How does TryOnDiffusion differ from previous virtual try-on solutions?

It replaces single-stage generators with a two-stage diffusion pipeline that improves warping realism and preserves garment fidelity, reducing artifacts in challenging poses.

What are the key features of TryOnDiffusion?

A two-stage Warping and Try-On Diffusion process, a Parallel-UNet design, robust handling of pose misalignments, and strong garment detail preservation.

What datasets are used in training and evaluation?

DeepFashion and VITON are used to train and evaluate the system across diverse clothing types and human poses.

Is the code for TryOnDiffusion publicly available?

Yes. The website links to the GitHub repository with code and trained models.

Who developed TryOnDiffusion?

A team from the Institute of Automation, Chinese Academy of Sciences, and the University of Chinese Academy of Sciences.

What is the main workflow of TryOnDiffusion?

First, a Warping Module aligns the garment with the target person’s pose; second, a Try-On Diffusion Module synthesizes the final image while preserving garment details.

Are supplementary materials or pretrained models available?

Yes. The website provides links to supplementary materials and pretrained models.

Can TryOnDiffusion handle complex clothing types and poses?

Yes. It is designed to handle large pose variations, occlusions, and diverse clothing categories.

Does TryOnDiffusion provide results or comparisons with other methods?

Yes. The website showcases qualitative results and comparisons with prior state-of-the-art, highlighting improved realism and garment fidelity.

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TryOnDiffusion

Name: TryOnDiffusion
Brand: TryOnDiffusion
Rating: 5 (1 reviews)
Author: TryOnDiffusion

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Last updated: August 31, 2025

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What is TryOnDiffusion?

TryOnDiffusion is a state-of-the-art, diffusion-based Virtual Try-On (VTON) framework that generates realistic try-on images in a zero-shot manner by combining an arbitrary person image with an in-shop garment. Its two-stage pipeline—Warping followed by Try-On Diffusion—leverages a Parallel-UNet design to robustly handle pose and garment misalignments while preserving fine clothing details. Trained and evaluated on DeepFashion and VITON, TryOnDiffusion demonstrates strong performance across challenging poses and clothing categories, with open-source code and pretrained models available and results validated in CVPR 2023.

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TryOnDiffusion's Top Features

Diffusion-based architecture for virtual try-on

Two-stage pipeline: Warping Module and Try-On Diffusion Module

Parallel-UNet design to preserve garment details during synthesis

Zero-shot try-on from arbitrary person and in-shop garment images

Robust handling of pose variations and warping misalignments

High-fidelity garment detail preservation and realism

Trained and evaluated on DeepFashion and VITON datasets

Open-source code and pretrained models available

Validated with results and comparisons on the project website

Research published and presented at CVPR 2023

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