Image Conditioning

Image conditioning in generative models focuses on controlling the output image by providing additional information, such as a second image, text prompt, or segmentation map, to guide the generation process. Current research emphasizes leveraging diffusion models and transformers, often incorporating techniques like adaptive layer normalization and attention mechanisms to improve control and realism, particularly when handling noisy or out-of-distribution inputs. This area is significant for applications ranging from medical imaging (e.g., generating contrast-enhanced MRI scans without contrast agents) to autonomous driving (e.g., synthesizing street views from bird's-eye maps) and beyond, offering improved efficiency and capabilities in various fields.