SD3.5-Flash: Distribution-Guided Distillation of Generative Flows
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.
68d5f6668ccd91bdd39ffdab
Hmrishav Bandyopadhyay
claimed_verified
2025-09-26T12:19:34.527Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/f0eCrzBrxz7Y9n25WkZ2v.png
Hmrishav Bandyopadhyay
user
Hmrishav
68d5f6668ccd91bdd39ffdac
Rahim Entezari
admin_assigned
2025-09-26T16:18:56.596Z
62fca7e7b17dbe0ceae54f04
/avatars/90c55bd5e0c6842586cc0ec5db387f89.svg
Rahim Entezari
user
rahiment
68d5f6668ccd91bdd39ffdad
Jim Scott
68d5f6668ccd91bdd39ffdae
Reshinth Adithyan
68d5f6668ccd91bdd39ffdaf
Yi-Zhe Song
68d5f6668ccd91bdd39ffdb0
Varun Jampani
admin_assigned
2025-09-26T16:19:13.126Z
630e7cfe3fc17ffc50f6e835
/avatars/0b742ff094a09f9374fafcd97ab9e002.svg
Varun Jampani
user
varunjampani
SD3.5-Flash: Distribution-Guided Distillation of Generative Flows
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.
SD3.5-Flash is an efficient few-step distillation framework that enhances image generation on consumer devices using rectified flow models with innovations like timestep sharing and split-timestep fine-tuning.
rectified flow models distribution matching objective timestep sharing split-timestep fine-tuning text encoder restructuring specialized quantization