Stable Diffusion 3.5 Large

    stableDiffusion35_large.safetensors Checkpoint / SD 3.5 Large

    Model Information
    Model Name
    Stable Diffusion 3.5 Large
    Version
    Large
    Creator
    theally
    Size
    15.33 GB
    Downloads
    20,402
    Torrent Details
    BTIH
    E15441E4D24D676DDF9222EB143EB1141D133515
    BTMH
    5A418D4830A171B6DCDE26552B40D229DD40CBFF25788E93528A51A3352C51B9
    SHA256
    FFEF7A279D9134626E6CE0D494FBA84FC1C7E720B3C7DF2D19A09DC3796D8F93
    Upload Date
    5 months ago
    Uploader
    CivitasBay.org
    Status
    2 Seeders
    0 Peers
    Info

    Please see our Quickstart Guide to Stable Diffusion 3.5 for all the latest info!

    Stable Diffusion 3.5 Large is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.

    Please note: This model is released under the Stability Community License. Visit Stability AI to learn or contact us for commercial licensing details.

    Model Description

    • Developed by: Stability AI

    • Model type: MMDiT text-to-image generative model

    • Model Description: This model generates images based on text prompts. It is a Multimodal Diffusion Transformer that use three fixed, pretrained text encoders, and with QK-normalization to improve training stability.

    License

    • Community License: Free for research, non-commercial, and commercial use for organizations or individuals with less than $1M in total annual revenue. More details can be found in the Community License Agreement. Read more at https://stability.ai/license.

    • For individuals and organizations with annual revenue above $1M: please contact us to get an Enterprise License.

    Implementation Details

    • QK Normalization: Implements the QK normalization technique to improve training Stability.

    • Text Encoders:

    • Training Data and Strategy:

      This model was trained on a wide variety of data, including synthetic data and filtered publicly available data.

    For more technical details of the original MMDiT architecture, please refer to the Research paper.

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