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Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstein Gradient Flow
To mitigate the time complexity of generative models, one-step generative models have recently emerged through direct mapping from noise to data in a single forward pass. However, the reward-guided fine-tuning method of one-step generative models remains largely unexplored. To address this, we consi
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