add SDXL model example to INC 3.x

examples/3.x_api/pytorch/diffusion_model/diffusers/stable_diffusion/smooth_quant/README.md

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+Step-by-Step
+============
+This document describes the step-by-step instructions to run [stable diffusion XL model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) using Smooth Quantization to accelerate inference while maintain the quality of output image.
+
+# Prerequisite
+
+## Environment
+Recommend python 3.9 or higher version.
+
+```shell
+pip install -r requirements.txt
+```
+**Note**: IPEX along with torch require nightly version (2.4) for compatibility. Please refer to [installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=cpu&version=main&os=linux%2fwsl2&package=source). 
+
+# Run
+
+To quantize the model:
+```bash
+python sdxl_smooth_quant.py --model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 --quantize --output_dir "./saved_results"
+```
+To load a quantized model:
+```bash
+python sdxl_smooth_quant.py --model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 --quantize --load --int8
+```
+
+# Results
+## Image Generated
+
+With caption `"A brown and white dog runs on some brown grass near a Frisbee that is just sailing above the ground."`, results of fp32 model and int8 model are listed left and right respectively.
+
+<p float="left">
+  <img src="./images/fp32.jpg" width = "300" height = "300" alt="bf16" align=center />
+  <img src="./images/int8.jpg" width = "300" height = "300" alt="int8" align=center />
+</p>
+
+## CLIP evaluation
+We have also evaluated CLIP scores on 5000 samples from COCO2014 validation dataset for FP32 model and INT8 model. CLIP results are listed below.
+
+| Precision            | FP32  | INT8  | 
+|----------------------|-------|-------|
+| CLIP on COCO2014 val  | 32.05 | 31.77 |
+
+We're using the mlperf_sd_inference [repo](https://github.com/ahmadki/mlperf_sd_inference) to evaluate CLIP scores. In order to support evaluation on quantized model,
+we made some modification on the script (`main.py`). Please use as following:
+```bash
+git clone https://github.com/ahmadki/mlperf_sd_inference.git
+cd mlperf_sd_inference
+mv ../main.py ./
+```
+After setting the environment as instructed in the repo, you can execute the modified `main.py` script to generate images:
+```bash
+python main.py \
+    --model-id stabilityai/stable-diffusion-xl-base-1.0 \
+    --quantized-unet ./saved_results \  # quantized model saving path, should include `qconfig.json` and `quantized_model.pt`
+    --precision fp32 \ 
+    --guidance 8.0 \
+    --steps 20 \
+    --latent-path latents.pt \
+    --base-output-dir ./output
+```
+Then you can compute CLIP score using the images generated by the quantized model:
+```bash
+python clip/clip_score.py \
+    --tsv-file captions_5k.tsv \
+    --image-folder ./output \  # Folder with the generated images
+    --device "cpu"     
+```