PixPerfect: Seamless Latent Diffusion Local Editing with Discriminative Pixel-Space Refinement

TL;DR

PixPerfect is a pixel-level refinement framework that improves the seamlessness and fidelity of local edits in images generated by latent diffusion models, addressing artifacts like seams and color mismatches.

Contribution

It introduces a discriminative pixel space, artifact simulation, and a direct pixel-space refiner to enhance local editing quality across various LDM architectures and tasks.

Findings

Significantly reduces visual artifacts in local image edits.

Improves perceptual quality and editing performance in benchmarks.

Demonstrates robustness across diverse LDM architectures and tasks.

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.