  Portrait editing is challenging for existing techniques due to difficulties
in preserving subject features like identity. In this paper, we propose a
training-based method leveraging auto-generated paired data to learn desired
editing while ensuring the preservation of unchanged subject features.
Specifically, we design a data generation process to create reasonably good
training pairs for desired editing at low cost. Based on these pairs, we
introduce a Multi-Conditioned Diffusion Model to effectively learn the editing
direction and preserve subject features. During inference, our model produces
accurate editing mask that can guide the inference process to further preserve
detailed subject features. Experiments on costume editing and cartoon
expression editing show that our method achieves state-of-the-art quality,
quantitatively and qualitatively.
