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To maintain training efficiency, we initially train a single model, which is then split into specialized models that are trained for the specific stages of the iterative generation process.
Our mission at Runway is to build the next generation of creative tools, powered by machine learning. This week we released Green Screen, a tool for cutting objects out of videos.
Patrick Esser is a Principal Research Scientist at Runway, leading applied research efforts including the core model behind Stable Diffusion, otherwise known as High-Resolution Image Synthesis with Latent Diffusion Models.
Our latent diffusion models (LDMs) achieve highly competitive performance on various tasks, including unconditional image generation, inpainting, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.
Gen-1: Structure and Content-Guided Video Synthesis with Diffusion Models Patrick Esser, Jonathan Granskog, Johnathan Chiu, Parmida Atighehchian, Anastasis Germanidis Runway
Figure 1. Single image inpainting approaches such as LaMa [27] (third col.) cannot propagate context from keyframes (second col.) to a target frame (first col.). By aggregating features globally across frames, transformer-based approaches (fourth col.) can propagate coarse context about a blue object that is visible in the keyframes but not in ...