PixPerfect: Seamless Latent Diffusion Local Editing with Discriminative Pixel-Space Refinement
This work addresses the problem of visible artifacts in localized image editing for users of generative models, representing an incremental improvement over existing methods.
The paper tackled pixel-level inconsistencies like chromatic shifts and seams in Latent Diffusion Models for local image editing by introducing PixPerfect, a pixel-level refinement framework that achieved substantial enhancements in perceptual fidelity and editing performance across benchmarks.
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.