Please see our <a target="_blank" rel="ugc" href="https://education.civitai.com/getting-started-with-stable-diffusion-3-5/">Quickstart Guide to Stable Diffusion 3.5</a> for all the latest info!<a target="_blank" rel="ugc" href="https://stability.ai/news/introducing-stable-diffusion-3-5">Stable Diffusion 3.5 Large</a> 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 <a target="_blank" rel="ugc" href="https://stability.ai/community-license-agreement">Stability Community License</a>. Visit <a target="_blank" rel="ugc" href="https://stability.ai/license">Stability AI</a> to learn or <a target="_blank" rel="ugc" href="https://stability.ai/enterprise">contact us</a> for commercial licensing details.<h3 id="model-description-xmjn0qtt6">Model Description</h3><ul><li>Developed by: Stability AI</li><li>Model type: MMDiT text-to-image generative model</li><li>Model Description: This model generates images based on text prompts. It is a <a target="_blank" rel="ugc" href="https://arxiv.org/abs/2403.03206">Multimodal Diffusion Transformer</a> that use three fixed, pretrained text encoders, and with QK-normalization to improve training stability.</li></ul><h3 id="license-43q6wfq84">License</h3><ul><li>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 <a target="_blank" rel="ugc" href="https://stability.ai/community-license-agreement">Community License Agreement</a>. Read more at <a target="_blank" rel="ugc" href="https://stability.ai/license">https://stability.ai/license</a>.</li><li>For individuals and organizations with annual revenue above $1M: please <a target="_blank" rel="ugc" href="https://stability.ai/enterprise">contact us</a> to get an Enterprise License.</li></ul><h3 id="implementation-details-dw87idyvo">Implementation Details</h3><ul><li>QK Normalization: Implements the QK normalization technique to improve training Stability.</li><li>Text Encoders：<ul><li>CLIPs: <a target="_blank" rel="ugc" href="https://github.com/mlfoundations/open_clip">OpenCLIP-ViT/G</a>, <a target="_blank" rel="ugc" href="https://github.com/openai/CLIP/tree/main">CLIP-ViT/L</a>, context length 77 tokens</li><li>T5: <a target="_blank" rel="ugc" href="https://huggingface.co/google/t5-v1_1-xxl">T5-xxl</a>, context length 77/256 tokens at different stages of training</li></ul></li><li>Training Data and Strategy:This model was trained on a wide variety of data, including synthetic data and filtered publicly available data.</li></ul>For more technical details of the original MMDiT architecture, please refer to the <a target="_blank" rel="ugc" href="https://stability.ai/news/stable-diffusion-3-research-paper">Research paper</a>.

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