PDDM: IP Image Design via Diffusion Model

Traditional methods and models, while capable of generating high-quality images, often require substantial human resources and time, limiting their feasibility for large-scale applications. To address these challenges, this paper proposes the Pose-Directed Diffusion Model. Through optimized training of this module, the model in this paper significantly reduces training time. Additionally, the model in this paper integrates the Pro-ControlNet module to enhance fine control during the image generation process. Importantly, this paper constructs a new CCC Image Collection dataset, featuring precise classifications, detailed annotations, and high-resolution images. The experimental results on the CCC Image Collection dataset demonstrate that our model performs excellently in terms of evaluation metrics such as MSE, PSNR, and SSIM. It shows significant improvement in training effectiveness on the original dataset, with outstanding performance both in terms of absolute error measurement and visual perception by the human eye.