393 lines
12 KiB
Plaintext
393 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "aa2c1ada",
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"metadata": {
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"id": "aa2c1ada"
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},
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"source": [
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"# Dreambooth\n",
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"### Notebook implementation by Joe Penna (@MysteryGuitarM on Twitter) - Improvements by David Bielejeski\n",
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"https://github.com/JoePenna/Dreambooth-Stable-Diffusion\n",
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"\n",
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"### If on runpod / vast.ai / etc, spin up an A6000 or A100 pod using a Stable Diffusion template with Jupyter pre-installed."
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]
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},
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{
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"cell_type": "markdown",
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"id": "7b971cc0",
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"metadata": {
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"id": "7b971cc0"
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},
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"source": [
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"## Build Environment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9e1bc458-091b-42f4-a125-c3f0df20f29d",
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"metadata": {
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"id": "9e1bc458-091b-42f4-a125-c3f0df20f29d",
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"#BUILD ENV\n",
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"!pip install omegaconf\n",
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"!pip install einops\n",
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"!pip install pytorch-lightning==1.6.5\n",
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"!pip install test-tube\n",
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"!pip install transformers\n",
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"!pip install kornia\n",
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"!pip install -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers\n",
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"!pip install -e git+https://github.com/openai/CLIP.git@main#egg=clip\n",
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"!pip install setuptools==59.5.0\n",
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"!pip install pillow==9.0.1\n",
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"!pip install torchmetrics==0.6.0\n",
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"!pip install -e .\n",
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"!pip install protobuf==3.20.1\n",
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"!pip install gdown\n",
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"!pip install pydrive\n",
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"!pip install -qq diffusers[\"training\"]==0.3.0 transformers ftfy\n",
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"!pip install -qq \"ipywidgets>=7,<8\"\n",
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"!pip install huggingface_hub\n",
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"!pip install ipywidgets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ddf7a43d",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"## Move the sd-v1-4.ckpt to the root of this directory as \"model.ckpt\"\n",
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"#actual_locations_of_model_blob = !readlink -f {downloaded_model_path}\n",
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"#!cp {actual_locations_of_model_blob[-1]} model.ckpt\n",
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"!wget 'https://prodesk.home.thijn.ovh/sd-v1-4.ckpt'\n",
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"!cp sd-v1-4.ckpt model.ckpt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "mxPL2O0OLvBW",
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"metadata": {
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"id": "mxPL2O0OLvBW"
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},
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"source": [
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"## Download pre-generated regularization images\n",
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"\n",
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"We've created the following image sets\n",
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"\n",
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"* man_euler - provided by Niko Pueringer (Corridor Digital) - euler @ 40 steps, CFG 7.5\n",
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"* man_unsplash - pictures from various photographers\n",
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"* person_ddim\n",
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"* woman_ddim - provided by David Bielejeski - ddim @ 50 steps, CFG 10.0\n",
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"\n",
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"`person_ddim` is recommended"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e7EydXCjOV1v",
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"metadata": {
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"id": "e7EydXCjOV1v"
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},
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"outputs": [],
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"source": [
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"# Grab the existing regularization images\n",
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"# Choose the dataset that best represents what you are trying to do and matches what you used for your token\n",
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"# man_euler, man_unsplash, person_ddim, woman_ddim\n",
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"dataset=\"person_ddim\"\n",
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"!rm -rf Stable-Diffusion-Regularization-Images-{dataset}\n",
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"!git clone https://github.com/djbielejeski/Stable-Diffusion-Regularization-Images-{dataset}.git\n",
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"\n",
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"!mkdir -p outputs/txt2img-samples/samples/{dataset}\n",
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"!mv -v Stable-Diffusion-Regularization-Images-{dataset}/{dataset}/*.* outputs/txt2img-samples/samples/{dataset}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "zshrC_JuMXmM",
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"metadata": {
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"id": "zshrC_JuMXmM"
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},
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"source": [
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"# Upload your training images\n",
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"Upload 10-20 images of someone to\n",
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"\n",
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"```\n",
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"/workspace/Dreambooth-Stable-Diffusion/training_samples\n",
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"```\n",
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"\n",
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"WARNING: Be sure to upload an *even* amount of images, otherwise the training inexplicably stops at 1500 steps.\n",
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"\n",
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"* 2-3 full body\n",
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"* 3-5 upper body \n",
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"* 5-12 close-up on face"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "60e37ee0",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"#@markdown Add here the URLs to the images of the concept you are adding\n",
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"urls = [\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121054.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121057.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121100.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121102.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121104.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121106.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121108.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121112.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121116.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121118.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121120.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121153.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121155.png\",\n",
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"\"https://prodesk.home.thijn.ovh/benji/IMG_20221011_121157.png\"\n",
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" ## You can add additional images here\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "df314e2e",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"#@title Download and check the images you have just added\n",
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"import os\n",
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"import requests\n",
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"from io import BytesIO\n",
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"from PIL import Image\n",
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"\n",
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"\n",
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"def image_grid(imgs, rows, cols):\n",
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" assert len(imgs) == rows*cols\n",
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"\n",
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" w, h = imgs[0].size\n",
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" grid = Image.new('RGB', size=(cols*w, rows*h))\n",
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" grid_w, grid_h = grid.size\n",
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"\n",
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" for i, img in enumerate(imgs):\n",
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" grid.paste(img, box=(i%cols*w, i//cols*h))\n",
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" return grid\n",
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"\n",
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"def download_image(url):\n",
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" try:\n",
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" response = requests.get(url)\n",
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" except:\n",
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" return None\n",
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" return Image.open(BytesIO(response.content)).convert(\"RGB\")\n",
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"\n",
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"images = list(filter(None,[download_image(url) for url in urls]))\n",
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"save_path = \"./training_samples\"\n",
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"if not os.path.exists(save_path):\n",
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" os.mkdir(save_path)\n",
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"[image.save(f\"{save_path}/{i}.png\", format=\"png\") for i, image in enumerate(images)]\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ad4e50df",
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"metadata": {
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"id": "ad4e50df"
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},
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"source": [
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"## Training\n",
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"\n",
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"If training a person or subject, keep an eye on your project's `logs/{folder}/images/train/samples_scaled_gs-00xxxx` generations.\n",
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"\n",
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"If training a style, keep an eye on your project's `logs/{folder}/images/train/samples_gs-00xxxx` generations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6fa5dd66-2ca0-4819-907e-802e25583ae6",
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"metadata": {
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"id": "6fa5dd66-2ca0-4819-907e-802e25583ae6",
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"tags": []
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},
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"outputs": [],
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"source": [
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"# START THE TRAINING\n",
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"project_name = \"benjiman\"\n",
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"batch_size = 1000\n",
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"class_word = \"person\" # << match this word to the class word from regularization images above\n",
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"reg_data_root = \"/workspace/Dreambooth-Stable-Diffusion/outputs/txt2img-samples/samples/\" + dataset\n",
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"\n",
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"!rm -rf training_samples/.ipynb_checkpoints\n",
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"!python \"main.py\" \\\n",
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" --base configs/stable-diffusion/v1-finetune_unfrozen.yaml \\\n",
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" -t \\\n",
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" --actual_resume \"model.ckpt\" \\\n",
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" --reg_data_root {reg_data_root} \\\n",
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" -n {project_name} \\\n",
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" --gpus 0, \\\n",
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" --data_root \"/workspace/Dreambooth-Stable-Diffusion/training_samples\" \\\n",
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" --batch_size {batch_size} \\\n",
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" --class_word class_word"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dc49d0bd",
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"metadata": {},
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"source": [
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"## Pruning (12GB to 2GB)\n",
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"We are working on having this happen automatically (TODO: PR's welcome)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "27cea333",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"directory_paths = !ls -d logs/*"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "965b4654",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# This version should automatically prune around 10GB from the ckpt file\n",
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"last_checkpoint_file = directory_paths[-1] + \"/checkpoints/last.ckpt\"\n",
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"!python \"scripts/prune-ckpt.py\" --ckpt {last_checkpoint_file}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b7a8cec3",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"last_checkpoint_file_pruned = directory_paths[-1] + \"/checkpoints/last-pruned.ckpt\"\n",
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"training_samples = !ls training_samples\n",
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"date_string = !date +\"%Y-%m-%dT%H-%M-%S\"\n",
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"file_name = date_string[-1] + \"_\" + project_name + \"_\" + str(len(training_samples)) + \"_training_images_\" + str(batch_size) + \"_batch_size_\" + class_word + \"_class_word.ckpt\"\n",
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"!mkdir -p trained_models\n",
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"!mv {last_checkpoint_file_pruned} trained_models/{file_name}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ff1a46d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Download your trained model file from `trained_models` and use in your favorite Stable Diffusion repo!\n",
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"!wget -nc 'https://prodesk.home.thijn.ovh/gijs/ai'\n",
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"!chmod 600 ai\n",
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"!scp -r -i ai -P 2387 -o StrictHostKeyChecking=no ./trained_models ai@home.thijn.ovh:/mnt/hdd/ai/"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d28d0139",
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"metadata": {},
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"source": [
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"## Generate Images With Your Trained Model!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "80ddb03b",
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"metadata": {},
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"outputs": [],
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"source": [
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"!python scripts/stable_txt2img.py \\\n",
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" --ddim_eta 0.0 \\\n",
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" --n_samples 1 \\\n",
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" --n_iter 10 \\\n",
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" --scale 7.0 \\\n",
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" --ddim_steps 50 \\\n",
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" --ckpt \"/workspace/Dreambooth-Stable-Diffusion/trained_models/CHANGEME.ckpt\" \\\n",
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" --prompt \"beautiful oil painting of benjiman with high detail of a gorgeous wood-elf ranger from dungeons and dragons in the desert by artgerm and greg rutkowski and thomas kinkade\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0e3c10d9-2c40-4f50-9cf4-97e88a57288c",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.13"
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},
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"vscode": {
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"interpreter": {
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"hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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