Diffusion models for data generation

The 2021 paper Diffusion Models Beat GANs on Image synthesis by two OpenAI research scientists Prafulla Dhariwal and Alex Nichol garnered a lot of interest in diffusion models for data generation.

Using the Frechet Inception Distance (FID) as the metrics for evaluation of generated images, they were able to achieve an FID score of 3.85 on a diffusion model trained on ImageNet data:

Figure 9.28: Selected samples of images generated from ImageNet (FID 3.85). Image Source: Dhariwal, Prafulla, and Alexander Nichol. “Diffusion models beat GANs on image synthesis.” Advances in Neural Information Processing Systems 34 (2021)

The idea behind diffusion models is very simple. We take our input image , and at each time step (forward step), we add a Gaussian noise to it (diffusion of noise) such that after time steps, the original image is no longer decipherable. And then find a model that can, starting from a noisy input,...