Search Results for "swap"
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Showing 32 open source projects for "swap"
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1
Deep-Live-Cam
Real time face swap and one-click video deepfake
Real time face swap and one-click video deepfake with only a single image. Choose a face (image with the desired face) and the target image/video (image/video in which you want to replace the face) and click on Start. Open File Explorer and navigate to the directory you select your output to be in. You will find a directory named <video_title> where you can see the frames being swapped in real time. -
2
TorchRL
A modular, primitive-first, python-first PyTorch library
...TorchRL provides PyTorch and python-first, low and high-level abstractions for RL that are intended to be efficient, modular, documented, and properly tested. The code is aimed at supporting research in RL. Most of it is written in Python in a highly modular way, such that researchers can easily swap components, transform them, or write new ones with little effort. -
3
DeepSeek-V3.2-Exp
An experimental version of DeepSeek model
DeepSeek-V3.2-Exp is an experimental release of the DeepSeek model family, intended as a stepping stone toward the next generation architecture. The key innovation in this version is DeepSeek Sparse Attention (DSA), a sparse attention mechanism that aims to optimize training and inference efficiency in long-context settings without degrading output quality. According to the authors, they aligned the training setup of V3.2-Exp with V3.1-Terminus so that benchmark results remain largely... -
4
agent heroes
Generate, animate and schedule your AI characters
...The framework is designed to integrate with modern large language models and external APIs, allowing agents to access tools, retrieve data, and perform actions dynamically. It also supports modular design, making it possible to extend agent capabilities or swap components without redesigning the entire system. agentheroes is particularly useful for automation, workflow orchestration, and experimentation with collaborative AI systems. -
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5
UltraRAG
Less Code, Lower Barrier, Faster Deployment
...The toolkit comes with built-in support for popular RAG datasets, large corpora, and canonical baselines, plus documentation that walks from “quick start” to debugging and case analysis. It encourages pipeline composition via configuration, enabling researchers to swap retrievers, rerankers, and generators without heavy refactoring. Community posts highlight its focus on reducing engineering overhead so more effort goes to experimental design. Backed by the OpenBMB org, it is actively maintained with tutorials and updates. -
6
Open-LLM-VTuber
Open source AI VTuber platform with voice chat and Live2D avatars
...Users can speak directly to the AI character, and the system can respond with a generated voice while animating a Live2D avatar to simulate a talking virtual personality. Open-LLM-VTuber is modular, allowing developers to swap or configure different language models, speech recognition engines, and voice synthesis systems depending on their needs. It can run locally and supports both offline and online AI services, giving users flexibility in how models and resources are used. Open-LLM-VTuber was originally inspired by the goal of recreating an AI VTuber experience using open source tools that work across multiple operating systems. -
7
xFormers
Hackable and optimized Transformers building blocks
xformers is a modular, performance-oriented library of transformer building blocks, designed to allow researchers and engineers to compose, experiment, and optimize transformer architectures more flexibly than monolithic frameworks. It abstracts components like attention layers, feedforward modules, normalization, and positional encoding, so you can mix and match or swap optimized kernels easily. One of its key goals is efficient attention: it supports dense, sparse, low-rank, and approximate attention mechanisms (e.g. FlashAttention, Linformer, Performer) via interchangeable modules. The library includes memory-efficient operator implementations in both Python and optimized C++/CUDA, ensuring that performance isn’t sacrificed for modularity. ... -
8
MCP Apps
Official repo for spec & SDK of MCP Apps protocol
...Designed to work with large language models and agent systems, Ext-Apps lets developers create plugins that extend core behaviors — such as search integration, data retrieval, workflow automation, or domain-specific tools — without rewriting the host system. The ecosystem emphasizes composability, meaning developers can stack, combine, or swap out apps to tailor functionality to specific use cases or industries. It also includes governance primitives so host platforms can enforce permissions, limit app capabilities, and audit inter-app communication. -
9
aisuite
Simple, unified interface to multiple Generative AI providers
...Using an interface similar to OpenAI's, aisuite makes it easy to interact with the most popular LLMs and compare the results. It is a thin wrapper around Python client libraries and allows creators to seamlessly swap out and test responses from different LLM providers without changing their code. Today, the library is primarily focused on chat completions. We will expand it to cover more use cases in the near future. Currently supported providers are - OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, HuggingFace and Ollama. To maximize stability, aisuite uses either the HTTP endpoint or the SDK for making calls to the provider. -
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10
Omnigent
A meta-harness for all your AI agents
Omnigent is a meta-harness for managing many AI agents through one shared layer. It works with Claude Code, Codex, Cursor, Pi, and custom YAML-defined agents, so users can swap or combine agent runtimes without rebuilding their workflows. Sessions can move across terminal, browser, desktop, and mobile interfaces while keeping messages, files, terminals, and subagents in sync. The platform supports collaboration, shared live sessions, co-driving, conversation forking, and remote access from deployed servers. ... -
11
Agently 4
Build GenAI application quick and easy
Agently is a Python framework for building generative-AI (“GenAI”) applications; it focuses on enabling developers to orchestrate AI agents, workflows, and event-driven logic in a robust, reusable way. With Agently, one can define agents that call different models, chain tasks, trigger workflows based on events, and switch models with minimal code changes. It abstracts away boilerplate around model API calls, tool usage, prompt management, and workflow state. The project aims at... -
12
Vision Transformer Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA
...It’s widely used as an educational reference for people learning transformers in vision and as a lightweight baseline for research prototypes. The project encourages experimentation—swap optimizers, change augmentations, or plug the transformer backbone into downstream tasks. -
13
DeepSeek-OCR
Contexts Optical Compression
...It supports local deployment, enabling organizations concerned about privacy or latency to run the pipeline on-premises rather than send sensitive documents to third-party cloud services. The codebase is written in Python with a focus on modularity: you can swap preprocessing, recognition, and post-processing components as needed for custom workflows. -
14
RealtimeTTS
Converts text to speech in realtime
...The library is engine-agnostic and plugs into a wide range of cloud and local TTS systems, including OpenAI, ElevenLabs, Azure, Coqui, Piper, StyleTTS2, Edge TTS, Google TTS, system TTS and others, so you can swap providers without rewriting your pipeline. It supports both internet-based engines and fully local engines, which lets you choose between privacy, cost, and quality trade-offs. RealtimeTTS also includes robustness features such as automatic fallbacks when a backend fails, so production systems can stay responsive even if one TTS provider is temporarily unavailable. -
15
Neuron AI
The PHP Agentic Framework to build production-ready AI driven apps
...The framework provides an Agent class that can be extended to inherit core capabilities like memory, tools, function calling, and retrieval-augmented generation. Its design is modular, so developers can swap model providers with minimal changes to their application code, which makes it practical for teams that need flexibility across vendors. The project also supports structured output, monitoring, MCP connectivity, and workflow patterns that include human-in-the-loop intervention when automated flows need review or correction. -
16
NVIDIA Earth2Studio
Open-source deep-learning framework
...It provides a unified API that lets researchers, data scientists, and engineers build complex forecasting and analysis pipelines by combining modular prognostic and diagnostic AI models with a diverse range of real-world data sources such as global forecast systems, reanalysis datasets, and satellite feeds. The toolkit makes it easy to run deterministic and ensemble forecasts, swap models interchangeably, and process large geophysical datasets with Xarray structures, enabling experimentation with state-of-the-art deep learning models for climate and atmospheric prediction. Users can extend Earth2Studio with optional model packs, advanced data interfaces, statistical operators, and backend integrations that support flexible workflows from simple tests to large-scale operational inference. -
17
NVIDIA NeMo
Toolkit for conversational AI
NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI... -
18
PokeeResearch-7B
Pokee Deep Research Model Open Source Repo
...The repository includes evaluation results on multi-step QA and research benchmarks, illustrating how web-time context boosts accuracy. Because the system is modular, you can swap the search component, reader, or policy to fit private deployments or different data domains. It’s aimed at developers who want a transparent, hackable research agent they can run locally or wire into existing workflows. -
19
Large Concept Model
Language modeling in a sentence representation space
...Probing tools help diagnose what the model knows—e.g., attribute recognition, relation understanding, or compositionality—so you can iterate on data and objectives. The design is modular, making it straightforward to swap backbones, change objectives, or integrate retrieval components. -
20
Trae Agent
LLM-based agent for general purpose software engineering tasks
Trae Agent is an open-source, LLM-based agent system also developed by ByteDance, focused primarily on automating software engineering workflows. It provides a command-line interface (CLI) that accepts natural-language instructions (e.g. “refactor this module,” “write a unit test,” “generate a REST API skeleton”), and then orchestrates tool-based workflows — such as file editing, shell/batch commands, code generation, code formatting or refactoring — to carry out complex engineering tasks.... -
21
Frevo
Frevo is probably the simplest tool for evolutionary design
...We identify these as the problem definition, solution representation and the optimization method. This structure enables the components to be designed separately allowing the user to easily swap and evaluate different configurations and methods or to connect an external simulation tool. The latest version in development is hosted at https://github.com/smartgrids-aau/Frevo -
22
OnnxStream
Lightweight inference library for ONNX files, written in C++
The challenge is to run Stable Diffusion 1.5, which includes a large transformer model with almost 1 billion parameters, on a Raspberry Pi Zero 2, which is a microcomputer with 512MB of RAM, without adding more swap space and without offloading intermediate results on disk. The recommended minimum RAM/VRAM for Stable Diffusion 1.5 is typically 8GB. Generally, major machine learning frameworks and libraries are focused on minimizing inference latency and/or maximizing throughput, all of which at the cost of RAM usage. So I decided to write a super small and hackable inference library specifically focused on minimizing memory consumption: OnnxStream. ... -
23
Roop
One-click face swap
Take a video and replace the face with a face of your choice. You only need one image of the desired face. No dataset, and no training. -
24
DeepFaceLive
Real-time face swap for PC streaming or video calls
You can swap your face from a webcam or the face in the video using trained face models. There is also a Face Animator module in DeepFaceLive app. You can control a static face picture using video or your own face from the camera. The quality is not the best, and requires fine face matching and tuning parameters for every face pair, but enough for funny videos and memes or real-time streaming at 25 fps using 35 TFLOPS GPU. -
25
e-Dokyumento
e-Dokyumento is web-based Document Management System (DMS)
...Download: https://sourceforge.net/projects/e-dokyumento/files/Releases/e-DokyuV3.iso/download 2. Boot and login with: "root" and "admin@123" 3. Create 2 partitions: SWAP and / mount 4. Login and move "/opt/drive" folder to root: "mv /opt/drive /" # Install on Ubuntu: https://sourceforge.net/projects/e-dokyumento/files/Install%20e-Dokyumento%20on%20Ubuntu%20Linux.pdf/download
