High Resolution Image Synthesis

High-resolution image synthesis aims to generate realistic and detailed images at resolutions exceeding 1024x1024 pixels, pushing the boundaries of generative modeling. Current research focuses on improving the efficiency and scalability of diffusion models, often employing architectures like U-Nets and transformers, and exploring techniques such as cascaded diffusion, latent diffusion, and training-free upsampling methods to achieve high-fidelity results at reduced computational cost. This field is significant for its potential impact on various applications, including medical imaging, remote sensing, and creative content generation, where high-resolution detail is crucial.