_FULL_IMAGE:figures/full_fig_p024_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: We compare with additional inference time baselines. [PITH_FULL_IMAGE:figures/full_fig_p025_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Extrapolation of edit strengths. One of the failure case of our method is it cannot generate edits with extrapolation well. In most cases, either it recreates the full edit image (s = 1), or reduce the extent of edit in extrapolation region [PITH_FULL_IMAGE:figures/full_fig_p026_24.png] view at source ↗

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Reference graph

Works this paper leans on

50 extracted references · 12 linked inside Pith · cited by 6 Pith papers

  1. [1]

    Mitra, and Peter Wonka

    Rameen Abdal, Peihao Zhu, Niloy J. Mitra, and Peter Wonka. Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous nor- malizing flows.ACM Trans. Graph., 40(3), 2021. 2, 3

    2021

  2. [2]

    Qwen2.5-vl technical report.arXiv preprint arXiv:2502.13923, 2025

    Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhao- hai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Jun- yang Lin. Qwen2.5-vl technical repor...

    Pith/arXiv arXiv 2025

  3. [3]

    Stephen Batifol, Andreas Blattmann, Frederic Boesel, Sak- sham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, et al. Flux. 1 kontext: Flow matching for in-context image generation and editing in latent space.arXiv e-prints, pages arXiv–2506,

  4. [4]

    In- structpix2pix: Learning to follow image editing instructions

    Tim Brooks, Aleksander Holynski, and Alexei A Efros. In- structpix2pix: Learning to follow image editing instructions. InProceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 18392–18402, 2023. 2

    2023

  5. [5]

    Viewpoint textual inversion: Discovering scene representa- tions and 3d view control in 2d diffusion models

    James Burgess, Kuan-Chieh Wang, and Serena Yeung-Levy. Viewpoint textual inversion: Discovering scene representa- tions and 3d view control in 2d diffusion models. InEu- ropean Conference on Computer Vision, pages 416–435. Springer, 2024. 4

    2024

  6. [6]

    Freemorph: Tuning-free generalized image morphing with diffusion model.arXiv preprint arXiv:2507.01953, 2025

    Yukang Cao, Chenyang Si, Jinghao Wang, and Ziwei Liu. Freemorph: Tuning-free generalized image morphing with diffusion model.arXiv preprint arXiv:2507.01953, 2025. 2, 4, 5, 7, 14

    Pith/arXiv arXiv 2025

  7. [7]

    Learning continuous 3d words for text-to- image generation

    Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix, Matthew Fisher, Radomir Mech, Andrew Markham, and Niki Trigoni. Learning continuous 3d words for text-to- image generation. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 6753–6762, 2024. 4

    2024

  8. [8]

    Marble: Material recomposition and blending in clip- space

    Ta Ying Cheng, Prafull Sharma, Mark Boss, and Varun Jam- pani. Marble: Material recomposition and blending in clip- space. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 13061–13071, 2025. 2, 3, 8

    2025

  9. [9]

    Fluxs- pace: Disentangled semantic editing in rectified flow trans- formers, 2024

    Yusuf Dalva, Kavana Venkatesh, and Pinar Yanardag. Fluxs- pace: Disentangled semantic editing in rectified flow trans- formers, 2024. 2, 6

    2024

  10. [10]

    Interpreting the weight space of customized dif- fusion models.Advances in Neural Information Processing Systems, 37:137334–137371, 2024

    Amil Dravid, Yossi Gandelsman, Kuan-Chieh Wang, Rameen Abdal, Gordon Wetzstein, Alexei Efros, and Kfir Aberman. Interpreting the weight space of customized dif- fusion models.Advances in Neural Information Processing Systems, 37:137334–137371, 2024. 3

    2024

  11. [11]

    Scaling recti- fied flow transformers for high-resolution image synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M ¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling recti- fied flow transformers for high-resolution image synthesis. InForty-first international conference on machine learning,

  12. [12]

    Dream- sim: Learning new dimensions of human visual similar- ity using synthetic data.arXiv preprint arXiv:2306.09344,

    Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dream- sim: Learning new dimensions of human visual similar- ity using synthetic data.arXiv preprint arXiv:2306.09344,

    Pith/arXiv arXiv

  13. [13]

    Stylegan-nada: Clip-guided domain adap- tation of image generators, 2021

    Rinon Gal, Or Patashnik, Haggai Maron, Gal Chechik, and Daniel Cohen-Or. Stylegan-nada: Clip-guided domain adap- tation of image generators, 2021. 7

    2021

  14. [14]

    Concept sliders: Lora adaptors for precise control in diffusion models

    Rohit Gandikota, Joanna Materzy ´nska, Tingrui Zhou, Anto- nio Torralba, and David Bau. Concept sliders: Lora adaptors for precise control in diffusion models. InEuropean Confer- ence on Computer Vision, pages 172–188. Springer, 2024. 2, 3

    2024

  15. [15]

    Sliderspace: Decomposing the visual capabilities of diffusion models.arXiv preprint arXiv:2502.01639, 2025

    Rohit Gandikota, Zongze Wu, Richard Zhang, David Bau, Eli Shechtman, and Nick Kolkin. Sliderspace: Decomposing the visual capabilities of diffusion models.arXiv preprint arXiv:2502.01639, 2025. 3

    Pith/arXiv arXiv 2025

  16. [16]

    Tokenverse: Versatile multi-concept personalization in token modulation space, 2025

    Daniel Garibi, Shahar Yadin, Roni Paiss, Omer Tov, Shiran Zada, Ariel Ephrat, Tomer Michaeli, Inbar Mosseri, and Tali Dekel. Tokenverse: Versatile multi-concept personalization in token modulation space, 2025. 2, 6

    2025

  17. [17]

    Prompt-to-prompt image editing with cross attention control

    Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. 2022. 2, 3, 19

    2022

  18. [18]

    beta-vae: Learning basic visual con- cepts with a constrained variational framework

    Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. beta-vae: Learning basic visual con- cepts with a constrained variational framework. InInterna- tional conference on learning representations, 2017. 3

    2017

  19. [19]

    Denoising diffu- sion probabilistic models, 2020

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffu- sion probabilistic models, 2020. 2

    2020

  20. [20]

    Deep feature consistent variational autoencoder

    Xianxu Hou, Linlin Shen, Ke Sun, and Guoping Qiu. Deep feature consistent variational autoencoder. In2017 IEEE winter conference on applications of computer vision (WACV), pages 1133–1141. IEEE, 2017. 3

    2017

  21. [21]

    Ganspace: Discovering interpretable gan con- trols

    Erik H ¨ark¨onen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris. Ganspace: Discovering interpretable gan con- trols. InProc. NeurIPS, 2020. 2, 3

    2020

  22. [22]

    Pnp inversion: Boosting diffusion-based editing with 3 lines of code.International Conference on Learning Representations (ICLR), 2024

    Xuan Ju, Ailing Zeng, Yuxuan Bian, Shaoteng Liu, and Qiang Xu. Pnp inversion: Boosting diffusion-based editing with 3 lines of code.International Conference on Learning Representations (ICLR), 2024. 7

    2024

  23. [23]

    A style-based generator architecture for generative adversarial networks

    Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. InProceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 4401–4410, 2019. 3

    2019

  24. [24]

    Concept steerers: Leveraging k-sparse autoencoders for controllable genera- tions.arXiv preprint arXiv:2501.19066, 2025

    Dahye Kim and Deepti Ghadiyaram. Concept steerers: Leveraging k-sparse autoencoders for controllable genera- tions.arXiv preprint arXiv:2501.19066, 2025. 8

    arXiv 2025

  25. [25]

    Diffusion models already have a semantic latent space.arXiv preprint arXiv:2210.10960, 2022

    Mingi Kwon, Jaeseok Jeong, and Youngjung Uh. Diffusion models already have a semantic latent space.arXiv preprint arXiv:2210.10960, 2022. 3

    Pith/arXiv arXiv 2022

  26. [26]

    Flux.https://github.com/ black-forest-labs/flux, 2024

    Black Forest Labs. Flux.https://github.com/ black-forest-labs/flux, 2024. 6

    2024

  27. [27]

    When stylegan meets stable diffusion: a w+ adapter for person- alized image generation

    Xiaoming Li, Xinyu Hou, and Chen Change Loy. When stylegan meets stable diffusion: a w+ adapter for person- alized image generation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2187–2196, 2024. 4

    2024

  28. [28]

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

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023. 14

    2023

  29. [29]

    Lightlab: Controlling light sources in images with diffusion models

    Nadav Magar, Amir Hertz, Eric Tabellion, Yael Pritch, Alex Rav-Acha, Ariel Shamir, and Yedid Hoshen. Lightlab: Controlling light sources in images with diffusion models. InProceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers, pages 1–11, 2025. 3

    2025

  30. [30]

    Precisecontrol: En- hancing text-to-image diffusion models with fine-grained at- tribute control

    Rishubh Parihar, VS Sachidanand, Sabariswaran Mani, Te- jan Karmali, and R Venkatesh Babu. Precisecontrol: En- hancing text-to-image diffusion models with fine-grained at- tribute control. InEuropean Conference on Computer Vision, pages 469–487. Springer, 2024. 4

    2024

  31. [31]

    Compass control: Multi object orientation control for text-to-image generation

    Rishubh Parihar, Vaibhav Agrawal, Sachidanand VS, and Venkatesh Babu Radhakrishnan. Compass control: Multi object orientation control for text-to-image generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 2791–2801, 2025. 4

    2025

  32. [32]

    Styleclip: Text-driven manipulation of stylegan imagery

    Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and Dani Lischinski. Styleclip: Text-driven manipulation of stylegan imagery. InProceedings of the IEEE/CVF In- ternational Conference on Computer Vision (ICCV), pages 2085–2094, 2021. 2, 3

    2085

  33. [33]

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

    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. 2

    Pith/arXiv arXiv 2023

  34. [34]

    Hierarchical text-conditional image gener- ation with clip latents.arXiv preprint arXiv:2204.06125, 1 (2):3, 2022

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image gener- ation with clip latents.arXiv preprint arXiv:2204.06125, 1 (2):3, 2022

    Pith/arXiv arXiv 2022

  35. [35]

    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. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 2, 14

    2022

  36. [36]

    Alchemist: Parametric control of material proper- ties with diffusion models

    Prafull Sharma, Varun Jampani, Yuanzhen Li, Xuhui Jia, Dmitry Lagun, Fredo Durand, Bill Freeman, and Mark Matthews. Alchemist: Parametric control of material proper- ties with diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24130–24141, 2024. 2, 3

    2024

  37. [37]

    Inter- preting the latent space of gans for semantic face editing

    Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou. Inter- preting the latent space of gans for semantic face editing. In CVPR, 2020. 2, 3

    2020

  38. [38]

    Emu edit: Precise image editing via recognition and gen- eration tasks

    Shelly Sheynin, Adam Polyak, Uriel Singer, Yuval Kirstain, Amit Zohar, Oron Ashual, Devi Parikh, and Yaniv Taigman. Emu edit: Precise image editing via recognition and gen- eration tasks. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8871– 8879, 2024. 2

    2024

  39. [39]

    Denois- ing diffusion implicit models, 2022

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denois- ing diffusion implicit models, 2022. 2

    2022

  40. [40]

    Ominicontrol: Minimal and uni- versal control for diffusion transformer.arXiv preprint arXiv:2411.15098, 2024

    Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang. Ominicontrol: Minimal and uni- versal control for diffusion transformer.arXiv preprint arXiv:2411.15098, 2024. 4, 12

    Pith/arXiv arXiv 2024

  41. [41]

    Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314, 2025

    Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianx- iao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jin- gren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fan...

    Pith/arXiv arXiv 2025

  42. [42]

    Generative inbetweening: Adapting image-to-video models for keyframe interpolation

    Xiaojuan Wang, Boyang Zhou, Brian Curless, Ira Kemelmacher-Shlizerman, Aleksander Holynski, and Steve Seitz. Generative inbetweening: Adapting image-to-video models for keyframe interpolation. InThe Thirteenth Inter- national Conference on Learning Representations. 4

  43. [43]

    Qwen-image technical report.arXiv preprint arXiv:2508.02324, 2025

    Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng-ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, et al. Qwen-image technical report.arXiv preprint arXiv:2508.02324, 2025. 2

    Pith/arXiv arXiv 2025

  44. [44]

    Omnigen2: Exploration to advanced multimodal generation.arXiv preprint arXiv:2506.18871, 2025

    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, 2025

    Pith/arXiv arXiv 2025

  45. [45]

    Omnigen: Unified image genera- tion

    Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xin- grun Xing, Ruiran Yan, Chaofan Li, Shuting Wang, Tiejun Huang, and Zheng Liu. Omnigen: Unified image genera- tion. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 13294–13304, 2025. 2

    2025

  46. [46]

    Diffmorpher: Unleashing the capability of dif- fusion models for image morphing

    Kaiwen Zhang, Yifan Zhou, Xudong Xu, Bo Dai, and Xin- gang Pan. Diffmorpher: Unleashing the capability of dif- fusion models for image morphing. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7912–7921, 2024. 4, 7

    2024

  47. [47]

    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. InProceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 586–595, 2018. 6, 18

    2018

  48. [48]

    Hive: Harnessing human feedback for instructional visual editing

    Shu Zhang, Xinyi Yang, Yihao Feng, Can Qin, Chia-Chih Chen, Ning Yu, Zeyuan Chen, Huan Wang, Silvio Savarese, Stefano Ermon, et al. Hive: Harnessing human feedback for instructional visual editing. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9026–9036, 2024. 2

    2024

  49. [49]

    In-context edit: Enabling instructional image editing with in- context generation in large scale diffusion transformer.arXiv preprint arXiv:2504.20690, 2025

    Zechuan Zhang, Ji Xie, Yu Lu, Zongxin Yang, and Yi Yang. In-context edit: Enabling instructional image editing with in- context generation in large scale diffusion transformer.arXiv preprint arXiv:2504.20690, 2025. 2

    Pith/arXiv arXiv 2025

  50. [50]

    category

    Tianyi Zhu, Dongwei Ren, Qilong Wang, Xiaohe Wu, and Wangmeng Zuo. Generative inbetweening through frame- wise conditions-driven video generation. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 27968–27978, 2025. 4 A. Appendix A.1. Implementation Details. We train slider projector along with a rank-4LoRA on all attention la...

    2025

This paper was first reviewed by deepseek-v4-flash on August 4, 2026.