Interpolating between Images with Diffusion Models
Keywords: image interpolation, image editing, latent diffusion model, denoising diffusion model, video generation
TL;DR: We present a method for zero-shot controlled interpolations between two input images, using a pre-trained diffusion model
Abstract: 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 derived from OpenPose. 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 \url{https://clintonjwang.github.io/interpolation}.
Submission Number: 1
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