Filtered to image generation · clear filter
This paper introduces a new framework for building strong image generators that can produce highly photorealistic images while accurately following editing instructions. Practitioners might care about the potential applications of this framework in fields like computer vision, graphics, and art.
This paper introduces Mage-Flow, a compact model for generating and editing high-resolution images, which can be trained efficiently and deployed on a single GPU. Practitioners might care about the potential applications of this model in interactive image editing and generation tasks.
This paper improves monocular depth estimation models by repurposing image generation models, using a diffusion transformer architecture, to produce sharper and more detailed depth maps that generalize well to out-of-distribution inputs. Practitioners might care about this research because it could lead to better performance in applications such as scene reconstruction and computational photography.