Deep learning's fast and accurate inference between material configurations and properties has been used to design digital composites with superior mechanical properties. However, initial training sets cannot explore a vast design space with astronomical numbers of possible combinations, and most DL methods cannot guarantee predictive power in unobserved domains that may contain optimal configurations. Active learning-based gradual DNN model update schemes were implemented, but this increased computational costs. We propose a single-shot training pipeline to predict stress/strain distribution and stiffness over configuration space far from the initial training set. Predicting a composite's load response requires predicting its stress/strain field. Two autoencoders and a cGAN predict high-resolution stress/strain fields from grid-averaged local fields inferred from binary digital composite configurations. Our pipeline accurately predicts the high-resolution stress/strain field distri-bution in the unseen volume fraction (VF) domain or for composites with configurations very different from the initial training set. The framework can predict high-resolution fields in many physical phenomena and design composite materials for engineering applications.