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Text-to-image models get a diversity boost without sacrificing quality, thanks to a new sampling method that disentangles and controls prompt-dependent and independent variations in the CLIP embedding space.
Fast weight models can now achieve significantly better long-context performance thanks to a new RL training framework that moves beyond next-token prediction.
