Fine Grained Control

Fine-grained control in generative models aims to achieve precise manipulation of specific attributes within generated outputs, such as images, videos, or music, going beyond coarse-grained control offered by simple parameters. Current research focuses on developing methods that leverage diffusion models, GANs, and transformer architectures, often incorporating techniques like textual inversion, concept sliders, and hierarchical semantic graphs to achieve this fine-grained control. This research is significant because it enables more nuanced and creative control in various applications, ranging from image editing and animation to music composition and personalized avatar creation, ultimately improving the usability and expressiveness of generative AI systems.