Sign In

Stable Diffusion 3.5 Large Turbo

145
4.3k
5.9k
40
Verified:
SafeTensor
Type
Checkpoint Trained
Stats
3,746
5,920
Reviews
Published
Oct 22, 2024
Base Model
SD 3.5 Large Turbo
Hash
AutoV2
FB64610BF8
Holiday 2024: 1 lights
theally's Avatar
theally
This Stability AI Model is licensed under the Stability AI Community License, Copyright (c) Stability AI Ltd. All Rights Reserved.
Powered by Stability AI

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.