Guided Diffusion

Guided diffusion is a generative modeling technique leveraging diffusion processes to synthesize data, primarily focusing on achieving high-fidelity and controllable generation. Current research emphasizes extending its applications beyond image generation to diverse fields like medical imaging, scientific simulations, and optimization problems, often incorporating techniques like feature alignment, ensemble methods, and various forms of guidance (e.g., text, physical constraints, or pre-trained features) to improve both the quality and efficiency of the generated data. This approach holds significant promise for addressing data scarcity, enhancing model robustness, and accelerating solutions in numerous scientific and engineering domains.