Semantic segmentation is a complex task for deep neural networks, especially when limited training data is available. Unlike image classification problems such as Imagenet, semantic segmentation requires a class prediction for every individual pixel rather than just an image-level class. This requires a high level of detail and can be difficult to achieve with limited labeled data. Obtaining labeled data for semantic segmentation is challenging, as it requires precise pixel annotation, which is time-consuming for humans.

The goal of this article is to get you up to speed on stable diffusion. You will learn the main use cases, how stable diffusion works, debugging options, how to use it to your advantage and how to extend it. I) Main use cases of stable diffusion There are a lot of options of how to use stable diffusion, but here are the four main use cases: Overview of the four main uses cases for stable diffusion.

An interesting way to think about making decisions is to consider each decision as a bet with yourself about future versions of your life. Annie Duke who was a professional poker player winning high amounts of price money and big titles wrote a fascinating book called “Thinking in Bets” (You can buy it here). I highly recommend reading it as she brings along a lot of fun and great examples, but if you want to get the gist of it, this post is for you.

Stable diffusion is all the rage in the deep learning community at the moment. It’s trending on Twitter at #stablediffusion and gaining large amounts of attention all over the internet. We’ll take a look into the reasons for all the attention to stable diffusion and more importantly see how it works under the hood by considering the well-written paper “High-resolution image synthesis with latent diffusion models” by Rombach et al which is the foundation of the system.

This is a follow-up to my previous post of Depthwise Separable Convolutions in PyTorch. This article is based on the nice CVPR paper titled “Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets” by Haase and Amthor. Previously I took a look at depthwise separable convolutions which are a drop-in replacement for standard convolutions, but focused on computational and parameter-based efficiency. Basically, you can gain similar results with a lot less parameters and FLOPs, so they are used in MobileNet style architectures.