Completed Class Conditional Diffusion Model. This version is intended to be trained on a class labeled dataset. It makes use of classifier-free guided diffusion to boost sample quality of the model when generating images from each class. The conditioning mechanism in the UNet (implemented together with Roy) simply adds an embedding of the class to the time embedding before passing them through the learnable reshaping layers for each block. Successfully trained on 3 class dataset, dogs, cats and wildlife.