Best AI tools for< Decorate For The Holidays >

HomeStage is an AI-powered virtual furnishing tool that allows users to instantly visualize how their space would look with different furniture and decor items. With just one click, users can transform their empty rooms into beautifully furnished spaces, helping them make informed decisions about interior design and furniture placement. HomeStage leverages advanced AI algorithms to accurately place virtual furniture in real-time, providing a realistic and immersive experience for users. Whether you're a homeowner, interior designer, or real estate agent, HomeStage is the perfect solution to bring your design ideas to life.

InteriorAI Room Planner is an AI-powered interior design tool that allows users to easily transform any room in their home. With just a photo, the AI technology can create a high-quality design that considers dimensions, lighting, and furniture. Users can also get personalized advice from an AI design expert to help them create a space that matches their style and preferences.

Quick Dreamviz is an instant dream home visualization tool that allows users to redesign their rooms using AI technology. With just a few clicks, users can upload a photo of their room, select a room type and theme, and watch as the AI generates a new design. Quick Dreamviz is perfect for anyone who wants to see how their dream home will look before it becomes a reality.

Designify is an AI-powered interior design service that allows users to redesign their rooms in seconds. Users simply need to take a picture of their room and choose the themes they like, and the AI will generate a new design. Designify offers over 60 different interior themes to choose from, so users can find the perfect fit for their space. The AI is also able to generate designs for unfurnished rooms, making it a great tool for people who are starting from scratch. Designify is a great way to get inspiration for your next interior design project, and it's also a great way to see how your room would look with different furniture and décor.

HomeDesignsAI is an AI-powered home design tool that allows users to redesign and decorate their homes in seconds. With over 75 design styles to choose from, users can create realistic and stunning designs for any room in their house. HomeDesignsAI is easy to use and can be used by anyone, regardless of their design experience. It is a great tool for homeowners, interior designers, and real estate agents alike.

Fontis.ai is an AI-powered art printing service that allows users to create unique and personalized prints from their own images. Users can choose from a variety of print options, including canvases, posters, mugs, stickers, and puzzles. Fontis.ai's AI technology analyzes the user's image and generates a unique print that is tailored to their individual style. The service is easy to use and affordable, making it a great option for anyone looking to create a unique and personalized gift.

PetPet is an AI-powered application that allows users to create custom pet portraits from their pet's photos. With the help of advanced artificial intelligence technology, users can easily turn a photo of their pet into a modern hand-painted style masterpiece in just minutes. The application offers a variety of stylish and interesting styles to choose from, including watercolor pet dog portraits, oil painting pet portraits, and more. Users can showcase their furry companions with premium quality pet portraits at an affordable price. PetPet.AI is the perfect solution for pet lovers looking to express their love for their pets through beautiful artwork.

Acrylic is an AI-powered application that allows users to create personalized paintings for home decor. The app leverages artificial intelligence technology to help users design unique artwork that can be previewed using augmented reality. With Acrylic, users can unleash their creativity, personalize their home decor, and order high-quality canvas paintings of their creations. The app offers a user-friendly interface with preset selections and supports easy purchasing through Apple Pay.

Roughly is a creative platform that allows users to bring their ideas to life through art and design. The platform enables users to dream like an artist, draw like a kid, and create like a professional. With a focus on various categories such as architecture, portraits, interiors, games, characters, landscapes, fashion, movies, sculptures, and sneakers, Roughly provides a space for users to unleash their creativity and imagination. The platform also emphasizes privacy and adheres to strict terms of service to protect user rights and content. Join Roughly to explore a world of artistic possibilities and turn your visions into reality.

AI Room Styles is a web-based home staging assistant that allows users to transform their room's pictures into different styles and colors. With 40 free monthly renderings and thousands of possible setups, users can easily create beautiful and realistic images of their rooms. The tool is perfect for individuals, architects, and real estate agents who want to quickly and easily improve the look of their rooms.

IACrea is an online platform that allows users to virtually stage and decorate their homes using artificial intelligence. With IACrea, users can upload photos of their rooms and choose from a variety of furniture and décor options to create realistic and visually appealing images. IACrea's AI technology automatically generates the staged images, making it quick and easy for users to see how their homes would look with different furnishings and décor.

Indesignify is an innovative platform that utilizes artificial intelligence to revolutionize interior design. It offers users the ability to decorate rooms, generate design ideas, and visualize spaces quickly and effortlessly. With Indesignify, users can build prototypes of interior designs in seconds, transform empty spaces into fully furnished rooms, explore a wide range of interior design styles, customize design rules, and generate diverse and stunning visualizations to boost conversion rates for real estate agents or secure more clients for interior designers.

Lifelike is a website that offers a wide range of lifelike products and services to enhance your daily life. From lifelike artificial plants and flowers to lifelike animal sculptures and lifelike human figurines, we provide high-quality and realistic items to add beauty and charm to your surroundings. Our lifelike products are meticulously crafted to resemble their real counterparts, making them perfect for home decor, events, and gifts. With Lifelike, you can bring a touch of nature and artistry into your life with ease.

Pattern Maker AI is a web-based tool that allows users to generate unique and creative seamless patterns. With a few simple clicks, users can create patterns that can be used for a variety of purposes, such as fabric design, wallpaper, and website backgrounds. Pattern Maker AI is easy to use and does not require any prior design experience. Simply select a few colors and shapes, and Pattern Maker AI will generate a pattern that is both unique and visually appealing.

MyRoomDesigner.ai is an online interior design tool that allows users to upload a photo of their room and apply different design styles and themes to it. With just a few clicks, users can transform any room in their home into a stylish and welcoming space. The tool is easy to use and does not require any prior design experience. Users can choose from a variety of popular interior design themes, including Farmhouse, Coastal, Modern, and Traditional. MyRoomDesigner.ai also allows users to share their styled photos with others.

Decorify.app is an AI-powered interior design tool that helps you create beautiful and functional spaces. With Decorify.app, you can easily design your dream room in any style, from modern to traditional. Simply upload a photo of your space and start designing! Decorify.app will provide you with a variety of furniture and décor options to choose from, and you can even see how your design will look in 3D.

Coated is an AI Interior Design Assistant that allows users to effortlessly transform any space by creating beautiful and personalized designs. Users can upload a photo, select a preferred style from a variety of options, and let Coated handle the rest. With 11+ predefined interior styles to choose from, users can easily decorate their spaces to reflect their unique preferences and tastes.

AI Coloring Pages is a website that allows users to generate unique and printable coloring pages using artificial intelligence. The website features a variety of different coloring pages, including animals, landscapes, and people. Users can also upload their own images to color. The website is easy to use and does not require any special software or skills. Simply select a coloring page, click on the "Generate" button, and then print out the coloring page.

myaipainting.com is an AI-powered painting assistant that helps you create beautiful, realistic paintings with just a few clicks. With our easy-to-use interface and powerful AI algorithms, you can create stunning works of art in minutes, even if you have no prior painting experience.

HomeStyler AI is an innovative interior design tool that allows users to easily imagine, decorate, and visualize their dream home. With the help of state-of-the-art AI technology, users can experiment with up to 14 different styles using just a picture of their space. The platform offers a user-friendly experience, making interior design accessible to everyone, regardless of their expertise. HomeStyler AI provides a wide range of features and benefits to help users bring their design ideas to life.

The Microsoft AI for Earth program hosts geospatial data on Azure that is important to environmental sustainability and Earth science. This repo hosts documentation and demonstration notebooks for all the data that is managed by AI for Earth. It also serves as a "staging ground" for the Planetary Computer Data Catalog.

LLMPerf is a tool designed for evaluating the performance of Language Model APIs. It provides functionalities for conducting load tests to measure inter-token latency and generation throughput, as well as correctness tests to verify the responses. The tool supports various LLM APIs including OpenAI, Anthropic, TogetherAI, Hugging Face, LiteLLM, Vertex AI, and SageMaker. Users can set different parameters for the tests and analyze the results to assess the performance of the LLM APIs. LLMPerf aims to standardize prompts across different APIs and provide consistent evaluation metrics for comparison.

This repository hosts the official code and data artifact for the paper 'Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context'. It introduces Monitor-Guided Decoding (MGD) for code generation using Language Models, where a monitor uses static analysis to guide the decoding. The repository contains datasets, evaluation scripts, inference results, a language server client 'multilspy' for static analyses, and implementation of various monitors monitoring for different properties in 3 programming languages. The monitors guide Language Models to adhere to properties like valid identifier dereferences, correct number of arguments to method calls, typestate validity of method call sequences, and more.

Project Miyagi showcases Microsoft's Copilot Stack in an envisioning workshop aimed at designing, developing, and deploying enterprise-grade intelligent apps. By exploring both generative and traditional ML use cases, Miyagi offers an experiential approach to developing AI-infused product experiences that enhance productivity and enable hyper-personalization. Additionally, the workshop introduces traditional software engineers to emerging design patterns in prompt engineering, such as chain-of-thought and retrieval-augmentation, as well as to techniques like vectorization for long-term memory, fine-tuning of OSS models, agent-like orchestration, and plugins or tools for augmenting and grounding LLMs.

The Microsoft Health Intelligence Machine Learning Toolbox is a repository that provides low-level and high-level building blocks for Machine Learning / AI researchers and practitioners. It simplifies and streamlines work on deep learning models for healthcare and life sciences by offering tested components such as data loaders, pre-processing tools, deep learning models, and cloud integration utilities. The repository includes two Python packages, 'hi-ml-azure' for helper functions in AzureML, 'hi-ml' for ML components, and 'hi-ml-cpath' for models and workflows related to histopathology images.

A fine-tuning guide for both OpenAI and Open-Source Large Language Models on Azure. Fine-Tuning retrains an existing pre-trained LLM using example data, resulting in a new 'custom' fine-tuned LLM optimized for task-specific examples. Use cases include improving LLM performance on specific tasks and introducing information not well represented by the base LLM model. Suitable for cases where latency is critical, high accuracy is required, and clear evaluation metrics are available. Learning path includes labs for fine-tuning GPT and Llama2 models via Dashboards and Python SDK.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

BitBLAS is a library for mixed-precision BLAS operations on GPUs, for example, the $W_{wdtype}A_{adtype}$ mixed-precision matrix multiplication where $C_{cdtype}[M, N] = A_{adtype}[M, K] \times W_{wdtype}[N, K]$. BitBLAS aims to support efficient mixed-precision DNN model deployment, especially the $W_{wdtype}A_{adtype}$ quantization in large language models (LLMs), for example, the $W_{UINT4}A_{FP16}$ in GPTQ, the $W_{INT2}A_{FP16}$ in BitDistiller, the $W_{INT2}A_{INT8}$ in BitNet-b1.58. BitBLAS is based on techniques from our accepted submission at OSDI'24.

Azure Functions OpenAI Extension is a project that adds support for OpenAI LLM (GPT-3.5-turbo, GPT-4) bindings in Azure Functions. It provides NuGet packages for various functionalities like text completions, chat completions, assistants, embeddings generators, and semantic search. The project requires .NET 6 SDK or greater, Azure Functions Core Tools v4.x, and specific settings in Azure Function or local settings for development. It offers features like text completions, chat completion, assistants with custom skills, embeddings generators for text relatedness, and semantic search using vector databases. The project also includes examples in C# and Python for different functionalities.

The Enterprise Azure OpenAI Hub is a comprehensive repository designed to guide users through the world of Generative AI on the Azure platform. It offers a structured learning experience to accelerate the transition from concept to production in an Enterprise context. The hub empowers users to explore various use cases with Azure services, ensuring security and compliance. It provides real-world examples and playbooks for practical insights into solving complex problems and developing cutting-edge AI solutions. The repository also serves as a library of proven patterns, aligning with industry standards and promoting best practices for secure and compliant AI development.

MSCCL++ is a GPU-driven communication stack for scalable AI applications. It provides a highly efficient and customizable communication stack for distributed GPU applications. MSCCL++ redefines inter-GPU communication interfaces, delivering a highly efficient and customizable communication stack for distributed GPU applications. Its design is specifically tailored to accommodate diverse performance optimization scenarios often encountered in state-of-the-art AI applications. MSCCL++ provides communication abstractions at the lowest level close to hardware and at the highest level close to application API. The lowest level of abstraction is ultra light weight which enables a user to implement logics of data movement for a collective operation such as AllReduce inside a GPU kernel extremely efficiently without worrying about memory ordering of different ops. The modularity of MSCCL++ enables a user to construct the building blocks of MSCCL++ in a high level abstraction in Python and feed them to a CUDA kernel in order to facilitate the user's productivity. MSCCL++ provides fine-grained synchronous and asynchronous 0-copy 1-sided abstracts for communication primitives such as `put()`, `get()`, `signal()`, `flush()`, and `wait()`. The 1-sided abstractions allows a user to asynchronously `put()` their data on the remote GPU as soon as it is ready without requiring the remote side to issue any receive instruction. This enables users to easily implement flexible communication logics, such as overlapping communication with computation, or implementing customized collective communication algorithms without worrying about potential deadlocks. Additionally, the 0-copy capability enables MSCCL++ to directly transfer data between user's buffers without using intermediate internal buffers which saves GPU bandwidth and memory capacity. MSCCL++ provides consistent abstractions regardless of the location of the remote GPU (either on the local node or on a remote node) or the underlying link (either NVLink/xGMI or InfiniBand). This simplifies the code for inter-GPU communication, which is often complex due to memory ordering of GPU/CPU read/writes and therefore, is error-prone.

FarmVibes.AI is a repository focused on developing multi-modal geospatial machine learning models for agriculture and sustainability. It enables users to fuse various geospatial and spatiotemporal datasets, such as satellite imagery, drone imagery, and weather data, to generate robust insights for agriculture-related problems. The repository provides fusion workflows, data preparation tools, model training notebooks, and an inference engine to facilitate the creation of geospatial models tailored for agriculture and farming. Users can interact with the tools via a local cluster, REST API, or a Python client, and the repository includes documentation and notebook examples to guide users in utilizing FarmVibes.AI for tasks like harvest date detection, climate impact estimation, micro climate prediction, and crop identification.

Vidur is a high-fidelity and extensible LLM inference simulator designed for capacity planning, deployment configuration optimization, testing new research ideas, and studying system performance of models under different workloads and configurations. It supports various models and devices, offers chrome trace exports, and can be set up using mamba, venv, or conda. Users can run the simulator with various parameters and monitor metrics using wandb. Contributions are welcome, subject to a Contributor License Agreement and adherence to the Microsoft Open Source Code of Conduct.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

This sample shows how to build a serverless chat experience with Retrieval-Augmented Generation using LangChain.js and Azure. The application is hosted on Azure Static Web Apps and Azure Functions, with Azure Cosmos DB for MongoDB vCore as the vector database. You can use it as a starting point for building more complex AI applications.

Scalene is a high-performance CPU, GPU, and memory profiler for Python that provides detailed information and runs faster than many other profilers. It incorporates AI-powered proposed optimizations, allowing users to generate optimization suggestions by clicking on specific lines or regions of code. Scalene separates time spent in Python from native code, highlights hotspots, and identifies memory usage per line. It supports GPU profiling on NVIDIA-based systems and detects memory leaks. Users can generate reduced profiles, profile specific functions using decorators, and suspend/resume profiling for background processes. Scalene is available as a pip or conda package and works on various platforms. It offers features like profiling at the line level, memory trends, copy volume reporting, and leak detection.

Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.

LLMLingua is a tool that utilizes a compact, well-trained language model to identify and remove non-essential tokens in prompts. This approach enables efficient inference with large language models, achieving up to 20x compression with minimal performance loss. The tool includes LLMLingua, LongLLMLingua, and LLMLingua-2, each offering different levels of prompt compression and performance improvements for tasks involving large language models.

RecAI is a project that explores the integration of Large Language Models (LLMs) into recommender systems, addressing the challenges of interactivity, explainability, and controllability. It aims to bridge the gap between general-purpose LLMs and domain-specific recommender systems, providing a holistic perspective on the practical requirements of LLM4Rec. The project investigates various techniques, including Recommender AI agents, selective knowledge injection, fine-tuning language models, evaluation, and LLMs as model explainers, to create more sophisticated, interactive, and user-centric recommender systems.

The Artificial Intelligence Controller Interface (AICI) lets you build Controllers that constrain and direct output of a Large Language Model (LLM) in real time. Controllers are flexible programs capable of implementing constrained decoding, dynamic editing of prompts and generated text, and coordinating execution across multiple, parallel generations. Controllers incorporate custom logic during the token-by-token decoding and maintain state during an LLM request. This allows diverse Controller strategies, from programmatic or query-based decoding to multi-agent conversations to execute efficiently in tight integration with the LLM itself.

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