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{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"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":"2023-05-20T02:42:49.162836Z","iopub.execute_input":"2023-05-20T02:42:49.163197Z","iopub.status.idle":"2023-05-20T02:42:49.176204Z","shell.execute_reply.started":"2023-05-20T02:42:49.163166Z","shell.execute_reply":"2023-05-20T02:42:49.175325Z"},"trusted":true},"execution_count":1,"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":"2023-05-20T02:42:49.178072Z","iopub.execute_input":"2023-05-20T02:42:49.178489Z","iopub.status.idle":"2023-05-20T02:42:49.192576Z","shell.execute_reply.started":"2023-05-20T02:42:49.178457Z","shell.execute_reply":"2023-05-20T02:42:49.191794Z"},"trusted":true},"execution_count":2,"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 内网穿透\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":"2023-05-20T02:42:49.194106Z","iopub.execute_input":"2023-05-20T02:42:49.194378Z","iopub.status.idle":"2023-05-20T02:42:49.209313Z","shell.execute_reply.started":"2023-05-20T02:42:49.194353Z","shell.execute_reply":"2023-05-20T02:42:49.208515Z"},"trusted":true},"execution_count":3,"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":"2023-05-20T02:42:49.211732Z","iopub.execute_input":"2023-05-20T02:42:49.212074Z","iopub.status.idle":"2023-05-20T02:42:49.226525Z","shell.execute_reply.started":"2023-05-20T02:42:49.212043Z","shell.execute_reply":"2023-05-20T02:42:49.225696Z"},"trusted":true},"execution_count":4,"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":{"execution":{"iopub.status.busy":"2023-05-20T02:42:49.227893Z","iopub.execute_input":"2023-05-20T02:42:49.228712Z","iopub.status.idle":"2023-05-20T02:42:49.239030Z","shell.execute_reply.started":"2023-05-20T02:42:49.228680Z","shell.execute_reply":"2023-05-20T02:42:49.238193Z"},"trusted":true},"execution_count":5,"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":{"execution":{"iopub.status.busy":"2023-05-20T02:42:49.316875Z","iopub.execute_input":"2023-05-20T02:42:49.317195Z","iopub.status.idle":"2023-05-20T02:42:49.334949Z","shell.execute_reply.started":"2023-05-20T02:42:49.317168Z","shell.execute_reply":"2023-05-20T02:42:49.334070Z"},"trusted":true},"execution_count":6,"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 $install_path/stable-diffusion-webui/\n #Use lastest version\n !git checkout 5ab7f213bec2f816f9c5644becb32eb72c8ffb89\n with open('launch.py', 'r') as f:\n content = f.read()\n with open('launch.py', 'w') as f:\n f.write('import ssl\\n')\n f.write('ssl._create_default_https_context = ssl._create_unverified_context\\n')\n f.write(content)\n print('stable-diffusion-webui已安装')\n if huggingface_use:\n webui_config_download(yun_files, huggiingface_repo_id)\n install_extensions(install_path, extensions)\n download_model()\n link_models()","metadata":{"execution":{"iopub.status.busy":"2023-05-20T02:42:49.340521Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from concurrent.futures import ThreadPoolExecutor\n# 安装插件，下载和同步模型\ndef install_extensions(install_path, extensions):\n print('安装插件，此处出现红条是正常的')\n os.chdir(os.path.join(install_path, 'stable-diffusion-webui'))\n os.makedirs('extensions', exist_ok=True)\n os.chdir('extensions')\n for ex in extensions:\n repo_name = ex.split('/')[-1]\n if not os.path.exists(repo_name):\n os.system('git clone ' + ex)\n \ndef download_link(link, target_folder):\n if link.startswith('https://huggingface.co/'):\n filename = re.search(r'[^/]+$', 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 !/opt/conda/envs/venv/bin/python3 launch.py {' '.join(args1)} --port 7861 --device-id=1\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 !/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 [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()","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":[]}]} |