Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation

Hugging Face Daily Papers Papers
one-step-generation text-to-image meanflow llm-encoders discriminative-features

Summary

Researchers extend MeanFlow one-step image generation from class labels to flexible text inputs by integrating highly-discriminative LLM-based text encoders, enabling efficient text-conditioned synthesis with improved performance.

Few-step generation has been a long-standing goal, with recent one-step generation methods exemplified by MeanFlow achieving remarkable results. Existing research on MeanFlow primarily focuses on class-to-image generation. However, an intuitive yet unexplored direction is to extend the condition from fixed class labels to flexible text inputs, enabling richer content creation. Compared to the limited class labels, text conditions pose greater challenges to the model's understanding capability, necessitating the effective integration of powerful text encoders into the MeanFlow framework. Surprisingly, although incorporating text conditions appears straightforward, we find that integrating powerful LLM-based text encoders using conventional training strategies results in unsatisfactory performance. To uncover the underlying cause, we conduct detailed analyses and reveal that, due to the extremely limited number of refinement steps in the MeanFlow generation, such as only one step, the text feature representations are required to possess sufficiently high discriminability. This also explains why discrete and easily distinguishable class features perform well within the MeanFlow framework. Guided by these insights, we leverage a powerful LLM-based text encoder validated to possess the required semantic properties and adapt the MeanFlow generation process to this framework, resulting in efficient text-conditioned synthesis for the first time. Furthermore, we validate our approach on the widely used diffusion model, demonstrating significant generation performance improvements. We hope this work provides a general and practical reference for future research on text-conditioned MeanFlow generation. The code is available at https://github.com/AMAP-ML/EMF.
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Paper page - Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation

Source: https://huggingface.co/papers/2604.18168

Abstract

Researchers extend MeanFlow generation from class labels to text inputs by integrating powerful LLM-based text encoders, overcoming limitations of few-step refinement through enhanced semantic feature representation.

Few-step generation has been a long-standing goal, with recent one-step generation methods exemplified byMeanFlowachieving remarkable results. Existing research onMeanFlowprimarily focuses onclass-to-image generation. However, an intuitive yet unexplored direction is to extend the condition from fixed class labels to flexible text inputs, enabling richer content creation. Compared to the limited class labels, text conditions pose greater challenges to the model’s understanding capability, necessitating the effective integration of powerful text encoders into theMeanFlowframework. Surprisingly, although incorporating text conditions appears straightforward, we find that integrating powerfulLLM-based text encodersusing conventional training strategies results in unsatisfactory performance. To uncover the underlying cause, we conduct detailed analyses and reveal that, due to the extremely limited number ofrefinement stepsin theMeanFlowgeneration, such as only one step, the text feature representations are required to possess sufficiently highdiscriminability. This also explains why discrete and easily distinguishable class features perform well within theMeanFlowframework. Guided by these insights, we leverage a powerful LLM-based text encoder validated to possess the requiredsemantic propertiesand adapt theMeanFlowgeneration process to this framework, resulting in efficienttext-conditioned synthesisfor the first time. Furthermore, we validate our approach on the widely useddiffusion model, demonstrating significantgeneration performance improvements. We hope this work provides a general and practical reference for future research on text-conditionedMeanFlowgeneration. The code is available at https://github.com/AMAP-ML/EMF.

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