This release emphasizes Stable Diffusion 3, Stability AI’s latest iteration of the Stable Diffusion family of models. It was introduced in [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://arxiv.org/abs/2403.03206) by Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach.
As the model is gated, before using it with `diffusers`, you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate.
bash
huggingface-cli login
The code below shows how to perform text-to-image generation with SD3:
python
import torch
from diffusers import StableDiffusion3Pipeline
pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
image = pipe(
"A cat holding a sign that says hello world",
negative_prompt="",
num_inference_steps=28,
guidance_scale=7.0,
).images[0]
image
Refer to [our documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_3) for learning all the optimizations you can apply to SD3 as well as the image-to-image pipeline.
Additionally, we support DreamBooth + LoRA fine-tuning of Stable Diffusion 3 through rectified flow. Check out [this directory](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md) for more details.