30 open-source projects similar to ofa-sys/one-peace, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best ONE PEACE alternative.
Qwen3-Omni is an omni-modal large language model designed to process and generate text, audio, images, and video within a single unified neural architecture. It functions as a real-time voice assistant and multimodal AI agent capable of reasoning across different media types and executing external tool-calling functions via APIs. The system supports low-latency conversational AI through autoregressive token streaming and natural turn-taking. It enables multilingual speech translation and generation across dozens of languages, featuring customizable speaker profiles and tones. The model's cap
Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through graphical user interface interactions. It functions as a computer use interface, utilizing vision-language grounding to translate natural language goals into precise screen coordinates and system actions. The project differentiates itself by combining structured accessibility tree inspection with vision-based element localization. It manages cross-application workflows by mapping conceptual descriptions to physical pixels and simulating low-level keyboard and mouse events to mov
This project is a computer control framework that uses multimodal vision models to simulate mouse and keyboard inputs for automating desktop tasks. It functions as an autonomous agent and vision-based orchestrator that interprets screen visuals to interact with user interfaces. The system employs vision language models and object detection to locate and click interface elements. It utilizes visual grounding to overlay numerical markers on UI components and uses optical character recognition to map on-screen text to precise pixel coordinates. The framework supports voice-controlled computing
Jaaz is a self-hosted AI design suite and multimodal workspace used for generating and editing images and videos. It functions as a design workspace where users can produce visual content and assets through a combination of local and cloud-based AI models. The project features a hybrid model orchestrator that routes requests between local model runners and remote APIs to balance data privacy with processing performance. It utilizes an infinite canvas collaborative tool for organizing storyboards and assets, and includes an image prompt optimizer to translate rough ideas into detailed generati
CLIPort: What and Where Pathways for Robotic Manipulation Mohit Shridhar, Lucas Manuelli, Dieter Fox CoRL 2021
Code for the paper "ViperGPT: Visual Inference via Python Execution for Reasoning"
TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
Official Repository for CVPR 2024 paper MultiPly: Reconstruction of Multiple People from Monocular Video in the Wild.
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming
Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting.
LLaVA-NeXT is a multimodal large language model framework and training toolkit designed to process interleaved images and video sequences to generate text. It functions as a visual language model that combines vision encoders with language models to perform complex reasoning, question answering, and video understanding. The system is capable of analyzing high-resolution images and temporal video frames to describe events, summarize actions, and reason across multiple visual inputs. It supports the interpretation of documents and charts, spatial environment analysis, and the generation of desc
Macaw-LLM: Multi-Modal Language Modeling with Image, Video, Audio, and Text Integration
MobileVLM: Vision Language Model for Mobile Devices
The ambition of the i-Code project is to build integrative and composable multimodal Artificial Intelligence. The "i" stands for integrative multimodal learning.
OmniParser is a multimodal interaction engine designed to function as a desktop automation agent. It interprets visual screen information to execute complex, multi-step tasks across operating system environments by bridging visual interface perception with language models. Through a continuous cycle of observation and command execution, the system grounds high-level natural language instructions into precise, coordinate-based actions. The project distinguishes itself by utilizing vision-based parsing to interact with software interfaces without requiring access to underlying application progr
TMLR23 Official implementation of UnIVAL: Unified Model for Image, Video, Audio and Language Tasks.
Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. (Correspondence )
MiniCPM-V is a multimodal large language model and vision-language system designed for complex visual and linguistic understanding. It functions as an on-device AI model, providing the capacity to process text, images, and video as a compact neural network. The project is specifically developed as an edge AI framework, utilizing quantization and weight sharding to run on memory-constrained mobile chipsets. This allows for the deployment of multimodal intelligence directly on mobile operating systems for local inference. Its capabilities cover multimodal content analysis of high-resolution im
ECCV 2024 Best Paper Candidate & TPAMI 2025 PointLLM: Empowering Large Language Models to Understand Point Clouds
Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation Mohit Shridhar, Lucas Manuelli, Dieter Fox CoRL 2022
X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages
【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment If you like our project, please give us a star ⭐ on GitHub for latest update.
Qwen2.5-VL is an autoregressive multimodal transformer designed to process interleaved sequences of text and visual tokens. It integrates visual feature embeddings into a shared language model space to perform cross-modal reasoning and generate coherent responses or structured layout code. The project distinguishes itself through vision-language-action mapping, allowing it to perceive visual interfaces and translate that perception into actionable commands for operating digital screens and robotic hardware. It employs dynamic-resolution image encoding and temporal-frame video indexing to hand
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors Yuan Tang  Xu Han  Xianzhi Li*  Qiao Yu  Yixue Hao  Long Hu  Min Chen Huazhong University of Science and Technology South China University of Technology
MiniGPT-4 is a multimodal AI framework and large language model that integrates vision encoders with language models to process and reason about combined image and text inputs. It functions as a vision-language model capable of image-based conversational AI, visual question answering, and multimodal logical reasoning. The project utilizes a pretrained vision-language integration strategy that connects a vision encoder to a language model via a linear projection layer. This approach employs frozen-backbone training to align visual representations with linguistic tokens while keeping the primar
This Repository provides an LLM-driven audio codec model, which can be used to build multi-modal LLMs (text and audio modalities). More details will be introduced as soon as. You can find the paper from https://arxiv.org/pdf/2406.10056