This paper proposes a generalizable model to synthesize high-fidelity clothing wrinkle deformation in 3D by learning from real data. Given the complex deformation behaviors of real-world clothing, this task presents significant challenges, primarily due to the lack of accurate ground-truth data. Obtaining high-fidelity 3D deformations requires special equipment like a multi-camera system, which is not easily scalable. To address this challenge, we decompose the clothing into a base surface and fine wrinkles, and introduce a new method that can generate wrinkles as high-frequency 3D displacement from coarse clothing deformation. Our method is conditioned by Green-Lagrange strain field—a local rotation-invariant measurement that is independent of body and clothing topology, enhancing its generalizability. Using limited real data (e.g., 3K) of a clothing, we train a diffusion model that can generate high-fidelity wrinkles from a coarse clothing mesh, conditioned on its strain field. Practically, we obtain the coarse clothing mesh using a body-conditioned VAE, ensuring compatibility of the deformation with the body pose. In our experiments, we demonstrate that our generative wrinkle model outperforms existing methods by synthesizing high-fidelity wrinkle deformation from novel body poses and clothing while preserving the quality comparable to the one from training data.