Upload autotrain.ipynb

autotrain.ipynb

@@ -0,0 +1,673 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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,\n",
+    "   \"batch_size\": 8,\n",
+    "   \"repo_id\": \"SmilingWolf/wd-v1-4-convnextv2-tagger-v2\",\n",
+    "   \"recursive\": True,\n",
+    "   \"remove_underscore\": True,\n",
+    "   \"general_threshold\": Threshold,\n",
+    "   \"character_threshold\": 0.35,\n",
+    "   \"caption_extension\": \".txt\",\n",
+    "   \"max_data_loader_n_workers\": 2,\n",
+    "   \"debug\": True,\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",
+    "final_args = f\"python tag_images_by_wd14_tagger.py {args}\"\n",
+    "if not NoAutoCaption :\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",
+    "    \"RealisticVision-v51\" : \"https://huggingface.co/phamhungd/GuoZovya/resolve/main/RealisticVision-v51.safetensors\",\n",
+    "    \"Anything-v3\" : \"https://huggingface.co/cag/anything-v3-1/resolve/main/anything-v3-1.safetensors\",\n",
+    "    \"Chilloutmix\" : \"https://civitai.com/api/download/models/11745\",\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 = \"sd vn\"\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\": 32,\n",
+    "         \"training_comment\": \"sdvn.me\",\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\": \"constant\",\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\": 2,\n",
+    "         \"logging_dir\": \"/content/Dreamboot-Config/logs\",\n",
+    "         \"log_prefix\": Loraname,\n",
+    "         \"lowram\": True,\n",
+    "         \"training_comment\" : \"train by sdvn.me train 1click\",\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 28\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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}