Stable Diffusion 3.5 Large BF16

Model

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.

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:

Implementation Details

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

Model Performance

See blog for our study about comparative performance in prompt adherence and aesthetic quality.

Using with Diffusers

Upgrade to the latest version of the 🧨 diffusers library

pip install -U diffusers

and then you can run

import torch
from diffusers import StableDiffusion3Pipeline

pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")

image = pipe(
    "A capybara holding a sign that reads Hello World",
    num_inference_steps=28,
    guidance_scale=3.5,
).images[0]
image.save("capybara.png")

Contact

Please report any issues with the model or contact us:

Safety issues: [email protected]
Security issues: [email protected]
Privacy issues: [email protected]
License and general: https://stability.ai/license
Enterprise license: https://stability.ai/enterprise

ford442
/

stable-diffusion-3.5-large-bf16