Component 1: Image Inversion
LEDITS++: Limitless Image Editing using Text-to-Image Models
Abstract
Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. Subsequent research efforts are aiming to exploit the capabilities of these models and leverage them for intuitive, textual image editing. However, existing methods often require time-consuming fine-tuning and lack native support for performing multiple edits simultaneously. To address these issues, we introduce LEDITS++ , an efficient yet versatile technique for image editing using text-to-image models. LEDITS++ re- quires no tuning nor optimization, runs in a few diffusion steps, natively supports multiple simultaneous edits, inherently limits changes to relevant image regions, and is architecture agnostic.
LEDITS++: Efficient and Versatile Textual Image Editing
To ease textual image editing, we present LEDITS++, a novel method for efficient and versatile image editing using text-to-image diffusion models. Firstly, LEDITS++ sets itself apart as a parameter-free solution requiring no fine-tuning nor any optimization. We derive characteristics of an edit-friendly noise space with a perfect input reconstruction, which were previously proposed for the DDPM sampling scheme, for a significantly faster multistep stochastic differential-equation (SDE) solver. This novel invertibility of the DPM-solver++ facilitates editing with LEDITS++ in as little as 20 total diffusion steps for inversion and inference combined. Moreover, LEDITS++ places a strong emphasis on semantic grounding to enhance the visual and contextual coherence of the edits. This ensures that changes are limited to the relevant regions in the image, preserving the original image’s fidelity as much as possible. LEDITS++ also provides users with the flexibility to combine multiple edits seamlessly, opening up new creative possibilities for intricate image manipulations. Finally, the approach is architecture-agnostic and compatible with any diffusion model, whether latent or pixel-based.
Methodology
The methodology of LEDITS++ can be broken down into three components: (1) efficient image inversion, (2) versatile textual editing, and (3) semantic grounding of image changes. More in-depth details and mathematical derivations of each component can be found in App
Component 2: Textual Editing
Component 3: Semantic Grounding
BibTeX
@article{
}