11 个仓库
Adaptive sampling algorithms that reduce computational costs by prioritizing relevant spatial features in video streams.
Distinct from Data Compression Algorithms: Distinct from general data compression: focuses on visual token reduction for neural inference.
Explore 11 awesome GitHub repositories matching data & databases · Visual Token Compression. Refine with filters or upvote what's useful.
WeUI is a mobile web UI library and design system consisting of CSS components and HTML templates. It is specifically designed to replicate the visual identity and interface of the WeChat messaging ecosystem, providing a standardized set of components to build responsive mobile web interfaces. The library functions as a stateless component system, utilizing a pure CSS architecture and HTML templates that rely on external JavaScript for interactivity. It employs a BEM-based class naming convention to manage component nesting and prevent style leakage across complex layouts. The framework incl
Uses visual tokens for identity, though not in the context of image tokenization.
MiniCPM-o is a multimodal large language model designed to function as a real-time conversational assistant on edge devices. By mapping text, image, video, and audio inputs into a unified latent space, the system enables simultaneous cross-modal reasoning and full-duplex interaction. It is built as an edge-side inference engine, utilizing quantized model weights to maintain high-performance processing on consumer hardware. The system distinguishes itself through its integrated speech synthesis and voice cloning capabilities, which allow for the generation of expressive, personalized vocal out
Implements adaptive visual token compression to balance inference speed and accuracy on edge devices.
DeepSeek-OCR is a vision processing framework designed to convert image-based text into machine-readable tokens for large language models. It functions as a document inference pipeline that encodes visual data into compact representations, enabling automated optical character recognition and document analysis workflows. The system distinguishes itself through a high-throughput architecture that utilizes hardware-accelerated batch inference to process large volumes of visual data. It incorporates dynamic resolution scaling to manage the balance between visual detail and token consumption, ensu
Compresses image content into optimized token representations for visual analysis.
This project is a comprehensive framework and toolkit for developing, optimizing, and deploying transformer-based models across multimodal, document intelligence, and natural language processing tasks. It provides a unified neural architecture that processes text, vision, audio, and document layout data through a shared set of weights, enabling researchers and developers to build foundational models that align cross-modal representations. The platform distinguishes itself through advanced training and inference strategies designed for large-scale deep learning. It incorporates specialized mec
Implements visual data tokenization to convert raw images into discrete tokens using encoder-decoder architectures.
Qwen2-VL is a multimodal large language model and vision language model designed to process and reason across text, images, and video content. It functions as a visual reasoning engine and a visual agent framework, capable of interpreting visual data to perform object detection, document parsing, and spatial reasoning. The model is distinguished by its ability to act as a video understanding model, processing hour-long videos with second-level indexing and event recall. It further differentiates itself through a visual agent capability that interacts with software interfaces and robotic hardw
Controls the resolution and pixel count of visual inputs to balance processing quality with memory constraints.
This project is a vision language model framework and vision-to-text pipeline designed for deploying and optimizing models that process both images and text. It provides an on-device inference engine and a vision language model framework to run quantized models locally on mobile and desktop hardware accelerators. The framework features a model quantization toolkit to reduce weight precision for lower memory footprints and increased execution speed on specialized silicon. It also includes an efficient vision encoder utilizing a hybrid encoding system to compress image tokens, which reduces pro
Implements a hybrid encoding system that reduces visual token counts to accelerate vision language model processing.
OmniRoute is a unified LLM API gateway that connects multiple AI providers to a single endpoint. Its primary purpose is to simplify the integration of various AI models into tools and agents by translating different provider formats into a standardized API. The project distinguishes itself through a multi-strategy request routing system that optimizes for cost, speed, and availability, including automatic model fallbacks and a circuit-breaker resilience model to isolate provider failures. It employs a local-first security posture, using AES-256-GCM encryption to store API keys and conversatio
Reduces billed token usage through semantic pruning and deduplication of prompts to maximize the context window.
DeepSeek-VL2 是一个多模态大语言模型和视觉语言系统,旨在分析视觉场景并生成描述性文本。它作为一个视觉问答和视觉定位模型,能够从文档中提取信息,并根据文本描述定位图像中的特定对象或区域。 该项目利用专家混合(mixture-of-experts)架构来处理组合的图像和文本输入。它通过增量预填充(incremental prefilling)针对推理进行了优化,从而降低了硬件上的 GPU 内存需求。 该模型涵盖多模态数据分析和视觉文档理解,包括对图表和布局的解释。它执行视觉推理和定位,以将文本查询与相应的视觉内容进行匹配。
Adjusts input image resolution and pixel counts to optimize the visual token budget.
LLaVA-NeXT 是一个多模态大语言模型框架和训练工具包,旨在处理交错的图像和视频序列以生成文本。它作为视觉语言模型,结合了视觉编码器与语言模型,能够执行复杂的推理、问答和视频理解任务。 该系统能够分析高分辨率图像和时序视频帧,从而描述事件、总结动作并跨多个视觉输入进行推理。它支持文档和图表解析、空间环境分析,以及为图像和视频生成描述性字幕。 该框架包含通过偏好优化来微调多模态模型的工具,以减少幻觉并提高准确性。它还提供了一个推理服务器,可通过 HTTP 后端将这些功能部署为 API 服务。
Implements dynamic resolution scaling to optimize the visual token budget while preserving high-resolution image details.
ClawRouter is an AI model router and API gateway designed to classify query complexity and assign prompts to the most efficient model tier. It operates as a multi-model AI proxy that orchestrates traffic between various large language models and AI media generators through a unified interface. The project distinguishes itself by integrating a non-custodial micropayment processor using the x402 protocol. This allows for per-request API access and USDC settlement on Base and Solana chains, replacing static API keys with wallet-based authentication and real-time budget enforcement. The system c
Reduces billed token counts by deduplicating messages and minifying data before sending requests.
SimpleMem is a persistent memory system for AI assistants designed to maintain context across different user chat sessions. It functions as a memory server and multimodal vector database that stores and retrieves information from text, images, audio, and video. The project features a context compression engine that distills interaction histories into compact units to reduce token consumption. It utilizes a distributed memory orchestrator and worker-thread parallel processing to reduce latency when querying large-scale dialogue datasets. The system implements a hybrid indexing approach combin
Reduces token consumption by compressing interaction histories into compact, non-redundant units.