DiffMorpher: Unleashing the Capability of Diffusion Models for Image
  Morphing
Kaiwen Zhang Yifan Zhou Xudong Xu Xingang Pan Bo Dai
Diffusion models have achieved remarkable image generation quality surpassing
previous generative models. However, a notable limitation of diffusion models,
in comparison to GANs, is their difficulty in smoothly interpolating between
two image samples, due to their highly unstructured latent space. Such a smooth
interpolation is intriguing as it naturally serves as a solution for the image
morphing task with many applications. In this work, we present DiffMorpher, the
first approach enabling smooth and natural image interpolation using diffusion
models. Our key idea is to capture the semantics of the two images by fitting
two LoRAs to them respectively, and interpolate between both the LoRA
parameters and the latent noises to ensure a smooth semantic transition, where
correspondence automatically emerges without the need for annotation. In
addition, we propose an attention interpolation and injection technique and a
new sampling schedule to further enhance the smoothness between consecutive
images. Extensive experiments demonstrate that DiffMorpher achieves starkly
better image morphing effects than previous methods across a variety of object
categories, bridging a critical functional gap that distinguished diffusion
models from GANs.