Conditional generative modeling with limited target data can benefit from representations learned in a related source population. We develop a transfer learning framework for conditional flow matching that accommodates distinct source and target response distributions and dimensions. Two procedures exploit a representation learned from source data: TFM-Freeze estimates a target velocity head with the representation fixed, whereas TFM-Adapt jointly learns the head and a representation adaptation under a functional anchoring penalty. For explicit neural network classes, we establish bounds on conditional squared Wasserstein risk that account for source estimation, target fitting, representation mismatch, and numerical integration. When the source representation admits a smooth correction, adaptation replaces the frozen mismatch contribution by the cost of learning the correction and a controllable regularization bias. An information lower bound identifies regimes in which adaptation attains a strictly faster conditional squared Wasserstein risk rate than any generator restricted to the frozen context information. We also develop independent validation procedure for selecting among fitted conditional generators. We show that the selection procedure satisfies an oracle inequality, and the switching rule guarantees, with high probability, no increase in conditional energy risk relative to a target-only generator. Simulations and real data applications illustrate the gains from our proposed methods and the effectiveness of target validation in guarding against negative transfer.

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