{"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/beyond-textual-constraints-learning-novel","title":"Beyond Textual Constraints: Learning Novel Diffusion Conditions with Fewer Examples","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Yuyang Yu","Bangzhen Liu","Chenxi Zheng","Xuemiao Xu","Huaidong Zhang","Shengfeng He"],"abstract":"    In this paper we delve into a novel aspect of learning novel diffusion conditions with datasets an order of magnitude smaller. The rationale behind our approach is the elimination of textual constraints during the few-shot learning process. To that end we implement two optimization strategies. The first prompt-free conditional learning utilizes a prompt-free encoder derived from a pre-trained Stable Diffusion model. This strategy is designed to adapt new conditions to the diffusion process by minimizing the textual-visual correlation thereby ensuring a more precise alignment between the generated content and the specified conditions. The second strategy entails condition-specific negative rectification which addresses the inconsistencies typically brought about by Classifier-free guidance in few-shot training contexts. Our extensive experiments across a variety of condition modalities demonstrate the effectiveness and efficiency of our framework yielding results comparable to those obtained with datasets a thousand times larger. Our codes are available at https://github.com/Yuyan9Yu/BeyondTextConstraint.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Yu_Beyond_Textual_Constraints_Learning_Novel_Diffusion_Conditions_with_Fewer_Examples_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Yu_Beyond_Textual_Constraints_Learning_Novel_Diffusion_Conditions_with_Fewer_Examples_CVPR_2024_paper.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":"beyond-textual-constraints-learning-novel","repo_url":"https://github.com/yuyan9yu/beyondtextconstraint","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"}],"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}