Stable Diffusion High resolution image synthesis with latent diffusion models

Stable diffusion is a model for synthetic image generation like Imagen, DALL.E-2 etc. It leverages diffusion models with classifier free guidance. It decomposes the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs). Using cross attention layers in the UNet it supports different forms of conditioning.

In this video, I will briefly provide an overview of the latent diffusion model and architecture of Stable Diffusion.

Here is the agenda:

00:00:00 What is Stable diffusion?
00:03:00 What are Latent Diffusion Models (LDMs)?
00:07:18 What is the architecture for LDMs?
00:10:50 Image Synthesis examples
00:11:50 Perceptual compression tradeoffs
00:16:40 Layout-to-image synthesis, Semantic synthesis, Super-resolution, Inpainting using Stable Diffusion.

For more details, please look at

For better understanding, also look at these videos:

Rombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. "High-resolution image synthesis with latent diffusion models." In CVPR, pp. 10684-10695. 2022.

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Hi sir, is there any instruction on how to train your own model (using your own images)? It woul be so good to make a tutorial video explaining the process.