fix notebook link

Author: echarlaix

notebooks/openvino/README.md

@@ -12,5 +12,5 @@ The notebooks have been tested with Python 3.8 and 3.10 on Ubuntu Linux.
 |:----------|:-------------|:-------------|------:|
 | [How to run inference with the OpenVINO](https://github.com/huggingface/optimum-intel/blob/main/notebooks/openvino/optimum_openvino_inference.ipynb) | Explains how to export your model to OpenVINO and to run inference with OpenVINO Runtime on various tasks| [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/optimum-intel/blob/main/notebooks/openvino/optimum_openvino_inference.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/optimum-intel/blob/main/notebooks/openvino/optimum_openvino_inference.ipynb)|
 | [How to quantize a question answering model with OpenVINO NNCF](https://github.com/huggingface/optimum-intel/blob/main/notebooks/openvino/question_answering_quantization.ipynb) | Show how to apply post-training quantization on a question answering model using [NNCF](https://github.com/openvinotoolkit/nncf) and to accelerate inference with OpenVINO| [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/optimum-intel/blob/main/notebooks/openvino/question_answering_quantization.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/optimum-intel/blob/main/notebooks/openvino/question_answering_quantization.ipynb)|
-| [Compare outputs of a quantized Stable Diffusion model with its full-precision counterpart](https://github.com/huggingface/optimum-intel/blob/main/notebooks/openvino/stable_diffusion_quantization.ipynb) | Show how to load and compare outputs from two Stable Diffusion models with different precision| [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/optimum-intel/blob/main/notebooks/openvino/stable_diffusion_quantization.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/optimum-intel/blob/main/notebooks/openvino/stable_diffusion_quantization.ipynb)|
+| [Compare outputs of a quantized Stable Diffusion model with its full-precision counterpart](https://github.com/huggingface/optimum-intel/blob/main/notebooks/openvino/stable_diffusion_optimization.ipynb) | Show how to load and compare outputs from two Stable Diffusion models with different precision| [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/optimum-intel/blob/main/notebooks/openvino/stable_diffusion_optimization.ipynb)| [![Open in AWS Studio](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/huggingface/optimum-intel/blob/main/notebooks/openvino/stable_diffusion_optimization.ipynb)|
 

notebooks/openvino/stable_diffusion_optimization.ipynb

@@ -69,9 +69,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "quantized_pipe = OVStableDiffusionPipeline.from_pretrained(\n",
-    "    \"OpenVINO/Stable-Diffusion-Pokemon-en-quantized\", compile=False\n",
-    ")\n",
+    "quantized_pipe = OVStableDiffusionPipeline.from_pretrained(\"OpenVINO/Stable-Diffusion-Pokemon-en-quantized\", compile=False)\n",
     "quantized_pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)\n",
     "quantized_pipe.compile()"
    ]
@@ -104,9 +102,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "optimized_pipe = OVStableDiffusionPipeline.from_pretrained(\n",
-    "    \"OpenVINO/stable-diffusion-pokemons-tome-quantized\", compile=False\n",
-    ")\n",
+    "optimized_pipe = OVStableDiffusionPipeline.from_pretrained(\"OpenVINO/stable-diffusion-pokemons-tome-quantized\", compile=False)\n",
     "optimized_pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)\n",
     "optimized_pipe.compile()"
    ]