You’ve seen text-to-image systems like Midjourney, DALL·E, and Google’s Nano Banana turn short prompts into vivid art. In this course, we’ll build that kind of model ourselves: a latent Stable Diffusion pipeline coded from scratch in PyTorch. You’ll see how attention, a UNet, and a VAE come together with diffusion to generate images – piece by piece, so the whole system finally “clicks.”
Why learn this? Whether you’re a developer who wants to understand the moving parts behind modern generative models, a researcher exploring diffusion techniques, or a creative coder who wants to customize models for your own projects, knowing how Stable Diffusion works under the hood lets you debug, extend, and innovate. You’ll move beyond treating models as black boxes and gain the skills to shape them.
We’ll start with core building blocks: linear projection, multi-head attention, self- and cross-attention, positional and sinusoidal time embeddings, normalization layers, residual connections, and convolutional blocks with upsampling. Then we connect the theory to practice with focused demos that implement attention in code, construct position-aware token embeddings, build time-aware residual blocks, and refine features inside convolutional modules.
