This presentation discusses model architectures for high-resolution image generation. Four key architectures are explained: Progressive Growing (low-to-high resolution training), Self-Attention (efficient high-resolution generation), Attention with Linear Units (channel-wise feature selection), and Style-based Generator (latent variable control of style and resolution). Each architecture's strengths and weaknesses are highlighted, along with examples and comparisons.