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Free Download Build Your Own AI Image Generator with Diffusion Models
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 28m | Size: 613.45 MB
Hands-on latent diffusion: UNet, VAE, attention, time embeddings, denoising, checkpoints
What you'll learn
Build a complete text-to-image Stable Diffusion pipeline from scratch in PyTorch
Implement the core components yourself: self- and cross-attention, a U-Net, and a VAE
Understand how diffusion generates images: DDPM denoising, latent space, and classifier-free guidance
Load pretrained weights into your own code and generate an image end to end
Trace, debug, and modify a real diffusion model implementation
Requirements
No experience needed!
Description
This course contains the use of artificial intelligence.
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.
From there, we'll assemble the full model. You'll implement a UNet tailored for image processing, wire in attention-based blocks, and integrate a convolutional VAE using the reparameterization trick to move between image space and compact latents. We'll cover Denoising Diffusion Probabilistic Models, stepwise denoising, and the shift to Latent Diffusion for efficient generation. You'll add classifier-free guidance to steer outputs during inference, and learn how to save, load, and manage model state with checkpoints and state dicts. Finally, we'll load and connect pretrained components and walk through the latent diffusion inference flow end-to-end.
Along the way, I'll keep things practical. Each concept is paired with a concise demo so you can see exactly how to implement it in PyTorch, from transforming token sequences into encoded representations to upsampling with convolutional refinement and producing the final output channels.
You get lifetime access to all lessons and demos, plus the Q&A section where you can ask questions when you get stuck. I'm here to help you reason about the design choices and get your code running.
If you're ready to understand how text becomes pixels - and write the code that makes it happen - join me and start building.
Who this course is for
Python developers who want to understand how modern image generators work under the hood
Machine learning learners who want hands-on experience building a diffusion model end to end
Data scientists comfortable with code who want implementation-level knowledge of diffusion models
Researchers and creative coders who want to customize or extend text-to-image models for their own projects
Anyone who has used tools like Midjourney, DALL·E, or Google's Nano Banana and is curious how they work
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