Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

arXiv:2403.03206v1 Announce Type: new
Abstract: Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling …

abstract arxiv cs.cv data diffusion diffusion models flow generative generative modeling image images modeling noise scaling synthesis transformers type videos