title: Semantica: An Adaptable Image-Conditioned Diffusion Model
summary: We investigate the task of adapting image generative models to different
  datasets without finetuneing. To this end, we introduce Semantica, an
  image-conditioned diffusion model capable of generating images based on the
  semantics of a conditioning image. Semantica is trained exclusively on
  web-scale image pairs, that is it receives a random image from a webpage as
  conditional input and models another random image from the same webpage. Our
  experiments highlight the expressivity of pretrained image encoders and
  necessity of semantic-based data filtering in achieving high-quality image
  generation. Once trained, it can adaptively generate new images from a dataset
  by simply using images from that dataset as input. We study the transfer
  properties of Semantica on ImageNet, LSUN Churches, LSUN Bedroom and SUN397.
paper_authors._id: 665015de8286a86212b7d3a7
paper_authors.name: Manoj Kumar
paper_authors.status: extracted_confirmed
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paper_authors.avatarUrl: /avatars/a23d4b5b6d456618f18a87aba4ecd822.svg
paper_authors.fullname: Manoj Kumar
paper_authors.type: user
paper_authors.user: mechcoder
paper_authors._id: 665015de8286a86212b7d3a8
paper_authors.name: Neil Houlsby
paper_authors.status: admin_assigned
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paper_authors.fullname: Neil Houlsby
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paper_authors.user: neilhoulsby
paper_authors._id: 665015de8286a86212b7d3a9
paper_authors.name: Emiel Hoogeboom
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-10-28T19:59:56.457Z
paper_authors._id: 671feaa3e269c38584cb065f
paper_authors.avatarUrl: /avatars/d7bc35ab98b208ef1d009aa9b629682a.svg
paper_authors.fullname: Emiel Hoogeboom
paper_authors.type: user
paper_authors.user: Emiel2
paper_title: Semantica: An Adaptable Image-Conditioned Diffusion Model
paper_summary: We investigate the task of adapting image generative models to different
  datasets without finetuneing. To this end, we introduce Semantica, an
  image-conditioned diffusion model capable of generating images based on the
  semantics of a conditioning image. Semantica is trained exclusively on
  web-scale image pairs, that is it receives a random image from a webpage as
  conditional input and models another random image from the same webpage. Our
  experiments highlight the expressivity of pretrained image encoders and
  necessity of semantic-based data filtering in achieving high-quality image
  generation. Once trained, it can adaptively generate new images from a dataset
  by simply using images from that dataset as input. We study the transfer
  properties of Semantica on ImageNet, LSUN Churches, LSUN Bedroom and SUN397.
paper_ai_keywords: semantica image-conditioned diffusion model pretrained image encoders data filtering