PixelFlow: Pixel-Space Generative Models with Flow
Authors: name: Shoufa Chen, name: Chongjian Ge, name: Shilong Zhang, name: Peize Sun, name: Ping Luo

We present PixelFlow, a family of image generation models that operate
directly in the raw pixel space, in contrast to the predominant latent-space
models. This approach simplifies the image generation process by eliminating
the need for a pre-trained Variational Autoencoder (VAE) and enabling the whole
model end-to-end trainable. Through efficient cascade flow modeling, PixelFlow
achieves affordable computation cost in pixel space. It achieves an FID of 1.98
on 256times256 ImageNet class-conditional image generation benchmark. The
qualitative text-to-image results demonstrate that PixelFlow excels in image
quality, artistry, and semantic control. We hope this new paradigm will inspire
and open up new opportunities for next-generation visual generation models.
Code and models are available at https://github.com/ShoufaChen/PixelFlow.