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.

• ComfyUI: Github, Example Workflow

• Huggingface Space: Space

• Diffusers: See below.

• GitHub: GitHub.

• API Endpoints:

  • Stability AI API

  • Deepinfra

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：

  • CLIPs: OpenCLIP-ViT/G, CLIP-ViT/L, context length 77 tokens

  • T5: T5-xxl, context length 77/256 tokens at different stages of training

• 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.