Mastering Generative Vision & Video: From GAN to Flow to DiT

Mastering Generative Vision and Video: From GAN to Flow to DiT

The Complete Engineering Guide to Modern Generative AI — Images, Video, and Audio-Visual Synthesis

Generative AI is no longer a research curiosity. It is the engine behind billion-dollar products, production pipelines at studios and startups, and the most sought-after engineering skillset in the AI job market today. Stable Diffusion, Sora, DALL-E, Runway, Midjourney, Kling, and Veo — every one of these systems is built on the architectural foundations this course teaches from first principles to production implementation.

This course picks up exactly where classical computer vision ends. You already understand CNNs, segmentation, and detection. Now it is time to master the generative side — the models that do not just recognize the visual world, but create, transform, and synthesize it.

"Mastering Generative Vision and Video: From GAN to Flow to DiT" is the only course that takes you through the complete evolution of generative architectures in a single, coherent learning journey. You will start with the foundational building blocks — Variational Autoencoders, GANs, and Vision Transformers — and progressively advance through Latent Diffusion Models, Flow Matching, ControlNet, Consistency Models, and finally Diffusion Transformers (DiT), the architecture powering Sora and the next generation of video generation systems.

The curriculum is structured around five modules covering 19 lectures of hands-on, implementation-focused content.

Module 0 ensures every student has the right foundation with VAEs, GANs, and ViT before entering the diffusion world.

Module 1 takes you from DDPM probability theory all the way to Flow Matching and ODE solvers.

Module 2 dives deep into control and acceleration — ControlNet, IP-Adapters, LCM Distillation, SDXL Turbo, and Flux Schnell.

Module 3 introduces spatiotemporal generation for video, covering DiT-based architectures, Sora, Veo 2, temporal attention, optical flow, and frame interpolation.

Module 4 closes the loop with generative audio-visual synchronization — neural audio synthesis with AudioLM and MusicGen, unified AV generation with Veo, lip-sync architectures with Wav2Lip, and latent audio-video alignment metrics.

This is not a course about prompting or using AI tools. This is an engineering course. You will understand the mathematics, implement the architectures, and build systems capable of generating images, videos, and synchronized audio-visual content.

Whether you are an AI engineer wanting to work on foundation model teams, a researcher building the next generation of generative systems, a developer integrating generative capabilities into production pipelines, or a technical entrepreneur building a generative AI product, this course gives you the complete, rigorous, and practical foundation to do it.

The demand for engineers who understand these systems at an architectural level is growing faster than the supply. This course is your path to becoming one of them.