Image to Image Generation

Image-to-image generation focuses on transforming a given input image into a new image based on specified criteria, aiming for high fidelity and controllability. Current research emphasizes improving the quality and realism of generated images, particularly in challenging scenarios like low-resolution faces within complex scenes, using techniques like diffusion models and incorporating multimodal information from sources such as large language models to guide the generation process. This field is significant for its applications in various domains, including image editing, enhancement, and the creation of synthetic datasets for training other AI models, particularly in agriculture and other data-scarce areas. Furthermore, ongoing work addresses the need for quantifiable uncertainty measures in generated images to improve reliability and trustworthiness.