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To generate an image, JoLT uses two streams that jointly denoise LR and HR latent images at each sampling step, and interconnect both branches to jointly integrate their information.
This work presents Gradient-free Analytical Trajectory Optimization Video Generation (GATO-Vid), a novel training-free and gradient-free approach for precise spatial guidance that significantly outperforms existing baselines in localization accuracy while introducing minimal computational overhead.
DiMaS is proposed, a Distribution-Matching Steering strategy tailored to flow-matching VLAs, which transports between representation distributions rather than shifting along a fixed direction, and it effectively controls behavior across two state-of-the-art VLAs.
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