muzishen/IMAGDressing

Health v2 · maintenance only

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1343 stars · 117 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

IMAGDressing-v1 is a diffusion-based framework for customizable virtual dressing that generates human images with fixed garments and controllable pose, face, and scene via a garment UNet with hybrid attention. It includes inference and training code, a Gradio WebUI, the CAMI evaluation metric, and the 300k-pair IGPair dataset.

Use cases

• generate model photos wearing a specific garment for online stores
• virtual try-on of clothes onto a person image
• change the outfit of a person in a photo via inpainting
• control pose and scene when generating dressed human images
• train a garment-consistent diffusion model on the IGPair dataset
• evaluate garment consistency of generated images with CAMI
• create cartoon-style virtual dressing images

When to choose

• you need garment-faithful human image generation rather than localized inpainting try-on
• you want flexible control over pose, face, and background with Stable Diffusion plugins like ControlNet and IP-Adapter
• you need a large garment-person paired dataset for training virtual dressing models

When to avoid

• you only need simple 2D warping-based try-on without generative synthesis
• you lack a GPU or cannot run Stable Diffusion locally
• you need production e-commerce integration out of the box rather than research code

Facets

library · maturity active

image-processing machine-learning deep-learning llm-inference computer-vision image-processing artificial-intelligence e-commerce python virtual-try-on diffusion-models stable-diffusion garment-generation text-to-image controlnet ip-adapter dataset gradio aaai-2025 gpu linux

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