Showing 4 open source projects for "foundation"

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  • 1
    Z-Image

    Z-Image

    Image generation model with single-stream diffusion transformer

    Z-Image is an efficient, open-source image generation foundation model built to make high-quality image synthesis more accessible. With just 6 billion parameters — far fewer than many large-scale models — it uses a novel “single-stream diffusion Transformer” architecture to deliver photorealistic image generation, demonstrating that excellence does not always require extremely large model sizes.
    Downloads: 22 This Week
    Last Update:
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  • 2
    HiDream-I1

    HiDream-I1

    Open-source image generative foundation model

    HiDream-I1 is an open-source image generation foundation model with 17 billion parameters. It is designed to produce high-quality images from text prompts while keeping inference practical through efficient model design. The project provides full, dev, and fast model variants with different inference step counts. It supports direct Python inference scripts, an interactive Gradio demo, and integration through the Hugging Face Diffusers library.
    Downloads: 2 This Week
    Last Update:
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  • 3
    Qwen-Image

    Qwen-Image

    Qwen-Image is a powerful image generation foundation model

    Qwen-Image is a powerful 20-billion parameter foundation model designed for advanced image generation and precise editing, with a particular strength in complex text rendering across diverse languages, especially Chinese. Built on the MMDiT architecture, it achieves remarkable fidelity in integrating text seamlessly into images while preserving typographic details and layout coherence.
    Downloads: 7 This Week
    Last Update:
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  • 4
    InvokeAI

    InvokeAI

    InvokeAI is a leading creative engine for Stable Diffusion models

    ...Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products. This fork is supported across Linux, Windows and Macintosh. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver). We do not recommend the GTX 1650 or 1660 series video cards. They are unable to run in half-precision mode and do not have sufficient VRAM to render 512x512 images.
    Downloads: 10 This Week
    Last Update:
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