---
pipeline_tag: text-to-image
license: other
license_name: sai-nc-community
license_link: https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICENSE.TXT
base_model: stabilityai/sdxl-turbo
language:
  - en
tags:
  - stable-diffusion
  - sdxl
  - onnxruntime
  - onnx
  - text-to-image
---
# Stable Diffusion XL Turbo for ONNX Runtime CUDA

## Introduction

This repository hosts the optimized onnx models of **SDXL Turbo** to accelerate inference with ONNX Runtime CUDA execution provider for Nvidia GPUs. It cannot run in other providers like CPU or DirectML.

The models are generated by [Olive](https://github.com/microsoft/Olive/tree/main/examples/stable_diffusion) with command like the following:
```
python stable_diffusion_xl.py --provider cuda --model_id stabilityai/sdxl-turbo --optimize --use_fp16_fixed_vae
```

See the [usage instructions](#usage-example) for how to run the SDXL pipeline with the ONNX files hosted in this repository.

## Model Description

- **Developed by:** Stability AI
- **Model type:** Diffusion-based text-to-image generative model
- **License:** [STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE](https://huggingface.co/stabilityai/sd-turbo/blob/main/LICENSE)
- **Model Description:** This is a conversion of the [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo) model for [ONNX Runtime](https://github.com/microsoft/onnxruntime) inference with CUDA execution provider.

The VAE decoder is converted from [sdxl-vae-fp16-fix](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix). There are slight discrepancies between its output and that of the original VAE, but the decoded images should be [close enough for most purposes](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/discussions/7#64c5c0f8e2e5c94bd04eaa80).

## Usage Example

Following the [demo instructions](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/models/stable_diffusion/README.md#run-demo-with-docker). Example steps:

0. Install nvidia-docker using these [instructions](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).

1. Clone onnxruntime repository.
```shell
git clone https://github.com/microsoft/onnxruntime
cd onnxruntime
```

2. Download the SDXL ONNX files from this repo
```shell
git lfs install
git clone https://huggingface.co/tlwu/sdxl-turbo-onnxruntime
```

3. Launch the docker
```shell
docker run --rm -it --gpus all -v $PWD:/workspace nvcr.io/nvidia/pytorch:23.10-py3 /bin/bash
```

4. Build ONNX Runtime from source
```shell
export CUDACXX=/usr/local/cuda-12.2/bin/nvcc
git config --global --add safe.directory '*'
sh build.sh --config Release  --build_shared_lib --parallel --use_cuda --cuda_version 12.2 \
            --cuda_home /usr/local/cuda-12.2 --cudnn_home /usr/lib/x86_64-linux-gnu/ --build_wheel --skip_tests \
            --use_tensorrt --tensorrt_home /usr/src/tensorrt \
            --cmake_extra_defines onnxruntime_BUILD_UNIT_TESTS=OFF \
            --cmake_extra_defines CMAKE_CUDA_ARCHITECTURES=80 \
            --allow_running_as_root
python3 -m pip install build/Linux/Release/dist/onnxruntime_gpu-*-cp310-cp310-linux_x86_64.whl --force-reinstall
```

If the GPU is not A100, change CMAKE_CUDA_ARCHITECTURES=80 in the command line according to the GPU compute capacity (like 89 for RTX 4090, or 86 for RTX 3090). If your machine has less than 64GB memory, replace --parallel by --parallel 4 --nvcc_threads 1  to avoid out of memory.
5. Install libraries and requirements
```shell
python3 -m pip install --upgrade pip
cd /workspace/onnxruntime/python/tools/transformers/models/stable_diffusion
python3 -m pip install -r requirements-cuda12.txt
python3 -m pip install --upgrade polygraphy onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com
```
6. Perform ONNX Runtime optimized inference
```shell
python3 demo_txt2img_xl.py \
  "starry night over Golden Gate Bridge by van gogh" \
  --version xl-turbo   \
  --engine-dir /workspace/sdxl-turbo-onnxruntime
```