Asymmetric Flow Models

Hugging Face Daily Papers Papers
flow-based-generation high-dimensional low-rank text-to-image pixel-space latent-models fine-tuning

Summary

Asymmetric Flow Modeling (AsymFlow) restricts noise prediction to low-rank subspaces for efficient high-dimensional flow-based generation, achieving state-of-the-art results on ImageNet and text-to-image tasks by fine-tuning from latent flow models.

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 256times256, 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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Cached at: 05/14/26, 04:17 AM

Paper page - Asymmetric Flow Models

Source: https://huggingface.co/papers/2605.12964

Abstract

Asymmetric Flow Modeling enables efficient high-dimensional flow-based generation by restricting noise prediction to low-rank subspaces while maintaining full-dimensional data prediction, achieving superior performance in pixel-space text-to-image generation through effective fine-tuning from latent models.

Flow-based generationin high-dimensional spaces is difficult becausevelocity predictionrequires modelinghigh-dimensional noise, even when data has stronglow-rank structure. We present Asymmetric Flow Modeling (AsymFlow), arank-asymmetric velocity parameterizationthat 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 256times256, AsymFlow achieves a leading 1.57 FID, outperforming prior DiT/JiT-likepixel diffusion modelsby a large margin. AsymFlow also provides the first-ever route for finetuning pretrainedlatent flow modelsinto pixel-space models: aligning the low-rank pixel subspace to thelatent spacegives 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-spacetext-to-image generation, beating its latent base on HPSv3, DPG-Bench, and GenEval while qualitatively showing substantially improved visual realism.

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#### Lakonik/AsymFlow-ImageNet Updatedabout 2 hours ago #### Lakonik/AsymFLUX.2-klein-9B Text-to-Image• Updatedabout 2 hours ago • 2

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