Detects, segments, and tracks every instance of an open-vocabulary concept in images and video from a text phrase or visual exemplar, not just one object per prompt. An 848M-param model reaching ~75-80% of human accuracy across 270K concepts.
A 20B-parameter MMDiT diffusion model that generates and edits images with accurate embedded text, including dense Chinese and English typography. Handles complex multi-line layouts and identity-preserving edits while keeping text legible.
Self-supervised vision foundation model producing dense, patch-level features that transfer to classification, segmentation, depth, and detection with a frozen backbone. Spans ViT-S (21M) to ViT-7B (6.7B params), plus ConvNeXt and satellite variants.
Converts images and PDFs into structured Markdown, HTML, or JSON while preserving layout, handling tables, math, handwriting, charts, and chemistry diagrams across 90+ languages. Runs locally via HuggingFace or against a vLLM server.
Turn plain-English requests into editable draw.io diagrams: the model writes the underlying draw.io XML, which renders live in an embedded canvas. Upload images, PDFs, or text to replicate, refine through chat, and roll back via version history.
Generates real-time, infinite-length portrait video from one reference image on a 12GB GPU. Combines implicit facial signals and 3D keypoints with step-distilled diffusion and autoregressive micro-chunk streaming for low-latency live use.
Detects and surgically removes Google's SynthID watermark from images using multi-resolution spectral analysis and a resolution-aware codebook; provides a V3 bypass with high PSNR and strong phase-coherence reduction. Research-focused and intended for analysis/defense, not misrepresentation.
Multimodal OCR and document-understanding toolkit for recognizing complex layouts, tables, formulas and code. Uses Multi-Token Prediction and stable RL for better training; ships as a 0.9B-parameter model with a Python SDK and deployment guides for vLLM, SGLang and Ollama.
Generates production-ready App Store and Google Play screenshots from app metadata and style preferences using AI. Scaffolds a Next.js project, composes ad-style slides with localized/RTL support, and exports PNGs at all required Apple and Google resolutions.
Turns 530+ GPT-Image2 examples into a structured 'Prompt-as-Code' library and style skill for reproducible image-generation workflows; includes a browsable gallery, 20+ industrial templates, and an agent-ready style skill for automation.
Distilled dev checkpoint of an image foundation model that natively unifies raw pixels and text tokens for text-to-image, image editing, long-text rendering, and subject-driven personalization at up to 2048×2048. The Dev variant targets faster (28-step) inference for iterative use and research.