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However, they often struggle to faithfully\ncapture the intended semantics of complex input prompts that include multiple\nsubjects. Recently, numerous layout-to-image extensions have been introduced to\nimprove user control, aiming to localize subjects represented by specific\ntokens. Yet, these methods often produce semantically inaccurate images,\nespecially when dealing with multiple semantically or visually similar\nsubjects. In this work, we study and analyze the causes of these limitations.\nOur exploration reveals that the primary issue stems from inadvertent semantic\nleakage between subjects in the denoising process. This leakage is attributed\nto the diffusion model's attention layers, which tend to blend the visual\nfeatures of different subjects. To address these issues, we introduce Bounded\nAttention, a training-free method for bounding the information flow in the\nsampling process. Bounded Attention prevents detrimental leakage among subjects\nand enables guiding the generation to promote each subject's individuality,\neven with complex multi-subject conditioning. Through extensive\nexperimentation, we demonstrate that our method empowers the generation of\nmultiple subjects that better align with given prompts and layouts.","upvotes":25,"discussionId":"66024834c618afa8d796e61d","ai_summary":"Bounded Attention addresses semantic leakage in text-to-image diffusion models by restricting information flow between subjects, improving the alignment with given prompts and layouts.","ai_keywords":["text-to-image diffusion models","layout-to-image extensions","denoising process","attention layers","semantic leakage","Bounded Attention","sampling process","multi-subject conditioning"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"6538119803519fddb4a17e10","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6538119803519fddb4a17e10/ffJMkdx-rM7VvLTCM6ri_.jpeg","isPro":false,"fullname":"samusenps","user":"samusenps","type":"user"},{"_id":"60c8d264224e250fb0178f77","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60c8d264224e250fb0178f77/i8fbkBVcoFeJRmkQ9kYAE.png","isPro":true,"fullname":"Adam Lee","user":"Abecid","type":"user"},{"_id":"655ac762cb17ec19ef82719b","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/655ac762cb17ec19ef82719b/1kDncYrGLYS_2SR8cNdAL.png","isPro":false,"fullname":"Welcome to matlok","user":"matlok","type":"user"},{"_id":"620783f24e28382272337ba4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/620783f24e28382272337ba4/zkUveQPNiDfYjgGhuFErj.jpeg","isPro":false,"fullname":"GuoLiangTang","user":"Tommy930","type":"user"},{"_id":"64ba8705bc7873649690b5ea","avatarUrl":"/avatars/61111de3aebf954e4299d6b56dd31219.svg","isPro":false,"fullname":"jizhongpeng","user":"jizhongpeng","type":"user"},{"_id":"64cbce5ebf67d9b76e8aa6e5","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/AadfIVTfx_SKwrgJo0I3_.png","isPro":false,"fullname":"mytoon","user":"mytoon","type":"user"},{"_id":"648eb1eb59c4e5c87dc116e0","avatarUrl":"/avatars/c636cea39c2c0937f01398c94ead5dad.svg","isPro":false,"fullname":"fdsqefsgergd","user":"T-representer","type":"user"},{"_id":"653a18000dfecb4b26dd2876","avatarUrl":"/avatars/fcf8a2ea58f6eca0a6196299c68fc8ad.svg","isPro":false,"fullname":"James Chang","user":"strategist922","type":"user"},{"_id":"643be4ff5ec6af9c331bb9fd","avatarUrl":"/avatars/2ffc111f81079925488f6ebfd27ae4ad.svg","isPro":false,"fullname":"Hardeep Kumar","user":"wrench1815","type":"user"},{"_id":"63143cde3ef0644e256d2647","avatarUrl":"/avatars/f77a97698e034d54d0ebf54c5f3f4e8d.svg","isPro":false,"fullname":"Gorantla Narendra","user":"narrinddhar","type":"user"},{"_id":"6362ddb7d3be91534c30bfd6","avatarUrl":"/avatars/dac76ebd3b8a08099497ec0b0524bc7c.svg","isPro":false,"fullname":"Art Atk","user":"ArtAtk","type":"user"},{"_id":"64498de5df4e6cb7eaedf512","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64498de5df4e6cb7eaedf512/PYLCe2Munx-yx96M5pyex.png","isPro":false,"fullname":"Gabriel Rodrigues","user":"fimbulvntr","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":2}">
Bounded Attention addresses semantic leakage in text-to-image diffusion models by restricting information flow between subjects, improving the alignment with given prompts and layouts.
AI-generated summary
Text-to-image diffusion models have an unprecedented ability to generate
diverse and high-quality images. However, they often struggle to faithfully
capture the intended semantics of complex input prompts that include multiple
subjects. Recently, numerous layout-to-image extensions have been introduced to
improve user control, aiming to localize subjects represented by specific
tokens. Yet, these methods often produce semantically inaccurate images,
especially when dealing with multiple semantically or visually similar
subjects. In this work, we study and analyze the causes of these limitations.
Our exploration reveals that the primary issue stems from inadvertent semantic
leakage between subjects in the denoising process. This leakage is attributed
to the diffusion model's attention layers, which tend to blend the visual
features of different subjects. To address these issues, we introduce Bounded
Attention, a training-free method for bounding the information flow in the
sampling process. Bounded Attention prevents detrimental leakage among subjects
and enables guiding the generation to promote each subject's individuality,
even with complex multi-subject conditioning. Through extensive
experimentation, we demonstrate that our method empowers the generation of
multiple subjects that better align with given prompts and layouts.