title: SSR-Encoder: Encoding Selective Subject Representation for
    Subject-Driven Generation
summary: Recent advancements in subject-driven image generation have led to zero-shot
  generation, yet precise selection and focus on crucial subject representations
  remain challenging. Addressing this, we introduce the SSR-Encoder, a novel
  architecture designed for selectively capturing any subject from single or
  multiple reference images. It responds to various query modalities including
  text and masks, without necessitating test-time fine-tuning. The SSR-Encoder
  combines a Token-to-Patch Aligner that aligns query inputs with image patches
  and a Detail-Preserving Subject Encoder for extracting and preserving fine
  features of the subjects, thereby generating subject embeddings. These
  embeddings, used in conjunction with original text embeddings, condition the
  generation process. Characterized by its model generalizability and efficiency,
  the SSR-Encoder adapts to a range of custom models and control modules.
  Enhanced by the Embedding Consistency Regularization Loss for improved
  training, our extensive experiments demonstrate its effectiveness in versatile
  and high-quality image generation, indicating its broad applicability. Project
  page: https://ssr-encoder.github.io
paper_authors._id: 658e62eaac02633c0d34f143
paper_authors.name: Yuxuan Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-11T08:26:16.058Z
paper_authors._id: 636b3f9ce3ad78bc68b67541
paper_authors.avatarUrl: /avatars/2b7e745953ae39e01222e99fb63b279e.svg
paper_authors.fullname: yuxuan
paper_authors.type: user
paper_authors.user: zzyx
paper_authors._id: 658e62eaac02633c0d34f144
paper_authors.name: Jiaming Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-19T22:15:33.184Z
paper_authors._id: 637c941588699fba70e29f70
paper_authors.avatarUrl: /avatars/1b6ce2faf0f8c98b987d8efc9b92cc31.svg
paper_authors.fullname: LIU JIAMING
paper_authors.type: user
paper_authors.user: jamesliu1217
paper_authors._id: 658e62eaac02633c0d34f145
paper_authors.name: Yiren Song
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-04T09:40:37.053Z
paper_authors._id: 64311a95034ecbefddd141ef
paper_authors.avatarUrl: /avatars/b6dc5ca373bedbaa368208517954c375.svg
paper_authors.fullname: Yiren Song
paper_authors.type: user
paper_authors.user: yiren98
paper_authors._id: 658e62eaac02633c0d34f146
paper_authors.name: Rui Wang
paper_authors._id: 658e62eaac02633c0d34f147
paper_authors.name: Hao Tang
paper_authors._id: 658e62eaac02633c0d34f148
paper_authors.name: Jinpeng Yu
paper_authors._id: 658e62eaac02633c0d34f149
paper_authors.name: Huaxia Li
paper_authors._id: 658e62eaac02633c0d34f14a
paper_authors.name: Xu Tang
paper_authors._id: 658e62eaac02633c0d34f14b
paper_authors.name: Yao Hu
paper_authors._id: 658e62eaac02633c0d34f14c
paper_authors.name: Han Pan
paper_authors._id: 658e62eaac02633c0d34f14d
paper_authors.name: Zhongliang Jing
paper_title: SSR-Encoder: Encoding Selective Subject Representation for
    Subject-Driven Generation
paper_summary: Recent advancements in subject-driven image generation have led to zero-shot
  generation, yet precise selection and focus on crucial subject representations
  remain challenging. Addressing this, we introduce the SSR-Encoder, a novel
  architecture designed for selectively capturing any subject from single or
  multiple reference images. It responds to various query modalities including
  text and masks, without necessitating test-time fine-tuning. The SSR-Encoder
  combines a Token-to-Patch Aligner that aligns query inputs with image patches
  and a Detail-Preserving Subject Encoder for extracting and preserving fine
  features of the subjects, thereby generating subject embeddings. These
  embeddings, used in conjunction with original text embeddings, condition the
  generation process. Characterized by its model generalizability and efficiency,
  the SSR-Encoder adapts to a range of custom models and control modules.
  Enhanced by the Embedding Consistency Regularization Loss for improved
  training, our extensive experiments demonstrate its effectiveness in versatile
  and high-quality image generation, indicating its broad applicability. Project
  page: https://ssr-encoder.github.io
paper_ai_keywords: SSR-Encoder Token-to-Patch Aligner Detail-Preserving Subject Encoder subject embeddings Embedding Consistency Regularization Loss