Text to Image AI Models - Different Methodologies and How It Works

Explore various text-to-image generation AI methodologies and their inner workings in this 18-minute video tutorial. Learn about four different methods: Autoregressive models, GANs, VQ-VAE Transformers, and Diffusion models. Discover how each approach works, including GANs' introduction, VQ-VAE's DALL-E mini/mega and ruDALL-E models, and Diffusion models' technology. Examine specific implementations like GLIDE, DALL-E 2, and Google's Imagen. Gain insights into Google Pathway Models and access GitHub resources for further exploration. Understand the evolution of text-to-image AI, from early successes to advanced systems like DALL-E 2 and Google Imagen, which demonstrate impressive capabilities in generating images from text descriptions.

- Content Intro
- 4 Different Methods
- Our Objective
- Text to Image Generation Methods
- Autoregressive Models
- GANs
- GANs Introduction
- VQ-VAE Transformers
- VQ-VAE - DALL-E mini/mega Models
- VQ-VAE - ruDALL-E Models
- Diffusion Models
- Diffusion Models Technology
- Diffusion Models - GLIDE by Open AI
- Diffusion Models - DALL-E 2 by Open AI
- Diffusion Models - Imagen by Google
- Google Pathway Models
- GitHub Resources
- Conclusion