TryOffDiff is a high-fidelity garment reconstruction technology that generates standardized garment images from a single photo of an individual wearing clothing. Unlike traditional virtual try-ons, this technology aims to extract normative garment images, which poses unique challenges in capturing garment shape, texture, and complex patterns. TryOffDiff ensures high fidelity and detail retention by utilizing Stable Diffusion and SigLIP-based visual conditions. Experiments on the VITON-HD dataset demonstrate that its approach outperforms baseline methods based on pose transfer and virtual try-on while requiring fewer preprocessing and postprocessing steps. TryOffDiff not only enhances the quality of e-commerce product images but also advances the evaluation of generative models and inspires future work in high-fidelity reconstruction.