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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2512.13006 |
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| _version_ | 1866917146733314048 |
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| author | Pu, Yifan Han, Yizeng Tang, Zhiwei Tang, Jiasheng Wang, Fan Zhuang, Bohan Huang, Gao |
| author_facet | Pu, Yifan Han, Yizeng Tang, Zhiwei Tang, Jiasheng Wang, Fan Zhuang, Bohan Huang, Gao |
| contents | Diffusion distillation has dramatically accelerated class-conditional image synthesis, but its applicability to open-ended text-to-image (T2I) generation is still unclear. We present the first systematic study that adapts and compares state-of-the-art distillation techniques on a strong T2I teacher model, FLUX.1-lite. By casting existing methods into a unified framework, we identify the key obstacles that arise when moving from discrete class labels to free-form language prompts. Beyond a thorough methodological analysis, we offer practical guidelines on input scaling, network architecture, and hyperparameters, accompanied by an open-source implementation and pretrained student models. Our findings establish a solid foundation for deploying fast, high-fidelity, and resource-efficient diffusion generators in real-world T2I applications. Code is available on github.com/alibaba-damo-academy/T2I-Distill. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Few-Step Distillation for Text-to-Image Generation: A Practical Guide Pu, Yifan Han, Yizeng Tang, Zhiwei Tang, Jiasheng Wang, Fan Zhuang, Bohan Huang, Gao Computer Vision and Pattern Recognition Diffusion distillation has dramatically accelerated class-conditional image synthesis, but its applicability to open-ended text-to-image (T2I) generation is still unclear. We present the first systematic study that adapts and compares state-of-the-art distillation techniques on a strong T2I teacher model, FLUX.1-lite. By casting existing methods into a unified framework, we identify the key obstacles that arise when moving from discrete class labels to free-form language prompts. Beyond a thorough methodological analysis, we offer practical guidelines on input scaling, network architecture, and hyperparameters, accompanied by an open-source implementation and pretrained student models. Our findings establish a solid foundation for deploying fast, high-fidelity, and resource-efficient diffusion generators in real-world T2I applications. Code is available on github.com/alibaba-damo-academy/T2I-Distill. |
| title | Few-Step Distillation for Text-to-Image Generation: A Practical Guide |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.13006 |