{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Fooocus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VjYy0F2gZIPR"
   },
   "outputs": [],
   "source": [
    "!pip install pygit2==1.12.2\n",
    "%cd /content\n",
    "!git clone https://github.com/lllyasviel/Fooocus.git\n",
    "%cd /content/Fooocus\n",
    "!python entry_with_update.py --share\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download checkpoints and LoRAs from civitai\n",
    "to download checkpoints and LoRAs from civitai you have to run Fooocus first\n",
    "\n",
    "https://civitai.com/models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# put the link of the model you want to download (checkpoint or LoRA)\n",
    "# link can specify a version id and if not specified the latest version will be downloaded\n",
    "# url example without version id: https://civitai.com/models/133005/juggernaut-xl\n",
    "# url example with version id: https://civitai.com/models/133005?modelVersionId=252902\n",
    "\n",
    "civitai_url = \"https://civitai.com/models/133005/juggernaut-xl\"\n",
    "\n",
    "# -----------------------------\n",
    "import requests\n",
    "from urllib.parse import urlparse\n",
    "from urllib.parse import parse_qs\n",
    "\n",
    "allowed_types = ['Checkpoint', 'LORA']\n",
    "save_locations = {\n",
    "    'Checkpoint': '/content/Fooocus/models/checkpoints',\n",
    "    'LORA': '/content/Fooocus/models/loras'\n",
    "}\n",
    "\n",
    "parsed_url = urlparse(civitai_url)\n",
    "\n",
    "model_id = parsed_url.path.split('/')[parsed_url.path.split('/').index('models') + 1]\n",
    "model_version_id = parse_qs(parsed_url.query).get('modelVersionId')\n",
    "\n",
    "url = \"https://civitai.com/api/v1/models/\" + model_id\n",
    "response = requests.get(url)\n",
    "\n",
    "if response.status_code != 200:\n",
    "  raise RuntimeError('model not found')\n",
    "\n",
    "data = response.json()\n",
    "model_type = data.get('type')\n",
    "\n",
    "if model_type not in allowed_types:\n",
    "  raise RuntimeError('model is not a checkpoint or LoRA')\n",
    "\n",
    "model_versions = data.get('modelVersions')\n",
    "\n",
    "selected_version = None\n",
    "\n",
    "if model_version_id:\n",
    "  for model_version in model_versions:\n",
    "    if str(model_version.get('id')) == model_version_id[0]:\n",
    "      selected_version = model_version\n",
    "else:\n",
    "  selected_version = model_versions[0]\n",
    "\n",
    "if selected_version is None:\n",
    "  raise RuntimeError(\"this version doesn't exist\")\n",
    "\n",
    "\n",
    "files = selected_version.get('files')\n",
    "primary_file = None\n",
    "\n",
    "for f in files:\n",
    "  if f.get('primary'):\n",
    "    primary_file = f\n",
    "\n",
    "download_url = primary_file.get('downloadUrl')\n",
    "file_name = primary_file.get('name')\n",
    "\n",
    "LOCATION = save_locations[model_type]\n",
    "%cd $LOCATION\n",
    "\n",
    "model_name = data.get('name')\n",
    "selected_version_name = selected_version.get('name')\n",
    "print(f'downloading {model_name} ({model_type} version {selected_version_name})')\n",
    "\n",
    "get_ipython().system(f'wget -O \"{file_name}\" \"{download_url}\"')"
   ]
  }
 ],
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  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
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  "language_info": {
   "name": "python"
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}