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Today's visual generation models excel at photorealism but still fail at the kind of spatial reasoning, long-term consistency, and causal understanding that truly intelligent visual generation demands.
Time conditioning, a seemingly crucial component of diffusion models like DDIM, can be entirely bypassed without sacrificing generation quality by carefully shaping the evolution of noisy data manifolds.
