ChipDiff: Staged Diffusion Model with Loss Gradient Guidance for Chinese Ink Painting Style Transfer

This paper presents ChipDiff, a novel staged diffusion framework specifically designed for Chinese ink-wash style transfer. While conventional generative models often struggle to capture the intricate monochromatic tonal layering and “bone-work” (structural brushstrokes) of traditional art, we propose a bifurcated sampling strategy guided by tailored loss gradients to reconcile structural preservation with stylistic refinement. Inspired by the traditional artistic workflow of “brushing-then-inking”, our approach decouples the denoising trajectory into two functional phases. In the first stage, we leverage a combination of content-structure, HED-edge, and semantic-divergence losses to anchor the global layout and latent skeleton. The second stage further refines the synthesis by injecting fine-grained ink-wash textures and anisotropic bleeding effects through multi-scale stylistic representation losses. Unlike prior single-stage methods, ChipDiff provides a mathematically grounded and artistically intuitive mechanism for controlled pattern generation. Extensive experiments, supported by our curated Traditional Chinese Ink-Wash Painting (TCWP) dataset and a comprehensive perception-aligned evaluation protocol, demonstrate that ChipDiff achieves superior performance in stylistic fidelity and structural integrity over state-of-the-art baselines. Project code is available at https://github.com/hengliusky/ChipDiff/.