From Noise to Masterpiece: Diffusion 완전정복 (DDPM to SANA)

Unlock the power of Diffusion Models with this hands-on masterclass! Go from theory to implementation, mastering the evolution of generative AI's most impactful models.

This course takes you on a deep dive into Diffusion Models, starting with the foundational DDPM (Denoising Diffusion Probabilistic Model) and progressing through cutting-edge architectures like LDM (Latent Diffusion Model), Stable Diffusion, DiT (Diffusion Transformer), and the latest advancements including PixArt and SANA.

Learn by doing! You'll implement each model from scratch using PyTorch, gaining practical skills to reproduce and apply these powerful techniques. We'll dissect the core principles behind each model, including:

• Model Origins: Understand the motivation and context for each architecture's development.
• Mathematical Foundations: Explore the key equations driving diffusion processes.
• Network Architectures: Analyze the inner workings of crucial components like U-Net, VAE, and Transformer networks.
• Training Process: Master concepts like Noise Schedules and Denoising Steps.
• Performance Enhancements: Discover the innovative ideas that push the boundaries of generative AI.

Gain a clear understanding of how Stable Diffusion builds upon LDM, and how DiT and SANA leverage Transformer structures for enhanced capabilities.

Whether you're a researcher, developer, or creator, this course provides a structured path to understanding the evolution of generative models. Stop just reading papers – start implementing, understanding, and reimagining the future of AI. Develop practical expertise in generative models and unlock new creative possibilities.