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Updated Colab to use RIFE_trained_model_v3.6.zip

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lazylion22
2022-02-11 18:34:19 +02:00
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parent 5b1b69b232
commit 7d472147aa
1 changed files with 42 additions and 42 deletions
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84
Colab_demo.ipynb
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@@ -1,24 +1,10 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Untitled0.ipynb",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/hzwer/arXiv2020-RIFE/blob/main/Colab_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -26,41 +12,41 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FypCcZkNNt2p"
},
"outputs": [],
"source": [
"!git clone https://github.com/hzwer/arXiv2020-RIFE"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "1wysVHxoN54f"
},
"outputs": [],
"source": [
"!gdown --id 1wsQIhHZ3Eg4_AfCXItFKqqyDMB4NS0Yd\n",
"!7z e RIFE_trained_model_HDv2.zip"
],
"execution_count": null,
"outputs": []
"!mkdir /content/arXiv2020-RIFE/train_log\n",
"%cd /content/arXiv2020-RIFE/train_log\n",
"!gdown --id 1APIzVeI-4ZZCEuIRE1m6WYfSCaOsi_7_\n",
"!7z e RIFE_trained_model_v3.6.zip"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AhbHfRBJRAUt"
},
"outputs": [],
"source": [
"!mkdir /content/arXiv2020-RIFE/train_log\n",
"!mv *.pkl /content/arXiv2020-RIFE/train_log/\n",
"%cd /content/arXiv2020-RIFE/\n",
"!gdown --id 1i3xlKb7ax7Y70khcTcuePi6E7crO_dFc\n",
"!pip3 install -r requirements.txt"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
@@ -73,15 +59,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dnLn4aHHPzN3"
},
"outputs": [],
"source": [
"!nvidia-smi\n",
"!python3 inference_video.py --exp=2 --video=demo.mp4 --montage --skip"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "markdown",
@@ -96,30 +82,44 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0zIBbVE3UfUD",
"cellView": "code"
"cellView": "code",
"id": "0zIBbVE3UfUD"
},
"outputs": [],
"source": [
"from IPython.display import display, Image\n",
"import moviepy.editor as mpy\n",
"display(mpy.ipython_display('demo_4X_100fps.mp4', height=256, max_duration=100.))"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tWkJCNgP3zXA"
},
"outputs": [],
"source": [
"!python3 inference_img.py --img demo/I0_0.png demo/I0_1.png\n",
"ffmpeg -r 10 -f image2 -i output/img%d.png -s 448x256 -vf \"split[s0][s1];[s0]palettegen=stats_mode=single[p];[s1][p]paletteuse=new=1\" output/slomo.gif\n",
"# Image interpolation"
],
"execution_count": null,
"outputs": []
]
}
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"include_colab_link": true,
"name": "Untitled0.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}