DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing

Abstract

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 mor-phing task with many applications. In this work, we address this limitation via DiffMorpher, an approach that enables smooth and natural image interpolation by harnessing the prior knowledge of a pretrained diffusion model. 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 correspon-dence automatically emerges without the need for annotation. In addition, we propose an attention interpolation and injection technique, an adaptive normalization adjustment method, 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.