This is an engineering course, not a prompting tutorial. It assumes you already understand CNNs and PyTorch, then takes you through the complete architectural evolution behind Stable Diffusion, Sora, Veo, and Flux, implementing each system rather than just describing it.

This Course Offers

• VAEs, GANs, and Vision Transformers built from scratch, covering reparameterisation, minimax training, and patch embeddings that underpin Stable Diffusion
• DDPM, Latent Diffusion Models, and Flow Matching, with ODE solvers and time step formulations used in systems like SD 3.5 and Flux
• Control and acceleration using ControlNet, IP Adapters, Consistency Models, and adversarial distillation techniques including SDXL Turbo and Flux Schnell
• Spatiotemporal video generation with Diffusion Transformers, temporal attention, and optical flow, referencing Sora, Veo 2, and Gen 3
• Generative audio visual synchronization covering neural audio synthesis with AudioLM and MusicGen, lip sync architectures like Wav2Lip, and latent alignment metrics

Why We Love This Course

1. It picks up where classical computer vision ends, so the prerequisites are clearly stated rather than assumed.
2. The progression is coherent, moving from VAEs through diffusion to DiT rather than jumping between unrelated techniques.
3. At thirty one and a half hours across three hundred and sixteen lessons with forty five downloadable resources, it is unusually thorough.
4. It addresses architecture and mathematics, which is what separates engineers who can customize foundation models from those who can only call APIs.

Generative AI engineering roles are growing faster than the supply of people who understand these systems at an architectural level. Do you want to use these tools, or build and modify them? Enrollment is open, though this course assumes existing deep learning and Python experience.

• This course is perfect for AI engineers and developers wanting to build generative image, video, and audio visual systems.
• It suits students who have completed foundational computer vision training and are ready for generative architectures.
• Machine learning researchers, software developers building generative products, and deep learning engineers specializing in generative vision will find the implementation focus directly applicable, though complete beginners are not the target audience.

• Completion of a foundational computer vision course covering CNNs, image classification, and deep learning, or equivalent practical experience, is expected.
• Solid Python skills and working knowledge of PyTorch or TensorFlow are required, including defining models, writing training loops, and running inference.
• A computer running Python 3.8 or higher with deep learning libraries installed is needed, and access to a GPU environment, including cloud platforms, is recommended for the labs.

Interested in exploring more lessons? Check out our full course library to continue building your skills and advancing your learning journey.