“Adversarial Diffusion Distillation”, Axel Sauer, Dominik Lorenz, Andreas Blattmann, Robin Rombach2023-11-28 (diffusion model, GAN)⁠:

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 4 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/.