Diffusion Models in Virtual Clothing Try-OnCvetković, Ana (Avtor) Peer, Peter (Mentor) Lampe, Ajda (Komentor) diffusion modelsvirtual try-onstable diffusionControlNetIP-AdapterVirtual try-on aims to synthesize new garments on a person image while preserving identity, pose, and scene context for applications such as e-commerce and design exploration. This thesis extends DiCTI, a text-guided diffusion inpainting pipeline, to reduce pose drift, improve mask coverage for loose garments, and enable finer fabric control than text alone. To address the limitations the proposed PMFR-DiCTI integrates DensePose-based ControlNet conditioning for pose preservation, a union mask combining DensePose and SegFormer clothing segmentation for more reliable garment masking, fabric reference image conditioning via IP-Adapter and region-selective editing. Evaluation on a VITON-HD subset (2250 generated images) shows improved realism and pose consistency, including a 57% reduction in KID metric and a 53% improvement in pose distance compared to the baseline. A user study with 30 participants further favors PMFR-DiCTI outputs across pose preservation, fabric accuracy, and garment structure with statistically significant preference. This work effectively addresses DiCTI's limitations while preserving zero-shot generalization, providing a practical framework for controllable garment synthesis.20262026-03-25 10:10:20Diplomsko delo/naloga181104sl