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README.md

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-# Introduction
-
-This demo application ("demoDiffusion") showcases the acceleration of [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion-v1-4) pipeline using TensorRT plugins.
-
-# Setup
-
-### Clone the TensorRT OSS repository
-
-```bash
-git clone git@github.com:NVIDIA/TensorRT.git -b release/8.5 --single-branch
-cd TensorRT
-git submodule update --init --recursive
-```
-
-### Launch TensorRT NGC container
-
-Install nvidia-docker using [these intructions](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker).
-
-```bash
-docker run --rm -it --gpus all -v $PWD:/workspace nvcr.io/nvidia/tensorrt:22.10-py3 /bin/bash
-```
-
-### (Optional) Install latest TensorRT release
-
-```bash
-python3 -m pip install --upgrade pip
-python3 -m pip install --upgrade tensorrt
-```
-> NOTE: Alternatively, you can download and install TensorRT packages from [NVIDIA TensorRT Developer Zone](https://developer.nvidia.com/tensorrt).
-
-### Build TensorRT plugins library
-
-Build TensorRT Plugins library using the [TensorRT OSS build instructions](https://github.com/NVIDIA/TensorRT/blob/main/README.md#building-tensorrt-oss).
-
-```bash
-export TRT_OSSPATH=/workspace
-
-cd $TRT_OSSPATH
-mkdir -p build && cd build
-cmake .. -DTRT_OUT_DIR=$PWD/out
-cd plugin
-make -j$(nproc)
-
-export PLUGIN_LIBS="$TRT_OSSPATH/build/out/libnvinfer_plugin.so"
-```
-
-### Install required packages
-
-```bash
-cd $TRT_OSSPATH/demo/Diffusion
-pip3 install -r requirements.txt
-
-# Create output directories
-mkdir -p onnx engine output
-```
-
-> NOTE: demoDiffusion has been tested on systems with NVIDIA A100, RTX3090, and RTX4090 GPUs, and the following software configuration.
-```
-cuda-python         11.8.1
-diffusers           0.7.2
-onnx                1.12.0
-onnx-graphsurgeon   0.3.25
-onnxruntime         1.13.1
-polygraphy          0.43.1
-tensorrt            8.5.1.7
-tokenizers          0.13.2
-torch               1.12.0+cu116
-transformers        4.24.0
-```
-
-> NOTE: optionally install HuggingFace [accelerate](https://pypi.org/project/accelerate/) package for faster and less memory-intense model loading.
-
-
-# Running demoDiffusion
-
-### Review usage instructions
-
-```bash
-python3 demo-diffusion.py --help
-```
-
-### HuggingFace user access token
-
-To download the model checkpoints for the Stable Diffusion pipeline, you will need a `read` access token. See [instructions](https://huggingface.co/docs/hub/security-tokens).
-
-```bash
-export HF_TOKEN=<your access token>
-```
-
-### Generate an image guided by a single text prompt
-
-```bash
-LD_PRELOAD=${PLUGIN_LIBS} python3 demo-diffusion.py "a beautiful photograph of Mt. Fuji during cherry blossom" --hf-token=$HF_TOKEN -v
-```
-
-# Restrictions
-- Upto 16 simultaneous prompts (maximum batch size) per inference.
-- For generating images of dynamic shapes without rebuilding the engines, use `--force-dynamic-shape`.
-- Supports images sizes between 256x256 and 1024x1024.