AI fitting

Kolors Virtual Try On is a platform that uses advanced AI technology to provide online virtual try-on services. It helps users visualize clothing products in a real environment through virtual modeling, reducing the cost of returns and exchanges caused by unsatisfactory sizes or unsatisfactory styles. Users can try on clothing anytime, anywhere and make smarter shopping choices. The platform is compatible with multiple platforms, provides personalized recommendations, and is mobile-friendly. Kolors Virtual Try On's privacy policy ensures the security of user data and all uploaded photos will be securely deleted after processing.

M&M VTO is a mix-and-match virtual try-on method that accepts multiple images of clothing, a text description of the clothing layout, and a picture of a person as input, and the output is a visualization of these clothes worn on a given person in a specified layout. The main advantages of this technology include: a single-stage diffusion model, without the need for super-resolution cascades, capable of mixing and matching multiple garments at 1024x512 resolution, while retaining and distorting complex garment details; the architectural design (VTO UNet Diffusion Transformer) can separate denoising and character-specific features, achieving an efficient identity-preserving fine-tuning strategy; controlling the layout of multiple garments through text input, specifically fine-tuning for virtual try-on tasks. The M&M VTO achieves state-of-the-art performance both qualitatively and quantitatively and opens up new possibilities for verbal guidance and multi-garment fitting.

Visual Try-On Chrome Extension is a Chrome browser plug-in that uses artificial intelligence image processing technology to allow users to virtually try on clothes on any e-commerce website. The plug-in captures the main product image through OpenAI GPT-4, uploads user images to Cloudinary, uses the Kolors model on Hugging Face for AI processing, and stores the results in the browser cache to improve usability. It protects user privacy and does not send personal data or pictures to the server, except when Hugging Face performs AI processing.

Kolors Virtual Try-On is a virtual try-on application that combines artificial intelligence and augmented reality technology to generate natural and beautiful try-on effects based on given model images and selected clothes. This product supports the entire process generation from model material pictures to model short videos, meeting the needs of e-commerce model material generation.

CatVTON is a virtual try-on technology based on the diffusion model, with lightweight network (899.06M parameters in total), efficient parameter training (49.57M trainable parameters) and simplified inference (<8G VRAM at 1024X768 resolution). It achieves fast and efficient virtual try-on effects through simplified network structure and reasoning process, which is especially suitable for the fashion industry and personalized recommendation scenarios.

Outfit Anyone is a client program that calls the interface for virtual try-on. This model is not open source, fixed and cannot be uploaded or modified. It only supports users to upload their own clothing.

IDM-VTON is a novel diffusion model for image-based virtual try-on tasks, which generates virtual try-on images with high realism and detail by combining high-level semantics and low-level features of visual encoders and UNet networks. The technology enhances the realism of generated images by providing detailed textual prompts, and further improves fidelity and realism in real-world scenarios through customization methods.

Diffuse to Choose is a diffusion-based image repair model mainly used in virtual try-on scenarios. It is able to preserve the details of reference items when repairing images and is capable of accurate semantic operations. By directly incorporating the detailed features of the reference image into the latent feature map of the main diffusion model, and combining perceptual losses to further preserve the details of the reference items, this model achieves a good balance between fast inference and high-fidelity details.

The fAIshion virtual fitting plug-in integrates AI technology to provide comprehensive pre-sales solutions for online shopping consumers. The main functions include AI-generated diversified try-on effects that adapt to different body types, ages and races, and provide AI size recommendations and automatic discount code matching for a personalized shopping experience.

Outfit Anyone is an ultra-high-quality virtual try-on product that allows users to try on different fashion styles without actually trying on the clothes. By employing a two-flow conditional diffusion model, Outfit Anyone is able to flexibly handle clothing deformation and produce more realistic effects. It's scalable and can adjust factors such as pose and body shape, and works on images from anime characters to real people. Outfit Anyone's performance in a variety of scenarios highlights its practicality and readiness for real-world applications.