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Diff for: ‎README.md

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# Diffusion deployment
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This repository contains scripts for the deployment of diffusion models (based on [diffusers](https://github.com/huggingface/diffusers)) on both GPU (Nvidia) and CPU (Intel). The aim is to significantly speed up the inference of diffusion models. It provides **a ~12x speedup on CPUs and a ~4x speedup on GPUs.**
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Integrated with [small-stable-diffusion-v0](https://huggingface.co/OFA-Sys/small-stable-diffusion-v0/), it could generate an image in just **5s** on the CPU.
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## CPU speedup
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We develop the diffusion deployment on CPU based on [Intel OpenVINO](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html). The pipeline `OpenVINOStableDiffusionPipeline` is modified from `OnnxStableDiffusionPipeline`. The code used here is in `pipeline_openvino_stable_diffusion.py`.
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#### Results
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Here are some experimental results on stable diffusion v1.4 for comparison with the default Pytorch CPU and Onnx pipeline.
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| Pipeline | Pytorch CPU | Onnx | OpenVINO |
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| --------- | ----------- | ------------ | --------------- |
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| Time Cost | 397s | 77s ± 2.56 s | 33.9 s ± 247 ms |
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| Speedup | 1 | 5.2 | 11.7 |
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*Test setting: CPU Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz / PNDM scheduler 50 steps)*
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#### Prerequisites
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+ diffuers
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+ transformers
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+ openvino runtime
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To install `openvino runtime`, you could simply use `pip install onnxruntime-openvino==1.13.0`.
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#### Usage
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To use this deployment, you could follow the following code:
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```py
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# Load a onnx pipeline firstly.
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onnx_pipe = OnnxStableDiffusionPipeline.from_pretrained(
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"OFA-Sys/small-stable-diffusion-v0",
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revision="onnx",
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provider="CPUExecutionProvider",
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)
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# Convert it to OpenVINO pipeline.
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import pipeline_openvino_stable_diffusion
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openvino_pipe = pipeline_openvino_stable_diffusion.OpenVINOStableDiffusionPipeline.from_onnx_pipeline(p3)
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# Generate images.
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images = openvino_pipe("an apple, 4k") .
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```
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## GPU speedup
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We develop the deployment on GPU based on TensorRT and its plugins.
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#### Comparison
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Here are some experimental results of stable-diffusion-v1-4 and [small-stable-diffusion-v0](https://huggingface.co/OFA-Sys/small-stable-diffusion-v0/).
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| Model\Pipeline | Pytorch GPU | TensorRT | TensorRT Plugin |
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| ---------------------- | ----------- | -------- | --------------- |
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| Stable diffusion | 3.94 | 1.44s | 1.07s |
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| Small stable diffusion | 2.7s | 1.01s | 0.65s |
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#### Prerequisites
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See the `gpu_requirements.txt` for requirments. To use plugins, we need `tensorrt>=8.5`. If you use `tensorrt==8.4`, you could run it by deleting `trt.init_libnvinfer_plugins(TRT_LOGGER, '')` in `gpu-trt-infer-demo.py` and not adding `PLUGIN_LIBS` to `LD_PRELOAD`.
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#### Usage
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```sh
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export PLUGIN_LIBS="/path/to/libnvinfer_plugin.so.8.5.1"
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export HF_TOKEN="Your_HF_TOKEN"
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mkdir -p onnx engine output
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LD_PRELOAD=${PLUGIN_LIBS} python3 demo-diffusion.py "a beautiful photograph of Mt. Fuji during cherry blossom" --enable-preview-features --hf-token=$HF_TOKEN -v
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```
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## Contributions
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Contributions to this repository are welcome. If you would like to contribute, please open a pull request and make sure to follow the existing code style.

Diff for: ‎cpu_requirements.txt

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diffusers
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transformers
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onnxruntime-openvino==1.13.0

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