test

potat1_text_to_video_diffusers.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github"
+      },
+      "source": [
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/camenduru/text-to-video-synthesis-colab/blob/main/potat1_text_to_video_diffusers.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#https://github.com/huggingface/diffusers/pull/2738 modified\n",
+        "!pip install git+https://github.com/huggingface/diffusers transformers accelerate imageio[ffmpeg] -U\n",
+        "!pip install -q torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 torchtext==0.14.1 torchdata==0.5.1 --extra-index-url https://download.pytorch.org/whl/cu116 -U"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "n2ibl97e2yM4"
+      },
+      "outputs": [],
+      "source": [
+        "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",
+        "import datetime\n",
+        "\n",
+        "pipe = DiffusionPipeline.from_pretrained(\"camenduru/potat1\", torch_dtype=torch.float16)\n",
+        "pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\n",
+        "pipe.enable_model_cpu_offload()\n",
+        "pipe.enable_vae_slicing()\n",
+        "\n",
+        "!mkdir /content/videos"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "prompt = 'Darth Vader surfing a wave' #@param {type:\"string\"}\n",
+        "negative_prompt = \"low quality\" #@param {type:\"string\"}\n",
+        "num_frames = 30 #@param {type:\"raw\"}\n",
+        "video_frames = pipe(prompt, negative_prompt=negative_prompt, width=1024, height=576, num_inference_steps=25, num_frames=num_frames).frames\n",
+        "output_video_path = export_to_video(video_frames)\n",
+        "\n",
+        "new_video_path = f'/content/videos/{datetime.datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")}.mp4'\n",
+        "!ffmpeg -y -i {output_video_path} -c:v libx264 -c:a aac -strict -2 {new_video_path} >/dev/null 2>&1\n",
+        "\n",
+        "print(output_video_path, '->', new_video_path)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!cp {new_video_path} /content/videos/tmp.mp4\n",
+        "mp4 = open('/content/videos/tmp.mp4','rb').read()\n",
+        "\n",
+        "decoded_vid = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
+        "HTML(f'<video width=400 controls><source src=\"{decoded_vid}\" type=\"video/mp4\"></video>')"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}