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.
Bring-your-own-key chat client that keeps every conversation in the local browser, never a server. One UI reaches OpenAI, Claude, Gemini, DeepSeek and a dozen more providers across web, desktop and mobile, with MCP, plugins, and one-click self-hosting.
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.
Enables real-time (≥30 fps) 1080p novel-view synthesis by representing scenes as optimized anisotropic 3D Gaussians plus a visibility-aware splatting renderer; provides the paper's reference implementation, pretrained models and viewers — high-quality training requires CUDA GPU and significant VRAM.
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.
Runs Stable Diffusion XL behind a Midjourney-style interface, hiding samplers, model swaps, and LoRA weights. A built-in GPT2 expander rewrites prompts into richer styling, and it works fully offline on as little as 4GB of Nvidia VRAM.
Applies deep learning workflows to geospatial data, covering imagery search, dataset preparation, model training, inference, visualization, and QGIS integration for remote sensing.
Swaps a face from a single photo onto a live webcam feed or video in real time, using the inswapper_128 model with GFPGAN enhancement. Runs on NVIDIA, Apple Silicon, and Intel GPUs, with a built-in filter that blocks explicit or sensitive media.
Browser-based editor for inspecting, editing, optimizing and publishing 3D Gaussian splats. Runs entirely in the browser with live preview, localization support, and export/publishing workflows — no install required, aimed at quick iteration and lightweight delivery.