Provides a comprehensive set of computer-vision algorithms and image/video processing utilities with multi-language bindings (C++, Python, Java), contrib modules, and community docs/forums — suitable for prototyping, production pipelines, and real-time applications.
Swaps faces in images and videos using deep learning, offering tools to extract faces, train generative models, and convert media via CLI or GUI for research, VFX, and ethical experimentation.
Canonical ILSVRC ImageNet-1k for 1,000-way image classification — provides roughly 1.2M labeled images (train/val/test) packaged as optimized Parquet for easy loading with Hugging Face Datasets, Dask, and Polars. Verify licensing and distribution constraints before use.
Enlarges and enhances low-resolution images using AI models (Real-ESRGAN) through a cross-platform desktop app. Runs on a local NCNN/Vulkan backend (requires a Vulkan-compatible GPU), offers an Electron GUI plus a CLI backend (upscayl-ncnn), and supports custom models for different image types.
Runs a self-hosted web server and React-based UI to generate, edit, and manage images using Stable Diffusion, SDXL and other foundation models. Includes a model manager, unified canvas, node-based workflows, gallery, and support for ckpt, diffusers and some gguf checkpoints.
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.
Asynchronous, reverse-engineered Python API for programmatic access to the Google Gemini web app — supports persistent cookie auth, streaming text, image/video/audio generation, deep-research workflows, model selection, and a CLI for automation and chatbots.
Generates images from text prompts using a 12-billion-parameter rectified-flow transformer trained with guidance distillation for more efficient sampling. Distributed with diffusers/ComfyUI support and multiple conditioning/editing variants; weights released under a non-commercial license.
High-resolution image and video generation codebase and models that run with far lower compute and memory than typical diffusion systems. Uses linear-attention DiT variants, aggressive latent compression, and inference-scaling to support text-to-image (up to 4K), fast one/few-step generation, and efficient video pipelines.
Multi‑modal closed-ended academic benchmark with 2,500 multiple-choice and short-answer exam questions spanning math, natural sciences, and humanities for automated grading. Curated by subject-matter experts, released under MIT, and includes a canary string to help prevent dataset leakage into model training.
A benchmark dataset for evaluating MLLM-driven interactive webpage code generation: provides prototyping screenshots, action.json interaction metadata, and example generation scripts across 127 webpages and 374 interactions to test dynamic UI-to-code capabilities.
Pan-cancer CT segmentation dataset for training and benchmarking medical-image segmentation models — packaged as a Hugging Face dataset with an estimated 10k–100k samples and linked arXiv references. Designed for model development and reproducible benchmarking; non-commercial license applies.