Conditional Diffusion

Conditional diffusion models are generative AI models that produce samples conditioned on specific inputs, such as text prompts, labels, or other images, aiming to generate high-quality, realistic outputs while adhering to the given constraints. Current research focuses on improving sampling efficiency, enhancing the robustness of conditional guidance (especially with noisy or unreliable inputs), and applying these models to diverse tasks including image generation, time series forecasting, medical image analysis, and inverse problems. This rapidly developing field has significant implications for various scientific domains and practical applications, offering powerful tools for data generation, analysis, and manipulation across diverse data modalities.