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Painterly image harmonization aims to insert photographic objects intopaintings and obtain artistically coherent composite images. Previous methodsfor this task mainly rely on inference optimization or generative adversarialnetwork, but they are either very time-consuming or struggling at fine controlof the foreground objects (e.g., texture and content details). To address theseissues, we propose a novel Painterly Harmonization stable Diffusion model(PHDiffusion), which includes a lightweight adaptive encoder and a Dual EncoderFusion (DEF) module. Specifically, the adaptive encoder and the DEF modulefirst stylize foreground features within each encoder. Then, the stylizedforeground features from both encoders are combined to guide the harmonizationprocess. During training, besides the noise loss in diffusion model, weadditionally employ content loss and two style losses, i.e., AdaIN style lossand contrastive style loss, aiming to balance the trade-off between stylemigration and content preservation. Compared with the state-of-the-art modelsfrom related fields, our PHDiffusion can stylize the foreground moresufficiently and simultaneously retain finer content. Our code and model areavailable at https://github.com/bcmi/PHDiffusion-Painterly-Image-Harmonization.

Painterly Image Harmonization using Diffusion Model