Image Generation

Image generation research focuses on creating realistic and diverse images from various inputs, such as text, sketches, or other images, aiming for greater control and efficiency. Current efforts center on refining diffusion and autoregressive models, exploring techniques like dynamic computation, disentangled feature representation, and multimodal integration to improve image quality, controllability, and computational efficiency. These advancements have significant implications for accessible communication, creative content production, and various computer vision tasks, offering powerful tools for both scientific investigation and practical applications. Ongoing work addresses challenges like handling multiple conditions, improving evaluation metrics, and mitigating biases and limitations in existing models.