[2605.12964] Asymmetric Flow Models

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Computer Science > Computer Vision and Pattern Recognition

arXiv:2605.12964 (cs)

[Submitted on 13 May 2026]

Title:Asymmetric Flow Models

Authors:Hansheng Chen, Jan Ackermann, Minseo Kim, Gordon Wetzstein, Leonidas Guibas<br>View a PDF of the paper titled Asymmetric Flow Models, by Hansheng Chen and 4 other authors

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Abstract:Flow-based generation in high-dimensional spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has strong low-rank structure. We present Asymmetric Flow Modeling (AsymFlow), a rank-asymmetric velocity parameterization that restricts noise prediction to a low-rank subspace while keeping data prediction full-dimensional. From this asymmetric prediction, AsymFlow analytically recovers the full-dimensional velocity without changing the network architecture or training/sampling procedures. On ImageNet 256$\times$256, AsymFlow achieves a leading 1.57 FID, outperforming prior DiT/JiT-like pixel diffusion models by a large margin. AsymFlow also provides the first-ever route for finetuning pretrained latent flow models into pixel-space models: aligning the low-rank pixel subspace to the latent space gives a seamless initialization that preserves the latent model's high-level semantics and structure, so finetuning mainly improves low-level mismatches rather than relearning pixel generation. We show that the pixel AsymFlow model finetuned from FLUX.2 klein 9B establishes a new state of the art for pixel-space text-to-image generation, beating its latent base on HPSv3, DPG-Bench, and GenEval while qualitatively showing substantially improved visual realism.

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Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as:<br>arXiv:2605.12964 [cs.CV]

(or<br>arXiv:2605.12964v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2605.12964

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Hansheng Chen [view email]<br>[v1]<br>Wed, 13 May 2026 03:58:01 UTC (24,782 KB)

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