SD3.5-Flash: Distribution-Guided Distillation of Generative Flows
This work addresses the problem of democratizing access to advanced generative AI for practical deployment across various devices, though it is incremental in improving efficiency and accessibility.
The paper tackled the problem of making high-quality image generation computationally efficient for consumer devices by introducing SD3.5-Flash, a distillation framework that reduces model complexity and enables rapid, memory-efficient deployment, as demonstrated through extensive evaluation and user studies showing it outperforms existing few-step methods.
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.