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| <title> LEDITS++: Limitless Image Editing using Text-to-Image Models </title> | |
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| <h1 class="title is-1 publication-title">LEDITS++: Limitless Image Editing using Text-to-Image Models</h1> | |
| <div class="is-size-5 publication-authors"> | |
| <span class="author-block"> | |
| <a>Manuel Brack</a>,</span> | |
| <span class="author-block"> | |
| <a>Linoy Tsaban</a>,</span> | |
| <span class="author-block"> | |
| <a>Katharina Kornmeier</a>,</span> | |
| <span class="author-block"> | |
| <a>Apolinário Passos</a>,</span> | |
| <p></p> | |
| <span class="author-block"> | |
| <a>Felix Friedrich</a>,</span> | |
| <span class="author-block"> | |
| <a>Patrick Schramowski</a>,</span> | |
| <span class="author-block"> | |
| <a>Kristian Kersting</a></span> | |
| <div class="is-size-5 publication-authors"> | |
| <span class="author-block">German Research Center for Artificial Intelligence (DFKI),</span> | |
| </div> | |
| <div class="is-size-5 publication-authors"> | |
| <span class="author-block">Computer Science Department, TU Darmstadt,</span> | |
| </div> | |
| <div class="is-size-5 publication-authors"> | |
| <span class="author-block">HuggingFace🤗,</span> | |
| </div> | |
| <div class="is-size-5 publication-authors"> | |
| <span class="author-block">Hessian.AI,</span> | |
| </div> | |
| <div class="is-size-5 publication-authors"> | |
| <span class="author-block">LAION,</span> | |
| </div> | |
| <div class="is-size-5 publication-authors"> | |
| <span class="author-block">Centre for Cognitive Science, TU Darmstadt</span> | |
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| <a href="https://huggingface.co/spaces/editing-images/ledtisplusplus" | |
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| <span>🤗 Demo</span> | |
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| </section> | |
| <section class="hero teaser"> | |
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| <!-- Abstract. --> | |
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| <h2 class="title is-3">Abstract</h2> | |
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| <p> | |
| 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. | |
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| alt="ledits++ teaser"/> | |
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| <!-- Introduction --> | |
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| <h2 class="title is-3">LEDITS++: Efficient and Versatile Textual Image Editing</h2> | |
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| <p> | |
| 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. | |
| </p> | |
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| alt="examples"/> | |
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| <h2 class="title is-3">Methodology | |
| </h2> | |
| </div> | |
| <p> | |
| 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 | |
| </p> | |
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| <h2 class="title is-4">Component 1: Image Inversion</h2> | |
| <p> | |
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| <h2 class="title is-4">Component 2: Textual Editing</h2> | |
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| <h2 class="title is-4">Component 3: Semantic Grounding</h2> | |
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| <h2 class="title">BibTeX</h2> | |
| <pre><code>@article{ | |
| }</code></pre> | |
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