PyTorch object detector built for shipping: train on your own data, then export to ONNX, CoreML, TFLite, or TensorRT with one command. Comes in five sizes (n/s/m/l/x) and adds instance-segmentation and classification heads beyond bounding-box detection.
Library for benchmarking, developing, and deploying deep-learning visual anomaly-detection models — includes ready-to-use model implementations (PatchCore, DINO-based), experiment/HPO tooling, OpenVINO export for edge inference, and a low-code Studio for deployment.
Offline desktop OCR for Windows and Linux that extracts text from screenshots, image batches, and scanned PDFs without requiring a network connection. Bundles multilingual offline engines (PaddleOCR / RapidOCR), supports ignore-regions, searchable PDF output, CLI and HTTP interfaces for automation and integration.
Browser-based control panel for running Stable Diffusion locally, built on Gradio. Bundles txt2img, img2img, inpainting, outpainting, and upscalers (ESRGAN, GFPGAN, CodeFormer), plus an extension ecosystem and support for NVIDIA, AMD, and Intel GPUs.
Turns text prompts into images through latent diffusion, from local-ready releases to professional SD 3.5 models. Its impact comes from deployability: self-hosting, API access, and community tooling made image generation broadly hackable.
Unifies successive YOLO generations — YOLOv8, YOLO11, YOLOv3 and newer — under one package and a single `YOLO` API spanning detection, segmentation, classification, pose, oriented boxes and tracking, plus one-line export to ONNX, TensorRT and CoreML.
Provides reusable computer-vision utilities for dataset loading/conversion, visualization/annotation of detections and segmentation, and connectors to popular detection frameworks—aimed at quick prototyping, dataset work, and visualization.
X-AnyLabeling is a powerful annotation tool integrated with an AI engine for fast and automatic labeling. Designed for multi-modal data engineers, it offers industrial-grade solutions for complex tasks. Supports images and videos, GPU acceleration, custom models, one-click inference for all task images, and import/export formats like COCO, VOC, YOLO. Handles classification, detection, segmentation, captioning, rotation, tracking, estimation, OCR, VQA, grounding, etc., with various annotation styles including polygons, rectangles, rotated boxes.
Create and run node-based generative AI workflows for images, video, 3D, and audio — reusable, shareable node graphs with custom nodes, live previews, and local/cloud runtime options. Open-source with Comfy Cloud and Hub for creators.
Provides an uncensored, self‑hostable studio for generating AI images, videos, and lip‑synced talking videos in browser or desktop. Integrates 200+ models via Muapi.ai, supports local inference (stable-diffusion.cpp), multi-image inputs and workflow automation — no content filters.
Reference implementation for Stability AI's diffusion models: SDXL base/refiner/Turbo for text-to-image, plus Stable Video Diffusion, SV3D, and SV4D for image-to-video and 4D synthesis. A modular engine separates samplers, guiders, and conditioners.
stable-diffusion.cpp is a pure C/C++ implementation for diffusion model inference, based on ggml, supporting models like Stable Diffusion (SD1.x, SD2.x, SDXL), Flux, Wan, Qwen Image, Z-Image, and more. It's lightweight with no external dependencies, supports backends like CPU, CUDA, Vulkan, Metal, and features like LoRA, ControlNet, LCM for efficient local image generation on platforms including Linux, Mac, Windows, and Android.