Adversarial Diffusion Distillation
Axel Sauer Dominik Lorenz Andreas Blattmann Robin Rombach
We introduce Adversarial Diffusion Distillation (ADD), a novel training
approach that efficiently samples large-scale foundational image diffusion
models in just 1-4 steps while maintaining high image quality. We use score
distillation to leverage large-scale off-the-shelf image diffusion models as a
teacher signal in combination with an adversarial loss to ensure high image
fidelity even in the low-step regime of one or two sampling steps. Our analyses
show that our model clearly outperforms existing few-step methods (GANs, Latent
Consistency Models) in a single step and reaches the performance of
state-of-the-art diffusion models (SDXL) in only four steps. ADD is the first
method to unlock single-step, real-time image synthesis with foundation models.
Code and weights available under
https://github.com/Stability-AI/generative-models and
https://huggingface.co/stabilityai/ .