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Dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) is a critical tool for breast cancer detection and diagnosis. Yet, its reliance on contrast agents presents risks, increases cost, and is contraindicated for certain patients, necessitating a contrast-agent-free alternative. In this paper, we use a conditional Generative Adversarial Network (GAN) with a multi-scale enhancement consistency loss for contrast agent-free breast DCE-MRI synthesis from a multi-parametric MRI input (T1w, multi-b-value DWI, and ADC maps). The proposed loss explicitly enforces consistency of the predicted enhancement maps across multiple scales. The model generates multiple DCE post-contrast phases simultaneously, reducing inference time and computational overhead. The proposed framework achieved the best performance among previous methods on public and in-house datasets. The code is available at https://github.com/minnelab/DCE-MRI-Syn.