Upload sd-webui-v2.1.4.ipynb

sd-webui-v2.1.4.ipynb

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+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## NovelAi sd-webui AI绘画项目 修复版（完全免费，无需任何配置！）\n**torch: 2.0.0+cu118  •  xformers: 0.0.19**\n\n# <span style=\"color:red; font-weight:bold;\">有问题请加qq群632428790 (691/2000)</span>\n### 急需一名宣传人员，无偿，会做B站视频就行","metadata":{}},{"cell_type":"markdown","source":"## 使用教程：https://www.kaggle.com/code/at2020dead/novelai-stable-diffusion/notebook ","metadata":{}},{"cell_type":"markdown","source":"<div class=\"alert alert-block alert-info\" style=\"font-size:14px; font-family:verdana;\">\n    📌 2023年3月5日更新：现在支持通过下载链接上传模型了，省去了下载模型后再上传后的麻烦.（）\n</div>\n<div class=\"alert alert-block alert-info\" style=\"font-size:14px; font-family:verdana;\">\n    📌 2023年5月15日更新：现在可以双开webui了，可以双线程跑图（GPU请选择 T4 x2 ， 将use2设置为True）\n</div>","metadata":{}},{"cell_type":"markdown","source":"# 注意事项/WARNING：\n- ### 1.将设置中的PERSISTENCE改为Files Only方便下次打开提高启动速度，第一次启动后下载Python环境包就不用下载第二次了\n- ### 2.检测到出现涩图会容易导致封号现象，建议到webui设置里把'始终保存所有生成的图像'和‘始终保存所有生成的宫格图’关了\n- ### 3.如果不能启动，请新建一个notebook并且重新导入","metadata":{}},{"cell_type":"markdown","source":"## Ai绘画模型下载站：\n#### [Civitai](http://civitai.com)\n \n#### [huggingface](http://huggingface.co)\n \n####   [pix.ink](http://pix.ink) ","metadata":{}},{"cell_type":"markdown","source":"<a href=\"https://sm.ms/image/V8hOxP5FAWfRqEi\" target=\"_blank\"><img src=\"https://s2.loli.net/2023/05/20/V8hOxP5FAWfRqEi.png\" ></a>","metadata":{}},{"cell_type":"code","source":"# 安装目录\ninstall_path=\"/kaggle/working\" #或者/kaggle\nupdata_webui = False  #是否开机自动更新webui\n\n# 重置变量 会删掉sd_webui重新安装\nreLoad = False\nupdata_webui = False\n\n#清理和打包生成的图片\nzip_output=True\nclear_output=True\n\n# 使用huggingface保存和载入webui配置文件\nhuggingface_use = True\nhuggingface_token_file = '/kaggle/input/hugfacetoken/hugfacetoken.txt'\nhuggiingface_repo_id = 'ACCA225/sdconfig'\n# 将会同步的文件\nyun_files = ['ui-config.json','config.json','styles.csv']","metadata":{"execution":{"iopub.status.busy":"2023-05-20T04:28:55.647489Z","iopub.execute_input":"2023-05-20T04:28:55.647877Z","iopub.status.idle":"2023-05-20T04:28:55.661388Z","shell.execute_reply.started":"2023-05-20T04:28:55.647847Z","shell.execute_reply":"2023-05-20T04:28:55.660185Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#模型和插件\n\n# 插件列表： git仓库地址\n# 不需要的插件在前面加 # ，插件地址之间需要用英语逗号隔开\nextensions = [\n    'https://github.com/Elldreth/loopback_scaler',\n    'https://github.com/jexom/sd-webui-depth-lib',\n    'https://github.com/AlUlkesh/stable-diffusion-webui-images-browser',\n    'https://github.com/camenduru/sd-civitai-browser',\n    'https://github.com/Mikubill/sd-webui-controlnet',\n    'https://github.com/nonnonstop/sd-webui-3d-open-pose-editor',\n    'https://github.com/dtlnor/stable-diffusion-webui-localization-zh_CN',\n    'https://github.com/opparco/stable-diffusion-webui-two-shot',\n    #'https://github.com/minicacas/stable-diffusion-webui-composable-lora',\n    'https://github.com/DominikDoom/a1111-sd-webui-tagcomplete',\n    'https://github.com/pkuliyi2015/multidiffusion-upscaler-for-automatic1111',\n    'https://github.com/KohakuBlueleaf/a1111-sd-webui-locon',\n    'https://github.com/hnmr293/sd-webui-cutoff',\n    'https://github.com/hako-mikan/sd-webui-lora-block-weight',\n    'https://github.com/butaixianran/Stable-Diffusion-Webui-Civitai-Helper',\n    'https://github.com/catppuccin/stable-diffusion-webui',\n    #'https://github.com/Nevysha/Cozy-Nest',\n    'https://github.com/Scholar01/sd-webui-mov2mov',\n    'https://github.com/toriato/stable-diffusion-webui-wd14-tagger',\n    'https://github.com/Physton/sd-webui-prompt-all-in-one'\n]\n\n# Stable Diffusion模型请放在这里（不用填模型的文件名，只填模型的目录即可）\nsd_model = [\n#'/kaggle/input/cetus-mix/',\n#'/kaggle/input/aom3ackpt',\n'/kaggle/input/9527-fp16',\n#'/kaggle/input/dalcefo-painting',\n            ]\n# Stable Diffusion模型下载链接放这里\nsd_model_urls=[\n#Counterfeit-V3.0\n'https://civitai.com/api/download/models/57618',\n'https://huggingface.co/datasets/sukaka/sd_models_fp16/resolve/main/cetusMix_Coda2.safetensors',\n'https://huggingface.co/datasets/sukaka/sd_models_fp16/resolve/main/cetusMix_Version35.safetensors',\n\n]\n\n# VAE模型请放在这里（不用填模型的文件名，只填模型的目录即可）\nvae_model = []\n#VAE模型下载链接放这里\nvae_model_urls=[\n'https://huggingface.co/datasets/sukaka/sd_models_fp16/resolve/main/clearvae.vae.pt',\n]\n\n# Lora模型的数据集路径请写在这里：\nlora_model = [\n#'/kaggle/input/lora-1',\n] \n# Lora模型下载链接放这里\nlora_model_urls=[\n#墨心\n'https://civitai.com/api/download/models/14856',\n#山楂糕\n'https://civitai.com/api/download/models/41580',\n]\n\n# ControlNet模型data请放在这里：\ncn_model = [\n]\n# controlnet模型下载链接放这里\ncn_model_urls = [\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors',\n]\n\n# Hypernetworks超网络模型路径请放在这里：\nhypernetworks_model = []\n#Hypernetworks超网络模型下载链接请放在这里\nhypernetworks_model_urls = []\n\n#放大算法路径请放在这里\nESRGAN = []\n#放大算法链接请放在这里\nESRGAN_urls = [\n'https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth',\n'https://huggingface.co/konohashinobi4/4xAnimesharp/resolve/main/4x-AnimeSharp.pth',\n'https://huggingface.co/lokCX/4x-Ultrasharp/resolve/main/4x-UltraSharp.pth',\n]\n\n# embeddings（pt文件）请放在这里:\nembeddings_model = [\n'/kaggle/input/bad-embedding',\n] \n# embeddings（pt文件）下载链接请放在这里:\nembeddings_model_urls=[\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/%E4%BA%BA%E4%BD%93%E4%BF%AE%E6%AD%A3/EasyNegative.pt',\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/%E4%BA%BA%E4%BD%93%E4%BF%AE%E6%AD%A3/bad-artist-anime.pt',\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/%E4%BA%BA%E4%BD%93%E4%BF%AE%E6%AD%A3/bad-hands-5.pt',\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/%E4%BA%BA%E4%BD%93%E4%BF%AE%E6%AD%A3/bad_prompt_version2.pt',\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/%E4%BA%BA%E4%BD%93%E4%BF%AE%E6%AD%A3/bad-image-v2-39000.pt',\n]\n\n#script文件导入\nscripts = []\n#script文件下载链接导入\nscripts_urls = [\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/repositories/k-diffusion/k_diffusion/sampling.py'\n]\n\n#tag词库文件导入\ntags = []\n","metadata":{"execution":{"iopub.status.busy":"2023-05-20T04:28:55.663857Z","iopub.execute_input":"2023-05-20T04:28:55.664518Z","iopub.status.idle":"2023-05-20T04:28:55.681429Z","shell.execute_reply.started":"2023-05-20T04:28:55.664483Z","shell.execute_reply":"2023-05-20T04:28:55.680308Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 是否使用pm2启动\nusepm2 = False\n# 使用pm2启动可以爆内存时自动重启，用于测试，非必要别开\n# 开启后需使用frp或者ngrok做内网穿透","metadata":{"execution":{"iopub.status.busy":"2023-05-20T04:28:55.683007Z","iopub.execute_input":"2023-05-20T04:28:55.683506Z","iopub.status.idle":"2023-05-20T04:28:55.693252Z","shell.execute_reply.started":"2023-05-20T04:28:55.683472Z","shell.execute_reply":"2023-05-20T04:28:55.692279Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#ngrok穿透\nngrok_use = False\nngrokTokenFile='/kaggle/input/ngroktoken/Authtoken.txt' # 非必填 存放ngrokToken的文件的路径\n#Frp 内网穿��\nuse_frpc = False\nfrpconfigfile = '/kaggle/input/testfrpc/frpc_8215127.ini'  # 非必填 frp 配置文件，本地端口 7860\n\n# 启动时默认加载的模型名称 填模型名称，名称建议带上文件名后缀\nusedCkpt = 'cetusMix_Coda2.safetensors'\n\n#启动参数\nargs = [\n    '--share',\n    '--xformers',\n    '--lowram',\n    '--no-hashing',\n    '--disable-nan-check',\n    '--enable-insecure-extension-access',\n    '--disable-console-progressbars',\n    '--enable-console-prompts',\n    '--disable-safe-unpickle',\n    '--no-gradio-queue',\n    '--no-half-vae',\n    '--api'\n]","metadata":{"execution":{"iopub.status.busy":"2023-05-20T04:28:55.696008Z","iopub.execute_input":"2023-05-20T04:28:55.697139Z","iopub.status.idle":"2023-05-20T04:28:55.703780Z","shell.execute_reply.started":"2023-05-20T04:28:55.697106Z","shell.execute_reply":"2023-05-20T04:28:55.702662Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"use2 = False#是否开启两个webui\n#ngrok穿透\nngrok_use1 = False\nngrokTokenFile1='/kaggle/input/ngroktoken/Authtoken1.txt' # 非必填 存放ngrokToken的文件的路径\n#Frp 内网穿透\nuse_frpc1 = False\nfrpconfigfile1 = '/kaggle/input/testfrpc1/frpc_8217098.ini'  # 非必填 frp 配置文件，本地端口 7861\n\n#第二个webui使用的模型\nusedCkpt1 = 'cetusMix_Coda2.safetensors'\n\n#启动参数\nargs1 = [\n    '--share',\n    '--xformers',\n    '--lowram',\n    '--no-hashing',\n    '--disable-nan-check',\n    '--enable-insecure-extension-access',\n    '--disable-console-progressbars',\n    '--enable-console-prompts',\n    '--disable-safe-unpickle',\n    '--no-gradio-queue',\n    '--no-half-vae',\n    '--api'\n]","metadata":{"execution":{"iopub.status.busy":"2023-05-20T04:28:55.705449Z","iopub.execute_input":"2023-05-20T04:28:55.706274Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#使用的库\nfrom pathlib import Path\nimport subprocess\nimport pandas as pd\nimport shutil\nimport os\nimport time\nimport re\nimport gc\nimport requests\nfrom concurrent.futures import ProcessPoolExecutor\nos.environ['install_path'] = install_path","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#功能函数，内存优化\ndef libtcmalloc():\n    if os.path.exists('/kaggle/temp'):\n        os.chdir('/kaggle')\n        os.chdir('temp')\n        os.environ[\"LD_PRELOAD\"] = \"libtcmalloc.so\"\n        print('内存优化已安装')\n    else:\n        \n        if use_frpc:\n            !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/datasets/ACCA225/Frp/resolve/main/frpc -d /kaggle/working/frpc -o frpc\n        os.system('pip install -q pyngrok ')\n        os.chdir('/kaggle')\n        os.makedirs('temp', exist_ok=True)\n        os.chdir('temp')\n        os.system('wget -qq  http://launchpadlibrarian.net/367274644/libgoogle-perftools-dev_2.5-2.2ubuntu3_amd64.deb')\n        os.system('wget -qq  https://launchpad.net/ubuntu/+source/google-perftools/2.5-2.2ubuntu3/+build/14795286/+files/google-perftools_2.5-2.2ubuntu3_all.deb')\n        os.system('wget -qq  https://launchpad.net/ubuntu/+source/google-perftools/2.5-2.2ubuntu3/+build/14795286/+files/libtcmalloc-minimal4_2.5-2.2ubuntu3_amd64.deb')\n        os.system('wget -qq  https://launchpad.net/ubuntu/+source/google-perftools/2.5-2.2ubuntu3/+build/14795286/+files/libgoogle-perftools4_2.5-2.2ubuntu3_amd64.deb')\n        os.system('apt install -qq libunwind8-dev -y')\n        !dpkg -i *.deb\n        os.environ[\"LD_PRELOAD\"] = \"libtcmalloc.so\"\n        !rm *.deb\n        print('内存优化已安装')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#功能函数，环境和sd_webui安装\ndef webui_config_download(yun_files, huggiingface_repo_id):\n    %cd $install_path/stable-diffusion-webui/\n    for yun_file in yun_files:\n        url = f'https://huggingface.co/datasets/{huggiingface_repo_id}/resolve/main/{yun_file}'\n        response = requests.head(url)\n        if response.status_code == 200:\n            result = subprocess.run(['wget', '-O', yun_file, url, '-q'], capture_output=True)\n            if result.returncode != 0:\n                print(f'Error: Failed to download {yun_file} from {url}')\n        else:\n            print(f'Error: Invalid URL {url}')\n    \ndef venv_install():\n    %cd /opt/conda/envs\n    if os.path.exists('venv'):\n        print('环境已安装')\n    else:\n        %cd /kaggle/working/\n        if not os.path.exists('venv.tar.gz'):\n            print('环境包下载中')\n            !wget https://huggingface.co/datasets/sukaka/venv_ai_drow/resolve/main/sd_webui_torch2_cu118_xf19.tar.gz -O venv.tar.gz\n        print('环境包已下载')\n        %cd /opt/conda/envs/\n        !mkdir venv\n        %cd venv\n        print('环境安装中')\n        !tar -xzf /kaggle/working/venv.tar.gz\n        !source /opt/conda/bin/activate venv\n        print('环境安装完毕')\n\n#安装webui\ndef install_webui():\n    %cd $install_path\n    if reLoad:\n        !rm -rf stable-diffusion-webui\n    if Path(\"stable-diffusion-webui\").exists():\n        if updata_webui:\n            %cd $install_path/stable-diffusion-webui/\n            !git pull\n        print('stable-diffusion-webui已安装')\n    else:\n        print('stable-diffusion-webui安装中')\n        #Download Automatic1111's Stable Diffusion Web UI\n        !git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui\n        %cd $install_path/stable-diffusion-webui/\n        #Use lastest version\n        !git checkout 5ab7f213bec2f816f9c5644becb32eb72c8ffb89\n        with open('launch.py', 'r') as f:\n            content = f.read()\n        with open('launch.py', 'w') as f:\n            f.write('import ssl\\n')\n            f.write('ssl._create_default_https_context = ssl._create_unverified_context\\n')\n            f.write(content)\n        print('stable-diffusion-webui已安装')\n    if huggingface_use:\n        webui_config_download(yun_files, huggiingface_repo_id)\n    install_extensions(install_path, extensions)\n    download_model()\n    link_models()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def pm2():\n    if usepm2:\n        !npm i -g pm2\n    ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from concurrent.futures import ThreadPoolExecutor\n# 安装插件，下载和同步模型\ndef install_extensions(install_path, extensions):\n    print('安装插件，此处出现红条是正常的')\n    os.chdir(os.path.join(install_path, 'stable-diffusion-webui'))\n    os.makedirs('extensions', exist_ok=True)\n    os.chdir('extensions')\n    for ex in extensions:\n        repo_name = ex.split('/')[-1]\n        if not os.path.exists(repo_name):\n            os.system('git clone ' + ex)\n    \ndef download_link(link, target_folder):\n    if link.startswith('https://huggingface.co/'):\n        filename = re.search(r'[^/]+$', link).group(0)\n        return f'aria2c --console-log-level=error -q -c -x 16 -s 16 -k 1M -d \"{target_folder}\" -o \"{filename}\" \"{link}\"'\n    else:\n        return f'aria2c --console-log-level=error -q -c -x 16 -s 16 -k 1M --remote-time -d \"{target_folder}\" \"{link}\"'\n\ndef download_links(links, target_folder):\n    tasks = []\n    for link in links:\n        tasks.append(download_link(link, target_folder))\n    return tasks\n\ndef download_links_all(tasks):\n    with ThreadPoolExecutor(max_workers=5) as executor:\n        for task in tasks:\n            executor.submit(os.system, task)\n        \n# 下载模型文件\ndef download_model():\n    os.chdir('/kaggle')\n    os.makedirs('models', exist_ok=True)\n    os.chdir('models')\n    os.makedirs('VAE', exist_ok=True)\n    os.makedirs('Stable-diffusion', exist_ok=True)\n    os.makedirs('Lora', exist_ok=True)\n    os.makedirs('cn-model', exist_ok=True)\n    os.makedirs('hypernetworks', exist_ok=True)\n    os.makedirs('ESRGAN', exist_ok=True)\n    tasks = []\n    tasks.extend(download_links(vae_model_urls, 'VAE'))\n    tasks.extend(download_links(sd_model_urls, 'Stable-diffusion'))\n    tasks.extend(download_links(lora_model_urls, 'Lora'))\n    tasks.extend(download_links(cn_model_urls, 'cn-model'))\n    tasks.extend(download_links(hypernetworks_model_urls, 'hypernetworks'))\n    tasks.extend(download_links(ESRGAN_urls, 'ESRGAN'))\n    tasks.extend(download_links(embeddings_model_urls, f'{install_path}/stable-diffusion-webui/embeddings'))\n    tasks.extend(download_links(scripts_urls, f'{install_path}/stable-diffusion-webui/scripts'))\n    download_links_all(tasks)\n\ndef create_symlinks(folder_paths, target_dir):\n    # Create target directory if it doesn't exist\n    if not os.path.exists(target_dir):\n        os.makedirs(target_dir)\n    # Remove broken symlinks in target directory\n    for filename in os.listdir(target_dir):\n        target_path = os.path.join(target_dir, filename)\n        if os.path.islink(target_path) and not os.path.exists(target_path):\n            os.unlink(target_path)\n    # Create new symlinks\n    for source_path in folder_paths:\n        if not os.path.exists(source_path):\n            continue\n        if os.path.isdir(source_path):\n            for filename in os.listdir(source_path):\n                source_file_path = os.path.join(source_path, filename)\n                target_file_path = os.path.join(target_dir, filename)\n                if not os.path.exists(target_file_path):\n                    os.symlink(source_file_path, target_file_path)\n                    print(f'Created symlink for {filename} in {target_dir}')\n        else:\n            filename = os.path.basename(source_path)\n            target_file_path = os.path.join(target_dir, filename)\n            if not os.path.exists(target_file_path):\n                os.symlink(source_path, target_file_path)\n                print(f'Created symlink for {filename} in {target_dir}')\n\n# 链接模型文件\ndef link_models():\n    cn_model.append('/kaggle/models/cn-model')\n    vae_model.append('/kaggle/models/VAE')\n    sd_model.append('/kaggle/models/Stable-diffusion')\n    lora_model.append('/kaggle/models/Lora')\n    hypernetworks_model.append('/kaggle/models/hypernetworks')\n    ESRGAN.append('/kaggle/models/ESRGAN')\n    \n    create_symlinks(vae_model,f'{install_path}/stable-diffusion-webui/models/VAE')\n    create_symlinks(sd_model,f'{install_path}/stable-diffusion-webui/models/Stable-diffusion')\n    create_symlinks(lora_model,f'{install_path}/stable-diffusion-webui/models/Lora')\n    create_symlinks(cn_model,f'{install_path}/stable-diffusion-webui/extensions/sd-webui-controlnet/models')\n    create_symlinks(embeddings_model,f'{install_path}/stable-diffusion-webui/embeddings')\n    create_symlinks(hypernetworks_model,f'{install_path}/stable-diffusion-webui/models/hypernetworks')\n    create_symlinks(ESRGAN,f'{install_path}/stable-diffusion-webui/models/ESRGAN')\n    create_symlinks(tags,f'{install_path}/stable-diffusion-webui/extensions/a1111-sd-webui-tagcomplete/tags')\n    create_symlinks(scripts,f'{install_path}/stable-diffusion-webui/scripts')\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 功能函数：内网穿透\n#ngrok\ndef ngrok_start(ngrokTokenFile: str, port: int, address_name: str, should_run: bool):\n    if not should_run:\n        print('Skipping ngrok start')\n        return\n    if Path(ngrokTokenFile).exists():\n        with open(ngrokTokenFile, encoding=\"utf-8\") as nkfile:\n            ngrokToken = nkfile.readline()\n        print('use nrgok')\n        from pyngrok import conf, ngrok\n        conf.get_default().auth_token = ngrokToken\n        conf.get_default().monitor_thread = False\n        ssh_tunnels = ngrok.get_tunnels(conf.get_default())\n        if len(ssh_tunnels) == 0:\n            ssh_tunnel = ngrok.connect(port, bind_tls=True)\n            print(f'{address_name}：' + ssh_tunnel.public_url)\n        else:\n            print(f'{address_name}：' + ssh_tunnels[0].public_url)\n    else:\n        print('skip start ngrok')\n\n#Frp内网穿透 \nimport subprocess\n\ndef install_Frpc(port, frpconfigfile, use_frpc):\n    if use_frpc:\n        subprocess.run(['chmod', '+x', '/kaggle/working/frpc/frpc'], check=True)\n        print(f'正在启动frp ，端口{port}')\n        subprocess.Popen(['/kaggle/working/frpc/frpc', '-c', frpconfigfile])\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#sd_webui启动\ndef start_webui1():\n    if use2:\n        install_Frpc('7861',frpconfigfile1,use_frpc1)\n        ngrok_start(ngrokTokenFile1,7861,'第二个webui',ngrok_use1)\n        !sleep 90\n        %cd $install_path/stable-diffusion-webui\n        args1.append(f'--ckpt=models/Stable-diffusion/{usedCkpt1}')\n        if usepm2:\n            !pm2 del webui_main\n            !pm2 flush webui_main\n            !pm2 start /opt/conda/envs/venv/bin/python3 -n webui_main -- launch.py {' '.join(args)}\n        else:\n            !/opt/conda/envs/venv/bin/python3 launch.py {' '.join(args)}\n    pass\n\ndef start_webui():\n    install_Frpc('7860',frpconfigfile,use_frpc)\n    ngrok_start(ngrokTokenFile,7860,'第一个webui',ngrok_use)\n    %cd $install_path/stable-diffusion-webui\n    args.append(f'--ckpt=models/Stable-diffusion/{usedCkpt}')\n    if usepm2:\n        !pm2 del webui_main\n        !pm2 flush webui_main\n        !pm2 start /opt/conda/envs/venv/bin/python3 -n webui_main -- launch.py {' '.join(args)}\n    else:\n        !/opt/conda/envs/venv/bin/python3 launch.py {' '.join(args)}","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def main():\n    startTicks = time.time()\n    os.system('apt -y install -qq aria2')\n    with ProcessPoolExecutor() as executor:\n        futures = []\n        for func in [pm2, install_webui, venv_install,libtcmalloc]:\n            futures.append(executor.submit(func))\n            time.sleep(0.5)\n        for future in futures:\n            future.result()\n    libtcmalloc()\n    ticks = time.time()\n    print(\"加载耗时:\",(ticks - startTicks),\"s\")\n    with ProcessPoolExecutor() as executor:\n        futures = []\n        for func in [start_webui, start_webui1]:\n            futures.append(executor.submit(func))\n            time.sleep(1)\n        for future in futures:\n            future.result()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#功能函数，清理打包上传\nfrom pathlib import Path\nfrom huggingface_hub import HfApi, login\n\ndef hugface_upload(huggingface_token_file, yun_files, repo_id):\n    if Path(huggingface_token_file).exists():\n        with open(huggingface_token_file, encoding=\"utf-8\") as nkfile:\n            hugToken = nkfile.readline()\n        if hugToken != '':\n            # 使用您的 Hugging Face 访问令牌登录\n            login(token=hugToken)\n            # 实例化 HfApi 类\n            api = HfApi()\n            print(\"HfApi 类已实例化\")\n            %cd $install_path/stable-diffusion-webui\n            # 使用 upload_file() 函数上传文件\n            print(\"开始上传文件...\")\n            for yun_file in yun_files:\n                if Path(yun_file).exists():\n                    response = api.upload_file(\n                        path_or_fileobj=yun_file,\n                        path_in_repo=yun_file,\n                        repo_id=repo_id,\n                        repo_type=\"dataset\"\n                    )\n                    print(\"文件上传完成\")\n                    print(f\"响应: {response}\")\n                else:\n                    print(f'Error: File {yun_file} does not exist')\n    else:\n        print(f'Error: File {huggingface_token_file} does not exist')\n\ndef clean_folder(folder_path):\n    if not os.path.exists(folder_path):\n        return\n    for filename in os.listdir(folder_path):\n        file_path = os.path.join(folder_path, filename)\n        if os.path.isfile(file_path):\n            os.remove(file_path)\n        elif os.path.isdir(file_path):\n            shutil.rmtree(file_path)\n\ndef zip_clear_updata():\n    if zip_output:\n        output_folder = f'{install_path}/stable-diffusion-webui/outputs/'\n        if os.path.exists(output_folder):\n            shutil.make_archive('/kaggle/working/图片', 'zip', output_folder)\n            print('图片已压缩到output')\n        else:\n            print(f'文件夹 {output_folder} 不存在，跳过压缩操作')\n    if clear_output:\n        %cd $install_path/stable-diffusion-webui/outputs/\n        clean_folder('img2img-images')\n        clean_folder('txt2img-images')\n        clean_folder('img2img-grids')\n        clean_folder('txt2img-grids')\n        clean_folder('extras-images')\n        print('清理完毕')\n    if huggingface_use == True:\n        hugface_upload(huggingface_token_file,yun_files,huggiingface_repo_id)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# start\nmain()\n!pm2 log","metadata":{"_kg_hide-input":true,"_kg_hide-output":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#跑图结束，手动执行，清理图片并打包到output方便下载,同时同步配置文件\nzip_clear_updata()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#模型下载器，手动执行后出现一个交互式表格\nimport os\ninstall_path=\"/kaggle/working\" \nos.environ['install_path'] = install_path\ndef model_down_tool():\n    import ipywidgets as widgets\n    from IPython.display import display\n    import subprocess\n    def download_with_aria2(link, file_path):\n        # 设置aria2c命令行参数\n        cmd = ['aria2c','--console-log-level=error', link, '-o', file_path, '-x', '16', '-s', '16', '-k', '1M']\n        # 调用aria2c下载文件\n        try:\n            subprocess.run(cmd, check=True)\n            print(f\"文件已保存到: {file_path}\")\n        except subprocess.CalledProcessError as e:\n            print(f\"下载失败: {e}\")\n    def sdmodel_down(link, model_name):\n        # 设置模型保存的文件夹路径\n        %cd $install_path\n        save_dir = 'stable-diffusion-webui/models/Stable-diffusion'\n        if not os.path.exists(save_dir):\n            os.makedirs(save_dir)\n    \n        # 设置模型保存的文件名\n        file_name = f\"{model_name}\"\n        file_path = os.path.join(save_dir, file_name)\n    \n        # 下载模型\n        download_with_aria2(link, file_path)\n    \n        print(f\"模型已保存到: {file_path}\")\n\n    def vae_down(link, model_name):\n        # 设置模型保存的文件夹路径\n        %cd $install_path\n        save_dir = 'stable-diffusion-webui/models/VAE'\n        if not os.path.exists(save_dir):\n            os.makedirs(save_dir)\n\n        # 设置模型保存的文件名\n        file_name = f\"{model_name}\"\n        file_path = os.path.join(save_dir, file_name)\n\n        # 下载模型\n        cmd = ['aria2c','--console-log-level=error', link, '-o', file_path, '-x', '16', '-s', '16', '-k', '1M']\n\n        print(f\"模型已保存到: {file_path}\")\n\n    def vae_down(link, model_name):\n        # 设置模型保存的文件夹路径\n        %cd $install_path\n        save_dir = 'stable-diffusion-webui/models/Lora'\n        if not os.path.exists(save_dir):\n            os.makedirs(save_dir)\n\n        # 设置模型保存的文件名\n        file_name = f\"{model_name}\"\n        file_path = os.path.join(save_dir, file_name)\n\n        # 下载模型\n        cmd = ['aria2c','--console-log-level=error', link, '-o', file_path, '-x', '16', '-s', '16', '-k', '1M']\n\n        print(f\"模型已保存到: {file_path}\")\n        \n    model_type = widgets.Dropdown(\n        options=['sd大模型', 'vae模型', 'Lora模型'],\n        description='模型类型:',\n        disabled=False,\n    )\n\n    link = widgets.Text(\n        value='',\n        placeholder='输入链接',\n        description='链接:',\n        disabled=False\n    )\n\n    model_name = widgets.Text(\n        value='',\n        placeholder='输入模型名称',\n        description='模型名:',\n        disabled=False\n    )\n\n    def on_submit(btn):\n        if model_type.value == 'sd大模型':\n            sdmodel_down(link.value, model_name.value)\n        elif model_type.value == 'vae模型':\n            vae_down(link.value, model_name.value)\n        else:\n            lora_down(link.value, model_name.value)\n\n    submit = widgets.Button(description=\"提交\")\n    submit.on_click(on_submit)\n\n    display(model_type, link, model_name, submit)\n#模型下载器\nmodel_down_tool()\n#safetensors","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}