Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Yannic Kilcher via YouTube

Overview

Explore an in-depth analysis of Parti, a groundbreaking autoregressive text-to-image model that demonstrates the power of scaling in AI. Delve into the model's architecture, impressive outputs, and its ability to generate crisp, accurate, and realistic images from complex prompts. Examine the datasets used, including PartiPrompts, and review experimental results that showcase Parti's capabilities. Learn about the model's strengths in combining arbitrary styles and concepts, as well as its potential limitations through failure case studies. Gain insights into the future of AI-generated art and content creation through this comprehensive examination of Parti's innovative approach to text-to-image generation.

Syllabus

- Introduction
- Example Outputs
- Model Architecture
- Datasets incl. PartiPrompts
- Experimental Results
- Picking a cherry tree
- Failure cases
- Final comments

Taught by

Yannic Kilcher

Reviews

Start your review of Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.