One-step Latent-free Image Generation with Pixel Mean Flows
Modern diffusion/flow-based models for image generation typically exhibit two core characteristics: (i) using multi-step sampling, and (ii) operating in a latent space. Recent advances have made encouraging progress on each aspect individually, paving the way toward one-step diffusion/flow without latents. In this work, we take a further step towards this goal and propose "pixel MeanFlow" (pMF). Our core guideline is to formulate the network output space and the loss space separately. The network target is designed to be on a presumed low-dimensional image manifold (i.e., x-prediction), while the loss is defined via MeanFlow in the velocity space. We introduce a simple transformation between the image manifold and the average velocity field. In experiments, pMF achieves strong results for one-step latent-free generation on ImageNet at 256x256 resolution (2.22 FID) and 512x512 resolution (2.48 FID), filling a key missing piece in this regime. We hope that our study will further advance the boundaries of diffusion/flow-based generative models.
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Yiyang Lu
697c6ad6a67238fac88cc277
Susie Lu
697c6ad6a67238fac88cc278
Qiao Sun
697c6ad6a67238fac88cc279
Hanhong Zhao
697c6ad6a67238fac88cc27a
Zhicheng Jiang
697c6ad6a67238fac88cc27b
Xianbang Wang
697c6ad6a67238fac88cc27c
Tianhong Li
697c6ad6a67238fac88cc27d
Zhengyang Geng
697c6ad6a67238fac88cc27e
Kaiming He
One-step Latent-free Image Generation with Pixel Mean Flows
Modern diffusion/flow-based models for image generation typically exhibit two core characteristics: (i) using multi-step sampling, and (ii) operating in a latent space. Recent advances have made encouraging progress on each aspect individually, paving the way toward one-step diffusion/flow without latents. In this work, we take a further step towards this goal and propose "pixel MeanFlow" (pMF). Our core guideline is to formulate the network output space and the loss space separately. The network target is designed to be on a presumed low-dimensional image manifold (i.e., x-prediction), while the loss is defined via MeanFlow in the velocity space. We introduce a simple transformation between the image manifold and the average velocity field. In experiments, pMF achieves strong results for one-step latent-free generation on ImageNet at 256x256 resolution (2.22 FID) and 512x512 resolution (2.48 FID), filling a key missing piece in this regime. We hope that our study will further advance the boundaries of diffusion/flow-based generative models.
Pixel MeanFlow introduces a one-step latent-free image generation method by separating network output space from loss space, achieving strong performance on ImageNet at multiple resolutions.
diffusion models flow-based models multi-step sampling latent space one-step generation image manifold MeanFlow velocity space x-prediction