DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation
Pixel diffusion aims to generate images directly in pixel space in an end-to-end fashion. This approach avoids the limitations of VAE in the two-stage latent diffusion, offering higher model capacity. Existing pixel diffusion models suffer from slow training and inference, as they usually model both high-frequency signals and low-frequency semantics within a single diffusion transformer (DiT). To pursue a more efficient pixel diffusion paradigm, we propose the frequency-DeCoupled pixel diffusion framework. With the intuition to decouple the generation of high and low frequency components, we leverage a lightweight pixel decoder to generate high-frequency details conditioned on semantic guidance from the DiT. This thus frees the DiT to specialize in modeling low-frequency semantics. In addition, we introduce a frequency-aware flow-matching loss that emphasizes visually salient frequencies while suppressing insignificant ones. Extensive experiments show that DeCo achieves superior performance among pixel diffusion models, attaining FID of 1.62 (256x256) and 2.22 (512x512) on ImageNet, closing the gap with latent diffusion methods. Furthermore, our pretrained text-to-image model achieves a leading overall score of 0.86 on GenEval in system-level comparison. Codes are publicly available at https://github.com/Zehong-Ma/DeCo.
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Zehong Ma
admin_assigned
2025-11-25T09:37:20.953Z
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ZehongMa
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Longhui Wei
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2025-12-09T20:10:59.671Z
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Longhui Wei
Joohnzxcv
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Shuai Wang
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2025-11-25T15:53:18.956Z
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wangshuai
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Shiliang Zhang
admin_assigned
2025-11-25T09:37:37.350Z
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shiliang zhang
slade96
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Qi Tian
DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation
Pixel diffusion aims to generate images directly in pixel space in an end-to-end fashion. This approach avoids the limitations of VAE in the two-stage latent diffusion, offering higher model capacity. Existing pixel diffusion models suffer from slow training and inference, as they usually model both high-frequency signals and low-frequency semantics within a single diffusion transformer (DiT). To pursue a more efficient pixel diffusion paradigm, we propose the frequency-DeCoupled pixel diffusion framework. With the intuition to decouple the generation of high and low frequency components, we leverage a lightweight pixel decoder to generate high-frequency details conditioned on semantic guidance from the DiT. This thus frees the DiT to specialize in modeling low-frequency semantics. In addition, we introduce a frequency-aware flow-matching loss that emphasizes visually salient frequencies while suppressing insignificant ones. Extensive experiments show that DeCo achieves superior performance among pixel diffusion models, attaining FID of 1.62 (256x256) and 2.22 (512x512) on ImageNet, closing the gap with latent diffusion methods. Furthermore, our pretrained text-to-image model achieves a leading overall score of 0.86 on GenEval in system-level comparison. Codes are publicly available at https://github.com/Zehong-Ma/DeCo.
The frequency-DeCoupled pixel diffusion framework improves image generation efficiency and quality by separating high-frequency details and low-frequency semantics, achieving superior performance compared to existing pixel diffusion models.
pixel diffusion pixel space VAE latent diffusion diffusion transformer (DiT) frequency-DeCoupled pixel diffusion lightweight pixel decoder semantic guidance frequency-aware flow-matching loss FID ImageNet GenEval