Title: PixPerfect: Seamless Latent Diffusion Local Editing with Discriminative Pixel-Space Refinement
Venue: NeurIPS 2025
Accepted: yes
Keywords: image editing

image refinement

diffusion model
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.


Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
## Strengths
* The writing in this paper is smooth and clear, with rich detail throughout.
* 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.
* 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.

## Weaknesses
* 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?

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.
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-06-05

comments: 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. 

The proposed method can be applied to any backbone inpainting methods and consistenly improves the inpatining quality.

STRENGTHS AND WEAKNESSES:
## Strengths
* **S1** The proposed method is simple and agnositc to the backbone. 
* **S2** Impressive quality with visible and clear improvement over previous methods in a comprehensive comparison.
* **S3** Well-written paper, easy to follow and understand.
* **S4** The tackled task is interesting for a broad community. 

## Weaknesses
* **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)?

QUESTIONS:
## Figures
* Figure 1: What does the dashed arrow to the decoder in latent space means?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nI 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nAdvantage：\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nSee questions", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}