# levihsu/ootdiffusion

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6,556 stars · 953 forks · Python · NOASSERTION

## Links

- GitHub: https://github.com/levihsu/OOTDiffusion
- awesome-repositories: https://awesome-repositories.com/repository/levihsu-ootdiffusion.md

## Description

OOTDiffusion is an AI virtual try-on system designed for controllable image synthesis. It generates images of people wearing specific clothing items by superimposing garments onto human figures for both half-body and full-body compositions.

The project facilitates digital fashion prototyping and virtual clothing fitting by creating garment-to-person overlays. It aims to maintain the original identity of the wearer and the specific details of the clothing during the synthesis process.

The system utilizes a latent diffusion model and conditioning-based image generation to control the output. It employs a cross-attention mechanism and dual-path feature fusion to align visual features from clothing images to the human figure.

## Tags

### Artificial Intelligence & ML

- [Latent Diffusion Models](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-models/latent-diffusion-models.md) — Based on a latent diffusion model that performs iterative denoising within a compressed latent space.
- [Fashion Visualization](https://awesome-repositories.com/f/artificial-intelligence-ml/fashion-visualization.md) — Facilitates digital fashion prototyping by creating visual previews of clothing on human bodies.
- [Feature Fusion Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-fusion-architectures.md) — Employs dual-path feature fusion to merge person and clothing representations for structural alignment and texture preservation.
- [Cross-Attention Mechanisms](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-architectures/cross-attention-mechanisms.md) — Utilizes cross-attention mechanisms to align garment visual features with specific spatial regions of the human figure.
- [Image-Conditioned Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/image-generation/image-conditioned-generation.md) — Implements generation guided by garment images and human poses as structural and stylistic references.
- [Variational Autoencoders](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/variational-autoencoders.md) — Utilizes a variational autoencoder to compress high-resolution images into low-dimensional tensors.
- [Iterative Denoising Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-architectures/u-net-architectures/iterative-denoising-pipelines.md) — Uses a U-Net based architecture for the iterative denoising process essential to image synthesis.
- [Image-to-Image Synthesis Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/image-to-image-synthesis-frameworks.md) — Implements a framework for merging garment and person images using reference guidance for precise overlays.

### Part of an Awesome List

- [Human Image and Video Generation](https://awesome-repositories.com/f/awesome-lists/ai/human-image-and-video-generation.md) — Generates realistic images of human figures wearing specific garments for half-body and full-body compositions. ([source](https://github.com/levihsu/ootdiffusion#readme))
- [Virtual Try-On Systems](https://awesome-repositories.com/f/awesome-lists/ai/virtual-try-on-systems.md) — Provides a deep learning system for mapping clothing items onto human images via virtual try-on.

### Graphics & Multimedia

- [Virtual Fitting Rooms](https://awesome-repositories.com/f/graphics-multimedia/room-visualizers/room-cleaning-queues/virtual-fitting-rooms.md) — Enables virtual fitting by visualizing how specific garments look on human figures for e-commerce applications.