SD3.5-Flash: Distribution-Guided Distillation of Generative Flows
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915513950535680 |
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| author | Bandyopadhyay, Hmrishav Entezari, Rahim Scott, Jim Adithyan, Reshinth Song, Yi-Zhe Jampani, Varun |
| author_facet | Bandyopadhyay, Hmrishav Entezari, Rahim Scott, Jim Adithyan, Reshinth Song, Yi-Zhe Jampani, Varun |
| contents | We present SD3.5-Flash, an efficient few-step distillation framework that brings high-quality image generation to accessible consumer devices. Our approach distills computationally prohibitive rectified flow models through a reformulated distribution matching objective tailored specifically for few-step generation. We introduce two key innovations: "timestep sharing" to reduce gradient noise and "split-timestep fine-tuning" to improve prompt alignment. Combined with comprehensive pipeline optimizations like text encoder restructuring and specialized quantization, our system enables both rapid generation and memory-efficient deployment across different hardware configurations. This democratizes access across the full spectrum of devices, from mobile phones to desktop computers. Through extensive evaluation including large-scale user studies, we demonstrate that SD3.5-Flash consistently outperforms existing few-step methods, making advanced generative AI truly accessible for practical deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21318 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SD3.5-Flash: Distribution-Guided Distillation of Generative Flows Bandyopadhyay, Hmrishav Entezari, Rahim Scott, Jim Adithyan, Reshinth Song, Yi-Zhe Jampani, Varun Computer Vision and Pattern Recognition Artificial Intelligence We present SD3.5-Flash, an efficient few-step distillation framework that brings high-quality image generation to accessible consumer devices. Our approach distills computationally prohibitive rectified flow models through a reformulated distribution matching objective tailored specifically for few-step generation. We introduce two key innovations: "timestep sharing" to reduce gradient noise and "split-timestep fine-tuning" to improve prompt alignment. Combined with comprehensive pipeline optimizations like text encoder restructuring and specialized quantization, our system enables both rapid generation and memory-efficient deployment across different hardware configurations. This democratizes access across the full spectrum of devices, from mobile phones to desktop computers. Through extensive evaluation including large-scale user studies, we demonstrate that SD3.5-Flash consistently outperforms existing few-step methods, making advanced generative AI truly accessible for practical deployment. |
| title | SD3.5-Flash: Distribution-Guided Distillation of Generative Flows |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.21318 |