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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "D4TNDJdRpPN9"
},
"source": [
"#Invoke AI Notebook\n",
"\n",
"Works on the free tier: Generating images with the SDXL base model and refiner. Adding SDXL models in diffusers format from HuggingFace.\n",
"\n",
"Works, but only with Colab Pro: Adding custom checkpoints and LoRAs."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ow5L4LUnr_Cs"
},
"source": [
"Step 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "MIhVvU8jkdm6"
},
"outputs": [],
"source": [
"#@markdown # Installing InvokeAI\n",
"\n",
"#@markdown Use Google Drive to store models (uses about 7 GB). Uncheck this if you don't have enough space in your Drive.\n",
"useGoogleDrive = False #@param {type:\"boolean\"}\n",
"\n",
"googleDriveModelsFolder = '/stablemodels' #@param {type:\"string\"}\n",
"\n",
"#@markdown This step usually takes about 5 minutes.\n",
"\n",
"#@markdown You can ignore the message about restarting the runtime.\n",
"import os\n",
"import subprocess\n",
"from google.colab import drive\n",
"if useGoogleDrive:\n",
" drive.mount('/content/drive')\n",
" if not googleDriveModelsFolder.startswith('/'):\n",
" googleDriveModelsFolder = '/' + googleDriveModelsFolder\n",
" modelsPath = \"/content/drive/MyDrive\"+googleDriveModelsFolder\n",
" if not modelsPath.endswith(\"/\"):\n",
" modelsPath = modelsPath + \"/\"\n",
"\n",
"env = os.environ.copy()\n",
"\n",
"!pip install 'InvokeAI[xformers]' --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117\n",
"\n",
"exit()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ERca0J67r8Ss"
},
"source": [
"Step 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "YTkFxvuH0BsX"
},
"outputs": [],
"source": [
"#@markdown # Configuration and downloading default models\n",
"\n",
"!mkdir /content/invokeai\n",
"!mkdir /content/invokeai/configs\n",
"\n",
"#@markdown Download only the default model in initial configuration.\n",
"#@markdown Checking this prevents running out of space in Colab.\n",
"\n",
"defaultOnly = True #@param {type:\"boolean\"}\n",
"skipWeights = True #@param {type:\"boolean\"}\n",
"noFullPrecision = True #@param {type:\"boolean\"}\n",
"#@markdown This step usually takes about 2 minutes with only the default model and no weights.\n",
"\n",
"#@markdown You can ignore \"File exists\" warnings in the output.\n",
"\n",
"cmd = 'invokeai-configure --root_dir /content/invokeai --yes'\n",
"\n",
"if defaultOnly:\n",
" cmd += ' --default_only'\n",
"\n",
"if skipWeights:\n",
" cmd += ' --skip-sd-weights'\n",
"\n",
"if noFullPrecision:\n",
" cmd += ' --no-full-precision'\n",
"\n",
"get_ipython().system(cmd)\n",
"\n",
"import fileinput\n",
"import os\n",
"def find(name, path):\n",
" for root, dirs, files in os.walk(path):\n",
" if name in files:\n",
" return os.path.join(root, name)\n",
"\n",
"if noFullPrecision:\n",
" model_install_file = find('model_install_backend.py', '/usr/local/lib')\n",
" print('modifying file ' + model_install_file)\n",
" for line in fileinput.input(model_install_file, inplace=True):\n",
" if ('precision = torch_dtype(choose_torch_device())' in line):\n",
" line = line.replace('torch_dtype(choose_torch_device())', 'torch.float16')\n",
" print(line, end='')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3owdtpnWsRoU",
"outputId": "a6873dfe-a211-427d-f158-b0865c5bf95e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"# Linking output images to Google Drive\n",
"outputDrivePath = '/content/drive/MyDrive/images/invoke-outputs' #@param {type:\"string\"}\n",
"# Full path to the output folder on Google Drive\n",
"\n",
"saveDatabase = True #@param {type:\"boolean\"}\n",
"from os import path\n",
"\n",
"from google.colab import drive\n",
"import os\n",
"from os import path\n",
"drive.mount('/content/drive')\n",
"\n",
"if not outputDrivePath.endswith('/'):\n",
" outputDrivePath = outputDrivePath + '/'\n",
"imagesDrivePath = outputDrivePath + 'images'\n",
"databaseDrivePath = outputDrivePath + 'databases'\n",
"if not path.exists(imagesDrivePath):\n",
" os.makedirs(imagesDrivePath, exist_ok=True)\n",
"\n",
"\n",
"outputsLocalPath = '/content/invokeai/outputs'\n",
"imagesLocalPath = '/content/invokeai/outputs/images'\n",
"\n",
"if not path.exists(outputsLocalPath):\n",
" os.makedirs(outputsLocalPath, exist_ok=True)\n",
"\n",
"import datetime\n",
"\n",
"if path.exists(imagesLocalPath):\n",
" cmd = f'mv {imagesLocalPath} {imagesLocalPath}-backup{datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")}'\n",
" get_ipython().system(cmd)\n",
"\n",
"cmd = f'ln -s {imagesDrivePath} {outputsLocalPath}'\n",
"get_ipython().system(cmd)\n",
"\n",
"# Linking the database\n",
"if saveDatabase:\n",
" if not path.exists(databaseDrivePath):\n",
" os.makedirs(databaseDrivePath, exist_ok=True)\n",
"\n",
" databaseLocalPath = '/content/invokeai/databases'\n",
"\n",
" cmd = f'mv {databaseLocalPath} {databaseLocalPath}-backup{datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")}'\n",
" get_ipython().system(cmd)\n",
"\n",
" cmd = f'ln -s {databaseDrivePath} /content/invokeai'\n",
" get_ipython().system(cmd)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jS0EJ4LosUFY"
},
"source": [
"Step 6: Load any SDXL models in diffusers format from Drive - Optional"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "sdaNxzYPsaXX"
},
"outputs": [],
"source": [
"# Adding custom SDXL models in diffusers format from Goole Drive\n",
"googleDriveModelFolder = '/content/drive/MyDrive/path-to-the-model' #@param {type:\"string\"}\n",
"#@markdown - Full path to the model folder on Google Drive\n",
"\n",
"#@markdown This can also be done from the Model Manager in the Web UI.\n",
"\n",
"updateModelsYaml = True\n",
"with open('/content/invokeai/configs/models.yaml') as f:\n",
" if googleDriveModelFolder in f.read():\n",
" updateModelsYaml = False\n",
"if updateModelsYaml:\n",
" with open('/content/invokeai/configs/models.yaml', 'a') as file:\n",
" folders = googleDriveModelFolder.split('/');\n",
" modelname = folders[len(folders)-1]\n",
" print(modelname)\n",
" lines = [\n",
" 'sdxl/main/' + modelname + ':\\n',\n",
" ' path: ' + googleDriveModelFolder + '\\n',\n",
" ' description: Stable Diffusion XL base model (12 GB)\\n',\n",
" ' variant: normal\\n',\n",
" ' format: diffusers\\n'\n",
" ]\n",
" file.writelines(lines)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T4xrUy3Gsomd"
},
"source": [
"Step 7: Starting the app"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nCiDkdSlqZhd"
},
"outputs": [],
"source": [
"def install_jemalloc():\n",
" !apt -y update -qq\n",
" !apt -y install libjemalloc-dev\n",
"install_jemalloc()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "e-IErS_AaNNz"
},
"outputs": [],
"source": [
"!apt-get install aria2\n",
"civitai_model_urls = \"https://civitai.com/api/download/models/157223?type=Model&format=SafeTensor&size=pruned&fp=fp16, https://civitai.com/api/download/models/138176?type=Model&format=SafeTensor&size=pruned&fp=fp32\" # @param {'type': 'string'}\n",
"url_list = civitai_model_urls.split(\", \")\n",
"for url in url_list:\n",
" !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M --content-disposition -d /content/invokeai/models/sd-1/main {url}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "QC6jE2afaVHy"
},
"outputs": [],
"source": [
"# @title Embeddings\n",
"import zipfile\n",
"embeddings_zip_url = 'https://github.com/Ysb321/supper/releases/download/emm/emm.zip, https://civitai.com/api/download/models/42247?type=Model&format=Other'\n",
"url_list = embeddings_zip_url.split(\", \")\n",
"for url in url_list:\n",
" !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M -d /content/invokeai/models/sd-1/embedding {url}\n",
"current_dir = '/content/invokeai/models/sd-1/embedding'\n",
"for entry in os.scandir(current_dir):\n",
" if entry.is_file() and entry.name.endswith('.zip'):\n",
" with zipfile.ZipFile(entry, 'r') as zip_ref:\n",
" zip_ref.extractall(current_dir)\n",
"!rm /content/invokeai/models/sd-1/embedding/*.zip"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "zPR0gqrAc97R"
},
"outputs": [],
"source": [
"# @title lorazip\n",
"import zipfile\n",
"lora_zip_url = 'https://huggingface.co/datasets/ysb123/yy/resolve/main/ppp.zip, https://huggingface.co/datasets/ysb123/yy/resolve/main/ddd.zip, https://huggingface.co/datasets/ysb123/yy/resolve/main/Lora.zip'\n",
"url_list = lora_zip_url.split(\", \")\n",
"for url in url_list:\n",
" !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M --content-disposition -d /content/invokeai/models/sd-1/lora {url}\n",
"directory = '/content/invokeai/models/sd-1/lora'\n",
"for filename in os.listdir(directory):\n",
" if '.' not in filename:\n",
" old_filepath = os.path.join(directory, filename)\n",
" new_filepath = os.path.join(directory, filename + '.zip')\n",
" os.rename(old_filepath, new_filepath)\n",
"current_dir = '/content/invokeai/models/sd-1/lora'\n",
"for entry in os.scandir(current_dir):\n",
" if entry.is_file() and entry.name.endswith('.zip'):\n",
" with zipfile.ZipFile(entry, 'r') as zip_ref:\n",
" zip_ref.extractall(current_dir)\n",
"!rm /content/invokeai/models/sd-1/lora/*.zip"
]
},
{
"cell_type": "code",
"source": [
"lora_url = 'https://civitai.com/api/download/models/139136' # @param {'type': 'string'}\n",
"url_list = lora_url.split(\", \")\n",
"for url in url_list:\n",
" !wget --content-disposition -P /content/invokeai/models/sd-1/lora {url}"
],
"metadata": {
"cellView": "form",
"id": "iOf2elAdGwqc"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "8P-UgO8Ysrlz"
},
"outputs": [],
"source": [
"#@markdown # Option 2: Starting the Web UI with ngrok\n",
"!pip install pyngrok\n",
"\n",
"from pyngrok import ngrok, conf\n",
"import fileinput\n",
"import sys\n",
"%env LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so.2\n",
"Ngrok_token = \"\" #@param {type:\"string\"}\n",
"#@markdown - Add ngrok token (obtainable from https://ngrok.com)\n",
"\n",
"#@markdown Only works with InvokeAI 3.0.2 and later\n",
"\n",
"share=''\n",
"if Ngrok_token!=\"\":\n",
" ngrok.kill()\n",
" srv=ngrok.connect(9090 , pyngrok_config=conf.PyngrokConfig(auth_token=Ngrok_token),\n",
" bind_tls=True).public_url\n",
" print(srv)\n",
" get_ipython().system(\"invokeai-web --root /content/invokeai/\")\n",
"else:\n",
" print('An ngrok token is required. You can get one on https://ngrok.com and paste it into the ngrok_token field.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "qN-IExD5XwOs"
},
"outputs": [],
"source": [
"#@markdown # Option 1: Starting the Web UI with Localtunnel\n",
"\n",
"%cd /content/invokeai/\n",
"!npm install -g localtunnel\n",
"\n",
"#@markdown Copy the IP address shown in the output above the line\n",
"#@markdown \"your url is: https://some-random-words.loca.lt\"\n",
"!wget -q -O - ipv4.icanhazip.com\n",
"\n",
"#@markdown Wait for the line that says \"Uvicorn running on http://127.0.0.1:9090 (Press CTRL+C to quit)\"\n",
"\n",
"#@markdown Click the localtunnel url and paste the IP you copied earlier to the \"Endpoint IP\" text field\n",
"!lt --port 9090 --local_https False & invokeai-web --root /content/invokeai/\n",
"\n",
"#@markdown If the UI shows a red dot that says 'disconnected' when hovered in the upper\n",
"#@markdown right corner and the Invoke button is disabled, change 'https' to 'http'\n",
"#@markdown in the browser's address bar and press enter.\n",
"#@markdown When the page reloads, the UI should work properly.\n"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |