May 28, 03:27 AM

Google and DeepMind Introduce EM Distillation for Efficient One-Step Diffusion Models on ImageNet

Google, in collaboration with Google DeepMind, has introduced EM Distillation (EMD) for one-step diffusion models, a new method aimed at improving the efficiency and quality of generative models. This technique outperforms existing one-step generative methods in terms of Fréchet Inception Distance (FID) on ImageNet-64 and ImageNet-128 datasets. EMD leverages maximum likelihood to distill pretrained diffusion models into one-step generators, effectively balancing between mode-seeking and mode-covering Kullback-Leibler divergence to better capture the teacher model's distribution. This advancement addresses the computationally expensive iterative process required by traditional diffusion models and the limitations of existing distillation methods, delivering competitive results on both ImageNet and Stable Diffusion benchmarks. The research was led by Z Xiao, D P Kingma, and others.

#Google #Google DeepMind #EM Distillation #Fréchet Inception Distance #ImageNet #Stable Diffusion

Written with ChatGPT (GPT-4o).

Sources

Additional media

Image #1 for story google-deepmind-introduce-em-distillation-efficient-one-step

Google and DeepMind Introduce EM Distillation for Efficient One-Step Diffusion Models on ImageNet

Sources

Additional media

Similar Stories