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notebooks/ltx-video/README.md

@@ -1,6 +1,6 @@
 # Sound Generation with Stable Audio Open and OpenVINO™
 
-[LTX-Video](https://github.com/Lightricks/LTX-Video) is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 24 FPS videos at a 768x512 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-resolution videos with realistic and varied content. The model is provided for both text-to-video as well as image+text-to-video usecases. In this example we show how to convert text-to-video pipeline in OpenVINO format and run inference.
+[LTX-Video](https://github.com/Lightricks/LTX-Video) is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 24 FPS videos at a 768x512 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-resolution videos with realistic and varied content. The model is provided for both text-to-video as well as image+text-to-video use cases. In this example we show how to convert text-to-video pipeline in OpenVINO format and run inference.
 
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notebooks/ltx-video/ltx-video.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# LTX Video and OpenVINO™ IR\n",
     "\n",
-    "[LTX-Video](https://github.com/Lightricks/LTX-Video) is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 24 FPS videos at a 768x512 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-resolution videos with realistic and varied content. The model is provided for both text-to-video as well as image+text-to-video usecases. In this example we show how to convert text-to-video pipeline in OpenVINO format and run inference.\n",
+    "[LTX-Video](https://github.com/Lightricks/LTX-Video) is the first DiT-based video generation model capable of generating high-quality videos in real-time. It produces 24 FPS videos at a 768x512 resolution faster than they can be watched. Trained on a large-scale dataset of diverse videos, the model generates high-resolution videos with realistic and varied content. The model is provided for both text-to-video as well as image+text-to-video use cases. In this example we show how to convert text-to-video pipeline in OpenVINO format and run inference.\n",
     "\n",
     "| | | | |\n",
     "|:---:|:---:|:---:|:---:|\n",
@@ -163,7 +163,12 @@
       ]
      },
      "execution_count": 3,
-     "metadata": {},
+     "metadata": {
+      "test_replace": {
+       "    num_frames=24,\n": "    num_frames=2,\n",
+       "    num_inference_steps=40,\n": "    num_inference_steps=2,\n"
+      }
+     },
      "output_type": "execute_result"
     }
    ],
@@ -508,7 +513,12 @@
        "  0%|          | 0/40 [00:00<?, ?it/s]"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "test_replace": {
+       "    num_frames=24,\n": "    num_frames=2,\n",
+       "    num_inference_steps=40,\n": "    num_inference_steps=2,\n"
+      }
+     },
      "output_type": "display_data"
     }
    ],