Less-to-More Generalization: Unlocking More Controllability by In-Context Generation
Overview
UNO-1M is a large dataset (~1M paired images) constructed by the in-context generation pipeline introduced in the UNO paper. Its advantages include highly diverse categories (>365 categories), high-resolution images (around 1024x1024), variable resolutions (different aspect ratios), high quality (produced by state-of-the-art text-to-image models), and high subject… See the full description on the dataset page: https://huggingface.co/datasets/bytedance-research/UNO-1M.
