DNPMorph: Direct Noise-Path Optimization for Smooth and Consistent Diffusion Morphing

Text-to-image diffusion models possess vast knowledge of our visual world, however unlocking this prior for the task of image morphing remains a significant challenge. Here, we introduce DNPMorph, a diffusion-based morphing method that utilizes a pretrained diffusion model and, unlike existing methods, directly optimizes the noise latents of a morph path so as to minimize smoothness objectives on the corresponding diffusion-generated frames. To make path-level optimization practical, we use a type of zero-order optimization strategy which does not require differentiating through the diffusion sampling process, essentially treating it as a black box. The path is initialized by spherical linear interpolation between the initial noises corresponding to the user-provided endpoint images, and is then refined in a coarse-to-fine manner. We provide a theoretical conditional descent analysis for our method, and empirically compare it to existing diffusion-based morphing methods on four benchmarks. DNPMorph outperforms competing methods on all datasets in terms of path-smoothness metrics and on three of four in terms of FID, establishing a new state-of-the-art for diffusion-based image morphing.