PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss

• URL: http://arxiv.org/abs/2602.02493v1
• Date: Mon, 02 Feb 2026 18:59:42 GMT
• Title: PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss
• Authors: Zehong Ma, Ruihan Xu, Shiliang Zhang,
• Abstract summary: We propose PixelGen, a simple pixel diffusion framework with perceptual supervision.<n>Instead of modeling the full image manifold, PixelGen introduces two complementary perceptual losses.<n>An LPIPS loss facilitates learning better local patterns, while a DINO-based perceptual loss strengthens global semantics.
• Score: 47.868429337792314
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pixel diffusion generates images directly in pixel space in an end-to-end manner, avoiding the artifacts and bottlenecks introduced by VAEs in two-stage latent diffusion. However, it is challenging to optimize high-dimensional pixel manifolds that contain many perceptually irrelevant signals, leaving existing pixel diffusion methods lagging behind latent diffusion models. We propose PixelGen, a simple pixel diffusion framework with perceptual supervision. Instead of modeling the full image manifold, PixelGen introduces two complementary perceptual losses to guide diffusion model towards learning a more meaningful perceptual manifold. An LPIPS loss facilitates learning better local patterns, while a DINO-based perceptual loss strengthens global semantics. With perceptual supervision, PixelGen surpasses strong latent diffusion baselines. It achieves an FID of 5.11 on ImageNet-256 without classifier-free guidance using only 80 training epochs, and demonstrates favorable scaling performance on large-scale text-to-image generation with a GenEval score of 0.79. PixelGen requires no VAEs, no latent representations, and no auxiliary stages, providing a simpler yet more powerful generative paradigm. Codes are publicly available at https://github.com/Zehong-Ma/PixelGen.

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