{"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/zero-shot-inversion-process-for-image","title":"Textual and Visual Prompt Fusion for Image Editing via Step-Wise Alignment","arxiv_id":"2308.15854","date":"2023-08-30","proceeding":null,"authors":["Zhanbo Feng","Zenan Ling","Xinyu Lu","Ci Gong","Feng Zhou","Wugedele Bao","Jie Li","Fan Yang","Robert C. Qiu"],"abstract":"The use of denoising diffusion models is becoming increasingly popular in the field of image editing. However, current approaches often rely on either image-guided methods, which provide a visual reference but lack control over semantic consistency, or text-guided methods, which ensure alignment with the text guidance but compromise visual quality. To resolve this issue, we propose a framework that integrates a fusion of generated visual references and text guidance into the semantic latent space of a \\textit{frozen} pre-trained diffusion model. Using only a tiny neural network, our framework provides control over diverse content and attributes, driven intuitively by the text prompt. Compared to state-of-the-art methods, the framework generates images of higher quality while providing realistic editing effects across various benchmark datasets.","url_abs":"https://arxiv.org/abs/2308.15854v3","url_pdf":"https://arxiv.org/pdf/2308.15854v3.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":"zero-shot-inversion-process-for-image","repo_url":"https://github.com/sadangelf/editing-via-step-wise-alignment","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"denoising","task_name":"Denoising"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}