DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing
This addresses a critical functional gap for users needing high-quality image morphing in applications like media and design, though it is incremental as it builds on existing diffusion model capabilities.
The paper tackles the problem of smooth image interpolation with diffusion models, which previously struggled compared to GANs, and introduces DiffMorpher to achieve this, resulting in starkly better morphing effects across various object categories.
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.