jyoung105 / sdxl-turbo

Adversarial Diffusion Distillation

  • Public
  • 34 runs
  • GitHub
  • Weights
  • Paper
  • License
Run with an API
Playground API Examples README Versions

SDXL-Turbo

SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation.

A real-time demo is available here: http://clipdrop.co/stable-diffusion-turbo Please note: For commercial use, please refer to https://stability.ai/license.

Introduction

SDXL-Turbo is a distilled version of SDXL 1.0, trained for real-time synthesis.

SDXL-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the technical report), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high image quality.

This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps.

  • Developed by: Stability AI
  • Funded by: Stability AI
  • Model type: Generative text-to-image model
  • Finetuned from model: SDXL 1.0 Base

For research purposes, we recommend our generative-models Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference).

Citation

@misc{2311.17042,
Author = {Axel Sauer and Dominik Lorenz and Andreas Blattmann and Robin Rombach},
Title = {Adversarial Diffusion Distillation},
Year = {2023},
Eprint = {arXiv:2311.17042},
}