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- Figure 1: Visualization of 512x512 images generated by our PixelGen. -
Illustration of Perceptual Manifold
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- 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. -
-- 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 introduce PixelGen, a simple pixel diffusion framework with perceptual loss. 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. -
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- We propose PixelGen, a simple yet effective pixel diffusion framework with perceptual supervision. PixelGen directly operates in the pixel domain without relying on latent representations, VAEs, or auxiliary stages. Following the x-prediction paradigm, the diffusion model predicts clean images instead of noise or velocity. To retain the benefits of flow matching, the predicted image is converted into velocity, resulting in a flow-matching objective. - PixelGen focuses on the perceptual manifold rather than the full image manifold. To this end, we introduce two complementary perceptual losses. An LPIPS loss emphasizes local textures and fine-grained details, while a Perceptual DINO (P-DINO) loss aligns global semantics using patch-level features from a frozen DINOv2 encoder. -
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- Perceptual supervision improves pixel diffusion by enhancing local details and global semantics. Starting from the JiT baseline, we progressively introduce the LPIPS loss and the P-DINO loss. As shown in Figure 5, the baseline model produces blurry images with weak structural consistency. After adding the LPIPS loss, local textures become sharper, and fine details are better preserved. This indicates that LPIPS effectively emphasizes perceptually important local patterns. When the P-DINO loss is further introduced, the generated images exhibit improved global structure and better semantics. These qualitative improvements are supported by quantitative results. The baseline model achieves an FID of 23.67 on ImageNet without classifier-free guidance. With LPIPS loss, the FID decreases to 10.00. After adding the P-DINO loss, it further drops to 7.46. This confirms that LPIPS and P-DINO provide complementary supervision. LPIPS focuses on local perceptual fidelity, while P-DINO enhances global semantics. Together, they guide the diffusion model toward a perceptually meaningful manifold. -
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- Figure 6: More Qualitative results of text-to-image generation at a 512x512 resolution. Our PixelGen supports multiple languages with the Qwen3 text encoder, such as Chinese and English.
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- Figure 7: Qualitative results of class-to-image generation at a 256x256 resolution.
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@misc{ma2025PixelGenfrequencyPixelGenupledpixeldiffusion,
- title={PixelGen: Pixel Diffusion Beats Latent Diffusion with Perceptual Loss},
- author={Zehong Ma and Ruihan Xu and Shiliang Zhang},
- year={2025},
- eprint={2602.02493},
- archivePrefix={arXiv},
- primaryClass={cs.CV},
- url={https://arxiv.org/abs/2602.02493},
- }