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SILO: Solving Inverse Problems with Latent Operators

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read SILO claims that a learned latent degradation operator lets latent diffusion models solve inverse problems entirely in latent space, improving perceptual quality while cutting runtime roughly 3-10x.

desk verdict SILO's latent-operator idea is fresh and the speedups look real, but the appendix's own CPSNR numbers show it is not actually enforcing measurement consistency, so the inverse-problem claim needs major qualification. read the letter →

arxiv 2501.11746 v1 pith:ZO3AZ7GP submitted 2025-01-20 cs.CV cs.AIcs.LG

Ron Raphaeli , Sean Man , Michael Elad This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords latentdiffusionmodelsinverseproblemsposteriorsamplinglearneddegradationoperatorimagerestorationconsistencyguidanceautoencoder-free
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

SILO proposes a way to solve inverse problems such as deblurring, super-resolution, inpainting, and JPEG decompression with latent diffusion models while keeping the entire restoration loop in the latent space. Its central claim is that a small learned operator $H_{\theta}$ can emulate the image-space degradation $A$ directly on latents, so measurement consistency is enforced without repeatedly decoding latents and differentiating through the autoencoder. The authors argue this removes the main source of blur and noise artifacts in prior latent-diffusion solvers and cuts runtime by a factor of roughly 3 to 10. Experiments on FFHQ and COCO report consistent gains in LPIPS, FID, and KID over LDPS, GML-DPS, PSLD, and ReSample. If the claim holds, the standard recipe for latent-diffusion inverse solvers changes: the autoencoder is used only once at the start and once at the end.

What carries the argument

The load-bearing object is the learned latent degradation operator $H_{\theta}$, a small neural network (a Readout Guidance network in the main experiments, a plain CNN in an ablation) trained with loss (20) to map denoised latents $\hat{z}_t^0$ to the encoding of the degraded measurement. It carries the argument because it converts the image-space data-consistency term into a latent-space norm: minimizing $\|w - H_{\theta}(\hat{z}_t^0, t)\|$ is used as a proxy for the pixel-space likelihood, justified by near-perfect autoencoder reconstruction on degraded images and a Lipschitz bound on the decoder.

What would settle it

Measure the correlation between the latent guidance gradient $\nabla_z \|w - H_{\theta}(\hat{z}_t^0)\|$ and the pixel-space likelihood gradient $\nabla_z \|y - A(D(\hat{z}_t^0))\|$ across diffusion timesteps for a degradation such as phase retrieval or very large noise. If the two gradients point in largely unrelated directions, the proxy fails and SILO's reconstructions should degrade accordingly.

Watch

Extended reading notes

Core claim

The central claim is that the likelihood score of a latent diffusion posterior can be approximated by a learned latent operator. Given a measurement $y$, a clean latent $z_0$, and a trained $H_{\theta}$ with $H_{\theta}(E(x)) \approx E(A(x))$, the paper derives the guidance gradient $\nabla_{z_t} \ln p(y|z_t) \approx -\tfrac{\text{Const}}{\sigma_y^2} \nabla_{z_t} \|w - H_{\theta}(\hat{z}_t^0)\|^2$, where $w = E(y)$. The argument rests on two steps: the autoencoder is near-lossless on degraded images, so pixel consistency can be rewritten in latent space, and the decoder's Lipschitz continuity lets the latent residual bound the pixel residual. Training $H_{\theta}$ by Eq. (20) minimizes the $\ell^1$ distance between $H_{\theta}(\hat{z}_t^0, t)$ and $E(y)$ over timesteps, so the operator learns not just the degradation but also the denoiser's time-dependent effect on latents. In Algorithm 1 the encoder and decoder are each invoked exactly once, with all gradient steps passing through the denoiser and $H_{\theta}$.

Load-bearing premise

The method assumes that the latent-space residual $\|E(y) - H_{\theta}(z)\|$ is a faithful proxy for the true pixel-space mismatch $\|y - A(x)\|$, an assumption supported only empirically by the autoencoder's near-reconstruction on degraded images and a Lipschitz bound with an unknown constant.

Editorial extensions

If this is right

  • The autoencoder is used only twice per restoration: once to encode the measurement and once to decode the final latent; every intermediate step happens in latent space.
  • On FFHQ and COCO, SILO reports lower LPIPS, FID, and KID than LDPS, GML-DPS, PSLD, and ReSample for blur, super-resolution, inpainting, and JPEG tasks.
  • Restoration runtime drops by roughly 3 times versus PSLD and about 10 times versus ReSample in the reported settings.
  • A separate $H_{\theta}$ must be trained for each degradation operator, but that one training run supports unlimited restorations for that operator.
  • The method benefits from better text conditioning and classifier-free guidance, so perceptual quality improves when the latent diffusion prior is stronger.

Reading between the lines

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

  • The paper leaves implicit that the same latent-operator recipe should transfer to other nonlinear degradations, such as learned camera pipelines or MRI undersampling, as long as a latent surrogate can be trained.
  • A natural extension not explored in the paper is to train $H_{\theta}$ to match gradients rather than just outputs, which could tighten the latent-space proxy and improve consistency where the Lipschitz bound is loose.
  • Because $E(y)$ must be a meaningful representation, SILO's success for a given degradation is tied to the autoencoder's behavior on degraded images; alternative encoders or learned measurement-to-latent maps could extend it to phase retrieval and other cases where $y$ is far from natural images.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper proposes SILO, a latent-space inverse problem solver for latent diffusion models. Instead of repeatedly decoding latents and differentiating through the decoder to enforce measurement consistency, SILO learns a small network Hθ that emulates the degradation operator A directly in the latent space. The measurement is encoded once, and the diffusion sampling is guided by minimizing ||E(y) - Hθ(ẑ_t0, t)||. The paper reports experiments on FFHQ and COCO with Gaussian blur, super-resolution ×4/×8, box inpainting, and JPEG, claiming improvements in LPIPS, FID, and KID and 3–10x speedups over PSLD, LDPS, GML-DPS, and ReSample.

Significance. If the consistency gap were resolved, the idea of learning a latent operator would be a valuable contribution to LDM-based inverse problems, since it removes the costly and artifact-prone differentiation through the autoencoder. The paper provides a systematic comparison, ablations (t-dependence, CNN vs. RG), and additional diversity results in the appendix. However, the central claim that SILO solves the inverse problem by posterior sampling is undermined by the measurement-consistency results reported only in the appendix.

major comments (3)
  1. [Sec. 5.3 and Appendix F (Tables 13–15)] The appendix reports CPSNR values that directly contradict the claim that the guidance in Eq. (19) enforces consistency with the measurement. For FFHQ super-resolution ×8, SILO(RV) reaches CPSNR 32.60 dB while PSLD and LDPS reach 40.91 and 38.73 dB; for Gaussian blur the gap is 32.17 dB vs. 44.18 dB and 42.69 dB. Since CPSNR = PSNR(A(x), A(ẋ̂)), a gap of 8–12 dB means the reconstructions, after re-applying the known degradation, do not match the measurements that define the inverse problem. The main tables (Tables 2 and 3) omit CPSNR, so the reported perceptual gains are not accompanied by evidence that the method samples p(x|y). The authors should include CPSNR in the main tables and either modify the method to enforce measurement consistency or substantially temper the claim that SILO is a posterior sampler.
  2. [Sec. 4.2, Eqs. (16)–(19)] The derivation of Eq. (19) is a proxy that is not sufficient for the guidance step. Eq. (18) states ||D(E(y*))−D(Hθ(z))||² ≤ C ||E(y*)−Hθ(z)||², but Algorithm 1 uses the gradient of the latent residual, not of the pixel residual. The Lipschitz constant C is unknown, and the gradient of the left-hand side is not controlled by the gradient of the right-hand side without additional assumptions on the Jacobian of D. Moreover, the approximation D(E(y))≈y in Eq. (16) is poor for inpainting and JPEG (PSNR ≈ 31 dB in Table 1). The paper should either prove a gradient bound or empirically verify that minimizing the latent residual reduces the pixel-space residual during the sampling trajectory; the current CPSNR results suggest it does not.
  3. [Sec. 4.4, Algorithm 1, step 9, and Eq. (19)] There is an inconsistency between the formula in Eq. (19), which uses the squared norm ||w−Hθ(ẑ_t0)||², and the algorithm, which computes the gradient of the unsquared norm ||w−ŵ_t||₂ (as described in the text: 'taking a gradient of the square root of the RHS in Eq. (18)'). The gradient of the norm differs from the gradient of the squared norm by a factor of 1/(2||·||), which changes the effective step size and the behavior of the guidance. The authors should clarify which objective is actually minimized and align Eq. (19), the text, and Algorithm 1.
minor comments (6)
  1. [Sec. 5.1] There are typos in the manuscript, for example 'groundn-truth' in Sec. 5.1 and 'degredations' in the caption of Fig. 4; these should be corrected.
  2. [Sec. 5.2] The network name 'Readout-Guidence' is likely a typo for 'Readout Guidance' (reference [35]); please fix.
  3. [Sec. 4.1 and Algorithm 1] The clamping operation w = clamp(E(y),−4,4) is introduced without explanation; a brief justification of the range would improve reproducibility.
  4. [Table 1] The column headers in Table 1 (e.g., 'x, f(x)', 'ynl, f(ynl)') are not self-explanatory; the caption should define these pairs and describe how PSNR is computed for each.
  5. [Appendix D and Sec. 5.3] The ReSample comparison uses an adapted version of the code with a different prior and image size, and the paper notes discrepancies with the originally reported ReSample numbers; this caveat should also appear in the main text near Tables 2 and 3.
  6. [Sec. 5.1] No seed variation or error bars are reported; since the algorithm is stochastic and the seed is fixed at 1000, it would strengthen the paper to run multiple seeds and report means and standard deviations for the key metrics.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the latent operator is trained on known degradations and evaluated on held-out data, and the derivation rests on an explicit Lipschitz surrogate rather than on its own conclusion.

full rationale

The paper's derivation chain is not circular under the quoted-equation test. Hθ is trained by Eq. (20), L = E[||Hθ(ẑ_t0, t) − E(y)||_1], on training images with known degradations, and its only use at inference time is as the guidance term in Algorithm 1, step 9. All reported quality metrics are computed on held-out FFHQ and COCO test images that were not used to train Hθ or the diffusion prior, so the central empirical claim is not statistically forced by construction. The key theoretical step, Eq. (18), is a Lipschitz bound on the decoder showing that the latent residual controls the pixel residual up to an unknown constant C; it does not assume the posterior-sampling conclusion, and the paper explicitly flags the D(E(y)) ≈ y approximation and leaves C unspecified. The self-citations in the background sections are standard references to prior work by the same group and are not load-bearing; the Hθ architecture is imported from external work [35] and is independently ablated with a simple CNN in Table 7. The reviewer-identified weakness, namely large CPSNR gaps in the appendix tables, is an empirical correctness concern about whether the learned operator faithfully enforces measurement consistency; it is not a circularity in the derivation. Therefore no specific circular step can be exhibited, and the honest finding is no significant circularity.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The method rests on standard autoencoder/diffusion assumptions plus the new heuristic that a trained latent operator can substitute for pixel-space consistency. The main unproven link is Eq. (19), which converts a Lipschitz bound into a gradient proxy; its validity is empirical rather than derived.

free parameters (2)
  • consistency scale eta = 0.5 (most tasks); 1.0 (inpainting)
    Controls the size of the latent guidance step in Algorithm 1, step 9. Chosen per task to trade measurement consistency against perceptual quality (Sec. 5.2 and Appendix C.3).
  • classifier-free guidance scale CFG = 1 or 4
    Test-time prompt guidance strength used in experiments (Tab. 5); not part of the core algorithm but affects reported results.
assumptions (5)
  • standard math The decoder D is Lipschitz continuous
    Invoked in Eq. (18) to bound the pixel-space distance by the latent-space distance; true for any finite neural network, but the constant C is unknown and unmeasured.
  • domain assumption D(E(y)) approximately equals y for degraded images
    Used in Eq. (16) to move the measurement fidelity term into latent space; tested empirically in Tab. 1 for common degradations, but not guaranteed for unusual measurements like phase retrieval.
  • domain assumption Htheta trained by Eq. (20) approximates E(A(x)) for test latents
    The learned operator is assumed to generalize from training to test images and degradations; no generalization bound is provided.
  • ad hoc to paper Gradient of ||w - Htheta(zhat_t0)|| is a valid proxy for the score likelihood grad_z ln p(y|z)
    The central heuristic of the method (Eq. 19), motivated by the Lipschitz proxy and by the DPS-style square-root gradient; not a proven likelihood approximation.
  • domain assumption Stable Diffusion v1.5 and Realistic Vision v5.1 provide a good latent prior
    The method's performance depends on the quality of the pretrained diffusion prior; standard assumption shared with all LDM inverse solvers.

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Cite this review

Pith. "Pith review of SILO: Solving Inverse Problems with Latent Operators." pith.science (2026). https://pith.science/paper/ZO3AZ7GP

@misc{pith2026250111746,
  author       = {Pith},
  title        = {Pith review of: SILO: Solving Inverse Problems with Latent Operators},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZO3AZ7GP}},
  note         = {Machine review of arXiv:2501.11746}
}
read the original abstract

Consistent improvement of image priors over the years has led to the development of better inverse problem solvers. Diffusion models are the newcomers to this arena, posing the strongest known prior to date. Recently, such models operating in a latent space have become increasingly predominant due to their efficiency. In recent works, these models have been applied to solve inverse problems. Working in the latent space typically requires multiple applications of an Autoencoder during the restoration process, which leads to both computational and restoration quality challenges. In this work, we propose a new approach for handling inverse problems with latent diffusion models, where a learned degradation function operates within the latent space, emulating a known image space degradation. Usage of the learned operator reduces the dependency on the Autoencoder to only the initial and final steps of the restoration process, facilitating faster sampling and superior restoration quality. We demonstrate the effectiveness of our method on a variety of image restoration tasks and datasets, achieving significant improvements over prior art.

Figures

Figures reproduced from arXiv: 2501.11746 by the authors.

Figure 1
Figure 1. Measurements and their corresponding reconstructions [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Computational schemes of prior work and SILO. (a): Prior work enforces consistency to the measurement in pixel space, resulting in differentiation through the decoder. (b): SILO keeps all calculations in the latent space. This allows faster reconstructions while improving their perceptual quality compared to prior work, as seen in [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Training scheme of the latent operator, Hθ. In train￾ing, gradients flow from L to update the parameters of Hθ. Note that no gradients pass through the pixel space. Hθ learns to mimic the effect of the degradation operator in the latent space, allowing us to use SILO to solve inverse problems using LDMs. 4.4. Reconstruction We are left with Q4, questioning the way to deploy the trained operator Hθ in solving inverse… view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Comparison of SILO to other methods. From left to right: the clean image, x, the measurement y, the reconstructions using SILO (with RV and SD), ReSample, and PSLD. Each row contains the image and a zoom-in to show the differences better. From top to bottom the degreda…
Figure 5
Figure 5. Figure 5: Restorations of masked images from the COCO dataset. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: The decoder’s artifacts. We present the likelihood’s gradient through the decoder (Eq. (23)) at different timesteps t. Each gradient is presented as four single-channel images, clipped to [−3, 3] and scaled by 102 for better visualization. At the bottom, we present the…
Figure 7
Figure 7. Figure 7: Diverse reconstructions. We present two measurements from FFHQ corrupted by a box mask at the top. Below each measure￾ment are several reconstructions using SILO. The reconstructions’ hyperparameters are identical over all the images except for the random seed used. We…
Figure 8
Figure 8. Figure 8: Architecture of the CNN used to get the results of the CNN-RV row in Tab. [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: Training loss when learning a RG Hθ to mimic the Super-resolution ×8 operator. resolution ×8 operator is shown in [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 11
Figure 11. Figure 11: Box inpainting with σy = 0.01, COCO dataset. Additional results. 8 [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]
Figure 12
Figure 12. Figure 12: Box inpainting with σy = 0.01, FFHQ dataset. Additional results. 9 [PITH_FULL_IMAGE:figures/full_fig_p020_12.png]
Figure 13
Figure 13. Figure 13: SR×4 with σy = 0.01, FFHQ dataset. Additional results. 10 [PITH_FULL_IMAGE:figures/full_fig_p021_13.png]
Figure 14
Figure 14. Figure 14: SR×8, FFHQ dataset. Additional results. 11 [PITH_FULL_IMAGE:figures/full_fig_p022_14.png]
Figure 15
Figure 15. Figure 15: JPEG with σy = 0.01, FFHQ dataset. Additional results. 12 [PITH_FULL_IMAGE:figures/full_fig_p023_15.png]
Figure 16
Figure 16. Figure 16: Gaussian blur with σy = 0.01, FFHQ dataset. Additional results. 13 [PITH_FULL_IMAGE:figures/full_fig_p024_16.png]
Figure 17
Figure 17. Figure 17: SR×8 with σy = 0.03, FFHQ dataset. Additional results. 14 [PITH_FULL_IMAGE:figures/full_fig_p025_17.png]

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Reference graph

Works this paper leans on

67 extracted references · 60 canonical work pages · cited by 2 Pith papers

  1. [1]

    Aharon, M

    M. Aharon, M. Elad, and A. Bruckstein. K-SVD: An al- gorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54 (11):4311–4322, 2006. 3

    work page 2006

  2. [2]

    Improving Image Genera- tion with Better Captions

    James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, Wesam Manassra, Prafulla Dhariwal, Casey Chu, Yunxin Jiao, and Aditya Ramesh. Improving Image Genera- tion with Better Captions. 1

    work page

  3. [3]

    Sutherland, Michael Arbel, and Arthur Gretton

    Mikołaj Bi ´nkowski, Danica J. Sutherland, Michael Arbel, and Arthur Gretton. Demystifying MMD GANs. In Inter- national Conference on Learning Representations, 2018. 6, 3

    work page 2018

  4. [4]

    The Perception-Distortion Tradeoff

    Yochai Blau and Tomer Michaeli. The Perception-Distortion Tradeoff. In Proceedings of the IEEE Conference on Com- puter Vision and Pattern Recognition , pages 6228–6237,

  5. [5]

    Chan, Xiran Wang, and Omar A

    Stanley H. Chan, Xiran Wang, and Omar A. Elgendy. Plug- and-Play ADMM for Image Restoration: Fixed-Point Con- vergence and Applications. IEEE Transactions on Computa- tional Imaging, 3(1):84–98, 2017. 3

    work page 2017

  6. [6]

    Trainable Nonlinear Reac- tion Diffusion: A Flexible Framework for Fast and Effective Image Restoration

    Yunjin Chen and Thomas Pock. Trainable Nonlinear Reac- tion Diffusion: A Flexible Framework for Fast and Effective Image Restoration. IEEE Trans. Pattern Anal. Mach. Intell., 39(6):1256–1272, 2017. 1, 3

    work page 2017

  7. [7]

    Diffusion Pos- terior Sampling for General Noisy Inverse Problems

    Hyungjin Chung, Jeongsol Kim, Michael Thompson Mc- cann, Marc Louis Klasky, and Jong Chul Ye. Diffusion Pos- terior Sampling for General Noisy Inverse Problems. In The Eleventh International Conference on Learning Representa- tions, 2022. 1, 3, 5

    work page 2022

  8. [8]

    Decom- posed Diffusion Sampler for Accelerating Large-Scale In- verse Problems

    Hyungjin Chung, Suhyeon Lee, and Jong Chul Ye. Decom- posed Diffusion Sampler for Accelerating Large-Scale In- verse Problems. In The Twelfth International Conference on Learning Representations, 2023. 3

    work page 2023

Show all 67 references
  1. [9]

    Prompt-tuning latent diffusion models for inverse problems, 2023

    Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, and Mauricio Delbracio. Prompt-tuning latent diffusion models for inverse problems, 2023. 2, 4, 6

    2023

  2. [10]

    Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

    Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, and Karen Egiazarian. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. IEEE Transac- tions on Image Processing, 16(8):2080–2095, 2007. 1

    2007

  3. [11]

    Diffusion Models Beat GANs on Image Synthesis

    Prafulla Dhariwal and Alexander Quinn Nichol. Diffusion Models Beat GANs on Image Synthesis. In Advances in Neural Information Processing Systems, 2021. 1, 2

    2021

  4. [12]

    Tweedie’s Formula and Selection Bias

    Bradley Efron. Tweedie’s Formula and Selection Bias. Jour- nal of the American Statistical Association, 106(496):1602– 1614, 2011. 3

    2011

  5. [13]

    Image Denoising Via Sparse and Redundant Representations Over Learned Dic- tionaries

    Michael Elad and Michal Aharon. Image Denoising Via Sparse and Redundant Representations Over Learned Dic- tionaries. IEEE Transactions on Image Processing, 15(12): 3736–3745, 2006. 1

    2006

  6. [14]

    Image Denoising: The Deep Learning Revolution and Beyond—A Survey Paper

    Michael Elad, Bahjat Kawar, and Gregory Vaksman. Image Denoising: The Deep Learning Revolution and Beyond—A Survey Paper. SIAM Journal on Imaging Sciences , 16(3): 1594–1654, 2023. 1, 3

    2023

  7. [15]

    Adaptive Compressed Sensing with Diffusion-Based Posterior Sam- pling

    Noam Elata, Tomer Michaeli, and Michael Elad. Adaptive Compressed Sensing with Diffusion-Based Posterior Sam- pling. In Computer Vision – ECCV 2024 , pages 290–308, Cham, 2025. Springer Nature Switzerland. 1

    2024

  8. [16]

    Sparsity based Poisson de- noising

    Raja Giryes and Michael Elad. Sparsity based Poisson de- noising. In 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, pages 1–5, 2012. 3

    2012

  9. [17]

    Weighted Nuclear Norm Minimization with Applica- tion to Image Denoising

    Shuhang Gu, Lei Zhang, Wangmeng Zuo, and Xiangchu Feng. Weighted Nuclear Norm Minimization with Applica- tion to Image Denoising. In 2014 IEEE Conference on Com- puter Vision and Pattern Recognition , pages 2862–2869,

    2014

  10. [18]

    Alarc´on

    Javier Gurrola-Ramos, Oscar Dalmau, and Teresa E. Alarc´on. A Residual Dense U-Net Neural Network for Im- age Denoising. IEEE Access, 9:31742–31754, 2021. 1

    2021

  11. [19]

    GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2017. 6, 3

    2017

  12. [20]

    Classifier-Free Diffusion Guidance

    Jonathan Ho and Tim Salimans. Classifier-Free Diffusion Guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021. 8, 3

    2021

  13. [21]

    Denoising Dif- fusion Probabilistic Models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising Dif- fusion Probabilistic Models. In Advances in Neural Infor- mation Processing Systems, pages 6840–6851. Curran Asso- ciates, Inc., 2020. 1, 3

    2020

  14. [22]

    Fu Jie Huang and Y . LeCun. Large-scale Learning with SVM and Convolutional for Generic Object Categorization. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), pages 284–291,

    2006

  15. [23]

    What is the best multi-stage architecture for object recognition? In 2009 IEEE 12th International Conference on Computer Vision, pages 2146–2153, 2009

    Kevin Jarrett, Koray Kavukcuoglu, Marc’Aurelio Ranzato, and Yann LeCun. What is the best multi-stage architecture for object recognition? In 2009 IEEE 12th International Conference on Computer Vision, pages 2146–2153, 2009. 8

    2009

  16. [24]

    Towards Flex- ible Blind JPEG Artifacts Removal

    Jiaxi Jiang, Kai Zhang, and Radu Timofte. Towards Flex- ible Blind JPEG Artifacts Removal. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 4997–5006, 2021. 3

    2021

  17. [25]

    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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4401– 4410, 2019. 6

    2019

  18. [26]

    Denoising Diffusion Restoration Models

    Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song. Denoising Diffusion Restoration Models. Advances in Neural Information Processing Systems, 35:23593–23606,

  19. [27]

    Regularization by Texts for Latent Diffusion Inverse Solvers, 2024

    Jeongsol Kim, Geon Yeong Park, Hyungjin Chung, and Jong Chul Ye. Regularization by Texts for Latent Diffusion Inverse Solvers, 2024. 2, 4

    2024

  20. [28]

    Kingma and Jimmy Ba

    Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization, 2017. 5

    2017

  21. [29]

    Kingma and Max Welling

    Diederik P. Kingma and Max Welling. Auto-Encoding Vari- ational Bayes, 2022. 2, 6 9

    2022

  22. [30]

    Im- ageNet Classification with Deep Convolutional Neural Net- works

    Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Im- ageNet Classification with Deep Convolutional Neural Net- works. In Advances in Neural Information Processing Sys- tems. Curran Associates, Inc., 2012. 6, 8, 2

    2012

  23. [31]

    LSDIR: A Large Scale Dataset for Image Restoration

    Yawei Li, Kai Zhang, Jingyun Liang, Jiezhang Cao, Ce Liu, Rui Gong, Yulun Zhang, Hao Tang, Yun Liu, Denis De- mandolx, Rakesh Ranjan, Radu Timofte, and Luc Van Gool. LSDIR: A Large Scale Dataset for Image Restoration. In 2023 IEEE/CVF Conference on Computer Vision and Pat- ter...

    2023

  24. [32]

    SwinIR: Image Restoration Using Swin Transformer

    Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. SwinIR: Image Restoration Using Swin Transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1833– 1844, 2021. 3

    2021

  25. [33]

    Lawrence Zitnick

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll´ar, and C. Lawrence Zitnick. Microsoft COCO: Common Objects in Context. In Computer Vision – ECCV 2014 , pages 740–755, Cham,

    2014

  26. [34]

    Decoupled Weight De- cay Regularization

    Ilya Loshchilov and Frank Hutter. Decoupled Weight De- cay Regularization. In International Conference on Learning Representations, 2018. 4

    2018

  27. [35]

    Gold- man, and Aleksander Holynski

    Grace Luo, Trevor Darrell, Oliver Wang, Dan B. Gold- man, and Aleksander Holynski. Readout Guidance: Learn- ing Control from Diffusion Features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8217–8227, 2024. 6, 4

    2024

  28. [36]

    High-Perceptual Quality JPEG Decoding via Posterior Sam- pling

    Sean Man, Guy Ohayon, Theo Adrai, and Michael Elad. High-Perceptual Quality JPEG Decoding via Posterior Sam- pling. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 1272–1282,

  29. [37]

    A Variational Perspective on Solving Inverse Problems with Diffusion Models

    Morteza Mardani, Jiaming Song, Jan Kautz, and Arash Vah- dat. A Variational Perspective on Solving Inverse Problems with Diffusion Models. In The Twelfth International Confer- ence on Learning Representations, 2023. 3

    2023

  30. [38]

    High Perceptual Quality Image De- noising With a Posterior Sampling CGAN

    Guy Ohayon, Theo Adrai, Gregory Vaksman, Michael Elad, and Peyman Milanfar. High Perceptual Quality Image De- noising With a Posterior Sampling CGAN. In Proceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 1805–1813, 2021. 1

    2021

  31. [39]

    Learning Transferable Visual Models From Natural Language Supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning Transferable Visual Models From Natural Language Supervision. InProceedings of the...

    2021

  32. [40]

    Inverse Methods for Atmospheric Sound- ing: Theory and Practice

    Clive D Rodgers. Inverse Methods for Atmospheric Sound- ing: Theory and Practice. In Inverse Methods for Atmo- spheric Sounding: Theory and Practice . WORLD SCIEN- TIFIC, 2000. 1

    2000

  33. [41]

    The Little Engine That Could: Regularization by Denoising (RED)

    Yaniv Romano, Michael Elad, and Peyman Milanfar. The Little Engine That Could: Regularization by Denoising (RED). SIAM Journal on Imaging Sciences , 10(4):1804– 1844, 2017. 3

    2017

  34. [42]

    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. 2, 3, 6

    2022

  35. [43]

    Dimakis, and Sanjay Shakkottai

    Litu Rout, Negin Raoof, Giannis Daras, Constantine Cara- manis, Alexandros G. Dimakis, and Sanjay Shakkottai. Solv- ing Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models, 2023. 2, 4, 6, 1

    2023

  36. [44]

    Beyond First-Order Tweedie: Solving Inverse Problems using La- tent Diffusion

    Litu Rout, Yujia Chen, Abhishek Kumar, Constantine Cara- manis, Sanjay Shakkottai, and Wen-Sheng Chu. Beyond First-Order Tweedie: Solving Inverse Problems using La- tent Diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 947...

    2024

  37. [45]

    Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi. Pho- torealistic Text-to-Image Diffusion Models with Deep Lan- gu...

    2022

  38. [46]

    Stablediffusionapi/realistic- vision-51 · Hugging Face

    Evgeny SG161222. Stablediffusionapi/realistic- vision-51 · Hugging Face. https://huggingface.co/stablediffusionapi/realistic-vision-

  39. [47]

    Very Deep Convo- lutional Networks for Large-Scale Image Recognition, 2015

    Karen Simonyan and Andrew Zisserman. Very Deep Convo- lutional Networks for Large-Scale Image Recognition, 2015. 6, 2

    2015

  40. [48]

    Deep Unsupervised Learning using Nonequilibrium Thermodynamics

    Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep Unsupervised Learning using Nonequilibrium Thermodynamics. In Proceedings of the 32nd International Conference on Machine Learning, pages 2256–2265. PMLR, 2015. 1

    2015

  41. [49]

    Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency

    Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, and Liyue Shen. Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency. In The Twelfth International Conference on Learning Representa- tions, 2023. 2, 4, 6, 1

    2023

  42. [50]

    Generative Modeling by Es- timating Gradients of the Data Distribution

    Yang Song and Stefano Ermon. Generative Modeling by Es- timating Gradients of the Data Distribution. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2019. 3

    2019

  43. [51]

    Generative Modeling by Estimating Gradients of the Data Distribution

    Yang Song and Stefano Ermon. Generative Modeling by Estimating Gradients of the Data Distribution. Advances in Neural Information Processing Systems, 32, 2019. 1

    2019

  44. [52]

    Kingma, Ab- hishek Kumar, Stefano Ermon, and Ben Poole

    Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Ab- hishek Kumar, Stefano Ermon, and Ben Poole. Score-Based Generative Modeling through Stochastic Differential Equa- tions. In International Conference on Learning Representa- tions, 2020. 1, 3

    2020

  45. [53]

    Solv- ing Inverse Problems in Medical Imaging with Score-Based Generative Models

    Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon. Solv- ing Inverse Problems in Medical Imaging with Score-Based Generative Models. In International Conference on Learn- ing Representations, 2021. 1

    2021

  46. [54]

    Venkatakrishnan, Charles A

    Singanallur V . Venkatakrishnan, Charles A. Bouman, and Brendt Wohlberg. Plug-and-Play priors for model based re- 10 construction. In 2013 IEEE Global Conference on Signal and Information Processing, pages 945–948, 2013. 3

    2013

  47. [55]

    A Connection Between Score Matching and Denoising Autoencoders

    Pascal Vincent. A Connection Between Score Matching and Denoising Autoencoders. Neural Computation, 23(7):1661– 1674, 2011. 3

    2011

  48. [56]

    Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution, 2024

    Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Jun- yang Lin. Qwen2-VL: Enhancing Vision-Language Model’s ...

    2024

  49. [57]

    ESRGAN: En- hanced Super-Resolution Generative Adversarial Networks

    Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy. ESRGAN: En- hanced Super-Resolution Generative Adversarial Networks. In Proceedings of the European Conference on Computer Vi- sion (ECCV) Workshops, pages 0–0, 2018. 3

    2018

  50. [58]

    Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

    Yinhuai Wang, Jiwen Yu, and Jian Zhang. Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model. In The Eleventh International Conference on Learning Rep- resentations, 2022. 1

    2022

  51. [59]

    RestoreFormer: High-Quality Blind Face Restoration From Undegraded Key-Value Pairs

    Zhouxia Wang, Jiawei Zhang, Runjian Chen, Wenping Wang, and Ping Luo. RestoreFormer: High-Quality Blind Face Restoration From Undegraded Key-Value Pairs. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17512–17521, 2022. 3

    2022

  52. [60]

    On Single Image Scale-Up Using Sparse-Representations

    Roman Zeyde, Michael Elad, and Matan Protter. On Single Image Scale-Up Using Sparse-Representations. In Curves and Surfaces , pages 711–730, Berlin, Heidelberg, 2012. Springer. 3

    2012

  53. [61]

    Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

    Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Transactions on Image Processing, 26(7):3142–3155, 2017. 3

    2017

  54. [62]

    Efros, Eli Shecht- man, and Oliver Wang

    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. 6

    2018

  55. [63]

    PerceptualSimilarity, 2018 https://github.com/richzhang/PerceptualSimilarity

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shecht- man, and Oliver Wang. PerceptualSimilarity, 2018 https://github.com/richzhang/PerceptualSimilarity. 6, 2

    2018

  56. [64]

    Denoising Dif- fusion Models for Plug-and-Play Image Restoration

    Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bi- han Wen, Radu Timofte, and Luc Van Gool. Denoising Dif- fusion Models for Plug-and-Play Image Restoration. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1219–1229, 2023. 3 ...

    2023

  57. [66]

    bi-domain

    (22) We categorize such solutions as part of the “bi-domain” family, as they compute gradients in both the pixel and la- tent domains. To the best of our knowledge, all existing methods leveraging LDMs for inverse problems (apart from SILO) fall within this category. In this s...

  58. [256]

    Hence, to give a fair comparison to ReSample, we had to adapt their publicly available code. PSLD. We made no modifications to the PSLD code, ex- cept for adapting the data-loading process to enable sam- pling from the COCO dataset. The hyperparameters used were identical to t...

  59. [2014]

    Springer International Publishing. 6

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.