RF-Inversion is a technology focused on image generation and editing, which achieves inversion and editing of images through stochastic differential equations (SDE). The main advantage of this technology is its ability to perform efficient image inversion and editing without requiring extra training, latent optimization, prompt adjustments, or complex attention processors. RF-Inversion excels in zero-shot inversion and editing, surpassing previous approaches, and has been validated by large-scale human evaluations indicating user preferences in stroke-to-image synthesis and semantic image editing. Background information shows that it was co-developed by researchers from the University of Texas at Austin and Google, with support from NSF grants and other research collaboration awards.