PixelFlow introduces a novel approach to image generation by operating directly in raw pixel space, eliminating the need for pre-trained Variational Autoencoders. This method enhances the image generation process with efficient cascade flow modeling, achieving a competitive FID score of 1.98 on the ImageNet benchmark while offering high-quality and semantically controlled image outputs. The work aims to inspire future developments in visual generation models.

PixelFlow introduces a novel family of image generation models that operate directly in pixel space, eliminating the need for pre-trained VAEs and allowing for end-to-end training. By utilizing efficient cascade flow modeling, it achieves impressive image quality with a low FID score of 1.98 on the ImageNet benchmark, showcasing its potential for both class-to-image and text-to-image tasks. The model aims to inspire future advancements in visual generation technologies.

UCGM is an official PyTorch implementation that provides a unified framework for training and sampling continuous generative models, such as diffusion and flow-matching models. It enables significant acceleration of sampling processes and efficient tuning of pre-trained models, achieving impressive FID scores across various datasets and resolutions. The framework supports diverse architectures and offers tools for both training and evaluating generative models.