title: Multi-LoRA Composition for Image Generation
summary: Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models
  for the accurate rendition of specific elements like distinct characters or
  unique styles in generated images. Nonetheless, existing methods face
  challenges in effectively composing multiple LoRAs, especially as the number of
  LoRAs to be integrated grows, thus hindering the creation of complex imagery.
  In this paper, we study multi-LoRA composition through a decoding-centric
  perspective. We present two training-free methods: LoRA Switch, which
  alternates between different LoRAs at each denoising step, and LoRA Composite,
  which simultaneously incorporates all LoRAs to guide more cohesive image
  synthesis. To evaluate the proposed approaches, we establish ComposLoRA, a new
  comprehensive testbed as part of this research. It features a diverse range of
  LoRA categories with 480 composition sets. Utilizing an evaluation framework
  based on GPT-4V, our findings demonstrate a clear improvement in performance
  with our methods over the prevalent baseline, particularly evident when
  increasing the number of LoRAs in a composition.
paper_authors._id: 65dd64e7ce348fc5b1214f3c
paper_authors.name: Ming Zhong
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paper_authors.fullname: Ming Zhong
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paper_authors.user: MingZhong
paper_authors._id: 65dd64e7ce348fc5b1214f3d
paper_authors.name: Yelong Shen
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paper_authors.fullname: Yelong Shen
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paper_authors.user: uuu6
paper_authors._id: 65dd64e7ce348fc5b1214f3e
paper_authors.name: Shuohang Wang
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paper_authors.fullname: Wang
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paper_authors.user: Shuohang
paper_authors._id: 65dd64e7ce348fc5b1214f3f
paper_authors.name: Yadong Lu
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paper_authors.fullname: Yadong Lu
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paper_authors.user: Adong17
paper_authors._id: 65dd64e7ce348fc5b1214f40
paper_authors.name: Yizhu Jiao
paper_authors._id: 65dd64e7ce348fc5b1214f41
paper_authors.name: Siru Ouyang
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paper_authors.fullname: Siru Ouyang
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paper_authors.user: siruo2
paper_authors._id: 65dd64e7ce348fc5b1214f42
paper_authors.name: Donghan Yu
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paper_authors.statusLastChangedAt: 2024-02-27T09:36:45.862Z
paper_authors._id: 65b01b8a29ae836e9ed5af24
paper_authors.avatarUrl: /avatars/a8b78a4b54d3f10858c5925521357001.svg
paper_authors.fullname: Donghan Yu
paper_authors.type: user
paper_authors.user: donghanyu
paper_authors._id: 65dd64e7ce348fc5b1214f43
paper_authors.name: Jiawei Han
paper_authors._id: 65dd64e7ce348fc5b1214f44
paper_authors.name: Weizhu Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-27T09:37:08.279Z
paper_authors._id: 64da876370446182be5b608d
paper_authors.avatarUrl: /avatars/e412fdc71404ecdf638e416846e3ebfb.svg
paper_authors.fullname: Weizhu Chen
paper_authors.type: user
paper_authors.user: chenweizhu
paper_title: Multi-LoRA Composition for Image Generation
paper_summary: Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models
  for the accurate rendition of specific elements like distinct characters or
  unique styles in generated images. Nonetheless, existing methods face
  challenges in effectively composing multiple LoRAs, especially as the number of
  LoRAs to be integrated grows, thus hindering the creation of complex imagery.
  In this paper, we study multi-LoRA composition through a decoding-centric
  perspective. We present two training-free methods: LoRA Switch, which
  alternates between different LoRAs at each denoising step, and LoRA Composite,
  which simultaneously incorporates all LoRAs to guide more cohesive image
  synthesis. To evaluate the proposed approaches, we establish ComposLoRA, a new
  comprehensive testbed as part of this research. It features a diverse range of
  LoRA categories with 480 composition sets. Utilizing an evaluation framework
  based on GPT-4V, our findings demonstrate a clear improvement in performance
  with our methods over the prevalent baseline, particularly evident when
  increasing the number of LoRAs in a composition.
paper_ai_summary: Two training-free methods, LoRA Switch and LoRA Composite, enhance multi-LoRA composition in text-to-image models, leading to improved image synthesis performance.
paper_ai_keywords: Low-Rank Adaptation LoRA multi-LoRA composition denoising step image synthesis ComposLoRA GPT-4V