Diffusion Based Image Generation with Diffuser (Full Course) - Class Conditioned Diffusion Model in Pytorch | Full Course Part 2 | By Dr Mohan Dash

Welcome to Intelligent Machines! I am Dr. Balyogi Mohan Dash. Previously, we learned how diffusion models generate structured data from random noise. But one major limitation was the lack of control over what gets generated. In this tutorial, we solve that by introducing class conditioning — teaching the model to generate images (or data points) belonging to a specific class using class labels. You will learn: ✅ What makes conditional diffusion different from unconditional diffusion ✅ How to modify the U-Net architecture to include class embeddings ✅ How conditioning helps control the generated outputs ✅ Step-by-step coding walkthrough of the conditional diffusion process ✅ Forward and reverse diffusion with class conditioning All code used in this video is available here: https://github.com/mohan696matlab/Diffusion_Gen_AI_Course Subscribe to the channel to follow this complete course and master diffusion models from theory to practical implementation. 🔗Links🔗 LinkedIn: https://www.linkedin.com/in/balyogi-mohan-dash/ GitHub: https://github.com/mohan696matlab Google Scholar: https://scholar.google.com/citations?user=jzcIElIAAAAJ&hl=en 📌 1:1 AI Consulting: https://topmate.io/balyogi_mohan_dash_phd/ 📌 freelance profile: https://www.upwork.com/freelancers/~012513f43380c0b7b7?mp_source=share WhatsApp inquiries are currently closed. Please reach out via email for any questions: [email protected]