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Optical Coherence Tomography Angiography (OCTA) is a revolutionary technology widely used in the diagnosis and management of fundus, skin and cardiovascular diseases. However, unavoidable movements, such as breathing, often introduce motion artifacts into OCTA images, which can significantly degrade image quality, obscure critical vascular details, and reduce the diagnostic reliability of the modality. Although recent advances in learning-based image inpainting methods for OCTA enface images have made notable progress in artifact removal, these methods typically require large amounts of accurately labeled data and the generation of pseudo stripes to construct paired training datasets. Additionally, the abundant structural information and flow intensity signals available in OCTA B-scans are often under-utilized. Here we proposed a novel method:B-scans to Enface Conditional Diffusion Guidance (B2E-CDG), which translates signal-void B-scans into normal B-scans. Moreover, the normal B-scans were introduced in a connection manner and the specified reference B-scans in a gradient-based manner as style feature guidance within a diffusion model. Importantly, conditional guidance facilitates a more controlled and precise generation process for flow signal recovery in B-scans. Notably, our method eliminates the need for labeled datasets and pseudo stripes, due to the repetitive scanning nature of OCTA inherently provides paired signal-void and normal B- scans. Our results demonstrated that B2E-CDG effectively removes motion artifacts and restores vascular and structural details. The proposed method shows superior performance in vascular recovery and artifact removal metrics, thereby improving the clinical utility and diagnostic reliability of OCTA.