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However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers efficient deployment. Moreover, existing methods struggle with multi-subject generation as they often blend identity among subjects. We present FastComposer which enables efficient, personalized, multi-subject text-to-image generation without fine-tuning. FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions <jats:italic>with only forward passes<\/jats:italic>. To address the identity blending problem in the multi-subject generation, FastComposer proposes <jats:italic>cross-attention localization<\/jats:italic> supervision during training, enforcing the attention of reference subjects localized to the correct regions in the target images. Naively conditioning on subject embeddings results in subject overfitting. FastComposer proposes <jats:italic>delayed subject conditioning<\/jats:italic> in the denoising step to maintain both identity and editability in subject-driven image generation. FastComposer generates images of multiple unseen individuals with different styles, actions, and contexts. It achieves 300<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\times $$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00d7<\/mml:mo>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>\u20132500<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$\\times $$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mo>\u00d7<\/mml:mo>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> speedup compared to fine-tuning-based methods and requires zero extra storage for new subjects. FastComposer paves the way for efficient, personalized, and high-quality multi-subject image creation. Code, model, and dataset are available here (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/mit-han-lab\/fastcomposer\" ext-link-type=\"uri\">https:\/\/github.com\/mit-han-lab\/fastcomposer<\/jats:ext-link>).<\/jats:p>","DOI":"10.1007\/s11263-024-02227-z","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T06:03:00Z","timestamp":1727157780000},"page":"1175-1194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":155,"title":["FastComposer: Tuning-Free Multi-subject Image Generation with Localized Attention"],"prefix":"10.1007","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7182-9284","authenticated-orcid":false,"given":"Guangxuan","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianwei","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William T.","family":"Freeman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fr\u00e9do","family":"Durand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Song","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"2227_CR1","unstructured":"Andonian, A., Osmany, S., Cui, A., Park, Y., Jahanian, A., Torralba, A., & Bau, D.(2021). 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