diff --git "a/Text_to_Video_with_Diffusers.ipynb" "b/Text_to_Video_with_Diffusers.ipynb"
new file mode 100644--- /dev/null
+++ "b/Text_to_Video_with_Diffusers.ipynb"
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+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🧨 Diffusers meets Video\n",
+        "\n",
+        "This colab showcases the new research text-to-video model by Alibaba and its integration with the diffusers library https://huggingface.co/damo-vilab/text-to-video-ms-1.7b "
+      ],
+      "metadata": {
+        "id": "OE8UPJPBdRKD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Check your GPU!\n",
+        "!nvidia-smi"
+      ],
+      "metadata": {
+        "id": "R6fBIouojIYa"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9bxNLonIVDQP"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Install dependencies\n",
+        "!pip install torch==2.0.0 git+https://github.com/huggingface/diffusers transformers accelerate imageio[ffmpeg]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Setup pipeline\n",
+        "import torch\n",
+        "from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler\n",
+        "from diffusers.utils import export_to_video\n",
+        "from IPython.display import HTML\n",
+        "from base64 import b64encode\n",
+        "\n",
+        "pipe = DiffusionPipeline.from_pretrained(\"damo-vilab/text-to-video-ms-1.7b\", torch_dtype=torch.float16, variant=\"fp16\")\n",
+        "pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n",
+        "pipe.enable_model_cpu_offload()\n",
+        "pipe.enable_vae_slicing()"
+      ],
+      "metadata": {
+        "id": "lNJpGpqaVZTH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Generate your video\n",
+        "prompt = 'Spiderman chatting with a llama' #@param {type:\"string\"}\n",
+        "video_duration_seconds = 3 #@param {type:\"integer\"}\n",
+        "num_frames = video_duration_seconds * 10\n",
+        "video_frames = pipe(prompt, negative_prompt=\"low quality\", num_inference_steps=25, num_frames=num_frames).frames\n",
+        "video_path = export_to_video(video_frames)"
+      ],
+      "metadata": {
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+      "cell_type": "code",
+      "source": [
+        "#@title Display the video\n",
+        "import imageio\n",
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.animation as animation\n",
+        "from skimage.transform import resize\n",
+        "from IPython.display import HTML\n",
+        "\n",
+        "def display_video(video):\n",
+        "    fig = plt.figure(figsize=(4.2,4.2))  #Display size specification\n",
+        "    fig.subplots_adjust(left=0, right=1, bottom=0, top=1)\n",
+        "    mov = []\n",
+        "    for i in range(len(video)):  #Append videos one by one to mov\n",
+        "        img = plt.imshow(video[i], animated=True)\n",
+        "        plt.axis('off')\n",
+        "        mov.append([img])\n",
+        "\n",
+        "    #Animation creation\n",
+        "    anime = animation.ArtistAnimation(fig, mov, interval=100, repeat_delay=1000)\n",
+        "\n",
+        "    plt.close()\n",
+        "    return anime\n",
+        "video = imageio.mimread(video_path)  #Loading video\n",
+        "HTML(display_video(video).to_html5_video())  #Inline video display in HTML5"
+      ],
+      "metadata": {
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+        "id": "CFmTs4ftecDU",
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+            "text/plain": [
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+              "\">\n",
+              "  Your browser does not support the video tag.\n",
+              "</video>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file