IDM-VTON introduces an innovative approach to enhance the consistency and authenticity of virtual try-on images, focusing on preserving garment details and improving overall visual quality. The method leverages sophisticated attention modules and detailed textual prompts to achieve these goals.

At the core of IDM-VTON are two essential components: the image prompt adapter and the GarmentNet UNet encoder. The image prompt adapter encodes high-level semantics of the garment image, while the GarmentNet extracts low-level features to preserve fine-grained details. By conditioning the diffusion model with these components, IDM-VTON achieves better consistency and authenticity in generating virtual try-on images.

The integration of detailed textual prompts for both garment and person images further enhances the accuracy and authenticity of the generated visuals. This customization method significantly improves the fidelity of virtual try-on images, particularly in real-world scenarios where authenticity is crucial.