Diffusion Based Image Generation with Diffuser (Full Course) - Hugging Face Diffusers Library: MNIST Generation | Full Course Part 4

#stablediffusion #aiart #python #generativeai #diffusion #pytorch Welcome to Intelligent Machines! I am Dr. Balyogi Mohan Dash. This video demonstrates using the Hugging Face Diffuser library for image generation, specifically focusing on the Amnesty dataset. It highlights the shift from manual implementation to a streamlined, ready-to-use diffusion pipeline. Key topics covered include the scheduler, the unit model with attention layers, and simplified noise addition/denoising steps. The library offers a significant advantage in terms of speed and ease of use, reducing the complexity of diffusion modeling. Training a diffusion model for MNIST digits is presented, showing improvements in image quality and recognizability with the library's implementation. Future content will focus on generating realistic human phases, emphasizing the library's versatility and efficiency. 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 - Introduction and Recap 00:00:35 - Overview of Hugging Face Diffusers 00:01:11 - Setting Up Data and Scheduler 00:03:00 - Building the UNet Model with Diffusers 00:06:39 - Image Generation using the Scheduler 00:07:31 - Training the Diffusion Model 00:09:04 - Results and Performance Comparison 00:09:38 - Summary and Next Steps WhatsApp inquiries are currently closed. Please reach out via email for any questions: [email protected]