Dan Bochman: How to Create AI Image Generation Models

Links- Codecrafters (sponsor): https://tej.as/codecrafters- FASHN AI: https://fashn.ai- Dan on X: https://x.com/danbochman- Aya on X: https://x.com/ayaboch- Tejas on X: https://x.com/tejaskumar_SummaryIn this conversation, we dive deep into the intricacies of AI, focusing on concepts like latent space, diffusion, and the evolution of image generation techniques. We explore how latent space serves as a condensed representation of features, the challenges faced by GANs, and how diffusion models have emerged as a more effective method for generating images from noise. The discussion also touches on the importance of quantization in AI models and the iterative approaches used in image generation. Chapters00:00 Dan Bochman02:25 Introduction to AI and Latent Space07:24 Understanding Latent Space and Its Importance12:29 The Concept of Diffusion in AI17:21 From Noise to Image Generation22:32 Challenges with GANs and the Emergence of Diffusion27:28 The Role of Quantization in AI Models32:26 Iterative Approaches in Image Generation35:51 The Noise of Life and Image Clarity37:09 Exploring Diffusion Models in Creative Generation39:00 Understanding Latent Space and Its Importance40:27 Diving Deeper into Loss Functions and Image Quality43:32 Signal to Noise Ratio in Image Generation45:54 The Transition to Latent Space for Better Learning48:44 The Power of Variational Autoencoders53:01 Navigating the Uncanny Valley in AI Generated Images57:43 Guidance in Image Generation and Fashion Applications01:10:24 Understanding Architecture in AI Models01:14:40 Training Diffusion Models: Getting Hands-On01:21:18 Fine-Tuning Techniques and Challenges01:26:53 The Accessibility of AI Model Development01:34:10 Navigating Funding and Research in AI01:46:45 Lessons Learned: The Builder's Journey Hosted on Acast. See acast.com/privacy for more information.