yisol/IDM-VTON

Features

• Virtual Try-On Systems - Implements a deep learning framework for synthesizing realistic images of people wearing specific garments.
• Fashion Visualization - Enables the creation of digital visual previews of clothing items on human bodies.
• Image-to-Image Diffusion Toolkits - Provides a diffusion-based toolkit for synthesizing high-fidelity try-on images from text and image prompts.
• Latent Diffusion Models - Utilizes latent diffusion models to create high-fidelity clothing overlays on human figures.
• Fashion Image Synthesis - Acts as a computational tool for generating synthetic fashion imagery through texture and pose blending.
• Virtual Fitting Rooms - Provides the underlying generative model to implement a digital virtual fitting room experience.
• Spatial Correlation Alignments - Implements attention-based feature alignment to correlate garment patches with human body geometry.
• Latent Space Encoders - Utilizes latent space encoders to process compressed image representations for reduced computational cost.
• Cross-Attention Conditioning - Steers the image generation network by injecting garment-specific embeddings via cross-attention conditioning.
• U-Net Architectures - Employs a U-Net architecture with skip connections to recover spatial details during the denoising process.
• Mask-Based Area Replacement - Uses spatial masks to isolate garment and person areas for precise generative replacement.
• Virtual Try-On Interfaces - Provides a web-based application interface for real-time visual demonstrations of garment try-ons.

Star history