PIXELS: Progressive Image Xemplar-based Editing with Latent Surgery

TL;DR

PIXELS is a novel framework that enables fine-grained, exemplar-based image editing using off-the-shelf diffusion models, allowing users to make localized and progressive modifications without retraining or fine-tuning models.

Contribution

The paper introduces PIXELS, a new inference-only method for exemplar-driven image editing that offers granular control and progressive updates, overcoming limitations of prior techniques.

Findings

Achieves high-quality, localized edits with improved quantitative metrics.

Enables progressive, region-specific modifications without retraining.

Demonstrates effectiveness across diverse base models and exemplar inputs.

Abstract

Recent advancements in language-guided diffusion models for image editing are often bottle-necked by cumbersome prompt engineering to precisely articulate desired changes. An intuitive alternative calls on guidance from in-the-wild image exemplars to help users bring their imagined edits to life. Contemporary exemplar-based editing methods shy away from leveraging the rich latent space learnt by pre-existing large text-to-image (TTI) models and fall back on training with curated objective functions to achieve the task. Though somewhat effective, this demands significant computational resources and lacks compatibility with diverse base models and arbitrary exemplar count. On further investigation, we also find that these techniques restrict user control to only applying uniform global changes over the entire edited region. In this paper, we introduce a novel framework for progressive exemplar-driven editing with off-the-shelf diffusion models, dubbed PIXELS, to enable customization by providing granular control over edits, allowing adjustments at the pixel or region level. Our method operates solely during inference to facilitate imitative editing, enabling users to draw inspiration from a dynamic number of reference images, or multimodal prompts, and progressively incorporate all the desired changes without retraining or fine-tuning existing TTI models. This capability of fine-grained control opens up a range of new possibilities, including selective modification of individual objects and specifying gradual spatial changes. We demonstrate that PIXELS delivers high-quality edits efficiently, leading to a notable improvement in quantitative metrics as well as human evaluation. By making high-quality image editing more accessible, PIXELS has the potential to enable professional-grade edits to a wider audience with the ease of using any open-source image generation model.