update doc about Olive

README.md

@@ -2,7 +2,7 @@
 pipeline_tag: text-to-image
 license: other
 license_name: sai-nc-community
-license_link: https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICENSE.TXT  
+license_link: https://huggingface.co/stabilityai/sdxl-turbo/blob/main/LICENSE.TXT
 base_model: stabilityai/sdxl-turbo
 language:
   - en
@@ -18,7 +18,10 @@ tags:
 
 ## Introduction
 
-This repository hosts the optimized versions of **SDXL Turbo** to accelerate inference with ONNX Runtime CUDA execution provider.
+This repository hosts the optimized versions of **SDXL Turbo** to accelerate inference with ONNX Runtime CUDA execution provider. 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.
 
@@ -39,28 +42,17 @@ The Canny control net is converted from [diffusers/controlnet-canny-sdxl-1.0](ht
 
 Below is average latency of generating an image of size 512x512 using NVIDIA A100-SXM4-80GB GPU:
 
-| Engine      | Batch Size | Steps | PyTorch 2.1     | ONNX Runtime CUDA |
+| Engine      | Batch Size | Steps | PyTorch 2.1 + Diffusers  | ONNX Runtime Demo |
 |-------------|------------|------ | ----------------|-------------------|
-| Static      | 1          |   1   | 109.4 ms        |  43.9 ms          |
-| Static      | 4          |   1   | 247.0 ms        | 121.1 ms          |
-| Static      | 1          |   4   | 171.1 ms        |  97.5 ms          |
-| Static      | 4          |   4   | 390.5 ms        | 248.0 ms          |
+| Static      | 1          |   1   | 109.4 ms        |  49.5 ms          |
+| Static      | 4          |   1   | 247.0 ms        | 143.1 ms          |
+| Static      | 1          |   4   | 171.1 ms        |  104.1 ms         |
+| Static      | 4          |   4   | 390.5 ms        | 271.69 ms         |
 
 
 Static means the engine is built for the given batch size and image size combination, and CUDA graph is used to speed up.
-For PyTorch 2.1, the UNet use channel last (NHWC) format, and compile the UNet with mode `reduce-overhead`. See [benchmark script](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/models/stable_diffusion/benchmark_controlnet.py) for detail.
-
-#### Latency for SDXL-Turbo with Canny Control Net
-
-Below is average latency of generating an image of size 512x512 with canny control net using NVIDIA A100-SXM4-80GB GPU:
-
-| Engine      | Batch Size | Steps | PyTorch 2.1     | ONNX Runtime CUDA |
-|-------------|------------|------ | ----------------|-------------------|
-| Static      | 1          |   1   | 160.0 ms        |  55.3 ms          |
-| Static      | 4          |   1   | 314.9 ms        | 144.4 ms          |
-| Static      | 1          |   4   | 251.9 ms        | 134.9 ms          |
-| Static      | 4          |   4   | 514.2 ms        | 332.6 ms          |
 
+For PyTorch 2.1, the UNet use channel last (NHWC) format, and compile the UNet with mode `reduce-overhead`. See [benchmark script](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/models/stable_diffusion/benchmark_controlnet.py) for detail.
 
 ## Usage Example
 
@@ -80,11 +72,6 @@ git lfs install
 git clone https://huggingface.co/tlwu/sdxl-turbo-onnxruntime
 ```
 
-If you want to try canny control net, get model from a branch:
-```shell
-git checkout canny_control_net
-```
-
 3. Launch the docker
 ```shell
 docker run --rm -it --gpus all -v $PWD:/workspace nvcr.io/nvidia/pytorch:23.10-py3 /bin/bash
@@ -118,16 +105,5 @@ python3 -m pip install --upgrade polygraphy onnx-graphsurgeon --extra-index-url
 python3 demo_txt2img_xl.py \
   "starry night over Golden Gate Bridge by van gogh" \
   --version xl-turbo   \
-  --work-dir /workspace/sdxl-turbo-onnxruntime
-```
-
-Generate an image using the canny control net:
-
-```shell
-wget https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png
-
-python3 demo_txt2img_xl.py --controlnet-type canny --controlnet-scale 0.5 --controlnet-image input_image_vermeer.png \
-        --version xl-turbo --height 1024 --width 1024 \
-        --work-dir /workspace/sdxl-turbo-onnxruntime \
-        "portrait of Mona Lisa with mysterious mysterious smile and mountain, river and forest in the background"
+  --engine-dir /workspace/sdxl-turbo-onnxruntime
 ```