1.1. Intro to the course
Python, Colab vs local, libraries and modules. Framework setup.
1.2. Introduction to the neural networks
Neuron, graph, loss function. Training as an optimization process. CPPN (image reproduction)
2.1. CNN: concept, features
Convolutions as graphics filters, how they process & store data.
2.2. Architectures & building principles
Style transfer, autoencoders, GAN. Features, iterative pipeline, network extensions.
3.1. Modern GANs
ProGAN, StyleGANs. Latent space - animation, blending, projection, special tricks.
3.2. StyleGAN2 training
Data preparation, training process. Augmentation.
4.1. Image transformations (image-to-image)
From pix2pix to StarGAN2. Applied methods overview.
4.2. Integration with TouchDesigner
OSC, NDI, built-in Python with TouchEngine
5.1. Multimodal synthesis with foundation models
CLIP, early iterative methods. CPPNs, Aphantasia, VQGAN.
5.2. Transformers & Diffusion models
Principles & specifics. Denoised & latent diffusion.
6.1. Stable Diffusion as the current generative tool #1
Architecture, use cases: txt2img, img2img, interpolations
6.2. SD control & finetuning
Controlnet. Textual inversion, custom diffusion, LoRA, ..
Development of the own project with visual AI/ML methods
Minimum skills (not strongly required, but get ready to learn): familiarity with command line (shell) operations.
Programming skills will greatly help with exploring specific details of the used instruments.
Prerequisites are flexible: the more you know, the more you get.
Complete course program would require Windows computer with a decent Nvidia GPU with at least 6Gb VRAM (hard minimum is 1060, reasonable level 2070+).
Users with Macs would be able to use Collab versions (no integration with TD in this case).
Partial payment option available: 50% before the start of the course and 50% 1 month after the start
All practical topics have corresponding tasks to complete as homeworks. Most of them require to repeat the actions from the lectures on your own supply.We recommend to execute them even if everything is clear in theory - to ensure that you won’t face unexpected issues in your future work.
During final week you will have to develop and complete your own project (with our assistance) as a kind of exam of the obtained knowledge.
10-12 hours per week should be sufficient to master necessary concepts and skills. You may want to invest more time for more exhaustve explorations though. Remember, the course program is somewhat open-ended: nearly all topics are not limited in scope, and have lots of options for extra developments.