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However, there exists a foundational mismatch between the distribution-mapping nature of generative models and the single-point optimization of standard TTT. In this paper, we demonstrate that this mismatch triggers \\textit{Prior Collapse}, a degenerate state where the model discards the text conditions and spatial latents, collapsing generations to the source video, or entangling the features of distinct regions. To resolve this, we propose \\textbf{ElasticTTT}, a novel framework that preserves the prior generative distribution and rescues generative elasticity. Specifically, we propose \\textit{Target Distribution Regularization} to prevent sharp memorization minima, \\textit{Contrastive CFG} to guide inference away from source biases, and \\textit{Asynchronous Noise Schedule} to preserve unedited regions. 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Specifically, we propose \\textit{Target Distribution Regularization} to prevent sharp memorization minima, \\textit{Contrastive CFG} to guide inference away from source biases, and \\textit{Asynchronous Noise Schedule} to preserve unedited regions. 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