PixPerfect: Seamless Latent Diffusion Local Editing with Discriminative Pixel-Space Refinement
Authors: Haitian Zheng
Yuan Yao
Yongsheng Yu
Yuqian Zhou
Jiebo Luo
Zhe Lin
Venue: NeurIPS 2025 poster ()
Keywords: image editing
image refinement
diffusion model
Primary area: applications
TL;DR: We introduce PixPerfect, a general pixel refinement framework that removes seams, color shifts, and texture artifacts from latent diffusion outputs, achieving seamless, high-quality image inpainting and local editing.

Abstract: Latent Diffusion Models (LDMs) have markedly advanced the quality of image inpainting and local editing. However, the inherent latent compression often introduces pixel-level inconsistencies, such as chromatic shifts, texture mismatches, and visible seams along editing boundaries. Existing remedies, including background-conditioned latent decoding and pixel-space harmonization, usually fail to fully eliminate these artifacts in practice and do not generalize well across different latent representations or tasks. We introduce PixPerfect, a pixel‐level refinement framework that delivers seamless, high-fidelity local edits across diverse LDM architectures and tasks. PixPerfect leverages (i) a differentiable discriminative pixel space that amplifies and suppresses subtle color and texture discrepancies, (ii) a comprehensive artifact simulation pipeline that exposes the refiner to realistic local editing artifacts during training, and (iii) a direct pixel-space refinement scheme that ensures broad applicability across diverse latent representations and tasks. Extensive experiments on inpainting, object removal, and insertion benchmarks demonstrate that PixPerfect substantially enhances perceptual fidelity and downstream editing performance, establishing a new standard for robust and high-fidelity localized image editing.

PixPerfect: Seamless Latent Diffusion Local Editing with Discriminative Pixel-Space Refinement
Haitian Zheng!*
Yuan Yao²-
Yongsheng Yu²
Yugian Zhou'
Jiebo Luo²
Zhe Lin'
Adobe Research
University of Rochester
[hazheng, yuqzhou, zlin)Gadobe com, yynobugdgmail.. com, yyu90@ur rochester edu, jlu.@cs.rochestee. rochester.edl
Abstract
Latent Diffusion Models (LDMs) have markedly advanced the quality image inpainting and local editing. However, the inherent latent compression often intro- duces pixel-level inconsistencies, such as chromatic shifts, texture mismatches, and visible seams along editing boundaries. Existing remedies, including background- conditioned latent decoding and pixel-space harmonization, usually fail to fully eliminate these artifacts in practice and do not generalize well across different latent representations or tasks. We introduce PixPerfect, a pixel-level refinement framework that delivers seamless, high-fidelit local edits across diverse LDM architectures and tasks. PixPerfect leverages (i) a differentiable discriminative pixel space that amplifies and suppresses subtle color and texture discrepancies, (ii) a comprehensive artifact simulation pipeline that exposes the refiner to realistic local editing artifacts during training, and (iii) direct pixel-space refinement scheme that ensures broad applicability across diverse latent representations and tasks. Extensive experiments on inpainting. object removal, and insertion benchmarks demonstrate that PixPerfect substantially enhances perceptual fidelity and down- stream editing performance, establishing new standard for robust and high-fidh sigdfiielityy localized image editing.
Introduction
aim to modify a specified image region reference image 41 while according high-level instructions, such text prompt [57 ensuring the coherence with surrounding pixels. As an atomic operation for interactive image editing, it plays fundamental role for applications ranging from object removal, insertion, to creative content regeneration. Recent advances in text-to-image generation models, particularly latent diffusion models (LDMs) (88) have driven remarkable progress in image inpainting
Image inpainting 28 13 and local editing 66 42 55
[3] 20
and local editing 7
55 These methods perform diffusion processes [8 in a low-dimensional
and semantically compressed latent space and demonstrate impressive capacity in generating complex and semantically coherent visual content guided by textual or structural cues.
However, latent local editing methods often struggle to maintain pixel-level consistency between synthesized regions and their surrounding context 47 Specifically, the latent encoding and decoding inherent in these approaches often introduces low-level compression errors, such color and textures mismatch, hindering the pixel-level matching with the original background. Furthermore, when the edited region pasted-back into the source image, small visual differences usually become more pronounced, producing visible mismatches at the boundaries. Such artifacts are usually hard to eliminate. In fact, our experiment found that more expressive latent representations, such as the 16-channel VAE in FLUX 3.. often exacerbate local editing artifacts due to poor generalization
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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during diffusion inference. Such visual artifacts, as shown in Fig. and Fig. 5 are mixture of: i) chromatic shifts editing boundaries, ii) misaligned textures, inconsistent noise or grain patterns, and iii) visible seams arising from content discontinuities and they typically persist across diverse inpainting and local editing methods, posing fundamental challenge that undermines both pereeptual fidelity and practical usability across a wide spectrum of editing applications.
Several approaches have been explored to mitigate local editing artifacts. First, latent-space modi- fications integrate background information during generation: Asymmetric-VO&E VQGAN enriches the latent decoder with partial background inputs for context awarre decoding 665 and ASUKA intro- duces color augmentation during decoder training to simulate uniform chromatic discrepancies [47]. Second. post-hoc pixel-level harmonization methods refine the synthesized region after generation: naive Poisson blending solves for seamless gradient transitions [36], while DifiHarmony++ employs a learning-basee harmonization model for pixel-level adjustment [63 Despite these advancements,
subtle hue shifts, texture mismatches, and content discontinuities often persist for those methods, as human perception remains sensitive minute visual discrepancies at editing boundaries. Moreover, the reliance specific latent space [65] 47 limits the generalization of these methods to alternative representations and diverse editing scenarios.
In this work, we identified three fundamental challenges associated with local editing artifacts and proposed PixPerfect as general-purpose, high-fidelity, and pixel-level artifact removal framework for inpainting and local editing. Our proposed framework addresses three unsolved issues:
(i) Subtle visual difference: how to eliminate persistent and cumbersome boundaries artifacts caused by subtle but perceptible pixel-value value difference or noise poise-pattern discrepancies?
(ii) Complexity of local-editing artifacts: how to handle the complexity of the artifacts that mixes chromatic shifts, inconsistent noise or grain patterns, and content discontinuities?
(iii) Generalization: how to develop a unified solution that generalizes across diverse latent diffusion models, latent spaces 38 or applications?
Consequently, we propose three novel contributions to address those issues. First, we propose a novel discrininative pixel space that transforms the RGB color space into more discriminative representation where subtle hue and texture mismatches between an edited region and its background become more perceptible. Such differentiable transformation is incorporated model sensitivity for more precise color and texture alignment. Second, we design a comprehensive data pipeline that simulates local editing artifacts in a realistic setting. Our data pipeline simulates a diverse set of inpainting degradations including non-uniform color shifts, texture inconsistencies, noise pattern variations, and content discontinuities, thereby enabling the refiner to learn across multiple failure modes. Third, we formulate the refinement process as pixel-Ievel refinement as opposed to latent decoding compared to prior works 655 47), yielding a general purposse solution for inpainting and local-editing artifact removal.
loss enhance the
With extensive evaluations and visualizations across diverse set of inpainting and local editing models, we demonstrate that the refiner can robustly correct local editing artifacts and consistently improve the performance and visual quality of wide range of existing methods. As such, our approach yields higher quantitative scores and visibly superior results, and noticeably, it significantly boosts a wide spectrum of state-of-the-ar methods for image inpainting, object removal and object insertion. All these evidence shows the effectiveness and necessity of our proposed scheme for achieving high-quality image inpainting and localized editing.
2 Related Work
Image Inpainting and Local Editing, Image inpainting and local editing are confined to masked
regions. GAN-based approaches [34 54
achieve structure recovery by learn- 37 and multi-scale feature fusion [56]. Diffusion-based methods, including RePaint 32.. Stable Diffusion inpainting (38), and FLUX-
60
4
43
28
[3]
ing semantic priors, often enhanced by attention 53
31
Fill (3], demonstrate strong inpainting capabilities but may produce inconsistencies around mask boundaries or hallucinate unfaithful content. Beyond pure inpainting, local editing extends the scope to broader semantic manipulations. Works like BrushNet (20) and CL.PPAaa [1] adopt masked diffusion pipelines guided by CLIP or user input, while OmniPaint (55) and FreeCompose (7) support both object removal and insertion. Other methods explore region-specific editing via

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GT
Projected GT
Encoder
Latent
Refiner
recon koss
Diseriminative Tone Mapping fo IR R
GAN recom loss
diffusion
model
background picelr editee pizefz
output
Final outpu
Projected oulput
Latent Space
Pixel Space
Discriminative Pixel Space
Figure 1: Given partially composited image generated by latent diffusion model, PixPerfect refines the output correct harmonization artifacts between the synthesized region and the background. discriminative color-space transformation is employed to enhance sensitivity to subtle chromatic and textural discrepancies, thereby improving color harmonization and texture coherence.
layout control [29]] or hybrid latent/image-spaa optimization [ 2 De- spite promising results, latent-diffusion based methods still struggle to seamlessly blend the edits and maintain local consistency. We propose the universal method PixPerfect to solve this issue.
augmentation 6
41
Improving Image Details for Diffusion Models. Latent diffusion models 33 B compress images
with VAES (25), causing loss of fine details. To solve this issue, various works have explored enhancing latent representations. Consistency-aware training (40 and frequency-aware VAEs [49
aim to preserve structure during compression, while decoder-based improvements 12
33 15 target
the reconstruction phase. In small domains. post-g generation methods (5 46) provides enhancement for generated contents. Masked generative priors [4 offer better fidelity by supervising denoising with partial reconstructions, However, these approaches still fall short of full consistency 48 47
We argue ensuring consistency in pixel space is a more refined approach and present pixel space refiner.
Image Harmonization and Refinement. Image harmonization aims to correct appearance in- consistencies between composited foreground and its background. Early learning cearning-based based meth- ods [44 8 30) cast harmonization as an image-to-image translation problem, adjusting the fore- ground's illumination and color match the context. Attention-based and multi-scale designs 15 [4 improve blending along object boundaries. Recent works introduce contrastive learning [6], transformer-based harmonization [19) and diffusion models 644 27 semantic shifts. In image
enhancement, generic correctors such as ARCNN for JPEG artifacts 110. lens aberration correction
[52]. and deep sharpening networks 45 26 further improve realism. However, existing harmo- nization methods are typically designed for manually composited inputs where the foreground is complete, well-defined object pasted into clean background. In contrast, diffusion- based inpainting involves -hhrpe masked regions with potentially complex semantics. Moreover, many harmonization models focus on aligning the foreground appearance, but do not explicitly enforce background consistency, making them less suitable for correcting seams introduced by latent diffu- sion. Our work bridges this gap by addressing both semantic restoration and -arauunnd--akrrunndd consistency within a unified refinement framework tailored to latent diffusion artifacts.
3 Methods
Inpainting and local editing aim to modify region of an original image Toris yielding an edited result agen that alters only pixels within a binary mask m E 0, HXW while preserving background content. LDM-based approaches frequently introduce inconsistencies along mask boundaries following latent decoding and background compositing. To overcome this limitation, PixPerfect employs pixel-level refinement network implemented a generative adversarial network (GAN) [14), denoted as G, to restore pixel-Ievel coherence. The refiner produces
pred
G gen m))
3

---

H
Masked image
GT
Oulput
JIGT)
fu(Output
RGB Pixel Space
Discriminative Pixal Space
Figure 2: The effect of discriminative pixel space on enhancing subtle background mismatch.
such that pred aligns closely with the pixel-consistent oracle image agt in both the edited region and its surroundings.
3.1 The Discriminative Pixel Space
Precise alignment color and texture between synthesized regions and the surrounding background is essential for achieving pixel consistent and seamless results. In the inpainting and image harmo- nization literature, this objective is commonly enforced via minimizing combination of l loss, perceptual losses and mask-conditioned adversarial loss on the pixel-space constrain chromatic and structural similarity,
Spixel-space = w1 Ppred - Egali + u2 -10((()red) - Φ )l1 + wg D(Igen m), (1)
where (i-) denotes perceptual feature extractor (58] D) is the mask-conditioned adversarial loss and w are balancing weights.
However, refinement networks trained with only these loss frequently produce outputs with subtle yet persistent hue shifts, mismatched texture or noise patterns, and visible seams that remain detectable by expert observers during high-fidelity editing. In fact, this issue arises across GAN-based inpaint-
ing 43 61,, image harmonization settings.
63 and latent decoding methods 65 47
across various
We argue that such visual distortions due the insensitivity of the conventional pixel-space objectives to subtle color or textures misalignments. To overcome this limitation, we introduce a discriminative pixel space that amplifies perceptual discrepancies between the synthesized region and its background. Specifically, we define a discriminative tone mapping function : IR3 - R3 parameterized by which transforms pixel value vector æ æ(p] [0, 1| at each location p into discriminative color space, thereby rendering color and texture mismatches more salient.
The visual effect of the tone mapping fa is illustrated in Fig, 2 After applying fo. both the prediction and the ground-trute are projected into the discriminative pixel space via Ypred = preep and Vgt. fz(zz,), respectively, where subtle color and texture mismatches between foreground and background are amplified. Accordingly, we define the discriminative pixel space loss as the combination of f1, perceptual and conditioned adversarial loss using the same weighting:
Cdisc-space W1
[peeed
ygall1 + w22 [[o(y)red) o ygu) I| W3 D' (glgen m.) (2)
Designing the tone mapping function fo is critical to enable the discriminative pixel space. In principle, fa must be differentiable, computationally lightweight, and adaptive to individual samples training stage. To satisfy these requirements, we parameterize fo polynomial regression, yielding a closed-form, sample-specific, differentiable mapping. Specifically, the regression is defined as:
Ye = EyPane
for each channel c E G, B). is the polynomial degree and 0 = (Pc.1, parameters. To facilitate content discrimination, the regression inputs are defined as the pixel values of the predicted image prrod and regression targets are specified by an image that amplifies the hue difference between Epred and Eeg within the composition mask:
... Pe.D) are the
Yamp Zgt (Ipred Egt).
where > controls the amplification strength. For the implementation, the Moore-Penrose pseudoinverse (35] is employed to compute the regression coefficients. To improve training stability,

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Non-uniform Color Shifting
Texture Pattern Mismatch
Content Discontinuity
Apply different texture in the hole and background
O
-I
Q
JPEG Jpeg artifact
Expand mask
Inpainter
Seamless on boundary
Noise pattem
Color
Gradient blending
Seam
jittering
In BE ERONE
O
Composite the original background
Tiim a Timg + (1-a) a) Fijt
Smoothing
VAE distortion
Figure 3: Artifact Simulation Pipelines. Each module illustrates one of the three simulated artifact types used to train our refiner. (Lefft) Non-uniform color shifting is created via local jittering and gradient blending. (Middle) Texture-pattern mismatches are simulated through JPEG artifacts, noise, and VAE decoding distortions applied selectively inside or outside the mask. (Right) Content discontinuity is emulated by expanding the mask boundary and applying off-the-shelf inpainting followed by background recomposition.
we apply balanced sampling to select an equal number of pixels inside and outside the mask, and is drawn uniformly from [20, 40]. Finally, the outputs are clamped to valid ranges after tone mapping.
As such, our overall training objective is a combination of the original pixel-space loss and the discriminative pixel-space loss:
= Cpixel -pixol-spac
space space
Ldisc-space-
(4)
3.2 Simulating Local Editing Artifacts
Training a high-quality refiner requires diverse, controllable supervision that reflects the degradations introduced by LDM-based inpainting and editing. Relying on real diffusion outputs for supervision poses two challenges:
(i) artifact distributions vary across models and prompts, hindering the construction of a consistent training set; and (ii) the ground-truth image zgt for a partially edited output & geen is often unavailable or ambiguous, particularly when large semantic gaps or content hallucinations occur.
To overcome these limitations, we design a synthetic artifact simulation pipeline that generates training pairs (Igge;; Fgg) by injecting controlled degradations into clean images. Unlike prior work [47] our simulation reproduces the complex, realistic local editing artifacts observed in diffusion outputs. Specifically, the pipeline applies mixture of non-uniform color shifts, texture-pattern inconsistencies, content-mismatch discontinuities, soft and hard boundary effects, and autoencoder reconstruction artifacts. Each degradation module operates exclusively within masked regions, preserving background integrity and providing a clear learning signal for harmonization. Please refer the supplementary material for further details.
Non-uniform Color Shifting, Inpainting and local editing often introduce chromatic discrepancies relative to surrounding background, which can be especially pronounced over heterogeneous regions (e.g., skylines). To model these effects, a non-uniform color augmentation pipeline is devised as opposed to 44 First, uniform color shifts simulated by applying random color jitter within the masked region (22. Next, non-uniform chromatic variations are synthesized by alpha-blending two independently color-jittered versions of the input using a randomly generated gradient mask. This gradient alpha map produces spatially varying hue and luminance shifts that closely mimic realistic color artifacts. The alpha-blending process can be formulated
Fism
Fimg + (1 a:) Tjit-
(5)
5

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Simulating Texture-Pattern Mismatch. Efffusion-basee inpainting often yields region-specific texture distortions such blurring, inconsistent noise patterns, smoothed details, the absence of background JPEG compression artifacts, and altered texture distributions due to latent decoding. To replicate these defects, the simulation pipeline introduces independent texture transformations within and outside the masked region. Specifically, the masked region undergoes random VAE reconstructions [38] [ and Gaussian smoothing, while the background is subjected to JPEG compres- sion artifacts. In addition, separate stochastic texture synthesis processes applied to foreground and background, generating distinct noise and detail characteristics that mimic real-world texture mismatches.
Simulating Content Discontinuities. Partial compositing of generated content onto existing back- grounds can induce slight misalignments or content discrepancies at mask boundaries, yielding visible seams that compromise object integrity. To emulate this artifact, an off off-the-shelf inpainting method 66 [60] reconstructs narrow band straddling the mask edge, perturbing pixels on both sides of the boundary. The original background pixels are then composited back into the masked region, producing training examples that exhibit realistic boundary discontinuities similar to those introduced by diffusion- based edits. This simulation supplies the refiner with explicit examples seam artifacts, enabling targeted correction.
Mixing Soft and Hard Boundary. Latent diffusion local edits often produce both feathered (soft) or abrupt (hard) seams that may be offset from the true composition boundary. To simulate these artifacts, we randomly perturb the composition mask for artifact generation with morphological dilation and erosion, then apply Gaussian blur with random kernel size to generate soft transitions. Blending content with these augmented masks enables our pipeline to generalize across both smooth and sharp seam artifacts.
3.3 Noise-adding and Inference-time Pooling
Following the recent GAN-based approach (21). we apply moderate Gaussian noise augmentation to the input pixels to stabilize the GAN training. Furthermore, inference-time scaling has recently been recently proven to be effective for multiple domains related LLM and GenAI models. Inspired by this idea, we propose simple yet effective inference-time pooling tricks to further boost the performance. Our intuition is by apply the refiner on different color jittering variation of the input image and perform pooling, a better refiner output can be produced. Specifically, given an initial image geen we propose N random color jittering inside the mask, resulting Zgen for E 1,,,,,,, Then we apply the refiner to the jittered image, and we propose to use the difference between input and refiner output || - G(zgon: m)]1 as an indicator to determine how well the input image is close to the grouth-truth. Finally, the best refiner output is selected as a: pred i = arg min; || popo G( ml)
(6)
(b') whereas
4 Experiments
4.1 Experiment Settings
Implementation details Our model built upon the CMGAN architecture [61] and trained on a curated dataset of approximately 300 million images at 1024x1024 resolution. Optimization uses Adam with a learning rate of 0.0005 and a batch size of 32. We interoperate larger perceptual and 11 weight, i.c. W 64, W2 5, wy 1 to enforce color consistency. the perceptual loss is computed using LPIPS 58 following [12. For the tone mapping function, the maximal polynomial degree is set to D 5 to avoid overfitting. Training is performed on cluster of 32 NVIDIA A100 GPUs within one week. Further details are provided in the supplementary material.
Evaluation Dataset We evaluate PixPerfect on three major tasks inse--niainninn.... object removal, and object insertion. For inpainting, we follow prior works and use two standard datasets: Places2 [62 and MISATO 47 Places2 is a large-scale scene-centric dataset from which we randomly sample 2000 validation images and apply irregular masks of varying shapes and sizes to simulate occlusions. MISATO consists of 2000 512x512 images, each paired with a generated mask, specifically curated for evaluating semantic inpainting. For object removal, use the RORDS dataset [39], which

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Datasel
MISATO
Places2
Method
FIDL
I.PIPS]
1.14
PSNR
U-IDSt
P-IDS
FIDL
I.PPSS.
1.14
PSNR1
U-IDS1
P-IDSt
SDv1.5 XS
18.15
0.229
0.068
19.01
9.55
4.03
21.45
0.270
0.088
17.22
11.34
5.04
SDvi.-PixPerfeet
13.25
0.171
0.044
20.40
17.24
10.89
18.91
0.228
0.067
18.07
15.05
8.82
SDv2 8S
18.68
0.236
0.067
19.04
8.24
3.83
21.13
0:271
0.086
17.25
11.16
5.29
SDv2-PixPerfect
16.28
0.189
0.048
19.81
13.13
7:71
19.12
0.231
0.059
17.90
15.45
8.82
FLUX-Fill FLUX-Fill-syVO
14.66
0.062
20.90
3.18
19.05
0.074
19.33
7.89
G
15,99
0.202
0.057
20.91
7.46
3.33
18.28
0.244
0.073
19.07
14,49
8.47
FLUX-FII-DH (63
14.02
0.190
0.056
20.89
10.38
4.79
18.18
0.236
0.071
18.99
10.48
4.74
MLUX-FillPPsPeriee
10.87
0.141
0.036
22.18
18.09
9.53
15.61
0.194
0.052
20.04
19.08
11.69
Table 1: Quantitative comparison on MISATO and Places2. Our method substantially improves upon existing inpainting approaches and significantly reduces the FID score for FLUX-Fill [
contains 500 image pairs with human-annotated foreground masks and corresponding clean back- ground ground-truthe For object insertion, we evaluate on dataset of 300 triplets, each comprising background image, foreground object, and ground-truth composite image.
Benchmark We evaluate the quality of generated images using a comprehensive set of metrics covering both perceptual similarity and pixel-Ievel accuracy. These include FID[T7] for distributional alignment, [PIPS[[8] for perceptual similarity, L1 and PSNR for reconstruction fidelity, and P-IDS U-IDS [60] to assess perceptual discriminability. In addition, for the object insertion task where ground truth composite image is not reliable, we report no-reference image quality scores such as MUSIQ (23] and MANIQA [51] to reflect global perceptual coherence. Together, these metrics provide a balanced evaluation across visual quality, semantic consistency, and low-level accuracy.
4.2 Comparison results
Inpainting PixPerfect is evaluated on latent diffusion- based inpainting models including SDv1.5, SDv2, and FLUX-Fill across MISATO and Places2 datasets in Tab PixPerfect consistently im- proves both perceptual (FID, LPIPS, IDS) and pixel-level (L1, PSNR) metrics over all baselines. Notably, the PLUX-rril-PixPerfeet achieves new state-of-the-art results. We further compare PixPer- fect with the decoder-based method Asymmetric VQ-GAN 65 and the darmonization-basee method DiffHarmony++ 63 On both datasets, PixPerfect outperforms these methods by a clear margin across all metrics, demonstrating its effectiveness in correcting diffusion-induced artifacts.
Qualitative comparisons in Fig. 4 further highlight the differences. We paste back original unmasked regions to reveal editing artifacts. Both Asymmetric VQ-GAN and DifiHarmony++ show noticeable color shifts or texture inconsistency, particularly around mask boundaries or semantically complex regions. In contrast, PixPerfect produces sharper transitions and better-aligned textures, yielding more coherent and natural completions.
Object removal We evaluate PixPerfect on four representative diffusion-based object removal models: BrushNet (20). CLIPAway [II]. PowerPaint [66], and OmniPaint (55). Tab. shows the results on the RORDS dataset. PixPerfect consistently improves all basclines across FID, LPIPS, LI, and PSNR, confirming its effectiveness across diverse range of architectures and scenes. Even for the strongest model OmniPaint, PixPerfect significantly reduces the FID from 23.05 to 18.87 and improves PSNR from 24.67 27. 76, demonstrating its capacity to refine already plausible outputs and further enhance structural fidelity and perceptual quality. Fig. shows representative example where editing artifacts overlap with soft shadows on reflective surfaces. PixPerfect eliminates the artifacts while preserving natural cues, resulting in coherent and visually consistent completions. These results highlight the robustness of PixPerfect in complex real-world removal scenarios and its utility general refinement module across diverse generative pipelines.
Object Insertion We evaluate PixPerfect on four representative cerffusion-basedd object insertion models: pbe [50], ObjectStitch [1), AnyDoor [6), and OmniPaint [55]. Tab. repootss quantitative results across both full-reference metrics (FID, LPIPS, L1) and no-reference perceptual scores (MUSIQ, MANIQA). PixPerfect consistently improves all baselines across all metrics, reflecting better structural alignment and visual fidelity after refinement. The gains are particularly large in models with more evident compositional artifacts, such as Pbe and ObjectStitch. Even in more
7

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FLUX-Fil
MISATO
SDv2 Inpaint
FLUX-Fill
Places2
SDVLS wmpain 1II
Masked image
Inpainted image Asymmetric VQGAN DiftHarmonyt+
PixPerfect
Figure 4: Qualitative comparison of inpainting results on MISATO 47 and Places2 [62] PixPer-
fect consistently restores coherent structure and appearance across mask boundaries, and produces more accurate color and texture transitions compared Asymmetrie VQGAN (65 and DiffHar- mony++ 63
Input image
After local editing
Refined
OmnnPaint Pinnt Removal
Anydoor Insertion
Figure 5: PixPerfect improves visual coherence of the local editing method OmniPaint (55) In the removal case, it removes the hue artifact while restoring realistic reflections and shadows. In the insertion example, aligns fine-graime textures around the boundary, producing seamless integration between the inserted object and its background.

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Method
FIDL
LPIPS|
L14
PSNR1
Method
FiDL
LPIPSL
L14
MUSIQU
MANIQAT
BrushNat 20
148.99
0.224
0.0604
20,00
Phe [50
97.53
0.269
0.0856
69.33
0.4746
+ PixPerfect
144.54
0.182
0.0390
21.31
PixPerfect
91.21
0.236
0.0793
71.04
0.5070
CLiPAway M
63.69
0.193
0.0393
20.78
ObjectSiich [
89.14
0.264
0.0838
69.27
0.4051
+ PiiaPerfect
54.78
0.113
0.0337
23.04
PixPPerfet
86.74
0.238
0.0790
71.38
0.5060
PowerPaint (6
53,33
0.170
0.0613
20.75
AnyDoor G
73.17
0.251
0.0794
68.53
0.4306
PixPerfect
43.40
0.0321
23.50
PixPerfect
71.74
0.223
0.0764
71.53
0.5058
OmniPaint 5S
23.05
0.094
0.0420
24.67
OmniPaint [SS]
56.80
0.186
0.0713
70.32
0.5029
PixPerfect
18.87
0.060
0.0206
27.96
PixPerfect
57.42
0.181
0.0678
71.54
0.5066
Table 2: Object removal results.
Table 3: Object insertion results.
advanced models, PixPerfect yields measurable improvements, indicating that subtle inconsistencies still prevalent in modern diffusion-based insertion pipelines and can be effectively resolved through targeted refinement. shows an insertion example the edited image suffers from severe texture mismatch. PixPerfect capable of restoring textures that are consistent with the background.
4.3 Ablation Studies
To validate the effectiveness of each component in PixPerfect, we conduct incremental ablation study based on FLUX-Fill, using the MISATO dataset for evaluation. Starting from the baseline, we progressively integrate each proposed module and report the results in Tab. 4 Applying a simple paste-back operation, which restores the unmasked regions from the original image, already yields substantial reductions in LPIPS and L1. This confirms that background distortion is key source of degradation in latent diffusion- inpainting.
Introducing the refineroour base architecture for artifact suppression-furtiie improves FID and LPIPS, though the gains remain modest without additional guidance. The key performance leap comes from adding the enhancement loss in the proposed discriminative pixel space. This transformation amplifies subtle chromatic and structural inconsistencies that are often overlooked in standard pixel space, enabling the refiner to align texture and color more precisely. As result, all metrics improve significantly.
A Ablation studies are further conducted on the variant of the discriminative pixel-space design. First, the influence of the polynomial degree in I examined by varying the degree value during training. Unless otherwise stated, our default degree is set to d = 6. Empirical observations indicate that leads to only limited tonal correction, reducing the loss effectiveness,
a low degree such as whereas excessively high degrees such as d = 10 tend to overemphasize local details and generate outputs that deviate from the natural image distribution thus degrading performance. In addition, examined a -igh--immnsional discriminative space variant that applies the discriminative trans- formation to VGG16 feature maps before computing the LPIPS loss. From the experiment, this variant achieves competitive performance, suggesting that our method is generalizable to a higher- dimensional discriminative space. However, its performance is slightly worse than the original pixel-space implementation, which we hypothesize is due the loss of spatial precision along editing boundaries caused by spatial downsampling of feature maps. Furthermore, an alternative pixel-space enhancement loss is evaluated by employing HAAR decomposition to separate both the prediction and ground truth into low- and then applied to each band, with larger weight assigned to the low-frequency term emphasize subtle chromatic variations. However, this design yields inferior results, as band-wise reweighting is difficult to integrate with perceptual or adversarial objectives, which are essential for high-quality image synthesis. Finally, incorporating the inference-time pooling strategy further stabilizes the predictions, mitigating noise and improving overall visual coherence.
components. Independent & losses are
Limitations and Broader Impacts
PixPerfect a refinement module designed to correct low-level artifacts in diffusion- -baed inpainting and local editing. While effective in improving color consistency and texture alignment, it cannot correct major semantic errors from the generative model. Its performance depends on the availability of reasonably accurate initial predictions and pre-defined edited regions.

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Method
FID
LPIPS|
L14
FLUX-fill 3
14.6585
0.1950
0.0621
+ paste-back
14.4022
0.1701
0.0395
+ refiner
13,9874
0.1698
0.0402
enhance loss (d=6, default)
10.9014
0.1425
0.0365
enhance loss (d=2) enhance loss (d=10)
11.2244
0.1431
0.0362
11.0018
0.1407
0.0361
enhance loss on VGG features
11.0525
0.1421
0.0360
Haar-based re-weighted loss
11.3816
0.1431
0.0375
+ inference time pooling (PixPerfect)
10.8675
0.1414
0.0363
Table 4: Ablation study on the MISATO dataset. Each component of PixPerfect progressively
improves inpainting quality across perceptual and pixel-wise metrics.
PixPerfect can support wide range of socially beneficial applications, such as accessible photo editing, digital restoration of historical or damaged media, and assistive tools for creators with limited visual or technical expertise, by improving visual fidelity and reducing editing artifacts. However, similar with other generative tools, may also be misused for producing more convincing manipulated content. The method does not introduce new identity or demographic biases, but inherits any biases present in upstream diffusion models and training datasets.
Conclusion
PixPerfect is presented general- purposs refinement module designed to rectify harmonization failures LDM-based inpainting and local editing. By integrating a discriminative pixel-space objective, realistic artifact simulation pipeline, and direct pixel-level refinement framework, Pix- Perfect effectively mitigates chromatic shifts, texture misalignments, and boundary seams exhibited by variety of diffusion-based editing models. Comprehensive evaluations across multiple tasks and architectures demonstrate substantial gains in perceptual fidelity and quantitative performance. Furthermore, lightweight, glug-and-play component, PixPerfect generalizes robustly diverse editing scenarios. These findings highlight the potential to pixel-consistett and reliable local image editing pipeline.
10

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Appendix
Supplementary Material for "PixPerfect: Seamless Latent Diffusion Local Editing with Discriminative Pixel-Space Refinement"
A The Latent Space Spatial Disentanglement Issue
Latent diffusion models operates on compact latent space. However, the latent space spa- tially entangled and not suitable for pixel-wise tasks. In this section, we study the latent space disentanglement issue.
Latent diffusion models encode images into a compressed latent space with an autoencoder. However, this latent representation lacks spatial disentanglement, limiting its suitability for fine-grained local editing. To illustrate this issue, we design a controlled experiment shown in Fig. 6 We encode both the original image and its masked counterpart using FLUX VAE [3] then construct hybrid latent by combining the unmasked background from the masked input with the masked region from the original. This ensures that the latent representation differs only within a small localized area.
the VAE decoder preserved spatial locality, such localized change would affect the reconstruc- tion outside the masked region. However, the decoded image exhibits global shifts in background appearance, even where latent features remain unchanged. This behavior highlights fundamental limitation of the latent space: local modifications can induce unintended global effects due to en- tangled representations. These observations motivate our refinement strategy, which operates in the pixel space to preserve spatial locality and ensure coherent integration between edited and unedited regions.
Residual heat map outside mask
Decoue
0.35
100
0.30
203
0.23
0.00
Deccu er
200
0.15
0.0
400
0.05
latents
200
0.00
100
200
200
420
Figure 6: Replacing only the masked region in latent space leads to background drift in the decoded image, suggesting spatial entanglement in satent-based inpainting.
B
More Experiments
B.1
Efficiency Analysis
While improving visual consistency and perceptual fidelity is the primary goal of our refinement framework, it is also critical that the added refinement stage does not not significantly increase the overall runtime. To this end, we analyze the computational cost PixPerfect comparison to the underlying latent diffusion sampling process.
Our refiner operates as single-stage ferdard network in the pixel space, and introduces negligible overhead compared to the iterative denoising procedure of diffusion models. For example, when applied to a 512x512 image on a single NVIDIA A100 GPU, the diffusion sampling with FLUX-Fil takes approximately 9.7 seconds, whereas our refiner adds only 2.7 seconds, accounting for only 21.8% of the total inference time.
Notably, our approach remains more efficient than additional diffusion- based refinement stage. This efficiency stems from two factors: (1) PixPerfect requires only single forward pass without iterative sampling, and (2) its architecture is lightweight and resolution-agnosice enabling low-latency

---

execution. Even when inference- time pooling is enabled, the overall runtime remains within 1.3x of the baseline, while yielding measurable improvements in visual quality.
These results indicate that PixPerfect can be seamlessly integrated into existing diffusion pipelines with minimal computational cost, offering substantial perceptual gains at fraction of the runtime.
B.2 Comparisons with Poisson Blending
In the main paper, we have presented the comparisons with decoder-based method Assymetric VQ-GAN [65] and harmoniiationn-aaseeee method DiffHarmony [64]. In this section we will provide additional analysis about Poisson blending. Poisson blending is a classical gradient-domain technique widely used for seamless image compositing. estimates smooth transition between a source (edited) region and target (background) image by solving for pixel values that minimize gradient differences while respecting boundary conditions.
However, applying Poisson blending in the context of inpainting or local editing typically requires access to a reliable gradient field within the masked region. In practice, this is often approximated using the ground truth content in the masked area to compute the desired gradients. While this produces visually smooth results, it introduces a critical ground-truth leakage isfue-iinoonnaion that is unavailable at test time is used during blending. Consequently, Poisson blending cannot be considered a fair or deployable baseline real-world settings.
Although Poisson blending relics on inaccessible ground-truth information, we still present some qualitative comparison results. We apply Poisson blending on the outputs of FLUX-FI -ill 3 using houud-truth--mate gradients to simulate its ideal behavior. 7 shows representative examples comparing our method with Poisson blending. While the latter can reduce abrupt seams at the boundary, it often introduces unnatural hue propagation and fails to correct texture inconsistencies or geometric artifacts introduced during the generation process. Furthermore, when the inpainted results differ from the original ground truth image, the Poisson blending will blend the masked part into the tone of the original ground truth and produce unnatural seams. In contrast, our method produces more coherent integration with the background, better preserves structural details, and eliminates color/texture artifacts without relying on inaccessible goound-ruuth information.
These results highlight that Poisson blending falls short correcting complex local editing artifacts. Our learning- baased refiner not only avoids the pitfalls of ground-truth leakage but also achieves better perceptual quality through semantically aware refinement.
B.3 More Qualitative results
To further illustrate the effectiveness and generalization of approach, we present additional qualitative results for the two local editing tasks: object removal and object insertion. These tasks requires image editing within a masked area and keep the background unchanged.
In the object removal examples shown in Fig. g, we present qualitative results from three represen- tative baselines: OmniPaint (55). PowerPaint [66] and CLIPAway [II]. As indicated by the red arrows, baseline inpainting results often exhibit low-level inconsistencies, such as chromatic shifts, particularly in regions of clean background such as floors and tables. In contrast, our method effec- tively eliminates these artifacts, yielding smooth and contextually coherent background completions without disrupting the surrounding scene geometry.
In the object insertion results shown in Fig. 9 we visualize our refinement performance on outputs from ObjectStitch [1). AnyDoor (6). and PBE (5). In these cases, challenges arise from the need to harmonize inserted objects with scene textures and lighting. As highlighted in the magnified insets, baseline results often suffer from blurry transitions, scale-inconsistent textures, unnatural object boundaries. Our method noticeably improves local consistency by refining high-frequency texture alignment, enhancing boundary sharpness, and reducing chromatic discrepancies leading to more realistic and visually pleasing composites.
Overall, these examples demonstrate the general applicability of our method across diverse models and editing scenarios. In both insertion and removal tasks, PixPerfect consistently enhances visual quality by resolving local inconsistencies that are challenging for latent diffusion models alone. We
12

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Original Image
Masked Image
Inpainted Image
Poisson Blending
PixPerfect
Figure 7: Qualitative comparison between method and Poisson blending for FLUX-Fill inpainting outputs. While Poisson blending reduces edge discontinuities, often introduces hue bleeding and fails to correct texture or structural artifacts. Further more in the cases where the inpainted results differ from the ground truth image (e.g. second row), poisson blending will tend to mimic the ground truth and produce unnatural results. In contrast, PixPerfect produces cleaner transitions, preserves scene structure, and avoids tone inconsistency without relying on inaccessible ground-truth information.
encourage readers to examine the highlighted regions closely to appreciate the subtle yet impactful improvements brought by approach.
C Implementation Details
Architecture and Training. The refiner is built on the CMGAN architecture [6]. However, we replace the bottleneck fully-connected layer with a global average pooling operation, thereby making the network fully convolutional. In addition, we apply channel pruning to reduce the model size. Our final model contains 41M parameters. Training employs R1 regularization with 7 1 and utilizes the CoModGAN mask generation scheme (59] generate random masks on-the-fly. During an initial warm-up phase, the discriminative pixel-space loss remains disabled. A constant learning rate of x 10-4 applied throughout the training.
Details on Color Shifting Augmentation. Three complementary color-shifting schemes are em- ployed. First, linear gradient color augmentation constructs mask by projecting normalized r-y coordinate grids onto a randomly oriented unit vector and normalizing the result; the final image obtained by alpha-blending this mask with color-jittered version of the input. Second, random blob color augmentation synthesizes one or more soft ellipses per image each defined by random center, semi-axes sampled from a fraction of the image dimensions, and a random rotation where pixel intensities decay smoothly from center to boundary: overlapping ellipses merge via a maximum operator to produce distinct, softly blended circular regions. Third, uniform jütter augmentation simulates spatially invariant color shifts by blending uniformly color-jittered image with the original input using a fixed blending ratio. We provide an artifact generation pipeline that describes the artifact types and their corresponding augmentation probabilities in
13

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Input image
After removal
Refined
OmmiPaint
PowerPaint
CLIPAway
Figure 8: Qualitative comparisons on object removal. Red arrows highlight residual artifacts such as color inconsistency produced by baseline diffusion models. Our method effectively eliminates such artifacts.
Input image
After insertion
Refined
ObjectStitch
Anydoor
Pbe
Figure 9: Qualitative comparisons on object insertion. The highlighted insets reveal artifacts in baseline results, such as blurry edges, inconsistent textures, and poor object blending. Our refinement enhances boundary sharpness, aligns local textures, and achieves more seamless visual integration.
14

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Table Summary of artifact types and their corresponding augmentation probabilities.
Artifact Type
Description
Probability
Content Discontinuity
Small misalignments / missing pixels near mask edges
0.5
Background Color Augmentation
Non-uniform hue / brightness variations
0.8
applied to the background
Foreground Color Augmentation
Non-uniform / uniform gradient color perturbations applied to the foreground region
0.8
Soft/Hard Boundary Mixing
Mixing soft / hard boundaries to mimic visual seams at compositional borders
1.0
Sensor Noise / JPEG/ Blur
Injecting noise / JPEG compression blur into foreground and/or background regions
0.5
VAE Compression Artifacts
Introducing compression artifacts sim- ulated by a pretrained VAE to the fore- ground
0.5
A minimal demo script for reproducing the "seam" artifacts of Flux inpainting 3 model. To facilitate reprodueibility, we attached minimal demo script that reproduces the boundary artifacts for the official FLUX-Fill model [
1 import torch
2 import numpy as np
3 from PIL import Image, ImageDraw from diffusers import FluxFillPipeline diffusers.utie utils import load_image
6
7 === Define input image path === hoput__mage__pee
"/your/inage/path: # TOD0: change to the input image path
Load input image
11 image load_ image (input_image patpputtinnee_aa_a___ path) convert (ee ("RGB") width, height miage.size
Generate irregular mask ptrpirrruulllammsssritttt... (widhti height, max_ sex_sshaee=5); mask Image Image.new("1' new("L" (width, height), 0)
16
draw = ImageDraw. Draw (mask)
randint (1, max_ max_shapes 1)): for range (np (np. random.randiitt
20
shape_typp np. random. choicc(("ellipsee "polygon"]) if shape_typp == "ellipse":
x0, y0 np. ap..randmm.o randint (0, width 50), np random. randint height
rrandom.randint
50)
x1, y1 x0 random. randint (40, 120), y0 www.rrand..aadai.... (40,
120)
draw. el ellipse ([x0, yo, x1, y1], fill-255)
else:
num_points np. random. randint (3,
www.ado...andnntto... +} for in range (num_ (num__ointss] points)]
raandom.randint.. randint (0, height)) points [(np.randon randint width), np random.
draw. wew.polyyonnpoiinee
fill=255)
return mask. convert (nnnverttRGGae ("RGB")
mask = generate_irregulart maakkmaaskkiii (width, height)
15

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=== Load FLUX inpeinting pipeline ===
pipe #luufil1Pipeeinee htt.:ppppppeie.ffooprreraaaaaeee
Mlack-forrett-lab//UUbB -er forch_dtype-tordd bfloat16 )"todac
=== Run PLUI-FeLL
output = pipe(
image=image, mask_image-mask,
prompt="",
height-height,
width-width,
guidance_scale-30 The default value provided on the official haggingface page
num rum_innference_stepeeeee # The default value provided on the official
huggingface page
max_sequenne__eegghhbboo & The default value provided on the official
huggingface page
) images
Composite: restore unmasked regions from original smage image_np mp. arrayr airaayimmgmm)
output_npp up np. array(outpus)
mask_np np. arrayray (mask. (onver(""")))
impainted_ap output_np.copy
impainted_np [mask___mmas__ppppp [mask_np 128] image_np [mask_np saagg___[[ass__pppp inpainted Image. fromarray (impainted_up)
128]
..- Save outputs =*2
("oriiiiiooi mask. save ("mask.png") inpainted save sww.ittted.aveeiaaaitttttte ("inpainted. png")
Code 1: A minimal demo script for reproducing the "seam" artifacts of Flux inpainting 3 model.
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NeurIPS Paper Checklist
(i) Claims
Question:
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The main claims made in the abstract and introduction accurately reflect the paper's contributions and scope.
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Reviews:
[{"review_id": "gnjoKyxnOp", "reviewer": "Reviewer_wXb9", "summary": "This paper introduces PixPerfect, a pixel-level refinement framework designed to address pixel-level inconsistencies (such as chromatic shifts, texture mismatches, and visible seams) that occur in local editing tasks using latent diffusion models (LDMs). The paper constructs a comprehensive artifact simulation pipeline to generate a large amount of diverse data and maps images from the RGB pixel space to a differentiable discriminative pixel space, amplifying the differences between the mask region and the original background region. This paper improves the performance of LDMs models in local editing tasks from both the data and algorithm levels.", "questions": "1. In Section 3.1, when designing the tone mapping function, the authors directly consider the polynomial regression approach. Is there any theoretical validation for this approach? Could you provide some references?\n\n2. In lines 151-152, the authors mention that this tone mapping function is a closed-form, sample-specific, differentiable mapping. What is the data volume based on which this tone mapping function is determined? Do the parameters of this mapping function need to be specially adjusted for each dataset? How to ensure the generalization ability of this mapping function?\n\n3. Can the parameters of this tone mapping function be adjusted? Will the degree of the polynomial affect the final result? There seems to be no relevant ablation experiment later.\n\n4. In Section 4.1, the authors state that they used a data volume of 300M. Are all these data generated through the artifact data production pipeline? Could you explain the data volume ratio of each artifact type?", "limitations": "See questions", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Advantage：\n1.The scenario studied in this paper is highly novel. Although it is relatively specific, it is crucial to the final effect of image editing. The proposed PixPerfect model successfully solves the problem of artifacts generated in image editing based on latent diffusion models (LDMs) and effectively improves the effect of image editing.\n2.In terms of algorithms, this paper proposes that using a discriminative pixel space during training can effectively solve the artifact problem in image editing. This is a simple and direct method, which only needs to add the GAN + reconstruction loss in the discriminative pixel space to the GAN + reconstruction loss obtained in the standard pixel space, and the dimensions of the standard pixel space and the discriminative pixel space are the same.\n3.The synthetic data pipeline proposed in this paper focuses on four aspects: non-uniform color shifting, texture-pattern mismatch, content discontinuities, and mixing soft and hard boundaries, which basically covers the common artifact problems in image editing.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for the detailed feedback. We are encouraged that the reviewer found that the reviewer found the **scenario studied is highly novel and crucial for image editing** and our method **better than or competitive with existing methods.** We appreciate the opportunity to address the concerns raised and clarify the details.\n\n### Regarding on Weaknesses and Questions\n\n> 1. In Section 3.1, when designing the tone mapping function, the authors directly consider the polynomial regression approach. Is there any theoretical validation for this approach? Could you provide some references?\n\nThe tone mapping function is designed to construct a discriminative pixel space that amplifies subtle inconsistencies. In the literature, classical color space transformations such as CIELUV and HSV [1,2] are established examples of non-linear remappings that make chromatic differences more perceptually uniform. Following this principle, we introduce the sample-adaptive low-degree polynomial mapping, which naturally emphasizes discrepancies at editing seams while minimally affecting consistent regions. This polynomial formulation ensures a closed-form solution and is fully differentiable, making it suitable for integration into the training pipeline.\n\n\n*[1] CIE. Uniform Color Spaces (CIELUV). Supplement 2 to CIE 15: Colorimetry, 1978.*  \n*[2] Smith, A. R. Color Gamut Transform Pairs. SIGGRAPH Proceedings, 1978 (introduces HSV).*\n\n> 2. In lines 151–152, the authors mention that this tone mapping function is a closed-form, sample-specific, differentiable mapping. What is the data volume based on which this tone mapping function is determined? Do the parameters of this mapping function need to be specially adjusted for each dataset? How to ensure the generalization ability of this mapping function?\n> \n\nTo clarify, the tone mapping function is not trained over a dataset, but is dynamically computed per image during training, based on a closed-form polynomial regression fitted to a set of pixels sampled from the foreground (masked) and background (unmasked) regions of that specific image. This sample-specific design ensures that the mapping always highlights the actual artifact region for in each training instance in a dynamic fashion, making the learning signal highly relevant and robust.\n\nSince the mapping is calculated with a closed-form solution for each individual sample, it does not require manual tuning or parameter adjustment across datasets. Its role is to amplify chromatic and textural differences in a differentiable way, providing a stronger training gradient for the refinement network. Generalization is achieved through the refiner, which learns to correct a wide range of artifact patterns, while the tone mapping serves as an auxiliary projection to emphasize discrepancies more effectively than conventional RGB losses.\n\n> 3. Can the parameters of this tone mapping function be adjusted? Will the degree of the polynomial affect the final result? There seems to be no relevant ablation experiment later.\n\nYes, the parameters of the polynomial tone mapping can be adjusted. Our empirical experiments show that low polynomial degree results only mild tonal adjustment, rendering the loss less effective. Conversely, higher degree tends to over-amplify details and produces images that deviate from the natural image distribution, thus hurts the performance. We therefore select a moderate degree as a practical trade-off. We also present the ablation results here.\n\n| Method | FID $\\downarrow$ | LPIPS $\\downarrow$ | L1 $\\downarrow$ |\n|-------|-------|-------|------|\n| 6 degrees **（PixPerfect）** | 10.8675 | 0.1414 | 0.0363 |\n| 2 degrees | 11.2244 | 0.1431 | 0.0362 |\n| 10 degrees | 11.0018 | 0.1407 | 0.0361 |\n\n> 4. In Section 4.1, the authors state that they used a data volume of 300M. Are all these data generated through the artifact data production pipeline? Could you explain the data volume ratio of each artifact type?\n\nThanks for raising this point. Yes, our artifact simulation pipeline is a function that can run efficiently during training, thus all the data are generated through the pipeline.\nThe artifact simulation pipeline is implemented by stochastically applying one or more artifact type in a sequential order. The type and probability of the operations in the following table. We will provide a detailed pseudo code in revision.\n\n| Artifact Type               | Description                                                        | Probability |\n|-----------------------------|--------------------------------------------------------------------|-------------|\n| Content Discontinuity       | Small misalignments or missing pixels near mask edges              | 0.5       |\n| Background Color Augmentation  | Non-uniform hue / brightness            | 0.8      |\n| Foreground Color Augmentation  | Non-uniform / uniform / gradient color augmentation           | 0.8      |\n| Soft/Hard Boundary Mixing      | Mixing soft and hard boundary to mimic seams           | 1.0      |\n| Adding sensor noise / JPEG artifacts / blurring | Adding noise to foreground and/or background         | 0.5     |\n| Adding VAE compression artifacts | Adding VAE compression artifact to foreground         | 0.5     |"}, {"review_id": "0WfkSWbvIo", "reviewer": "Reviewer_x4mB", "summary": "This paper introduces PixPerfect, a pixel-level refinement framework designed to address artifacts from local edits by inpainting models. The pipeline includes standard latent space inpainting, a pixel-space refiner, and a discriminative pixel space to enhance and correct subtle color and texture inconsistencies. Additionally, the authors propose an artifact simulation pipeline during training, covering non-uniform color shifts, texture mismatches, and content discontinuities. Extensive experiments and ablation studies are presented to demonstrate the effectiveness of the method.", "questions": "I see the responses to my two main concerns: (1) why do we need to use regression, rather than FFT or HAAR? (2) the baseline quality of FLUX-Fill. \n\nBesides, I have some further questions or small points:\n* Is the trained refiner a general module that works across different LDM inpainting models, or are there separate refiners for each model?\n* Since the training uses the synthetic artifact simulation pipeline, does this mean the LDM inpainting model is not involved during the refiner's training?\n* The font size in the tables is inconsistent, for example, in Table 4.\n* What base model is used in Figure 5?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n* The paper is well-written and easy to understand.\n* The motivation is clear, focusing on solving artifacts caused by LDM models during the inpainting process.\n* The synthetic artifact simulation pipeline is well-designed and appears effective.\n* The evaluations are comprehensive, covering benchmarks like MISATO, Places2, and RORDS, and comparing against SOTA methods.\n\nWeaknesses\n* I understand that the purpose of $y_{amp}$ is to highlight error maps and focus more on problematic areas. However, it is unclear why the authors chose polynomial regression with amplified attention instead of using frequency-domain techniques like FFT or HAAR, which could also re-weight error areas. This choice is not explained or validated through ablation studies.\n* I also have doubts about the results in Figure 4. From my experience with FLUX-Fill, the boundary artifacts shown in column 2 don't seem as severe and obvious. I tested FLUX-Fill models during my review and did not observe such artifacts. Could you clarify the hyper-parameter settings, such as guidance scale or controlling strength, used during inference?\n* If FLUX-Fill already produces decent results, the practical impact of the proposed method may be limited.", "was_revised": true, "final_justification": "The rebuttal by the authors has addressed my concerns. I decide to increase my rating as a positive score.", "rebuttal": "We sincerely thank the reviewer for the valuable feedback and appreciate your recognition of our contributions, including **clear motivation**, **a well-designed simulation pipeline** and **comprehensive benchmark**. We will address all raised concerns as follows.\n\n> 1. I understand that the purpose of  is to highlight error maps and focus more on problematic areas. However, it is unclear why the authors chose polynomial regression with amplified attention instead of using frequency-domain techniques like FFT or HAAR, which could also re-weight error areas. This choice is not explained or validated through ablation studies.\n\nThank you for raising this insightful point. In early experiments we did test frequency-domain re-weighting: we decomposed prediction image with HAAR decomposition, amplified the low-frequency sub-bands, and used the recombined predicted image to compute the training loss. However, this approach consistently produced overly smooth, blurry results. In fact, the unequal scaling of sub-bands disrupted the balance between low- and high-frequency components, leading disrupted textures and noise patterns, thus hinding the network to learn to predict the correct textures that matchs the ground truth. \n\nWe further examined frequency-domain decomposition by training a model that employs a Haar-based re-weighted loss. In this variant, the prediction and ground truth are each decomposed into low- and high-frequency bands; separate $\\ell_{1}$ losses are computed for the two bands, and the low-frequency term is assigned a larger weight to target subtle colour shifts. As the following table shows, this approach performs worse than our method. Moreover, band-wise re-weighting is feasible with losses such as $\\ell_{1}$ or $\\ell_{2}$; it is incompatible with perceptual or GAN objectives, which are shown crucial for image synthesis.\n\n| Method | FID $\\downarrow$ | LPIPS $\\downarrow$ | L1 $\\downarrow$ |\n|-------|-------|-------|------|\n| ours loss **（PixPerfect）** | 10.8675 | 0.1414 | 0.0363 |\n| Haar-based re-weighted loss | 11.3816 | 0.1431 | 0.0375 |\n\nBy contrast, the polynomial-regression we ultimately adopted is an **element-wise transformation** in pixel space. Because it modifies each pixel without altering the spatial relationship with neighbouring pixels, it preserves subtle texture and noise patterns while still amplfying subtle color difference.\n\n\n> 2. I also have doubts about the results in Figure 4. From my experience with FLUX-Fill, the boundary artifacts shown in column 2 don't seem as severe and obvious. I tested FLUX-Fill models during my review and did not observe such artifacts. If FLUX-Fill already produces decent results, the practical impact of the proposed method may be limited.\n\nWe sincerely thank the reviewer for taking the time to run hands-on tests. In addition, we would like to clearify our evaluation setting. In our experiment, we use the official FLUX-Fill model and the default hyper-parameters running on 512px resolution and paste the original background (Line 32, 254), following the standardized inpainting setting. We found that FLUX-Fill often produce inconsistent color or textures patterns that are different from the input background. In fact, the artifacts shown in **Figure 4** persist across most samples in the testing sets. *To facilitate reproducibility, we attached a minimal demo script at the end of this rebuttal that reproduces the boundary artifacts for the official FLUX-Fill model.*\n\nWe want to further emphasize that such issues are **common** and **fundemantal** challenges for LDM local editing models. The compression nature of the latent space in LDMs inherently introduces distortion in low-level patterns. As a result, texture mismatches and noise artifacts remain difficult to avoid. Recent studies [1,2] further demonstrate that the latent space is inherently spatially entangled, making it ill-suited for enforcing pixel-accurate boundary consistency. These observations underscore the need for a pixel-space framework that can explicitly address and correct inconsistencies at the pixel level.\n\n*[1] Hou, Xingzhong, et al. \"Towards Seamless Borders: A Method for Mitigating Inconsistencies in Image Inpainting and Outpainting.\" arXiv preprint arXiv:2506.12530 (2025).*  \n*[2] Wang, Yikai, et al. \"Towards Enhanced Image Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency.\" CVPR'25.*\n\n> 3. Is the trained refiner a general module that works across different LDM inpainting models, or are there separate refiners for each model?\n\nThank you for the question. The refiner is a single, model-agnostic module. It is trained once and then applied without any additional fine-tuning to all LDM-based inpainting and local-editing backbones evaluated in the paper.\n\n> 4. Since the training uses the synthetic artifact simulation pipeline, does this mean the LDM inpainting model is not involved during the refiner's training?\n\nYes. Our artifact simulation pipeline is agnostic to different diffusion-model. This design enables our trained refiner to serve as a general-purpose refinement module for various LDM-based editing pipelines without retraining. (L166-L169)\n\n> 5. What base model is used in Figure 5?\n\nAs indicated in the left caption of Figure 5, the base model is OmniPaint for object insertion and Anydoor for object removal.\n\n\n\n> 6. The font size in the tables is inconsistent, for example, in Table 4.\n\nThanks for pointing out. We will fix this issue in the final version.\n\n---\n*Demo code for reproducing FLUX-Fill seams*\nThe following snippet provides a minimal setup for observing hue seams and texture discontinuities introduced by FLUX-Fill. Please ensure the model weights and dependencies are properly set up.\n\n```python\nimport torch\nimport numpy as np\nfrom PIL import Image, ImageDraw\nfrom diffusers import FluxFillPipeline\nfrom diffusers.utils import load_image\n\n# === Define input image path ===\ninput_image_path = \"/your/image/path\" # TODO: change to the input image path\n\n# === Load input image ===\nimage = load_image(input_image_path).convert(\"RGB\")\nwidth, height = image.size\n\n# === Generate irregular mask ===\ndef generate_irregular_mask(width, height, max_shapes=5):\n    mask = Image.new(\"L\", (width, height), 0)\n    draw = ImageDraw.Draw(mask)\n\n    for _ in range(np.random.randint(1, max_shapes + 1)):\n        shape_type = np.random.choice([\"ellipse\", \"polygon\"])\n        if shape_type == \"ellipse\":\n            x0, y0 = np.random.randint(0, width - 50), np.random.randint(0, height - 50)\n            x1, y1 = x0 + np.random.randint(40, 120), y0 + np.random.randint(40, 120)\n            draw.ellipse([x0, y0, x1, y1], fill=255)\n        else:\n            num_points = np.random.randint(3, 8)\n            points = [(np.random.randint(0, width), np.random.randint(0, height)) for _ in range(num_points)]\n            draw.polygon(points, fill=255)\n\n    return mask.convert(\"RGB\")\n\nmask = generate_irregular_mask(width, height)\n\n# === Load FLUX inpainting pipeline ===\npipe = FluxFillPipeline.from_pretrained(\n    \"black-forest-labs/FLUX.1-Fill-dev\",\n    torch_dtype=torch.bfloat16\n).to(\"cuda\")\n\n# === Run FLUX-Fill ===\noutput = pipe(\n    image=image,\n    mask_image=mask,\n    prompt=\"\",\n    height=height,\n    width=width,\n    guidance_scale=30, # The default value provided on the official huggingface page\n    num_inference_steps=50, # The default value provided on the official huggingface page\n    max_sequence_length=512 # The default value provided on the official huggingface page\n).images[0]\n\n# === Composite: restore unmasked regions from original image ===\nimage_np = np.array(image)\noutput_np = np.array(output)\nmask_np = np.array(mask.convert(\"L\"))\ninpainted_np = output_np.copy()\ninpainted_np[mask_np < 128] = image_np[mask_np < 128]\ninpainted = Image.fromarray(inpainted_np)\n\n# === Save outputs ===\nimage.save(\"original.png\")\nmask.save(\"mask.png\")\ninpainted.save(\"inpainted.png\")\n```"}, {"review_id": "BA2e3JDmlH", "reviewer": "Reviewer_JDA3", "summary": "This paper introduces a refining method for generative in- and outpainting approaches. Recent generative models shine on conditional image generation, but they often struggle with pixel-perfect in- and outpainting, often leading to noticeable artifacts inside the masked region. The authors propose to solve these artifacts with a pixel-space refiner module, trained with various image degradations. Additionally, a pixel space color discriminator is inroduced to help localizing subtle color mismatches between the edited and original regions. \n\nThe proposed method can be applied to any backbone inpainting methods and consistenly improves the inpatining quality.", "questions": "## Figures\n* Figure 1: What does the dashed arrow to the decoder in latent space means?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n* **S1** The proposed method is simple and agnositc to the backbone. \n* **S2** Impressive quality with visible and clear improvement over previous methods in a comprehensive comparison.\n* **S3** Well-written paper, easy to follow and understand.\n* **S4** The tackled task is interesting for a broad community. \n\n## Weaknesses\n* **W1 - Ablating design choices** Although the authors did a good job on evaluating the key components of the method, it would be interesting to see if how the quality changes with the specific design choices. E.g. what if the discriminative pixel space is not 3 dimensional, but more (or less)?", "was_revised": true, "final_justification": "The paper provides a simple way to improve upon a heavily used downstream task of diffusion models: inpainting; making it interesting for a wide community. My only remaning concern is to see an ablation on the disciminative space dimensionality, which should be possible to show in the final revision.", "rebuttal": "We sincerely thank the reviewer for the valuable comments and we are grateful for your recognition of the merits of our methods, including **simple and backbone-agnostic design**, **impressive quality with clear improvement**. We also appreciate your acknowledgment that the task we address is **of interest to a broad community**.\n\nBased on the positive assessment, we would like to respectfully elaborate on the remaining questions.\n\n> 1. It would be interesting to see if how the quality changes with the specific design choices. E.g. what if the discriminative pixel space is not 3 dimensional, but more (or less)?\n\nThank you for this insightful suggestion. Inspired by the classic color space transformation [1,2], we adopt a direct mapping from the standard RGB space to a discriminative pixel space of equal dimensionality. This keeps the transformation both simple and easily interpretable.\n\n\nHere we present the ablation results of design choices for (1) different polynomial regression degrees (2) other methods such as HAAR.\n\n| Method | FID $\\downarrow$ | LPIPS $\\downarrow$ | L1 $\\downarrow$ |\n|-------|-------|-------|------|\n| 6-degree regression loss **（PixPerfect）** | 10.8675 | 0.1414 | 0.0363 |\n| 2-degree regression loss  | 11.2244 | 0.1431 | 0.0362 |\n| 10-degree regression loss | 11.0018 | 0.1407 | 0.0361 |\n| HAAR loss  | 11.3816 | 0.1431 | 0.0375 |\n\n\nIt will be interesting to test other designs. This can be left as future work.\n\n\n> 2. What does the dashed arrow to the decoder in latent space means?\n\nThe dashed arrow denotes an optional pathway where in certain design variants, the decoder can take the original image as an additional condition to improve background consistency [3]. We will clarify this in the final version.\n\n*[1] CIE. *Uniform Color Spaces (CIELUV)*. Supplement 2 to **CIE 15: Colorimetry**, 1978.*  \n*[2] Smith, A. R. *Color Gamut Transform Pairs*. SIGGRAPH Proceedings, 1978 (introduces HSV).*  \n*[3] Z. Zhu, et al. Designing a better asymmetric vqgan for stablediffusion, arXiv:2306.04632, 2023.*"}, {"review_id": "sfpWDTv7oL", "reviewer": "Reviewer_W1zs", "summary": "This paper proposes a refinement method for inpainted or local-edited images produced by various generative models. The approach involves training a GAN network on large-scale data with various types of simulated artifacts. Meanwhile, two types of loss functions, one in the pixel space and the other in a discriminative pixel space, are designed to optimize the network. The proposed method is compatible with various image generation models, demonstrating strong generalizability. It also offers certain advantages in terms of efficiency.", "questions": "* I have a question regarding the amount of data required for training: to what extent does the model's performance depend on the amount of data? After all, 300 million images is not a small dataset.", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "## Strengths\n* The writing in this paper is smooth and clear, with rich detail throughout.\n* The proposed approach demonstrates strong generalizability and can be applied to various image local-editing methods. Moreover, it exhibits excellent inference performance, which is highly practical for real-world applications.\n* The experiments in this paper are relatively comprehensive, and the effectiveness of the proposed approach is significant from the perspective of both visualization and quantitative analysis.\n\n## Weaknesses\n* I wonder whether pixel-level inconsistencies along editing boundaries are actually a serious issue. If there are any relevant experiments or data, could you please provide them? Additionally, would simply achieving good performance with the LDM used for editing be sufficient to address this problem?", "was_revised": true, "final_justification": "My concerns have been largely addressed. During the rebuttal stage, the authors provided thorough explanations. Reviewer wXb9 also raised questions regarding the dataset construction, and the authors gave detailed clarifications about the data composition. Overall, this paper achieves strong results with a simple approach, which is the key factor behind my high evaluation of it.", "rebuttal": "We appreciate your positive and constructive feedback. We are especially grateful for the recognition of our work, including **strong generality**, **highly practical for real-world applications**, **comprehensive experiments**. We appreciate the opportunity to address the remaining questions.\n\n> 1. \"I wonder whether pixel-level inconsistencies along editing boundaries are actually a serious issue. If there are any relevant experiments or data, could you please provide them? Additionally, would simply achieving good performance with the LDM used for editing be sufficient to address this problem?\"\n\nWe appreciate the reviewer for raising this important point. In our experiments, we found that the pixel-level inconsistency is a **systematic issue** that presists across a wide range of inpainting and editing models, including the most recent state-of-the-art methods such as FLUX-Fill [1] and OmniPaint [2]. In fact, the artifacts shown in **Figure 4** and **Figure 5** persist across most samples in the testing sets, showing clearly visible hue seams and texture discontinuities. To facilitate reproducibility, we have included demo code at the end of this rebuttal to allow reviewers to observe this pixel-level inconsistency.\n\nWhile improving the performance of the latent diffusion model (LDM) used for editing could potentially reduce the severity of these artifacts, we would like to emphasize that the **compression nature** of the latent space in LDMs inherently introduces distortion in low-level patterns. As a result, texture mismatches and noise artifacts remain difficult to avoid. Recent studies [3, 4] further demonstrate that the latent space is inherently spatially entangled, making it ill-suited for enforcing pixel-accurate boundary consistency. These observations underscore the need for a pixel-space framework that can explicitly address and correct inconsistencies at the pixel level.\n\n*[1] Black Forest Labs. FLUX.1 Fill: open-weight rectified-flow transformer for inpainting & outpainting, 2025.  \n[2] Yu, Yongsheng, et al. \"Omnipaint: Mastering object-oriented editing via disentangled insertion-removal inpainting.\", ICCV'25.  \n[3] Hou, Xingzhong, et al. \"Towards Seamless Borders: A Method for Mitigating Inconsistencies in Image Inpainting and Outpainting.\" arXiv preprint arXiv:2506.12530 (2025).  \n[4] Wang, Yikai, et al. \"Towards Enhanced Image Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency.\" CVPR'25.*\n\n> 2. I have a minor suggestion regarding the formatting: for Table 1, it would be more intuitive to place the same models on adjacent rows, for example, SDv1.5 and SDv1.5-PixPerfect, since the paper is not comparing differences between different LDMs, but rather the effect of applying PixPerfect to the same LDM.\n\nThank you for the suggestion. We will make sure to adjust the formatting to better highlight the effect of our method.\n\n---\n**Demo code for reproducing FLUX-Fill seams**  \nThe following snippet provides a minimal setup for observing hue seams and texture discontinuities introduced by FLUX-Fill. Please ensure the model weights and dependencies are properly set up.\n```python\nimport torch\nimport numpy as np\nfrom PIL import Image, ImageDraw\nfrom diffusers import FluxFillPipeline\nfrom diffusers.utils import load_image\n\n# === Define input image path ===\ninput_image_path = \"/your/image/path\" # TODO: change to the input image path\n\n# === Load input image ===\nimage = load_image(input_image_path).convert(\"RGB\")\nwidth, height = image.size\n\n# === Generate irregular mask ===\ndef generate_irregular_mask(width, height, max_shapes=5):\n    mask = Image.new(\"L\", (width, height), 0)\n    draw = ImageDraw.Draw(mask)\n\n    for _ in range(np.random.randint(1, max_shapes + 1)):\n        shape_type = np.random.choice([\"ellipse\", \"polygon\"])\n        if shape_type == \"ellipse\":\n            x0, y0 = np.random.randint(0, width - 50), np.random.randint(0, height - 50)\n            x1, y1 = x0 + np.random.randint(40, 120), y0 + np.random.randint(40, 120)\n            draw.ellipse([x0, y0, x1, y1], fill=255)\n        else:\n            num_points = np.random.randint(3, 8)\n            points = [(np.random.randint(0, width), np.random.randint(0, height)) for _ in range(num_points)]\n            draw.polygon(points, fill=255)\n\n    return mask.convert(\"RGB\")\n\nmask = generate_irregular_mask(width, height)\n\n# === Load FLUX inpainting pipeline ===\npipe = FluxFillPipeline.from_pretrained(\n    \"black-forest-labs/FLUX.1-Fill-dev\",\n    torch_dtype=torch.bfloat16\n).to(\"cuda\")\n\n# === Run inpainting ===\noutput = pipe(\n    image=image,\n    mask_image=mask,\n    prompt=\"\",\n    height=height,\n    width=width,\n    guidance_scale=30, # The default value provided on the official huggingface page\n    num_inference_steps=50, # The default value provided on the official huggingface page\n    max_sequence_length=512 # The default value provided on the official huggingface page\n).images[0]\n\n# === Composite: restore unmasked regions from original image ===\nimage_np = np.array(image)\noutput_np = np.array(output)\nmask_np = np.array(mask.convert(\"L\"))\ninpainted_np = output_np.copy()\ninpainted_np[mask_np < 128] = image_np[mask_np < 128]\ninpainted = Image.fromarray(inpainted_np)\n\n# === Save outputs ===\nimage.save(\"original.png\")\nmask.save(\"mask.png\")\ninpainted.save(\"inpainted.png\")\n```"}]

Author rebuttal:

Meta-review: This paper introduces PixPerfect, a pixel-level refinement framework for addressing artifacts in local image editing tasks with latent diffusion models. After reviewing the rebuttal, all participating reviewers agree that the authors have adequately addressed concerns regarding the theoretical motivation for the polynomial tone-mapping function, the sample-specific and differentiable nature of the mapping, and the training data composition. Remaining minor points, such as ablations on the dimensionality of the discriminative pixel space and clarification of baseline hyperparameters, are suggested for inclusion in the final version. Although reviewer wXb9 did not participate in the discussion, the initial review also highlighted the novelty of the paper. 

Overall, the paper presents a novel and practically relevant solution, with strong empirical results and a well-designed training pipeline. The AC therefore recommends acceptance, encouraging the authors to incorporate additional ablation studies and clarifications in the final version.
Decision: Accept (poster)