SD-Turbo Model Card

SD-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. We release SD-Turbo as a research artifact, and to study small, distilled text-to-image models. For increased quality and prompt understanding, we recommend SDXL-Turbo.

Model Details

Model Description

SD-Turbo is a distilled version of Stable Diffusion 2.1, trained for real-time synthesis. SD-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the technical report), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high image quality. This approach uses score distillation to leverage large-scale off-the-shelf image diffusion models as a teacher signal and combines this with an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps.

Developed by: Stability AI
Funded by: Stability AI
Model type: Generative text-to-image model
Finetuned from model: Stable Diffusion 2.1

Model Sources

For research purposes, we recommend our generative-models Github repository (https://github.com/Stability-AI/generative-models), which implements the most popular diffusion frameworks (both training and inference).

Repository: https://github.com/Stability-AI/generative-models
Paper: https://stability.ai/research/adversarial-diffusion-distillation
Demo [for the bigger SDXL-Turbo]: http://clipdrop.co/stable-diffusion-turbo

Evaluation

The charts above evaluate user preference for SD-Turbo over other single- and multi-step models. SD-Turbo evaluated at a single step is preferred by human voters in terms of image quality and prompt following over LCM-Lora XL and LCM-Lora 1.5.

Note: For increased quality, we recommend the bigger version SDXL-Turbo. For details on the user study, we refer to the research paper.

Uses

Direct Use

The model is intended for research purposes only. Possible research areas and tasks include

Research on generative models.
Research on real-time applications of generative models.
Research on the impact of real-time generative models.
Safe deployment of models which have the potential to generate harmful content.
Probing and understanding the limitations and biases of generative models.
Generation of artworks and use in design and other artistic processes.
Applications in educational or creative tools.

Excluded uses are described below.

Diffusers

pip install diffusers transformers accelerate --upgrade

Text-to-image:

SD-Turbo does not make use of guidance_scale or negative_prompt, we disable it with guidance_scale=0.0. Preferably, the model generates images of size 512x512 but higher image sizes work as well. A single step is enough to generate high quality images.

from diffusers import AutoPipelineForText2Image
import torch

pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sd-turbo", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")

prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe."
image = pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0.0).images[0]

Image-to-image:

When using SD-Turbo for image-to-image generation, make sure that num_inference_steps * strength is larger or equal to 1. The image-to-image pipeline will run for int(num_inference_steps * strength) steps, e.g. 0.5 * 2.0 = 1 step in our example below.

from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
import torch

pipe = AutoPipelineForImage2Image.from_pretrained("stabilityai/sd-turbo", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")

init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png").resize((512, 512))
prompt = "cat wizard, gandalf, lord of the rings, detailed, fantasy, cute, adorable, Pixar, Disney, 8k"

image = pipe(prompt, image=init_image, num_inference_steps=2, strength=0.5, guidance_scale=0.0).images[0]

Out-of-Scope Use

The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. The model should not be used in any way that violates Stability AI's Acceptable Use Policy.

Limitations and Bias

Limitations

The quality and prompt alignment is lower than that of SDXL-Turbo.
The generated images are of a fixed resolution (512x512 pix), and the model does not achieve perfect photorealism.
The model cannot render legible text.
Faces and people in general may not be generated properly.
The autoencoding part of the model is lossy.

Recommendations

The model is intended for research purposes only.

How to Get Started with the Model

Check out https://github.com/Stability-AI/generative-models