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A novel dense supervision strategy boosts image editing model performance by leveraging over 1,000 fine-grained concepts from a massive dataset of 12 million pairs.
Rarity-aware sampling and spatial consistency techniques enable discrete diffusion to achieve high-quality image super-resolution with fewer decoding steps than traditional methods.
A 4B-parameter model, InternVL-U, outperforms 14B-parameter models in multimodal generation and editing, proving that size isn't everything.
