Diffusion Based Image Generation with Diffuser (Full Course) - Diffusion Models for Images: Implementing a Conditional U-Net for MNIST from Scratch Pytorch Part 3

#stablediffusion #aiart #python #generativeai #diffusion #pytorch Welcome to Intelligent Machines! I am Dr. Balyogi Mohan Dash, and in this video, we start a new series on Diffusion Models – the foundation behind today’s most powerful image and video generation systems. In this video, you will learn: Implement a Conditional Diffusion Model (DDPM) from scratch using PyTorch. Focuses on generating MNIST handwritten digits conditioned on a specific class label (0-9). Details the process of applying diffusion to images, contrasting it with the 2D point examples from previous videos. Explains the architecture and implementation of the U-Net convolutional network, including how time and class embeddings are integrated. Covers the full pipeline: data preprocessing, forward diffusion (adding noise), the training loop, and the reverse denoising process (generating images). A great starting point for understanding the core mechanics of image generation with diffusion models before using high-level libraries. 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 TIME STAMPS 00:00 Intro & Transition to Image Diffusion (MNIST) 02:01 Data Preparation & Forward Diffusion Process 04:56 The U-Net Model: Architecture & Embeddings 09:14 Training, Denoising Loop & Results (PyTorch Scratch) 11:57 Conclusion & What's Next (The Diffusers Library) WhatsApp inquiries are currently closed. Please reach out via email for any questions: [email protected]