Efficient Zero-shot Inpainting with Decoupled Diffusion Guidance
Authors: Badr MOUFAD
Yazid Janati
Navid Bagheri Shouraki
Alain Oliviero Durmus
Thomas Hirtz
Eric Moulines
Jimmy Olsson
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion models
zero-shot
guidance
Primary area: generative models

Abstract: Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remains consistent with observed regions. In particular, zero-shot approaches that leverage a pretrained diffusion model, without any retraining, have been shown to achieve highly effective reconstructions. However, state-of-the-art zero-shot methods typically rely on a sequence of surrogate likelihood functions, whose scores are used as proxies for the ideal score. This procedure however requires vector-Jacobian products through the denoiser at every reverse step, introducing significant memory and runtime overhead. To address this issue, we propose a new likelihood surrogate that yields simple and efficient to sample Gaussian posterior transitions, sidestepping the  backpropagation through the denoiser network. Our extensive experiments show that our method achieves strong observation consistency compared with fine-tuned baselines and produces coherent, high-quality reconstructions, all while significantly reducing inference cost.

Published as a conference paper at ICLR 2026
EFFICIENT ZERO-SHOT INPAINTING WITH DECOUPLED DIFFUSION GUIDANCE
Badr Moufad'. Navid Bagheri Shouraki Alain Oliviero Durmus!
Thomas Hirtz Eric Moulines Jimmy Olsson Yazid Janati
'CMAP, Ecole Polytechnique IIsstitute of Foundation Models MBBZAI *EPITA "Sorbonne University 'Lagrange Mathematics and Computing Research Center TEPITA Research Lab *KTH Royal Institute of Technology
ABSTRACT
Diffusion models have emerged as powerful priors for image editing tasks such inpainting and local modification, where the objective is to generate realistic content that remains consistent with observed regions. In particular, zero-shot approaches that leverage pretrained diffusion model, without any retraining, have been shown to achieve highly effective reconstructions. However, state-of-the-art zero-shot methods typically rely on sequence surrogate likelihood functions, whose scores are used as proxies for the ideal score. This procedure however requires vector-Jacobian products through the denoiser at every reverse step, in- troducing significant memory and runtime overhead. To address this issue, we propose a new likelihood surrogate that yields simple and efficient to sample Gaus- sian posterior transitions, sidestepping the backpropagation through the denoiser network. Our extensive experiments show that our method achieves strong ob- servation consistency compared with fine-tuned baselines and produces coherent, high-quality reconstructions, all while significantly reducing inference cost. Code available at https: /githhb....../. com/ Yazi/azzaaaa dJ aJanati/// i/ding.
INTRODUCTION
We focus on inpainting problems in computer vision, which play a central role in applications ranging from photo restoration to content creation and interactive design. Given an image with prescribed missing pixels, the objective is to generate semantically coherent completion while ensuring strict consistency with the observed region. The importance of this task has motivated extensive research, spanning both classical approaches and, more recently, generative modeling with diffusion models (Rombach et al., 2022; Esser al., 2024; Batifol al., 2025; Wu al., 2025).
To address this problem, two main ceffusion-based approaches have been popularized. The first relies on training conditional diffusion models tailored specific editing setup. These models directly approximate the conditional distribution of interest (Saharia al., 2022: Wang et al., 2023a; Kawar et al., 2023; Huang et al., 2025) and take as side inputs additional information such as mask, a text prompt, reference pixels (Saharia et al., 2022; Wang et al., 2023a; Kawar et al., 2023; Huang et al., 2025). An alternative approach, which has recently attracted growing attention, is zero-shot image editing, requiring extra training fine-tuning In this formulation, the task is cast as a Bayesian inverse problem: the pre-trained diffusion model serves as a prior, while a likelihood term enforces fidelity the observations, and the resulting posterior distribution defines the reconstructions (Song & Ermon, 2019; Song et al., 2021b; Kadkhodaie & Simoncelli, 2020; Kawar et al., 2022: Lugmayr et al., 2022; Avrahami et al., 2022: Chung et al., 2023; Mardani et al., 2024; Rout et al., 2024a). Sampling from this posterior is achieved by approximating the score functions associated with the diffusion model adapted to this distribution. This plug- and--aaa paradigm has been investigated across variety
* *Authors contributed equally Correspondence: (badr mou fadépo locoppoyyteee ytechn' i que.edu), (yazid, d.d. janat @mbzuai aac.aaiaaccaaaa.. ac.ae)

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Published as a conference paper at ICLR 2026
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Figure I: Zero-shot inpainting edits generated by DING (50 NFES) for different masking patterns using Stable Diffusion 3.5 (medium). Given masked inputs (left column), the model fills the missing regions according to diverse textual prompts.
of inverse problems, from image restoration to scientific imaging, and has demonstrated strong editing performance without task-specific training.
While current zero-shot methods are appealing. they face a critical practical limitation. Implemen- tations of strong zero-shot posterior sampling with diffusion priors typically rely on the twisting function proposed by Ho et al. (2022); Chung et al. (2023); Song et al. (2023a), which corresponds to the likelihood evaluated at the denoiser's output given the observation. Simulating the corresponding reverse diffusion process requires computing gradients of the denoiser with respect to its input. This in turn entails repeated backpropagation through the denoiser network and costly vector-Jacobian product (VJP) evaluations. This makes such methods computationally demanding, memory intensive, and often slower than training dedicated conditional model.
Contributions. We propose new VJP-free framework for zero-shot inpainting with a pre-trained diffusion prior. Our key idea is to approximate the intractable twisted posterior-sampling transitions by closed-form mixture distribution that can be sampled exactly, thereby climinating the need for VJP evaluations and backpropagation through the denoiser. Concretely, we modify the twisting function of Ho al. (2022); Chung al. (2023) that it evaluates the denoiser an independent draw from the pretrained transition. This decoupling breaks the dependency between the denoiser and the arguments of the transition density. As result, our method provides posterior transitions that can be sampled efficiently for zero-shot inpainting with latent diffusion models. We demonstrate through extensive experiments on Stable Diffusion (SD) 3.5 that our method, coined DECOUPLED INPAINTING GUIDANCE (DING), consistently outperforms state-of-the-art guidance methods under low NFE budgets. achieves, across three benchmarks, the best trade-off between fidelity to the visible content and realism of the reconstructions, while being both faster and more memory memory-effiiiet than competing approaches. Remarkably, even without any task specific fine-tuning. it outperforms an SD 3 model that has been fine-tuned for image editing, confirming the effectiveness and practicality of our framework.
2 BACKGROUND
Diffusion models Denoising diffusion models (DDMs) (Sohl-Dickstein et al., 2015; Song & Ernon, 2019; Ho et al., 2020) define a generative process for a data distribution Po by constructing continuous path (p.)ee of distributions between po and pl A0 Ta)- More precisely,
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Pr Law(Xx), where
4==a, + t Xo~ ~ Po- X1 ~ P1
(2.1)
Here Xo and X1 supposed to be independent and (0t)) and (0))) are deterministic, non-increasing and non-decreasing, respectively, schedules with boundary conditions (oo, (o),o)) := (1.0) and (a1: 01) == (o, 1). Typical choices include the variance variance-preserving preserving schedule, satisfying af + of 1 (Ho et al., 2020; Dhariwal & Nichol, 2021). and the linear schedule, defined by (a,, = (1- t, (Lipman et al., 2023; Esser et al., 2024; Gao et al., 2024). The path (p.))ejo.1] defines an interpolation that gradually transforms the clean data distribution Po into the Gaussian reference distribution P1- To generate new samples, DDMs simulate a time-reversed Markov chain. Given a decreasing sequence (fx) iteratively applied to map a sample from Plz+1 to one from Pa.- thereby progressively denoising until convergence to the clean distribution Po-
) of time steps with fK and to 0, reverse transitions are
The DDIM framework (Song al., 2021a) introduces a general family of reverse transitions for denoising diffusion models. It defines a new schedule (1)))=(0..) 1]. satisfying v S oz for all i E [0, 1]. along with family of transition densities given for s < by
P2 16 22 (x. (x. X1) Xt xx=x:],
(2.2)
where o3 (x, No. x1) = N(x,: 0,x0 Vo? n/. x1. I) and the random variables (Xo, X₁ X1)
are defined as in (2.1). By construction, this family satisfies the marginalization property P. (x.) Pa (x, X1) Pa (x,) dx, (Song et al., 2021a, Appendix B). Thus, (P (PPuite)1))= K-1 defines consistent set of reverse transitions, enabling stepwise sampling from the sequence (Ptx)<=or In practice, however, these transitions are intractable. A common approximation is to replace Xo and X1 in (2.2) by their conditional expectations (Ho et al., 2020; Song et al., 2021a). More precisely, let xg(;t) denote a parametric estimator of (x,x,,t) E[ = X₁ = (x: &, (x,, t))/ /r, we set (x(,) == (xt a, (x, f))/ Then the parametric model proposed by Ho et al. (2020); Song et al. (2021a) corresponds to approximating each Pl 12 by
E[Xg Xx = xt). Since
n.6 Pie 1441
== o7 1 (X1, |80 (x (2+1),, X9 (X (1+11))))
(2.3)
For O. Poir, (,2x,, is simply defined as the Dirac mass at (xf, t1). the rest of the paper we omit the superscript 17 when there is no ambiguity.
9.0
Image editing. In this work, we address the task of image editing via inpainting. We assume access to some reference image E Rd that must be modified while remaining consistent with prescribed set of observed pixels. Let m C {I,..... d} denote the index set of missing (masked) pixels, and let m (1,.....d d} \ m be the index set of observed (unmasked) pixels, with cardinality |m| dy. For any x E Rdd and C f1,....,d) d} denote by x(i] TR the subvector formed by the components of x with indices i. The observation is thus given by y == x, [m], and the objective is to synthesize reconstruction & such that [[m] y while generating the missing region x x[m] in a realistic and semantically coherent manner with respect to the observed pixels. In the Bayesian formulation, the data distribution Po serves as a prior over natural images, while the observation model is encoded by a Gaussian likelihood on the observed coordinates:
4o (y|x) = ((m). og Tay))
(2.4)
The parameter 0y > 0 serves as relaxation factor: smaller values enforce strict adherence to the observation, while larger values permit controlled deviations from x., thereby facilitating the reconstruction process. In this Bayesian framework, the target distribution from which we aim to sample is the posterior distribution
(xoo)) x (o(y(x)) Po (xx).
(2.5)
Inference-time guidance. As observed in the seminal works of Song & Ermon (2019); Kadkhodaie & Simoncelli (2020); Song et al. (2021b); Kawar et al. (2021), approximate sampling from the posterior distribution can be performed by biasing the denoising process with guidance terms, without requiring any additional fine-tuning. The central idea is to modify the sampling dynamics of diffusion
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Algorithm Posterior sampling with decoupled guidance
Input: decreasing timesteps (tx (==)==x with tx L, to 0; original image x,; mask m: DDIM parameters (Th k==)
2: y x. m X (o)
3: for k = K - 1 to do
4:
Xn f x²(x, 1x+1)
5:
&1 t (x 0i111 x0) 80)
6:
u + ore Xo (oi
1//2/2211 &
sampl 1 ing (3.4) */
(w,w)) w') lid (0a.II
7:
8:
2 2 p +
9:
XP + (z at Sg tx /
10:
7 2 /Mt.. + +0 0
11:
x(mi t p[m] + "e m
12:
x(m (1 - r) + 7 c,,y y + or x1 x [m) + ar, By (w
(1-7))/(mm
13:
end for
14:
Return: xx(x
models on-the-fly that the generated samples both satisfy the likelihood constraint (y(+)) and remain plausible under the prior More precisely, a standard approach is to approximate the iterative updates of diffusion model defined to target the posterior To (-ly). This in turn entails deriving an approximation of the posterior denoiser (x(X,, tly) == f X. Toie (xo (xooas [x,, y) dx where Tone (Xp x, y) x To (xoly)N(xxx)) 0; of The denoiser &o(:, fly) is related to the prior denoiser via the identity
(x,, fly) = (x(x,) + a Vx, log (x)x)))
(2.6)
where the additional term is referred to as the guidance term; see Daras et al. (2024, Eq. 2.15 and 2.17). is defined as the logarithmic gradient of the propagated likelihood
(y(x)) = / fo(y|x)) (x0) Pore (x (x) dxo with Pou (xolx.) x Px(x)) N(x;; 0,X9, af)); (2.7)
see Daras et al. (2024, Equation 2.20). Since the pre-trained parametric approximation ( i) the
prior denoiser (-, (-,t) is already available, estimating (.,-,tly) reduces to computing the intractable score term Vx, log (y xx). A widely adopted approximation (Ho et al., 2022; Chung et al., 2023)
replaces Poie (xx) in (2.7) by Dirac mass at the denoiser estimate xx t), yielding
(y)x:)) == (y(y (xx())))))) ())
(2.8)
This approximation is often combined with a suitable rescaling weight (possibly depending on x,); see Ho et al. (2022, Equation 8) and Chung al. (2023, Algorithm 1). Substituting this into identity (2.6) yields approximation of the posterior denoiser, which in turn defines approximate diffusion model for (-ly).
METHOD
The methods discussed in the previous section rely on the likelihood approximation (2.8), which is then inserted into (2.6). However, computing this term requires differentiating through the denoisers (,(), at each timestep &.. This operation is computationally demanding: it increases memory usage, slows down the sampling process, and reduces scalability. By contrast, fine-tuned conditional diffusion models bypass these inference costs once training is complete, but at the expense of per-task retraining. This highlights a fundamental trade-off: zero-shot posterior sampling eliminates the need for retraining, but incurs substantial overhead during inference. Our goal is to bridge this gap by designing a zero-shot posterior sampler that removes the need for backpropagation through the denoiser while preserving the effectiveness of guidance.
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Reverse transitions for the posterior. Our method builds upon the alternative sampling strategy introduced in Wu et al. (2023); Zhang et al. (2023); Janati et al. (2024). Instead of initializing the interpolation (2.1) with the prior Xp Pn- we consider the same process initialized from the posterior distribution Xo ~ T-y- This yields a new family of random variables whose marginals nH <x(x+ly) = N(x;; 0g TfTa) To (xxol) dxn, in analogy with the prior family (Pe)1E(o.1) Moreover, the DDIM transitions associated with (I, ((y))))))) are given by Janati al. (2025b, Equation 1.17):
x - (X, x), x (x(x.) Pale (xx(x;).
(3.1)
which defines a valid Markov chain with marginals (T.,, (by)) This chain defines a path between the Gaussian reference (O Ia) and the posterior distribution To (-1y). However, the presence of the likelihood term (y(xe) makes also these transitions intractable. To address this issue, prior works (Zhang et al., 2023; Wu et al., 2023) introduced the surrogate transitions proportional to X, + (e(y)xs))1 ) (x),, for fixed x and y, where & (y)-) are defined in (2.8). These transitions are then approximated using either variational inference (Janati al., 2024; Pandey al., 2025) or sequential Monte Carlo methods (Wu et al., 2023). However, similar to the methods described in the previous section, these approximations rely on the approximate guidance term and thus suffer from inflated memory usage and higher runtime.
Our likelihood approximation. To address this limitation, we draw inspiration from (2.8) to propose lightweight approximation, designed to eliminate the need for VJP evaluations through the denoiser. Using the relation x xx(x,)) 8) = (x, o, o, (x,, s))/aa, a,, we first rewrite the standard likelihood approximation (2.8) in terms of the noise prediction 8 (xx, s) according
(x(x)) = foyl(x, - o, xx., s))/a.).
Based on this parametrization, then introduce the following alternative approximation
E x., 2.) (o ox s))/aa):
(3.2)
where the noise predictor is evaluated at z, Idd, which serves as proxy for x,. A key feature of this decoupling is that it enables lightweight updates, avoids costly denoiser backpropagation, and still provides high-quality reconstructions. Then, similarly to (3.1), we define
# (x,, Iz., x,,y) y) x ( x,, z.) p3ie (x, (x:)
This leads us to propose the surrogate
2 (x = E [ (x Zo, y)]
(3.3)
where Z. (-xxe, for (3.1). The transition # (x, y) generally lacks closed-form ex- pression; nevertheless, since it has a mixture structure, it allows for straightforward and efficient sampling. Sampling from # (x,|xx, can be performed by first drawing Z, from (f and then sampling from #5 (x,) /Z,, Xt, y). Moreover, as we will now show, in the case of inpainting, the second step can be carried out exactly.
.9.9 Let 14 (xx;1) denote the mean of the Gaussian reverse transition Paj (-Ix:). In the case of inpainting (2.4), standard Gaussian conjugacy results (Bishop, 2006, Equation 2. 261 show that # (-Iz,, y) admits closed-form form Gaussian expression
# (x. 2o, Xr, y) (xx[mmm) u (xcn) (gLo)d,)
x N X. [m]: (1 - 7wee) (xx:r) [m] + 7sir (a.yy + 0, o.8f(Za, [m)) ozog Ia,). (3.4)
with Yout := 1/22 + azoz). A derivation is provided in Appendix A.1. Thus, sample X, from (3.3) can be drawn exactly by, first, generating realization z, of Z, ~ p7. (-1x.) and, second, sampling X. [m] and X, [m] conditionally independently from the two Gaussian distributions in (3.4); see Algorithm 1 for pseudocode of this approach, which refer to DING (see Section 1).
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Practical implementation. A key practical feature of our method is that it depends a single hyperparameter: the sequence (7) Ie of standard deviations, which controls the level of stochas- ticity in the DDIM reverse process. This choice is particularly critical in the low-NFE regime, where only a few function evaluations are available and the variance schedule strongly influences both observation fidelity and perceptual quality. In all experiments, we adopt the schedule 7/2 o2 01). An ablation study of this parameter is reported in Section 4.
Beyond this hyperparameter. an important practical consideration is that most large-scale diffusion models for high-resolution image generation operate in a compressed latent space rather than in pixel space (Rombach et al.. 2022; Esser et al., 2024). To apply our algorithm in this setting, we must therefore formulate the inpainting task in the latent space. Denote by Enc the encoder, X, the pixel-space ground-truth image and M the corresponding pixel-space mask. Following Avrahami et al. (2022), we set x, == Enc( X.) ), the observation to y == x. [.[m] where m is downsampled version of the pixel-space mask M. We illustrate in Figure that masking in the latent spaces translates to masking in the pixel despite the nonlinearity of the decoder. Since the encoder reduces spatial resolution by a fixed factor (e.g., 8 = 8 in Esser et al. (2024)), we construct the latent mask m by average pooling the binary pixel-space mask M with kernel and stride s. Each latent site is assigned the fraction of unmasked pixels within its receptive field. These fractional values are then thresholded (typically at 0.5) to produce binary mask; in other words, latent site is marked as observed if the majority of its underlying pixels are unmasked. In practice, the mask m is provided as a channel image and broadcast across all latent channels when applied x.. Finally, we apply Algorithm with (x., y, m) thus defined in the latent space.
Related methods. Our work shares similarities with various recent approaches to zero-shot diffusion guidance, which now briefly review. The closest line of work comprises variants of the replacement method (Song& & Ermon, 2019; Song et al., 2021b), which follows the same structure as Algorithm 1. In these schemes, the masked coordinates of the state updated according to the standard DDIM transition (Line 11), while the unmasked coordinates are replaced by a direct update that enforces consistency with the observation y (Line 12). In its simplest form, the method performs ancestral sampling with the transition
1 (x) y) = Nxx, [m]: (x)r)) nrLa-d,) N(x, m a,y, ogld,))
(3.5)
ie., the unmasked state is set to noisy version of the observation a,y + W. where W. (O, Ia, ); see Song & Ermon (2019, Algorithm 2) and Song et al. (2021b, Appendix 1.2). Avrahami et al. (2022) extended this approach to the latent space using downsampled mask. The method was later refined in RePaint (Lugmayr et al., 2022), which improves sample quality by performing multiple back-and-forth updates: after applying the replacement step from fx+1 to f.. forward noising step is applied from &y back to fx+1, and this cycle is repeated several times. Several works have combined the replacement method with sequential Monte Carlo (SMC) sampling (Trippe et al., 2023; Cardoso et al., 2023; Dou & Song, 2024; Corenflos et al., 2025; Zhao, 2025). In particular, Cardoso et al. (2023) update the unmasked coordinates of each particle using a Gaussian transition whose mean is a convex combination of the DDIM mean and the rescaled observation av,y. In the inpainting framework, the recently proposed PnP-Flow (Martin et al., 2025) reduces to using similar transitions without relying on SMC, i.e., by using single particle. We explicitly compare the update rules in Cardoso et al. (2023); Martin et al. (2025) with ours in Appendix A.2, where we also discuss additional related work.
4 EXPERIMENTS
In this section, extensively evaluate the inpainting performance of DING when used with different large-scale models. We benchmark its performance on multiple datasets against several state-of-the- art baselines. We further analyze the relevance of our modeling choices, specifically the formulation of the approximation in (3.2) and the schedule of DDIM standard deviations (f/)tejo.1). through a series of targeted ablations.
Models and datasets. We evaluate method on Stable Diffusion 3.5 (medium) (Esser et al., 2024). We set the CFG scale to 2. Our experiments cover three datasets: FFHO (Karras et al., 2019), DTV2K (Agustsson & Timofte, 2017), and Peechench (Ju et al., 2024). For FFHQ, we use the first.

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Observatin
Ground Truth
DlvG
FLowCurer
DRo
PxP Puow
Rrober
DAPS
RESSMPLE
FSLD
Figure 2:
Examples of reconstructions on FFHQ and D1V2K with 50 NFEs.
5k images and condition generation on the prompt "a high-quality photo of a face". For DIV2K, we include both training and validation splits (900 images in total), and generate captions for each image using BLIP-2 (Li et al., 2023); see Appendix for details. All FFHQ and DIV2K images are resized to resolution of 768 x 768. The P PIE-Bench dataset contains 700 images of resolution 512 x 512, each paired with an inpainting mask and an edit caption. After removing cases where the mask completely covers the image, the resulting evaluation set contains 556 images.
Evaluation and masks. For FFHQ and DIV2K, we evaluate inpainting performance under four rectangular masking configurations: (i) right half of the image (Half), (ii) upper half (Top), (iii) lower half (Bottom), and (iv) a central 512 512 square (Center). In contrast, P PIE-Bench provides irregular masks with diverse spatial patterns; see Appendix C for examples. Unless otherwise stated, we set By 0.01 across all tasks. Since exact posterior sampling is infeasible, inpainting quality using proxy metrics. To measure distributional alignment with the dataset, we report both FID and patch FID (pFID) (Chai et al., 2022), the latter offering finer granularity for high-resolution evaluation. Following the standard FID protocol, we extract 10 random 256 x 256 patches per image, yielding total of 50k patches. To quantify consistency with the observed content, we compute context PSNR (cPSNR), defined as the PSNR over the unmasked region only, We further report LPIPS (Zhang et al., 2018) relative to the ground truth evaluate perceptual similarity, which is especially relevant for FFHQ where facial symmetries make reconstructions visually close the reference. For PIE-Bench. which includes edit captions, we additionally report -core (Radford et al., 2021) on both the full image (CLIP) and the edited region (CLIP-ED) following Ju al. (2024). Together, these metrics provide a comprehensive evaluation of inpainting quality. While each captures different aspect of performance, none should be interpreted in isolation.
assess
Baselines. We compare against seven state-of-the-art baselines:
Table 1: Memory and runtime.
FLOWCHEF (Patel et al., 2024), FLOWDPS (Kim et al., 2025),
DAPS (Zhang et al., 2025), REDDIFF (Mardani et al., 2024),
Method
Time
Mem, (CiR)
RESAMPLE (Song et al., 2024), PSLD (Rout et al., 2024b).
BLENDED-
PNP-FLOW (Martin et al., 2025), DIFFPIR (Zhu et al., 2023).
DAPS
9.1
22.09
DDNM
3.1
22.09
DDNM (Wang et al., 2023b) and BLENDED-DIFF (Avrahami et al., 2023) For the main comparison, all methods are evaluated under a fixed budget of 50 NFEs. Since our method requires two denoiser evaluations per diffusion step,
DIEPIRR
3.1
22.09
FLOWCHEF
FLOWDPS
22.10
PXP-FLow
3.1
22.09
PSLD
744
24.19
25 steps to match this budget. We focus on this low-NFE regime as it reflects realistic settings, where inference is constrained by latency and compute. To ensure
REDDIFF
RESAMPLE
DING (OUES)
2.9
22,09
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Table 2: Top: Quantitative results FFEQ 768 768 with 5k samples. Bottom: DIV2K 768 x 768 with 900 samples. For FID, pFID, and LPIPS, the lower the better. For cPSNR, the higher the better. 50 NFEs were used.
Half
Methed
FID
cPSNR
LPPPS
FID
oPNNR
1PPIS
FID
pFIT
oPSNR
1.PIPS
FID
pFTt)
cPSNR
1
FEH1
x
BLENDED-Dur
23.5
31.32
35.3
36.7
32.8
15.8
32.05
19.8
DAPS
17.
30.50
0.36
35.1
54.5
31:15
0.32
30.5
0.39
51.8
27.6
0.34
DDNM
12.3
13.68
31,27
0.33
24,5
34.8
31.61
0.27
22.3
23.2
31. 3,.82
0.36
38.3
19.6
30.51
0.32
Dur PIR
12.1
11.25
30.91
0.36
19.4
19.6
31.67
0.30
19.7
14.1
32,07
1.36
30.2
11.4
30.74
11.35
FLOWCHEF
203
16.5
30.41
0.36
29.3
35.0
31.00
0.31
27.8
21.1
31
0.36
35.9
22.7
29.94
0.35
FLOWDPS
36.2
49.0
26.72
0.46
49.5
79.9
23.36
0.53
52.1
74.2
24.15
0.56
72.3
71.5
23. 36.06
11.55
PNP-FLOW
20.5
33.4
30.62
0.37
36.5
65.1
31,67
0.32
33.6
42.7
$1.30 11.54
0.38
56.8
33.4
20.
0.33
PSLD
116.3
73.8
6.89
0.81
98.0
1
10.09
0.73
120.5
75.4
7.06
0.81
107.0
70.4
5.46
0.81
REDDEY
28.5
30.7
41.8
35.10
76.4
0133
RESAMMPE
103.4
78.
(ours)
9.6
31,03
15.5
14.0
12.5
DIV2K
768
BLENDED-DIFE
43.6
12.9
26.01
54.8
20.2
76.43
0.35
44.8
13.22
25.
0.30
13.1
26.85
DAPS
$1.0
38.4
25.92
0.46
74.8
67.6
26.14
0.41
54.8
41.0
25.
0.44
61.2
39,7
26.71
0.50
DDNM
42.5
21.2
26.01
0.41
57.7
38.5
26.61
0.37
45.7
21.3
25.36
0142
49.6
23.2
26.81
11.45
DIFEPIR
41.1
12.9
26.09
0.37
$2.8
21.4
26.
0.34
43.5
13.1
25.36
0.39
44.9
14.9
26.91
0.39
CLOWCHEP
43.3
12.3
25.78
0.36
53.5
22.3
26.27
0.32
45.0
13.8
25. 09.09
0.37
40.9
13.2
26.57
0.37
FLOWDPS
50.8
33.2
21:30
0.49
70.5
62.8
38.38
0.63
64.1
$7.9
17:43
0.65
64.2
57.3
19.06
0.63
INP-FLOW
54.2
42.7
26.00
0.46
79.7
71.4
26.63
0.44
57.1
33.5
25.19
0.44
51.7
50.8
26.61
0.50
PSLD
66.4
9:89
11.79
6.41
1.775
REnDEF
54.3
23.25
23.41
0.52
RESAMPLE
80.8
63.8
23.76
33.1
24.06
(curs)
13.0
25.00
50.7
19.5
36.41
41.4
13.7
25.
13.4
26.72
fairness, all methods are run in the latent space with downsampled masks, and extensive hyperpa- rameter tuning is performed for each baseline on each dataset. For baselines that require VJP or backpropagation through the denoiser, we report their actual runtime and memory costs, ensuring that comparisons reflect effective inference cost rather than nominal NFE counts. Average runtime and memory usage across all the experiments, measured on H100 GPUs, are provided in Table 1.
4.1
MAIN RESULTS
Tables 2 and 3 summarize the results FFHO, DIV2K
and P PIe-Bench, respectively. On FFHQ (Table 3), DING achieves the best performance on all masks and almost all the metrics. In particular, it improves both pFID and FID by significant margins over the strongest com- peting method FLOWCHEF It also obtains the highest cPSNR scores, indicating a faithful reconstruction of the visible content, while simultaneously producing visually coherent completions with the lowest LPIPS. On DV2K,, the comparison is more nuanced. DING consistently at- tains the best FID and LPIPS across all four masks, while remaining competitive baselines on cPSNR. On PIE-Bench, DING achieves the best results on all metrics except cPSNR and CLIP. We note, however, that although FLOWDPS and PSLD obtain slightly higher CLIP scores, they perform markedly worse on fidelity and perceptual quality metrics, suggesting that their improvements in CLIP may reflect metric hacking rather than genuine reconstruction quality. We provide qualitative comparisons of the reconstruction in Figure 7 and Appendix C.
Table 3: Results on PIE-Bench with 556
samples and 50 NFEs.
Methel
FID
pein
<PSNR
1PPS
CIIP
[2.IPET
BLENDED-DOS
65.5
22.0
26.32
23.15
DDNM
61.4
26.9
27.29
36.27
22.56
DUTFER
63.5
25.4
23.99
0.30
36.21
23.04
PLORDass
66.1
27.
26.84
8.30
26.03
23.47
POWDES
74.6
47.7
05
5.45
36.35
22.79
PHP-FLOW
65.8
0.34
25.62
PSLD
56.2
21.81
00.1
58.2
0.57
21.37
21
RESAMPLE
71.0
33.9
24.45
0.35
25.71
22.03
DENSIOUSS
03.4
34.7
27
0.30
26.30
23.36
pFID and comparable to strong
We now compare DING with a Stable Diffusion 3 model fine-tuned for inpainting', trained on 12M images at 1024 x 1024 resolution. To ensure faireess, both models are evaluated under the same runtime budget (2.2s), which corresponds to 56 NFEs for DING and 28 NFEs for the fine-tuned baseline. We also provide the results for the finetuned model using 56 NFEs. The results are given in Tables and 4. Across FFHQ DIV2K, and PTE-Bench, DING con- sistently outperforms the fine-tuned SD3 model on all reported metrics. The gains are especially pronounced in <PSNR, where DING achieves 8-10 dB higher fidelity to the observed pixels. This indicates that our method preserves the known content far more accurately while still producing realistic completions, as confirmed by
Table 4: Results on the P BKBchh with 556 samples.
Method
FID
pFID
<PSNR
LPIPS
CLIP
CLIP-ED
SD3 Empaint (28)
68.7
30.5
18.85
0.34
26.37
23.10
SDS Impaint (56)
68.2
30.3
19.03
0.34
26.38
23.03
DING tours)
26.63
23.70
hhttps://huggingffaae co/at mama- creat ve/S033Cont Inpainting
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Published as a conference paper at ICLR 2026
lower FID and LPIPS. On PIE-Bench, DING further improves the fine-tuned baseline every metric, including perceptual ones (pFID. LPIPS), while also yielding stronger text-image aligmment (CLIP, CLIP-ED). See Figure for qualitative comparison of the reconstructions. These results demonstrate that, even without task-specific fine-tuning on a large amount of images, DING not only matches but surpasses specialized SD3 inpaint model. Overall, these results show that method provides the strongest overall trade-off between realism and fidelity under low NFE budgets.
Table DING compared to SD3 fine-tuned (28 and 56 NFEs) for inpainting tasks.
Top
pFlD
<PSNR
pFI
<PSNR
LPIPS
FID
pFTD
CPINR
LPIPS
pFID
<PSNR
FFHI
512
x
S133 Impaint (28)
23.5
10.7
21.69
0.37
62.1
13.9
22.18
0.31
34.7
17.8
21.64
0.36
42.4
16.5
21.78
0.37
SDS Inpaiit (16)
23.7
10.3
21.53
0.37
63.7
34.4
21.94
0.31
35.4
16.3
21.41
0.36
43.8
16.8
21.53
0.36
DING (ours)
9,3
31,40
0.32
20.2
15.5
31,39
0.28
17.3
8,4
31.96
0.33
33.8
12.2
31.27
0.34
DIV2K
Inpoins
22.1
16.6
0.12
SD3 Impuini (36)
45.1
17.91
0.40
36.2
20.5
18.63
0.36
48.6
16.1
16.16
0.41
50.3
17.2
19.02
0.41
DING (ours)
41.5
14.2
26.09
0.37
32.4
21.3
26.53
0.33
43.7
13.8
25.47
0.38
45.4
15.4
26.94
0.38
4.2 ABLATIONS
Doubled NFE per diffusion step. Because the Xi-predictor must be evaluated at the proxy variable (Line 9 in Algorithm 1), our algorithm requires two NFEs per diffusion step. An immediate question is whether this overhead is needed. To explore this, we introduce variant in which the noise prediction from the previous step is reused instead of being recomputed at the proxy. We coin this variant Delayed DING, where Line is replaced by x + (x Tz, (x,, t(+1))//ax, and we further set 7t = o2 az), which we found to yield the best performance in this setting. A quantitative comparison with the original DING reported in Table 6, showing that while Delayed
DING reduces the NFE cost per step, it consistently underperforms across metrics and masking patterns, indicating that the doubled NFE is necessary to retain the full effectiveness of our approach.
Table 6: Delayed DING compared to DING on FFHQ (5k samples) and DIV2K (900 samples) with 50 NFEs.
Method
Hall
Center
Top
Bottom
FFD
FID
cPSNR
1PIPS
PFID
FID
cPSNR
1.PIPS
PFID)
FID
cPSNR
1.PIPS
pFID
FID
cPSNR
I.PIPS
FTHQ
708
29.21
29.90
9.9
29.84
12.3
6.6
31.03
15.5
14.0
31.38
19.2
12.5
31.64
0.34
29.6
8.6
30.50
DIV2K 768 x 768
Delayed DING
43,9
15.9
24.88
0.36
55.6
24.3
25.52
0.32
45.3
16.8
24.36
0.38
47.8
14.6
25.64
0.38
DING
39.2
13.0
25.90
0.35
30.7
19.5
26
0.31
41.4
13.7
25.19
0.37
43.4
13,4
26.72
0.37
DDIM schedule. Here we proceed to compare the behavior our algorithm under different schedules (no)eejo.1)- For this purpose we compare against some natural candidates. (A): we consider the DDPM schedule used in Ho et al. (2020) and which corresponds to using in (2.2) standard
deviation that depends on both s and t, iee. (t) = (o (as/ a, ) (/2/)////// 1/2//tt (B): as we cannot use deterministic sampling in our approach, we rescale the DDPM schedule (A) with 0.01 to approach deterministic sampling. (C): 1/s = o., which is the maximum allowed standard deviation (2.2). In this scenario, the transition is 15.%% (x
N(x.; (x,, !), TT))) and resembles the prior transition which corresponds to slower decay of the standard deviation compared to our default choice 7/, = o, a.). The results on FFHQ with 5k samples and 50 NFEs are reported in Table 7. We observe that the rescaled DDPM schedule (B) degrades significantly across all metrics, while (A) and (C) yield nearly identical performance. This suggests that maintaining sufficient stochasticity at the beginning of the diffusion process is crucial for strong performance. Among the alternatives, (D) performs best; still, it outperformed by our default schedule, confirming the benefit of faster decay of (ne).
used in Martin et al. (2025). (D): 7)s d.vl-

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Published as a conference paper at ICLR 2026
Table 7: Ablation results for the DDIM schedule (nt) on FFHQ 768
768 with 5k samples and 50 NFEs.
Method
Half
Center
Top
Bottom
pFID
<PSNR
pFID
FID
pFID
pFID
(A)
13.9
14.0
31.19
0.36
19.1
25.5
31.50
0.30
21.8
18.9
31.80
0.38
35.1
15.1
30.70
0.35
21.5
18.7
26.06
0.41
29.0
31.9
26.23
0.35
31.7
21.1
26.64
0.41
48.4
28.6
25,56
0.40
13.9
$1.19
25.5
31.50
0.30
21.8
15.6
0.35
(D)
10.2
10.7
31.33
0.33
16.7
19.0
31.70
0.27
19.6
15.7
31.95
0.35
31.6
12.0
30.81
0.32
Detaul
9.6
6.6
31,03
0.33
15.5
14.0
31.38
0.27
19.7
12.5
31.64
0.34
29.6
8.6
30.50
0,33
CONCLUSION
We have introduced DING, novel liffusion-basse method for zero-shot inpainting that operates fully in the latent space and enables fast, memory-efficient inference under low-NFE budgets. Through extensive experiments across multiple benchmarks, outperforms existing zero-shot approaches and even surpasses a fine-tuned Stable Diffusion 3 model for image editing, despite requiring no expensive training. Notably, our method produces globally coherent reconstructions while preserving the visible content with high fidelity.
have shown that DING consistently
Limitations and future directions. While these results highlight the effectiveness and practicality of DING, several avenues remain open. An important limitation of our current approach is that performance does not monotonically improve as the compute budget increases. Ideally, one would like reconstruction accuracy to keep improving with additional sampling steps, potentially beyond the standard diffusion horizon, but we observe diminishing returns due to the limitations of our current DDIM schedule. Addressing this issue, for example by designing guidance schemes noise schedules that continue to scale gracefully with compute, remains an important direction for future work. Moreover, while our framework is fully operational in the latent space, its applicability is currently limited to inpainting, as this is the only observation operator we can reliably lift to the latent space. Extending the method to accommodate more general forward operators and a broader class of inverse problems, while preserving the same level of efficiency achieved for inpainting, is challenging yet promising direction for future research.
Reproducibility statement. We place strong emphasis reproducibility. To this end, we provide the full source code of our method along with implementations of all baseline methods used in the paper. Our repository also includes scripts reproduce every experiment, as well configuration files specifying all hyperparameters and settings for each baseline and experimental setup. Together, these resources ensure that all results reported in this work can be fully reproduced and easily extended.
Ethics statement. While the proposed approach demonstrates clear benefits for applications in restoration, accessibility, and creative media, it also lies at the borderline of ethical considerations. Diffusion-basee inpainting methods can be misappropriated for producing deceptive or harmful content, such manipulated images synthetic media that obscure authenticity. This dual-use nature highlights the need for proactive safeguards, including transparent usage guidelines, traceable model outputs, and continued development of forensic detection tools to ensure responsible integration of such technologies.
ACKNOWLEDGEMENT
The work of Badr Moufad has been supported by Technology Innovation Institute (TII), project Fed2Learn. The work of Eric Moulines has been partly funded by the European Union (ERC- 0002-SYG-OCEAN-100077 10000..... Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. This work was granted access to the HPC resources of IDRIS under the allocations 2025-AD01 1015980 and 2025-AD01 101 6484 made by GENCI.
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Published as a conference paper at ICLR 2026
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Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi S. Jaakkola. Diffusion probabilistic modeling protein backbones in 3d for the motif- scaffolding problem. In The Eleventh International Conference on Learning Representations, 2023,
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Yinhuai Wang. Jiwen Yu, and Jian Zhang. Zero-shot image restoration using denoising diffusion null-space model. In The Eleventh International Conference on Learning Representations, 2023b. URL /ttptt:::///peeeeeveee
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Yuanzhi Zhu, Kai Zhang, Jingyun Liang, Jiezhang Cao, Bihan Wen, Radu Timofte, and Lue Van Gool. Denoising diffusion models for Bug--and-paa image restoration, In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 2219-1229, 2023.
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Latent
Pixel
Figure 3: Latent-space masking and its correspondence to pixel space using central square mask. The encoder and decoder Stable Diffusion (medium) were used. The first row shows latent images alongside encoded mask applied to each, while the second row shows their decoded counterparts. Notice that the masked regions in the latent space translate directly to analogous masked regions in pixel space. For that sake of visualization, since the latent images have 16 channels, apply PCA and visualize the first 3 components.
A METHOD DETAILS
A. DERIVATION OF THE POSTERIOR (3.1)
Recall that given Z,, the posterior transition of interest is
#1 (x, (2,, y) x (y X 2.) Pait (x.(x;)
n.8
Denoting by ÿ. = azy + x (z. 8) m the effective observation, we have that
(y|xs n.) oc N(v x. m a oz
and since the reverse transition writes
P (xxx (x.) = N(x, [m]: (x):)) [m) n/ Ia- N(x, mm u (x.m) [m] ITTA,
we obtain
# (x, X1,
N(x, m p (xm))
[m] 17. Ld
N( [m] a² o Lay [xx (x.r)))m nTا
J N m af N[ P (xx:7) im ولاجارا dx im The formula (3.4) follows by applying Bishop (2006, equation 2.116) to the second normalized transition on the right-hand side.
A.2 COMPARISON WITH RELATED WORKS
We start by providing explicit comparison with the closest works.
Comparison with the transition in Cardoso et al. (2023). Let E [0, 1] be timestep such that By 0,/0,- Such a always exists when the linear schedule is used for example. The transition used in the SMC algorithm in Cardoso et al. (2023) for s > is given by
T (x, (x:) & N(a.y; X. M of La, )p3
n.0 (xx (x:)
(A.1)
Using the same conjugation formulas as in the previous section, we find that #0 (x,
/m)./// (xxr) [m]. TTd,)
x Nxx [m], (1 - (xx + Jaro,y, of Rae Tay)) (A.2)
This to be contrasted with our update, where of- = oz - (as/ (ass/a))) )o? and %/1r = (n2 + -.
given a sample Zz.
# (x, 2.. x) N (x,, [m] 0 [m), 7/ Td)
m (1 Yols 14 ait (x)m) m + Yalt (a.y + 0
8) m))
o La,)
where No == n (n) + aZ
Hence, MCGDIFF differs from DING on the choice of effective observation, which in this case is ÿ. a,y, the choice of variance in the transition and the coefficient of the convex combination. From () it can be seen that MCGDIFF assumes the approximate model N(a.y: x, [m), of Ia,) for the true likelihood f, (y|xx,))) x.) (2.7).
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Comparison with the transition in Zhu et al. (2023) and Martin et al. (2025). We now write explicitely the algorithm PNP-FLOW (Martin et al., 2025, Algorithm 3) adapted to the inpainting problem consider, see Algorithm 2. We have simply adapted the notations and used F(x) lly - x[mm]l²/ in Martin et al. (2025, Algorithm 3). Thus, the transition used in Algorithm 2 is
(x, x N(x, [m), a, x (X:: t) [m], Lf
x N X, [m], (1 Ye
a (x,, () [m] + Ta
asy, o vTTa-da))
og
dy
In the case of the DDIM schedule 7/s = a. we have that / (x,) = og 80 ( xx. b), and the MCGDIFF transition (Cardoso et al., 2023) (A.2) writes
f (x, xx,) = N(x, [m], x [m], x N(x, m Ys a )),2 Xx, m + Han a You
Hence, the main difference lies the coefficient of the convex combination and the variance used.
Algorithm 2 PNN-FLOW reinterpreted
Input: Decreasing timesteps (tx) with tx to O; adaptive stepsizes (7x k==)==K-
2: Initialize: X [d
3: for k = K - to do
4: No m - (1 7h
&
m
76 03
5:
N(a.: Ta)
6: x + ar 20 + Oth
7: Xy f (x, tk)
8: end for
9: Return: Xo
Comparison with the transition in Kim et al. (2025); Patel et al. (2024). Here we explicitely write the transition of FLOWDPS for the inpainting case in order understand the main differences without our method. For this purpose we rewrite Kim et al. (2025, Algorithm 1) using our notations. We note that the algorithm is written for the linear schedule o₁ of DDIM schedule comparison with Algorithm 1. We also assume for the sake of simplicity that the optimization problem is solved exactly in Kim et al. (2025, line 7) (since there is no decoder as we solve the inverse problem the latent space). The algorithm is given in Algorithm 3. In the specific setting where the linear schedule is used, setting " or in Algorithm 2 recovers Algorithm 3 when ? = otc Finally, we note that FLOWDPS can be understood as a noisy version of the FLOWCHEF algorithm (Patel et al., 2024) and overall, follows the line of work of methods that learn residual that is then used to translate the denoiser (Bansal et al., 2023; Zhu et al., 2023).
a₂ and the choice oz d ot. but we still write it with general notations to streamline the
Comparison with DUIPIR (Zhu et al., 2023) and DDNM (Wang et al., 2023b). We provide the DIFFPIR algorithm (Zhu et al., 2023, Algorithm 1) adapted to our inpainting case using our own notation in Algorithm 4. In Line 6 we write the exact solution the optimization problem in the original algorithm. We write the associated transition in convenient form that allows a seamless comparison with our algorithm. Define 7t == of/(ot + )azog). Then, the transition
(x,
Xe. y) = N(x, im]: p (xem) [m], 1/ Td
N x, [ (1 no ) (x)) im + 7 (asy + - -//)1/1// a.y ) n7 Ily)
corresponds to one step of Algorithm 4. We highlight key distinctions:
Setting 1/ of recovers the same transition as in PNP-FLOW.
The main distinction lies in the mean of the Gaussian transition for the unmasked region: it is convex combination of y (x;; v)) [m] and an effective observation o,y + (o 1 [m]
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Algorithm 3 FLOWDPS reinterpreted
Input: decreasing timesteps (tx k== with tx 1, to 0; original image x,; mask m:
DDIM parameters (Tx) k=
2: y x, m
3: x ~ (0,
4: for =
to do Xo f- xx, (R+1)
5:
6:
X1 t (x (x- 0ie+ 1x- x0) 0t+11
7:
Xy m t -an m ot
8:
μ ar, 2o + (o 11p 1/2
9:
(Oa,
10: x x+ T
11: end for
12: Return: X
ory)/or- In algorithm, the effective observation instead takes the form a.y + og xx 8) [m] We estimate the residual noise using the pre-trained model at timestep whereas DIFFPIR computes it as (x: [ a,y)/o,
This residual noise is scaled differently: by (a? - -m DIFFPIR, and by a, in our method.
The convex combination coefficient in cases is Ysit (1/ +zgoz) whereas for DIFFPIR it is set to 7 of/ afoy))
Finally, the noise-free (ay = 0) version of DIFFPIR recovers the DDNM algorithm (Zhang et al., 2023).
Algorithm 4 DIFFPIR reinterpreted
Input: Decreasing timesteps (t*)k= with tk to scaling A: original image x,; mask
m: DDIM parameters (7% (mk)==K
2: y x, [m]
3: x ~ (2)
4: for = K
do
5: Åo + -x,(xx 1x+1)
X Xaga
6:
&n m th A
y
+ 1
X m
7:
X1 + (x ale+1 200/Obkt1
8:
w ~~ N La)
x+ Xo + (0? - -12) 1/2x1 + 7k
9:
10: end for
11: Return: x
Further related methods. Here we continue our discussion of VJP-free methods. The DAPS algorithm (Zhang al., 2025) proposes sampling, given the previous state Xis+1 clean state Xo by performing Langevin Monte Carlo steps on the posterior distribtion Toite (+|X y). This step performed approximately by replacing the prior transition Porry (+|X with a Gaussian approximation centered at the denoiser X (x 42+1. Then, given Xo. the next state X,, is drawn from N (o., of. Ia).
One important aspect of our method that we circumvent differentiation through the denoiser also the decoder, the diffusion models consider operate in the latent space. We do by downsampling the mask into the latent space. In contrast, the recent work of Spagnoletti et al. (2025) also circumvents differentiation through the denoiser, but does by lifting the latent states into pixel space and optimizing the likelihood there. The result the optimization then projected back into the latent space and then undergoes mack-and-frtth noise-denoising steps.
Finally, several recent works (Mardani et al., 2024: Zilberstein et al., 2025; Erbach et al., 2025) adopt variational perspective: the target distribution is approximated by Gaussian distribution whose
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parameters are iteratively estimated by minimizing combination of an observation-fides fidelity loss and a score- motte-mathhing-- like loss.
VJP-based methods. A broad class of zero-shot approaches builds the guidance approximation (2.8) to estimate Vx, log & xz). Song et al. (2023a) approximate Poli by a Gaussian with mean (fl;,t t) and a tuned covariance. For the inpainting setting in (2.4), plugging this approximation into (2.7) yields an integral that can be computed in a closed form, providing a proxy for (y()))). Several works exploit the link between the covariance of Pole(.|x, (x,) and the Jacobian of the denoiser (Meng al., 2021). This observation underpins the methods of Finzi al. (2023), Stevens et al. (2023), and Boys et al. (2023), which derive likelihood scores by estimating or inverting the Jacobian. These approaches require solving large linear systems and backpropagating through the denoiser, both computationally expensive operations. To reduce cost, these works assume locally constant Jacobian around X,, but updates still involve either explicit matrix inversion or repeated VJPs. In practice, diagonal approximations based row sums are commonly used to approximate the covariance matrix (Boys et al., 2023), or conjugate gradient methods are employed to circumvent the need for full matrix inversion (Rozet et al.. 2024). For general likelihoods (yl-)) Song al. (2023b) combine the Gaussian posterior model Song et al. (2023a) with Monte Carlo sampling to approximate (-)-)). In the latent setting, Rout et al. (2024h) apply the DPS approximation jointly with regularizer that encourages latent variables to remain near encoder-decoder fixed points. Other methods modify the sampling dynamics. Moufad et al. (2025) propose two-stage procedure: the chain is first moved to an earlier time & < tg. where the DPS approximation is applied to sample from an approximate conditional at f, before returning to la via additional noising steps. Janati et al. (2025a) incorporate related idea into Gibbs sampling framework. Overall, these methods remain fundamentally VJP-based and inherit substantial memory and runtime overhead from repeated backpropagation through the denoiser. By contrast, our decoupled guidance relies exclusively on forward denoiser evaluations and closed-form Gaussian updates, thereby eliminating VJPs entirely while retaining performance.
competitive
For complete review of zero-shot posterior sampling methods see Daras et al. (2024); Janati et al. (2025b); Chung et al. (2025).
A.3 BEНAVIIR UNDER INCREASED RUNTIME.
We extend the ablation study in Section 4.2 by examining the behavior of DING when the number of NFEs is increased. Specifically, we vary the budget from 20 to 500 NFEs on the DIV2K dataset and report results across different masking patterns; see Figure 4. All metrics improve steadily as the budget grows, reaching their best values around 200 NFEs (10s runtime). Beyond this point, performance saturates and exhibits slight degradation 500 NFEs. These results suggest that our default DDIM schedule well suited to low and mid-NFE regimes-which are most relevant for practical settings but may not be fully optimized for larger budgets.
FID
PFTD
I.PIPS
<PSNR
0.45
26-
50
25
0.30
24-
23
0
10
20
10
10
15
Runtime (5)
Figure 4: Performance of DING on DIV2K under varying NEE budgets (20 to 500) across different masking patterns. Runtimes are measured H100 GPU,
A.4 LIMITATION
We observed that the quality of reconstructions is highly sensitive the specificity of the textual prompt. When the prompt is under-specified or lacks sufficient semantic detail, the resulting samples may exhibit reduced coherence, particularly in large masked regions where contextual consistency
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Masked image
"man face under a hut"
"man face
tall brick building surrounded by city streets under clear blue sky."
Masked image
"a building"
Figure Effect of prompt precision on inpainting quality.
critical. This issue manifests as mismatched textures or backgrounds, or inconsistent object boundaries, even when the visible area is faithfully preserved. To illustrate this behavior, we compare reconstructions obtained with well-defined prompts against those generated using vague or ambiguous ones. Examples are provided in Figure 5 and 6.
A.5 BIAS IN GAUSSIAN CASE
For the sake of simplicity we assume that Po NOO,, E) where Σ is covariance matrix. We also write the likelihood as fo xx) = N(y; P Xo ogIa,) where Pm E Rdyxd xd is the matrix satisfying
P = x(m]. Define D. = a, E( + ofta) [Taa- Then, the denoiser and noise predictors are given by
xx,,,,t) = D,x, (x,, t) = oe (Ia ozDi) xx
We consider hereafter the DDIM transitions p" (x (x:) = N(x.i Mele (x:s m), Ia) where
( (x, + V -
In this section we analyze the bias of the DING one-step transition relative the posterior transition involving the DPS likelihood (2.8); i.e. we compare the transition
ding (x, xx, = RR
ding alt [x,
7.. 7. X1.))
(A.3)
where Za ~ p210(-1x)) and
ding (x, Zg. X.. x lo(x)* X. 0.x1 02x, )p (xxlxx))
as
against
adps (x,,xxx) lo (X () 3))3)
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"A red mountain train climbing steep track surrounded by grass and mountains the hackground"
Masked image
"a train on hill"
"aerial view of downtown Washington
Masked image
"city view"
Figure 6: Effect of prompt precision on inpainting quality.
We define M = P.PPa Pm. which is an orthogonal projection matrix since M = M, PmP.T m = Lay.
and thus M = M. We also introduce the quantity
e, = [| 0 L2)
which quantifies how far the Jacobian of the denoiser (,(,, s) deviates from the Jacobian of the DING denoiser approximation on the observed coordinates. In the following proposition, we characterize the asymptotic behavior of the DPS and DING posterior transition means and covariances as 7/s + O, and we express the mean bias in terms of e.. In Proposition 2, we also provide explicit upper bound on E, in terms of the schedule and the minimum eigenvalue of the prior covariance E.
Proposition 1. Both (-Ixe, y) and # ciingg (-1x;, y) Gaussian distributions with mean and dps covariance respectively (po (x,y), y), Idpsy and (p (x,,y,, Iding) satisfying
ppdss Iding| = Onn)
and
ding 14a1c xx, @ (e.((ly))) + [M + +221lam)
(
11 (x.,
as 7)s O.
Proof. Using the standard Gaussian conjugation formula (Bishop, 2006, equation 2.116), we have dds that # st (x, y) = N(x; mit (xr, y), DdPs) with
dps
dps (Xt, y) == Edps (11 Me (x1; + +0y 2D_ p.T)) mait
-ait = (n=22 + ay Pp Pm -1
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Next, for the DING transition, first set (z())) (o0g/aa) (Z Gaussian conjugacy with Pale (x) = (xx: (aie(X(; 7), LL gives
dding (x, 2: Xx: y) = N (x.: Eding (1x ( 17) + +oy a 1 (y by (Z.))) Saing S
and Sdine (1, L + ag Boy P Pm) Since the mean of this conditional distribution is clearly ding
affine in Z, we integrate out, yielding that # (x,, (x, = N(x;; m
air yx, E where
ding m,it (x, Si (n= Mai Main (x(in) + 2 P
+ a, P P Pmmmm (la a Mele)
diiing
n
dina P PimlI a,D,) (2s P Pm(ll a,D.))"
Eding
ofa?
Small-noise regime. We now study the behavior of both transitions when the DDIM kernel variance n tends zero. For simplicity we define
Kaps = oy DT P
Kaing = a= 0,-00gM
and Rg = la - a.Ds, M Pm. Then,
Edp (11=21 + Kdps)
>>ding a|t
(n, LLa + Kding)
nh (11,
La + MM (n1 2Ia + Kding)
We use throughout that for any fixed matrix K, we have that when n/ [|||| llop < 1,
|||| 11 1/ Kllp
La - - K) (T))))
[[+()))] 1/8
(A.4)
This follows from the standard Neumann (geometric) series expansion. Applying (A.4) with n/ min(1/ Kdpsllopp 1/ diigg [lop); we get
ydps
(Id 1/. Kdps) (1h)), sit
ding (1a 1/ Kding) + a²0
11s
M M, M + Om!))
and thus
ddps y.ding (( Plugging these expansions in the mean terms, we find that
m, (x,, y) (xir 7) + 1 D....T KapsMan(Xxnn) + O(n!), (xt,))= = (xir(x;;1) 1) + (a, 2 TT + +0-20 Moutst
dps
ding
Mapast - Keing/ss + Onn)).
m att
This yields
y) m
dps
m
(x., y) = ding
D PIPP + +a, MM (xxin) - (a R - DJ. (MD..)ae((xxii) + Onr)),
We now proceed to further upper bound the leading term. Define E. = Do - a, Igg. Then Rg =- and we have that
dps (x..))
ding m (xt, x).
m sit
11
o . (x E. M M + O(n))
with M -P...m which is orthogonal projection matrix since M M and PT m La, and thus 2 MM. We proceed by bouding each term of
F.T PITy 0, -11F. M MMss((x))) F.. M MeFptesss
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separately. Define E, || llop. Then, since P.lyy E range(M), have Mv Hence E PLy E_ M (ME,) (MME)) (yy)
where we have used that M T M = M. By the operator norm inequality, and the fact that |[yl lyyl we get
E P <
|M || = P ==llyll.
Next, using the same operator norm inequality we get that
_ M pa (x;; ml I e PallMMaae(xx which yields the desired bound.
| M (x m)]l & eall
Proposition 2 (Upperbound on 6,). We have that
Es Os 02 Amin (2) + o²
where (min(() is the smallest eigenvalue of
Proof. By noting that (a. + o2 = -a, -1 oIL we get the alternative expression
a² (a [a -o
Eg
By the submultiplicatiitty of the operator norm and the fact that M is a non-trivial orthogonal projection matrix, we have that
1
| Mflop S o1 0is Amin (a?5 oo
0g 0 Amin (E) + o²
B DETAILS ABOUT THE EXPERIMENTS
B.I MODELS
We use both the SD and SD 3.5 (medium) (Esser al., 2024) models with the linear schedule 0t 1 L and 2. The fine-tuned baseline, which we refer to as SD3 Inpaint, is based on the publicly available model? trained for inpainting with ControlNet-augmente been finetuned on a large dataset of approximately 12 million 1024 X 1024 maageemaskk pairs to the masked image and the mask
f. In all the experiments run the zero-shot methods with guidance scale
version of Stable Diffusion 3. It has directly predict high-quality inpainted completions conditioned itself. We have found the model to perform well also on lower resolutions, despite not undergoing multi-resolltionn training. Examples of image editing of lower resolution images presented in the the HuggingFace page of the smae project. We run this baseline using guidance scale of 7 for optimal results.
Finally, all experiments use bf o loatlf 6 for model forward passes (and backward passes for baselines that require it), with other computations performed in float32.
B.2 MASK DOWNSAMPLING
To construct the mask in the latent space, we start from the original binary mask defined in pixel space. Since the encoder reduces spatial resolution by fixed factor (here, 8), we downsample the pixel-space mask to match the resolution of the latent representation. This is done by applying bilinear interpolation with antialiasing. The resulting low-resolution mask captures the proportion of masked pixels within each latent receptive field. Finally, threshold this downsampled mask at 0.95 to obtain a binary latent mask, slightly overestimating the masked region to prevent boundary artifacts during sampling.
co/al mama creat ve De//nD3-omme rol net inpainting
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B.3 IMPLEMENTATION OF THE BASELINES
Here, we give implementation details of the baselines. We stress that each baseline is run in the latent spuce, and thus no method computes the gradient w.r.t. the input of the decoder. We also manually tuned each baseline for the considered tasks. We provide the used hyperparameters in Table 8.
BLENDED-DiEE We implemented Avrahami al. (2023, Algorithm 1) following their official code'. The codebase includes an additional hyperparameter, b blendi wwwhgggpepeeteeee which determines at what fraction of the inference steps blending begins. We set zero, applying blending across all steps produced the best results. A key detail is the original implementation is that the observed region (background) is re-noised to the noise level defined by the current timestep; see Avrahami et al. (2023, step 1-2 within the for loop in Algo 1), yet the reconstructed region (foreground) has less noise comes from applying a DDIM transition. This causes the background and foreground to follow different noise levels, and hence, introduces minor artifacts in the final reconstructions. We fixed this issue in our implementation by matching the two noise levels.
DAPS. We adapt Zhang et al. (2025, Algorithm 1) based on the released code* to the flow matching formulation. We found that using Langevin as MCMC sampler for enforcing data consistency works the best for low NFE regime.
DIFFPIR. We make Zhu et al. (2023, Algorithm 1) compatible with the flow matching formulation with step 4 being implemented in the case of mask operator. We found in practice that the hyper- parameter À has little impact on the quality of reconstructions and hence we use the recommended values À = 1 On the other hand for the second hyperparameter Ç, we find that using Ç = 0.3 yielded the best reconstructions.
DDNM. We adapt the implementation in the released code to the flow matching formulation with the step 4 in Wang et al. (2023b, Algorithm 3) being implemented for a mask operator. The official implementation uses DDIM transition in step 5 of Algorithm whose stochasticity is controlled by the hyperparemters y). As recommended, we set the latter to r = 0.85.
FLOWCHEF & FLOWDPS, For both algorithms, we adapt the implementations available in the released codes FLOWCHEF? to our codebase. We observe that the two algorithms are quite similar, with FLOWDPS being distinct by adding stochasticity between iterations.
We reimplement Martin et al. (2025, Algorithm 3) while taking reference the released code?. For the stepsizes on data fidelity term, we find that a constant scheduler with higher stepsize enables the algorithm fit the observation, mitigate the smooth and blurring effects in the reconstruction and hence yield better reconstructions.
PSLD. We implement the PSLD algorithm provided in Rout et al. (2024b, Algorithm 2). We find that PSLD algorithm requires several diffusion steps, e.g. at least 150 diffusion steps, to yield good results. Unfortunately, we were not able to make it work well for the low NFE setup.
REDDIFF. We implement Mardani et al. (2024, Algorithm 1) based the official code 10 and adapt it to the flow matching formulation. We initialize the algorithm with sample for a standard Gaussian. For low NFE setups, we find that using a constant weight schedule yields better results, namely in terms fitting the observation and providing consistent reconstructions.
fifusioo
httpe://jtthob..ccmmmmnn///////p
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RESAMPLE. We reimplemented Song et al. (2024, Algorithm 1) based the provided implementa- tion details in Song et al. (2024, Appendix) and the reference code" " As noted in Janati et al. (2025a), set the tolerance for optimizing the data consistency to the noise level dy. Since are working with low NEFs, we set the frequency at which hard data consistency is applied (skip step size) to 5. That aside, we found that the algorithm requires several diffusion steps (200) in order to output good enough reconstructions. We note that removing the DPS step in the data consistency steps reduces the quality of the reconstructions.
Table 8: Hyperparameters for cach algorithm (using the same notations as in their paper) and task variations. indicates identical across tasks.
Algorithm
Base hyperparameters
Latent tasks
Half
Top
Bottom
Center
Strip
BLENDED-
iglendiing peroentage
New a
steps
DAPS
50
6,
HONE sunpiez Lamgernn
DDNM
50
0.85
DIFEPTR
50
A
FLOWCHEF
50
10
FLOWDPS
simp_aize
atapuaize
stapuauce
PNP-FLOW
stylu comslatt
The
To
The 0.8
To
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Published as a conference paper at ICLR 2026
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Figure 7: Comparison of DING and finetuned SD3 on PIE-Bench. Both methods have the same runtime 2.2s.
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Published as a conference paper at ICLR 2026
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Figure 8: Comparison of DING and zero-shot baselines PIE-Bench. All methods use 50 NFEs.
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Published as a conference paper at ICLR 2026
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Figure 9: Comparison of DING and zero-shot baselines on -IE-Bench. All methods use 50 NFEs.
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Published as a conference paper at ICLR 2026
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Figure 10: Comparison of DING and zero-shot baselines on -TE-Bench. All methods use 50 NFEs.
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Published as a conference paper at ICLR 2026
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Figure 11: Comparison of DING and zero-shot baselines on PIE-Bench, All methods use 50 NFEs.
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Reviews:
[{"review_id": "zuLisqGi13", "reviewer": "Reviewer_SWvq", "summary": "This paper introduces DING (Decoupled INpainting GuidANCE), a zero-shot inpainting method that leverages a novel likelihood surrogate to enable efficient sampling with pre-trained diffusion models. By decoupling the denoiser evaluation from the likelihood surrogate, DING eliminates the need for costly vector-Jacobian products (VJPs) or backpropagation through the denoiser at each sampling step, leading to significant reductions in runtime and memory usage. Extensive experiments on benchmarks including FFHQ, DIV2K, and PIE-Bench demonstrate that DING achieves a strong trade-off between fidelity and realism, outperforming both state-of-the-art zero-shot methods and a fine-tuned Stable Diffusion 3 inpainting model, particularly under low NFE (number of function evaluations) budgets.", "questions": "1. Could the authors provide a more systematic analysis of where DING tends to fail? For instance, how does it perform on very large or very small masked regions, or in cases with significant semantic discontinuities? Does it ever overfit to the observed pixels at the expense of realism or coherence?\n2. Are the authors able to include comparisons with recent methods such as “Inpaint Anything,” “DiffEdit,” or other zero-shot image-editing approaches? If not, could they at least provide a qualitative or quantitative discussion of how DING might compare in terms of performance and efficiency?\n3. The method is currently tailored for inpainting. Could the authors comment on the feasibility of extending the decoupled guidance framework to other inverse problems (e.g., super-resolution, deblurring) while maintaining similar efficiency gains?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed decoupled likelihood surrogate enables VJP-free sampling, directly addressing a major bottleneck in prior zero-shot diffusion guidance methods. This results in significant reductions in both runtime and memory usage, as empirically validated in Table 1.\n2.  Across three challenging inpainting benchmarks (FFHQ, DIV2K, PIE-Bench), DING consistently outperforms existing zero-shot baselines and even surpasses a specialized fine-tuned SD3 model. The results are supported by both quantitative metrics (Tables 2–7) and compelling qualitative examples (Figures 4–8).\n3.   The method operates efficiently in latent space and requires only forward passes through the denoiser, making it highly suitable for real-world applications with constrained computational resources.\n4. The paper includes thorough ablation studies (e.g., on the necessity of doubled NFEs per step in Table 6, and DDIM noise schedules in Table 7), and baselines are carefully tuned to ensure fair comparisons.", "weaknesses": "1. While the decoupled likelihood surrogate is a clear improvement in efficiency and simplicity, it can be viewed as an incremental extension of existing VJP-free or mixture-based guidance methods (e.g., Wu et al., 2023; Janati et al., 2024/2025a). The paper could more explicitly articulate its conceptual departure from these prior works to better highlight its foundational contribution.\n 2. The manuscript lacks a systematic analysis of failure cases or challenging scenarios, such as highly irregular masks, extreme semantic gaps, or out-of-distribution content. A more detailed discussion of limitations would strengthen the paper’s practical relevance.\n3. Although the paper compares against several strong baselines, it omits recent zero-shot inpainting or image-editing methods such as “Inpaint Anything,” “DiffEdit,” or “pix2pix-zero.” Including these would provide a more comprehensive evaluation of DING’s relative performance and efficiency.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7NrJ1kHEzo", "reviewer": "Reviewer_MmE3", "summary": "This paper proposes a new idea for diffusion-based inpainting based on classifier guidance. Classifier guidance is a well-established technique for conditioning, but it has some shortcomings (the $t$-step conditioning distributions have to be all known). The paper expands on some well-known approximation ideas to devise a new form of transition, which does not require the additional backpropagation step for the classifier guidance. Based on the approximated conditioned transition formulation, the paper introduces an auxiliary variable to make the transition probability a mixture distribution. Now, the transition can be performed exactly in a Monte Carlo manner. Experiments demonstrate that the proposed method achieves superb performance with large gaps.", "questions": "Please see the above weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Clever idea. I like the reformulation technique. Incorporating the Dirac approximation idea into the conditional transition formulation is one thing, but the auxiliary variable idea is also a nice touch.\n\n- State-of-the-art performance with large gaps.", "weaknesses": "- I'm generally convinced by the formulation and the results, but one question remains: Seeing \"Related methods\" section and Appendix A.2, I cannot help but think whether the proposed method is just providing an alternative story to already existing methods. Appendix A.2 highlights some technical differences, but it is difficult to grasp the distinct value of the proposed method. What is the practical/core advantage of the proposed method in terms of theory/effectiveness compared to the methods mentioned in those sections? On the other hand, PnPFlow shows much worse performance in the experiments. Why is this?\n\n- My review score is currently 6, but it is solely due to the above concern. I'll decide my final score after seeing the rebuttal.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tkFz2n2hpq", "reviewer": "Reviewer_PHGS", "summary": "This paper introduces DING (Decoupled INpainting Guidance), a zero-shot diffusion-based inpainting method that eliminates the need for backpropagation through the denoiser during inference. Existing zero-shot inpainting methods require computing vector–Jacobian products (VJPs) through the diffusion denoiser network at every reverse step, which leads to high computational and memory costs.\nDING addresses this by proposing a decoupled likelihood approximation, where the denoiser is evaluated at an independent proxy variable rather than the current latent state. This removes the dependency between the denoiser and the transition density, enabling closed-form Gaussian posterior sampling without backpropagation. The authors evaluate DING on multiple benchmarks using Stable Diffusion 3.5 as the pretrained prior. DING demonstrates superior trade-offs between reconstruction fidelity, perceptual realism, and computational efficiency compared to recent zero-shot inpainting and posterior-sampling methods such as FLOWCHEF, DAPS, and PSLD. The proposed method even outperforms a fine-tuned SD3 inpainting model despite requiring no additional training.", "questions": "1. Can the authors clarify how runtime and memory are measured? Specifically, does the reported 2.9s runtime include both proxy sampling and denoiser evaluations (2× per step)?\n2. Since the derivation assumes a Gaussian likelihood over masked pixels, how easily can this framework extend to deblurring, super-resolution, or compressive sensing tasks?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The computational inefficiency of current zero-shot diffusion-based inpainting methods is a relevant and well-identified issue.\n+ The “decoupling” of the denoiser input and the posterior likelihood evaluation is a simple but effective modification that removes the need for costly gradient computations.\n+ Experimental results show clear improvements in runtime and memory consumption.\n+ DING achieves consistently better or comparable scores in FID, pFID, LPIPS, and cPSNR across datasets and even outperforms fine-tuned baselines under a fair computational budget.", "weaknesses": "- While the decoupling trick is brilliant, the method builds upon a well-studied line of “posterior sampling with diffusion priors”, such as DPS, FLOWDPS, DAPS, PnP-FLOW, and mainly contributes an engineering-level efficiency improvement rather than a new theoretical insight.\n- The derivation of the closed-form Gaussian posterior is mathematically sound, but the paper lacks deeper analysis or guarantees about the approximation error introduced by the decoupling. The Bayesian justification is somewhat heuristic.\n- DING is designed only for inpainting, as the Gaussian observation model is directly tied to pixel-wise missing-region reconstruction. The paper’s claim that the approach can generalize to “other inverse problems” remains speculative.\n- The paper is dense with mathematical notation and references, for example, multiple layers of priors, posterior transitions, likelihood surrogates, which makes it unnecessarily hard to follow, especially in Sections 2–3.\n- All experiments use Stable Diffusion 3.5 with standard image domains. It would strengthen the work to include more challenging or domain-specific cases (e.g., medical or scientific imaging) where zero-shot efficiency matters most.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cMlWArUj3B", "reviewer": "Reviewer_S6KU", "summary": "The paper proposes DING, a zero-shot diffusion-based inpainting framework that eliminates vector-Jacobian product (VJP) computations by decoupling the likelihood evaluation from the denoiser input. The method claims efficiency gains (reduced memory/runtime) while maintaining or surpassing the quality of prior zero-shot and fine-tuned baselines on FFHQ, DIV2K, and PIE-Bench datasets.", "questions": "1. *Regarding weakness 1:* In equation (3.4), authors define the mixture of Gaussians (over $\\mathbf{z}_s$) that DING samples from. But the true posterior transition π($\\mathbf{x}_s​$∣$\\mathbf{x}_t$​, $\\mathbf{y}$) is not Gaussian as it depends nonlinearly on the denoiser. So under what conditions does the Gaussian mixture in Eq. (3.4) approximate the true posterior transition? Is there a theoretical approximation guarantee?\n2. *Regarding weakness 1:* By decoupling the denoiser input from the latent, does DING still produce unbiased posterior means and variances?\n3. *Regarding weakness 3:* Can authors comment on DING’s performance when $σ_y$ > 0? Does the method remain stable and accurate when the measurement noise increases?\n4. *Regarding weakness 4:* Would DING still outperform the fine-tuned inpainting model if both used the same number of diffusion steps, and the fine-tuned model employed its default CFG weighting of 7.0 instead of the reduced value of 4.0?\n5. *Regarding weakness 4:* In Figure 4 and Table 4, the fine-tuned inpainting model appears to modify observed pixels, resulting in lower cPSNR. This behavior is unexpected. Can the authors explain the cause? Is it due to suboptimal hyperparameters, the 512×512 test resolution differing from the model’s 1024×1024 training resolution, or an architectural limitation?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is well-written, and the mathematical exposition is clear.\n2. The authors provide extensive experimental results across multiple datasets and metrics, consistently showing DING’s advantages. They also make a commendable effort to ensure fair comparisons, clearly documenting baseline hyperparameters.\n3. The inclusion of runtime and memory analyses further strengthens the empirical evaluation.", "weaknesses": "1. The mathematical derivation in Section 3.2 relies on several unverified approximations (see questions below). These steps seem heuristic and lack theoretical justification. Moreover, the closed-form update in Eq. (3.4) depends critically on Gaussian-Gaussian conjugacy, which holds only for linear masking operators. As a result, the proposed method is mathematically fragile and inherently task-specific. Given this restriction, there is little reason to prefer a task-agnostic zero-shot formulation over simply training a dedicated inpainting model, since DING offers no clear theoretical or practical advantage beyond the inpainting setting.\n2. The paper evaluates against general posterior-sampling methods but omits comparison to established fast zero-shot inverse-problem solvers such as DDNM and DiffPIR, which also target inpainting and avoid VJP overhead. DDNM, in particular, is well known for producing high-quality inpainting results when $σ_y$=0.\n3. Although the derivation includes a measurement-noise parameter ($σ_y$) in the likelihood and posterior update (Eq. 3.4), all experiments appear to assume a noise-free setting ($σ_y$=0.0). Consequently, the practical behavior of DING under noisy observations is unknown. Since $σ_y$ directly controls the trade-off between data fidelity and prior regularization, omitting this analysis limits the method’s generality and makes it unclear whether the surrogate posterior remains effective when measurements are noisy.\n4. DING is reported to outperform an SD3.5-Inpainting model trained for 12 M iterations with ControlNet conditioning. This is counterintuitive to me, as I’d expect a model of that scale, trained so extensively for a dedicated inpainting task, to possess a stronger conditional prior in principle. Although the authors match wall-clock runtime between DING and the fine-tuned SD3.5-Inpainting model, the comparison remains asymmetric. DING performs 56 denoiser evaluations (iterative posterior refinement), whereas the fine-tuned model runs only 28 steps of one-shot generation.\n\n**Minor Comments:**\n- Lines 397-402, the authors repeat themselves across two consecutive sentences.\n- Including results on random inpainting masks would also strengthen the evaluation and demonstrate robustness across mask types.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)