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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.
Achieve strong spatio-temporal video grounding with only 10M trainable parameters by smartly adapting pre-trained 2D visual-language models.