Stable Diffusion 3.5 Large Turbo

    stableDiffusion35_largeTurbo.safetensors Checkpoint / SD 3.5 Large Turbo

    Model Information
    Model Name
    Stable Diffusion 3.5 Large Turbo
    Version
    Large Turbo
    Creator
    theally
    Size
    15.33 GB
    Downloads
    8,439
    Torrent Details
    BTIH
    7EF0E8C120EFC984963DFEB7A058B1FF524E0303
    BTMH
    EA716E7F9D94ACBCC072900FDBDC580EC691893EF12370BED3719141DBA8EAD3
    SHA256
    FB64610BF8D73EB064B8D528EEF85D062BF2B4B1204FF7BC73E57AD28B24489C
    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 Turbo is a Multimodal Diffusion Transformer (MMDiT) text-to-image model with Adversarial Diffusion Distillation (ADD) that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency, with a focus on fewer inference steps.

    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 an ADD-distilled Multimodal Diffusion Transformer that use three fixed, pretrained text encoders, and with QK-normalization.

    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.

    Model Sources

    For local or self-hosted use, we recommend ComfyUI for node-based UI inference, or diffusers or GitHub for programmatic use.

    Implementation Details

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

    • Adversarial Diffusion Distillation (ADD) (see the technical report), which allows sampling with 4 steps at high image quality.

    • 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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