---
title: sdxl-turbo
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/sdxl-turbo"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/sdxl-turbo.md"
repository: AI-ModelScope/sdxl-turbo
last_updated: 2023-12-03
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
parameters: 13.9B
tensor_type:
  - F32
  - F16
library_name:
  - pytorch
  - safetensors
  - diffusers
frameworks:
  - Pytorch
downloads: 75908
stars: 30
---

# sdxl-turbo

> sdxl-turbo - AI-ModelScope 在 ModelScope 开源的模型。SDXL-Turbo 是 SDXL 1.0 的精炼版本，经过实时合成训练。 SDXL-Turbo 基于一种称为对抗扩散蒸馏 (ADD) 的新颖训练方法（请参阅技术报告），该方法允许在高图像质量下以 1 到 4 个步骤对大规模基础图像扩散模型进行采样。 这种方法使用分数蒸馏来利用大规模现成的图像扩散模型作为教师信号，并将其与对抗性损失相结合，以确保即使在一个或两个采样步骤的低步骤状态下也能确保高图像保真度。

- **Repository**: AI-ModelScope/sdxl-turbo
- **Tasks**: text-to-image-synthesis
- **Parameters**: 13.9B
- **Downloads**: 75908
- **Stars**: 30
- **Last updated**: 2023-12-03

Source: https://www.modelscope.cn/models/AI-ModelScope/sdxl-turbo

---

# SDXL-Turbo Model Card

<!-- Provide a quick summary of what the model is/does. -->
![row01](output_tile.jpg)
SDXL-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation.
A real-time demo is available here: http://clipdrop.co/stable-diffusion-turbo

## Model Details

### Model Description
SDXL-Turbo is a distilled version of [SDXL 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), trained for real-time synthesis. 
SDXL-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the [technical report](https://stability.ai/research/adversarial-diffusion-distillation)), 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:** [SDXL 1.0 Base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)

### 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:** http://clipdrop.co/stable-diffusion-turbo


## Evaluation
![comparison1](image_quality_one_step.png)
![comparison2](prompt_alignment_one_step.png)
The charts above evaluate user preference for SDXL-Turbo over other single- and multi-step models.
SDXL-Turbo evaluated at a single step is preferred by human voters in terms of image quality and prompt following over LCM-XL evaluated at four (or fewer) steps.
In addition, we see that using four steps for SDXL-Turbo further improves performance.
For details on the user study, we refer to the [research paper](https://stability.ai/research/adversarial-diffusion-distillation).


## 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**:

SDXL-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.

```py
from diffusers import AutoPipelineForText2Image
import torch
from modelscope import snapshot_download

model_dir = snapshot_download("AI-ModelScope/sdxl-turbo")

pipe = AutoPipelineForText2Image.from_pretrained(model_dir, 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.save("image.png")
```

- **Image-to-image**:

When using SDXL-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.

```py
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
from modelscope import snapshot_download
model_dir = snapshot_download("AI-ModelScope/sdxl-turbo")
pipe = AutoPipelineForImage2Image.from_pretrained(model_dir,  variant="fp16")

init_image = load_image("image.png").resize((512, 512))

prompt = "A cinematic shot of a baby racoon wearing an intricate italian priest robe, 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]
image.save("image1.png")
```

### 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](https://stability.ai/use-policy).

## Limitations and Bias

### Limitations
- 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