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1
Intro to Flux
2
Rectified Flow Transformers
3
What Do Diffusion Models Do
4
Understanding the Loss Equation
5
How Latent Diffusion Transformers Work
6
Rectified Flow Transformers Architecture
7
Rectified Flow Transformer Diagram
8
The Datasets They Used
9
Improved Captions Synthetic Data
10
Data Preprocessing
11
Our Results Fine-Tuning
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore a technical deep dive video examining the research paper that influenced Flux, a leading generative image model that surpassed DALL-E and PIXART. Learn about the fundamentals of Rectified Flow Transformers, diffusion model mechanics, and loss equation analysis. Discover the inner workings of Latent Diffusion Transformers, examine detailed architectural diagrams, and understand the datasets and preprocessing techniques used. Follow along as the presenter breaks down the model's components, from synthetic data generation with improved captions to fine-tuning results, providing a comprehensive understanding of this breakthrough in AI image generation technology.

Inside the Rectified Flow Transformer Model - Understanding Advanced Image Generation

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