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AI-powered fashion visualization and virtual try-on toolkit for consent-based garment editing, outfit prototyping, and creative design workflows. https://undress.design/edit?utm_source=gist.github.com/chordswallowthrust/undress-design

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✨ Undress Design

Undress Design is an AI-assisted fashion visualization project for experimenting with clothing concepts, outfit variations, garment replacement, and virtual styling.

The project is intended for designers, developers, creative teams, and researchers working with responsible image-generation workflows.

🚀 Features

  • 👗 Virtual garment visualization
  • 🎨 Outfit and style variations
  • 🧵 Fashion concept prototyping
  • ✂️ Clothing segmentation and masking
  • 🖼️ Design reference generation
  • ✅ Consent-based image editing workflows
  • ⚙️ API-ready integration architecture

🌐 Website

Learn more and try the platform:

Try Undress Design

🛡️ Responsible Use

Undress Design is designed exclusively for lawful, ethical, and consent-based applications.

Users must:

  • ✅ process only images they own or have explicit permission to use;
  • 🤝 obtain informed consent from every identifiable person depicted;
  • 🚫 avoid sexualized, deceptive, abusive, or non-consensual edits;
  • 🔞 avoid generating content involving minors;
  • 🔒 respect privacy, intellectual-property, and publicity rights;
  • 🏷️ clearly label AI-generated or AI-edited media where appropriate.

This project must not be used to remove clothing from photographs, generate intimate imagery, impersonate individuals, or create misleading representations of real people.

💡 Use Cases

  • 👚 Virtual try-on experiences
  • 🎭 Fashion moodboards
  • 🛍️ E-commerce product visualization
  • 🦸 Costume and character design
  • 🧥 Garment replacement prototypes
  • 📢 Creative advertising concepts
  • 🔬 Research into responsible generative AI

📌 Status

This repository contains project information, integration examples, and documentation related to the Undress Design platform.

⚠️ Disclaimer

AI-generated results may contain visual inaccuracies. Users are responsible for reviewing generated content and ensuring that its creation and distribution comply with applicable laws, platform policies, and consent requirements.

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