FREPix: Frequency-Heterogeneous Flow Matching for Pixel-Space Image Generation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913098984587264 |
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| author | Lin, Mingfeng Chen, Jiakun Han, Liang Nie, Liqiang |
| author_facet | Lin, Mingfeng Chen, Jiakun Han, Liang Nie, Liqiang |
| contents | Pixel-space diffusion has re-emerged as a promising alternative to latent-space generation because it avoids the representation bottleneck introduced by VAEs. Yet most existing methods still treat image generation as a frequency-homogeneous process, overlooking the distinct roles and learning dynamics of low- and high-frequency components. To address this, we propose FREPix, a FREquency-heterogeneous flow matching framework for Pixel-space image generation. FREPix explicitly decomposes generation into low- and high-frequency components, assigns them separate transport paths, predicts them with a factorized network, and trains them with a frequency-aware objective. In this way, coarse-to-fine generation becomes an explicit design principle rather than an implicit behavior. On ImageNet class-to-image generation, FREPix achieves competitive results among pixel-space generation models, reaching 1.91 FID at $256\times256$ and 2.38 FID at $512\times512$, with particularly strong behavior in the low-NFE regime. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_06421 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FREPix: Frequency-Heterogeneous Flow Matching for Pixel-Space Image Generation Lin, Mingfeng Chen, Jiakun Han, Liang Nie, Liqiang Computer Vision and Pattern Recognition Machine Learning Pixel-space diffusion has re-emerged as a promising alternative to latent-space generation because it avoids the representation bottleneck introduced by VAEs. Yet most existing methods still treat image generation as a frequency-homogeneous process, overlooking the distinct roles and learning dynamics of low- and high-frequency components. To address this, we propose FREPix, a FREquency-heterogeneous flow matching framework for Pixel-space image generation. FREPix explicitly decomposes generation into low- and high-frequency components, assigns them separate transport paths, predicts them with a factorized network, and trains them with a frequency-aware objective. In this way, coarse-to-fine generation becomes an explicit design principle rather than an implicit behavior. On ImageNet class-to-image generation, FREPix achieves competitive results among pixel-space generation models, reaching 1.91 FID at $256\times256$ and 2.38 FID at $512\times512$, with particularly strong behavior in the low-NFE regime. |
| title | FREPix: Frequency-Heterogeneous Flow Matching for Pixel-Space Image Generation |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2605.06421 |