While conditional diffusion models have achieved remarkable success in generation tasks, they memorize spurious correlations within training data. This causes them to deviate from ideal target distributions. Minimizing a covariate shift-adjusted target risk can address this issue. However, retraining models from scratch for different distributions is computationally prohibitive. To overcome this limitation, we propose DR-Diff, a framework that efficiently fine-tunes pre-trained conditional diffusion models. Specifically, DR-Diff leverages a functional gradient approach to derive a pseudo-target. This pseudo-target captures the residual between the base model's noise prediction and the ground-truth noise under the target distribution. Training the model to fit this pseudo-target enables efficient target adaptation. Theoretically, we prove that DR-Diff guarantees target risk reduction at the population level. It also achieves a pseudo-doubly robust property, providing resilience against density ratio estimation errors and base model misspecifications. Empirically, DR-Diff matches the noise prediction accuracy of models trained from scratch on synthetic datasets. Furthermore, it mitigates inter-group disparities in downstream classification tasks.

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