The Annotated Diffusion Model — overview of DDPM capabilities and diffusion exemplars (GLIDE, DALL-E 2)

AI Impact Summary

The content outlines the DDPM diffusion-model framework, detailing a forward diffusion process that adds Gaussian noise with a variance schedule and a learned reverse process parameterized by a neural network. It highlights diffusion-model exemplars such as GLIDE, DALL-E 2, Latent Diffusion, and ImageGen, signaling a growth in diffusion-based generative capabilities that engineering teams may need to support or compete with. The write-up emphasizes PyTorch-based implementation and training objectives around predicting the reverse mean, which has implications for the required tooling, compute, and data pipelines. Given a CAPABILITY update, plan for increasing GPU-accelerated inference and consider tradeoffs between diffusion steps and latency.