{"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/differentially-private-adaptation-of","title":"Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings","arxiv_id":"2411.14639","date":"2024-11-22","proceeding":null,"authors":["Pura Peetathawatchai","Wei-Ning Chen","Berivan Isik","Sanmi Koyejo","Albert No"],"abstract":"We introduce a novel method for adapting diffusion models under differential privacy (DP) constraints, enabling privacy-preserving style and content transfer without fine-tuning model weights. Traditional approaches to private adaptation, such as DP-SGD, incur significant computational and memory overhead when applied to large, complex models. In addition, when adapting to small-scale specialized datasets, DP-SGD incurs large amount of noise that significantly degrades the performance. Our approach instead leverages an embedding-based technique derived from Textual Inversion (TI) and adapted with differentially private mechanisms. We apply TI to Stable Diffusion for style adaptation using two private datasets: a collection of artworks by a single artist and pictograms from the Paris 2024 Olympics. Experimental results show that the TI-based adaptation achieves superior fidelity in style transfer, even under strong privacy guarantees.","url_abs":"https://arxiv.org/abs/2411.14639v2","url_pdf":"https://arxiv.org/pdf/2411.14639v2.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":"differentially-private-adaptation-of","repo_url":"https://github.com/poonpura/DPAgg-TI","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"},{"task_slug":"style-transfer","task_name":"Style Transfer"}],"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}