The Variational Auto Encoder paper [1], and the DDPM paper[2] are pretty much all you need for this, [6[ is also good but covered by [2]. Going through the deri

The Variational Auto Encoder paper [1], and the DDPM paper[2] are pretty much all you need for this, [6[ is also good but covered by [2]. Going through the derivations helped solidify things for me. I haven't read [9] but looks very promising, authors include Jonathan Ho, and D. Kingma who authored [2] and [1] respectively. From there [3,4] show improvements to DDPMs, [5] shows that diffusion models can be very general. [7,8] show diffusion models from the view of score matching. [1] AutoEncoding Variational Bayes [2] Denoising Diffusion Probabilistic Models [3] Denoising Implicit Models [4] Improved Denoising Diffusion Probabilistic Models [5] Cold Diffusion [6] Deep Unsupervised Learning using Nonequilibrium Thermodynamics [7] Generative Modeling by Estimating Gradients of the Data Distribution [8] Score-Based Generative Modeling through Stochastic Differential Equations [9] Variational Diffusion Models