A new method in image inversion called ReNoise aims to improve the fidelity and editability of diffusion-based inversion algorithms. ReNoise addresses the challenges faced when inverting images generated with a small number of denoising steps, offering iterative refinement without adding substantial computational overhead.

ReNoise reverses the sampling process crucial to diffusion-based image synthesis, guiding the inversion trajectory of real images towards the original noise source. By leveraging the pretrained UNet model and sampled noise, ReNoise iteratively refines the reconstruction accuracy of inversion algorithms, focusing on models like Stable Diffusion (SD) and SDXL Turbo. It aims to mitigate the poor reconstruction accuracy associated with few-step denoising processes.