Best AI tools for< Try Clothes >

IDM-VTON online is an AI virtual try-on tool that allows users to try on clothes virtually. Users can upload their human picture and garment picture, select the garment category, and quickly obtain an AI-generated photo of themselves wearing the selected clothing item. The tool is designed to enhance the online shopping experience, personalized recommendations, social media interactions, and fashion design displays.

Kolors Virtual Try On is an AI-powered fashion fitting tool that allows users to virtually try on clothing and accessories. By uploading images of themselves and the garments they want to try, users can quickly generate realistic virtual try-on results. The cutting-edge AI technology behind Kolors Virtual Try On ensures fast and accurate processing, providing users with a diverse range of clothing options to experiment with. The tool offers a secure platform with advanced encryption to protect user privacy, making it a convenient and reliable solution for exploring different fashion styles.

Avumi is an AI-powered 3D Fashion Virtual Try-on tool designed for E-commerce businesses. It allows customers to virtually try on clothes with accuracy, enhancing the online shopping experience. Avumi provides a seamless and interactive way for users to visualize how clothing items would look on them before making a purchase decision. The tool leverages advanced AI technology to create realistic virtual try-on experiences, helping businesses reduce returns and increase customer satisfaction.

FancyAI is an AI-powered photo and video editing tool that offers features like background removal, image enhancement, virtual try-on, and video background changing. It helps users effortlessly create professional visuals for marketing and e-commerce purposes. The tool is designed to streamline the process of creating eye-catching content and improving image quality without the need for advanced design skills.

Woy AI Tools Directory is a comprehensive platform showcasing the best and latest AI tools in 2024. It features a wide range of AI applications designed to enhance various aspects of daily life, from CV building and content generation to image enhancement and video creation. Users can explore cutting-edge AI technologies across different domains, such as recruitment, fashion, text-to-speech, translation, and more. The platform aims to simplify complex tasks, boost productivity, and personalize user experiences through innovative AI solutions.

Glasses Gone is an AI-powered online tool that allows users to easily remove eyeglasses from portrait photos in just minutes. The tool is designed to work best with simple frame colors and clear lenses, offering features such as highlighting eye-glasses with precision brush, AI enhancement for cleanup and editing, and bulk pricing options for cost-effective usage. Users can also explore additional features like erasing backgrounds, replacing backgrounds, trying on clothes virtually, creating pixel art, and accessing a gallery of AI-edited photos. Glasses Gone provides a seamless and efficient solution for enhancing portrait photos with its advanced AI technology.

Change Clothes AI is an innovative AI-powered online tool that revolutionizes the way we try on clothes. By utilizing cutting-edge AI algorithms, the application analyzes user photos and garment images to seamlessly create realistic images of individuals wearing new outfits. With a user-friendly interface and hyperrealistic results, Change Clothes AI eliminates the guesswork in online shopping, allowing users to visualize themselves in different styles effortlessly. The application offers a free trial and aims to provide a fun and functional experience for exploring endless outfit possibilities.

Outfit Changer is a revolutionary AI-powered platform that transforms the fashion experience with its virtual try-on technology. It empowers users to explore a vast collection of clothing styles and virtually try them on, accommodating various body types and offering a realistic and inclusive experience. Outfit Changer's advanced AI ensures high-quality garment fitting, enhanced detailing, and the ability to create outfits for animation characters, making it a versatile tool for fashion enthusiasts and professionals alike.

Outfit.fm is an AI-powered online dressing room that allows users to try on any outfit they want instantly. It uses AI to create realistic images of the user wearing the clothes, so they can see how they look before they buy. Outfit.fm is free to use while in beta, and no signup is required.

HeyBeauty is an innovative AI-driven platform that revolutionizes the online shopping experience by offering a virtual dressing room powered by cutting-edge artificial intelligence technology. Users can try on clothes digitally, receive personalized style recommendations, and benefit from accurate size predictions, all from the comfort of their homes. HeyBeauty aims to make online shopping interactive, convenient, and tailored to individual preferences.

West Idol is an AI-powered application that allows users to create beautiful and consistent AI-generated characters effortlessly. With the ability to do a photoshoot in just 14 seconds, the platform offers a range of features such as creating brand new faces, trying on clothes virtually, and more. Users can choose from different pricing plans tailored for students, individuals, and businesses, each offering various benefits like AI-enhancing tools, commercial usage licenses, and priority support. Packed with features like fast rendering, AI inpainting, upscaling, text-to-image conversion, and background removal, West Idol provides a seamless and intuitive design for users to unleash their creativity.

Photo AI™ is a cutting-edge AI application that allows users to generate over 120 unique avatar styles and high-quality AI photographs instantly. Users can create AI models of themselves, conduct virtual photo shoots, and explore various creative looks with the help of AI-generated avatars. The application offers features like AI fashion week, AI makeup try-on, and AI OnlyFans, enabling users to experiment with different styles and settings without the need for expensive photographers. With Photo AI™, users can create photorealistic scenes, videos, and even try on clothes virtually, making it a versatile tool for content creators, photographers, and individuals looking to enhance their digital identity.

Online AI Baby Generator is an AI application that predicts the appearance of a future child based on the facial features of the parents. It uses advanced algorithms to analyze parental photos, extract facial features, and combine them statistically to render the future child's appearance. The tool respects genetic inheritance, ensures privacy by encrypting and erasing user photos, and offers continuous innovation with features like changing hairstyles and clothes for future children. Users can upload parental photos, select gender and photo dimensions, and receive the prediction within minutes. The tool is designed for entertainment purposes and not for medical or genetic analysis.

DressMeUp is an AI-powered virtual dressing room platform that allows users to virtually try on dresses from the comfort of their own home. The platform offers personalized recommendations based on body shape and size, providing a seamless shopping experience with endless options to explore. Users can browse through an extensive collection of dresses, visualize their perfect fit, and receive tailored suggestions. DressMeUp aims to revolutionize the way people shop for clothing by leveraging AI technology to enhance the online shopping experience.

Pyxer is an AI fashion application that helps users find the right clothes, jewelry, hairstyles, and portraits effortlessly. It acts as a personal stylist, fashion photographer, and shopping assistant, utilizing artificial intelligence to generate outfits and enhance photos. Pyxer values user privacy and offers personalized recommendations based on individual preferences. Users can explore the latest trends, experiment with different looks, and create stunning portraits with ease. The application aims to simplify the fashion discovery process and provide a unique shopping experience tailored to each user's style.

DDoS-Guard is a web security service that protects websites from distributed denial-of-service (DDoS) attacks. It checks the user's browser before granting access to the website, ensuring a secure browsing experience. The service provides automatic protection against DDoS attacks and ensures the smooth functioning of websites. DDoS-Guard is trusted by many websites to safeguard their online presence and maintain uninterrupted service for their users.

VirtualFit is an AI application that allows users to change their style within seconds using advanced AI technology. Users can upload their photo, specify what they want to change (e.g., clothes, hairstyle), and let the powerful AI algorithm do the rest. VirtualFit offers a range of features such as outfit replacement, image restoration, generative fill, object recoloring, and background removal. The application is designed to provide users with an affordable and user-friendly solution for enhancing their photos without the need for complex editing software like Photoshop.

Outfit Anyone AI is a free online AI clothes changer that allows users to explore endless fashion possibilities by virtually trying on different outfits. Users can upload their own model photo, choose or customize outfits, and generate new looks within seconds. The application offers a seamless shopping experience, high-quality garment fitting, and personalized styling options. With Outfit Anyone AI, users can transform their appearance from casual to corporate, try on various outfits virtually, and refresh their style for different occasions.

What If? App is an AI-powered application that allows users to perform face swaps on photos, videos, and GIFs with ease. The app supports deep face swaps, enabling users to become anyone they desire. With realistic AI quality and intuitive interface, users can unleash their creativity by morphing faces and creating fun memes. The app is accessible online without the need for any additional software downloads. What If? App offers a user-friendly experience and is free to try, making face swapping effortless for everyone.

Dressplay is an AI-powered outfit product that allows users to virtually change their clothing styles effortlessly. By selecting target clothing areas, target persons, and desired clothing items, users can generate realistic outfit combinations. The application leverages advanced AI technology to provide accurate and visually appealing results, making it a convenient tool for fashion enthusiasts and individuals looking to experiment with different looks.

Awesome OpenVINO is a curated list of AI projects based on the OpenVINO toolkit, offering a rich assortment of projects, libraries, and tutorials covering various topics like model optimization, deployment, and real-world applications across industries. It serves as a valuable resource continuously updated to maximize the potential of OpenVINO in projects, featuring projects like Stable Diffusion web UI, Visioncom, FastSD CPU, OpenVINO AI Plugins for GIMP, and more.

Cool-GenAI-Fashion-Papers is a curated list of resources related to GenAI-Fashion, including papers, workshops, companies, and products. It covers a wide range of topics such as fashion design synthesis, outfit recommendation, fashion knowledge extraction, trend analysis, and more. The repository provides valuable insights and resources for researchers, industry professionals, and enthusiasts interested in the intersection of AI and fashion.

The collection of pre-trained, state-of-the-art AI models. ailia SDK is a self-contained, cross-platform, high-speed inference SDK for AI. The ailia SDK provides a consistent C++ API across Windows, Mac, Linux, iOS, Android, Jetson, and Raspberry Pi platforms. It also supports Unity (C#), Python, Rust, Flutter(Dart) and JNI for efficient AI implementation. The ailia SDK makes extensive use of the GPU through Vulkan and Metal to enable accelerated computing. # Supported models 323 models as of April 8th, 2024

llamafile is a tool that enables users to distribute and run Large Language Models (LLMs) with a single file. It combines llama.cpp with Cosmopolitan Libc to create a framework that simplifies the complexity of LLMs into a single-file executable called a 'llamafile'. Users can run these executable files locally on most computers without the need for installation, making open LLMs more accessible to developers and end users. llamafile also provides example llamafiles for various LLM models, allowing users to try out different LLMs locally. The tool supports multiple CPU microarchitectures, CPU architectures, and operating systems, making it versatile and easy to use.

Haystack is an open-source framework for building production-ready LLM applications, retrieval-augmented generative pipelines, and state-of-the-art search systems that work intelligently over large document collections. It lets you quickly try out the latest models in natural language processing (NLP) while being flexible and easy to use.

LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

The Latent Browser is a desktop application designed like a web browser, which hallucinates web search results (the resultds are fictious and are generated by a LLM) and web pages. It is a web application designed to run locally on your machine and is 99% React, Tailwind, TypeScript, and NextJS. The runtime is Tauri, which is written in Rust. The Latent Browser is still under development and some things may be broken when you try it.

Dataflow-oriented robotic application (dora-rs) is a framework that makes creation of robotic applications fast and simple. Building a robotic application can be summed up as bringing together hardwares, algorithms, and AI models, and make them communicate with each others. At dora-rs, we try to: make integration of hardware and software easy by supporting Python, C, C++, and also ROS2. make communication low latency by using zero-copy Arrow messages. dora-rs is still experimental and you might experience bugs, but we're working very hard to make it stable as possible.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

The LLM Twin Course is a free, end-to-end framework for building production-ready LLM systems. It teaches you how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices. The course is split into 11 hands-on written lessons and the open-source code you can access on GitHub. You can read everything and try out the code at your own pace.

HuixiangDou is a **group chat** assistant based on LLM (Large Language Model). Advantages: 1. Design a two-stage pipeline of rejection and response to cope with group chat scenario, answer user questions without message flooding, see arxiv2401.08772 2. Low cost, requiring only 1.5GB memory and no need for training 3. Offers a complete suite of Web, Android, and pipeline source code, which is industrial-grade and commercially viable Check out the scenes in which HuixiangDou are running and join WeChat Group to try AI assistant inside. If this helps you, please give it a star ⭐

Vespa documentation is served using GitHub Project pages with Jekyll. To edit documentation, check out and work off the master branch in this repository. Documentation is written in HTML or Markdown. Use a single Jekyll template _layouts/default.html to add header, footer and layout. Install bundler, then $ bundle install $ bundle exec jekyll serve --incremental --drafts --trace to set up a local server at localhost:4000 to see the pages as they will look when served. If you get strange errors on bundle install try $ export PATH=“/usr/local/opt/[email protected]/bin:$PATH” $ export LDFLAGS=“-L/usr/local/opt/[email protected]/lib” $ export CPPFLAGS=“-I/usr/local/opt/[email protected]/include” $ export PKG_CONFIG_PATH=“/usr/local/opt/[email protected]/lib/pkgconfig” The output will highlight rendering/other problems when starting serving. Alternatively, use the docker image `jekyll/jekyll` to run the local server on Mac $ docker run -ti --rm --name doc \ --publish 4000:4000 -e JEKYLL_UID=$UID -v $(pwd):/srv/jekyll \ jekyll/jekyll jekyll serve or RHEL 8 $ podman run -it --rm --name doc -p 4000:4000 -e JEKYLL_ROOTLESS=true \ -v "$PWD":/srv/jekyll:Z docker.io/jekyll/jekyll jekyll serve The layout is written in denali.design, see _layouts/default.html for usage. Please do not add custom style sheets, as it is harder to maintain.

STORM is a LLM system that writes Wikipedia-like articles from scratch based on Internet search. While the system cannot produce publication-ready articles that often require a significant number of edits, experienced Wikipedia editors have found it helpful in their pre-writing stage. **Try out our [live research preview](https://storm.genie.stanford.edu/) to see how STORM can help your knowledge exploration journey and please provide feedback to help us improve the system 🙏!**

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

Databend is an open-source cloud data warehouse built in Rust, offering fast query execution and data ingestion for complex analysis of large datasets. It integrates with major cloud platforms, provides high performance with AI-powered analytics, supports multiple data formats, ensures data integrity with ACID transactions, offers flexible indexing options, and features community-driven development. Users can try Databend through a serverless cloud or Docker installation, and perform tasks such as data import/export, querying semi-structured data, managing users/databases/tables, and utilizing AI functions.

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=