title: PixelFlow: Pixel-Space Generative Models with Flow
summary: 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.
paper_authors._id: 67f86da6ac109135e18e150f
paper_authors.name: Shoufa Chen
paper_authors._id: 67f86da6ac109135e18e1510
paper_authors.name: Chongjian Ge
paper_authors._id: 67f86da6ac109135e18e1511
paper_authors.name: Shilong Zhang
paper_authors._id: 67f86da6ac109135e18e1512
paper_authors.name: Peize Sun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-20T15:03:58.388Z
paper_authors._id: 640dc9bf8512ec51d7f0ac1a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/640dc9bf8512ec51d7f0ac1a/sT4rdEoQbzfW6D3xDVdqt.jpeg
paper_authors.fullname: peizesun
paper_authors.type: user
paper_authors.user: peizesun
paper_authors._id: 67f86da6ac109135e18e1513
paper_authors.name: Ping Luo
paper_title: PixelFlow: Pixel-Space Generative Models with Flow
paper_summary: 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.
paper_ai_summary: PixelFlow is an end-to-end trainable image generation model operating directly in pixel space, achieving high-quality results without a pre-trained VAE and efficient computation cost.
paper_ai_keywords: PixelFlow Variational Autoencoder (VAE) cascade flow modeling FID class-conditional text-to-image