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arXiv · 2504.07963

PixelFlow: Pixel-Space Generative Models with Flow

Shoufa Chen·Chongjian Ge·Shilong Zhang·Peize Sun·Ping Luo
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Abstract

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 256$\times$256 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.

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Shoufa Chen, Chongjian Ge, Shilong Zhang, Peize Sun, Ping Luo. 2025-04-10. PixelFlow: Pixel-Space Generative Models with Flow. https://arxiv.org/abs/2504.07963

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