To address the challenges of the absence of baseline ground truth and the underutilization of visible light information in infrared and visible light image fusion using denoising diffusion models， this study introduces a novel conditional diffusion and multi-channel high-low frequency parallel infrared and visible light image fusion model. First， a conditional diffusion model is developed， employing a splicing technique to generate spliced source images as ground truth during training， thereby facilitating an optimal prior distribution for feature extraction in infrared and visible images. During the reverse denoising process， a multi-channel likelihood correction module is incorporated to effectively model the intricate multi-channel distribution of these images. Subsequently， a detail-adaptive denoising network is proposed to perform multi-channel high- and low-frequency feature extraction for infrared and visible light images. The model also integrates a multi-channel high- and low-frequency parallel fusion module within the fusion network， which utilizes a regional consistency fusion network and a multi-channel low-frequency feature fusion network to merge high- and low-frequency features， respectively. This approach introduces a trainable diffusion-based paradigm for feature extraction in infrared and visible light image fusion tasks， leveraging specialized convolutional neural networks for feature integration. Comparative experiments on the MSRS and RoadScene datasets， against nine state-of-the-art methods， reveal that the proposed model improves the average performance across eight objective evaluation metrics by 4.52% to 59.62%. The method demonstrates superior performance in maintaining color fidelity and preserving texture details， aligning well with human visual perception， and proves robust in handling diverse lighting and environmental conditions for infrared and visible light image fusion tasks.