High Resolution Image Generation

High-resolution image generation aims to create realistic and detailed images at significantly larger scales than previously possible, focusing on overcoming limitations in existing models. Current research heavily utilizes diffusion models, often enhanced with techniques like progressive upscaling and attention mechanisms (e.g., Swin Transformers), alongside variational autoencoders (VAEs) and generative adversarial networks (GANs), sometimes in hybrid quantum-classical approaches. These advancements are improving the quality, efficiency, and controllability of high-resolution image synthesis, with implications for various fields including medical imaging, art creation, and virtual/augmented reality.