title: Interpolating between Images with Diffusion Models
summary: One little-explored frontier of image generation and editing is the task of
  interpolating between two input images, a feature missing from all currently
  deployed image generation pipelines. We argue that such a feature can expand
  the creative applications of such models, and propose a method for zero-shot
  interpolation using latent diffusion models. We apply interpolation in the
  latent space at a sequence of decreasing noise levels, then perform denoising
  conditioned on interpolated text embeddings derived from textual inversion and
  (optionally) subject poses. For greater consistency, or to specify additional
  criteria, we can generate several candidates and use CLIP to select the highest
  quality image. We obtain convincing interpolations across diverse subject
  poses, image styles, and image content, and show that standard quantitative
  metrics such as FID are insufficient to measure the quality of an
  interpolation. Code and data are available at
  https://clintonjwang.github.io/interpolation.
paper_authors._id: 64bf855486e7fb5b8aaa2989
paper_authors.name: Clinton J. Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T09:10:42.853Z
paper_authors._id: 6260d9038144532e6ff059f3
paper_authors.avatarUrl: /avatars/61b9a140db8757d8b2f76512609cf3cc.svg
paper_authors.fullname: Clinton Wang
paper_authors.type: user
paper_authors.user: clintonjwang
paper_authors._id: 64bf855486e7fb5b8aaa298a
paper_authors.name: Polina Golland
paper_title: Interpolating between Images with Diffusion Models
paper_summary: One little-explored frontier of image generation and editing is the task of
  interpolating between two input images, a feature missing from all currently
  deployed image generation pipelines. We argue that such a feature can expand
  the creative applications of such models, and propose a method for zero-shot
  interpolation using latent diffusion models. We apply interpolation in the
  latent space at a sequence of decreasing noise levels, then perform denoising
  conditioned on interpolated text embeddings derived from textual inversion and
  (optionally) subject poses. For greater consistency, or to specify additional
  criteria, we can generate several candidates and use CLIP to select the highest
  quality image. We obtain convincing interpolations across diverse subject
  poses, image styles, and image content, and show that standard quantitative
  metrics such as FID are insufficient to measure the quality of an
  interpolation. Code and data are available at
  https://clintonjwang.github.io/interpolation.
paper_ai_keywords: latent diffusion models interpolation latent space denoising noise levels interpolated text embeddings textual inversion CLIP FID