SD3.5-Flash: Distribution-Guided Distillation of Generative Flows
Authors: name: Hmrishav Bandyopadhyay, name: Rahim Entezari, name: Jim Scott, name: Reshinth Adithyan, name: Yi-Zhe Song, name: Varun Jampani
Organization: Stability AI

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.