Fun With Diffusion Models!
Overview This project focuses on implementing a diffusion model to generate images. The diffusion model is a generative model that generates images by iteratively applying a series of transformations to a noise image. The model is trained to generate images that are similar to the training data. The project is divided into two parts. In Part 1, we implement the forward process of the diffusion model and explore different denoising techniques using a pre-trained diffusion model. In Part 2, we train a single-step denoising UNet and extend it to include time-conditioning and class-conditioning. ...