ProtoDiffusion: Classifier-Free Diffusion Guidance with Prototype
  Learning
Gulcin Baykal Halil Faruk Karagoz Taha Binhuraib Gozde Unal
Diffusion models are generative models that have shown significant advantages
compared to other generative models in terms of higher generation quality and
more stable training. However, the computational need for training diffusion
models is considerably increased. In this work, we incorporate prototype
learning into diffusion models to achieve high generation quality faster than
the original diffusion model. Instead of randomly initialized class embeddings,
we use separately learned class prototypes as the conditioning information to
guide the diffusion process. We observe that our method, called ProtoDiffusion,
achieves better performance in the early stages of training compared to the
baseline method, signifying that using the learned prototypes shortens the
training time. We demonstrate the performance of ProtoDiffusion using various
datasets and experimental settings, achieving the best performance in shorter
times across all settings.