EMA-VFI-WebUI is a web-based graphical user interface (GUI) for the EMA-VFI AI-based movie restoration tool. It provides a user-friendly interface for accessing the various features of EMA-VFI, including frame interpolation, frame search, video inflation, video resynthesis, frame restoration, video blending, file conversion, file resequencing, FPS conversion, GIF to MP4 conversion, and frame upscaling. The web UI makes it easy to use EMA-VFI's powerful features without having to deal with the command line interface.

OmAgent is an open-source agent framework designed to streamline the development of on-device multimodal agents. It enables agents to empower various hardware devices, integrates speed-optimized SOTA multimodal models, provides SOTA multimodal agent algorithms, and focuses on optimizing the end-to-end computing pipeline for real-time user interaction experience. Key features include easy connection to diverse devices, scalability, flexibility, and workflow orchestration. The architecture emphasizes graph-based workflow orchestration, native multimodality, and device-centricity, allowing developers to create bespoke intelligent agent programs.

PURE (Process-sUpervised Reinforcement lEarning) is a framework that trains a Process Reward Model (PRM) on a dataset and fine-tunes a language model to achieve state-of-the-art mathematical reasoning capabilities. It uses a novel credit assignment method to calculate return and supports multiple reward types. The final model outperforms existing methods with minimal RL data or compute resources, achieving high accuracy on various benchmarks. The tool addresses reward hacking issues and aims to enhance long-range decision-making and reasoning tasks using large language models.

Nexa SDK is a comprehensive toolkit supporting ONNX and GGML models for text generation, image generation, vision-language models (VLM), and text-to-speech (TTS) capabilities. It offers an OpenAI-compatible API server with JSON schema mode and streaming support, along with a user-friendly Streamlit UI. Users can run Nexa SDK on any device with Python environment, with GPU acceleration supported. The toolkit provides model support, conversion engine, inference engine for various tasks, and differentiating features from other tools.

Xorbits Inference (Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command. Whether you are a researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full potential of cutting-edge AI models.

KubeSphere is a distributed operating system for cloud-native application management, using Kubernetes as its kernel. It provides a plug-and-play architecture, allowing third-party applications to be seamlessly integrated into its ecosystem. KubeSphere is also a multi-tenant container platform with full-stack automated IT operation and streamlined DevOps workflows. It provides developer-friendly wizard web UI, helping enterprises to build out a more robust and feature-rich platform, which includes most common functionalities needed for enterprise Kubernetes strategy.

The MiniAiLive Face Recognition LivenessDetection Server SDK provides system integrators with fast, flexible, and extremely precise facial recognition that can be deployed across various scenarios, including security, access control, public safety, fintech, smart retail, and home protection. The SDK is fully on-premise, meaning all processing happens on the hosting server, and no data leaves the server. The project structure includes bin, cpp, flask, model, python, test_image, and Dockerfile directories. To set up the project on Linux, download the repo, install system dependencies, and copy libraries into the system folder. For Windows, contact MiniAiLive via email. The C++ example involves replacing the license key in main.cpp, building the project, and running it. The Python example requires installing dependencies and running the project. The Python Flask example involves replacing the license key in app.py, installing dependencies, and running the project. The Docker Flask example includes building the docker image and running it. To request a license, contact MiniAiLive. Contributions to the project are welcome by following specific steps. An online demo is available at https://demo.miniai.live. Related products include MiniAI-Face-Recognition-LivenessDetection-AndroidSDK, MiniAI-Face-Recognition-LivenessDetection-iOS-SDK, MiniAI-Face-LivenessDetection-AndroidSDK, MiniAI-Face-LivenessDetection-iOS-SDK, MiniAI-Face-Matching-AndroidSDK, and MiniAI-Face-Matching-iOS-SDK. MiniAiLive is a leading AI solutions company specializing in computer vision and machine learning technologies.

X-AnyLabeling is a robust annotation tool that seamlessly incorporates an AI inference engine alongside an array of sophisticated features. Tailored for practical applications, it is committed to delivering comprehensive, industrial-grade solutions for image data engineers. This tool excels in swiftly and automatically executing annotations across diverse and intricate tasks.

AITER is AMD’s centralized repository that supports various high performance AI operators for AI workloads acceleration. It serves as a unified platform for customer operator-level requests, catering to different customer needs. Developers can focus on operators and customers can integrate this collection into their own frameworks. Features include C++ and Python level APIs, kernels from triton/ck/asm, support for inference, training, GEMM, and communication kernels for workarounds in any kernel-framework combination for any architecture limitation.

OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

PowerInfer is a high-speed Large Language Model (LLM) inference engine designed for local deployment on consumer-grade hardware, leveraging activation locality to optimize efficiency. It features a locality-centric design, hybrid CPU/GPU utilization, easy integration with popular ReLU-sparse models, and support for various platforms. PowerInfer achieves high speed with lower resource demands and is flexible for easy deployment and compatibility with existing models like Falcon-40B, Llama2 family, ProSparse Llama2 family, and Bamboo-7B.

Auto-News is an automatic news aggregator tool that utilizes Large Language Models (LLM) to pull information from various sources such as Tweets, RSS feeds, YouTube videos, web articles, Reddit, and journal notes. The tool aims to help users efficiently read and filter content based on personal interests, providing a unified reading experience and organizing information effectively. It features feed aggregation with summarization, transcript generation for videos and articles, noise reduction, task organization, and deep dive topic exploration. The tool supports multiple LLM backends, offers weekly top-k aggregations, and can be deployed on Linux/MacOS using docker-compose or Kubernetes.

RD-Agent is a tool designed to automate critical aspects of industrial R&D processes, focusing on data-driven scenarios to streamline model and data development. It aims to propose new ideas ('R') and implement them ('D') automatically, leading to solutions of significant industrial value. The tool supports scenarios like Automated Quantitative Trading, Data Mining Agent, Research Copilot, and more, with a framework to push the boundaries of research in data science. Users can create a Conda environment, install the RDAgent package from PyPI, configure GPT model, and run various applications for tasks like quantitative trading, model evolution, medical prediction, and more. The tool is intended to enhance R&D processes and boost productivity in industrial settings.

UI-TARS-desktop is a desktop application that provides a native GUI Agent based on the UI-TARS model. It offers features such as natural language control powered by Vision-Language Model, screenshot and visual recognition support, precise mouse and keyboard control, cross-platform support (Windows/MacOS/Browser), real-time feedback and status display, and private and secure fully local processing. The application aims to enhance the user's computer experience, introduce new browser operation features, and support the advanced UI-TARS-1.5 model for improved performance and precise control.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

fastRAG is a research framework designed to build and explore efficient retrieval-augmented generative models. It incorporates state-of-the-art Large Language Models (LLMs) and Information Retrieval to empower researchers and developers with a comprehensive tool-set for advancing retrieval augmented generation. The framework is optimized for Intel hardware, customizable, and includes key features such as optimized RAG pipelines, efficient components, and RAG-efficient components like ColBERT and Fusion-in-Decoder (FiD). fastRAG supports various unique components and backends for running LLMs, making it a versatile tool for research and development in the field of retrieval-augmented generation.

EMA-VFI-WebUI is a web-based graphical user interface (GUI) for the EMA-VFI AI-based movie restoration tool. It provides a user-friendly interface for accessing the various features of EMA-VFI, including frame interpolation, frame search, video inflation, video resynthesis, frame restoration, video blending, file conversion, file resequencing, FPS conversion, GIF to MP4 conversion, and frame upscaling. The web UI makes it easy to use EMA-VFI's powerful features without having to deal with the command line interface.

This repository contains self-trained single image super resolution (SISR) models. The models are trained on various datasets and use different network architectures. They can be used to upscale images by 2x, 4x, or 8x, and can handle various types of degradation, such as JPEG compression, noise, and blur. The models are provided as safetensors files, which can be loaded into a variety of deep learning frameworks, such as PyTorch and TensorFlow. The repository also includes a number of resources, such as examples, results, and a website where you can compare the outputs of different models.

QualityScaler is a Windows app powered by AI to enhance, upscale, and de-noise photographs and videos. It provides an easy-to-use GUI for upscaling images and videos using multiple AI models. The tool supports automatic image tiling and merging to avoid GPU VRAM limitations, resizing images/videos before upscaling, and interpolation between the original and upscaled content. QualityScaler is written in Python and utilizes external packages such as torch, onnxruntime-directml, customtkinter, OpenCV, moviepy, and nuitka. It requires Windows 11 or Windows 10, at least 8GB of RAM, and a Directx12 compatible GPU with 4GB VRAM or more. The tool aims to continue improving with upcoming versions by adding new features, enhancing performance, and supporting additional AI architectures.

RealScaler is a Windows app powered by RealESRGAN AI to enhance, upscale, and de-noise photos and videos. It provides an easy-to-use GUI for upscaling images and videos using multiple AI models. The tool supports automatic image tiling and merging to avoid GPU VRAM limitations, resizing images/videos before upscaling, interpolation between original and upscaled content, and compatibility with various image and video formats. RealScaler is written in Python and requires Windows 11/10, at least 8GB RAM, and a Directx12 compatible GPU with 4GB VRAM. Future versions aim to enhance performance, support more GPUs, offer a new GUI with Windows 11 style, include audio for upscaled videos, and provide features like metadata extraction and application from original to upscaled files.

EMA-VFI-WebUI is a web-based graphical user interface (GUI) for the EMA-VFI AI-based movie restoration tool. It provides a user-friendly interface for accessing the various features of EMA-VFI, including frame interpolation, frame search, video inflation, video resynthesis, frame restoration, video blending, file conversion, file resequencing, FPS conversion, GIF to MP4 conversion, and frame upscaling. The web UI makes it easy to use EMA-VFI's powerful features without having to deal with the command line interface.

3D content creation tools.

EasyAIVtuber is a tool designed to animate 2D waifus by providing features like automatic idle actions, speaking animations, head nodding, singing animations, and sleeping mode. It also offers API endpoints and a web UI for interaction. The tool requires dependencies like torch and pre-trained models for optimal performance. Users can easily test the tool using OBS and UnityCapture, with options to customize character input, output size, simplification level, webcam output, model selection, port configuration, sleep interval, and movement extension. The tool also provides an API using Flask for actions like speaking based on audio, rhythmic movements, singing based on music and voice, stopping current actions, and changing images.

Deep-Live-Cam is a software tool designed to assist artists in tasks such as animating custom characters or using characters as models for clothing. The tool includes built-in checks to prevent unethical applications, such as working on inappropriate media. Users are expected to use the tool responsibly and adhere to local laws, especially when using real faces for deepfake content. The tool supports both CPU and GPU acceleration for faster processing and provides a user-friendly GUI for swapping faces in images or videos.

Anim v0.1.0 is an animation tool that allows users to convert videos to animations using mixamorig characters. It features FK animation editing, object selection, embedded Python support (only on Windows), and the ability to export to glTF and FBX formats. Users can also utilize Mediapipe to create animations. The tool is designed to assist users in creating animations with ease and flexibility.

This repository provides a tool for AI voice cloning, allowing users to generate synthetic speech that closely resembles a target speaker's voice. The tool is designed to be user-friendly and accessible, with a graphical user interface that guides users through the process of training a voice model and generating synthetic speech. The tool also includes a variety of features that allow users to customize the generated speech, such as the pitch, volume, and speaking rate. Overall, this tool is a valuable resource for anyone interested in creating realistic and engaging synthetic speech.

Subtitles by fframes is a free, local, on-device AI video transcription tool with a user-friendly GUI. It allows users to transcribe video content, edit transcribed cues, style the subtitles, and render them directly onto the video. The tool provides a convenient way to create accurate subtitles for videos without the need for an internet connection.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.