Pith. sign in

REVIEW 2 major objections 3 minor 80 references

TurboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion

T0 review · 2 major / 3 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read TurboClear claims that object-effect removal can be compressed into a single denoising step, matching multi-step quality at up to 665x lower computational cost.

desk verdict Solid one-step object-effect removal with real speedups; the evaluation mostly supports it, but the central claim would be stronger with a metric computed inside the effect mask. read the letter →

arxiv 2608.01288 v1 pith:BFMMEQPR submitted 2026-08-02 cs.CV

Jiawei Guo , Junxian Li , Yixin Tang , Bingya Zhang , Jiaxin Lu , Yulun Zhang , Shangchen Zhou This is my paper · ORCID
classification cs.CV
keywords object-effectremovalone-stepdistillationregion-calibrateddistributionmatchinglearnablespatialfusionSDXLinpaintingdiffusionmodelaccelerationasymmetryimageediting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that object-effect removal—erasing a target object along with its shadows, reflections, and occlusion traces—can be compressed into a single denoising step without losing the task's asymmetric behavior, where affected pixels regenerate while unaffected pixels stay untouched. The authors argue that generic one-step distillation methods fail because their objectives are global and treat the whole image uniformly, so they design a region-calibrated objective that applies distribution-matching pressure only inside the object-effect mask. They also introduce a learned spatial fusion module that blends the generated removal stream with the original input image using per-pixel gates learned from local evidence. If the claim holds, real-time object removal becomes practical on consumer hardware while preserving removal quality.

What carries the argument

The load-bearing object is the object-effect mask $m_e$, used only in training, which defines where removal happens. RDM is a masked and normalized distribution-matching gradient $g_{\text{RDM}} = m_e^\ell \odot g_{\text{DMD}} / (s+\varepsilon)$ that aligns affected regions with the teacher's generative distribution while keeping background anchored via paired reconstruction losses. LSF is a lightweight convolutional head that predicts residual corrections to a logit-transformed attention prior, producing per-pixel gates $\alpha_z$ and $\alpha_x$ that blend the one-step latent prediction $\hat{z}_0$ with the input latent $z_y$, then blend the decoded result with the original image; the LSF training loss uses $m_e$ supervision and gate regularization to force high generation weight inside the effect region and low weight in safe background. Together these carry the asymmetry argument.

What would settle it

Take a set of paired images where a visible shadow or reflection extends beyond the annotated object mask, train or fine-tune TurboClear with an object-effect mask deliberately eroded to exclude that effect, and check whether the output still contains the residual effect; if it does, the central removal claim fails in exactly the regime the mask is supposed to cover.

Watch

Extended reading notes

Core claim

The central claim is that TurboClear, an SDXL-based one-step student distilled from the multi-step model ObjectClear, removes both the masked object and its associated visual effects in a single UNet evaluation while keeping background content unchanged, and does so at roughly 27–40x lower denoising computation than ObjectClear and 628–665x lower than OmniPaint, with comparable or better PSNR, LPIPS, and local metrics on OBER-Test and RORD-Val. The paper argues that the two ingredients—Region-Calibrated Distribution Matching (RDM), which masks the distribution-matching gradient to the effect region, and Learnable Spatial Fusion (LSF), which learns pixel- and latent-level gates between the generated stream and the input stream—are what preserve the edit-and-preserve asymmetry, and ablation comparisons against LCM, DMD2, RAD, and attention-guided fusion support that division of labor.

Load-bearing premise

The training pipeline assumes the object-effect mask $m_e$ is available and accurately covers the object plus every visual effect it causes; if that mask misses a shadow, a reflection, or an occlusion trace, the student is never taught to remove those pixels and may anchor them as background.

Editorial extensions

If this is right

  • One-step inference with no classifier-free guidance at test time brings per-image denoising FLOPs down to 1.59 T at 512x512 and 3.21 T at 960x540, which would allow object-effect removal in roughly 0.04 seconds on an A800 and open the door to real-time deployment.
  • RDM-type masking of the distribution-matching gradient is reported to beat generic one-step objectives (LCM, DMD2, DMD2+GAN, RAD) on both global and local fidelity, suggesting that region calibration, not just the distillation recipe, drives one-step removal quality.
  • LSF is shown to outperform attention-guided fusion under the same RDM student, with strict confinement of change to the effect region, so learned gating from local stream evidence is a better fusion rule than attention alone.
  • TurboClear generalizes from 512x512 training to 960x540 inputs, with quality metrics on RORD-Val remaining competitive, indicating the one-step student does not overfit the training resolution.
  • The paper's user study says TurboClear matches or beats its teacher ObjectClear in 69.0% of pairwise judgments, so the acceleration is not bought by a systematic perceptual regression.

Reading between the lines

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

  • The same region-calibrated distillation and learned two-stream gating could transfer to other asymmetric editing tasks, such as object insertion, style transfer confined to a subject, or text-guided local editing, where one region must change while the rest must stay identical; the paper does not test this.
  • A weak-supervision variant that discovers the effect mask automatically, for example from shadow and reflection cues, would remove the annotation bottleneck the paper acknowledges in Section H; whether RDM degrades gracefully under noisy $m_e$ is an open testable question.
  • Because peak memory is still 7,962 MiB, one-step distillation alone does not solve edge deployment; combining RDM/LSF with quantization or structured pruning is a direct extension the paper leaves implicit.
  • RDM's normalization term makes the gradient scale comparable across different effect-region sizes, which implies the objective could transfer to datasets with widely varying object sizes without re-tuning; the paper does not ablate this.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 3 minor

Summary. TurboClear proposes a one-step SDXL-based object-effect removal model that combines Region-Calibrated Distribution Matching (RDM) for distillation with a Learnable Spatial Fusion (LSF) module for inference-time blending. The student is distilled from the multi-step ObjectClear teacher, using the object-effect mask m_e as privileged training supervision to mask and normalize distribution-matching gradients and to supervise paired reconstruction and localization losses. LSF learns spatial gates that fuse the one-step removal stream with an identity-preserving reference stream. The paper reports large denoising FLOPs reductions and latency speedups relative to ObjectClear and OmniPaint, with quantitative results on OBER-Test and RORD-Val, ablations, a double-blind user study, and an appendix with paired bootstrap confidence intervals and a function-class analysis for LSF.

Significance. If the central claim is fully established, TurboClear would be a practically significant contribution: it demonstrates that one-step distillation with region-calibrated objectives and learned spatial fusion can retain the asymmetric edit-and-preserve behavior required for object-effect removal while cutting inference cost by one to two orders of magnitude. The paper has notable strengths: the method is clearly motivated, the ablations separate the effects of distillation strategy and fusion strategy, paired bootstrap intervals are provided in the appendix, the user study is double-blind, and the Appendix E analysis is a parameter-free function-class argument. The public code release is also a concrete reproducibility asset. The main weakness is that the evaluation never directly measures removal quality inside the object-effect mask m_e, even though the method's training objectives are explicitly calibrated to that mask; this gap currently prevents the central 'comparable or better visual removal quality' claim from being fully verified for the effect component of the task.

major comments (2)
  1. [Experiment (Evaluation protocol) and Appendix A (Quality metric definitions)] All reported removal-quality metrics are computed on the object mask M_o or its bounding box, never on the object-effect mask m_e that defines the task. Equation (20) computes PSNR-M inside M_o, LPIPS-L uses the bounding box B(M_o), PSNR-BG is evaluated on the complement 1-M_o, and DISTS-Local uses B(M_o). Since RDM masks and normalizes the distribution-matching gradient with m_e (Eqs. 6-9), the paired losses split on m_e (Eq. 10), and the LSF supervision and gate regularization depend on m_e (Eqs. 17-18), the 'effect' component of object-effect removal is never directly measured. A student that leaves shadows, reflections, or occlusion traces just outside M_o could score well on PSNR-M and its residuals would be diluted in global metrics. I request that the authors add effect-mask metrics on OBER-Test, e.g., PSNR and LPIPS computed inside m_e or inside m_e \ M_o, using the existing per-sample object-effect masks. Without such an evaluation, the central claim of 'comparable or better visual removal quality' for object-effect removal is not directly supported for the effect region.
  2. [Method (Learnable Spatial Fusion) and Limitations (Section H)] The LSF design and its gate regularization L_alpha (Eq. 18) explicitly encourage copying the input in regions classified as safe background, i.e., alpha_x near zero outside the dilated effect mask m_e^+. If m_e is imperfect or if visual effects extend beyond it, the learned gate can preserve residual effects in the output while actually improving metrics such as PSNR-BG by copying the original background. This is not an internal inconsistency, but it is a deployment risk directly tied to the quality of m_e annotations, and the paper itself notes in Section H that obtaining object-effect masks for new domains is expensive. The proposed m_e-based metrics would directly quantify whether TurboClear removes effects inside the annotated effect region and whether residuals leak outside it; without them, the model's behavior on real images where m_e is unavailable or inaccurate remains untested.
minor comments (3)
  1. [Appendix A (Denoising FLOPs measurement)] The headline speedup factors (40.04x vs. ObjectClear, 665x vs. OmniPaint) are based on theoretical denoising FLOPs that explicitly exclude condition encoders, VAE encoding/decoding, and the LSF head. This is transparently stated, but readers may conflate 'denoising FLOPs' with total inference cost; the latency numbers in Table 4 are more comprehensive and already support the speedup claim, so I suggest adding an end-to-end FLOPs figure or clearly labeling the headline numbers as backbone-only.
  2. [Appendix B (One-step warm-up and initialization)] The fixed one-step DDIM timestep 399, the fixed prompt 'remove the instance of object', and object-token index 5 are important implementation choices that are reported only in the appendix; mentioning them in the main Experiments section would make the method easier to reproduce.
  3. [Table 5 and Table 6] In the comparison against OmniPaint, TurboClear is worse on DISTS-Local and MUSIQ (Table 5, with CIs in Table 6 that exclude zero for DISTS-L). The abstract's phrase 'comparable or better visual removal quality' is broadly supported by the other metrics, but a sentence acknowledging this trade-off would improve precision.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the core derivation distills an external teacher and is evaluated on held-out targets.

full rationale

TurboClear's training pipeline is a standard one-step distillation: the student is initialized from and distilled against ObjectClear, a fixed multi-step teacher (Section 'Experiment Settings', 'Implementation details'), and the RDM objective (Eqs. 5-9) is computed from the difference between teacher and fake-score residuals masked by m_e, not from a target that is fed back as a prediction. The paired losses in Eq. 10 and the LSF losses in Eq. 17 are supervised by clean targets x*, which are held-out ground truths, not model outputs. The reported quality metrics (PSNR, PSNR-M, LPIPS, LPIPS-L, DISTS, etc.) are measured against those held-out targets on OBER-Test and RORD-Val; none of the headline efficiency numbers (40.04x, 665x) is implied by the method equations, and the Appendix E inequality is a parameter-free function-class statement that does not depend on the fitted gates. The overlapping-author baselines FlashClear and ObjectClear appear as comparisons and as the distillation teacher; this is self-citation but it is not load-bearing circularity, because the teacher is an externally trained model and the quality claims are independently measured against ground truth and in a blinded user study. The limitation that effect-region quality is not directly evaluated inside m_e is an evaluation-coverage gap, not a circular derivation.

Assumptions & free parameters 6 free parameters · 7 assumptions · 0 invented entities

No new physical or conceptual entities are postulated. RDM masks and LSF gates are trained network components; the object-effect mask is an annotation inherited from the task setup. The free parameters listed are the hand-chosen settings most directly affecting the central efficiency and quality claims; additional standard hyperparameters (learning rates, batch sizes, network widths) are in the appendices but do not uniquely determine the central result.

free parameters (6)
  • One-step sampling timestep t_s = 399 (fixed DDIM timestep)
    Chosen by hand during warm-up and inference; it defines the single-step schedule that the whole method depends on.
  • RDM stage loss weights = 0.1 L_RDM, 0.1 L_mask, 0.1 L_bg, 1.0 LPIPS, 0.01 L_loc
    Manual weights balancing region-calibrated distillation against reconstruction; the ablation in Table 3 depends on this balance.
  • Fake-to-student update ratio = 5 updates per student step
    Controls how closely the fake score model tracks the evolving student; manually set.
  • DMD timestep sampling range = [20, 980]
    Uniform sampling range for distribution matching; manual and not justified theoretically.
  • LSF loss weights and gate regularization = (1.0, 0.5, 0.2) for L1 and LPIPS; (0.05, 0.05, 0.01) for alpha regularizers
    Hand-selected so the gate copies background and regenerates the object region; directly shapes the fusion output.
  • Training iteration counts = 1K warm-up, 25K RDM, 10K LSF
    Stopping decisions made by hand; earlier or later stopping would change the reported quality.
assumptions (7)
  • domain assumption Object-effect masks m_e are available and accurate during training.
    RDM masks all distribution-matching gradients to m_e, reconstruction losses split foreground and background by m_e, and LSF supervision depends on it; if m_e omits effects, removal fails (Method section; Limitations H).
  • domain assumption Paired clean targets x* exist and are spatially aligned with input y.
    Warm-up, RDM reconstruction terms, and LSF loss all regress to x*; unpaired or misaligned targets would break training.
  • domain assumption The SDXL inpainting UNet and VAE provide a sufficiently accurate latent space for object removal.
    TurboClear is built on SDXL and initialized from ObjectClear, so representational limits of those models are inherited.
  • ad hoc to paper Fixed one-step DDIM timestep 399 with CFG disabled is a valid student sampler.
    The paper chooses this schedule without a derivation; it is a tuned hyperparameter (Appendix B).
  • ad hoc to paper The fixed prompt 'remove the instance of object' and object-token index 5 identify the target in cross-attention.
    RDM localization and LSF attention prior assume the object token is known; the paper hardcodes index 5 (Appendix B) and does not describe selection for arbitrary prompts.
  • domain assumption The teacher ObjectClear defines a valid target distribution for object-effect removal.
    The student is initialized from and distilled from ObjectClear; teacher artifacts bound the student (Experiment settings).
  • standard math Standard latent diffusion forward process and denoiser equations (Eq. 1-4) correctly represent the teacher and fake score models.
    Taken from Ho et al. 2020 and Song et al. 2021; standard background not introduced here.

how reviews work

0 comments
Cite this review

Pith. "Pith review of TurboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion." pith.science (2026). https://pith.science/paper/BFMMEQPR

@misc{pith2026260801288,
  author       = {Pith},
  title        = {Pith review of: TurboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BFMMEQPR}},
  note         = {Machine review of arXiv:2608.01288}
}
abstract

Recently, diffusion-based removal methods have achieved promising visual quality in removing both target objects and their associated effects. However, they typically rely on multi-step denoising, leading to high inference cost. Directly applying existing one-step distillation methods is also suboptimal, since their global objectives lack explicit region-wise calibration and may weaken the asymmetric edit-and-preserve behavior required by object-effect removal. To address these challenges, we propose TurboClear, a one-step SDXL-based object-effect removal model. During training, we design Region-Calibrated Distribution Matching (RDM) for region-aware distillation to preserve the teacher model's asymmetric edit-and-preserve behavior. Furthermore, we propose Learnable Spatial Fusion (LSF) for lightweight inference-time fusion. Extensive experiments show that TurboClear significantly improves inference efficiency while maintaining competitive visual quality. TurboClear reduces the computational overhead by up to $40.04\times$ compared to ObjectClear, and by up to $665\times$ against the Flux-based method OmniPaint, all while maintaining comparable or better visual removal quality. Code is available at https://github.com/GuoCalix/TurboClear.

Figures

Figures reproduced from arXiv: 2608.01288 by the authors.

Figure 1
Figure 1. PSNR-Params-FLOPs comparison of mask- and [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of TurboClear. (a) RDM spatially calibrates the one-step distribution-matching gradient using the object [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Visualizing the spatial asymmetry enabled by LSF. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison on five representative OBER-Wild samples without ground-truth targets. TurboClear removes [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Visual comparison of AGF and LSF implemented [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Additional qualitative comparisons on eight OBER-Wild samples without ground-truth targets (part 1). [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Additional qualitative comparisons on eight OBER-Wild samples without ground-truth targets (part 2). [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

80 extracted references · 49 canonical work pages

  1. [1]

    Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

    Clancey, William J. Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83)

  2. [2]

    Classification Problem Solving

    Clancey, William J. Classification Problem Solving. Proceedings of the Fourth National Conference on Artificial Intelligence

  3. [3]

    , title =

    Robinson, Arthur L. , title =. 1980 , doi =. https://science.sciencemag.org/content/208/4447/1019.full.pdf , journal =

    1980

  4. [4]

    New Ways to Make Microcircuits Smaller---Duplicate Entry

    Robinson, Arthur L. New Ways to Make Microcircuits Smaller---Duplicate Entry. Science

  5. [5]

    Clancey and Glenn Rennels , abstract =

    Diane Warner Hasling and William J. Clancey and Glenn Rennels , abstract =. Strategic explanations for a diagnostic consultation system , journal =. 1984 , issn =. doi:https://doi.org/10.1016/S0020-7373(84)80003-6 , url =

    doi:10.1016/s0020-7373(84)80003-6 1984

  6. [6]

    and Rennels, Glenn R

    Hasling, Diane Warner and Clancey, William J. and Rennels, Glenn R. and Test, Thomas. Strategic Explanations in Consultation---Duplicate. The International Journal of Man-Machine Studies

  7. [7]

    Poligon: A System for Parallel Problem Solving

    Rice, James. Poligon: A System for Parallel Problem Solving

  8. [8]

    Transfer of Rule-Based Expertise through a Tutorial Dialogue

    Clancey, William J. Transfer of Rule-Based Expertise through a Tutorial Dialogue

Show all 80 references
  1. [9]

    The Engineering of Qualitative Models

    Clancey, William J. The Engineering of Qualitative Models

  2. [10]

    2023 , eprint=

    Attention Is All You Need , author=. 2023 , eprint=

    2023

  3. [11]

    Pluto: The 'Other' Red Planet

    NASA. Pluto: The 'Other' Red Planet

  4. [12]

    ICLR , year=

    Sdedit: Guided image synthesis and editing with stochastic differential equations , author=. ICLR , year=

  5. [13]

    ICLR , year=

    Podell, Dustin and English, Zion and Lacey, Kyle and Blattmann, Andreas and Dockhorn, Tim and M. ICLR , year=

  6. [14]

    CVPR , year=

    High-resolution image synthesis with latent diffusion models , author=. CVPR , year=

  7. [15]

    WACV , year=

    Latentpaint: Image inpainting in latent space with diffusion models , author=. WACV , year=

  8. [16]

    CVPR , year=

    Structure matters: Tackling the semantic discrepancy in diffusion models for image inpainting , author=. CVPR , year=

  9. [17]

    Yu, Yingchen and Zhan, Fangneng and Lu, Shijian and Pan, Jianxiong and Ma, Feiying and Xie, Xuansong and Miao, Chunyan , booktitle=

  10. [18]

    Xie, Shaoan and Zhang, Zhifei and Lin, Zhe and Hinz, Tobias and Zhang, Kun , booktitle=

  11. [19]

    NeurIPS , year=

    Clipaway: Harmonizing focused embeddings for removing objects via diffusion models , author=. NeurIPS , year=

  12. [20]

    ECCV , year=

    A task is worth one word: Learning with task prompts for high-quality versatile image inpainting , author=. ECCV , year=

  13. [21]

    AAAI , year=

    Designedit: Multi-layered latent decomposition and fusion for unified & accurate image editing , author=. AAAI , year=

  14. [22]

    AAAI , year=

    Attentive eraser: Unleashing diffusion model’s object removal potential via self-attention redirection guidance , author=. AAAI , year=

  15. [23]

    Li, Ruibin and Yang, Tao and Guo, Song and Zhang, Lei , booktitle=

  16. [24]

    arXiv preprint arXiv:2501.07397 , year=

    Omnieraser: Remove objects and their effects in images with paired video-frame data , author=. arXiv preprint arXiv:2501.07397 , year=

    arXiv

  17. [25]

    Winter, Daniel and Cohen, Matan and Fruchter, Shlomi and Pritch, Yael and Rav-Acha, Alex and Hoshen, Yedid , booktitle=

  18. [26]

    Zhu, Zixin and Li, Haoxiang and Feng, Xuelu and Wu, He and Qiao, Chunming and Yuan, Junsong , booktitle=

  19. [27]

    CVPR , year=

    Precise Object and Effect Removal with Adaptive Target-Aware Attention , author=. CVPR , year=

  20. [28]

    ICCV , year=

    Omnipaint: Mastering object-oriented editing via disentangled insertion-removal inpainting , author=. ICCV , year=

  21. [29]

    NeurIPS , year=

    Improved distribution matching distillation for fast image synthesis , author=. NeurIPS , year=

  22. [30]

    CVPR , year=

    One-step diffusion with distribution matching distillation , author=. CVPR , year=

  23. [31]

    ICLR , year=

    Simplifying, stabilizing and scaling continuous-time consistency models , author=. ICLR , year=

  24. [32]

    ICML , year=

    Consistency models , author=. ICML , year=

  25. [33]

    arXiv preprint arXiv:2310.04378 , year=

    Latent consistency models: Synthesizing high-resolution images with few-step inference , author=. arXiv preprint arXiv:2310.04378 , year=

    arXiv

  26. [34]

    2023 , journal=

    Inst-Inpaint: Instructing to Remove Objects with Diffusion Models , author=. 2023 , journal=

    2023

  27. [35]

    WACV , year=

    Resolution-robust large mask inpainting with fourier convolutions , author=. WACV , year=

  28. [36]

    Lugmayr, Andreas and Danelljan, Martin and Romero, Andres and Yu, Fisher and Timofte, Radu and Van Gool, Luc , booktitle=

  29. [37]

    Jiang, Longtao and Wang, Zhendong and Bao, Jianmin and Zhou, Wengang and Chen, Dongdong and Shi, Lei and Chen, Dong and Li, Houqiang , booktitle=

  30. [38]

    Liu, Yi and Zhou, Hao and Cui, Benlei and Shang, Wenxiang and Lin, Ran , booktitle=

  31. [39]

    Nichol, Alex and Dhariwal, Prafulla and Ramesh, Aditya and Shyam, Pranav and Mishkin, Pamela and McGrew, Bob and Sutskever, Ilya and Chen, Mark , journal=

  32. [40]

    Saharia, Chitwan and Chan, William and Chang, Huiwen and Lee, Chris and Ho, Jonathan and Salimans, Tim and Fleet, David and Norouzi, Mohammad , booktitle=

  33. [41]

    CVPR , year=

    Blended diffusion for text-driven editing of natural images , author=. CVPR , year=

  34. [42]

    Brooks, Tim and Holynski, Aleksander and Efros, Alexei A , booktitle=

  35. [43]

    CVPR , year=

    Imagic: Text-based real image editing with diffusion models , author=. CVPR , year=

  36. [44]

    ICCV , year=

    Adding conditional control to text-to-image diffusion models , author=. ICCV , year=

  37. [45]

    ECCV , year=

    Brushnet: A plug-and-play image inpainting model with decomposed dual-branch diffusion , author=. ECCV , year=

  38. [46]

    CVPR , year=

    Anydoor: Zero-shot object-level image customization , author=. CVPR , year=

  39. [47]

    ICLR , year=

    Hd-painter: high-resolution and prompt-faithful text-guided image inpainting with diffusion models , author=. ICLR , year=

  40. [48]

    NeurIPS , year=

    Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps , author=. NeurIPS , year=

  41. [49]

    ICLR , year=

    Pseudo numerical methods for diffusion models on manifolds , author=. ICLR , year=

  42. [50]

    NeurIPS , year=

    Unipc: A unified predictor-corrector framework for fast sampling of diffusion models , author=. NeurIPS , year=

  43. [51]

    CVPR , year=

    ChordEdit: One-Step Low-Energy Transport for Image Editing , author=. CVPR , year=

  44. [52]

    ICLR , year=

    Progressive distillation for fast sampling of diffusion models , author=. ICLR , year=

  45. [53]

    CVPR , year=

    On distillation of guided diffusion models , author=. CVPR , year=

  46. [54]

    arXiv preprint arXiv:2402.19159 , year=

    Trajectory Consistency Distillation , author=. arXiv preprint arXiv:2402.19159 , year=

    arXiv

  47. [55]

    2024 , journal=

    PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator , author=. 2024 , journal=

    2024

  48. [56]

    arXiv preprint arXiv:2404.13686 , year=

    Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis , author=. arXiv preprint arXiv:2404.13686 , year=

    arXiv

  49. [57]

    arXiv preprint arXiv:2101.02388 , year=

    Knowledge distillation in iterative generative models for improved sampling speed , author=. arXiv preprint arXiv:2101.02388 , year=

    arXiv

  50. [58]

    arXiv preprint arXiv:2403.06807 , year=

    Multistep Consistency Models , author=. arXiv preprint arXiv:2403.06807 , year=

    arXiv

  51. [59]

    arXiv preprint arXiv:2404.13903 , year=

    Accelerating Image Generation with Sub-path Linear Approximation Model , author=. arXiv preprint arXiv:2404.13903 , year=

    arXiv

  52. [60]

    ICML , year=

    Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation , author=. ICML , year=

  53. [61]

    ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling , year=

    BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping , author=. ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling , year=

    2023

  54. [62]

    CVPR , year=

    SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation , author=. CVPR , year=

  55. [63]

    ECCV , year=

    Adversarial diffusion distillation , author=. ECCV , year=

  56. [64]

    arXiv preprint arXiv:2402.13929 , year=

    Sdxl-lightning: Progressive adversarial diffusion distillation , author=. arXiv preprint arXiv:2402.13929 , year=

    arXiv

  57. [65]

    CVPR , year=

    Ufogen: You forward once large scale text-to-image generation via diffusion gans , author=. CVPR , year=

  58. [66]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Flowedit: Inversion-free text-based editing using pre-trained flow models , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  59. [67]

    2026 , journal=

    FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching , author=. 2026 , journal=

    2026

  60. [68]

    2020 , journal=

    Denoising Diffusion Probabilistic Models , author=. 2020 , journal=

    2020

  61. [69]

    ICLR , year=

    Denoising diffusion implicit models , author=. ICLR , year=

  62. [70]

    arXiv preprint arXiv:1312.6114 , year=

    Auto-encoding variational bayes , author=. arXiv preprint arXiv:1312.6114 , year=

    arXiv

  63. [71]

    CVPR , year=

    Selective Kernel Networks , author=. CVPR , year=

  64. [72]

    TPAMI , year=

    A closed-form solution to natural image matting , author=. TPAMI , year=

  65. [73]

    Sagong, Min-Cheol and Yeo, Yoon-Jae and Jung, Seung-Won and Ko, Sung-Jea , booktitle=

  66. [74]

    CVPR , year=

    The unreasonable effectiveness of deep features as a perceptual metric , author=. CVPR , year=

  67. [75]

    IEEE transactions on pattern analysis and machine intelligence , year=

    Image quality assessment: Unifying structure and texture similarity , author=. IEEE transactions on pattern analysis and machine intelligence , year=

  68. [76]

    ICCV , year=

    Musiq: Multi-scale image quality transformer , author=. ICCV , year=

  69. [77]

    AAAI , year=

    Exploring clip for assessing the look and feel of images , author=. AAAI , year=

  70. [78]

    NeurIPS , year=

    Gans trained by a two time-scale update rule converge to a local nash equilibrium , author=. NeurIPS , year=

  71. [79]

    Communications of the ACM , year=

    Generative adversarial networks , author=. Communications of the ACM , year=

  72. [80]

    2026 , howpublished =

    GPT-5.6 System Card , author =. 2026 , howpublished =

    2026

Pith tools

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