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--- |
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pipeline_tag: text-to-image |
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license: other |
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license_name: sai-nc-community |
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license_link: https://huggingface.co/stabilityai/sd-turbo/blob/main/LICENSE.TXT |
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base_model: stabilityai/sd-turbo |
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language: |
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- en |
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tags: |
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- stable-diffusion |
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- sdxl |
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- onnxruntime |
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- onnx |
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- text-to-image |
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--- |
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# Stable Diffusion XL Turbo for ONNX Runtime |
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## Introduction |
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This repository hosts the optimized versions of **SD Turbo** to accelerate inference with ONNX Runtime CUDA execution provider. |
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See the [usage instructions](#usage-example) for how to run the SDXL pipeline with the ONNX files hosted in this repository. |
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## Model Description |
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- **Developed by:** Stability AI |
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- **Model type:** Diffusion-based text-to-image generative model |
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- **License:** [STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE](https://huggingface.co/stabilityai/sd-turbo/blob/main/LICENSE) |
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- **Model Description:** This is a conversion of the [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo) model for [ONNX Runtime](https://github.com/microsoft/onnxruntime) inference with CUDA execution provider. |
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## Performance Comparison |
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#### Latency |
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Below is average latency of generating an image of size 512x512 using NVIDIA A100-SXM4-80GB GPU: |
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| Engine | Batch Size | Steps | PyTorch 2.1 | ONNX Runtime CUDA | |
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|-------------|------------|------ | ----------------|-------------------| |
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| Static | 1 | 1 | 85.3 ms | 32.9 ms | |
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| Static | 4 | 1 | 213.8 ms | 97.5 ms | |
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| Static | 1 | 4 | 117.4 ms | 62.5 ms | |
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| Static | 4 | 4 | 294.3 ms | 168.3 ms | |
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Static means the engine is built for the given batch size and image size combination, and CUDA graph is used to speed up. |
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## Usage Example |
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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: |
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0. Install nvidia-docker using these [instructions](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html). |
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1. Clone onnxruntime repository. |
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```shell |
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git clone https://github.com/microsoft/onnxruntime |
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cd onnxruntime |
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``` |
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2. Download the SDXL ONNX files from this repo |
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```shell |
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git lfs install |
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git clone https://huggingface.co/tlwu/sdxl-turbo-onnxruntime |
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``` |
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3. Launch the docker |
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```shell |
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docker run --rm -it --gpus all -v $PWD:/workspace nvcr.io/nvidia/pytorch:23.10-py3 /bin/bash |
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``` |
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4. Build ONNX Runtime from source |
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```shell |
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export CUDACXX=/usr/local/cuda-12.2/bin/nvcc |
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git config --global --add safe.directory '*' |
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sh build.sh --config Release --build_shared_lib --parallel --use_cuda --cuda_version 12.2 \ |
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--cuda_home /usr/local/cuda-12.2 --cudnn_home /usr/lib/x86_64-linux-gnu/ --build_wheel --skip_tests \ |
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--use_tensorrt --tensorrt_home /usr/src/tensorrt \ |
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--cmake_extra_defines onnxruntime_BUILD_UNIT_TESTS=OFF \ |
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--cmake_extra_defines CMAKE_CUDA_ARCHITECTURES=80 \ |
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--allow_running_as_root |
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python3 -m pip install build/Linux/Release/dist/onnxruntime_gpu-*-cp310-cp310-linux_x86_64.whl --force-reinstall |
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``` |
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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. |
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5. Install libraries and requirements |
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```shell |
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python3 -m pip install --upgrade pip |
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cd /workspace/onnxruntime/python/tools/transformers/models/stable_diffusion |
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python3 -m pip install -r requirements-cuda12.txt |
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python3 -m pip install --upgrade polygraphy onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com |
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``` |
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6. Perform ONNX Runtime optimized inference |
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```shell |
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python3 demo_txt2img.py \ |
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"starry night over Golden Gate Bridge by van gogh" \ |
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--version sd-turbo \ |
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--work-dir /workspace/sd-turbo-onnxruntime |
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``` |
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