LEDITS++: Limitless Image Editing using Text-to-Image Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909231360245760 |
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| author | Brack, Manuel Friedrich, Felix Kornmeier, Katharina Tsaban, Linoy Schramowski, Patrick Kersting, Kristian Passos, Apolinário |
| author_facet | Brack, Manuel Friedrich, Felix Kornmeier, Katharina Tsaban, Linoy Schramowski, Patrick Kersting, Kristian Passos, Apolinário |
| contents | Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However, existing image-to-image methods are often inefficient, imprecise, and of limited versatility. They either require time-consuming finetuning, deviate unnecessarily strongly from the input image, and/or lack support for multiple, simultaneous edits. To address these issues, we introduce LEDITS++, an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second, our methodology supports multiple simultaneous edits and is architecture-agnostic. Third, we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_16711 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LEDITS++: Limitless Image Editing using Text-to-Image Models Brack, Manuel Friedrich, Felix Kornmeier, Katharina Tsaban, Linoy Schramowski, Patrick Kersting, Kristian Passos, Apolinário Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However, existing image-to-image methods are often inefficient, imprecise, and of limited versatility. They either require time-consuming finetuning, deviate unnecessarily strongly from the input image, and/or lack support for multiple, simultaneous edits. To address these issues, we introduce LEDITS++, an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second, our methodology supports multiple simultaneous edits and is architecture-agnostic. Third, we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods. |
| title | LEDITS++: Limitless Image Editing using Text-to-Image Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2311.16711 |