{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/plug-and-play-diffusion-features-for-text","title":"Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation","arxiv_id":"2211.12572","date":"2022-11-22","proceeding":"CVPR 2023 1","authors":["Narek Tumanyan","Michal Geyer","Shai Bagon","Tali Dekel"],"abstract":"Large-scale text-to-image generative models have been a revolutionary breakthrough in the evolution of generative AI, allowing us to synthesize diverse images that convey highly complex visual concepts. However, a pivotal challenge in leveraging such models for real-world content creation tasks is providing users with control over the generated content. In this paper, we present a new framework that takes text-to-image synthesis to the realm of image-to-image translation -- given a guidance image and a target text prompt, our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text, while preserving the semantic layout of the source image. Specifically, we observe and empirically demonstrate that fine-grained control over the generated structure can be achieved by manipulating spatial features and their self-attention inside the model. This results in a simple and effective approach, where features extracted from the guidance image are directly injected into the generation process of the target image, requiring no training or fine-tuning and applicable for both real or generated guidance images. We demonstrate high-quality results on versatile text-guided image translation tasks, including translating sketches, rough drawings and animations into realistic images, changing of the class and appearance of objects in a given image, and modifications of global qualities such as lighting and color.","url_abs":"https://arxiv.org/abs/2211.12572v1","url_pdf":"https://arxiv.org/pdf/2211.12572v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"plug-and-play-diffusion-features-for-text","repo_url":"https://github.com/MichalGeyer/plug-and-play","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"plug-and-play-diffusion-features-for-text","repo_url":"https://github.com/Shilin-LU/TF-ICON","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"plug-and-play-diffusion-features-for-text","repo_url":"https://github.com/michalgeyer/pnp-diffusers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"plug-and-play-diffusion-features-for-text","repo_url":"https://github.com/thu-cvml/texturediffusion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"text-based-image-editing","task_name":"Text-based Image Editing"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-based-image-editing-on-pie-bench","task":"Text-based Image Editing","dataset":"PIE-Bench","model":"DDIM Inversion+Plug-and-Play","rank_in_archive_order":13,"of":18,"metrics":{"Background LPIPS":"113.46","Background PSNR":"22.28","CLIPSIM":"25.41","Structure Distance":"28.22"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.12572","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.12572"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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