ColorizeDiffusion: Improving Reference-Based Sketch Colorization with Latent Diffusion Model

Diffusion models have achieved great success in dual-conditioned image generation. However, they still face significant challenges in image-guided sketch colorization, where reference and sketch images usually exhibit different semantics and spatial structures. This mismatch, termed 'distribution shift' in this peper, results in various artifacts and degrades the colorization quality. To address this issue, we conducted thorough investigations into the image-prompted latent diffusion model and developed a two-stage training framework to mitigate the effects of distribution shift based on our analysis. Comprehensive quantitative comparisons, qualitative evaluations, and user studies were performed to demonstrate the superiority of our proposed methods. Additionally, ablation studies were conducted to assess the impact of the distribution shift and the selection of reference embeddings. Codes are made publicly available at https://github.com/tellurion-kanata/colorizeDiffusion.