Generative AI

7.

Image and Visual Generation

7.1.

Image Synthesis Fundamentals

7.1.1.

Pixel-Level Generation

7.1.1.1.

Autoregressive Image Models

7.1.1.2.

Convolutional Approaches

7.1.2.

Latent Space Generation

7.1.2.1.

VAE Image Generation

7.1.2.2.

GAN Image Synthesis

7.1.2.3.

Diffusion Image Models

7.2.

Text-to-Image Generation

7.2.1.

Multimodal Understanding

7.2.1.1.

CLIP Architecture

7.2.1.2.

Vision-Language Models

7.2.2.

Conditioning Mechanisms

7.2.2.1.

Cross-Attention Conditioning

7.2.2.2.

Classifier Guidance

7.2.2.3.

Classifier-Free Guidance

7.2.3.

7.2.3.1.

DALL-E Architecture

7.2.3.2.

Stable Diffusion

7.2.3.3.

Midjourney Approach

7.2.3.4.

7.3.

Image Editing and Manipulation

7.3.1.

Inpainting Techniques

7.3.1.1.

Context-Aware Filling

7.3.1.2.

Semantic Inpainting

7.3.2.

7.3.2.1.

Neural Style Transfer

7.3.2.2.

7.3.2.3.

Arbitrary Style Transfer

7.3.3.

Image-to-Image Translation

7.3.3.1.

Pix2Pix Framework

7.3.3.2.

CycleGAN Approach

7.3.3.3.

Unpaired Translation

7.4.

Advanced Visual Generation

7.4.1.

Super-Resolution

7.4.1.1.

Single Image Super-Resolution

7.4.1.2.

Real-World Super-Resolution

7.4.2.

Video Generation

7.4.2.1.

Frame Prediction

7.4.2.2.

Text-to-Video Synthesis

7.4.2.3.

Video Style Transfer

7.4.3.

7.4.3.1.

Neural Radiance Fields

7.4.3.2.

3D Object Synthesis

7.4.3.3.

Text-to-3D Generation

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8. Audio and Speech Generation