Datasets:

Bluwynd
/

LoraTrain15

+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "efSw4FzN89ia",
+        "outputId": "e8733152-31fd-420f-8a14-0bca6af46641"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into '/content/Kohya-Colab'...\n",
+            "remote: Enumerating objects: 2158, done.\u001b[K\n",
+            "remote: Counting objects: 100% (893/893), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (231/231), done.\u001b[K\n",
+            "remote: Total 2158 (delta 738), reused 671 (delta 662), pack-reused 1265 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (2158/2158), 4.43 MiB | 6.82 MiB/s, done.\n",
+            "Resolving deltas: 100% (1418/1418), done.\n",
+            "The following additional packages will be installed:\n",
+            "  libaria2-0 libc-ares2\n",
+            "The following NEW packages will be installed:\n",
+            "  aria2 libaria2-0 libc-ares2\n",
+            "0 upgraded, 3 newly installed, 0 to remove and 49 not upgraded.\n",
+            "Need to get 1,513 kB of archives.\n",
+            "After this operation, 5,441 kB of additional disk space will be used.\n",
+            "Selecting previously unselected package libc-ares2:amd64.\n",
+            "(Reading database ... 123621 files and directories currently installed.)\n",
+            "Preparing to unpack .../libc-ares2_1.18.1-1ubuntu0.22.04.3_amd64.deb ...\n",
+            "Unpacking libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n",
+            "Selecting previously unselected package libaria2-0:amd64.\n",
+            "Preparing to unpack .../libaria2-0_1.36.0-1_amd64.deb ...\n",
+            "Unpacking libaria2-0:amd64 (1.36.0-1) ...\n",
+            "Selecting previously unselected package aria2.\n",
+            "Preparing to unpack .../aria2_1.36.0-1_amd64.deb ...\n",
+            "Unpacking aria2 (1.36.0-1) ...\n",
+            "Setting up libc-ares2:amd64 (1.18.1-1ubuntu0.22.04.3) ...\n",
+            "Setting up libaria2-0:amd64 (1.36.0-1) ...\n",
+            "Setting up aria2 (1.36.0-1) ...\n",
+            "Processing triggers for man-db (2.10.2-1) ...\n",
+            "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n",
+            "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n",
+            "\n",
+            "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n",
+            "\n",
+            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
+            "\n",
+            "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n",
+            "\n",
+            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
+            "\n",
+            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
+            "\n",
+            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
+            "\n",
+            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
+            "\n",
+            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
+            "\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100.3/100.3 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m266.3/266.3 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.0/4.0 MB\u001b[0m \u001b[31m54.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.9/43.9 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m191.5/191.5 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m82.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m125.7/125.7 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.7/61.7 MB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.6/41.6 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m503.1/503.1 kB\u001b[0m \u001b[31m28.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m825.8/825.8 kB\u001b[0m \u001b[31m41.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m475.2/475.2 MB\u001b[0m \u001b[31m130.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "import zipfile\n",
+        "import shutil\n",
+        "from subprocess import getoutput\n",
+        "from IPython.utils import capture\n",
+        "import random\n",
+        "import concurrent.futures\n",
+        "from tqdm import tqdm\n",
+        "from PIL import Image\n",
+        "import time\n",
+        "import re\n",
+        "import json\n",
+        "import glob\n",
+        "import gdown\n",
+        "import requests\n",
+        "import subprocess\n",
+        "from urllib.parse import urlparse, unquote\n",
+        "from pathlib import Path\n",
+        "import toml\n",
+        "\n",
+        "#root_dir\n",
+        "root_dir = \"/content\"\n",
+        "deps_dir = os.path.join(root_dir,\"deps\")\n",
+        "repo_dir = os.path.join(root_dir,\"Kohya-Colab\")\n",
+        "training_dir = os.path.join(root_dir,\"Dreamboot-Config\")\n",
+        "pretrained_model = os.path.join(root_dir,\"pretrained_model\")\n",
+        "vae_dir = os.path.join(root_dir,\"vae\")\n",
+        "config_dir = os.path.join(training_dir,\"config\")\n",
+        "\n",
+        "#repo_dir\n",
+        "accelerate_config = os.path.join(repo_dir, \"accelerate_config/config.yaml\")\n",
+        "tools_dir = os.path.join(repo_dir,\"tools\")\n",
+        "finetune_dir = os.path.join(repo_dir,\"finetune\")\n",
+        "\n",
+        "for store in [\"root_dir\", \"deps_dir\", \"repo_dir\", \"training_dir\", \"pretrained_model\", \"vae_dir\", \"accelerate_config\", \"tools_dir\", \"finetune_dir\", \"config_dir\"]:\n",
+        "  with capture.capture_output() as cap:\n",
+        "    %store {store}\n",
+        "    del cap\n",
+        "\n",
+        "repo_url = \"https://github.com/phamhungd/Kohya-Colab\"\n",
+        "bitsandytes_main_py = \"/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py\"\n",
+        "branch = \"\"\n",
+        "verbose = False\n",
+        "\n",
+        "def read_file(filename):\n",
+        "    with open(filename, \"r\") as f:\n",
+        "        contents = f.read()\n",
+        "    return contents\n",
+        "\n",
+        "\n",
+        "def write_file(filename, contents):\n",
+        "    with open(filename, \"w\") as f:\n",
+        "        f.write(contents)\n",
+        "\n",
+        "\n",
+        "def clone_repo(url):\n",
+        "    if not os.path.exists(repo_dir):\n",
+        "        os.chdir(root_dir)\n",
+        "        !git clone {url} {repo_dir}\n",
+        "    else:\n",
+        "        os.chdir(repo_dir)\n",
+        "        !git pull origin {branch} if branch else !git pull\n",
+        "\n",
+        "\n",
+        "def install_dependencies():\n",
+        "    s = getoutput('nvidia-smi')\n",
+        "\n",
+        "    if 'T4' in s:\n",
+        "        !sed -i \"s@cpu@cuda@\" library/model_util.py\n",
+        "\n",
+        "    !pip install {'-q' if not verbose else ''} --upgrade -r requirements.txt\n",
+        "\n",
+        "    from accelerate.utils import write_basic_config\n",
+        "\n",
+        "    if not os.path.exists(accelerate_config):\n",
+        "        write_basic_config(save_location=accelerate_config)\n",
+        "\n",
+        "\n",
+        "def remove_bitsandbytes_message(filename):\n",
+        "    welcome_message = \"\"\"\n",
+        "def evaluate_cuda_setup():\n",
+        "    print('')\n",
+        "    print('='*35 + 'BUG REPORT' + '='*35)\n",
+        "    print('Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues')\n",
+        "    print('For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link')\n",
+        "    print('='*80)\"\"\"\n",
+        "\n",
+        "    new_welcome_message = \"\"\"\n",
+        "def evaluate_cuda_setup():\n",
+        "    import os\n",
+        "    if 'BITSANDBYTES_NOWELCOME' not in os.environ or str(os.environ['BITSANDBYTES_NOWELCOME']) == '0':\n",
+        "        print('')\n",
+        "        print('=' * 35 + 'BUG REPORT' + '=' * 35)\n",
+        "        print('Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues')\n",
+        "        print('For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link')\n",
+        "        print('To hide this message, set the BITSANDBYTES_NOWELCOME variable like so: export BITSANDBYTES_NOWELCOME=1')\n",
+        "        print('=' * 80)\"\"\"\n",
+        "\n",
+        "    contents = read_file(filename)\n",
+        "    new_contents = contents.replace(welcome_message, new_welcome_message)\n",
+        "    write_file(filename, new_contents)\n",
+        "\n",
+        "\n",
+        "def main():\n",
+        "    os.chdir(root_dir)\n",
+        "\n",
+        "    for dir in [\n",
+        "        deps_dir,\n",
+        "        training_dir,\n",
+        "        config_dir,\n",
+        "        pretrained_model,\n",
+        "        vae_dir\n",
+        "    ]:\n",
+        "        os.makedirs(dir, exist_ok=True)\n",
+        "\n",
+        "    clone_repo(repo_url)\n",
+        "\n",
+        "    if branch:\n",
+        "        os.chdir(repo_dir)\n",
+        "        status = os.system(f\"git checkout {branch}\")\n",
+        "        if status != 0:\n",
+        "            raise Exception(\"Failed to checkout branch or commit\")\n",
+        "\n",
+        "    os.chdir(repo_dir)\n",
+        "\n",
+        "    !apt install aria2 {'-qq' if not verbose else ''}\n",
+        "\n",
+        "    install_dependencies()\n",
+        "    time.sleep(3)\n",
+        "\n",
+        "    remove_bitsandbytes_message(bitsandytes_main_py)\n",
+        "\n",
+        "    os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n",
+        "    os.environ[\"BITSANDBYTES_NOWELCOME\"] = \"1\"\n",
+        "    os.environ[\"SAFETENSORS_FAST_GPU\"] = \"1\"\n",
+        "\n",
+        "    cuda_path = \"/usr/local/cuda-11.8/targets/x86_64-linux/lib/\"\n",
+        "    ld_library_path = os.environ.get(\"LD_LIBRARY_PATH\", \"\")\n",
+        "    os.environ[\"LD_LIBRARY_PATH\"] = f\"{ld_library_path}:{cuda_path}\"\n",
+        "\n",
+        "main()\n",
+        "\n",
+        "\n",
+        "print(f\"Your train data directory : {train_data_dir}\")\n",
+        "\n",
+        "os.chdir(finetune_dir)\n",
+        "\n",
+        "config = {\n",
+        "    \"_train_data_dir\": train_data_dir,  # Manter a referência original\n",
+        "    \"batch_size\": 8,  # Valor do segundo código\n",
+        "    \"repo_id\": \"SmilingWolf/wd-v1-4-convnextv2-tagger-v2\",  # Valor do segundo código\n",
+        "    \"beam_search\": beam_search,  # Manter a referência original\n",
+        "    \"min_length\": min_length,  # Manter a referência original\n",
+        "    \"max_length\": max_length,  # Manter a referência original\n",
+        "    \"debug\": True,  # Do segundo código\n",
+        "    \"caption_extension\": \".txt\",  # Valor do segundo código\n",
+        "    \"max_data_loader_n_workers\": 2,  # Valor do segundo código\n",
+        "    \"recursive\": True,  # Do segundo código\n",
+        "    \"remove_underscore\": True,  # Do segundo código\n",
+        "    \"general_threshold\": Threshold,  # Do segundo código\n",
+        "    \"character_threshold\": 0.50  # Do segundo código\n",
+        "}\n",
+        "\n",
+        "args = \"\"\n",
+        "for k, v in config.items():\n",
+        "    if k.startswith(\"_\"):\n",
+        "        args += f'\"{v}\" '\n",
+        "    elif isinstance(v, str):\n",
+        "        args += f'--{k}=\"{v}\" '\n",
+        "    elif isinstance(v, bool) and v:\n",
+        "        args += f\"--{k} \"\n",
+        "    elif isinstance(v, float) and not isinstance(v, bool):\n",
+        "        args += f\"--{k}={v} \"\n",
+        "    elif isinstance(v, int) and not isinstance(v, bool):\n",
+        "        args += f\"--{k}={v} \"\n",
+        "\n",
+        "# Verificar qual script executar com base em NoAutoCaption\n",
+        "if 'NoAutoCaption' not in locals() or not NoAutoCaption:\n",
+        "    final_args = f\"python tag_images_by_wd14_tagger.py {args}\"\n",
+        "else:\n",
+        "    final_args = f\"python make_captions.py {args}\"\n",
+        "\n",
+        "os.chdir(finetune_dir)\n",
+        "!{final_args}\n",
+        "\n",
+        "os.chdir(root_dir)\n",
+        "\n",
+        "extension = \".txt\"\n",
+        "custom_tag = CustomCaption\n",
+        "\n",
+        "def read_file(filename):\n",
+        "   with open(filename, \"r\") as f:\n",
+        "         contents = f.read()\n",
+        "   return contents\n",
+        "\n",
+        "def write_file(filename, contents):\n",
+        "   with open(filename, \"w\") as f:\n",
+        "         f.write(contents)\n",
+        "\n",
+        "def process_tags(filename, custom_tag, append, remove_tag):\n",
+        "   contents = read_file(filename)\n",
+        "   tags = [tag.strip() for tag in contents.split(',')]\n",
+        "   custom_tags = [tag.strip() for tag in custom_tag.split(',')]\n",
+        "\n",
+        "   for custom_tag in custom_tags:\n",
+        "         custom_tag = custom_tag.replace(\"_\", \" \")\n",
+        "         if remove_tag:\n",
+        "            while custom_tag in tags:\n",
+        "               tags.remove(custom_tag)\n",
+        "         else:\n",
+        "            if custom_tag not in tags:\n",
+        "               if append:\n",
+        "                     tags.append(custom_tag)\n",
+        "               else:\n",
+        "                     tags.insert(0, custom_tag)\n",
+        "\n",
+        "   contents = ', '.join(tags)\n",
+        "   write_file(filename, contents)\n",
+        "\n",
+        "def process_directory(train_data_dir, tag, append, remove_tag, recursive):\n",
+        "   for filename in os.listdir(train_data_dir):\n",
+        "         file_path = os.path.join(train_data_dir, filename)\n",
+        "         if os.path.isdir(file_path) and recursive:\n",
+        "            process_directory(file_path, tag, append, remove_tag, recursive)\n",
+        "         elif filename.endswith(extension):\n",
+        "            process_tags(file_path, tag, append, remove_tag)\n",
+        "\n",
+        "if not any(\n",
+        "   [filename.endswith(extension) for filename in os.listdir(train_data_dir)]\n",
+        "):\n",
+        "   for filename in os.listdir(train_data_dir):\n",
+        "         if filename.endswith((\".png\", \".jpg\", \".jpeg\", \".webp\", \".bmp\")):\n",
+        "            open(\n",
+        "               os.path.join(train_data_dir, filename.split(\".\")[0] + extension),\n",
+        "               \"w\",\n",
+        "            ).close()\n",
+        "if not NoAutoCaption :\n",
+        "    process_directory(train_data_dir, custom_tag, False, False, True)\n",
+        "\n",
+        "#3.Setting\n",
+        "\n",
+        "MODEL_URLS = {\n",
+        "    \"GSMaletoPhotoreal_v4\" : \"https://civitai.com/api/download/models/164715\",\n",
+        "    \"GSMaletoFusion_v1\" : \"https://civitai.com/api/download/models/138518\",\n",
+        "    \"GSMaletoAnime_v1\" : \"https://civitai.com/api/download/models/503605\",\n",
+        "}\n",
+        "MODEL_URL = MODEL_URLS.get(Model, Model)\n",
+        "drive_dir = os.path.join(root_dir, \"drive/MyDrive\")\n",
+        "def get_supported_extensions():\n",
+        "    return tuple([\".ckpt\", \".safetensors\", \".pt\", \".pth\"])\n",
+        "\n",
+        "def get_filename(url, quiet=True):\n",
+        "    extensions = get_supported_extensions()\n",
+        "\n",
+        "    if url.startswith(drive_dir) or url.endswith(tuple(extensions)):\n",
+        "        filename = os.path.basename(url)\n",
+        "    else:\n",
+        "        response = requests.get(url, stream=True)\n",
+        "        response.raise_for_status()\n",
+        "\n",
+        "        if 'content-disposition' in response.headers:\n",
+        "            content_disposition = response.headers['content-disposition']\n",
+        "            filename = re.findall('filename=\"?([^\"]+)\"?', content_disposition)[0]\n",
+        "        else:\n",
+        "            url_path = urlparse(url).path\n",
+        "            filename = unquote(os.path.basename(url_path))\n",
+        "\n",
+        "    if filename.endswith(tuple(get_supported_extensions())):\n",
+        "        return filename\n",
+        "    else:\n",
+        "        return None\n",
+        "\n",
+        "def get_most_recent_file(directory):\n",
+        "    files = glob.glob(os.path.join(directory, \"*\"))\n",
+        "    if not files:\n",
+        "        return None\n",
+        "    most_recent_file = max(files, key=os.path.getmtime)\n",
+        "    basename = os.path.basename(most_recent_file)\n",
+        "\n",
+        "    return most_recent_file\n",
+        "\n",
+        "def parse_args(config):\n",
+        "    args = []\n",
+        "\n",
+        "    for k, v in config.items():\n",
+        "        if k.startswith(\"_\"):\n",
+        "            args.append(f\"{v}\")\n",
+        "        elif isinstance(v, str) and v is not None:\n",
+        "            args.append(f'--{k}={v}')\n",
+        "        elif isinstance(v, bool) and v:\n",
+        "            args.append(f\"--{k}\")\n",
+        "        elif isinstance(v, float) and not isinstance(v, bool):\n",
+        "            args.append(f\"--{k}={v}\")\n",
+        "        elif isinstance(v, int) and not isinstance(v, bool):\n",
+        "            args.append(f\"--{k}={v}\")\n",
+        "\n",
+        "    return args\n",
+        "def aria2_download(dir, filename, url):\n",
+        "    aria2_config = {\n",
+        "        \"console-log-level\"         : \"error\",\n",
+        "        \"summary-interval\"          : 10,\n",
+        "        \"continue\"                  : True,\n",
+        "        \"max-connection-per-server\" : 16,\n",
+        "        \"min-split-size\"            : \"1M\",\n",
+        "        \"split\"                     : 16,\n",
+        "        \"dir\"                       : dir,\n",
+        "        \"out\"                       : filename,\n",
+        "        \"_url\"                      : url,\n",
+        "    }\n",
+        "    aria2_args = parse_args(aria2_config)\n",
+        "    subprocess.run([\"aria2c\", *aria2_args])\n",
+        "\n",
+        "def gdown_download(url, dst, filepath):\n",
+        "    if \"/uc?id/\" in url:\n",
+        "        return gdown.download(url, filepath, quiet=False)\n",
+        "    elif \"/file/d/\" in url:\n",
+        "        return gdown.download(url=url, output=filepath, quiet=False, fuzzy=True)\n",
+        "    elif \"/drive/folders/\" in url:\n",
+        "        os.chdir(dst)\n",
+        "        return gdown.download_folder(url, quiet=True, use_cookies=False)\n",
+        "\n",
+        "def download(url, dst):\n",
+        "    print(f\"Starting downloading from {url}\")\n",
+        "    filename = get_filename(url)\n",
+        "    filepath = os.path.join(dst, filename)\n",
+        "\n",
+        "    if \"drive.google.com\" in url:\n",
+        "        gdown = gdown_download(url, dst, filepath)\n",
+        "    else:\n",
+        "        if \"huggingface.co\" in url and \"/blob/\" in url:\n",
+        "            url = url.replace(\"/blob/\", \"/resolve/\")\n",
+        "        aria2_download(dst, filename, url)\n",
+        "\n",
+        "    print(f\"Download finished: {filepath}\")\n",
+        "    return filepath\n",
+        "\n",
+        "def get_gpu_name():\n",
+        "    try:\n",
+        "        return subprocess.check_output(\"nvidia-smi --query-gpu=name --format=csv,noheader,nounits\", shell=True).decode('ascii').strip()\n",
+        "    except:\n",
+        "        return None\n",
+        "\n",
+        "def main():\n",
+        "    global model_path, vae_path\n",
+        "    model_path, vae_path = None, None\n",
+        "    download_targets = {\n",
+        "        \"model\": (MODEL_URL, pretrained_model),\n",
+        "    }\n",
+        "    for target, (url, dst) in download_targets.items():\n",
+        "        if url and not url.startswith(f\"PASTE {target.upper()} URL OR GDRIVE PATH HERE\"):\n",
+        "            filepath = download(url, dst)\n",
+        "            if target == \"model\":\n",
+        "                model_path = filepath\n",
+        "            print()\n",
+        "    if model_path:\n",
+        "        print(f\"Selected model: {model_path}\")\n",
+        "\n",
+        "if Model.startswith(\"/content/drive/\"):\n",
+        "  model_path = Model\n",
+        "  print(f\"Diffusers model is loaded : {Model}\")\n",
+        "else:\n",
+        "  main()\n",
+        "\n",
+        "!aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt -d /content/VAE -o VAE84EMA.vae.pt\n",
+        "vae = \"/content/VAE/VAE84EMA.vae.pt\"\n",
+        "\n",
+        "#Dataset Config\n",
+        "\n",
+        "activation_word = \"GSGI\"\n",
+        "caption_extension = \".txt\"\n",
+        "token_to_captions = False\n",
+        "dataset_repeats = Repeats\n",
+        "keep_tokens = 0\n",
+        "flip_aug = False\n",
+        "\n",
+        "if ',' in activation_word or ' ' in activation_word:\n",
+        "    words = activation_word.replace(',', ' ').split()\n",
+        "    class_token = words[-1]\n",
+        "\n",
+        "\n",
+        "def read_file(filename):\n",
+        "    with open(filename, \"r\") as f:\n",
+        "        contents = f.read()\n",
+        "    return contents\n",
+        "\n",
+        "\n",
+        "def write_file(filename, contents):\n",
+        "    with open(filename, \"w\") as f:\n",
+        "        f.write(contents)\n",
+        "\n",
+        "\n",
+        "def get_supported_images(folder):\n",
+        "    supported_extensions = (\".png\", \".jpg\", \".jpeg\", \".webp\", \".bmp\")\n",
+        "    return [file for ext in supported_extensions for file in glob.glob(f\"{folder}/*{ext}\")]\n",
+        "\n",
+        "\n",
+        "def get_subfolders_with_supported_images(folder):\n",
+        "    subfolders = [os.path.join(folder, subfolder) for subfolder in os.listdir(folder) if os.path.isdir(os.path.join(folder, subfolder))]\n",
+        "    return [subfolder for subfolder in subfolders if len(get_supported_images(subfolder)) > 0]\n",
+        "\n",
+        "\n",
+        "def process_tags(filename, custom_tag, remove_tag):\n",
+        "    contents = read_file(filename)\n",
+        "    tags = [tag.strip() for tag in contents.split(',')]\n",
+        "    custom_tags = [tag.strip() for tag in custom_tag.split(',')]\n",
+        "\n",
+        "    for custom_tag in custom_tags:\n",
+        "        custom_tag = custom_tag.replace(\"_\", \" \")\n",
+        "        # if remove_tag:\n",
+        "        #     while custom_tag in tags:\n",
+        "        #         tags.remove(custom_tag)\n",
+        "        # else:\n",
+        "        if custom_tag not in tags:\n",
+        "            tags.insert(0, custom_tag)\n",
+        "\n",
+        "    contents = ', '.join(tags)\n",
+        "    write_file(filename, contents)\n",
+        "\n",
+        "\n",
+        "def process_folder_recursively(folder):\n",
+        "    for root, _, files in os.walk(folder):\n",
+        "        for file in files:\n",
+        "            if file.endswith(caption_extension):\n",
+        "                file_path = os.path.join(root, file)\n",
+        "                extracted_class_token = get_class_token_from_folder_name(root, folder)\n",
+        "                train_supported_images = get_supported_images(train_data_dir)\n",
+        "                tag = extracted_class_token if extracted_class_token else activation_word if train_supported_images else \"\"\n",
+        "                if not tag == \"\":\n",
+        "                    process_tags(file_path, tag, remove_tag=(not token_to_captions))\n",
+        "\n",
+        "\n",
+        "def get_num_repeats(folder):\n",
+        "    folder_name = os.path.basename(folder)\n",
+        "    try:\n",
+        "        repeats, _ = folder_name.split('_', 1)\n",
+        "        num_repeats = int(repeats)\n",
+        "    except ValueError:\n",
+        "        num_repeats = dataset_repeats\n",
+        "\n",
+        "    return num_repeats\n",
+        "\n",
+        "\n",
+        "def get_class_token_from_folder_name(folder, parent_folder):\n",
+        "    if folder == parent_folder:\n",
+        "        return class_token\n",
+        "\n",
+        "    folder_name = os.path.basename(folder)\n",
+        "    try:\n",
+        "        _, concept = folder_name.split('_', 1)\n",
+        "        return concept\n",
+        "    except ValueError:\n",
+        "        return \"\"\n",
+        "\n",
+        "train_supported_images = get_supported_images(train_data_dir)\n",
+        "train_subfolders = get_subfolders_with_supported_images(train_data_dir)\n",
+        "\n",
+        "subsets = []\n",
+        "config = {\n",
+        "    \"general\": {\n",
+        "        \"enable_bucket\": True,\n",
+        "        \"caption_extension\": caption_extension,\n",
+        "        \"shuffle_caption\": True,\n",
+        "        \"keep_tokens\": keep_tokens,\n",
+        "        \"bucket_reso_steps\": 64,\n",
+        "        \"bucket_no_upscale\": False,\n",
+        "    },\n",
+        "    \"datasets\": [\n",
+        "        {\n",
+        "            \"resolution\": resolution,\n",
+        "            \"min_bucket_reso\": 320 if resolution > 640 else 256,\n",
+        "            \"max_bucket_reso\": 1280 if resolution > 640 else 1024,\n",
+        "            \"caption_dropout_rate\": 0,\n",
+        "            \"caption_tag_dropout_rate\": 0,\n",
+        "            \"caption_dropout_every_n_epochs\": 0,\n",
+        "            \"flip_aug\": flip_aug,\n",
+        "            \"color_aug\": False,\n",
+        "            \"face_crop_aug_range\": None,\n",
+        "            \"subsets\": subsets,\n",
+        "        }\n",
+        "    ],\n",
+        "}\n",
+        "\n",
+        "if token_to_captions and keep_tokens < 2:\n",
+        "    keep_tokens = 1\n",
+        "\n",
+        "process_folder_recursively(train_data_dir)\n",
+        "\n",
+        "if train_supported_images:\n",
+        "    subsets.append({\n",
+        "        \"image_dir\": train_data_dir,\n",
+        "        \"class_tokens\": activation_word,\n",
+        "        \"num_repeats\": dataset_repeats,\n",
+        "    })\n",
+        "\n",
+        "for subfolder in train_subfolders:\n",
+        "    num_repeats = get_num_repeats(subfolder)\n",
+        "    extracted_class_token = get_class_token_from_folder_name(subfolder, train_data_dir)\n",
+        "    subsets.append({\n",
+        "        \"image_dir\": subfolder,\n",
+        "        \"class_tokens\": extracted_class_token if extracted_class_token else None,\n",
+        "        \"num_repeats\": num_repeats,\n",
+        "    })\n",
+        "\n",
+        "for subset in subsets:\n",
+        "    if not glob.glob(f\"{subset['image_dir']}/*.txt\"):\n",
+        "        subset[\"class_tokens\"] = activation_word\n",
+        "\n",
+        "dataset_config = os.path.join(config_dir, \"dataset_config.toml\")\n",
+        "\n",
+        "for key in config:\n",
+        "    if isinstance(config[key], dict):\n",
+        "        for sub_key in config[key]:\n",
+        "            if config[key][sub_key] == \"\":\n",
+        "                config[key][sub_key] = None\n",
+        "    elif config[key] == \"\":\n",
+        "        config[key] = None\n",
+        "\n",
+        "config_str = toml.dumps(config)\n",
+        "\n",
+        "with open(dataset_config, \"w\") as f:\n",
+        "    f.write(config_str)\n",
+        "\n",
+        "print(config_str)\n",
+        "\n",
+        "#Config\n",
+        "optimizer_args = False\n",
+        "conv_dim = 4\n",
+        "conv_alpha = 1\n",
+        "\n",
+        "network_module = \"networks.lora\"\n",
+        "network_args = \"\"\n",
+        "\n",
+        "config = {\n",
+        "   \"model_arguments\": {\n",
+        "         \"v2\": False,\n",
+        "         \"v_parameterization\": False,\n",
+        "         \"pretrained_model_name_or_path\": model_path,\n",
+        "         \"vae\": vae,\n",
+        "   },\n",
+        "   \"additional_network_arguments\": {\n",
+        "         \"no_metadata\": False,\n",
+        "         \"unet_lr\": float(unet_lr),\n",
+        "         \"text_encoder_lr\": float(text_encoder_lr),\n",
+        "         \"network_module\": network_module,\n",
+        "         \"network_dim\": 64,\n",
+        "         \"network_alpha\": 48,\n",
+        "         \"training_comment\": \"GSGI Trainer\",\n",
+        "   },\n",
+        "   \"optimizer_arguments\": {\n",
+        "         \"optimizer_type\": \"AdamW8bit\",\n",
+        "         \"optimizer_args\": eval(optimizer_args) if optimizer_args else None,\n",
+        "         \"learning_rate\": unet_lr,\n",
+        "         \"max_grad_norm\": 1.0,\n",
+        "         \"lr_scheduler\": \"cosine_with_restarts\",\n",
+        "         \"lr_scheduler_num_cycles\": 4,\n",
+        "   },\n",
+        "   \"dataset_arguments\": {\n",
+        "         \"cache_latents\": True,\n",
+        "         \"debug_dataset\": False,\n",
+        "         \"vae_batch_size\": Batch_size,\n",
+        "   },\n",
+        "   \"training_arguments\": {\n",
+        "         \"output_dir\": output_dir,\n",
+        "         \"output_name\": Loraname,\n",
+        "         \"save_precision\": \"fp16\",\n",
+        "         \"save_every_n_epochs\": save_n_epochs_type_value,\n",
+        "         \"train_batch_size\": Batch_size,\n",
+        "         \"max_token_length\": 225,\n",
+        "         \"mem_eff_attn\": False,\n",
+        "         \"xformers\": True,\n",
+        "         \"max_train_epochs\": num_epochs,\n",
+        "         \"max_data_loader_n_workers\": 8,\n",
+        "         \"persistent_data_loader_workers\": True,\n",
+        "         \"gradient_checkpointing\": False,\n",
+        "         \"gradient_accumulation_steps\": 1,\n",
+        "         \"mixed_precision\": \"fp16\",\n",
+        "         \"clip_skip\": 1,\n",
+        "         \"logging_dir\": \"/content/Dreamboot-Config/logs\",\n",
+        "         \"log_prefix\": Loraname,\n",
+        "         \"lowram\": True,\n",
+        "         \"training_comment\" : \"train by GSGI Trainer\",\n",
+        "   },\n",
+        "   \"sample_prompt_arguments\": {\n",
+        "         \"sample_every_n_steps\": 200,\n",
+        "         \"sample_every_n_epochs\": 1,\n",
+        "         \"sample_sampler\": \"euler\",\n",
+        "   },\n",
+        "   \"dreambooth_arguments\": {\n",
+        "         \"prior_loss_weight\": 1,\n",
+        "   },\n",
+        "   \"saving_arguments\": {\n",
+        "         \"save_model_as\": \"safetensors\",\n",
+        "   },\n",
+        "}\n",
+        "SamplePrompt = f\"{Loraname},front view, masterpiece,best quality\"\n",
+        "sample_str = f\"\"\"\n",
+        "  {SamplePrompt}\\\n",
+        "  --n lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry \\\n",
+        "  --w 512 \\\n",
+        "  --h 768 \\\n",
+        "  --l 7 \\\n",
+        "  --s 30\n",
+        "\"\"\"\n",
+        "config_path = os.path.join(config_dir, \"config_file.toml\")\n",
+        "prompt_path = os.path.join(config_dir, \"sample_prompt.txt\")\n",
+        "\n",
+        "for key in config:\n",
+        "    if isinstance(config[key], dict):\n",
+        "        for sub_key in config[key]:\n",
+        "            if config[key][sub_key] == \"\":\n",
+        "                config[key][sub_key] = None\n",
+        "    elif config[key] == \"\":\n",
+        "        config[key] = None\n",
+        "\n",
+        "config_str = toml.dumps(config)\n",
+        "\n",
+        "def write_file(filename, contents):\n",
+        "    with open(filename, \"w\") as f:\n",
+        "        f.write(contents)\n",
+        "\n",
+        "write_file(config_path, config_str)\n",
+        "write_file(prompt_path, sample_str)\n",
+        "\n",
+        "print(config_str)\n",
+        "\n",
+        "os.chdir(repo_dir)\n",
+        "\n",
+        "\n",
+        "train_file = \"train_network.py\"\n",
+        "ConfigFolder = \"/content/Dreamboot-Config/config\"\n",
+        "sample_prompt = f\"{ConfigFolder}/sample_prompt.txt\"\n",
+        "config_file = f\"{ConfigFolder}/config_file.toml\"\n",
+        "dataset_config = f\"{ConfigFolder}/dataset_config.toml\"\n",
+        "accelerate_conf = {\n",
+        "    \"config_file\" : accelerate_config,\n",
+        "    \"num_cpu_threads_per_process\" : 1,\n",
+        "}\n",
+        "\n",
+        "train_conf = {\n",
+        "    \"sample_prompts\" : sample_prompt,\n",
+        "    \"dataset_config\" : dataset_config,\n",
+        "    \"config_file\" : config_file\n",
+        "}\n",
+        "\n",
+        "def train(config):\n",
+        "    args = \"\"\n",
+        "    for k, v in config.items():\n",
+        "        if k.startswith(\"_\"):\n",
+        "            args += f'\"{v}\" '\n",
+        "        elif isinstance(v, str):\n",
+        "            args += f'--{k}=\"{v}\" '\n",
+        "        elif isinstance(v, bool) and v:\n",
+        "            args += f\"--{k} \"\n",
+        "        elif isinstance(v, float) and not isinstance(v, bool):\n",
+        "            args += f\"--{k}={v} \"\n",
+        "        elif isinstance(v, int) and not isinstance(v, bool):\n",
+        "            args += f\"--{k}={v} \"\n",
+        "\n",
+        "    return args\n",
+        "\n",
+        "accelerate_args = train(accelerate_conf)\n",
+        "train_args = train(train_conf)\n",
+        "final_args = f\"accelerate launch {accelerate_args} {train_file} {train_args}\"\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}