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Diffusion models no longer need to simultaneously plan and paint; inserting discrete "visual thought" tokens between VLMs and DiTs decouples high-level semantic reasoning from pixel synthesis for tighter alignment and direct intermediate control.
Reinforcement learning boosts multimodal performance, raising task scores and creating unexpected synergies between image generation and editing.
OmniGen-AR can seamlessly generate images from a wide array of conditions, outperforming existing methods that are limited to single-modality inputs.
