Few-Step Distillation for Text-to-Image Generation: A Practical Guide
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.
6941639365f1e24a117808a1
Yifan Pu
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2025-12-17T14:04:35.661Z
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yifan pu
yifanpu001
user
yifanpu001
6941639365f1e24a117808a2
Yizeng Han
6941639365f1e24a117808a3
Zhiwei Tang
6941639365f1e24a117808a4
Jiasheng Tang
6941639365f1e24a117808a5
Fan Wang
6941639365f1e24a117808a6
Bohan Zhuang
6941639365f1e24a117808a7
Gao Huang
Few-Step Distillation for Text-to-Image Generation: A Practical Guide
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.
A systematic study adapts diffusion distillation techniques to text-to-image generation, providing guidelines for successful implementation and deployment.
diffusion distillation class-conditional image synthesis text-to-image generation FLUX.1-lite unified framework discrete class labels free-form language prompts input scaling network architecture hyperparameters diffusion generators