Diffusion Inversion

Diffusion inversion aims to find the latent noise representation of an image within a diffusion model, enabling manipulation and editing of real-world images. Current research focuses on improving the accuracy and efficiency of inversion algorithms, employing techniques like Newton-Raphson methods, coupled transformations, and optimized scheduling of noise perturbations within diffusion processes (e.g., DDIM). These advancements are crucial for enhancing image editing capabilities within generative models, leading to improved image manipulation, generation of image variations, and potentially impacting applications such as image restoration and few-shot learning.