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miaoshouai-assistant/.gitignore

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+# for vim
+.~
+.*.swp
+*~
+
+# for MacOS
+.DS_Store
+
+__pycache__/
+
+.idea/
+
+logs/
+flagged/
+
+configs/civitai_models.json
+configs/liandange_models.json
+configs/gpt_index.json
+configs/model_hash.json
+covers/*
+cache/*
+assets

miaoshouai-assistant/.gitmodules

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+[submodule "assets"]
+	path = assets
+	url = https://e.coding.net/miaoshouai/miaoshou-stable-diffusion-webui/assets.git

miaoshouai-assistant/LICENSE

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miaoshouai-assistant/README.md

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+# miaoshouai-assistant
+
+[English](README.md) / [中文](README_CN.md)
+
+MiaoshouAI Assistant for [Automatic1111 WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
+
+1.6 Add VRAM garbage collection for image generation so that VRAM is freed up after every run; if your run into out of memory, just go to Boot Assistant and click "VRAM Release" to free up the memory.
+1.5 Rewrote how assets are loaded to largely reduce the size and installation time for the extension. (extension reinstall is needed for this version and up). Added download covers for all models.
+1.4 Add new feature for using GPT to generate prompts. Fixed sub folder support for model management</br>
+1.3 Add support for LyCoris(just put them in the lora folder, <a herf="">LyCoris extension</a> is needed.); Cleanup work for git to reduce project size</br>
+1.2 Add support for model search. Allow model to load directly from model management. Now you can update model source directly under update tab.</br>
+1.1 Add support for Lora, embedding and hypernetwork models under model manangement. bug fixes.
+
+### Installation
+In Automatic1111 WebUI, go to `Extensions Tab`->`Install from URL`, copy the following address in "**URL for extension's git repository**".
+
+```sh
+https://github.com/miaoshouai/miaoshouai-assistant.git
+```
+
+Click `Install`, wait until it's done. Go to `Settings`-> `Reload UI`
+
+### Usage
+
+##### Boot Assistant
+
+<p align="center">
+   <img src="https://msdn.miaoshouai.com/msai/kt/ez/boot_assistant_en.png"/>
+</p>
+
+- Allows you to change your webui boot settings including:
+GPU optimization, UI Theme, enable port listening, xformers, auto launch, etc.
+
+- Allows you to save all your settings to your webui-user.bat/other boot script for your webui.
+
+##### Model Management
+
+<p align="center">
+   <img src="https://msdn.miaoshouai.com/msai/kt/ez/model_manager_en.png"/>
+</p>
+
+- Allows you to view all your models and view the model civitai prompts and parameters from the covers.
+You can easily send these civitai prompts to txt2img/img2img/inpainting/extra
+
+##### Model Downloader
+
+<p align="center">
+   <img src="https://msdn.miaoshouai.com/msai/kt/ez/model_downloader.gif"/>
+</p>
+
+- Allows you to search and download models from civitai.com or liandange.com, find the model type you want
+
+<p align="center">
+   <img src="https://msdn.miaoshouai.com/msai/kt/ez/controlnet_download.gif"/>
+</p>
+
+- It also allows you to find 1.5, 2.1 official models, controlnet models or different vae models.
+

miaoshouai-assistant/README_CN.md

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+# 喵手助理
+[English](README.md) / [中文](README_CN.md)
+
+喵手助理 [Automatic1111 WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
+
+1.6 增加了显存自动清理功能。在启动助手中启用后可以在每次生图后自动清理显存。
+1.5 重构了assets的读取方式，大幅减少了下载时间和插件大小（使用1.5+版本需要重新安装）; 增加了一键下载封面功能。
+1.4 添加了使用GPT来生成咒语的功能; 修复了模型管理中对子文件夹的支持。
+1.3 增加了模型下载和管理功能中对LyCoris的支持(你需要将LoCoris模型放置在Lora目录下, 需要安装<a herf="https://github.com/KohakuBlueleaf/a1111-sd-webui-lycoris"> LyCoris插件 </a>)</br>
+1.2 增加了模型搜索功能，可以直接在模型管理下load模型，增加了插件和模型源更新功能。</br>
+1.1 模型管理下增加了对Lora, embedding and hypernetwork等模型的支持。修复了启动时与秋叶启动器的冲突。
+
+### 安装
+在 Automatic1111 WebUI 中，前往 `扩展插件`-> `从URL安装`，在`扩展插件的git仓库网址`中复制以下地址。
+
+```sh
+https://github.com/miaoshouai/miaoshouai-assistant.git
+```
+
+点击`安装`，等待安装完成。然后前往`设置` -> `重新加载界面`
+
+### 使用方法
+##### 启动助手
+
+<p align="center">
+   <img src="https://msdn.miaoshouai.com/msai/kt/ez/boot_assistant_en.png"/>
+</p>
+
+- 允许您更改 WebUI 启动设置，包括：
+GPU 优化、UI 主题、启用端口监听、xformers、自动启动等。
+
+- 允许您将所有设置保存到 webui-user.bat/其他启动脚本。
+
+##### 模型管理
+
+<p align="center">
+   <img src="https://msdn.miaoshouai.com/msai/kt/ez/model_manager.png"/>
+</p>
+
+- 允许您查看所有模型，并从封面查看模型 civitai 提示和参数。
+您可以非常容易的将 civitai 提示词发送到 txt2img/img2img/inpainting/extra
+
+##### 模型下载器
+
+<p align="center">
+   <img src="https://msdn.miaoshouai.com/msai/kt/ez/model_downloader.gif"/>
+</p>
+
+- 允许您从 civitai.com 或 liandange.com 搜索和下载模型，找到您想要的模型类型
+
+<p align="center">
+   <img src="https://msdn.miaoshouai.com/msai/kt/ez/controlnet_download.gif"/>
+</p>
+
+- 喵手助理还能让您找到 1.5, 2.1 的官方模型、controlnet 模型以及不同的 vae 模型。

miaoshouai-assistant/configs/controlnet.json

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+[
+    {
+        "id": 1,
+        "name": "Controlnet 1.0",
+        "description": "<p>This repository hosts pruned .safetensors modules of ControlNet, by lllyasviel and T2I-Adapters, TencentARC Team\n\nThe modules are meant for this extension for AUTOMATIC1111/stable-diffusion-webui, but should work for different webuis too if they have it implemented. cheers!\uD83E\uDD42</p></li></ol>",
+        "type": "Controlnet",
+        "poi": false,
+        "nsfw": false,
+        "allowNoCredit": true,
+        "allowCommercialUse": "Rent",
+        "allowDerivatives": true,
+        "allowDifferentLicense": true,
+        "stats": {
+            "downloadCount": 865,
+            "favoriteCount": 190,
+            "commentCount": 7,
+            "ratingCount": 2,
+            "rating": 5
+        },
+        "creator": {
+            "username": "lllyasviel",
+            "image": "https://huggingface.co/webui/ControlNet-modules-safetensors"
+        },
+        "tags": [],
+        "modelVersions": [
+            {
+                "id": 1,
+                "modelId": 7019,
+                "name": "Controlnet 1.0",
+                "createdAt": "2023-02-06T13:52:34.605Z",
+                "updatedAt": "2023-02-06T13:52:34.605Z",
+                "trainedWords": [],
+                "baseModel": "SD 1.5",
+                "earlyAccessTimeFrame": 0,
+                "description": "<p></p>",
+                "files": [
+                    {
+                        "name": "control_canny-fp16.safetensors",
+                        "id": 1,
+                        "sizeKB": 161261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_canny-fp16.safetensors",
+                        "primary": false
+                    },
+                    {
+                        "name": "control_depth-fp16.safetensors",
+                        "id": 2,
+                        "sizeKB": 741261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_depth-fp16.safetensors",
+                        "primary": true
+                    },
+                    {
+                        "name": "control_hed-fp16.safetensors",
+                        "id": 3,
+                        "sizeKB": 741261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_hed-fp16.safetensors",
+                        "primary": false
+                    },
+                    {
+                        "name": "control_mlsd-fp16.safetensors",
+                        "id": 4,
+                        "sizeKB": 741261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_mlsd-fp16.safetensors",
+                        "primary": false
+                    },
+                    {
+                        "name": "control_normal-fp16.safetensors",
+                        "id": 5,
+                        "sizeKB": 741261.130859375,
+                        "type": "Model",
+                        "format": "Checkpoint",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_normal-fp16.safetensors",
+                        "primary": false
+                    },
+                    {
+                        "name": "control_openpose-fp16.safetensors",
+                        "id": 6,
+                        "sizeKB": 741261.130859375,
+                        "type": "Model",
+                        "format": "Checkpoint",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_openpose-fp16.safetensors",
+                        "primary": false
+                    },
+                    {
+                        "name": "control_scribble-fp16.safetensors",
+                        "id": 7,
+                        "sizeKB": 741261.130859375,
+                        "type": "Model",
+                        "format": "Checkpoint",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_scribble-fp16.safetensors",
+                        "primary": false
+                    },
+                    {
+                        "name": "control_seg-fp16.safetensors",
+                        "id": 8,
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+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
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+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
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+                        "hashes": {
+                            "AutoV2": "833E816807",
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+                        "pickleScanMessage": "No Pickle imports",
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+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
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+    {
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+        "name": "Controlnet 1.1",
+        "description": "<p>Stable Diffusion v2-1 Model Card\\nThis model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available here.\\n\\nThis stable-diffusion-2-1 model is fine-tuned from stable-diffusion-2 (768-v-ema.ckpt) with an additional 55k steps on the same dataset (with punsafe=0.1), and then fine-tuned for another 155k extra steps with punsafe=0.98.\\n\\nUse it with the stablediffusion repository: download the v2-1_768-ema-pruned.ckpt here.\\nUse it with \\uD83E\\uDDE8 diffusers\\nModel Details\\nDeveloped by: Robin Rombach, Patrick Esser\\n\\nModel type: Diffusion-based text-to-image generation model\\n\\nLanguage(s): English\\n\\nLicense: CreativeML Open RAIL++-M License\\n\\nModel Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H).\\n\\nResources for more information: GitHub Repository.</p></li></ol>\",</p></li></ol>",
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+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
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+                        "hashes": {
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+                        "hashes": {
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+                        },
+                        "downloadUrl": "https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge.pth",
+                        "primary": false
+                    },
+                    {
+                        "name": "control_v11p_sd15s2_lineart_anime.pth",
+                        "id": 30,
+                        "sizeKB": 1521261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15s2_lineart_anime.pth",
+                        "primary": false
+                    },
+                    {
+                        "name": "control_v11f1e_sd15_tile.pth",
+                        "id": 31,
+                        "sizeKB": 1521261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth",
+                        "primary": false
+                    },
+                    {
+                        "name": "control_v2p_sd15_mediapipe_face.pth",
+                        "id": 32,
+                        "sizeKB": 1521261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/control_v2p_sd15_mediapipe_face.pth",
+                        "primary": false
+                    },
+                    {
+                        "name": "control_v2p_sd21_mediapipe_face.pth",
+                        "id": 33,
+                        "sizeKB": 1521261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/CrucibleAI/ControlNetMediaPipeFace/resolve/main/control_v2p_sd21_mediapipe_face.pth",
+                        "primary": false
+                    }
+                ],
+                "images": [
+                    {
+                        "url": "https://msdn.miaoshouai.com/msai/kt/ez/9900922.png"
+                    }
+                ]
+            }
+        ]
+    }
+]

miaoshouai-assistant/configs/hugging_face.json

@@ -0,0 +1,198 @@
+[
+    {
+        "id": 1,
+        "name": "Negative Embeddings",
+        "description": "<p>This is a collection of negative embeddings for Stable Diffusion negative prompts</p>",
+        "type": "TextualInversion",
+        "poi": false,
+        "nsfw": false,
+        "allowNoCredit": true,
+        "allowCommercialUse": "Rent",
+        "allowDerivatives": true,
+        "allowDifferentLicense": true,
+        "stats": {
+            "downloadCount": 865,
+            "favoriteCount": 190,
+            "commentCount": 7,
+            "ratingCount": 2,
+            "rating": 5
+        },
+        "creator": {
+            "username": "multiple authors",
+            "image": ""
+        },
+        "tags": [],
+        "modelVersions": [
+            {
+                "id": 1,
+                "modelId": 7019,
+                "name": "Controlnet 1.0",
+                "createdAt": "2023-02-06T13:52:34.605Z",
+                "updatedAt": "2023-02-06T13:52:34.605Z",
+                "trainedWords": [],
+                "baseModel": "SD 1.5",
+                "earlyAccessTimeFrame": 0,
+                "description": "<p></p>",
+                "files": [
+                    {
+                        "name": "EasyNegative.safetensors",
+                        "id": 1,
+                        "sizeKB": 25261.130859375,
+                        "type": "Model",
+                        "format": "Safetensors",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/datasets/gsdf/EasyNegative/resolve/main/EasyNegative.safetensors",
+                        "primary": false
+                    },
+                    {
+                        "name": "EasyNegativeV2.safetensors",
+                        "id": 2,
+                        "sizeKB": 50261.130859375,
+                        "type": "Model",
+                        "format": "Safetensors",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/gsdf/Counterfeit-V3.0/resolve/main/embedding/EasyNegativeV2.safetensors",
+                        "primary": true
+                    },
+                    {
+                        "name": "bad-hands-5.pt",
+                        "id": 3,
+                        "sizeKB": 7261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/yesyeahvh/bad-hands-5/resolve/main/bad-hands-5.pt",
+                        "primary": false
+                    },
+                    {
+                        "name": "bad_prompt.pt",
+                        "id": 4,
+                        "sizeKB": 51261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/datasets/Nerfgun3/bad_prompt/blob/main/bad_prompt.pt",
+                        "primary": false
+                    },
+                    {
+                        "name": "bad_prompt_version2.pt",
+                        "id": 5,
+                        "sizeKB": 25261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/datasets/Nerfgun3/bad_prompt/resolve/main/bad_prompt_version2.pt",
+                        "primary": false
+                    },
+                    {
+                        "name": "badquality.pt",
+                        "id": 6,
+                        "sizeKB": 34261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/p1atdev/badquality/resolve/main/badquality.pt",
+                        "primary": false
+                    },
+                    {
+                        "name": "bad-artist-anime.pt",
+                        "id": 7,
+                        "sizeKB": 7261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/nick-x-hacker/bad-artist/resolve/main/bad-artist-anime.pt",
+                        "primary": false
+                    },
+                    {
+                        "name": "bad-artist.pt",
+                        "id": 8,
+                        "sizeKB": 7261.130859375,
+                        "type": "Model",
+                        "format": "Pth",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
+                            "SHA256": "833E816807E8D3658EBC65436908F690603A8E0E8D26143A90050F32958C9E4E",
+                            "CRC32": "87F68C12",
+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/nick-x-hacker/bad-artist/resolve/main/bad-artist.pt",
+                        "primary": false
+                    }
+                ],
+                "images": [
+                    {
+                        "url": "https://msdn.miaoshouai.com/msai/kt/ez/9900922.png"
+                    }
+                ]
+            }
+        ]
+    }
+]

miaoshouai-assistant/configs/model_hash.json

@@ -0,0 +1,671 @@
+{
+    "elldrethsVividMix_v20VividEr.safetensors [0f7f264114]": "0f7f264114",
+    "fkingScifiV2_r41958.safetensors [d6a44b9b90]": "d6a44b9b90",
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+    "galaxytimemachinesGTM_v1.safetensors [e406a20c1b]": "e406a20c1b",
+    "Grapefruit.safetensors [4fc8d3739f]": "4fc8d3739f",
+    "grapeGrapefruitHenta_grapefruitV11.safetensors [4a6e5cd1c8]": "4a6e5cd1c8",
+    "GrapeLikeDreamFruit.ckpt [c9278e23a7]": "c9278e23a7",
+    "gta5ArtworkDiffusion_v1.ckpt [607aa02fb8]": "607aa02fb8",
+    "hAS3Dkx10B_3Dkx10B.safetensors [1d45c7c094]": "1d45c7c094",
+    "HAs_3DKX 1.1.ckpt [998f6b580e]": "998f6b580e",
+    "hasdx_hasedsdx.safetensors [fafc72c6fd]": "fafc72c6fd",
+    "hassanblend1512And_hassanblend1512.safetensors [f05dd9e62f]": "f05dd9e62f",
+    "hassanBlendAllVersio_hassanBlend14.safetensors [b08fdba169]": "b08fdba169",
+    "Healys_Anime_Blend.ckpt [8416edf88c]": "8416edf88c",
+    "healySAnimeBlend_17.ckpt [8416edf88c]": "8416edf88c",
+    "homoerotic_v2.safetensors [b656369cf7]": "b656369cf7",
+    "Inkpunk-Diffusion-v2.ckpt [2182245415]": "2182245415",
+    "instruct-pix2pix-00-22000.safetensors [fbc31a67aa]": "fbc31a67aa",
+    "jaksCreepyCritter_sd21768px.ckpt [67d399388b]": "67d399388b",
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+    "ligneClaireAnime_v1.safetensors [672977e447]": "672977e447",
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+    "mixProV3_v3.safetensors [b6928134bb]": "b6928134bb",
+    "modernDisney_v1.ckpt [8067368533]": "8067368533",
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+    "pfg_111Safetensors.safetensors [ca42a7a009]": "ca42a7a009",
+    "princessZeldaLora_v1.safetensors [53d6abf3cb]": "53d6abf3cb",
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+    "analogDream3D_10.ckpt [4575363662]": "4575363662",
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miaoshouai-assistant/configs/official_models.json

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+[
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+        "name": "stable-diffusion-v1-5",
+        "description": "<p>Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at \uD83E\uDD17's Stable Diffusion blog.\n\nThe Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on \"laion-aesthetics v2 5+\" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.\n\nYou can use this both with the \uD83E\uDDE8Diffusers library and the RunwayML GitHub repository.</p></li></ol>",
+        "type": "Checkpoint",
+        "poi": false,
+        "nsfw": false,
+        "allowNoCredit": true,
+        "allowCommercialUse": "Rent",
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+        "allowDifferentLicense": true,
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+            "ratingCount": 2,
+            "rating": 5
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+        "creator": {
+            "username": "creativeml-openrail-m",
+            "image": "https://huggingface.co/runwayml/stable-diffusion-v1-5"
+        },
+        "tags": [
+            "horror",
+            "dzislaw beksinskidzi",
+            "kappa_neuro",
+            "paintings"
+        ],
+        "modelVersions": [
+            {
+                "id": 1,
+                "modelId": 7019,
+                "name": "Zdzislaw Beksinski LORA",
+                "createdAt": "2023-02-06T13:52:34.605Z",
+                "updatedAt": "2023-02-06T13:52:34.605Z",
+                "trainedWords": [],
+                "baseModel": "SD 1.5",
+                "earlyAccessTimeFrame": 0,
+                "description": "<p></p>",
+                "files": [
+                    {
+                        "name": "v1-5-pruned-emaonly.ckpt",
+                        "id": 1,
+                        "sizeKB": 4471261.130859375,
+                        "type": "Model",
+                        "format": "CKPT",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
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+                        "downloadUrl": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt",
+                        "primary": false
+                    },
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+                        "id": 2,
+                        "sizeKB": 4471261.130859375,
+                        "type": "Model",
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+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
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+                        "sizeKB": 7971261.130859375,
+                        "type": "Model",
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+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
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+                        "downloadUrl": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt",
+                        "primary": false
+                    },
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+                        "sizeKB": 7971261.130859375,
+                        "type": "Model",
+                        "format": "Safetensors",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
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+                        },
+                        "downloadUrl": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.safetensors",
+                        "primary": false
+                    },
+                    {
+                        "name": "sd-v1-5-inpainting.ckpt",
+                        "id": 5,
+                        "sizeKB": 4471261.130859375,
+                        "type": "Model",
+                        "format": "Checkpoint",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "833E816807",
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+                            "BLAKE3": "7A28D3D22520D61B58338791522A14B7B33A6A9CE552FF7D68D7E2688CAB3266"
+                        },
+                        "downloadUrl": "https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt",
+                        "primary": false
+                    }
+                ],
+                "images": [
+                    {
+                        "url": "https://msdn.miaoshouai.com/msai/kt/ez/9900922.png"
+                    }
+                ]
+            }
+        ]
+    },
+    {
+        "id": 2,
+        "name": "stable-diffusion-2-1",
+        "description": "<p>Stable Diffusion v2-1 Model Card\nThis model card focuses on the model associated with the Stable Diffusion v2-1 model, codebase available here.\n\nThis stable-diffusion-2-1 model is fine-tuned from stable-diffusion-2 (768-v-ema.ckpt) with an additional 55k steps on the same dataset (with punsafe=0.1), and then fine-tuned for another 155k extra steps with punsafe=0.98.\n\nUse it with the stablediffusion repository: download the v2-1_768-ema-pruned.ckpt here.\nUse it with \uD83E\uDDE8 diffusers\nModel Details\nDeveloped by: Robin Rombach, Patrick Esser\n\nModel type: Diffusion-based text-to-image generation model\n\nLanguage(s): English\n\nLicense: CreativeML Open RAIL++-M License\n\nModel Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H).\n\nResources for more information: GitHub Repository.</p></li></ol>",
+        "type": "Checkpoint",
+        "poi": false,
+        "nsfw": false,
+        "allowNoCredit": true,
+        "allowCommercialUse": "Rent",
+        "allowDerivatives": true,
+        "allowDifferentLicense": true,
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+    {
+        "id": 3,
+        "name": "VAE",
+        "description": "<p>Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at \uD83E\uDD17's Stable Diffusion blog.\n\nThe Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on \"laion-aesthetics v2 5+\" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling.\n\nYou can use this both with the \uD83E\uDDE8Diffusers library and the RunwayML GitHub repository.</p></li></ol>",
+        "type": "VAE",
+        "poi": false,
+        "nsfw": false,
+        "allowNoCredit": true,
+        "allowCommercialUse": "Rent",
+        "allowDerivatives": true,
+        "allowDifferentLicense": true,
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+            "username": "creativeml-openrail-m",
+            "image": "https://huggingface.co/runwayml/stable-diffusion-v1-5"
+        },
+        "tags": [],
+        "modelVersions": [
+            {
+                "id": 3,
+                "modelId": 3,
+                "name": "VAE",
+                "createdAt": "2023-02-06T13:52:34.605Z",
+                "updatedAt": "2023-02-06T13:52:34.605Z",
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+                    {
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+                    },
+                    {
+                        "name": "Anything-V3.0.vae.pt",
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+                        "primary": false
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+                    {
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+                        "sizeKB": 447126.130859375,
+                        "type": "Model",
+                        "format": "Checkpoint",
+                        "pickleScanResult": "Success",
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+                        "scannedAt": "2023-02-06T13:55:58.281Z",
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+                        "downloadUrl": "https://huggingface.co/hakurei/waifu-diffusion-v1-4/resolve/main/vae/kl-f8-anime2.ckpt",
+                        "primary": false
+                    },
+                    {
+                        "name": "sdxl_vae.safetensors",
+                        "id": 7,
+                        "sizeKB": 360126.130859375,
+                        "type": "Model",
+                        "format": "Safetensors",
+                        "pickleScanResult": "Success",
+                        "pickleScanMessage": "No Pickle imports",
+                        "virusScanResult": "Success",
+                        "scannedAt": "2023-02-06T13:55:58.281Z",
+                        "hashes": {
+                            "AutoV2": "551EAC7037",
+                            "SHA256": "551EAC7037CE58DE1EF4447F16D48664C1E67F0E27AF50A06B1A6D458B571E0C",
+                            "CRC32": "551EAC7037",
+                            "BLAKE3": "551EAC7037CE58DE1EF4447F16D48664C1E67F0E27AF50A06B1A6D458B571E0C"
+                        },
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+                    }
+                ],
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+                    {
+                        "url": "https://msdn.miaoshouai.com/msai/kt/ez/9900922.png"
+                    }
+                ]
+            }
+        ]
+    }
+]

miaoshouai-assistant/configs/settings.json

@@ -0,0 +1,20 @@
+{
+    "boot_settings": {
+        "drp_args_vram": "CPU Only",
+        "drp_args_theme": "Dark Mode",
+        "txt_args_listen_port": "7860",
+        "Enable xFormers": "--xformers",
+        "No Half": "--no-half",
+        "No Half VAE": "False",
+        "Enable API": "--api",
+        "Auto Launch": "--autolaunch",
+        "Allow Local Network Access": "False",
+        "drp_choose_version": "Official Release",
+        "txt_args_more": "",
+        "auto_vram": true,
+        "disable_log_console_output": true,
+        "model_source": "liandange.com",
+        "my_model_source": "civitai.com",
+        "openai_api": ""
+    }
+}

miaoshouai-assistant/configs/webui-macos-env.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+####################################################################
+#                          macOS defaults                          #
+# Please modify webui-user.sh to change these instead of this file #
+####################################################################
+
+if [[ -x "$(command -v python3.10)" ]]
+then
+    python_cmd="python3.10"
+fi
+
+export install_dir="$HOME"
+export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
+export TORCH_COMMAND="pip install torch==1.12.1 torchvision==0.13.1"
+export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
+export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71"
+export PYTORCH_ENABLE_MPS_FALLBACK=1
+
+####################################################################

miaoshouai-assistant/configs/webui-user-launch.bat

@@ -0,0 +1,10 @@
+echo off
+set GIT_PYTHON_GIT_EXECUTABLE=git\\bin\\git.exe
+
+echo ��������AI��Դվ 
+echo http://resource.miaoshouai.com ��ȡ������Դ
+set GIT_PYTHON_REFRESH=quiet
+call update.bat
+
+python\python.exe webui.py --autolaunch --api --xformers --medvram --deepdanbooru
+pause

miaoshouai-assistant/configs/webui-user.sh

@@ -0,0 +1,47 @@
+#!/bin/bash
+#########################################################
+# Uncomment and change the variables below to your need:#
+#########################################################
+
+# Install directory without trailing slash
+#install_dir="/home/$(whoami)"
+
+# Name of the subdirectory
+#clone_dir="stable-diffusion-webui"
+
+# Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention"
+# uncomment the line below based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon
+export COMMANDLINE_ARGS="--skip-torch-cuda-test --no-half --use-cpu all"
+
+# python3 executable
+#python_cmd="python3"
+
+# git executable
+#export GIT="git"
+
+# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
+#venv_dir="venv"
+
+# script to launch to start the app
+#export LAUNCH_SCRIPT="launch.py"
+
+# install command for torch
+#export TORCH_COMMAND="pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113"
+
+# Requirements file to use for stable-diffusion-webui
+#export REQS_FILE="requirements_versions.txt"
+
+# Fixed git repos
+#export K_DIFFUSION_PACKAGE=""
+#export GFPGAN_PACKAGE=""
+
+# Fixed git commits
+#export STABLE_DIFFUSION_COMMIT_HASH=""
+#export TAMING_TRANSFORMERS_COMMIT_HASH=""
+#export CODEFORMER_COMMIT_HASH=""
+#export BLIP_COMMIT_HASH=""
+
+# Uncomment to enable accelerated launch
+#export ACCELERATE="True"
+
+###########################################

miaoshouai-assistant/install.py

@@ -0,0 +1,31 @@
+import launch
+import os
+import gzip
+import io
+import git
+import shutil
+
+def install_preset_models_if_needed():
+    assets_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), "assets")
+    configs_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), "configs")
+
+    for model_filename in ["civitai_models.json", "liandange_models.json", "gpt_index.json"]:
+        gzip_file = os.path.join(assets_folder, f"{model_filename}.gz")
+        target_file = os.path.join(configs_folder, f"{model_filename}")
+        if not os.path.exists(target_file):
+            with gzip.open(gzip_file, "rb") as compressed_file:
+                with io.TextIOWrapper(compressed_file, encoding="utf-8") as decoder:
+                    content = decoder.read()
+                    with open(target_file, "w") as model_file:
+                        model_file.write(content)
+
+req_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), "requirements.txt")
+
+with open(req_file) as file:
+    for lib in file:
+        lib = lib.strip()
+        if not launch.is_installed(lib):
+            launch.run_pip(f"install {lib}", f"Miaoshou assistant requirement: {lib}")
+
+install_preset_models_if_needed()
+

miaoshouai-assistant/requirements.txt

@@ -0,0 +1,8 @@
+psutil
+rehash
+tqdm
+openai
+numba
+gpt_index==0.4.24
+langchain==0.0.132
+gradio_client==0.2.7

miaoshouai-assistant/scripts/assistant/__init__.py

@@ -0,0 +1 @@
+__all__ = ["miaoshou"]

miaoshouai-assistant/scripts/assistant/miaoshou.py

@@ -0,0 +1,462 @@
+import os
+import sys
+import typing as t
+
+import gradio as gr
+from modules.sd_models import CheckpointInfo
+from modules.call_queue import wrap_queued_call
+import launch
+import modules
+from modules import shared
+from scripts.msai_logging.msai_logger import Logger
+from scripts.runtime.msai_prelude import MiaoshouPrelude
+from scripts.runtime.msai_runtime import MiaoshouRuntime
+import modules.generation_parameters_copypaste as parameters_copypaste
+from modules.ui_components import ToolButton
+
+
+class MiaoShouAssistant(object):
+    # default css definition
+    default_css = '#my_model_cover{width: 100px;} #my_model_trigger_words{width: 200px;}'
+
+    def __init__(self) -> None:
+        self.logger = Logger()
+        self.prelude = MiaoshouPrelude()
+        self.runtime = MiaoshouRuntime()
+        self.refresh_symbol = '\U0001f504'
+        self.coffee_symbol = '\U0001f9cb' # 🧋
+        self.folder_symbol = '\U0001f4c2'  # 📂
+
+    def on_event_ui_tabs_opened(self) -> t.List[t.Optional[t.Tuple[t.Any, str, str]]]:
+        with gr.Blocks(analytics_enabled=False, css=MiaoShouAssistant.default_css) as miaoshou_assistant:
+            self.create_subtab_boot_assistant()
+            self.create_subtab_model_management()
+            self.create_subtab_model_download()
+            self.create_subtab_update()
+
+        return [(miaoshou_assistant.queue(), "Miaoshou Assistant", "miaoshou_assistant")]
+
+    def create_subtab_boot_assistant(self) -> None:
+        with gr.TabItem('Boot Assistant', elem_id="boot_assistant_tab") as boot_assistant:
+            with gr.Row():
+                with gr.Column(elem_id="col_model_list"):
+                    gpu, theme, port, chk_args, txt_args, webui_ver = self.runtime.get_default_args()
+                    gr.Markdown(value="Argument settings")
+                    with gr.Row():
+                        self.drp_gpu = gr.Dropdown(label="VRAM Size", elem_id="drp_args_vram",
+                                              choices=list(self.prelude.gpu_setting.keys()),
+                                              value=gpu, interactive=True)
+                        self.drp_theme = gr.Dropdown(label="UI Theme", choices=list(self.prelude.theme_setting.keys()),
+                                                value=theme,
+                                                elem_id="drp_args_theme", interactive=True)
+                        self.txt_listen_port = gr.Text(label='Listen Port', value=port, elem_id="txt_args_listen_port",
+                                                  interactive=True)
+
+                    with gr.Row():
+
+                        self.chk_group_args = gr.CheckboxGroup(choices=list(self.prelude.checkboxes.keys()), value=chk_args,
+                                                          show_label=False)
+                    self.additional_args = gr.Text(label='COMMANDLINE_ARGS (Divide by space)', value=txt_args,
+                                              elem_id="txt_args_more", interactive=True)
+
+                    with gr.Row():
+                        with gr.Column():
+                            txt_save_status = gr.Markdown(visible=False, interactive=False, show_label=False)
+                            drp_choose_version = gr.Dropdown(label="WebUI Version",
+                                                             choices=['Official Release', 'Python Integrated'],
+                                                             value=webui_ver, elem_id="drp_args_version",
+                                                             interactive=True)
+                            gr.HTML(
+                                '<div><p>*Save your settings to webui-user.bat file. Use Python Integrated only if your'
+                                ' WebUI is extracted from a zip file and does not need python installation</p></div>')
+                            save_settings = gr.Button(value="Save Boot Settings", elem_id="btn_arg_save_setting")
+
+                    with gr.Row():
+                        # with gr.Column():
+                        #    settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="ms_settings_submit")
+                        # with gr.Column():
+                        btn_apply = gr.Button(value='Apply Settings', variant='primary',
+                                                   elem_id="ms_settings_restart_gradio")
+                        restart_gradio = gr.Button(value='Restart WebUI', variant='primary',
+                                                   elem_id="ms_settings_restart_gradio")
+
+                        '''def mod_args(drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args):
+                          global commandline_args
+      
+                          get_final_args(drp_gpu, drp_theme, txt_listen_port, hk_group_args, additional_args)
+      
+                          print(commandline_args)
+                          print(sys.argv)
+                          #if '--xformers' not in sys.argv:
+                            #sys.argv.append('--xformers')
+      
+                        settings_submit.click(mod_args, inputs=[drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args], outputs=[])'''
+
+                    save_settings.click(self.runtime.change_boot_setting,
+                                        inputs=[drp_choose_version, self.drp_gpu, self.drp_theme, self.txt_listen_port, self.chk_group_args,
+                                                self.additional_args], outputs=[txt_save_status])
+                    btn_apply.click(
+                        self.save_cmdline_args,
+                        inputs=[self.drp_gpu, self.drp_theme, self.txt_listen_port, self.chk_group_args,
+                                self.additional_args],
+                        outputs=[txt_save_status],
+                    )
+
+                    def request_restart():
+                        shared.state.interrupt()
+                        shared.state.need_restart = True
+                        launch.prepare_environment()
+                        #launch.start()
+
+                    restart_gradio.click(
+                        request_restart,
+                        _js='restart_reload',
+                        inputs=[],
+                        outputs=[],
+                    )
+
+                with gr.Column():
+                    with gr.Row():
+                        machine_settings = self.prelude.get_sys_info()
+                        txt_sys_info = gr.TextArea(value=machine_settings, lines=20, max_lines=20,
+                                                   label="System Info",
+                                                   show_label=False, interactive=False)
+                    with gr.Row():
+                        sys_info_refbtn = gr.Button(value="Refresh")
+
+                    with gr.Row():
+                        md_vram_release = gr.Markdown(visible=False, interactive=False, value='Memory Released', show_label=False)
+                    with gr.Row():
+                        chk_auto_release = gr.Checkbox(value=self.prelude.boot_settings['auto_vram'], label='Enable Auto Memory Release')
+                        reload_button = gr.Button('Forc VRAM Release')
+
+
+        self.drp_gpu.change(self.runtime.update_xformers, inputs=[self.drp_gpu, self.chk_group_args], outputs=[self.chk_group_args])
+        sys_info_refbtn.click(self.prelude.get_sys_info, None, txt_sys_info)
+        chk_auto_release.change(self.runtime.change_auto_vram, inputs=[chk_auto_release])
+        reload_button.click(self.runtime.force_mem_release, outputs=[md_vram_release])
+
+    def create_subtab_model_management(self) -> None:
+        with gr.TabItem('Model Management', elem_id="model_management_tab") as tab_model_manager:
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        gr.Markdown(value="If you want your model directory to be other than [your_webui_dir]\\models, select a new directory down below. "
+                                          "Your default models directory needs to be removed first before you apply. Make sure do your backups!")
+                        md_result = gr.Markdown(visible=False, value="")
+                    with gr.Row():
+                        model_folder_path = gr.Textbox("", label="Model path", placeholder="Copy & paste your model destination folder here", interactive=True)
+                        #open_folder_button = ToolButton(value=self.folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else "open_folder_metadata_editor")
+                        refresh_models_button = ToolButton(value=self.refresh_symbol, elem_id="hidden_element")
+                    with gr.Row():
+                        btn_connect_modeldir = gr.Button(value="Apply Virtual Model Folder")
+
+                    with gr.Row().style(equal_height=True):
+                        my_search_text = gr.Textbox(
+                            label="Model name",
+                            show_label=False,
+                            max_lines=1,
+                            placeholder="Enter model name",
+                        )
+                        btn_my_search = gr.Button("Search")
+
+                    with gr.Row():
+                        my_model_source_dropdown = gr.Dropdown(
+                            choices=["civitai.com", "liandange.com"],
+                            value=self.runtime.my_model_source,
+                            label="Select Model Source",
+                            type="value",
+                            show_label=True,
+                            elem_id="my_model_source").style(full_width=False)
+
+                        mtypes = list(self.prelude.model_type.keys())
+                        my_model_type = gr.Radio(mtypes,
+                                              show_label=False, value='Checkpoint', elem_id="my_model_type",
+                                              interactive=True).style(full_width=True)
+
+                    with gr.Row():
+                        my_models = self.runtime.get_local_models('', my_model_type.value)
+                        self.runtime.ds_my_models = gr.Dataset(
+                            components=[gr.Image(visible=False, label='Cover', elem_id='my_model_cover'),
+                                        gr.Textbox(visible=False, label='ModelId'),
+                                        gr.Textbox(visible=False, label='Name/Version'),
+                                        gr.Textbox(visible=False, label='File Name')],
+                            elem_id='my_model_lib',
+                            label="My Models",
+                            headers=None,
+                            samples=my_models,
+                            samples_per_page=50)
+                with gr.Column():
+                    self.runtime.ds_my_model_covers = gr.Dataset(components=[gr.HTML(visible=False)],
+                                                    elem_id='my_model_covers',
+                                                    label="Model Cover",
+                                                    headers=None,
+                                                    samples=[],
+                                                    samples_per_page=10)
+                    with gr.Row(variant='panel'):
+                        c_image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True,
+                                         type="pil", visible=True)
+
+                    with gr.Row(variant='panel'):
+                        with gr.Column():
+
+                            btn_load_model = gr.HTML(
+                                value=f'<div class="lg secondary gradio-button svelte-1ipelgc" style="text-align: center;"' \
+                                        f'onclick="return selectCheckpoint()">Load Model</div>',
+                                visible=True)
+                        with gr.Column():
+                            btn_delete_model = gr.Button(visible=True, value='Delete Model')
+                        with gr.Column():
+                            with gr.Row():
+                                btn_set_all_covers = gr.Button(visible=True, value='Download Cover for Listed Models')
+                            with gr.Row():
+                                btn_set_cover = gr.Button(visible=False, value='Set as Cover')
+
+                    with gr.Row(variant='panel'):
+                        generation_info = gr.Textbox(label='prompt', interactive=False, visible=True, elem_id="imginfo_generation_info")
+                    with gr.Row(variant='panel'):
+                        display_text = 'Select a model and type some text here, ChatGPT will generate prompt for you. Supports different text in different languages.'
+                        display_value = ''
+
+                        if self.prelude.boot_settings['openai_api'] == '':
+                            print('a')
+                            display_text = 'Set your OpenAI api key in Setting & Update first: https://platform.openai.com/account/api-keys'
+                            display_value = display_text
+
+                        self.txt_main_prompt = gr.Textbox(label='Let ChatGPT write your prompt', placeholder=display_text, value=display_value, interactive=True, visible=True, elem_id="txt_main_prompt")
+                    with gr.Row(variant='panel'):
+                        with gr.Row():
+                            btn_generate_prompt = gr.Button(value="Use GPT to Generate Prompt")
+                        with gr.Row():
+                            buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+
+                        for tabname, button in buttons.items():
+                            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                                paste_button=button, tabname=tabname, source_text_component=generation_info,
+                                source_image_component=c_image,
+                            ))
+
+                    with gr.Row(variant='panel'):
+                        html_my_model = gr.HTML(visible=False)
+
+        btn_delete_model.click(self.runtime.delete_model, inputs=[self.runtime.ds_my_models, my_search_text, my_model_type], outputs=[self.runtime.ds_my_models])
+        btn_set_all_covers.click(self.runtime.set_all_covers, inputs=[my_search_text, my_model_type], outputs=[self.runtime.ds_my_models])
+        btn_set_cover.click(self.runtime.set_cover, inputs=[self.runtime.ds_my_models, c_image, my_search_text, my_model_type], outputs=[self.runtime.ds_my_models])
+        #open_folder_button.click(self.runtime.open_folder, inputs=[model_folder_path], outputs=[model_folder_path])
+        btn_connect_modeldir.click(self.runtime.change_model_folder, inputs=[model_folder_path], outputs=[md_result])
+        refresh_models_button.click(self.runtime.refresh_local_models, inputs=[my_search_text, my_model_type], outputs=[self.runtime.ds_my_models])
+        my_model_source_dropdown.change(self.switch_my_model_source,
+                                     inputs=[my_model_source_dropdown, my_model_type],
+                                     outputs=[self.runtime.ds_my_models])
+
+        btn_my_search.click(self.runtime.search_my_model, inputs=[my_search_text, my_model_type], outputs=[self.runtime.ds_my_models])
+        my_model_type.change(self.runtime.update_my_model_type, inputs=[my_search_text, my_model_type], outputs=[self.runtime.ds_my_models])
+        btn_generate_prompt.click(self.runtime.get_gpt_prompt, inputs=[self.runtime.ds_my_models, my_model_type, self.txt_main_prompt], outputs=[generation_info])
+
+        self.runtime.ds_my_models.click(self.runtime.get_my_model_covers,
+                                     inputs=[self.runtime.ds_my_models, my_model_type],
+                                     outputs=[self.runtime.ds_my_model_covers, html_my_model, btn_load_model])
+
+        self.runtime.ds_my_model_covers.click(self.runtime.update_cover_info,
+                                        inputs=[self.runtime.ds_my_models, self.runtime.ds_my_model_covers],
+                                        outputs=[btn_set_cover, generation_info, c_image])
+
+
+
+        def tab_model_manager_select():
+            self.runtime.active_model_set = 'my_model_set'
+
+        tab_model_manager.select(tab_model_manager_select, inputs=[], outputs=[])
+
+
+    def create_subtab_model_download(self) -> None:
+        with gr.TabItem('Model Download', elem_id="model_download_tab") as tab_downloads:
+            with gr.Row():
+                with gr.Column(elem_id="col_model_list"):
+                    with gr.Row().style(equal_height=True):
+                        model_source_dropdown = gr.Dropdown(choices=["civitai.com", "liandange.com", "official_models", 'hugging_face', "controlnet"],
+                                                            value=self.runtime.model_source,
+                                                            label="Select Model Source",
+                                                            type="value",
+                                                            show_label=True,
+                                                            elem_id="model_source").style(full_width=True)
+
+                        #btn_fetch = gr.Button("Fetch")
+
+                    with gr.Row().style(equal_height=True):
+                        search_text = gr.Textbox(
+                            label="Model name",
+                            show_label=False,
+                            max_lines=1,
+                            placeholder="Enter model name",
+                        )
+                        btn_search = gr.Button("Search")
+
+                    with gr.Row().style(equal_height=True):
+                        nsfw_checker = gr.Checkbox(label='NSFW', value=False, elem_id="chk_nsfw", interactive=True)
+                        model_type = gr.Radio(["All"] + list(self.prelude.model_type.keys()),
+                                              show_label=False, value='All', elem_id="rad_model_type",
+                                              interactive=True).style(full_width=True)
+
+                    images = self.runtime.get_images_html()
+                    self.runtime.ds_models = gr.Dataset(
+                        components=[gr.HTML(visible=False)],
+                        headers=None,
+                        type="values",
+                        label="Models",
+                        samples=images,
+                        samples_per_page=60,
+                        elem_id="model_dataset").style(type="gallery", container=True)
+
+                with gr.Column(elem_id="col_model_info"):
+                    with gr.Row():
+                        self.runtime.ds_cover_gallery = gr.Dataset(
+                            components=[gr.HTML(visible=False)],
+                            headers=None,
+                            type="values",
+                            label="Cover",
+                            samples=[],
+                            samples_per_page=10,
+                            elem_id="ds_cover_gallery").style(type="gallery", container=True)
+
+                    with gr.Row():
+                        with gr.Column():
+                            download_summary = gr.HTML('<div><span>No downloading tasks ongoing</span></div>')
+                            downloading_status = gr.Button(value=f"{self.refresh_symbol} Refresh Downloading Status",
+                                                           elem_id="ms_dwn_status")
+                    with gr.Row():
+                        model_dropdown = gr.Dropdown(choices=['Select Model'], label="Models", show_label=False,
+                                                     value='Select Model', elem_id='ms_dwn_button',
+                                                     interactive=True)
+
+                        is_civitai_model_source_active = self.runtime.model_source == "civitai.com"
+                        with gr.Row(variant="panel"):
+                            dwn_button = gr.Button(value='Download',
+                                                   visible=is_civitai_model_source_active, elem_id='ms_dwn_button')
+                            open_url_in_browser_newtab_button = gr.HTML(
+                                value='<div class="lg secondary gradio-button svelte-1ipelgc" style="text-align: center;">'
+                                      '<a style="text-align: center;" href="http://www.liandange.com/models" '
+                                      'target="_blank">Download</a></div>',
+                                visible=not is_civitai_model_source_active)
+                    with gr.Row():
+                        model_info = gr.HTML(visible=True)
+
+        nsfw_checker.change(self.runtime.set_nsfw, inputs=[search_text, nsfw_checker, model_type],
+                            outputs=self.runtime.ds_models)
+
+        model_type.change(self.runtime.search_model, inputs=[search_text, nsfw_checker, model_type], outputs=self.runtime.ds_models)
+
+        #btn_fetch.click(self.runtime.refresh_all_models, inputs=[], outputs=self.runtime.ds_models)
+
+        btn_search.click(self.runtime.search_model, inputs=[search_text, nsfw_checker, model_type], outputs=self.runtime.ds_models)
+
+        self.runtime.ds_models.click(self.runtime.get_model_info,
+                                     inputs=[self.runtime.ds_models],
+                                     outputs=[
+                                         self.runtime.ds_cover_gallery,
+                                         model_dropdown,
+                                         model_info,
+                                         open_url_in_browser_newtab_button
+                                     ])
+
+        dwn_button.click(self.runtime.download_model, inputs=[model_dropdown], outputs=[download_summary])
+        downloading_status.click(self.runtime.get_downloading_status, inputs=[], outputs=[download_summary])
+
+        model_source_dropdown.change(self.switch_model_source,
+                                     inputs=[model_source_dropdown],
+                                     outputs=[self.runtime.ds_models, dwn_button, open_url_in_browser_newtab_button])
+
+        def tab_downloads_select():
+            self.runtime.active_model_set = 'model_set'
+
+        tab_downloads.select(tab_downloads_select, inputs=[], outputs=[])
+
+    def create_subtab_update(self) -> None:
+        with gr.TabItem('Setting & Update', elem_id="about_update") as tab_update:
+            with gr.Row():
+                md_api_res = gr.Markdown(visible=False)
+            with gr.Row():
+                if self.prelude.boot_settings['openai_api'] == '':
+                    display_text = 'Enter you OpenAI API Key here, you can get it from https://platform.openai.com/account/api-keys'
+                else:
+                    display_text = self.prelude.boot_settings['openai_api']
+                txt_gptapi = gr.Textbox(label='OpenAI API Key', value=display_text)
+            with gr.Row():
+                btn_update_gptapi = gr.Button(value="Update API Key")
+            with gr.Row():
+                txt_update_result = gr.Markdown(visible=False)
+            with gr.Row():
+                btn_check_update = gr.Button(value="Check for Update")
+            with gr.Row():
+                chk_dont_update_ms = gr.Checkbox(visible=False, label="Do not update model source", value=False)
+                btn_update = gr.Button(visible=False, value="Update Miaoshouai Assistant")
+            with gr.Row():
+                gr.Markdown(value="About")
+            with gr.Row():
+                gr.HTML(
+                    f"""
+                    <div><p>
+                    This extension is created to improve some of the use experience for automatic1111 webui.</br>
+                    It is free of charge, use it as you wish, please DO NOT sell this extension.</br>
+                    Follow us on github, discord and give us suggestions, report bugs. support us with love or coffee~</br></br>
+                    Cheers~</p>
+                    <p style="text-align: left;">
+                        <a target="_blank" href="https://github.com/miaoshouai/miaoshouai-assistant"><img src="https://img.shields.io/github/followers/miaoshouai?style=social" style="display: inline;" alt="MiaoshouAI GitHub"/></a>
+                        <a href="https://discord.gg/S22Jgn3rtz"><img src="https://img.shields.io/discord/1086407792451129374?label=Discord" style="display: inline;" alt="Discord server"></a>
+                        <a target="_blank" href="https://jq.qq.com/?_wv=1027&k=p5ZhOHAh">【QQ群：256734228】</a>
+                    </p>
+
+                    """
+                )
+
+            btn_check_update.click(self.runtime.check_update, inputs=[], outputs=[txt_update_result, chk_dont_update_ms, btn_update])
+            btn_update_gptapi.click(self.runtime.update_gptapi, inputs=[txt_gptapi], outputs=[md_api_res, self.txt_main_prompt])
+            btn_update.click(self.runtime.update_program, inputs=[chk_dont_update_ms], outputs=[txt_update_result])
+
+    def save_cmdline_args(self, drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args):
+        #print(drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args)
+        self.runtime.get_final_args(drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args)
+        #print('request_restart: cmd_arg  = ', self.runtime.cmdline_args)
+        #print('request_restart: sys.argv = ', sys.argv)
+
+        # reset args
+        sys.argv = [sys.argv[0]]
+        os.environ['COMMANDLINE_ARGS'] = ""
+        #print('remove', sys.argv)
+
+        for arg in list(dict.fromkeys(self.runtime.cmdline_args)):
+            sys.argv.append(arg)
+
+        print('saved args', sys.argv)
+        #launch.start()
+        return gr.Markdown.update(value="Settings Saved", visible=True)
+
+    def switch_model_source(self, new_model_source: str):
+        self.runtime.model_source = new_model_source
+        show_download_button = self.runtime.model_source != "liandange.com"
+        images = self.runtime.get_images_html()
+        self.runtime.ds_models.samples = images
+
+        if self.runtime.model_source not in ['official_models', 'hugging_face', 'controlnet']:
+            self.runtime.update_boot_setting('model_source', self.runtime.model_source)
+
+        return (
+            gr.Dataset.update(samples=images),
+            gr.Button.update(visible=show_download_button),
+            gr.HTML.update(visible=not show_download_button)
+        )
+
+    def switch_my_model_source(self, new_model_source: str, model_type):
+        self.runtime.my_model_source = new_model_source
+        my_models = self.runtime.get_local_models('', model_type)
+        self.runtime.ds_my_models.samples = my_models
+
+        if self.runtime.my_model_source not in ['official_models', 'hugging_face', 'controlnet']:
+            self.runtime.update_boot_setting('my_model_source', self.runtime.my_model_source)
+
+        return gr.Dataset.update(samples=my_models)
+
+    def introception(self) -> None:
+        self.runtime.introception()
+
+

miaoshouai-assistant/scripts/download/__init__.py

@@ -0,0 +1 @@
+__all__ = ["msai_downloader_manager"]

miaoshouai-assistant/scripts/download/msai_downloader_manager.py

@@ -0,0 +1,251 @@
+import asyncio
+import os.path
+import queue
+import time
+import requests
+import typing as t
+from threading import Thread, Lock
+
+from scripts.download.msai_file_downloader import MiaoshouFileDownloader
+from scripts.msai_logging.msai_logger import Logger
+from scripts.msai_utils.msai_singleton import MiaoshouSingleton
+import scripts.msai_utils.msai_toolkit as toolkit
+from urllib.request import Request, urlopen
+
+
+class DownloadingEntry(object):
+    def __init__(self, target_url: str = None, local_file: str = None,
+                 local_directory: str = None, estimated_total_size: float = 0., expected_checksum: str = None):
+        self._target_url = target_url
+        self._local_file = local_file
+        self._local_directory = local_directory
+        self._expected_checksum = expected_checksum
+
+        self._estimated_total_size = estimated_total_size
+        self._total_size = 0
+        self._downloaded_size = 0
+
+        self._downloading = False
+        self._failure = False
+
+    @property
+    def target_url(self) -> str:
+        return self._target_url
+
+    @property
+    def local_file(self) -> str:
+        return self._local_file
+
+    @property
+    def local_directory(self) -> str:
+        return self._local_directory
+
+    @property
+    def expected_checksum(self) -> str:
+        return self._expected_checksum
+
+    @property
+    def total_size(self) -> int:
+        return self._total_size
+
+    @total_size.setter
+    def total_size(self, sz: int) -> None:
+        self._total_size = sz
+
+    @property
+    def downloaded_size(self) -> int:
+        return self._downloaded_size
+
+    @downloaded_size.setter
+    def downloaded_size(self, sz: int) -> None:
+        self._downloaded_size = sz
+
+    @property
+    def estimated_size(self) -> float:
+        return self._estimated_total_size
+
+    def is_downloading(self) -> bool:
+        return self._downloading
+
+    def start_download(self) -> None:
+        self._downloading = True
+
+    def update_final_status(self, result: bool) -> None:
+        self._failure = (result is False)
+        self._downloading = False
+
+    def is_failure(self) -> bool:
+        return self._failure
+
+
+class AsyncLoopThread(Thread):
+    def __init__(self):
+        super(AsyncLoopThread, self).__init__(daemon=True)
+        self.loop = asyncio.new_event_loop()
+        self.logger = Logger()
+        self.logger.info("looper thread is created")
+
+    def run(self):
+        asyncio.set_event_loop(self.loop)
+        self.logger.info("looper thread is running")
+        self.loop.run_forever()
+
+
+class MiaoshouDownloaderManager(metaclass=MiaoshouSingleton):
+    _downloading_entries: t.Dict[str, DownloadingEntry] = None
+
+    def __init__(self):
+        if self._downloading_entries is None:
+            self._downloading_entries = {}
+            self.message_queue = queue.Queue()
+
+            self.logger = Logger()
+            self.looper = AsyncLoopThread()
+            self.looper.start()
+            self.logger.info("download manager is ready")
+            self._mutex = Lock()
+
+    def consume_all_ready_messages(self) -> None:
+        """
+        capture all enqueued messages, this method should not be used if you are iterating over the message queue
+        :return:
+            None
+        :side-effect:
+            update downloading entries' status
+        """
+        while True:
+            # self.logger.info("fetching the enqueued message")
+            try:
+                (aurl, finished_size, total_size) = self.message_queue.get(block=False, timeout=0.2)
+                # self.logger.info(f"[+] message ([{finished_size}/{total_size}]   {aurl}")
+                try:
+                    self._mutex.acquire(blocking=True)
+                    self._downloading_entries[aurl].total_size = total_size
+                    self._downloading_entries[aurl].downloaded_size = finished_size
+                finally:
+                    self._mutex.release()
+            except queue.Empty:
+                break
+
+    def iterator(self) -> t.Tuple[float, float]:
+
+        while True:
+            self.logger.info("waiting for incoming message")
+
+            try:
+                (aurl, finished_size, total_size) = self.message_queue.get(block=True)
+                self.logger.info(f"[+] message ([{finished_size}/{total_size}]   {aurl}")
+                try:
+                    self._mutex.acquire(blocking=True)
+                    self._downloading_entries[aurl].total_size = total_size
+                    self._downloading_entries[aurl].downloaded_size = finished_size
+
+                    tasks_total_size = 0.
+                    tasks_finished_size = 0.
+
+                    for e in self._downloading_entries.values():
+                        tasks_total_size += e.total_size
+                        tasks_finished_size += e.downloaded_size
+
+                    yield tasks_finished_size, tasks_total_size
+                finally:
+                    self._mutex.release()
+            except queue.Empty:
+                if len(asyncio.all_tasks(self.looper.loop)) == 0:
+                    self.logger.info("all downloading tasks finished")
+                    break
+
+    async def _submit_task(self, download_entry: DownloadingEntry) -> None:
+        try:
+            self._mutex.acquire(blocking=True)
+            if download_entry.target_url in self._downloading_entries:
+                self.logger.warn(f"{download_entry.target_url} is already downloading")
+                return
+            else:
+                download_entry.start_download()
+                self._downloading_entries[download_entry.target_url] = download_entry
+        finally:
+            self._mutex.release()
+
+        file_downloader = MiaoshouFileDownloader(
+            target_url=download_entry.target_url,
+            local_file=download_entry.local_file,
+            local_directory=download_entry.local_directory,
+            channel=self.message_queue if download_entry.estimated_size else None,
+            estimated_total_length=download_entry.estimated_size,
+            expected_checksum=download_entry.expected_checksum,
+        )
+
+        result: bool = await self.looper.loop.run_in_executor(None, file_downloader.download_file)
+
+        try:
+            self._mutex.acquire(blocking=True)
+            self._downloading_entries[download_entry.target_url].update_final_status(result)
+        finally:
+            self._mutex.release()
+
+    def download(self, source_url: str, target_file: str, estimated_total_size: float,
+                 expected_checksum: str = None) -> None:
+        self.logger.info(f"start to download '{source_url}'")
+
+        target_dir = os.path.dirname(target_file)
+        target_filename = os.path.basename(target_file)
+        download_entry = DownloadingEntry(
+            target_url=source_url,
+            local_file=target_filename,
+            local_directory=target_dir,
+            estimated_total_size=estimated_total_size,
+            expected_checksum=expected_checksum
+        )
+
+        asyncio.run_coroutine_threadsafe(self._submit_task(download_entry), self.looper.loop)
+
+    def tasks_summary(self) -> t.Tuple[int, int, str]:
+        self.consume_all_ready_messages()
+
+        total_tasks_num = 0
+        ongoing_tasks_num = 0
+        failed_tasks_num = 0
+
+        try:
+            description = "<div>"
+            self._mutex.acquire(blocking=True)
+            for name, entry in self._downloading_entries.items():
+                if entry.estimated_size is None:
+                    continue
+
+                total_tasks_num += 1
+
+                if entry.total_size > 0.:
+                    description += f"<p>{entry.local_file} ({toolkit.get_readable_size(entry.total_size)}) : "
+                else:
+                    description += f"<p>{entry.local_file} ({toolkit.get_readable_size(entry.estimated_size)}) : "
+
+                if entry.is_downloading():
+                    ongoing_tasks_num += 1
+                    finished_percent = entry.downloaded_size/entry.estimated_size * 100
+                    description += f'<span style="color:blue;font-weight:bold">{round(finished_percent, 2)} %</span>'
+                elif entry.is_failure():
+                    failed_tasks_num += 1
+                    description += '<span style="color:red;font-weight:bold">failed!</span>'
+                else:
+                    description += '<span style="color:green;font-weight:bold">finished</span>'
+                description += "</p><br>"
+        finally:
+            self._mutex.release()
+            pass
+
+        description += "</div>"
+        overall = f"""
+                    <h4>
+                        <span style="color:blue;font-weight:bold">{ongoing_tasks_num}</span> ongoing, 
+                        <span style="color:green;font-weight:bold">{total_tasks_num - ongoing_tasks_num - failed_tasks_num}</span> finished, 
+                        <span style="color:red;font-weight:bold">{failed_tasks_num}</span> failed.
+                    </h4>
+                    <br>
+                    <br>
+                   """
+
+        return ongoing_tasks_num, total_tasks_num, overall + description
+
+

miaoshouai-assistant/scripts/download/msai_file_downloader.py

@@ -0,0 +1,222 @@
+import os
+import pickle
+import queue
+import time
+import typing as t
+from pathlib import Path
+from urllib.parse import urlparse
+
+import rehash
+import requests
+from requests.adapters import HTTPAdapter
+from tqdm import tqdm
+from urllib3.util import Retry
+
+import scripts.msai_utils.msai_toolkit as toolkit
+from scripts.msai_logging.msai_logger import Logger
+
+
+class MiaoshouFileDownloader(object):
+    CHUNK_SIZE = 1024 * 1024
+
+    def __init__(self, target_url: str = None,
+                 local_file: str = None, local_directory: str = None, estimated_total_length: float = 0.,
+                 expected_checksum: str = None,
+                 channel: queue.Queue = None,
+                 max_retries=5) -> None:
+        self.logger = Logger()
+
+        self.target_url: str = target_url
+        self.local_file: str = local_file
+        self.local_directory = local_directory
+        self.expected_checksum = expected_checksum
+        self.max_retries = max_retries
+
+        self.accept_ranges: bool = False
+        self.estimated_content_length = estimated_total_length
+        self.content_length: int = -1
+        self.finished_chunk_size: int = 0
+
+        self.channel = channel  # for communication
+
+        # Support 3 retries and backoff
+        retry_strategy = Retry(
+            total=3,
+            backoff_factor=1,
+            status_forcelist=[429, 500, 502, 503, 504],
+            method_whitelist=["HEAD", "GET", "OPTIONS"]
+        )
+        adapter = HTTPAdapter(max_retries=retry_strategy)
+        self.session = requests.Session()
+        self.session.mount("https://", adapter)
+        self.session.mount("http://", adapter)
+
+        # inform message receiver at once
+        if self.channel:
+            self.channel.put_nowait((
+                self.target_url,
+                self.finished_chunk_size,
+                self.estimated_content_length,
+            ))
+
+    # Head request to get file-length and check whether it supports ranges.
+    def get_file_info_from_server(self, target_url: str) -> t.Tuple[bool, float]:
+        try:
+            headers = {"Accept-Encoding": "identity"}  # Avoid dealing with gzip
+            response = requests.head(target_url, headers=headers, allow_redirects=True)
+            response.raise_for_status()
+            content_length = None
+            if "Content-Length" in response.headers:
+                content_length = int(response.headers['Content-Length'])
+            accept_ranges = (response.headers.get("Accept-Ranges") == "bytes")
+            return accept_ranges, float(content_length)
+        except Exception as ex:
+            self.logger.info(f"HEAD Request Error: {ex}")
+            return False, self.estimated_content_length
+
+    def download_file_full(self, target_url: str, local_filepath: str) -> t.Optional[str]:
+        try:
+            checksum = rehash.sha256()
+            headers = {"Accept-Encoding": "identity"}  # Avoid dealing with gzip
+
+            with tqdm(total=self.content_length, unit="byte", unit_scale=1, colour="GREEN",
+                      desc=os.path.basename(self.local_file)) as progressbar, \
+                    self.session.get(target_url, headers=headers, stream=True, timeout=5) as response, \
+                    open(local_filepath, 'wb') as file_out:
+                response.raise_for_status()
+
+                for chunk in response.iter_content(MiaoshouFileDownloader.CHUNK_SIZE):
+                    file_out.write(chunk)
+                    checksum.update(chunk)
+                    progressbar.update(len(chunk))
+                    self.update_progress(len(chunk))
+
+        except Exception as ex:
+            self.logger.info(f"Download error: {ex}")
+            return None
+
+        return checksum.hexdigest()
+
+    def download_file_resumable(self, target_url: str, local_filepath: str) -> t.Optional[str]:
+        # Always go off the checkpoint as the file was flushed before writing.
+        download_checkpoint = local_filepath + ".downloading"
+        try:
+            resume_point, checksum = pickle.load(open(download_checkpoint, "rb"))
+            assert os.path.exists(local_filepath)  # catch checkpoint without file
+            self.logger.info("File already exists, resuming download.")
+        except Exception as e:
+            self.logger.error(f"failed to load downloading checkpoint - {download_checkpoint} due to {e}")
+            resume_point = 0
+            checksum = rehash.sha256()
+            if os.path.exists(local_filepath):
+                os.remove(local_filepath)
+            Path(local_filepath).touch()
+
+        assert (resume_point < self.content_length)
+
+        self.finished_chunk_size = resume_point
+
+        # Support resuming
+        headers = {"Range": f"bytes={resume_point}-", "Accept-Encoding": "identity"}
+        try:
+            with tqdm(total=self.content_length, unit="byte", unit_scale=1, colour="GREEN",
+                      desc=os.path.basename(self.local_file)) as progressbar, \
+                    self.session.get(target_url, headers=headers, stream=True, timeout=5) as response, \
+                    open(local_filepath, 'r+b') as file_out:
+                response.raise_for_status()
+                self.update_progress(resume_point)
+                file_out.seek(resume_point)
+
+                for chunk in response.iter_content(MiaoshouFileDownloader.CHUNK_SIZE):
+                    file_out.write(chunk)
+                    file_out.flush()
+                    resume_point += len(chunk)
+                    checksum.update(chunk)
+                    pickle.dump((resume_point, checksum), open(download_checkpoint, "wb"))
+                    progressbar.update(len(chunk))
+                    self.update_progress(len(chunk))
+
+                # Only remove checkpoint at full size in case connection cut
+                if os.path.getsize(local_filepath) == self.content_length:
+                    os.remove(download_checkpoint)
+                else:
+                    return None
+
+        except Exception as ex:
+            self.logger.error(f"Download error: {ex}")
+            return None
+
+        return checksum.hexdigest()
+
+    def update_progress(self, finished_chunk_size: int) -> None:
+        self.finished_chunk_size += finished_chunk_size
+
+        if self.channel:
+            self.channel.put_nowait((
+                self.target_url,
+                self.finished_chunk_size,
+                self.content_length,
+            ))
+
+    # In order to avoid leaving extra garbage meta files behind this
+    # will overwrite any existing files found at local_file. If you don't want this
+    # behaviour you can handle this externally.
+    # local_file and local_directory could write to unexpected places if the source
+    # is untrusted, be careful!
+    def download_file(self) -> bool:
+        success = False
+        try:
+            # Need to rebuild local_file_final each time in case of different urls
+            if not self.local_file:
+                specific_local_file = os.path.basename(urlparse(self.target_url).path)
+            else:
+                specific_local_file = self.local_file
+
+            download_temp_dir = toolkit.get_user_temp_dir()
+            toolkit.assert_user_temp_dir()
+
+            if self.local_directory:
+                os.makedirs(self.local_directory, exist_ok=True)
+
+            specific_local_file = os.path.join(download_temp_dir, specific_local_file)
+
+            self.accept_ranges, self.content_length = self.get_file_info_from_server(self.target_url)
+            self.logger.info(f"Accept-Ranges: {self.accept_ranges}. content length: {self.content_length}")
+            if self.accept_ranges and self.content_length:
+                download_method = self.download_file_resumable
+                self.logger.info("Server supports resume")
+            else:
+                download_method = self.download_file_full
+                self.logger.info(f"Server doesn't support resume.")
+
+            for i in range(self.max_retries):
+                self.logger.info(f"Download Attempt {i + 1}")
+                checksum = download_method(self.target_url, specific_local_file)
+                if checksum:
+                    match = ""
+                    if self.expected_checksum:
+                        match = ", Checksum Match"
+
+                    if self.expected_checksum and self.expected_checksum != checksum:
+                        self.logger.info(f"Checksum doesn't match. Calculated {checksum} "
+                                         f"Expecting: {self.expected_checksum}")
+                    else:
+                        self.logger.info(f"Download successful{match}. Checksum {checksum}")
+                        success = True
+                        break
+                time.sleep(1)
+
+            if success:
+                self.logger.info(f"{self.target_url} [DOWNLOADED COMPLETELY]")
+                if self.local_directory:
+                    target_local_file = os.path.join(self.local_directory, self.local_file)
+                else:
+                    target_local_file = self.local_file
+                toolkit.move_file(specific_local_file, target_local_file)
+            else:
+                self.logger.info(f"{self.target_url} [  FAILED  ]")
+
+        except Exception as ex:
+            self.logger.info(f"Unexpected Error: {ex}")  # Only from block above
+
+        return success

miaoshouai-assistant/scripts/main.py

@@ -0,0 +1,27 @@
+import modules
+import modules.scripts as scripts
+
+from scripts.assistant.miaoshou import MiaoShouAssistant
+from scripts.runtime.msai_runtime import MiaoshouRuntime
+
+
+class MiaoshouScript(scripts.Script):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def title(self):
+        return "Miaoshou Assistant"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        return ()
+
+    def postprocess(self, p, processed):
+        self.runtime = MiaoshouRuntime()
+        self.runtime.mem_release()
+        return None
+
+assistant = MiaoShouAssistant()
+modules.script_callbacks.on_ui_tabs(assistant.on_event_ui_tabs_opened)

miaoshouai-assistant/scripts/msai_logging/__init__.py

@@ -0,0 +1 @@
+__all__ = ["msai_logger"]

miaoshouai-assistant/scripts/msai_logging/msai_logger.py

@@ -0,0 +1,100 @@
+import datetime
+import logging
+import logging.handlers
+import os
+import typing as t
+
+from scripts.msai_utils.msai_singleton import MiaoshouSingleton
+
+
+class Logger(metaclass=MiaoshouSingleton):
+    _dataset = None
+
+    KEY_TRACE_PATH = "trace_path"
+    KEY_INFO = "info"
+    KEY_ERROR = "error"
+    KEY_JOB = "job"
+
+    def _do_init(self, log_folder: str, disable_console_output: bool = False) -> None:
+        # Setup trace_path with empty string by default, it will be assigned with valid content if needed
+        self._dataset = {Logger.KEY_TRACE_PATH: ""}
+
+        print(f"logs_location: {log_folder}")
+        os.makedirs(log_folder, exist_ok=True)
+
+        # Setup basic logging configuration
+        logging.basicConfig(level=logging.INFO,
+                            filemode='w',
+                            format='%(asctime)s - %(filename)s [line:%(lineno)d] - %(levelname)s: %(message)s')
+
+        # Setup info logging
+        self._dataset[Logger.KEY_INFO] = logging.getLogger(Logger.KEY_INFO)
+        msg_handler = logging.FileHandler(os.path.join(log_folder, "info.log"),
+                                          "a",
+                                          encoding="UTF-8")
+        msg_handler.setLevel(logging.INFO)
+        msg_handler.setFormatter(
+            logging.Formatter(fmt='%(asctime)s - %(filename)s [line:%(lineno)d] - %(levelname)s: %(message)s'))
+        self._dataset[Logger.KEY_INFO].addHandler(msg_handler)
+
+        # Setup error logging
+        self._dataset[Logger.KEY_ERROR] = logging.getLogger(Logger.KEY_ERROR)
+        error_handler = logging.FileHandler(
+            os.path.join(log_folder, f'error_{datetime.date.today().strftime("%Y%m%d")}.log'),
+            mode="a",
+            encoding='UTF-8')
+        error_handler.setLevel(logging.ERROR)
+        error_handler.setFormatter(
+            logging.Formatter(
+                fmt=f"{self._dataset.get('trace_path')}:\n "
+                    f"%(asctime)s - %(filename)s [line:%(lineno)d] - %(levelname)s: %(message)s"))
+        self._dataset[Logger.KEY_ERROR].addHandler(error_handler)
+
+        # Setup job logging
+        self._dataset[Logger.KEY_JOB] = logging.getLogger(Logger.KEY_JOB)
+        job_handler = logging.FileHandler(os.path.join(log_folder, "jobs.log"),
+                                          mode="a",
+                                          encoding="UTF-8")
+        self._dataset[Logger.KEY_JOB].addHandler(job_handler)
+
+        if disable_console_output:
+            for k in [Logger.KEY_INFO, Logger.KEY_JOB, Logger.KEY_ERROR]:
+                l: logging.Logger = self._dataset[k]
+                l.propagate = not disable_console_output
+
+    def __init__(self, log_folder: str = None, disable_console_output: bool = False) -> None:
+        if self._dataset is None:
+            try:
+                self._do_init(log_folder=log_folder, disable_console_output=disable_console_output)
+            except Exception as e:
+                print(e)
+
+    def update_path_info(self, current_path: str) -> None:
+        self._dataset[Logger.KEY_TRACE_PATH] = current_path
+
+    def callback_func(self, exc_type: t.Any, exc_value: t.Any, exc_tracback: t.Any) -> None:
+        self._dataset[Logger.KEY_JOB].error(f"job failed for {self._dataset[Logger.KEY_TRACE_PATH]}")
+        self._dataset[Logger.KEY_INFO].error(f"{self._dataset[Logger.KEY_TRACE_PATH]}\n, callback_func: ",
+                                             exc_info=(exc_type, exc_value, exc_tracback))
+
+    def debug(self, fmt, *args, **kwargs) -> None:
+        l: logging.Logger = self._dataset[Logger.KEY_INFO]
+        l.debug(fmt, *args, **kwargs, stacklevel=2)
+
+    def info(self, fmt, *args, **kwargs) -> None:
+        l: logging.Logger = self._dataset[Logger.KEY_INFO]
+        l.info(fmt, *args, **kwargs, stacklevel=2)
+
+    def warn(self, fmt, *args, **kwargs) -> None:
+        l: logging.Logger = self._dataset[Logger.KEY_INFO]
+        l.warn(fmt, *args, **kwargs, stacklevel=2)
+
+    def error(self, fmt, *args, **kwargs) -> None:
+        l: logging.Logger = self._dataset[Logger.KEY_ERROR]
+        l.error(fmt, *args, **kwargs, stacklevel=2)
+
+    def job(self, fmt, *args, **kwargs) -> None:
+        l: logging.Logger = self._dataset[Logger.KEY_JOB]
+        l.info(fmt, *args, **kwargs, stacklevel=2)
+
+

miaoshouai-assistant/scripts/msai_utils/__init__.py

@@ -0,0 +1 @@
+__all__ = ["msai_singleton", "msai_toolkit"]

miaoshouai-assistant/scripts/msai_utils/msai_singleton.py

@@ -0,0 +1,8 @@
+class MiaoshouSingleton(type):
+    _instances = {}
+
+    def __call__(cls, *args, **kwargs):
+        if cls not in cls._instances:
+            cls._instances[cls] = super(MiaoshouSingleton, cls).__call__(*args, **kwargs)
+            cls._instances[cls].__init__(*args, **kwargs)
+        return cls._instances[cls]

miaoshouai-assistant/scripts/msai_utils/msai_toolkit.py

@@ -0,0 +1,88 @@
+import json
+import os
+import platform
+import shutil
+import typing as t
+from datetime import datetime
+from pathlib import Path
+
+
+def read_json(file) -> t.Any:
+    try:
+        with open(file, "r", encoding="utf-8-sig") as f:
+            return json.load(f)
+    except Exception as e:
+        print(e)
+        return None
+
+
+def write_json(file, content) -> None:
+    try:
+        with open(file, 'w') as f:
+            json.dump(content, f, indent=4)
+    except Exception as e:
+        print(e)
+
+
+def get_args(args) -> t.List[str]:
+    parameters = []
+    idx = 0
+    for arg in args:
+        if idx == 0 and '--' not in arg:
+            pass
+        elif '--' in arg:
+            parameters.append(rf'{arg}')
+            idx += 1
+        else:
+            parameters[idx - 1] = parameters[idx - 1] + ' ' + rf'{arg}'
+
+    return parameters
+
+
+def get_readable_size(size: int, precision=2) -> str:
+    if size is None:
+        return ""
+
+    suffixes = ['B', 'KB', 'MB', 'GB', 'TB']
+    suffixIndex = 0
+    while size >= 1024 and suffixIndex < len(suffixes):
+        suffixIndex += 1  # increment the index of the suffix
+        size = size / 1024.0  # apply the division
+    return "%.*f%s" % (precision, size, suffixes[suffixIndex])
+
+
+def get_file_last_modified_time(path_to_file: str) -> datetime:
+    if path_to_file is None:
+        return datetime.now()
+
+    if platform.system() == "Windows":
+        return datetime.fromtimestamp(os.path.getmtime(path_to_file))
+    else:
+        stat = os.stat(path_to_file)
+        return datetime.fromtimestamp(stat.st_mtime)
+
+
+def get_not_found_image_url() -> str:
+    return "https://msdn.miaoshouai.com/msdn/userimage/not-found.svg"
+
+
+def get_user_temp_dir() -> str:
+    return os.path.join(Path.home().absolute(), ".miaoshou_assistant_download")
+
+
+def assert_user_temp_dir() -> None:
+    os.makedirs(get_user_temp_dir(), exist_ok=True)
+
+
+def move_file(src: str, dst: str) -> None:
+    if not src or not dst:
+        return
+
+    if not os.path.exists(src):
+        return
+
+    if os.path.exists(dst):
+        os.remove(dst)
+
+    os.makedirs(os.path.dirname(dst), exist_ok=True)
+    shutil.move(src, dst)

miaoshouai-assistant/scripts/runtime/__init__.py

@@ -0,0 +1,5 @@
+__all__ = ["msai_prelude", "msai_runtime"]
+
+from . import msai_prelude as prelude
+
+prelude.MiaoshouPrelude().load()

miaoshouai-assistant/scripts/runtime/msai_prelude.py

@@ -0,0 +1,209 @@
+import os
+import platform
+import sys
+import typing as t
+
+import psutil
+import torch
+
+import launch
+from modules import shared
+from scripts.msai_logging.msai_logger import Logger
+from scripts.msai_utils import msai_toolkit as toolkit
+from scripts.msai_utils.msai_singleton import MiaoshouSingleton
+import modules
+
+
+class MiaoshouPrelude(metaclass=MiaoshouSingleton):
+    _dataset = None
+
+    def __init__(self) -> None:
+        # Potential race condition, not call in multithread environment
+        if MiaoshouPrelude._dataset is None:
+            self._init_constants()
+
+            MiaoshouPrelude._dataset = {
+                "log_folder": os.path.join(self.ext_folder, "logs")
+            }
+
+            disable_log_console_output: bool = False
+            if self.all_settings.get("boot_settings"):
+                if self.all_settings["boot_settings"].get("disable_log_console_output") is not None:
+                    disable_log_console_output = self.all_settings["boot_settings"].get("disable_log_console_output")
+
+            self._logger = Logger(self._dataset["log_folder"], disable_console_output=disable_log_console_output)
+
+    def _init_constants(self) -> None:
+        self._api_url = {
+            "civitai.com": "https://model-share.com/v1/models",
+            "liandange.com": "https://model-api.liandange.com/model/api/models",
+        }
+        self._ext_folder = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", ".."))
+        self._setting_file = os.path.join(self.ext_folder, "configs", "settings.json")
+        self._model_hash_file = os.path.join(self.ext_folder, "configs", "model_hash.json")
+        self._gpt_index = os.path.join(self.ext_folder, "configs", "gpt_index.json")
+        self._assets_folder = os.path.join(self.ext_folder, "assets")
+        self._cache_folder = os.path.join(self.ext_folder, "cache")
+        self._cover_folder = os.path.join(self.ext_folder, "covers")
+        self._no_preview_img = os.path.join(modules.paths.script_path, "html", "card-no-preview.png")
+        self._model_json = {
+            'civitai.com': os.path.join(self.ext_folder, 'configs', 'civitai_models.json'),
+            'liandange.com': os.path.join(self.ext_folder, 'configs', 'liandange_models.json'),
+            'official_models': os.path.join(self.ext_folder, 'configs', 'official_models.json'),
+            'hugging_face': os.path.join(self.ext_folder, 'configs', 'hugging_face.json'),
+            'controlnet': os.path.join(self.ext_folder, 'configs', 'controlnet.json')
+        }
+        self._checkboxes = {
+            'Enable xFormers': '--xformers',
+            'No Half': '--no-half',
+            'No Half VAE': '--no-half-vae',
+            'Enable API': '--api',
+            'Auto Launch': '--autolaunch',
+            'Allow Local Network Access': '--listen',
+        }
+
+        self._gpu_setting = {
+            'CPU Only': '--precision full --no-half --use-cpu SD GFPGAN BSRGAN ESRGAN SCUNet CodeFormer --all',
+            'GTX 16xx': '--lowvram --xformers --precision full --no-half',
+            'Low: 4-6G VRAM': '--xformers --lowvram',
+            'Med: 6-8G VRAM': '--xformers --medvram',
+            'Normal: 8+G VRAM': '',
+        }
+
+        self._theme_setting = {
+            'Auto': '',
+            'Light Mode': '--theme light',
+            'Dark Mode': '--theme dark',
+        }
+        self._ENV_EXCLUSION = ['COLAB_GPU', 'RUNPOD_POD_ID']
+        self._model_type = {'Checkpoint': f'{os.path.join(shared.models_path,"Stable-diffusion")}',
+                            'LORA': f'{os.path.join(shared.models_path,"Lora")}',
+                            'LoCon': f'{os.path.join(shared.models_path, "LyCORIS")}',
+                            "TextualInversion": f'{os.path.join(shared.script_path,"embeddings")}',
+                            "Hypernetwork": f'{os.path.join(shared.models_path,"hypernetworks")}'
+        }
+
+        self._gpt_type = ['gpt-3.5-turbo', 'text-davinci-003']
+
+    @property
+    def ext_folder(self) -> str:
+        return self._ext_folder
+
+    @property
+    def log_folder(self) -> str:
+        return self._dataset.get("log_folder")
+
+    @property
+    def all_settings(self) -> t.Any:
+        return toolkit.read_json(self._setting_file)
+
+    @property
+    def boot_settings(self) -> t.Any:
+        all_setting = self.all_settings
+        if all_setting:
+            return all_setting['boot_settings']
+        else:
+            return None
+
+    def api_url(self, model_source: str) -> t.Optional[str]:
+        return self._api_url.get(model_source)
+
+    @property
+    def setting_file(self) -> str:
+        return self._setting_file
+
+    @property
+    def ENV_EXCLUSION(self) -> list[str]:
+        return self._ENV_EXCLUSION
+
+    @property
+    def model_hash_file(self) -> str:
+        return self._model_hash_file
+
+    @property
+    def gpt_index(self) -> str:
+        return self._gpt_index
+
+    @property
+    def cache_folder(self) -> str:
+        return self._cache_folder
+
+    @property
+    def assets_folder(self) -> str:
+        return self._assets_folder
+
+    @property
+    def cover_folder(self) -> str:
+        return self._cover_folder
+
+    @property
+    def no_preview_img(self) -> str:
+        return self._no_preview_img
+
+    @property
+    def checkboxes(self) -> t.Dict[str, str]:
+        return self._checkboxes
+
+    @property
+    def gpu_setting(self) -> t.Dict[str, str]:
+        return self._gpu_setting
+
+    @property
+    def theme_setting(self) -> t.Dict[str, str]:
+        return self._theme_setting
+
+    @property
+    def model_type(self) -> t.Dict[str, str]:
+        return self._model_type
+
+    @property
+    def gpt_type(self) -> t.Dict[str, str]:
+        return self._gpt_type
+
+    @property
+    def model_json(self) -> t.Dict[str, t.Any]:
+        return self._model_json
+
+    def update_model_json(self, site: str, models: t.Dict[str, t.Any]) -> None:
+        if self._model_json.get(site) is None:
+            self._logger.error(f"cannot save model info for {site}")
+            return
+
+        self._logger.info(f"{self._model_json[site]} updated")
+        toolkit.write_json(self._model_json[site], models)
+
+    def load(self) -> None:
+        self._logger.info("start to do prelude")
+        self._logger.info(f"cmdline args: {' '.join(sys.argv[1:])}")
+
+    @classmethod
+    def get_sys_info(cls) -> str:
+        sys_info = 'System Information\n\n'
+
+        sys_info += r'OS Name: {0} {1}'.format(platform.system(), platform.release()) + '\n'
+        sys_info += r'OS Version: {0}'.format(platform.version()) + '\n'
+        sys_info += r'WebUI Version: {0}'.format(
+            f'https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{launch.commit_hash()}') + '\n'
+        sys_info += r'Torch Version: {0}'.format(getattr(torch, '__long_version__', torch.__version__)) + '\n'
+        sys_info += r'Python Version: {0}'.format(sys.version) + '\n\n'
+        sys_info += r'CPU: {0}'.format(platform.processor()) + '\n'
+        sys_info += r'CPU Cores: {0}/{1}'.format(psutil.cpu_count(logical=False), psutil.cpu_count(logical=True)) + '\n'
+
+        try:
+            sys_info += r'CPU Frequency: {0} GHz'.format(round(psutil.cpu_freq().max/1000,2)) + '\n'
+        except Exception as e:
+            sys_info += r'CPU Frequency: N/A GHz' + '\n'
+
+        sys_info += r'CPU Usage: {0}%'.format(psutil.cpu_percent()) + '\n\n'
+        sys_info += r'RAM: {0}'.format(toolkit.get_readable_size(psutil.virtual_memory().total)) + '\n'
+        sys_info += r'Memory Usage: {0}%'.format(psutil.virtual_memory().percent) + '\n\n'
+        for i in range(torch.cuda.device_count()):
+            sys_info += r'Graphics Card{0}: {1} ({2})'.format(i, torch.cuda.get_device_properties(i).name,
+                                                              toolkit.get_readable_size(
+                                                                  torch.cuda.get_device_properties(
+                                                                      i).total_memory)) + '\n'
+            sys_info += r'Available VRAM: {0}'.format(toolkit.get_readable_size(torch.cuda.mem_get_info(i)[0])) + '\n'
+
+        return sys_info
+
+

miaoshouai-assistant/scripts/runtime/msai_runtime.py

@@ -0,0 +1,1232 @@
+import datetime
+import fileinput
+import os
+import platform
+import re
+import shutil
+import sys
+import io
+import time
+import typing as t
+import gzip
+import git
+import gradio as gr
+import requests
+from bs4 import BeautifulSoup
+import subprocess
+import modules
+import random
+from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
+import openai
+import gc
+import json
+#import tkinter as tk
+#from tkinter import filedialog, ttk
+import modules.devices as devices
+import torch
+from numba import cuda
+from modules import shared, sd_hijack, sd_samplers, processing
+from modules.sd_models import CheckpointInfo
+from scripts.download.msai_downloader_manager import MiaoshouDownloaderManager
+from scripts.msai_logging.msai_logger import Logger
+from scripts.msai_utils import msai_toolkit as toolkit
+from scripts.runtime.msai_prelude import MiaoshouPrelude
+
+
+class MiaoshouRuntime(object):
+    def __init__(self):
+        self.cmdline_args: t.List[str] = None
+        self.logger = Logger()
+        self.prelude = MiaoshouPrelude()
+        self._old_additional: str = None
+        self._model_set: t.List[t.Dict] = None
+        self._my_model_set: t.List[t.Dict] = None
+        self._active_model_set: str = None
+        self._model_set_last_access_time: datetime.datetime = None
+        self._my_model_set_last_access_time: datetime.datetime = None
+        self._ds_models: gr.Dataset = None
+        self._ds_cover_gallery: gr.Dataset = None
+        self._ds_my_models: gr.Dataset = None
+        self._ds_my_model_covers: gr.Dataset = None
+        self._allow_nsfw: bool = False
+        self._model_source: str = "civitai.com"  # civitai is the default model source
+        self._my_model_source: str = "civitai.com"
+        self._git_address: str = "https://github.com/miaoshouai/miaoshouai-assistant.git"
+
+        # TODO: may be owned by downloader class
+        self.model_files = []
+
+        self.downloader_manager = MiaoshouDownloaderManager()
+
+
+    def get_default_args(self, commandline_args: t.List[str] = None):
+        if commandline_args is None:
+            commandline_args: t.List[str] = toolkit.get_args(sys.argv[1:])
+        commandline_args = list(map(lambda x: x.replace('theme=', 'theme '), commandline_args))
+        self.cmdline_args = list(dict.fromkeys(commandline_args))
+
+        self.logger.info(f"default commandline args: {commandline_args}")
+
+        checkbox_values = []
+        additional_args = ""
+        saved_setting = self.prelude.boot_settings
+
+        gpu = saved_setting.get('drp_args_vram')
+        theme = saved_setting.get('drp_args_theme')
+        port = saved_setting.get('txt_args_listen_port')
+
+        for arg in commandline_args:
+            if 'theme' in arg:
+                theme = [k for k, v in self.prelude.theme_setting.items() if v == arg][0]
+            if 'port' in arg:
+                port = arg.split(' ')[-1]
+
+        for chk in self.prelude.checkboxes:
+            for arg in commandline_args:
+                if self.prelude.checkboxes[chk] == arg and chk not in checkbox_values:
+                    checkbox_values.append(chk)
+
+        gpu_arg_list = [f'--{i.strip()}' for i in ' '.join(list(self.prelude.gpu_setting.values())).split('--')]
+        for arg in commandline_args:
+            if 'port' not in arg \
+                    and arg not in list(self.prelude.theme_setting.values()) \
+                    and arg not in list(self.prelude.checkboxes.values()) \
+                    and arg not in gpu_arg_list:
+                additional_args += (' ' + rf'{arg}')
+
+        self._old_additional = additional_args
+        webui_ver = saved_setting['drp_choose_version']
+
+        return gpu, theme, port, checkbox_values, additional_args.replace('\\', '\\\\').strip(), webui_ver
+
+    def add_arg(self, args: str = "") -> None:
+        for arg in args.split('--'):
+            if f"--{arg.strip()}" not in self.cmdline_args and arg.strip() != '':
+                self.logger.info(f'add arg: {arg.strip()}')
+                self.cmdline_args.append(f'--{arg.strip()}')
+
+        #remove duplicates
+        self.cmdline_args = list(dict.fromkeys(self.cmdline_args))
+        #print('added dup',self.cmdline_args)
+
+    def remove_arg(self, args: str = "") -> None:
+        arg_keywords = ['port', 'theme']
+
+        for arg in args.split('--'):
+            if arg in arg_keywords:
+                for cmdl in self.cmdline_args:
+                    if arg in cmdl:
+                        self.cmdline_args.remove(cmdl)
+                        break
+            elif f'--{arg.strip()}' in self.cmdline_args and arg.strip() != '':
+                self.cmdline_args.remove(f'--{arg.strip()}')
+
+        # remove duplicates
+        self.cmdline_args = list(dict.fromkeys(self.cmdline_args))
+        #print('removed dup',self.cmdline_args)
+
+    def get_final_args(self, gpu, theme, port, checkgroup, more_args) -> None:
+        # remove duplicates
+        self.cmdline_args = list(dict.fromkeys(self.cmdline_args))
+        # gpu settings
+        for s1 in self.prelude.gpu_setting:
+            if s1 in gpu:
+                for s2 in self.prelude.gpu_setting:
+                    if s2 != s1:
+                        self.remove_arg(self.prelude.gpu_setting[s2])
+                self.add_arg(self.prelude.gpu_setting[s1])
+
+        if port != '7860':
+            self.add_arg(f'--port {port}')
+        else:
+            self.remove_arg('--port')
+
+        # theme settings
+        self.remove_arg('--theme')
+        for t in self.prelude.theme_setting:
+            if t == theme:
+                self.add_arg(self.prelude.theme_setting[t])
+                break
+
+        # check box settings
+        for chked in checkgroup:
+            self.logger.info(f'checked:{self.prelude.checkboxes[chked]}')
+            self.add_arg(self.prelude.checkboxes[chked])
+
+        for unchk in list(set(list(self.prelude.checkboxes.keys())) - set(checkgroup)):
+            print(f'unchecked:{unchk}')
+            self.remove_arg(self.prelude.checkboxes[unchk])
+
+        # additional commandline settings
+        self.remove_arg(self._old_additional)
+        self.add_arg(more_args.replace('\\\\', '\\'))
+        self._old_additional = more_args.replace('\\\\', '\\')
+
+    def refresh_all_models(self) -> None:
+        self.install_preset_models_if_needed(True)
+        if self.ds_models:
+            self.ds_models.samples = self.model_set
+            self.ds_models.update(samples=self.model_set)
+        else:
+            self.logger.error(f"ds models is null")
+
+    def get_images_html(self, search: str = '', chk_nsfw: bool = False, model_type: str = 'All') -> t.List[str]:
+        self.logger.info(f"get_image_html: model_type = {model_type}, and search pattern = '{search}'")
+
+        model_cover_thumbnails = []
+        model_format = []
+
+        if self.model_set is None:
+            self.logger.error("model_set is null")
+            return []
+
+        self.logger.info(f"{len(self.model_set)} items inside '{self.model_source}'")
+
+        search = search.lower()
+        for model in self.model_set:
+            try:
+                if model.get('type') is not None \
+                        and model.get('type') not in model_format:
+                    model_format.append(model['type'])
+
+                if search == '' or \
+                        (model.get('name') is not None and search.lower() in model.get('name').lower()) \
+                        or (model.get('description') is not None and search.lower() in model.get('description').lower()):
+
+                    self._allow_nsfw = chk_nsfw
+                    if (model_type == 'All' or model_type in model.get('type')) \
+                            and (self.allow_nsfw or (not self.allow_nsfw and not model.get('nsfw'))):
+                        model_cover_thumbnails.append([
+                            [f"""
+                                <div style="display: flex; align-items: center;">
+                                    <div id="{str(model.get('id'))}" style="margin-right: 10px;" class="model-item">
+                                        <img referrerpolicy="no-referrer" src="{model['modelVersions'][0]['images'][0]['url'].replace('width=450', 'width=100')}" style="width:100px;">
+                                    </div>
+                                    <div style="flex:1; width:100px;">
+                                        <h3 style="text-align:left; word-wrap:break-word;">{model.get('name')}</h3>
+                                        <p  style="text-align:left;">Type: {model.get('type')}</p>
+                                        <p  style="text-align:left;">Rating: {model.get('stats')['rating']}</p>
+                                    </div>
+                                </div>
+                             """],
+                            model['id']])
+            except Exception:
+                continue
+
+        return model_cover_thumbnails
+
+    # TODO: add typing hint
+    def update_boot_settings(self, version, drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args, auto_vram):
+        boot_settings = self.prelude.boot_settings
+        boot_settings['drp_args_vram'] = drp_gpu
+        boot_settings["drp_args_theme"] = drp_theme
+        boot_settings['txt_args_listen_port'] = txt_listen_port
+        for chk in chk_group_args:
+            self.logger.debug(chk)
+            boot_settings[chk] = self.prelude.checkboxes[chk]
+        boot_settings['txt_args_more'] = additional_args
+        boot_settings['drp_choose_version'] = version
+        boot_settings['auto_vram'] = auto_vram
+
+        all_settings = self.prelude.all_settings
+        all_settings['boot_settings'] = boot_settings
+
+        toolkit.write_json(self.prelude.setting_file, all_settings)
+
+    def update_boot_setting(self, setting, value):
+        boot_settings = self.prelude.boot_settings
+        boot_settings[setting] = value
+
+        all_settings = self.prelude.all_settings
+        all_settings['boot_settings'] = boot_settings
+        toolkit.write_json(self.prelude.setting_file, all_settings)
+
+    def change_auto_vram(self, auto_vram):
+        self.update_boot_setting('auto_vram', auto_vram)
+
+    def mem_release(self):
+        try:
+            gc.collect()
+            devices.torch_gc()
+            torch.cuda.empty_cache()
+            gc.collect()
+
+            print('Miaoshouai boot assistant: Memory Released!')
+        except:
+            print('Miaoshouai boot assistant: Memory Release Failed...!')
+
+    def force_mem_release(self):
+        try:
+            if hasattr(sd_samplers, "create_sampler_original_md"):
+                sd_samplers.create_sampler = sd_samplers.create_sampler_original_md
+                del sd_samplers.create_sampler_original_md
+            if hasattr(processing, "create_random_tensors_original_md"):
+                processing.create_random_tensors = processing.create_random_tensors_original_md
+                del processing.create_random_tensors_original_md
+
+            cuda.select_device(0)
+            cuda.close()
+            cuda.select_device(0)
+            self.mem_release()
+            msg = 'Memory Released! (May not work if you already got CUDA out of memory error)'
+        except Exception as e:
+            msg = f'Memory Release Failed! ({str(e)})'
+
+        return gr.Markdown.update(visible=True, value=msg)
+
+        return gr.Markdown.update(visible=True, value=msg)
+    def get_all_models(self, site: str) -> t.Any:
+        return toolkit.read_json(self.prelude.model_json[site])
+
+    def update_model_json(self, site: str, models: t.Any) -> None:
+        toolkit.write_json(self.prelude.model_json[site], models)
+
+    def get_hash_from_json(self, chk_point: CheckpointInfo) -> CheckpointInfo:
+        model_hashes = toolkit.read_json(self.prelude.model_hash_file)
+
+        if len(model_hashes) == 0 or chk_point.title not in model_hashes.keys():
+            chk_point.shorthash = self.calculate_shorthash(chk_point)
+            model_hashes[chk_point.title] = chk_point.shorthash
+            toolkit.write_json(self.prelude.model_hash_file, model_hashes)
+        else:
+            chk_point.shorthash = model_hashes[chk_point.title]
+
+        return chk_point
+
+    def calculate_shorthash(self, chk_point: CheckpointInfo):
+        if chk_point.sha256 is None:
+            return
+        else:
+            return chk_point.sha256[0:10]
+
+
+    def update_my_model_type(self, search_txt, model_type) -> t.Dict:
+        my_models = self.get_local_models(search_txt, model_type)
+        self.ds_my_models.samples = my_models
+
+        return gr.Dataset.update(samples=my_models)
+
+    def get_local_models(self, search_txt='', model_type='Checkpoint') -> t.List[t.Any]:
+        models = []
+
+        for root, dirs, files in os.walk(self.prelude.model_type[model_type]):
+            for file in files:
+                mpath = os.path.join(root, file)
+
+                fname, ext = os.path.splitext(file)
+                if ext in ['.ckpt', '.safetensors', '.pt'] and file != 'scaler.pt' and (search_txt in fname or search_txt == ''):
+                    chkpt_info = modules.sd_models.get_closet_checkpoint_match(file)
+                    if chkpt_info is None:
+                        chkpt_info = CheckpointInfo(os.path.join(root, file))
+
+                    if chkpt_info.sha256 is None and chkpt_info.shorthash is None:
+                        chkpt_info = self.get_hash_from_json(chkpt_info)
+
+                    model_info = self.search_model_info(chkpt_info, mpath, model_type)
+                    fname = re.sub(r'\[.*?\]', "", chkpt_info.title)
+
+                    if model_info is not None:
+                        models.append(model_info)
+                    else:
+                        self.logger.info(
+                            f"{chkpt_info.title}, {chkpt_info.hash}, {chkpt_info.shorthash}, {chkpt_info.sha256}")
+                        models.append([
+                            self.prelude.no_preview_img,
+                            0,
+                            [os.path.basename(fname)],
+                            [mpath.replace(self.prelude.model_type[model_type]+'\\', '')]])
+
+        return models
+
+
+    def refresh_local_models(self, search_txt, model_type) -> t.Dict:
+        my_models = self.get_local_models(search_txt, model_type)
+        self.ds_my_models.samples = my_models
+
+        return gr.Dataset.update(samples=my_models)
+
+    def delete_model(self, model, search_txt, model_type):
+        fname = model[3][0]
+        mfolder = self.prelude.model_type[model_type]
+        mpapth = os.path.join(mfolder, fname)
+
+        os.remove(mpapth)
+        my_models = self.get_local_models(search_txt, model_type)
+        self.ds_my_models.samples = my_models
+
+        return gr.Dataset.update(samples=my_models)
+
+    def set_all_covers(self, search_txt, model_type):
+        for model in self.ds_my_models.samples:
+            try:
+                if model[0] == self.prelude.no_preview_img and model[1] != 0:
+                    img_list, l1, htmlDetail, h2 = self.get_model_info(model)
+                    soup = BeautifulSoup(img_list[0][0])
+                    cover_url = soup.findAll('img')[0]['src'].replace('width=150', 'width=450')
+
+                    fname = model[3][0]
+                    mname, ext = os.path.splitext(fname)
+                    mfolder = self.prelude.model_type[model_type]
+                    dst = os.path.join(mfolder, f'{mname}.jpg')
+
+                    if fname is not None and not os.path.exists(dst):
+                        if self.my_model_source == 'liandange.com':
+                            cover_url = soup.findAll('img')[0]['src'].replace('/w/150', '/w/450')
+                        r = requests.get(cover_url, timeout=30, stream=True)
+                        r.raw.decode_content = True
+                        with open(dst, 'wb') as f:
+                            shutil.copyfileobj(r.raw, f)
+            except Exception as e:
+                print(model[1], cover_url, dst, str(e))
+                continue
+
+        my_models = self.get_local_models(search_txt, model_type)
+        self.ds_my_models.samples = my_models
+
+        return gr.Dataset.update(samples=my_models)
+
+    def set_cover(self, model, cover, search_txt, model_type):
+        fname = model[3][0]
+        mname, ext = os.path.splitext(fname)
+        mfolder = self.prelude.model_type[model_type]
+
+        dst = os.path.join(mfolder, f'{mname}.jpg')
+        cover.save(dst)
+
+        my_models = self.get_local_models(search_txt, model_type)
+        self.ds_my_models.samples = my_models
+
+        return gr.Dataset.update(samples=my_models)
+
+
+
+    def search_model_info(self, chkpt_info: CheckpointInfo, mpath: str, model_type: str) -> t.Optional[t.List[t.Any]]:
+
+        lookup_sha256 = chkpt_info.sha256
+        lookup_shash = chkpt_info.shorthash
+        fname = re.sub(r'\[.*?\]', "", chkpt_info.title)
+        if '\\' in fname:
+            fname = fname.split('\\')[-1]
+
+        self.logger.info(f"lookup_sha256: {lookup_sha256}, lookup_shash: {lookup_shash}, fname: {fname}")
+
+        res = None
+        if lookup_sha256 is None and lookup_shash is None and fname is None:
+            return None
+
+        prefix, ext = os.path.splitext(mpath)
+
+        if os.path.exists(f'{prefix}.jpg'):
+            cover_img = os.path.join(os.path.dirname(mpath), f'{os.path.basename(prefix)}.jpg')
+        elif os.path.exists(f'{prefix}.png'):
+            cover_img = os.path.join(os.path.dirname(mpath), f'{os.path.basename(prefix)}.png')
+        elif os.path.exists(f'{prefix}.webp'):
+            cover_img = os.path.join(os.path.dirname(mpath), f'{os.path.basename(prefix)}.webp')
+        else:
+            cover_img = self.prelude.no_preview_img
+
+        if not os.path.exists(self.prelude.cover_folder):
+            os.mkdir(self.prelude.cover_folder)
+
+        dst = os.path.join(self.prelude.cover_folder, os.path.basename(cover_img))
+        try:
+            if cover_img != self.prelude.no_preview_img and os.path.exists(cover_img) and os.path.exists(dst):
+                dst_size = os.stat(dst).st_size
+                cover_size = os.stat(cover_img).st_size
+                if dst_size != cover_size:
+                    print('update to new cover')
+                    shutil.copyfile(cover_img, dst)
+            elif cover_img != self.prelude.no_preview_img and os.path.exists(cover_img) and not os.path.exists(dst):
+                shutil.copyfile(cover_img, dst)
+            elif cover_img == self.prelude.no_preview_img:
+                dst = cover_img
+        except Exception as e:
+            dst = self.prelude.no_preview_img
+
+        for model in self.my_model_set:
+            match = False
+
+            for ver in model['modelVersions']:
+                for file in ver['files']:
+                    if fname == file['name']:
+                        match = True
+                    elif lookup_sha256 is not None and 'SHA256' in file['hashes'].keys():
+                        match = (lookup_sha256.upper() == file['hashes']['SHA256'].upper())
+                    elif lookup_shash is not None:
+                        match = (lookup_shash[:10].upper() in [h.upper() for h in file['hashes'].values()])
+
+                    if match:
+                        mid = model['id']
+
+                        res = [
+                            dst,
+                            mid,
+                            [f"{model['name']}/{ver['name']}"],
+                            [mpath.replace(self.prelude.model_type[model_type]+'\\', '')]
+                        ]
+
+            if match:
+                break
+
+        return res
+
+    def update_xformers(self, gpu, checkgroup):
+        if '--xformers' in self.prelude.gpu_setting[gpu]:
+            if 'Enable xFormers' not in checkgroup:
+                checkgroup.append('Enable xFormers')
+
+        return checkgroup
+
+    def set_nsfw(self, search='', nsfw_checker=False, model_type='All') -> t.Dict:
+        self._allow_nsfw = nsfw_checker
+        new_list = self.get_images_html(search, model_type)
+        if self._ds_models is None:
+            self.logger.error(f"_ds_models is not initialized")
+            return {}
+
+        self._ds_models.samples = new_list
+        return self._ds_models.update(samples=new_list)
+
+    def search_model(self, search='', chk_nsfw=False, model_type='All') -> t.Dict:
+        if self._ds_models is None:
+            self.logger.error(f"_ds_models is not initialized")
+            return {}
+
+        new_list = self.get_images_html(search, chk_nsfw, model_type)
+
+        self._ds_models.samples = new_list
+        return self._ds_models.update(samples=new_list)
+
+    def search_my_model(self, search_txt='', model_type='Checkpoint') -> t.Dict:
+        if self._ds_models is None:
+            self.logger.error(f"_ds_models is not initialized")
+            return {}
+
+        new_list = self.get_local_models(search_txt, model_type)
+
+        self._ds_my_models.samples = new_list
+        return self._ds_my_models.update(samples=new_list)
+
+    def get_model_byid(self, mid, model_source) -> t.List:
+        response = requests.get(self.prelude.api_url(model_source) + f'/{mid}')
+        payload = response.json()
+        if payload.get("success") is not None and not payload.get("success"):
+            return []
+
+        return [payload]
+
+    def get_model_info(self, models) -> t.Tuple[t.List[t.List[str]], t.Dict, str, t.Dict]:
+        drop_list = []
+        cover_imgs = []
+        htmlDetail = "<div><p>No info found</p></div>"
+
+        mid = models[1]
+
+        # TODO: use map to enhance the performances
+        if self.active_model_set == 'model_set':
+            if self.model_source == "civitai.com" or self.model_source == "liandange.com":
+                m_list = self.get_model_byid(mid, self.model_source)
+            else:
+                m_list = [e for e in self.model_set if e['id'] == mid]
+        else:
+            if self.my_model_source == "civitai.com" or self.my_model_source == "liandange.com":
+                m_list = self.get_model_byid(mid, self.my_model_source)
+                self._allow_nsfw = True
+            else:
+                m_list = [e for e in self.my_model_set if e['id'] == mid]
+
+        if m_list is not None and len(m_list) > 0:
+            m = m_list[0]
+        else:
+            return [[]], {}, htmlDetail, {}
+
+        self.model_files.clear()
+
+        download_url_by_default = None
+        if m and m.get('modelVersions') and len(m.get('modelVersions')) > 0:
+            latest_version = m['modelVersions'][0]
+
+            if latest_version.get('images') and isinstance(latest_version.get('images'), list):
+                for img in latest_version['images']:
+                    if self.allow_nsfw or (not self.allow_nsfw and (not img.get('nsfw') or img.get('nsfw') in ['None', 'Soft'])):
+                        if img.get('url'):
+                            cover_imgs.append([f'<img referrerpolicy="no-referrer" src="{img["url"].replace("width=450","width=150").replace("/w/100", "/w/150")}" style="width:150px;">'])
+
+            if latest_version.get('files') and isinstance(latest_version.get('files'), list):
+                for file in latest_version['files']:
+                    # error checking for mandatory fields
+                    if file.get('id') is not None and file.get('downloadUrl') is not None:
+                        item_name = None
+                        if file.get('name'):
+                            item_name = file.get('name')
+                        if not item_name and latest_version.get('name'):
+                            item_name = latest_version['name']
+                        if not item_name:
+                            item_name = "unknown"
+
+                        self.model_files.append({
+                            "id:": file['id'],
+                            "url": file['downloadUrl'],
+                            "name": item_name,
+                            "type": m['type'] if m.get('type') else "unknown",
+                            "size": file['sizeKB'] * 1024 if file.get('sizeKB') else "unknown",
+                            "format": file['format'] if file.get('format') else "unknown",
+                            "cover": cover_imgs[0][0] if len(cover_imgs) > 0 else toolkit.get_not_found_image_url(),
+                        })
+                        file_size = toolkit.get_readable_size(file['sizeKB'] * 1024) if file.get('sizeKB') else ""
+                        if file_size:
+                            drop_list.append(f"{item_name} ({file_size})")
+                        else:
+                            drop_list.append(f"{item_name}")
+
+                        if not download_url_by_default:
+                            download_url_by_default = file.get('downloadUrl')
+
+            htmlDetail = '<div>'
+            if m.get('name'):
+                htmlDetail += f"<h1>{m['name']}</h1></br>"
+            if m.get('stats') and m.get('stats').get('downloadCount'):
+                htmlDetail += f"<p>Downloads: {m['stats']['downloadCount']}</p>"
+            if m.get('stats') and m.get('stats').get('rating'):
+                htmlDetail += f"<p>Rating: {m['stats']['rating']}</p>"
+            if m.get('creator') and m.get('creator').get('username'):
+                htmlDetail += f"<p>Author: {m['creator']['username']}</p></div></br></br>"
+            if latest_version.get('name'):
+                htmlDetail += f"<div><table><tbody><tr><td>Version:</td><td>{latest_version['name']}</td></tr>"
+            if latest_version.get('updatedAt'):
+                htmlDetail += f"<tr><td>Updated Time:</td><td>{latest_version['updatedAt']}</td></tr>"
+            if m.get('type'):
+                htmlDetail += f"<tr><td>Type:</td><td>{m['type']}</td></tr>"
+            if latest_version.get('baseModel'):
+                htmlDetail += f"<tr><td>Base Model:</td><td>{latest_version['baseModel']}</td></tr>"
+            htmlDetail += f"<tr><td>NFSW:</td><td>{m.get('nsfw') if m.get('nsfw') is not None else 'false'}</td></tr>"
+            if m.get('tags') and isinstance(m.get('tags'), list):
+                htmlDetail += f"<tr><td>Tags:</td><td>"
+                for t in m['tags']:
+                    htmlDetail += f'<span style="margin-right:5px>{t}</span>'
+                htmlDetail += "</td></tr>"
+            if latest_version.get('trainedWords'):
+                htmlDetail += f"<tr><td>Trigger Words:</td><td>"
+                for t in latest_version['trainedWords']:
+                    htmlDetail += f'<span style="margin-right:5px;">{t}</span>'
+                htmlDetail += "</td></tr>"
+            htmlDetail += "</tbody></table></div>"
+            htmlDetail += f"<div>{m['description'] if m.get('description') else 'N/A'}</div>"
+
+        self._ds_cover_gallery.samples = cover_imgs
+
+        return (
+            cover_imgs,
+            gr.Dropdown.update(choices=drop_list, value=drop_list[0] if len(drop_list) > 0 else []),
+            htmlDetail,
+            gr.HTML.update(value=f'<p style="text-align: center;">'
+                                 f'<a style="text-align: center;" href="{download_url_by_default}" '
+                                 'target="_blank">Download</a></p>')
+        )
+
+    def get_my_model_covers(self, model, model_type):
+        img_list, l1, htmlDetail, h2 = self.get_model_info(model)
+        if self._ds_my_model_covers is None:
+            self.logger.error(f"_ds_my_model_covers is not initialized")
+            return {}
+
+        new_html = '<div></div>'
+        if htmlDetail is not None:
+            new_html = htmlDetail.split('</tbody></table></div>')[0] + '</tbody></table></div>'
+
+        cover_list = []
+        for img_link in img_list:
+            cover_html = '<div style="display: flex; align-items: center;">\n'
+            cover_html += f'<div style = "margin-right: 10px;" class ="model-item" >\n'
+            if len(img_link) > 0:
+                cover_html += f'{img_link[0]}\n'
+
+            cover_html += '</div>\n</div>'
+            cover_list.append([cover_html])
+
+        if model_type == 'TextualInversion':
+            mname, ext = os.path.splitext(model[3][0])
+            button_html = '<div class ="lg secondary gradio-button svelte-1ipelgc" style="text-align: center;" ' \
+                            f'onclick="return cardClicked(&quot;txt2img&quot;, &quot;{mname}&quot;, true)"><a href="javascript:void(0)">Send to Prompt</a></div>'
+
+        elif model_type == 'LORA':
+            mname, ext = os.path.splitext(model[3][0])
+            button_html = '<div class ="lg secondary gradio-button svelte-1ipelgc" style="text-align: center;" ' \
+                          f'onclick="return cardClicked(&quot;txt2img&quot;, &quot;<lora:{mname}:&quot; + opts.extra_networks_default_multiplier + &quot;>&quot;, false)"><a href="javascript:void(0)">Send to Prompt</a></div>'
+        elif model_type.upper() == 'LoCon'.upper():
+            mname, ext = os.path.splitext(model[3][0])
+            button_html = '<div class ="lg secondary gradio-button svelte-1ipelgc" style="text-align: center;" ' \
+                          f'onclick="return cardClicked(&quot;txt2img&quot;, &quot;<lyco:{mname}:&quot; + opts.extra_networks_default_multiplier + &quot;>&quot;, false)"><a href="javascript:void(0)">Send to Prompt</a></div>'
+        else:
+            mpath = os.path.join(self.prelude.model_type[model_type], model[3][0])
+            checkpoint_info = CheckpointInfo(mpath)
+            button_html = f'<div class="lg secondary gradio-button svelte-1ipelgc" style="text-align: center;"' \
+                          f'onclick="return selectCheckpoint(&quot;{checkpoint_info.title}&quot;)"><a href="javascript:void(0)">Load Model</a></div>'
+
+        self._ds_my_model_covers.samples = cover_list
+        return self._ds_my_model_covers.update(samples=cover_list), gr.HTML.update(visible=True, value=new_html), gr.HTML.update(visible=True, value=button_html)
+
+
+    def update_cover_info(self, model, covers):
+
+        soup = BeautifulSoup(covers[0])
+        cover_url = soup.findAll('img')[0]['src'].replace('width=150', 'width=450')
+
+        if self.my_model_set is None:
+            self.logger.error("model_set is null")
+            return []
+
+        mid = model[1]
+        m_list = self.get_model_byid(mid, self.my_model_source)
+        if m_list is not None or m_list != []:
+            m = m_list[0]
+        else:
+            return {}, {}
+
+        generation_info = ''
+        fname = None
+        for mv in m['modelVersions']:
+            for img in mv['images']:
+                if img['url'] == cover_url:
+                    if img['meta'] is not None and img['meta'] != '':
+                        try:
+                            meta = img['meta']
+                            generation_info += f"{meta['prompt']}\n"
+                            if meta['negativePrompt'] is not None:
+                                generation_info += f"Negative prompt: {meta['negativePrompt']}\n"
+                            generation_info += f"Steps: {meta['steps']}, Sampler: {meta['sampler']}, "
+                            generation_info += f"CFG scale: {meta['cfgScale']}, Seed: {meta['seed']}, Size: {meta['Size']},"
+                            if meta['Model hash'] is not None:
+                                generation_info += f"Model hash: {meta['Model hash']}"
+
+                        except Exception as e:
+                            self.logger.info(f"generation_info error:{str(e)}")
+                            pass
+
+                    if not os.path.exists(self.prelude.cache_folder):
+                        os.mkdir(self.prelude.cache_folder)
+
+                    if self.my_model_source == 'civitai.com':
+                        fname = os.path.join(self.prelude.cache_folder, f"{cover_url.split('/')[-1]}.jpg")
+                    elif self.my_model_source == 'liandange.com':
+                        fname = os.path.join(self.prelude.cache_folder, cover_url.split('?')[0].split('/')[-1])
+
+                    break
+
+        if fname is not None and not os.path.exists(fname):
+            if self.my_model_source == 'liandange.com':
+                cover_url = soup.findAll('img')[0]['src'].replace('/w/150', '/w/450')
+            r = requests.get(cover_url, timeout=30, stream=True)
+            r.raw.decode_content = True
+            with open(fname, 'wb') as f:
+                shutil.copyfileobj(r.raw, f)
+
+        return gr.Button.update(visible=True), gr.Text.update(value=generation_info), gr.Image.update(value=fname)
+
+    def get_downloading_status(self):
+        (_, _, desc) = self.downloader_manager.tasks_summary()
+        return gr.HTML.update(value=desc)
+
+    def download_model(self, filename: str):
+        model_path = modules.paths.models_path
+        script_path = modules.paths.script_path
+
+        urls = []
+        for _, f in enumerate(self.model_files):
+            if not f.get('name'):
+                continue
+            model_fname = re.sub(r"\s*\(\d+(?:\.\d*)?.B\)\s*$", "", f['name'])
+
+            if model_fname in filename:
+                m_pre, m_ext = os.path.splitext(model_fname)
+                cover_fname = f"{m_pre}.jpg"
+                soup = BeautifulSoup(f['cover'])
+                cover_link = soup.findAll('img')[0]['src'].replace('/w/150', '/w/450').replace('width=150', 'width=450')
+
+                if f['type'] == 'LORA':
+                    cover_fname = os.path.join(model_path, 'Lora', cover_fname)
+                    model_fname = os.path.join(model_path, 'Lora', model_fname)
+                elif f['type'].upper() == 'LoCon'.upper():
+                    cover_fname = os.path.join(model_path, 'LyCORIS', cover_fname)
+                    model_fname = os.path.join(model_path, 'LyCORIS', model_fname)
+                elif f['type'] == 'VAE':
+                    cover_fname = os.path.join(model_path, 'VAE', cover_fname)
+                    model_fname = os.path.join(model_path, 'VAE', model_fname)
+                elif f['type'] == 'TextualInversion':
+                    cover_fname = os.path.join(script_path, 'embeddings', cover_fname)
+                    model_fname = os.path.join(script_path, 'embeddings', model_fname)
+                elif f['type'] == 'Hypernetwork':
+                    cover_fname = os.path.join(model_path, 'hypernetworks', cover_fname)
+                    model_fname = os.path.join(model_path, 'hypernetworks', model_fname)
+                elif f['type'] == 'Controlnet':
+                    cover_fname = os.path.join(shared.script_path, 'extensions', 'sd-webui-controlnet', 'models', cover_fname)
+                    model_fname = os.path.join(shared.script_path, 'extensions', 'sd-webui-controlnet', 'models', model_fname)
+                else:
+                    cover_fname = os.path.join(model_path, 'Stable-diffusion', cover_fname)
+                    model_fname = os.path.join(model_path, 'Stable-diffusion', model_fname)
+
+                urls.append((cover_link, f['url'], f['size'], cover_fname, model_fname))
+                break
+
+        for (cover_url, model_url, total_size, local_cover_name, local_model_name) in urls:
+            self.downloader_manager.download(
+                source_url=cover_url,
+                target_file=local_cover_name,
+                estimated_total_size=None,
+            )
+            self.downloader_manager.download(
+                source_url=model_url,
+                target_file=local_model_name,
+                estimated_total_size=total_size,
+            )
+
+        #
+        # currently, web-ui is without queue enabled.
+        #
+        # webui_queue_enabled = False
+        # if webui_queue_enabled:
+        #     start = time.time()
+        #     downloading_tasks_iter = self.downloader_manager.iterator()
+        #     for i in progressbar.tqdm(range(100), unit="byte", desc="Models Downloading"):
+        #         while True:
+        #             try:
+        #                 finished_bytes, total_bytes = next(downloading_tasks_iter)
+        #                 v = finished_bytes / total_bytes
+        #                 print(f"\n v = {v}")
+        #                 if isinstance(v, float) and int(v * 100) < i:
+        #                     print(f"\nv({v}) < {i}")
+        #                     continue
+        #                 else:
+        #                     break
+        #             except StopIteration:
+        #                 break
+        #
+        #         time.sleep(0.5)
+        #
+        #     self.logger.info(f"[downloading] finished after {time.time() - start} secs")
+
+        time.sleep(2)
+
+        #self.model_files.clear()
+        return gr.HTML.update(value=f"<h4>{len(urls)} downloading tasks added into task list</h4>")
+
+    def install_preset_models_if_needed(self, update_ds: bool):
+        assets_folder = os.path.join(self.prelude.ext_folder, "assets")
+        configs_folder = os.path.join(self.prelude.ext_folder, "configs")
+
+        for model_filename in ["civitai_models.json", "liandange_models.json", "gpt_index.json"]:
+            gzip_file = os.path.join(assets_folder, f"{model_filename}.gz")
+            target_file = os.path.join(configs_folder, f"{model_filename}")
+
+            if not os.path.exists(gzip_file):
+                self.relocate_assets_if_needed()
+                sub_repo = git.Repo(self.prelude.assets_folder)
+                sub_repo.git.fetch(all=True)
+                sub_repo.git.reset('origin', hard=True)
+
+            if update_ds or not os.path.exists(target_file):
+                with gzip.open(gzip_file, "rb") as compressed_file:
+                    with io.TextIOWrapper(compressed_file, encoding="utf-8") as decoder:
+                        content = decoder.read()
+                        with open(target_file, "w") as model_file:
+                            model_file.write(content)
+
+                print('Data source unpacked successfully')
+
+    def relocate_assets_if_needed(self):
+        repo = git.Repo(self.prelude.ext_folder)
+        print('Updating asset repo...')
+        try:
+            old_repo = True
+            if os.path.exists(self.prelude.assets_folder):
+                for filename in os.listdir(self.prelude.assets_folder):
+                    if '.git' in filename:
+                        old_repo = False
+                        break
+
+                if old_repo:
+                    shutil.rmtree(self.prelude.assets_folder)
+
+            for submodule in repo.submodules:
+                submodule.update(init=True)
+        except Exception as e:
+            print('error', str(e))
+
+    def get_dir_and_file(self, file_path):
+        dir_path, file_name = os.path.split(file_path)
+        return (dir_path, file_name)
+
+    def open_folder(self, folder_path=''):
+        if not any(var in os.environ for var in self.prelude.ENV_EXCLUSION) and sys.platform != 'darwin':
+            current_folder_path = folder_path
+
+            initial_dir, initial_file = self.get_dir_and_file(folder_path)
+
+            #root = tk.Tk()
+            #root.wm_attributes('-topmost', 1)
+            #root.withdraw()
+            #folder_path = filedialog.askdirectory(initialdir=initial_dir)
+            #root.destroy()
+
+            if folder_path == '':
+                folder_path = current_folder_path
+
+        return folder_path
+
+    def change_model_folder(self, folder_path=''):
+
+        res = 'Model folder is linked successfully'
+        if folder_path == '':
+            return gr.Markdown.update(value='No directory is set', visible=True)
+
+        try:
+            src = shared.models_path
+            # Destination file path
+            dst = folder_path
+
+            # Create a symbolic link
+            # pointing to src named dst
+            # using os.symlink() method
+            subprocess.check_call('mklink /J "%s" "%s"' % (src, dst), shell=True)
+        except Exception as e:
+            res = str(e)
+
+        return gr.Markdown.update(value=res, visible=True)
+
+    def change_boot_setting(self, version, drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args):
+        self.get_final_args(drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args)
+        self.logger.info(f'saved_cmd: {self.cmdline_args}')
+
+        if version == 'Official Release':
+            target_webui_user_file = "webui-user.bat"
+        else:
+            target_webui_user_file = "webui-user-launch.bat"
+        script_export_keyword = "export"
+        if platform.system() == "Linux":
+            target_webui_user_file = "webui-user.sh"
+        elif platform.system() == "Darwin":
+            target_webui_user_file = "webui-macos-env.sh"
+        else:
+            script_export_keyword = "set"
+
+        filepath = os.path.join(modules.shared.script_path, target_webui_user_file)
+        self.logger.info(f"to update: {filepath}")
+
+        msg = 'Result: Setting Saved.'
+        if version == 'Official Release':
+            try:
+                if not os.path.exists(filepath):
+                    shutil.copyfile(os.path.join(self.prelude.ext_folder, 'configs', target_webui_user_file), filepath)
+
+                with fileinput.FileInput(filepath, inplace=True, backup='.bak') as file:
+                    for line in file:
+                        if 'COMMANDLINE_ARGS' in line:
+                            rep_txt = ' '.join(self.cmdline_args).replace('\\', '\\\\')
+                            line = f'{script_export_keyword} COMMANDLINE_ARGS={rep_txt}\n'
+                        sys.stdout.write(line)
+
+            except Exception as e:
+                msg = f'Error: {str(e)}'
+        else:
+            try:
+                if not os.path.exists(filepath):
+                    shutil.copyfile(os.path.join(self.prelude.ext_folder, 'configs', target_webui_user_file), filepath)
+
+                new_data = ''
+                with open(filepath, 'r+') as file:
+                    data = file.readlines()
+                    for line in data:
+                        if 'webui.py' in line:
+                            rep_txt = ' '.join(self.cmdline_args).replace('\\', '\\\\')
+                            line = f"python\python.exe webui.py {rep_txt}\n"
+                        new_data += line
+                    file.seek(0)
+                    file.write(new_data)
+                    file.truncate()
+
+            except Exception as e:
+                msg = f'Error: {str(e)}'
+
+        self.update_boot_settings(version, drp_gpu, drp_theme, txt_listen_port, chk_group_args, additional_args)
+        return gr.update(value=msg, visible=True)
+
+    def check_update(self):
+        update_status = 'latest'
+        show_update = False
+        repo = git.Repo(self.prelude.ext_folder)
+        print('Checking updates for miaoshouai-assistant...')
+        for fetch in repo.remote().fetch(dry_run=True):
+            if fetch.flags != fetch.HEAD_UPTODATE:
+                show_update = True
+                update_status = "behind"
+                break
+
+        print('Checking updates for data source...')
+        if os.path.exists(self.prelude.assets_folder):
+            fcount = len([entry for entry in os.listdir(self.prelude.assets_folder) if os.path.isfile(os.path.join(self.prelude.assets_folder, entry))])
+
+        if not os.path.exists(self.prelude.assets_folder) or fcount <= 0:
+            self.relocate_assets_if_needed()
+            show_update = True
+            update_status = "behind"
+        else:
+            try:
+                asset_repo = git.Repo(self.prelude.asset_folder)
+                for fetch in asset_repo.remote().fetch(dry_run=True):
+                    if fetch.flags != fetch.HEAD_UPTODATE:
+                        show_update = True
+                        update_status = "behind"
+                        break
+            except Exception as e:
+                self.logger.info(f"Error during checking asset, try to relocate.\n{str(e)}")
+                self.relocate_assets_if_needed()
+                show_update = True
+                update_status = "behind"
+
+        return gr.Markdown.update(visible=True, value=update_status), gr.Checkbox.update(visible=show_update), gr.Button.update(visible=show_update)
+
+    def process_prompt(self, model, model_type, prompt: str):
+        text_replace = {'/': '|', 'a girl': '1girl', 'a boy': '1boy', 'a women': '1women', 'a man': '1man'}
+        for rep in text_replace.keys():
+            prompt = prompt.strip().lower().replace(rep, text_replace[rep])
+
+        try:
+            mid = model[1]
+            m_list = self.get_model_byid(mid, self.my_model_source)
+            if m_list is not None or m_list != []:
+                m = m_list[0]
+            else:
+                return prompt
+        except Exception as e:
+            self.logger.info(f"generation_info error:{str(e)}")
+            return prompt
+
+        generation_info = ''
+        for mv in m['modelVersions']:
+            img_cnt = len(mv['images'])
+            img = mv['images'][random.randint(0, img_cnt-1)]
+            if img['meta'] is not None and img['meta'] != '':
+                try:
+                    meta = img['meta']
+
+                    lora = ''
+                    if model_type == 'LORA':
+                        mname, ext = os.path.splitext(model[3][0])
+                        lora = f', <lora:{mname}:0.7>'
+                    elif model_type.upper() == 'LoCon'.upper():
+                        mname, ext = os.path.splitext(model[3][0])
+                        lora = f', <lyco:{mname}:0.7>'
+
+                    tw_count = len(mv['trainedWords'])
+                    if tw_count > 0:
+                        twords = mv['trainedWords'][random.randint(0, tw_count-1)]
+                        generation_info += f"{prompt}, {twords}{lora}\n"
+                    else:
+                        generation_info += f"{prompt}{lora}\n"
+
+                    if meta['negativePrompt'] is not None:
+                        generation_info += f"Negative prompt: {meta['negativePrompt']}\n"
+                    generation_info += f"Steps: {meta['steps']}, Sampler: {meta['sampler']}, "
+                    generation_info += f"CFG scale: {meta['cfgScale']}, Seed: -1, Size: {meta['Size']},"
+                    if meta['Model hash'] is not None:
+                        generation_info += f"Model hash: {meta['Model hash']}"
+
+                except Exception as e:
+                    self.logger.info(f"generation_info error:{str(e)}")
+                    return generation_info
+
+            break
+
+        return generation_info
+
+    def get_gpt_prompt(self, model, model_type, main_prompt):
+        os.environ["OPENAI_API_KEY"] = self.prelude.boot_settings['openai_api']
+
+        if model is None:
+            return gr.TextArea.update(value='Please select a model first')
+
+        if not os.path.exists(self.prelude.gpt_index):
+            self.install_preset_models_if_needed(True)
+
+        index = GPTSimpleVectorIndex.load_from_disk(self.prelude.gpt_index)
+        max_tokens = 4000
+
+        try:
+            response = openai.Completion.create(
+                engine="text-davinci-003",
+                prompt=f"translate the following text into English:\n{main_prompt}",
+                max_tokens=max_tokens,
+                n=1,
+                stop=None,
+                temperature=0.5,
+            )
+            res_prompt = response.choices[0].text.strip().replace('Translation:', '')
+            gpt_prompt = 'give me a prompt for: ' + res_prompt
+
+            response = index.query(gpt_prompt, response_mode="compact")
+            res_prompt = self.process_prompt(model, model_type, response.response)
+        except Exception as e:
+            res_prompt = str(e)
+            return gr.TextArea.update(value=res_prompt)
+
+        return gr.TextArea.update(value=res_prompt)
+
+    def update_gptapi(self, apikey):
+        if apikey == '':
+            res = 'Please enter a valid API Key'
+            gpt_hint_text = 'Set your OpenAI api key in Setting & Update first: https://platform.openai.com/account/api-keys'
+            value_text = gpt_hint_text
+        else:
+            self.update_boot_setting('openai_api', apikey)
+            os.environ["OPENAI_API_KEY"] = apikey
+            res = 'API Key updated'
+            gpt_hint_text = 'Select a model and type some text here, ChatGPT will generate prompt for you. Supports different text in different languages.'
+            value_text = ''
+
+        return gr.Markdown.update(value=res, visible=True), gr.Textbox.update(placeholder=gpt_hint_text, value=value_text)
+
+    def update_program(self, dont_update_ms=False):
+        result = "Update successful, restart to take effective."
+        try:
+            print('Updating miaoshouai-assistant...')
+            repo = git.Repo(self.prelude.ext_folder)
+            # Fix: `error: Your local changes to the following files would be overwritten by merge`,
+            # because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
+            repo.git.fetch(all=True)
+            repo.git.reset('origin', hard=True)
+            if not dont_update_ms:
+                sub_repo = git.Repo(self.prelude.assets_folder)
+                sub_repo.git.fetch(all=True)
+                sub_repo.git.reset('origin', hard=True)
+                self.install_preset_models_if_needed(True)
+        except Exception as e:
+            result = str(e)
+
+        return gr.Markdown.update(visible=True, value=result)
+
+
+    @property
+    def model_set(self) -> t.List[t.Dict]:
+        try:
+            self.install_preset_models_if_needed(False)
+            self.logger.info(f"access to model info for '{self.model_source}'")
+            model_json_mtime = toolkit.get_file_last_modified_time(self.prelude.model_json[self.model_source])
+
+            if self._model_set is None or self._model_set_last_access_time is None \
+                    or self._model_set_last_access_time < model_json_mtime:
+                self._model_set = self.get_all_models(self.model_source)
+                self._model_set_last_access_time = model_json_mtime
+                self.logger.info(f"load '{self.model_source}' model data from local file")
+        except Exception as e:
+            self.refresh_all_models()
+            self._model_set_last_access_time = datetime.datetime.now()
+
+        return self._model_set
+
+    @property
+    def my_model_set(self) -> t.List[t.Dict]:
+        try:
+            self.install_preset_models_if_needed(False)
+            self.logger.info(f"access to model info for '{self.my_model_source}'")
+            model_json_mtime = toolkit.get_file_last_modified_time(self.prelude.model_json[self.my_model_source])
+
+            if self._my_model_set is None or self._my_model_set_last_access_time is None \
+                or self._my_model_set_last_access_time < model_json_mtime:
+                self._my_model_set = self.get_all_models(self.my_model_source)
+                self._my_model_set_last_access_time = model_json_mtime
+                self.logger.info(f"load '{self.my_model_source}' model data from local file")
+        except Exception as e:
+            self.refresh_all_models()
+            self._my_model_set_last_access_time = datetime.datetime.now()
+
+        return self._my_model_set
+
+
+    @property
+    def allow_nsfw(self) -> bool:
+        return self._allow_nsfw
+
+    @property
+    def old_additional_args(self) -> str:
+        return self._old_additional
+
+    @property
+    def ds_models(self) -> gr.Dataset:
+        return self._ds_models
+
+    @ds_models.setter
+    def ds_models(self, newone: gr.Dataset):
+        self._ds_models = newone
+
+    @property
+    def ds_cover_gallery(self) -> gr.Dataset:
+        return self._ds_cover_gallery
+
+    @ds_cover_gallery.setter
+    def ds_cover_gallery(self, newone: gr.Dataset):
+        self._ds_cover_gallery = newone
+
+    @property
+    def ds_my_models(self) -> gr.Dataset:
+        return self._ds_my_models
+
+    @ds_my_models.setter
+    def ds_my_models(self, newone: gr.Dataset):
+        self._ds_my_models = newone
+
+    @property
+    def ds_my_model_covers(self) -> gr.Dataset:
+        return self._ds_my_model_covers
+
+    @ds_my_model_covers.setter
+    def ds_my_model_covers(self, newone: gr.Dataset):
+        self._ds_my_model_covers = newone
+
+    @property
+    def model_source(self) -> str:
+        return self._model_source
+
+    @model_source.setter
+    def model_source(self, newone: str):
+        self.logger.info(f"model source changes from {self.model_source} to {newone}")
+        self._model_source = newone
+        self._model_set_last_access_time = None  # reset timestamp
+
+    @property
+    def my_model_source(self) -> str:
+        return self._my_model_source
+
+    @my_model_source.setter
+    def my_model_source(self, newone: str):
+        self.logger.info(f"model source changes from {self.my_model_source} to {newone}")
+        self._my_model_source = newone
+        self._my_model_set_last_access_time = None  # reset timestamp
+
+    @property
+    def active_model_set(self) -> str:
+        return self._active_model_set
+
+    @active_model_set.setter
+    def active_model_set(self, newone: str):
+        self.logger.info(f"model set changes from {self.active_model_set} to {newone}")
+        self._active_model_set = newone
+
+    @property
+    def git_address(self) -> str:
+        return self._git_address
+
+    def introception(self) -> None:
+        (gpu, theme, port, checkbox_values, extra_args, ver) = self.get_default_args()
+
+        print("################################################################")
+        print("MIAOSHOU ASSISTANT ARGUMENTS:")
+
+        print(f"  gpu = {gpu}")
+        print(f"  theme = {theme}")
+        print(f"  port = {port}")
+        print(f"  checkbox_values = {checkbox_values}")
+        print(f"  extra_args = {extra_args}")
+        print(f"  webui ver = {ver}")
+
+        print("################################################################")
+