修改架构，增加可视化
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@@ -126,3 +126,4 @@ output/
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models/
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models/
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input/
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input/
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test/
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test/
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tests/
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@@ -1,6 +1,10 @@
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include LICENSE
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include LICENSE
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include README.md
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include README.md
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recursive-include src *.py
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recursive-include web *.js *.css
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recursive-include locales *.json
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recursive-exclude * __pycache__
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recursive-exclude * __pycache__
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recursive-exclude * *.py[co]
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recursive-exclude * *.py[co]
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@@ -1,209 +1,37 @@
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# SmartSaveImage - 智能图片保存节点
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# SmartSaveImage
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[](https://opensource.org/licenses/MIT)
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一个带实时路径预览的 ComfyUI 图片保存节点。节点菜单中搜索 **智能保存图片**。
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[](https://www.python.org/downloads/)
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[](https://github.com/comfyanonymous/ComfyUI)
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一个功能强大的ComfyUI自定义节点包，提供智能的文件夹管理和图片保存功能。
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## 使用方式
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## 🚀 安装
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1. 连接 `images`。
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2. 选择保存位置；自定义模式可填写绝对路径或相对 `output` 的路径。
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3. 填写子目录规则和文件名规则。
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4. 在“保存结果预览”中确认最终目录与示例文件名。
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### 方法一：通过ComfyUI Manager安装（推荐）
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模板变量默认折叠，展开后点击变量即可插入当前规则输入框。
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1. 打开ComfyUI Manager
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2. 搜索 "SmartSaveImage"
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3. 点击安装
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### 方法二：手动安装
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## 模板变量
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```bash
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cd ComfyUI/custom_nodes
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- 时间：`%date:yyyy-MM-dd%`、`%year%`、`%month%`、`%day%`、`%hour%`、`%minute%`、`%second%`
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git clone https://github.com/kjqwer/SmartSaveImage.git
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- 模型：`%model%`、`%model_full%`、`%unet%`、`%lora%`、`%vae%`
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cd SmartSaveImage
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- 采样：`%seed%`、`%steps%`、`%cfg%`、`%sampler%`、`%scheduler%`
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pip install -r requirements.txt
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- 图片：`%width%`、`%height%`、`%prompt%`、`%batch%`
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例如，保存到自定义根目录下的模型文件夹：
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```text
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子目录规则：krea/%model%
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文件名规则：image
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同名冲突：自动编号
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```
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```
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### 方法三：下载ZIP
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## 保存行为
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1. 下载本仓库的ZIP文件
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2. 解压到 `ComfyUI/custom_nodes/` 目录
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3. 重启ComfyUI
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## 🌟 主要特性
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- 支持 PNG、JPEG、WebP；PNG 默认压缩等级为 4，与 ComfyUI 自带保存节点一致，可在 0-9 间调整且始终无损；WebP 使用无损保存。
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- 自动编号不会覆盖已有文件；覆盖模式也会保证批量图片互不覆盖。
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- PNG 使用 ComfyUI 原生元数据字段；关闭“嵌入工作流”后不写入生成信息。
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- 保存到 `output`、`input`、`temp` 时直接使用对应预览类型；其他绝对目录使用临时预览。
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- 模型名自动从工作流读取，也可在“模型来源”中手动指定。
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- **智能文件夹管理** - 自动创建有组织的文件夹结构
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插件只依赖 ComfyUI 已包含的 Pillow 与 NumPy，不需要额外配置环境。
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- **灵活的保存选项** - 支持多种图片格式和质量设置
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- **元数据嵌入** - 自动提取并保存工作流信息
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- **批量处理** - 高效处理多张图片
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- **用户友好** - 直观的界面和丰富的选项
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## 📦 节点介绍
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### 智能文件夹管理器 (SmartFolderManager)
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负责创建和管理文件夹结构，从工作流中自动提取元数据。
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### 智能图片保存器 (SmartImageSaver)
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负责保存图片，支持多种格式、压缩选项和元数据嵌入。
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## 🚀 快速开始
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### 基本使用流程
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1. **添加智能文件夹管理器节点**
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- 在ComfyUI中搜索"智能文件夹管理器"
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- 将要保存的图片连接到管理器的images输入
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2. **配置文件夹结构**
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- 使用开关控制各层文件夹：日期、模型、种子、提示词、自定义
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- 设置基础文件夹路径
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- 可选择连接外部节点（模型、条件、潜在空间）获取更多信息
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3. **添加智能图片保存器节点**
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- 搜索"智能图片保存器"
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- 将文件夹管理器的三个输出全部连接到保存器：
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- images → images
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- folder_path → folder_path
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- metadata_json → metadata_json
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4. **配置保存选项**
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- 选择文件格式和质量设置
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- 设置文件名和预览模式
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## 📁 文件夹结构配置
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### 灵活的层级控制
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现在可以通过开关独立控制每一层文件夹的创建：
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- **日期文件夹** (`enable_date_folder`)
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- 按日期组织：`2024-11-15/`
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- 可自定义格式：`yyyy-MM-dd`, `yyyy/MM/dd` 等
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- 可选择包含时间：`2024-11-15_14-30-25/`
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- **模型文件夹** (`enable_model_folder`)
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- 按模型组织：`sdxl_base/`
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- 自动从工作流提取模型名称
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- 支持手动指定或从模型节点输入
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- **种子文件夹** (`enable_seed_folder`)
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- 按种子组织：`seed_12345/`
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- 自动从工作流提取种子值
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- 支持手动设置种子
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- **提示词文件夹** (`enable_prompt_folder`)
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- 按提示词组织：`beautiful_landscape/`
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- 可设置最大长度，自动清理非法字符
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- 支持手动输入或从条件节点获取
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- **自定义文件夹** (`enable_custom_folder`)
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- 完全自定义：`my_project/`
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- 可以是任意文件夹名称
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### 组合示例
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- 全开：`2024-11-15/sdxl_base/seed_12345/beautiful_landscape/my_project/`
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- 仅日期+模型：`2024-11-15/sdxl_base/`
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- 仅种子+自定义：`seed_12345/experiment_01/`
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## 🖼️ 图片保存选项
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### 文件格式支持
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- **PNG** - 无损压缩，支持透明度
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- **JPEG** - 有损压缩，文件较小
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- **WebP** - 现代格式，支持无损和有损
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- **BMP** - 位图格式
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- **TIFF** - 高质量格式
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### 质量设置
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- **JPEG质量**：1-100（推荐95）
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- **WebP质量**：1-100（推荐90）
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- **WebP无损**：启用无损压缩
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- **PNG压缩**：0-9级别（推荐6）
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### 文件命名选项
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- **文件名前缀**：自定义前缀
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- **添加时间戳**：在文件名中包含时间
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- **添加计数器**：多张图片时的序号
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- **计数器设置**：起始值和位数
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## 🔧 高级功能
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### 智能元数据获取
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- **外部节点优先**：连接外部节点时优先从节点获取信息
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- **工作流自动提取**：没有外部输入时从工作流中自动提取
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- **图片尺寸检测**：直接从图片数据中获取准确尺寸
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- **手动补充**：仅在需要时手动输入补充信息
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### 元数据嵌入
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- **参数记录**：保存采样器、CFG、步数等技术参数
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- **工作流保存**：可选择嵌入完整工作流信息
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- **多格式支持**：PNG使用PngInfo，JPEG/WebP使用EXIF
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### 预览模式
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- **保存并预览**：保存文件同时在界面显示
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- **仅预览**：只在界面显示，不保存文件
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- **仅保存**：只保存文件，不显示预览
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### 文件管理
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- **覆盖保护**：避免意外覆盖现有文件
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- **自动重命名**：文件冲突时自动生成新名称
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- **备份功能**：覆盖前创建备份文件
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## 💡 使用技巧
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### 推荐工作流设置
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1. **日常使用**
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- 开启：日期文件夹 + 模型文件夹
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- 文件格式：PNG（质量优先）或WebP（体积优先）
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- 预览模式：保存并预览
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2. **批量实验**
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- 开启：日期文件夹 + 种子文件夹 + 自定义文件夹
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- 启用计数器，使用描述性前缀
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- 考虑JPEG格式节省空间
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3. **项目管理**
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- 开启：自定义文件夹（项目名）+ 模型文件夹 + 种子文件夹
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- 手动设置模型和种子确保一致性
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- 嵌入完整元数据便于追溯
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4. **连接外部节点获取准确信息**
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- 将CheckpointLoader的MODEL输出连接到model_input
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- 将CLIPTextEncode的CONDITIONING连接到conditioning_positive/negative
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- 将KSampler的LATENT输出连接到latent_input
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- 连接后会优先使用外部节点的信息，无需手动选择模型
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### 文件夹路径设置
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- **相对路径**：基于ComfyUI输出目录
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- `output` 或留空 → 默认输出目录
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- `my_project` → `ComfyUI/output/my_project/`
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- **绝对路径**：指定完整路径
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- `D:/AI_Images/` → 直接保存到指定位置
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### 元数据利用
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生成的图片会包含丰富的元数据信息：
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- 在图片查看器中可以看到生成参数
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- 便于后续复现相同效果
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- 支持批量分析和管理
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## ⚠️ 注意事项
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1. **路径权限**：确保ComfyUI对目标文件夹有写入权限
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2. **文件名长度**：避免过长的文件名（建议<200字符）
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3. **特殊字符**：文件名会自动清理非法字符
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4. **磁盘空间**：注意监控存储空间，特别是使用无损格式时
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## 🔄 更新日志
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### v0.1.0
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- 重新设计的模块化架构
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- 改进的元数据提取功能
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- 更灵活的文件夹管理选项
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- 增强的错误处理和用户反馈
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## 🤝 支持与反馈
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如果遇到问题或有改进建议，欢迎反馈！
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---
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*享受更智能的图片保存体验！* 🎨
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+8
-11
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"""智能保存图片 - ComfyUI 节点包"""
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"""智能保存图片 - ComfyUI 节点包"""
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import os
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__author__ = "kj"
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__email__ = "2990346238@qq.com"
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__version__ = "2.0.0"
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from .src.SmartSaveImage import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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# 前端资源目录（可视化面板 JS/CSS）
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WEB_DIRECTORY = "./web"
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__all__ = [
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__all__ = [
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"NODE_CLASS_MAPPINGS",
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"NODE_CLASS_MAPPINGS",
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"NODE_DISPLAY_NAME_MAPPINGS",
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"NODE_DISPLAY_NAME_MAPPINGS",
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"WEB_DIRECTORY",
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"WEB_DIRECTORY",
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]
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]
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__author__ = "kj"
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__email__ = "2990346238@qq.com"
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__version__ = "0.1.0"
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# 从子包导入节点映射
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from .src.SmartSaveImage import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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# 声明Web目录以支持国际化 - 指向当前目录让ComfyUI能找到locales文件夹
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WEB_DIRECTORY = os.path.dirname(__file__)
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{
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{
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"SmartFolderManager": "Smart Folder Manager",
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"SmartSaveImage": "Smart Save Image"
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"SmartImageSaver": "Smart Image Saver"
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}
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}
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{
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{
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"SmartFolderManager": {
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"SmartSaveImage": {
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"display_name": "Smart Folder Manager",
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"display_name": "Smart Save Image",
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"description": "Smart folder manager - manages folder structure and metadata collection",
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"description": "Preview the destination and organize folders and filenames with templates",
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"inputs": {
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"inputs": {
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"images": {
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"images": {"name": "Images"},
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"name": "Images",
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"root_mode": {"name": "Save Location"},
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"tooltip": "Input images for extracting size info and passing to save node"
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"custom_root": {"name": "Custom Path"},
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},
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"folder_template": {"name": "Subfolder Rule"},
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"base_folder": {
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"filename_template": {"name": "Filename Rule"},
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"name": "Base Folder",
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"file_format": {"name": "Format"},
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"tooltip": "Base folder path, can be relative or absolute path"
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"collision_mode": {"name": "Name Collision"},
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},
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"save_mode": {"name": "Save Mode"},
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"create_subfolders": {
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"manual_model": {"name": "Model Source"},
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"name": "Create Subfolders",
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"embed_workflow": {"name": "Embed Workflow"},
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"tooltip": "Whether to automatically create subfolders"
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"counter_digits": {"name": "Counter Digits"},
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},
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"png_compression": {"name": "PNG Compression"}
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"enable_date_folder": {
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"name": "Enable Date Folder",
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"tooltip": "Whether to create date folder"
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},
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"enable_model_folder": {
|
|
||||||
"name": "Enable Model Folder",
|
|
||||||
"tooltip": "Whether to create model folder"
|
|
||||||
},
|
|
||||||
"enable_seed_folder": {
|
|
||||||
"name": "Enable Seed Folder",
|
|
||||||
"tooltip": "Whether to create seed folder"
|
|
||||||
},
|
|
||||||
"enable_prompt_folder": {
|
|
||||||
"name": "Enable Prompt Folder",
|
|
||||||
"tooltip": "Whether to create prompt folder"
|
|
||||||
},
|
|
||||||
"enable_custom_folder": {
|
|
||||||
"name": "Enable Custom Folder",
|
|
||||||
"tooltip": "Whether to use custom folder"
|
|
||||||
},
|
|
||||||
"date_format": {
|
|
||||||
"name": "Date Format",
|
|
||||||
"tooltip": "Date format: yyyy=year MM=month dd=day hh=hour mm=minute ss=second"
|
|
||||||
},
|
|
||||||
"include_time": {
|
|
||||||
"name": "Include Time",
|
|
||||||
"tooltip": "Whether to include time in date"
|
|
||||||
},
|
|
||||||
"model_source": {
|
|
||||||
"name": "Model Source",
|
|
||||||
"tooltip": "Model info source: auto=from metadata/external input, manual=manual selection",
|
|
||||||
"options": {
|
|
||||||
"auto": "Auto Detect",
|
|
||||||
"manual": "Manual Select"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"manual_model_name": {
|
|
||||||
"name": "Manual Model Name",
|
|
||||||
"tooltip": "Manual model selection (only effective when model_source=manual)"
|
|
||||||
},
|
|
||||||
"model_input": {
|
|
||||||
"name": "Model Input",
|
|
||||||
"tooltip": "Input from model loader node (only effective when model_source=auto)"
|
|
||||||
},
|
|
||||||
"seed": {
|
|
||||||
"name": "Seed",
|
|
||||||
"tooltip": "Seed value (manual input or connect external node)"
|
|
||||||
},
|
|
||||||
"positive_prompt": {
|
|
||||||
"name": "Positive Prompt",
|
|
||||||
"tooltip": "Positive prompt (manual input or connect external node)"
|
|
||||||
},
|
|
||||||
"negative_prompt": {
|
|
||||||
"name": "Negative Prompt",
|
|
||||||
"tooltip": "Negative prompt (manual input or connect external node)"
|
|
||||||
},
|
|
||||||
"custom_subfolder": {
|
|
||||||
"name": "Custom Subfolder",
|
|
||||||
"tooltip": "Custom subfolder name"
|
|
||||||
},
|
|
||||||
"model_short_name": {
|
|
||||||
"name": "Model Short Name",
|
|
||||||
"tooltip": "Use model short name (remove extension)"
|
|
||||||
},
|
|
||||||
"prompt_max_length": {
|
|
||||||
"name": "Prompt Max Length",
|
|
||||||
"tooltip": "Maximum length for prompt folder name"
|
|
||||||
},
|
|
||||||
"sanitize_names": {
|
|
||||||
"name": "Sanitize Names",
|
|
||||||
"tooltip": "Clean illegal characters in filenames"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": {
|
|
||||||
"0": {
|
|
||||||
"name": "Images",
|
|
||||||
"tooltip": "Passed image data"
|
|
||||||
},
|
|
||||||
"1": {
|
|
||||||
"name": "Folder Path",
|
|
||||||
"tooltip": "Generated folder path"
|
|
||||||
},
|
|
||||||
"2": {
|
|
||||||
"name": "Metadata JSON",
|
|
||||||
"tooltip": "Extracted metadata JSON"
|
|
||||||
}
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"SmartImageSaver": {
|
|
||||||
"display_name": "Smart Image Saver",
|
|
||||||
"description": "Smart image saver - handles image saving, format conversion and compression",
|
|
||||||
"inputs": {
|
|
||||||
"images": {
|
|
||||||
"name": "Images",
|
|
||||||
"tooltip": "Images to save (usually from SmartFolderManager)"
|
|
||||||
},
|
|
||||||
"folder_path": {
|
|
||||||
"name": "Folder Path",
|
|
||||||
"tooltip": "Save path (from SmartFolderManager)"
|
|
||||||
},
|
|
||||||
"metadata_json": {
|
|
||||||
"name": "Metadata JSON",
|
|
||||||
"tooltip": "Metadata JSON (from SmartFolderManager)"
|
|
||||||
},
|
|
||||||
"filename_prefix": {
|
|
||||||
"name": "Filename Prefix",
|
|
||||||
"tooltip": "Filename prefix"
|
|
||||||
},
|
|
||||||
"file_format": {
|
|
||||||
"name": "File Format",
|
|
||||||
"tooltip": "Image format",
|
|
||||||
"options": {
|
|
||||||
"png": "PNG Format",
|
|
||||||
"jpeg": "JPEG Format",
|
|
||||||
"webp": "WebP Format",
|
|
||||||
"bmp": "BMP Format",
|
|
||||||
"tiff": "TIFF Format"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"preview_mode": {
|
|
||||||
"name": "Preview Mode",
|
|
||||||
"tooltip": "Save mode",
|
|
||||||
"options": {
|
|
||||||
"save_and_preview": "Save and Preview",
|
|
||||||
"preview_only": "Preview Only",
|
|
||||||
"save_only": "Save Only"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"add_timestamp": {
|
|
||||||
"name": "Add Timestamp",
|
|
||||||
"tooltip": "Add timestamp to filename"
|
|
||||||
},
|
|
||||||
"add_counter": {
|
|
||||||
"name": "Add Counter",
|
|
||||||
"tooltip": "Add counter (for multiple images)"
|
|
||||||
},
|
|
||||||
"counter_start": {
|
|
||||||
"name": "Counter Start",
|
|
||||||
"tooltip": "Counter start value"
|
|
||||||
},
|
|
||||||
"counter_padding": {
|
|
||||||
"name": "Counter Padding",
|
|
||||||
"tooltip": "Counter padding (zero-fill)"
|
|
||||||
},
|
|
||||||
"jpeg_quality": {
|
|
||||||
"name": "JPEG Quality",
|
|
||||||
"tooltip": "JPEG quality (1-100)"
|
|
||||||
},
|
|
||||||
"webp_quality": {
|
|
||||||
"name": "WebP Quality",
|
|
||||||
"tooltip": "WebP quality (1-100)"
|
|
||||||
},
|
|
||||||
"webp_lossless": {
|
|
||||||
"name": "WebP Lossless",
|
|
||||||
"tooltip": "WebP lossless compression"
|
|
||||||
},
|
|
||||||
"png_compression": {
|
|
||||||
"name": "PNG Compression",
|
|
||||||
"tooltip": "PNG compression level (0-9)"
|
|
||||||
},
|
|
||||||
"embed_metadata": {
|
|
||||||
"name": "Embed Metadata",
|
|
||||||
"tooltip": "Embed metadata into image"
|
|
||||||
},
|
|
||||||
"embed_workflow": {
|
|
||||||
"name": "Embed Workflow",
|
|
||||||
"tooltip": "Embed workflow information"
|
|
||||||
},
|
|
||||||
"overwrite_existing": {
|
|
||||||
"name": "Overwrite Existing",
|
|
||||||
"tooltip": "Overwrite existing files"
|
|
||||||
},
|
|
||||||
"create_backup": {
|
|
||||||
"name": "Create Backup",
|
|
||||||
"tooltip": "Create backup for overwritten files"
|
|
||||||
},
|
|
||||||
"optimize_size": {
|
|
||||||
"name": "Optimize Size",
|
|
||||||
"tooltip": "Optimize file size"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": {
|
|
||||||
"0": {
|
|
||||||
"name": "Images",
|
|
||||||
"tooltip": "Saved images"
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
@@ -1,4 +1,3 @@
|
|||||||
{
|
{
|
||||||
"SmartFolderManager": "智能文件夹管理",
|
"SmartSaveImage": "智能保存图片"
|
||||||
"SmartImageSaver": "智能图片保存"
|
|
||||||
}
|
}
|
||||||
|
|||||||
+15
-201
@@ -1,206 +1,20 @@
|
|||||||
{
|
{
|
||||||
"SmartFolderManager": {
|
"SmartSaveImage": {
|
||||||
"display_name": "智能文件夹管理",
|
"display_name": "智能保存图片",
|
||||||
"description": "智能文件夹管理器 - 负责管理文件夹结构和元数据收集",
|
"description": "预览最终路径，并用模板组织目录和文件名",
|
||||||
"inputs": {
|
"inputs": {
|
||||||
"images": {
|
"images": {"name": "图像", "tooltip": "需要保存的图片或图片批次"},
|
||||||
"name": "图像",
|
"root_mode": {"name": "保存位置"},
|
||||||
"tooltip": "输入图片，用于提取尺寸信息并传递给保存节点"
|
"custom_root": {"name": "自定义路径"},
|
||||||
},
|
"folder_template": {"name": "子目录规则"},
|
||||||
"base_folder": {
|
"filename_template": {"name": "文件名规则"},
|
||||||
"name": "基础文件夹",
|
"file_format": {"name": "格式"},
|
||||||
"tooltip": "基础文件夹路径，可以是相对路径或绝对路径"
|
"collision_mode": {"name": "同名冲突"},
|
||||||
},
|
"save_mode": {"name": "保存模式"},
|
||||||
"create_subfolders": {
|
"manual_model": {"name": "模型来源"},
|
||||||
"name": "创建子文件夹",
|
"embed_workflow": {"name": "嵌入工作流"},
|
||||||
"tooltip": "是否自动创建子文件夹"
|
"counter_digits": {"name": "序号位数"},
|
||||||
},
|
"png_compression": {"name": "PNG 压缩"}
|
||||||
"enable_date_folder": {
|
|
||||||
"name": "启用日期文件夹",
|
|
||||||
"tooltip": "是否创建日期文件夹"
|
|
||||||
},
|
|
||||||
"enable_model_folder": {
|
|
||||||
"name": "启用模型文件夹",
|
|
||||||
"tooltip": "是否创建模型文件夹"
|
|
||||||
},
|
|
||||||
"enable_seed_folder": {
|
|
||||||
"name": "启用种子文件夹",
|
|
||||||
"tooltip": "是否创建种子文件夹"
|
|
||||||
},
|
|
||||||
"enable_prompt_folder": {
|
|
||||||
"name": "启用提示词文件夹",
|
|
||||||
"tooltip": "是否创建提示词文件夹"
|
|
||||||
},
|
|
||||||
"enable_custom_folder": {
|
|
||||||
"name": "启用自定义文件夹",
|
|
||||||
"tooltip": "是否使用自定义文件夹"
|
|
||||||
},
|
|
||||||
"date_format": {
|
|
||||||
"name": "日期格式",
|
|
||||||
"tooltip": "日期格式: yyyy=年 MM=月 dd=日 hh=时 mm=分 ss=秒"
|
|
||||||
},
|
|
||||||
"include_time": {
|
|
||||||
"name": "包含时间",
|
|
||||||
"tooltip": "是否在日期中包含时间"
|
|
||||||
},
|
|
||||||
"model_source": {
|
|
||||||
"name": "模型来源",
|
|
||||||
"tooltip": "模型信息来源：auto=从元数据/外部输入获取，manual=手动选择",
|
|
||||||
"options": {
|
|
||||||
"auto": "自动获取",
|
|
||||||
"manual": "手动选择"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"manual_model_name": {
|
|
||||||
"name": "手动模型名",
|
|
||||||
"tooltip": "手动选择模型（仅在model_source=manual时生效）"
|
|
||||||
},
|
|
||||||
"model_input": {
|
|
||||||
"name": "模型输入",
|
|
||||||
"tooltip": "从模型加载器节点输入（仅在model_source=auto时生效）"
|
|
||||||
},
|
|
||||||
"seed": {
|
|
||||||
"name": "随机种子",
|
|
||||||
"tooltip": "种子值（手动输入或连接外部节点）"
|
|
||||||
},
|
|
||||||
"positive_prompt": {
|
|
||||||
"name": "正向提示词",
|
|
||||||
"tooltip": "正向提示词（手动输入或连接外部节点）"
|
|
||||||
},
|
|
||||||
"negative_prompt": {
|
|
||||||
"name": "负向提示词",
|
|
||||||
"tooltip": "负向提示词（手动输入或连接外部节点）"
|
|
||||||
},
|
|
||||||
"custom_subfolder": {
|
|
||||||
"name": "自定义子文件夹",
|
|
||||||
"tooltip": "自定义子文件夹名称"
|
|
||||||
},
|
|
||||||
"model_short_name": {
|
|
||||||
"name": "模型短名称",
|
|
||||||
"tooltip": "使用模型短名称（去除扩展名）"
|
|
||||||
},
|
|
||||||
"prompt_max_length": {
|
|
||||||
"name": "提示词最大长度",
|
|
||||||
"tooltip": "提示词文件夹名最大长度"
|
|
||||||
},
|
|
||||||
"sanitize_names": {
|
|
||||||
"name": "清理文件名",
|
|
||||||
"tooltip": "清理文件名中的非法字符"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": {
|
|
||||||
"0": {
|
|
||||||
"name": "图像",
|
|
||||||
"tooltip": "传递的图片数据"
|
|
||||||
},
|
|
||||||
"1": {
|
|
||||||
"name": "文件夹路径",
|
|
||||||
"tooltip": "生成的文件夹路径"
|
|
||||||
},
|
|
||||||
"2": {
|
|
||||||
"name": "元数据JSON",
|
|
||||||
"tooltip": "提取的元数据JSON"
|
|
||||||
}
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"SmartImageSaver": {
|
|
||||||
"display_name": "智能图片保存",
|
|
||||||
"description": "智能图片保存器 - 负责图片保存、格式转换和压缩",
|
|
||||||
"inputs": {
|
|
||||||
"images": {
|
|
||||||
"name": "图像",
|
|
||||||
"tooltip": "要保存的图片（通常来自SmartFolderManager）"
|
|
||||||
},
|
|
||||||
"folder_path": {
|
|
||||||
"name": "文件夹路径",
|
|
||||||
"tooltip": "保存路径（来自SmartFolderManager）"
|
|
||||||
},
|
|
||||||
"metadata_json": {
|
|
||||||
"name": "元数据JSON",
|
|
||||||
"tooltip": "元数据JSON（来自SmartFolderManager）"
|
|
||||||
},
|
|
||||||
"filename_prefix": {
|
|
||||||
"name": "文件名前缀",
|
|
||||||
"tooltip": "文件名前缀"
|
|
||||||
},
|
|
||||||
"file_format": {
|
|
||||||
"name": "文件格式",
|
|
||||||
"tooltip": "图片格式",
|
|
||||||
"options": {
|
|
||||||
"png": "PNG格式",
|
|
||||||
"jpeg": "JPEG格式",
|
|
||||||
"webp": "WebP格式",
|
|
||||||
"bmp": "BMP格式",
|
|
||||||
"tiff": "TIFF格式"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"preview_mode": {
|
|
||||||
"name": "预览模式",
|
|
||||||
"tooltip": "保存模式",
|
|
||||||
"options": {
|
|
||||||
"save_and_preview": "保存并预览",
|
|
||||||
"preview_only": "仅预览",
|
|
||||||
"save_only": "仅保存"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"add_timestamp": {
|
|
||||||
"name": "添加时间戳",
|
|
||||||
"tooltip": "在文件名中添加时间戳"
|
|
||||||
},
|
|
||||||
"add_counter": {
|
|
||||||
"name": "添加计数器",
|
|
||||||
"tooltip": "添加计数器（多张图片时）"
|
|
||||||
},
|
|
||||||
"counter_start": {
|
|
||||||
"name": "计数器起始值",
|
|
||||||
"tooltip": "计数器起始值"
|
|
||||||
},
|
|
||||||
"counter_padding": {
|
|
||||||
"name": "计数器填充",
|
|
||||||
"tooltip": "计数器位数（补零）"
|
|
||||||
},
|
|
||||||
"jpeg_quality": {
|
|
||||||
"name": "JPEG质量",
|
|
||||||
"tooltip": "JPEG质量（1-100）"
|
|
||||||
},
|
|
||||||
"webp_quality": {
|
|
||||||
"name": "WebP质量",
|
|
||||||
"tooltip": "WebP质量（1-100）"
|
|
||||||
},
|
|
||||||
"webp_lossless": {
|
|
||||||
"name": "WebP无损",
|
|
||||||
"tooltip": "WebP无损压缩"
|
|
||||||
},
|
|
||||||
"png_compression": {
|
|
||||||
"name": "PNG压缩",
|
|
||||||
"tooltip": "PNG压缩级别（0-9）"
|
|
||||||
},
|
|
||||||
"embed_metadata": {
|
|
||||||
"name": "嵌入元数据",
|
|
||||||
"tooltip": "嵌入元数据到图片"
|
|
||||||
},
|
|
||||||
"embed_workflow": {
|
|
||||||
"name": "嵌入工作流",
|
|
||||||
"tooltip": "嵌入工作流信息"
|
|
||||||
},
|
|
||||||
"overwrite_existing": {
|
|
||||||
"name": "覆盖已存在文件",
|
|
||||||
"tooltip": "覆盖已存在的文件"
|
|
||||||
},
|
|
||||||
"create_backup": {
|
|
||||||
"name": "创建备份",
|
|
||||||
"tooltip": "为覆盖的文件创建备份"
|
|
||||||
},
|
|
||||||
"optimize_size": {
|
|
||||||
"name": "优化文件大小",
|
|
||||||
"tooltip": "优化文件大小"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"outputs": {
|
|
||||||
"0": {
|
|
||||||
"name": "图像",
|
|
||||||
"tooltip": "保存的图片"
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
+2
-2
@@ -4,8 +4,8 @@ build-backend = "setuptools.build_meta"
|
|||||||
|
|
||||||
[project]
|
[project]
|
||||||
name = "SmartSaveImage"
|
name = "SmartSaveImage"
|
||||||
version = "0.0.1"
|
version = "2.0.0"
|
||||||
description = "A node for easy save"
|
description = "A visual, template-driven image saver for ComfyUI"
|
||||||
authors = [
|
authors = [
|
||||||
{name = "kj", email = "2990346238@qq.com"}
|
{name = "kj", email = "2990346238@qq.com"}
|
||||||
]
|
]
|
||||||
|
|||||||
@@ -1,3 +1,2 @@
|
|||||||
Pillow>=8.0.0
|
Pillow>=8.0.0
|
||||||
piexif>=1.1.3
|
|
||||||
numpy>=1.19.0
|
numpy>=1.19.0
|
||||||
|
|||||||
@@ -2,6 +2,14 @@
|
|||||||
|
|
||||||
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
|
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
|
||||||
|
|
||||||
|
# 注册实时预览路由（在无 server 的测试环境中会静默跳过）
|
||||||
|
try:
|
||||||
|
from .server_routes import register_routes
|
||||||
|
|
||||||
|
register_routes()
|
||||||
|
except Exception: # pragma: no cover
|
||||||
|
pass
|
||||||
|
|
||||||
__all__ = [
|
__all__ = [
|
||||||
"NODE_CLASS_MAPPINGS",
|
"NODE_CLASS_MAPPINGS",
|
||||||
"NODE_DISPLAY_NAME_MAPPINGS",
|
"NODE_DISPLAY_NAME_MAPPINGS",
|
||||||
|
|||||||
@@ -1,12 +0,0 @@
|
|||||||
"""SmartSaveImage 核心模块"""
|
|
||||||
|
|
||||||
from .metadata import MetadataExtractor, MetadataBuilder
|
|
||||||
from .path_utils import PathManager
|
|
||||||
from .image_utils import ImageProcessor
|
|
||||||
|
|
||||||
__all__ = [
|
|
||||||
"MetadataExtractor",
|
|
||||||
"MetadataBuilder",
|
|
||||||
"PathManager",
|
|
||||||
"ImageProcessor",
|
|
||||||
]
|
|
||||||
@@ -1,168 +0,0 @@
|
|||||||
"""图片处理工具模块"""
|
|
||||||
|
|
||||||
import os
|
|
||||||
import json
|
|
||||||
import numpy as np
|
|
||||||
from PIL import Image, PngImagePlugin
|
|
||||||
import piexif
|
|
||||||
from typing import Dict, Any, Optional, Tuple
|
|
||||||
|
|
||||||
|
|
||||||
class ImageProcessor:
|
|
||||||
"""图片处理器"""
|
|
||||||
|
|
||||||
def __init__(self):
|
|
||||||
self.supported_formats = {
|
|
||||||
"png": {"extension": ".png", "pil_format": "PNG"},
|
|
||||||
"jpeg": {"extension": ".jpg", "pil_format": "JPEG"},
|
|
||||||
"webp": {"extension": ".webp", "pil_format": "WEBP"},
|
|
||||||
"bmp": {"extension": ".bmp", "pil_format": "BMP"},
|
|
||||||
"tiff": {"extension": ".tiff", "pil_format": "TIFF"},
|
|
||||||
}
|
|
||||||
|
|
||||||
def tensor_to_pil(self, tensor_image) -> Image.Image:
|
|
||||||
"""将tensor图片转换为PIL图片"""
|
|
||||||
# 处理批次维度
|
|
||||||
if len(tensor_image.shape) == 4:
|
|
||||||
tensor_image = tensor_image.squeeze(0)
|
|
||||||
|
|
||||||
# 转换为numpy数组并调整范围到0-255
|
|
||||||
image_np = (tensor_image.cpu().numpy() * 255).astype(np.uint8)
|
|
||||||
|
|
||||||
# 转换为PIL图片
|
|
||||||
return Image.fromarray(image_np)
|
|
||||||
|
|
||||||
def prepare_save_kwargs(self, file_format: str, quality_settings: Dict[str, Any]) -> Dict[str, Any]:
|
|
||||||
"""准备保存参数"""
|
|
||||||
format_info = self.supported_formats.get(file_format, self.supported_formats["png"])
|
|
||||||
save_kwargs = {"format": format_info["pil_format"]}
|
|
||||||
|
|
||||||
if file_format == "png":
|
|
||||||
save_kwargs["compress_level"] = quality_settings.get("png_compression", 6)
|
|
||||||
save_kwargs["optimize"] = quality_settings.get("optimize_size", False)
|
|
||||||
|
|
||||||
elif file_format == "jpeg":
|
|
||||||
save_kwargs["quality"] = quality_settings.get("jpeg_quality", 95)
|
|
||||||
save_kwargs["optimize"] = quality_settings.get("optimize_size", False)
|
|
||||||
|
|
||||||
elif file_format == "webp":
|
|
||||||
if quality_settings.get("webp_lossless", False):
|
|
||||||
save_kwargs["lossless"] = True
|
|
||||||
else:
|
|
||||||
save_kwargs["quality"] = quality_settings.get("webp_quality", 90)
|
|
||||||
save_kwargs["method"] = 6 # 最佳压缩方法
|
|
||||||
|
|
||||||
return save_kwargs
|
|
||||||
|
|
||||||
def add_png_metadata(self, pil_image: Image.Image, metadata_text: Optional[str],
|
|
||||||
workflow_data: Optional[Dict], save_kwargs: Dict) -> Dict:
|
|
||||||
"""为PNG格式添加元数据"""
|
|
||||||
if metadata_text or workflow_data:
|
|
||||||
pnginfo = PngImagePlugin.PngInfo()
|
|
||||||
|
|
||||||
if metadata_text:
|
|
||||||
pnginfo.add_text("parameters", metadata_text)
|
|
||||||
|
|
||||||
if workflow_data:
|
|
||||||
pnginfo.add_text("workflow", json.dumps(workflow_data))
|
|
||||||
|
|
||||||
save_kwargs["pnginfo"] = pnginfo
|
|
||||||
|
|
||||||
return save_kwargs
|
|
||||||
|
|
||||||
def add_exif_metadata(self, metadata_text: Optional[str],
|
|
||||||
workflow_data: Optional[Dict]) -> Optional[bytes]:
|
|
||||||
"""创建EXIF元数据（用于JPEG和WebP）"""
|
|
||||||
if not metadata_text and not workflow_data:
|
|
||||||
return None
|
|
||||||
|
|
||||||
try:
|
|
||||||
exif_dict = {}
|
|
||||||
|
|
||||||
if metadata_text:
|
|
||||||
# 将元数据添加到UserComment字段
|
|
||||||
exif_dict["Exif"] = {
|
|
||||||
piexif.ExifIFD.UserComment: b"UNICODE\0" + metadata_text.encode("utf-16be")
|
|
||||||
}
|
|
||||||
|
|
||||||
if workflow_data:
|
|
||||||
# 将工作流数据添加到ImageDescription字段
|
|
||||||
workflow_json = json.dumps(workflow_data)
|
|
||||||
exif_dict["0th"] = {
|
|
||||||
piexif.ImageIFD.ImageDescription: f"Workflow:{workflow_json}"
|
|
||||||
}
|
|
||||||
|
|
||||||
return piexif.dump(exif_dict)
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[ImageProcessor] EXIF元数据创建失败: {e}")
|
|
||||||
return None
|
|
||||||
|
|
||||||
def save_image(self, tensor_image, filepath: str, file_format: str,
|
|
||||||
quality_settings: Dict[str, Any], metadata_text: Optional[str] = None,
|
|
||||||
embed_workflow: bool = False, workflow_data: Optional[Dict] = None) -> bool:
|
|
||||||
"""保存图片并嵌入元数据"""
|
|
||||||
try:
|
|
||||||
# 转换为PIL图片
|
|
||||||
pil_image = self.tensor_to_pil(tensor_image)
|
|
||||||
|
|
||||||
# 准备保存参数
|
|
||||||
save_kwargs = self.prepare_save_kwargs(file_format, quality_settings)
|
|
||||||
|
|
||||||
# 根据格式添加元数据
|
|
||||||
if file_format == "png":
|
|
||||||
# PNG使用PngInfo
|
|
||||||
save_kwargs = self.add_png_metadata(
|
|
||||||
pil_image, metadata_text,
|
|
||||||
workflow_data if embed_workflow else None,
|
|
||||||
save_kwargs
|
|
||||||
)
|
|
||||||
|
|
||||||
elif file_format in ["jpeg", "webp"]:
|
|
||||||
# JPEG和WebP使用EXIF
|
|
||||||
exif_bytes = self.add_exif_metadata(
|
|
||||||
metadata_text,
|
|
||||||
workflow_data if embed_workflow else None
|
|
||||||
)
|
|
||||||
if exif_bytes:
|
|
||||||
save_kwargs["exif"] = exif_bytes
|
|
||||||
|
|
||||||
# 保存图片
|
|
||||||
pil_image.save(filepath, **save_kwargs)
|
|
||||||
return True
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[ImageProcessor] 保存图片失败 {filepath}: {e}")
|
|
||||||
return False
|
|
||||||
|
|
||||||
def create_backup(self, filepath: str) -> bool:
|
|
||||||
"""为现有文件创建备份"""
|
|
||||||
if not os.path.exists(filepath):
|
|
||||||
return True
|
|
||||||
|
|
||||||
try:
|
|
||||||
backup_path = filepath + ".backup"
|
|
||||||
|
|
||||||
# 如果备份已存在，先删除
|
|
||||||
if os.path.exists(backup_path):
|
|
||||||
os.remove(backup_path)
|
|
||||||
|
|
||||||
# 重命名原文件为备份
|
|
||||||
os.rename(filepath, backup_path)
|
|
||||||
print(f"[ImageProcessor] 创建备份: {backup_path}")
|
|
||||||
return True
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[ImageProcessor] 创建备份失败: {e}")
|
|
||||||
return False
|
|
||||||
|
|
||||||
def get_image_info(self, tensor_image) -> Tuple[int, int]:
|
|
||||||
"""获取图片尺寸信息"""
|
|
||||||
if len(tensor_image.shape) == 4:
|
|
||||||
# 批次格式: (batch, height, width, channels)
|
|
||||||
return tensor_image.shape[2], tensor_image.shape[1] # width, height
|
|
||||||
elif len(tensor_image.shape) == 3:
|
|
||||||
# 单张格式: (height, width, channels)
|
|
||||||
return tensor_image.shape[1], tensor_image.shape[0] # width, height
|
|
||||||
else:
|
|
||||||
return 0, 0
|
|
||||||
@@ -1,201 +0,0 @@
|
|||||||
"""元数据提取和构建模块"""
|
|
||||||
|
|
||||||
import json
|
|
||||||
import os
|
|
||||||
from datetime import datetime
|
|
||||||
from typing import Dict, Any, Optional
|
|
||||||
|
|
||||||
|
|
||||||
class MetadataExtractor:
|
|
||||||
"""从ComfyUI工作流中提取元数据"""
|
|
||||||
|
|
||||||
def __init__(self):
|
|
||||||
self.supported_checkpoint_nodes = [
|
|
||||||
"CheckpointLoaderSimple",
|
|
||||||
"CheckpointLoader",
|
|
||||||
"CheckpointLoaderV2"
|
|
||||||
]
|
|
||||||
self.supported_sampler_nodes = [
|
|
||||||
"KSampler",
|
|
||||||
"KSamplerAdvanced",
|
|
||||||
"SamplerCustom",
|
|
||||||
"SamplerCustomAdvanced",
|
|
||||||
# 添加更多可能的采样器节点类型
|
|
||||||
]
|
|
||||||
self.supported_text_nodes = [
|
|
||||||
"CLIPTextEncode",
|
|
||||||
"CLIPTextEncodeSDXL",
|
|
||||||
"T5TextEncode"
|
|
||||||
]
|
|
||||||
|
|
||||||
def extract_from_workflow(self, extra_pnginfo: Optional[Dict]) -> Dict[str, Any]:
|
|
||||||
"""从工作流信息中提取元数据"""
|
|
||||||
metadata = {
|
|
||||||
"model": None,
|
|
||||||
"seed": None,
|
|
||||||
"steps": None,
|
|
||||||
"cfg": None,
|
|
||||||
"sampler": None,
|
|
||||||
"scheduler": None,
|
|
||||||
"positive_prompt": None,
|
|
||||||
"negative_prompt": None,
|
|
||||||
"width": None,
|
|
||||||
"height": None,
|
|
||||||
}
|
|
||||||
|
|
||||||
if not isinstance(extra_pnginfo, dict):
|
|
||||||
return metadata
|
|
||||||
|
|
||||||
workflow = extra_pnginfo.get("workflow", {})
|
|
||||||
if not isinstance(workflow, dict):
|
|
||||||
return metadata
|
|
||||||
|
|
||||||
nodes_data = workflow.get("nodes", [])
|
|
||||||
text_prompts = [] # 收集所有文本提示
|
|
||||||
|
|
||||||
for node in nodes_data:
|
|
||||||
if not isinstance(node, dict):
|
|
||||||
continue
|
|
||||||
|
|
||||||
class_type = node.get("class_type", "")
|
|
||||||
inputs = node.get("inputs", {})
|
|
||||||
|
|
||||||
# 提取模型信息
|
|
||||||
if class_type in self.supported_checkpoint_nodes:
|
|
||||||
if "ckpt_name" in inputs and not metadata["model"]:
|
|
||||||
metadata["model"] = inputs["ckpt_name"]
|
|
||||||
|
|
||||||
# 提取采样器信息
|
|
||||||
elif class_type in self.supported_sampler_nodes:
|
|
||||||
if "seed" in inputs and metadata["seed"] is None:
|
|
||||||
metadata["seed"] = inputs["seed"]
|
|
||||||
if "steps" in inputs and metadata["steps"] is None:
|
|
||||||
metadata["steps"] = inputs["steps"]
|
|
||||||
if "cfg" in inputs and metadata["cfg"] is None:
|
|
||||||
metadata["cfg"] = inputs["cfg"]
|
|
||||||
if "sampler_name" in inputs and not metadata["sampler"]:
|
|
||||||
metadata["sampler"] = inputs["sampler_name"]
|
|
||||||
if "scheduler" in inputs and not metadata["scheduler"]:
|
|
||||||
metadata["scheduler"] = inputs["scheduler"]
|
|
||||||
|
|
||||||
# 提取文本提示
|
|
||||||
elif class_type in self.supported_text_nodes:
|
|
||||||
text = inputs.get("text", "")
|
|
||||||
if text and isinstance(text, str):
|
|
||||||
text_prompts.append(text.strip())
|
|
||||||
|
|
||||||
# 分配正负提示词（通常第一个是正向，第二个是负向）
|
|
||||||
if text_prompts:
|
|
||||||
metadata["positive_prompt"] = text_prompts[0]
|
|
||||||
if len(text_prompts) > 1:
|
|
||||||
metadata["negative_prompt"] = text_prompts[1]
|
|
||||||
|
|
||||||
return metadata
|
|
||||||
|
|
||||||
def extract_from_prompt(self, prompt: Optional[Dict]) -> Dict[str, Any]:
|
|
||||||
"""从prompt参数中提取元数据（备用方案）"""
|
|
||||||
metadata = {
|
|
||||||
"model": None,
|
|
||||||
"seed": None,
|
|
||||||
"steps": None,
|
|
||||||
"cfg": None,
|
|
||||||
"sampler": None,
|
|
||||||
"scheduler": None,
|
|
||||||
}
|
|
||||||
|
|
||||||
if not isinstance(prompt, dict):
|
|
||||||
return metadata
|
|
||||||
|
|
||||||
for node_id, node_data in prompt.items():
|
|
||||||
if not isinstance(node_data, dict):
|
|
||||||
continue
|
|
||||||
|
|
||||||
class_type = node_data.get("class_type", "")
|
|
||||||
inputs = node_data.get("inputs", {})
|
|
||||||
|
|
||||||
if class_type in self.supported_checkpoint_nodes and not metadata["model"]:
|
|
||||||
metadata["model"] = inputs.get("ckpt_name")
|
|
||||||
elif class_type in self.supported_sampler_nodes:
|
|
||||||
if metadata["seed"] is None:
|
|
||||||
metadata["seed"] = inputs.get("seed")
|
|
||||||
if metadata["steps"] is None:
|
|
||||||
metadata["steps"] = inputs.get("steps")
|
|
||||||
if metadata["cfg"] is None:
|
|
||||||
metadata["cfg"] = inputs.get("cfg")
|
|
||||||
if not metadata["sampler"]:
|
|
||||||
metadata["sampler"] = inputs.get("sampler_name")
|
|
||||||
if not metadata["scheduler"]:
|
|
||||||
metadata["scheduler"] = inputs.get("scheduler")
|
|
||||||
|
|
||||||
return metadata
|
|
||||||
|
|
||||||
|
|
||||||
class MetadataBuilder:
|
|
||||||
"""构建用于保存的元数据"""
|
|
||||||
|
|
||||||
def build_parameters_text(self, metadata: Dict[str, Any]) -> str:
|
|
||||||
"""构建参数文本（用于嵌入图片）"""
|
|
||||||
parts = []
|
|
||||||
|
|
||||||
# 正向提示词
|
|
||||||
positive = metadata.get("positive_prompt")
|
|
||||||
if positive:
|
|
||||||
parts.append(str(positive))
|
|
||||||
|
|
||||||
# 负向提示词
|
|
||||||
negative = metadata.get("negative_prompt")
|
|
||||||
if negative:
|
|
||||||
parts.append(f"Negative prompt: {negative}")
|
|
||||||
|
|
||||||
# 技术参数
|
|
||||||
params = []
|
|
||||||
|
|
||||||
steps = metadata.get("steps")
|
|
||||||
if steps is not None:
|
|
||||||
params.append(f"Steps: {steps}")
|
|
||||||
|
|
||||||
sampler = metadata.get("sampler")
|
|
||||||
scheduler = metadata.get("scheduler")
|
|
||||||
if sampler:
|
|
||||||
if scheduler and scheduler != "normal":
|
|
||||||
params.append(f"Sampler: {sampler} {scheduler}")
|
|
||||||
else:
|
|
||||||
params.append(f"Sampler: {sampler}")
|
|
||||||
|
|
||||||
cfg = metadata.get("cfg")
|
|
||||||
if cfg is not None:
|
|
||||||
params.append(f"CFG scale: {cfg}")
|
|
||||||
|
|
||||||
seed = metadata.get("seed")
|
|
||||||
if seed is not None:
|
|
||||||
params.append(f"Seed: {seed}")
|
|
||||||
|
|
||||||
width = metadata.get("width")
|
|
||||||
height = metadata.get("height")
|
|
||||||
if width and height:
|
|
||||||
params.append(f"Size: {width}x{height}")
|
|
||||||
|
|
||||||
model = metadata.get("model")
|
|
||||||
if model:
|
|
||||||
model_name = os.path.splitext(os.path.basename(model))[0]
|
|
||||||
# 这里可以添加模型哈希计算，但为了性能考虑暂时省略
|
|
||||||
params.append(f"Model: {model_name}")
|
|
||||||
|
|
||||||
if params:
|
|
||||||
parts.append(", ".join(params))
|
|
||||||
|
|
||||||
return "\n".join(parts)
|
|
||||||
|
|
||||||
def build_metadata_json(self, folder_path: str, structure_mode: str,
|
|
||||||
workflow_metadata: Dict, user_inputs: Dict) -> str:
|
|
||||||
"""构建完整的元数据JSON"""
|
|
||||||
metadata = {
|
|
||||||
"folder_path": folder_path,
|
|
||||||
"structure_mode": structure_mode,
|
|
||||||
"timestamp": datetime.now().isoformat(),
|
|
||||||
"workflow_metadata": workflow_metadata,
|
|
||||||
"user_inputs": user_inputs,
|
|
||||||
"version": "1.0"
|
|
||||||
}
|
|
||||||
|
|
||||||
return json.dumps(metadata, ensure_ascii=False, indent=2)
|
|
||||||
@@ -1,230 +0,0 @@
|
|||||||
"""路径管理和文件名处理工具"""
|
|
||||||
|
|
||||||
import os
|
|
||||||
import re
|
|
||||||
from datetime import datetime
|
|
||||||
from typing import List, Dict, Any, Optional
|
|
||||||
|
|
||||||
|
|
||||||
class PathManager:
|
|
||||||
"""路径管理器"""
|
|
||||||
|
|
||||||
def __init__(self):
|
|
||||||
# Windows文件名非法字符
|
|
||||||
self.illegal_chars = r'[<>:"/\\|?*]'
|
|
||||||
# 日期格式替换表
|
|
||||||
self.date_formats = {
|
|
||||||
"yyyy": lambda dt: f"{dt.year:04d}",
|
|
||||||
"yy": lambda dt: f"{dt.year % 100:02d}",
|
|
||||||
"MM": lambda dt: f"{dt.month:02d}",
|
|
||||||
"dd": lambda dt: f"{dt.day:02d}",
|
|
||||||
"hh": lambda dt: f"{dt.hour:02d}",
|
|
||||||
"mm": lambda dt: f"{dt.minute:02d}",
|
|
||||||
"ss": lambda dt: f"{dt.second:02d}",
|
|
||||||
}
|
|
||||||
|
|
||||||
def sanitize_filename(self, name: str, max_length: int = 100) -> str:
|
|
||||||
"""安全地清理文件名，移除非法字符和路径遍历攻击"""
|
|
||||||
if not name:
|
|
||||||
return "untitled"
|
|
||||||
|
|
||||||
name = str(name)
|
|
||||||
|
|
||||||
# 使用os.path.basename确保只获取文件名部分，防止路径遍历
|
|
||||||
name = os.path.basename(name)
|
|
||||||
|
|
||||||
# 如果basename后为空（比如输入是路径分隔符），使用默认名称
|
|
||||||
if not name or name in ('.', '..'):
|
|
||||||
return "untitled"
|
|
||||||
|
|
||||||
# 移除或替换非法字符
|
|
||||||
name = re.sub(self.illegal_chars, '_', name)
|
|
||||||
|
|
||||||
# 处理空格和特殊字符
|
|
||||||
name = re.sub(r'\s+', '_', name) # 多个空格替换为下划线
|
|
||||||
name = re.sub(r'[^\w\-_.]', '_', name) # 只保留字母数字下划线破折号点
|
|
||||||
name = re.sub(r'_+', '_', name) # 多个下划线合并为一个
|
|
||||||
|
|
||||||
# 移除首尾的下划线和点
|
|
||||||
name = name.strip('_.')
|
|
||||||
|
|
||||||
# 防止Windows保留名称
|
|
||||||
reserved_names = [
|
|
||||||
'CON', 'PRN', 'AUX', 'NUL',
|
|
||||||
'COM1', 'COM2', 'COM3', 'COM4', 'COM5', 'COM6', 'COM7', 'COM8', 'COM9',
|
|
||||||
'LPT1', 'LPT2', 'LPT3', 'LPT4', 'LPT5', 'LPT6', 'LPT7', 'LPT8', 'LPT9'
|
|
||||||
]
|
|
||||||
name_upper = name.upper()
|
|
||||||
if name_upper in reserved_names or name_upper.split('.')[0] in reserved_names:
|
|
||||||
name = f"_{name}"
|
|
||||||
|
|
||||||
# 限制长度
|
|
||||||
if len(name) > max_length:
|
|
||||||
name = name[:max_length].rstrip('_.')
|
|
||||||
|
|
||||||
return name if name else "untitled"
|
|
||||||
|
|
||||||
def format_date(self, date_format: str, include_time: bool = False) -> str:
|
|
||||||
"""格式化日期字符串"""
|
|
||||||
now = datetime.now()
|
|
||||||
|
|
||||||
# 如果需要包含时间但格式中没有时间部分，自动添加
|
|
||||||
if include_time and not any(t in date_format for t in ["hh", "mm", "ss"]):
|
|
||||||
date_format += "_hh-mm-ss"
|
|
||||||
|
|
||||||
# 替换日期格式标记
|
|
||||||
result = date_format
|
|
||||||
for pattern, formatter in self.date_formats.items():
|
|
||||||
result = result.replace(pattern, formatter(now))
|
|
||||||
|
|
||||||
return result
|
|
||||||
|
|
||||||
def build_folder_structure(self, base_path: str, structure_mode: str,
|
|
||||||
metadata: Dict[str, Any], user_inputs: Dict[str, Any]) -> List[str]:
|
|
||||||
"""构建文件夹结构路径段"""
|
|
||||||
path_segments = []
|
|
||||||
|
|
||||||
if structure_mode == "date":
|
|
||||||
date_str = self.format_date(
|
|
||||||
user_inputs.get("date_format", "yyyy-MM-dd"),
|
|
||||||
user_inputs.get("include_time", False)
|
|
||||||
)
|
|
||||||
path_segments.append(date_str)
|
|
||||||
|
|
||||||
elif structure_mode == "model":
|
|
||||||
model = user_inputs.get("model_name") or metadata.get("model")
|
|
||||||
if model:
|
|
||||||
if user_inputs.get("model_short_name", True):
|
|
||||||
model = os.path.splitext(os.path.basename(model))[0]
|
|
||||||
path_segments.append(self.sanitize_filename(model))
|
|
||||||
|
|
||||||
elif structure_mode == "seed":
|
|
||||||
seed = user_inputs.get("seed_value")
|
|
||||||
if seed is None:
|
|
||||||
seed = metadata.get("seed")
|
|
||||||
if seed is not None:
|
|
||||||
path_segments.append(f"seed_{seed}")
|
|
||||||
|
|
||||||
elif structure_mode == "prompt":
|
|
||||||
prompt = user_inputs.get("prompt_text") or metadata.get("positive_prompt")
|
|
||||||
if prompt:
|
|
||||||
max_len = user_inputs.get("prompt_max_length", 50)
|
|
||||||
prompt_clean = str(prompt).replace("\n", " ").strip()[:max_len]
|
|
||||||
path_segments.append(self.sanitize_filename(prompt_clean))
|
|
||||||
|
|
||||||
elif structure_mode == "custom":
|
|
||||||
custom_path = user_inputs.get("custom_path", "")
|
|
||||||
if custom_path:
|
|
||||||
# 支持变量替换
|
|
||||||
variables = {
|
|
||||||
"{date}": self.format_date(user_inputs.get("date_format", "yyyy-MM-dd")),
|
|
||||||
"{model}": self.sanitize_filename(
|
|
||||||
user_inputs.get("model_name") or metadata.get("model", "model")
|
|
||||||
),
|
|
||||||
"{seed}": str(user_inputs.get("seed_value") or metadata.get("seed", 0)),
|
|
||||||
"{prompt}": self.sanitize_filename(
|
|
||||||
(user_inputs.get("prompt_text") or metadata.get("positive_prompt", ""))[:50]
|
|
||||||
),
|
|
||||||
}
|
|
||||||
|
|
||||||
for var, value in variables.items():
|
|
||||||
custom_path = custom_path.replace(var, value)
|
|
||||||
|
|
||||||
# 分割路径并清理
|
|
||||||
segments = [s.strip() for s in custom_path.split("/") if s.strip()]
|
|
||||||
path_segments.extend([self.sanitize_filename(s) for s in segments])
|
|
||||||
|
|
||||||
elif structure_mode == "auto":
|
|
||||||
# 自动模式：日期/模型/种子的组合
|
|
||||||
date_str = self.format_date("yyyy-MM-dd")
|
|
||||||
path_segments.append(date_str)
|
|
||||||
|
|
||||||
# 添加模型
|
|
||||||
model = user_inputs.get("model_name") or metadata.get("model")
|
|
||||||
if model:
|
|
||||||
if user_inputs.get("model_short_name", True):
|
|
||||||
model = os.path.splitext(os.path.basename(model))[0]
|
|
||||||
path_segments.append(self.sanitize_filename(model))
|
|
||||||
|
|
||||||
# 添加种子
|
|
||||||
seed = user_inputs.get("seed_value")
|
|
||||||
if seed is None:
|
|
||||||
seed = metadata.get("seed")
|
|
||||||
if seed is not None:
|
|
||||||
path_segments.append(f"seed_{seed}")
|
|
||||||
|
|
||||||
# 限制文件夹深度
|
|
||||||
max_depth = user_inputs.get("max_folder_depth", 5)
|
|
||||||
path_segments = path_segments[:max_depth]
|
|
||||||
|
|
||||||
return [seg for seg in path_segments if seg]
|
|
||||||
|
|
||||||
def resolve_base_path(self, base_folder: str, default_output_dir: str) -> str:
|
|
||||||
"""安全地解析基础路径"""
|
|
||||||
from ..utils.validators import InputValidator
|
|
||||||
|
|
||||||
if not base_folder or base_folder.lower() in ["", "output", "."]:
|
|
||||||
return default_output_dir
|
|
||||||
|
|
||||||
# 使用安全的路径连接方法
|
|
||||||
return InputValidator.secure_path_join(base_folder, "")
|
|
||||||
|
|
||||||
def build_full_path(self, base_folder: str, structure_mode: str,
|
|
||||||
metadata: Dict[str, Any], user_inputs: Dict[str, Any],
|
|
||||||
default_output_dir: str) -> str:
|
|
||||||
"""构建完整的文件夹路径"""
|
|
||||||
# 解析基础路径
|
|
||||||
base_path = self.resolve_base_path(base_folder, default_output_dir)
|
|
||||||
|
|
||||||
# 构建子文件夹结构
|
|
||||||
path_segments = self.build_folder_structure(base_path, structure_mode, metadata, user_inputs)
|
|
||||||
|
|
||||||
if path_segments:
|
|
||||||
final_path = os.path.join(base_path, *path_segments)
|
|
||||||
else:
|
|
||||||
final_path = base_path
|
|
||||||
from ..utils.validators import InputValidator
|
|
||||||
return InputValidator.secure_path_join(final_path, "")
|
|
||||||
|
|
||||||
def ensure_directory_exists(self, path: str) -> bool:
|
|
||||||
"""确保目录存在，如果不存在则创建"""
|
|
||||||
try:
|
|
||||||
os.makedirs(path, exist_ok=True)
|
|
||||||
return True
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[PathManager] 创建目录失败: {path}, 错误: {e}")
|
|
||||||
return False
|
|
||||||
|
|
||||||
def generate_unique_filename(self, directory: str, base_filename: str,
|
|
||||||
extension: str, overwrite: bool = False) -> str:
|
|
||||||
filename = f"{base_filename}{extension}"
|
|
||||||
filepath = os.path.join(directory, filename)
|
|
||||||
if not os.path.exists(filepath) or overwrite:
|
|
||||||
return filename
|
|
||||||
m = re.match(r"^(.*)_(\d+)$", base_filename)
|
|
||||||
if m:
|
|
||||||
root = m.group(1)
|
|
||||||
num_str = m.group(2)
|
|
||||||
padding = len(num_str)
|
|
||||||
try:
|
|
||||||
num = int(num_str)
|
|
||||||
except ValueError:
|
|
||||||
num = 1
|
|
||||||
while True:
|
|
||||||
num += 1
|
|
||||||
new_filename = f"{root}_{str(num).zfill(padding)}{extension}"
|
|
||||||
if not os.path.exists(os.path.join(directory, new_filename)):
|
|
||||||
return new_filename
|
|
||||||
if num > 9999999:
|
|
||||||
break
|
|
||||||
else:
|
|
||||||
counter = 1
|
|
||||||
while True:
|
|
||||||
new_filename = f"{base_filename}_{counter:03d}{extension}"
|
|
||||||
if not os.path.exists(os.path.join(directory, new_filename)):
|
|
||||||
return new_filename
|
|
||||||
counter += 1
|
|
||||||
if counter > 9999999:
|
|
||||||
break
|
|
||||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")[:17]
|
|
||||||
return f"{base_filename}_{timestamp}{extension}"
|
|
||||||
@@ -1,118 +0,0 @@
|
|||||||
from inspect import cleandoc
|
|
||||||
class Example:
|
|
||||||
"""
|
|
||||||
A example node
|
|
||||||
|
|
||||||
Class methods
|
|
||||||
-------------
|
|
||||||
INPUT_TYPES (dict):
|
|
||||||
Tell the main program input parameters of nodes.
|
|
||||||
IS_CHANGED:
|
|
||||||
optional method to control when the node is re executed.
|
|
||||||
|
|
||||||
Attributes
|
|
||||||
----------
|
|
||||||
RETURN_TYPES (`tuple`):
|
|
||||||
The type of each element in the output tulple.
|
|
||||||
RETURN_NAMES (`tuple`):
|
|
||||||
Optional: The name of each output in the output tulple.
|
|
||||||
FUNCTION (`str`):
|
|
||||||
The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute()
|
|
||||||
OUTPUT_NODE ([`bool`]):
|
|
||||||
If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example.
|
|
||||||
The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected.
|
|
||||||
Assumed to be False if not present.
|
|
||||||
CATEGORY (`str`):
|
|
||||||
The category the node should appear in the UI.
|
|
||||||
execute(s) -> tuple || None:
|
|
||||||
The entry point method. The name of this method must be the same as the value of property `FUNCTION`.
|
|
||||||
For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`.
|
|
||||||
"""
|
|
||||||
def __init__(self):
|
|
||||||
pass
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def INPUT_TYPES(s):
|
|
||||||
"""
|
|
||||||
Return a dictionary which contains config for all input fields.
|
|
||||||
Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
|
|
||||||
Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
|
|
||||||
The type can be a list for selection.
|
|
||||||
|
|
||||||
Returns: `dict`:
|
|
||||||
- Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
|
|
||||||
- Value input_fields (`dict`): Contains input fields config:
|
|
||||||
* Key field_name (`string`): Name of a entry-point method's argument
|
|
||||||
* Value field_config (`tuple`):
|
|
||||||
+ First value is a string indicate the type of field or a list for selection.
|
|
||||||
+ Secound value is a config for type "INT", "STRING" or "FLOAT".
|
|
||||||
"""
|
|
||||||
return {
|
|
||||||
"required": {
|
|
||||||
"image": ("Image", { "tooltip": "This is an image"}),
|
|
||||||
"int_field": ("INT", {
|
|
||||||
"default": 0,
|
|
||||||
"min": 0, #Minimum value
|
|
||||||
"max": 4096, #Maximum value
|
|
||||||
"step": 64, #Slider's step
|
|
||||||
"display": "number" # Cosmetic only: display as "number" or "slider"
|
|
||||||
}),
|
|
||||||
"float_field": ("FLOAT", {
|
|
||||||
"default": 1.0,
|
|
||||||
"min": 0.0,
|
|
||||||
"max": 10.0,
|
|
||||||
"step": 0.01,
|
|
||||||
"round": 0.001, #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
|
|
||||||
"display": "number"}),
|
|
||||||
"print_to_screen": (["enable", "disable"],),
|
|
||||||
"string_field": ("STRING", {
|
|
||||||
"multiline": False, #True if you want the field to look like the one on the ClipTextEncode node
|
|
||||||
"default": "Hello World!"
|
|
||||||
}),
|
|
||||||
},
|
|
||||||
}
|
|
||||||
|
|
||||||
RETURN_TYPES = ("IMAGE",)
|
|
||||||
#RETURN_NAMES = ("image_output_name",)
|
|
||||||
DESCRIPTION = cleandoc(__doc__)
|
|
||||||
FUNCTION = "test"
|
|
||||||
|
|
||||||
#OUTPUT_NODE = False
|
|
||||||
#OUTPUT_TOOLTIPS = ("",) # Tooltips for the output node
|
|
||||||
|
|
||||||
CATEGORY = "Example"
|
|
||||||
|
|
||||||
def test(self, image, string_field, int_field, float_field, print_to_screen):
|
|
||||||
if print_to_screen == "enable":
|
|
||||||
print(f"""Your input contains:
|
|
||||||
string_field aka input text: {string_field}
|
|
||||||
int_field: {int_field}
|
|
||||||
float_field: {float_field}
|
|
||||||
""")
|
|
||||||
#do some processing on the image, in this example I just invert it
|
|
||||||
image = 1.0 - image
|
|
||||||
return (image,)
|
|
||||||
|
|
||||||
"""
|
|
||||||
The node will always be re executed if any of the inputs change but
|
|
||||||
this method can be used to force the node to execute again even when the inputs don't change.
|
|
||||||
You can make this node return a number or a string. This value will be compared to the one returned the last time the node was
|
|
||||||
executed, if it is different the node will be executed again.
|
|
||||||
This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash
|
|
||||||
changes between executions the LoadImage node is executed again.
|
|
||||||
"""
|
|
||||||
#@classmethod
|
|
||||||
#def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
|
|
||||||
# return ""
|
|
||||||
|
|
||||||
|
|
||||||
# A dictionary that contains all nodes you want to export with their names
|
|
||||||
# NOTE: names should be globally unique
|
|
||||||
NODE_CLASS_MAPPINGS = {
|
|
||||||
"Example": Example
|
|
||||||
}
|
|
||||||
|
|
||||||
# A dictionary that contains the friendly/humanly readable titles for the nodes
|
|
||||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
||||||
"Example": "Example Node"
|
|
||||||
}
|
|
||||||
@@ -1,22 +1,18 @@
|
|||||||
"""SmartSaveImage 节点模块"""
|
"""SmartSaveImage 节点模块"""
|
||||||
|
|
||||||
from .folder_manager import SmartFolderManager
|
from .smart_save import SmartSaveImage
|
||||||
from .image_saver import SmartImageSaver
|
|
||||||
|
|
||||||
# 导出节点映射
|
# 节点 ID 保持为 SmartSaveImage，旧工作流可无缝复用
|
||||||
NODE_CLASS_MAPPINGS = {
|
NODE_CLASS_MAPPINGS = {
|
||||||
"SmartFolderManager": SmartFolderManager,
|
"SmartSaveImage": SmartSaveImage,
|
||||||
"SmartImageSaver": SmartImageSaver,
|
|
||||||
}
|
}
|
||||||
|
|
||||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||||
"SmartFolderManager": "SmartFolderManager",
|
"SmartSaveImage": "智能保存图片",
|
||||||
"SmartImageSaver": "SmartImageSaver",
|
|
||||||
}
|
}
|
||||||
|
|
||||||
__all__ = [
|
__all__ = [
|
||||||
"SmartFolderManager",
|
"SmartSaveImage",
|
||||||
"SmartImageSaver",
|
|
||||||
"NODE_CLASS_MAPPINGS",
|
"NODE_CLASS_MAPPINGS",
|
||||||
"NODE_DISPLAY_NAME_MAPPINGS",
|
"NODE_DISPLAY_NAME_MAPPINGS",
|
||||||
]
|
]
|
||||||
|
|||||||
@@ -1,412 +0,0 @@
|
|||||||
"""智能文件夹管理器节点"""
|
|
||||||
|
|
||||||
import os
|
|
||||||
import folder_paths
|
|
||||||
from ..core import MetadataExtractor, MetadataBuilder, PathManager
|
|
||||||
from ..utils import InputValidator
|
|
||||||
|
|
||||||
|
|
||||||
class SmartFolderManager:
|
|
||||||
"""智能文件夹管理器 - 负责管理文件夹结构和元数据收集"""
|
|
||||||
|
|
||||||
CATEGORY = "SmartSave"
|
|
||||||
RETURN_TYPES = ("IMAGE", "STRING", "STRING")
|
|
||||||
RETURN_NAMES = ("images", "folder_path", "metadata_json")
|
|
||||||
FUNCTION = "generate_path"
|
|
||||||
|
|
||||||
# 为翻译插件提供类名标识
|
|
||||||
@classmethod
|
|
||||||
def get_comfy_class(cls):
|
|
||||||
return "SmartFolderManager"
|
|
||||||
|
|
||||||
def __init__(self):
|
|
||||||
self.metadata_extractor = MetadataExtractor()
|
|
||||||
self.metadata_builder = MetadataBuilder()
|
|
||||||
self.path_manager = PathManager()
|
|
||||||
self.validator = InputValidator()
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def INPUT_TYPES(cls):
|
|
||||||
return {
|
|
||||||
"required": {
|
|
||||||
"images": ("IMAGE", {"tooltip": "Input images for extracting size info and passing to save node"}),
|
|
||||||
"base_folder": ("STRING", {
|
|
||||||
"default": "output",
|
|
||||||
"multiline": False,
|
|
||||||
"tooltip": "Base folder path, can be relative or absolute path"
|
|
||||||
}),
|
|
||||||
"create_subfolders": ("BOOLEAN", {
|
|
||||||
"default": True,
|
|
||||||
"tooltip": "Whether to automatically create subfolders"
|
|
||||||
}),
|
|
||||||
},
|
|
||||||
"optional": {
|
|
||||||
# 文件夹层级开关
|
|
||||||
"enable_date_folder": ("BOOLEAN", {
|
|
||||||
"default": True,
|
|
||||||
"tooltip": "Whether to create date folder"
|
|
||||||
}),
|
|
||||||
"enable_model_folder": ("BOOLEAN", {
|
|
||||||
"default": True,
|
|
||||||
"tooltip": "Whether to create model folder"
|
|
||||||
}),
|
|
||||||
"enable_seed_folder": ("BOOLEAN", {
|
|
||||||
"default": False,
|
|
||||||
"tooltip": "Whether to create seed folder"
|
|
||||||
}),
|
|
||||||
"enable_prompt_folder": ("BOOLEAN", {
|
|
||||||
"default": False,
|
|
||||||
"tooltip": "Whether to create prompt folder"
|
|
||||||
}),
|
|
||||||
"enable_custom_folder": ("BOOLEAN", {
|
|
||||||
"default": False,
|
|
||||||
"tooltip": "Whether to use custom folder"
|
|
||||||
}),
|
|
||||||
|
|
||||||
# 日期相关
|
|
||||||
"date_format": ("STRING", {
|
|
||||||
"default": "yyyy-MM-dd",
|
|
||||||
"multiline": False,
|
|
||||||
"tooltip": "Date format: yyyy=year MM=month dd=day hh=hour mm=minute ss=second"
|
|
||||||
}),
|
|
||||||
"include_time": ("BOOLEAN", {
|
|
||||||
"default": False,
|
|
||||||
"tooltip": "Whether to include time in date"
|
|
||||||
}),
|
|
||||||
|
|
||||||
# 模型信息来源选择
|
|
||||||
"model_source": (["auto", "manual"], {
|
|
||||||
"default": "auto",
|
|
||||||
"tooltip": "Model info source: auto=from metadata/external input, manual=manual selection"
|
|
||||||
}),
|
|
||||||
"manual_model_name": (folder_paths.get_filename_list("checkpoints"), {
|
|
||||||
"tooltip": "Manual model selection (only effective when model_source=manual)"
|
|
||||||
}),
|
|
||||||
"model_input": ("MODEL", {
|
|
||||||
"tooltip": "Input from model loader node (only effective when model_source=auto)"
|
|
||||||
}),
|
|
||||||
|
|
||||||
# 种子信息
|
|
||||||
"seed": ("STRING", {
|
|
||||||
"default": "0",
|
|
||||||
"multiline": False,
|
|
||||||
"tooltip": "Seed value (manual input or connect external node)"
|
|
||||||
}),
|
|
||||||
|
|
||||||
# 提示词信息
|
|
||||||
"positive_prompt": ("STRING", {
|
|
||||||
"default": "",
|
|
||||||
"multiline": True,
|
|
||||||
"tooltip": "Positive prompt (manual input or connect external node)"
|
|
||||||
}),
|
|
||||||
"negative_prompt": ("STRING", {
|
|
||||||
"default": "",
|
|
||||||
"multiline": True,
|
|
||||||
"tooltip": "Negative prompt (manual input or connect external node)"
|
|
||||||
}),
|
|
||||||
|
|
||||||
# 自定义路径
|
|
||||||
"custom_subfolder": ("STRING", {
|
|
||||||
"default": "",
|
|
||||||
"multiline": False,
|
|
||||||
"tooltip": "Custom subfolder name"
|
|
||||||
}),
|
|
||||||
|
|
||||||
# 显示选项
|
|
||||||
"model_short_name": ("BOOLEAN", {
|
|
||||||
"default": True,
|
|
||||||
"tooltip": "Use model short name (remove extension)"
|
|
||||||
}),
|
|
||||||
"prompt_max_length": ("INT", {
|
|
||||||
"default": 50,
|
|
||||||
"min": 10,
|
|
||||||
"max": 200,
|
|
||||||
"tooltip": "Maximum length for prompt folder name"
|
|
||||||
}),
|
|
||||||
"sanitize_names": ("BOOLEAN", {
|
|
||||||
"default": True,
|
|
||||||
"tooltip": "Clean illegal characters in filenames"
|
|
||||||
}),
|
|
||||||
},
|
|
||||||
"hidden": {
|
|
||||||
"prompt": "PROMPT",
|
|
||||||
"extra_pnginfo": "EXTRA_PNGINFO",
|
|
||||||
},
|
|
||||||
}
|
|
||||||
|
|
||||||
def generate_path(self, images, base_folder, create_subfolders,
|
|
||||||
enable_date_folder=True, enable_model_folder=True,
|
|
||||||
enable_seed_folder=False, enable_prompt_folder=False, enable_custom_folder=False,
|
|
||||||
date_format="yyyy-MM-dd", include_time=False,
|
|
||||||
model_source="auto", manual_model_name=None, model_input=None,
|
|
||||||
seed="0", positive_prompt="", negative_prompt="",
|
|
||||||
custom_subfolder="", model_short_name=True, prompt_max_length=50, sanitize_names=True,
|
|
||||||
prompt=None, extra_pnginfo=None):
|
|
||||||
"""生成文件夹路径和元数据"""
|
|
||||||
|
|
||||||
# 验证输入
|
|
||||||
if not self.validator.validate_folder_path(base_folder):
|
|
||||||
print(f"[SmartFolderManager] 无效的基础文件夹路径: {base_folder}")
|
|
||||||
base_folder = "output"
|
|
||||||
|
|
||||||
if date_format and not self.validator.validate_date_format(date_format):
|
|
||||||
print(f"[SmartFolderManager] 无效的日期格式: {date_format}")
|
|
||||||
date_format = "yyyy-MM-dd"
|
|
||||||
|
|
||||||
# 清理输入字符串
|
|
||||||
positive_prompt = self.validator.sanitize_input_string(positive_prompt, prompt_max_length * 2)
|
|
||||||
negative_prompt = self.validator.sanitize_input_string(negative_prompt, prompt_max_length * 2)
|
|
||||||
custom_subfolder = self.validator.sanitize_input_string(custom_subfolder, 100)
|
|
||||||
|
|
||||||
# 从图片中提取尺寸信息
|
|
||||||
image_metadata = {}
|
|
||||||
if images is not None and len(images) > 0:
|
|
||||||
try:
|
|
||||||
# 获取第一张图片的尺寸
|
|
||||||
if len(images.shape) == 4: # [batch, height, width, channels]
|
|
||||||
height, width = images.shape[1], images.shape[2]
|
|
||||||
elif len(images.shape) == 3: # [height, width, channels]
|
|
||||||
height, width = images.shape[0], images.shape[1]
|
|
||||||
else:
|
|
||||||
height, width = 0, 0
|
|
||||||
|
|
||||||
image_metadata["width"] = width
|
|
||||||
image_metadata["height"] = height
|
|
||||||
print(f"[SmartFolderManager] 从图片提取尺寸: {width}x{height}")
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[SmartFolderManager] 提取图片尺寸失败: {e}")
|
|
||||||
image_metadata["width"] = 0
|
|
||||||
image_metadata["height"] = 0
|
|
||||||
|
|
||||||
# 1. 根据用户选择构建元数据
|
|
||||||
final_metadata = {}
|
|
||||||
|
|
||||||
# 模型信息
|
|
||||||
if model_source == "manual":
|
|
||||||
final_metadata["model"] = manual_model_name
|
|
||||||
else: # auto
|
|
||||||
# 优先从外部输入获取
|
|
||||||
if model_input is not None:
|
|
||||||
external_model = self.extract_model_from_input(model_input)
|
|
||||||
if external_model:
|
|
||||||
final_metadata["model"] = external_model
|
|
||||||
else:
|
|
||||||
# 从工作流元数据获取
|
|
||||||
workflow_metadata = self.metadata_extractor.extract_from_prompt(prompt)
|
|
||||||
final_metadata["model"] = workflow_metadata.get("model")
|
|
||||||
else:
|
|
||||||
# 从工作流元数据获取
|
|
||||||
workflow_metadata = self.metadata_extractor.extract_from_prompt(prompt)
|
|
||||||
final_metadata["model"] = workflow_metadata.get("model")
|
|
||||||
|
|
||||||
# 种子信息
|
|
||||||
final_metadata["seed"] = seed
|
|
||||||
|
|
||||||
# 提示词信息
|
|
||||||
final_metadata["positive_prompt"] = positive_prompt if positive_prompt.strip() else None
|
|
||||||
final_metadata["negative_prompt"] = negative_prompt if negative_prompt.strip() else None
|
|
||||||
|
|
||||||
# 尺寸信息（直接从图片获取）
|
|
||||||
final_metadata["width"] = image_metadata.get("width", 0)
|
|
||||||
final_metadata["height"] = image_metadata.get("height", 0)
|
|
||||||
|
|
||||||
# 2. 构建文件夹路径
|
|
||||||
path_segments = []
|
|
||||||
base_path = self.path_manager.resolve_base_path(base_folder, folder_paths.get_output_directory())
|
|
||||||
|
|
||||||
# 按开关添加各层文件夹
|
|
||||||
if enable_date_folder:
|
|
||||||
date_str = self.path_manager.format_date(date_format, include_time)
|
|
||||||
path_segments.append(date_str)
|
|
||||||
|
|
||||||
if enable_model_folder and final_metadata.get("model"):
|
|
||||||
model_name = final_metadata["model"]
|
|
||||||
if model_short_name:
|
|
||||||
model_name = os.path.splitext(os.path.basename(model_name))[0]
|
|
||||||
if sanitize_names:
|
|
||||||
model_name = self.path_manager.sanitize_filename(model_name)
|
|
||||||
path_segments.append(model_name)
|
|
||||||
|
|
||||||
if enable_seed_folder and final_metadata.get("seed") is not None:
|
|
||||||
seed_str = f"seed_{final_metadata['seed']}"
|
|
||||||
path_segments.append(seed_str)
|
|
||||||
|
|
||||||
if enable_prompt_folder and final_metadata.get("positive_prompt"):
|
|
||||||
prompt_text = final_metadata["positive_prompt"]
|
|
||||||
prompt_clean = prompt_text.replace("\n", " ").strip()[:prompt_max_length]
|
|
||||||
if sanitize_names:
|
|
||||||
prompt_clean = self.path_manager.sanitize_filename(prompt_clean)
|
|
||||||
path_segments.append(prompt_clean)
|
|
||||||
|
|
||||||
if enable_custom_folder and custom_subfolder:
|
|
||||||
if sanitize_names:
|
|
||||||
custom_subfolder = self.path_manager.sanitize_filename(custom_subfolder)
|
|
||||||
path_segments.append(custom_subfolder)
|
|
||||||
|
|
||||||
if path_segments:
|
|
||||||
folder_path = os.path.join(base_path, *path_segments)
|
|
||||||
else:
|
|
||||||
folder_path = base_path
|
|
||||||
folder_path = InputValidator.secure_path_join(folder_path, "")
|
|
||||||
|
|
||||||
# 创建文件夹
|
|
||||||
if create_subfolders:
|
|
||||||
if not self.path_manager.ensure_directory_exists(folder_path):
|
|
||||||
print(f"[SmartFolderManager] 创建文件夹失败，使用默认输出目录")
|
|
||||||
folder_path = folder_paths.get_output_directory()
|
|
||||||
else:
|
|
||||||
print(f"[SmartFolderManager] 文件夹路径: {folder_path}")
|
|
||||||
|
|
||||||
# 构建用户输入记录
|
|
||||||
user_inputs = {
|
|
||||||
"enable_date_folder": enable_date_folder,
|
|
||||||
"enable_model_folder": enable_model_folder,
|
|
||||||
"enable_seed_folder": enable_seed_folder,
|
|
||||||
"enable_prompt_folder": enable_prompt_folder,
|
|
||||||
"enable_custom_folder": enable_custom_folder,
|
|
||||||
"date_format": date_format,
|
|
||||||
"include_time": include_time,
|
|
||||||
"model_source": model_source,
|
|
||||||
"manual_model_name": manual_model_name,
|
|
||||||
"seed": seed,
|
|
||||||
"positive_prompt": positive_prompt,
|
|
||||||
"negative_prompt": negative_prompt,
|
|
||||||
"custom_subfolder": custom_subfolder,
|
|
||||||
}
|
|
||||||
|
|
||||||
# 构建元数据JSON
|
|
||||||
metadata_json = self.metadata_builder.build_metadata_json(
|
|
||||||
folder_path, "flexible", final_metadata, user_inputs
|
|
||||||
)
|
|
||||||
|
|
||||||
return (images, folder_path, metadata_json)
|
|
||||||
|
|
||||||
def extract_model_from_input(self, model_input):
|
|
||||||
"""从模型输入中提取模型名称（增强版）"""
|
|
||||||
if model_input is None:
|
|
||||||
return None
|
|
||||||
|
|
||||||
try:
|
|
||||||
# 检查ModelPatcher的关键属性
|
|
||||||
key_attrs = ['model_path', 'checkpoint_path', 'ckpt_path', 'model_file', 'filename']
|
|
||||||
for attr in key_attrs:
|
|
||||||
if hasattr(model_input, attr):
|
|
||||||
value = getattr(model_input, attr)
|
|
||||||
if value:
|
|
||||||
return value
|
|
||||||
|
|
||||||
# 尝试从ModelPatcher的特殊属性中提取信息
|
|
||||||
# 检查model_options（可能包含模型信息）
|
|
||||||
if hasattr(model_input, 'model_options') and model_input.model_options:
|
|
||||||
if isinstance(model_input.model_options, dict):
|
|
||||||
for key in ['model_path', 'checkpoint_path', 'filename']:
|
|
||||||
if key in model_input.model_options:
|
|
||||||
value = model_input.model_options[key]
|
|
||||||
if value:
|
|
||||||
return value
|
|
||||||
|
|
||||||
# 检查attachments（可能包含模型信息）
|
|
||||||
if hasattr(model_input, 'attachments') and model_input.attachments:
|
|
||||||
if isinstance(model_input.attachments, dict):
|
|
||||||
for key, value in model_input.attachments.items():
|
|
||||||
if isinstance(value, str) and ('.safetensors' in value or '.ckpt' in value):
|
|
||||||
return value
|
|
||||||
|
|
||||||
# 最后的尝试：检查parent属性
|
|
||||||
if hasattr(model_input, 'parent') and model_input.parent:
|
|
||||||
parent_model = self.extract_model_from_input(model_input.parent)
|
|
||||||
if parent_model:
|
|
||||||
return parent_model
|
|
||||||
|
|
||||||
# 字典格式
|
|
||||||
if isinstance(model_input, dict):
|
|
||||||
return model_input.get('model_path') or model_input.get('checkpoint_path')
|
|
||||||
|
|
||||||
# 尝试其他可能的属性名
|
|
||||||
possible_attrs = ['checkpoint_path', 'ckpt_path', 'model_file', 'filename', 'path']
|
|
||||||
for attr in possible_attrs:
|
|
||||||
if hasattr(model_input, attr):
|
|
||||||
value = getattr(model_input, attr)
|
|
||||||
if value:
|
|
||||||
return value
|
|
||||||
|
|
||||||
# 如果是tuple或list，检查第一个元素
|
|
||||||
if isinstance(model_input, (tuple, list)) and len(model_input) > 0:
|
|
||||||
return self.extract_model_from_input(model_input[0])
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
pass # 静默处理错误
|
|
||||||
|
|
||||||
return None
|
|
||||||
|
|
||||||
def extract_from_external_inputs(self, model_input, conditioning_positive, conditioning_negative, latent_input):
|
|
||||||
"""从外部输入节点提取元数据"""
|
|
||||||
metadata = {}
|
|
||||||
|
|
||||||
# 从模型输入提取模型名称
|
|
||||||
if model_input is not None:
|
|
||||||
try:
|
|
||||||
# 尝试多种方式从模型对象中提取信息
|
|
||||||
model_name = None
|
|
||||||
|
|
||||||
# 方法1: 检查是否有model_path属性
|
|
||||||
if hasattr(model_input, 'model_path'):
|
|
||||||
model_name = model_input.model_path
|
|
||||||
|
|
||||||
# 方法2: 检查model对象的属性
|
|
||||||
elif hasattr(model_input, 'model'):
|
|
||||||
model_obj = model_input.model
|
|
||||||
if hasattr(model_obj, 'model_path'):
|
|
||||||
model_name = model_obj.model_path
|
|
||||||
elif hasattr(model_obj, 'checkpoint_path'):
|
|
||||||
model_name = model_obj.checkpoint_path
|
|
||||||
|
|
||||||
# 方法3: 检查是否是字典格式
|
|
||||||
elif isinstance(model_input, dict):
|
|
||||||
model_name = model_input.get('model_path') or model_input.get('checkpoint_path')
|
|
||||||
|
|
||||||
if model_name:
|
|
||||||
metadata["model"] = model_name
|
|
||||||
print(f"[SmartFolderManager] 从外部输入提取模型: {model_name}")
|
|
||||||
else:
|
|
||||||
print(f"[SmartFolderManager] 模型输入已连接，但无法提取模型名称")
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[SmartFolderManager] 从模型输入提取信息失败: {e}")
|
|
||||||
|
|
||||||
# 从conditioning提取提示词
|
|
||||||
# 注意：ComfyUI的conditioning对象通常不直接包含原始文本
|
|
||||||
# 但我们可以尝试一些方法
|
|
||||||
if conditioning_positive is not None:
|
|
||||||
try:
|
|
||||||
# conditioning通常是一个包含编码后数据的复杂结构
|
|
||||||
# 我们标记有外部conditioning输入，但文本提取仍依赖工作流
|
|
||||||
metadata["has_positive_conditioning"] = True
|
|
||||||
print(f"[SmartFolderManager] 检测到正向条件输入")
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[SmartFolderManager] 处理正向条件输入失败: {e}")
|
|
||||||
|
|
||||||
if conditioning_negative is not None:
|
|
||||||
try:
|
|
||||||
metadata["has_negative_conditioning"] = True
|
|
||||||
print(f"[SmartFolderManager] 检测到负向条件输入")
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[SmartFolderManager] 处理负向条件输入失败: {e}")
|
|
||||||
|
|
||||||
# 从latent提取尺寸信息
|
|
||||||
if latent_input is not None:
|
|
||||||
try:
|
|
||||||
if isinstance(latent_input, dict) and "samples" in latent_input:
|
|
||||||
samples = latent_input["samples"]
|
|
||||||
if hasattr(samples, 'shape') and len(samples.shape) >= 3:
|
|
||||||
# latent通常是 [batch, channels, height, width]
|
|
||||||
# 需要乘以8因为latent空间是1/8分辨率
|
|
||||||
height = samples.shape[-2] * 8
|
|
||||||
width = samples.shape[-1] * 8
|
|
||||||
metadata["width"] = width
|
|
||||||
metadata["height"] = height
|
|
||||||
print(f"[SmartFolderManager] 从latent提取尺寸: {width}x{height}")
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[SmartFolderManager] 从latent提取尺寸失败: {e}")
|
|
||||||
|
|
||||||
return metadata
|
|
||||||
@@ -1,332 +0,0 @@
|
|||||||
"""智能图片保存器节点"""
|
|
||||||
|
|
||||||
import os
|
|
||||||
import json
|
|
||||||
from datetime import datetime
|
|
||||||
import folder_paths
|
|
||||||
import nodes
|
|
||||||
from ..core import MetadataBuilder, ImageProcessor, PathManager
|
|
||||||
from ..utils import InputValidator
|
|
||||||
|
|
||||||
|
|
||||||
class SmartImageSaver:
|
|
||||||
"""智能图片保存器 - 负责图片保存、格式转换和压缩"""
|
|
||||||
|
|
||||||
CATEGORY = "SmartSave"
|
|
||||||
OUTPUT_NODE = True
|
|
||||||
RETURN_TYPES = ("IMAGE",)
|
|
||||||
RETURN_NAMES = ("images",)
|
|
||||||
FUNCTION = "save_images"
|
|
||||||
|
|
||||||
# 为翻译插件提供类名标识
|
|
||||||
@classmethod
|
|
||||||
def get_comfy_class(cls):
|
|
||||||
return "SmartImageSaver"
|
|
||||||
|
|
||||||
def __init__(self):
|
|
||||||
self.metadata_builder = MetadataBuilder()
|
|
||||||
self.image_processor = ImageProcessor()
|
|
||||||
self.path_manager = PathManager()
|
|
||||||
self.validator = InputValidator()
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def INPUT_TYPES(cls):
|
|
||||||
return {
|
|
||||||
"required": {
|
|
||||||
"images": ("IMAGE", {"tooltip": "Images to save (usually from SmartFolderManager)"}),
|
|
||||||
"folder_path": ("STRING", {
|
|
||||||
"default": "",
|
|
||||||
"multiline": False,
|
|
||||||
"tooltip": "Save path (from SmartFolderManager)"
|
|
||||||
}),
|
|
||||||
"metadata_json": ("STRING", {
|
|
||||||
"default": "",
|
|
||||||
"multiline": True,
|
|
||||||
"tooltip": "Metadata JSON (from SmartFolderManager)"
|
|
||||||
}),
|
|
||||||
"filename_prefix": ("STRING", {
|
|
||||||
"default": "image",
|
|
||||||
"multiline": False,
|
|
||||||
"tooltip": "Filename prefix"
|
|
||||||
}),
|
|
||||||
"file_format": (["png", "jpeg", "webp", "bmp", "tiff"], {
|
|
||||||
"default": "png",
|
|
||||||
"tooltip": "Image format"
|
|
||||||
}),
|
|
||||||
"preview_mode": (["save_and_preview", "preview_only", "save_only"], {
|
|
||||||
"default": "save_and_preview",
|
|
||||||
"tooltip": "Save mode"
|
|
||||||
}),
|
|
||||||
},
|
|
||||||
"optional": {
|
|
||||||
# 文件名选项
|
|
||||||
"add_timestamp": ("BOOLEAN", {
|
|
||||||
"default": False,
|
|
||||||
"tooltip": "Add timestamp to filename"
|
|
||||||
}),
|
|
||||||
"add_counter": ("BOOLEAN", {
|
|
||||||
"default": True,
|
|
||||||
"tooltip": "Add counter (for multiple images)"
|
|
||||||
}),
|
|
||||||
"counter_start": ("INT", {
|
|
||||||
"default": 1,
|
|
||||||
"min": 0,
|
|
||||||
"max": 99999,
|
|
||||||
"tooltip": "Counter start value"
|
|
||||||
}),
|
|
||||||
"counter_padding": ("INT", {
|
|
||||||
"default": 4,
|
|
||||||
"min": 1,
|
|
||||||
"max": 10,
|
|
||||||
"tooltip": "Counter padding (zero-fill)"
|
|
||||||
}),
|
|
||||||
|
|
||||||
# 图片质量选项
|
|
||||||
"jpeg_quality": ("INT", {
|
|
||||||
"default": 95,
|
|
||||||
"min": 1,
|
|
||||||
"max": 100,
|
|
||||||
"tooltip": "JPEG quality (1-100)"
|
|
||||||
}),
|
|
||||||
"webp_quality": ("INT", {
|
|
||||||
"default": 90,
|
|
||||||
"min": 1,
|
|
||||||
"max": 100,
|
|
||||||
"tooltip": "WebP quality (1-100)"
|
|
||||||
}),
|
|
||||||
"webp_lossless": ("BOOLEAN", {
|
|
||||||
"default": False,
|
|
||||||
"tooltip": "WebP lossless compression"
|
|
||||||
}),
|
|
||||||
"png_compression": ("INT", {
|
|
||||||
"default": 6,
|
|
||||||
"min": 0,
|
|
||||||
"max": 9,
|
|
||||||
"tooltip": "PNG compression level (0-9)"
|
|
||||||
}),
|
|
||||||
|
|
||||||
# 元数据选项
|
|
||||||
"embed_metadata": ("BOOLEAN", {
|
|
||||||
"default": True,
|
|
||||||
"tooltip": "Embed metadata into image"
|
|
||||||
}),
|
|
||||||
"embed_workflow": ("BOOLEAN", {
|
|
||||||
"default": True,
|
|
||||||
"tooltip": "Embed workflow information"
|
|
||||||
}),
|
|
||||||
|
|
||||||
# 高级选项
|
|
||||||
"overwrite_existing": ("BOOLEAN", {
|
|
||||||
"default": False,
|
|
||||||
"tooltip": "Overwrite existing files"
|
|
||||||
}),
|
|
||||||
"create_backup": ("BOOLEAN", {
|
|
||||||
"default": False,
|
|
||||||
"tooltip": "Create backup for overwritten files"
|
|
||||||
}),
|
|
||||||
"optimize_size": ("BOOLEAN", {
|
|
||||||
"default": False,
|
|
||||||
"tooltip": "Optimize file size"
|
|
||||||
}),
|
|
||||||
},
|
|
||||||
"hidden": {
|
|
||||||
"prompt": "PROMPT",
|
|
||||||
"extra_pnginfo": "EXTRA_PNGINFO",
|
|
||||||
},
|
|
||||||
}
|
|
||||||
|
|
||||||
def generate_filename(self, prefix, index, add_timestamp, add_counter,
|
|
||||||
counter_start, counter_padding, file_format):
|
|
||||||
"""生成文件名"""
|
|
||||||
# 清理前缀
|
|
||||||
prefix = self.path_manager.sanitize_filename(prefix) if prefix else "image"
|
|
||||||
|
|
||||||
parts = [prefix]
|
|
||||||
|
|
||||||
# 添加时间戳
|
|
||||||
if add_timestamp:
|
|
||||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
|
||||||
parts.append(timestamp)
|
|
||||||
|
|
||||||
# 添加计数器
|
|
||||||
if add_counter:
|
|
||||||
counter = str(counter_start + index).zfill(counter_padding)
|
|
||||||
parts.append(counter)
|
|
||||||
|
|
||||||
# 组合文件名
|
|
||||||
filename = "_".join(parts)
|
|
||||||
|
|
||||||
# 添加扩展名
|
|
||||||
extensions = {
|
|
||||||
"png": ".png",
|
|
||||||
"jpeg": ".jpg",
|
|
||||||
"webp": ".webp",
|
|
||||||
"bmp": ".bmp",
|
|
||||||
"tiff": ".tiff"
|
|
||||||
}
|
|
||||||
|
|
||||||
filename += extensions.get(file_format, ".png")
|
|
||||||
return filename
|
|
||||||
|
|
||||||
def parse_metadata_json(self, metadata_json):
|
|
||||||
"""解析元数据JSON"""
|
|
||||||
if not metadata_json:
|
|
||||||
return {}
|
|
||||||
|
|
||||||
try:
|
|
||||||
return json.loads(metadata_json)
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[SmartImageSaver] 解析元数据JSON失败: {e}")
|
|
||||||
return {}
|
|
||||||
|
|
||||||
def save_images(self, images, folder_path, metadata_json, filename_prefix, file_format, preview_mode,
|
|
||||||
add_timestamp=False, add_counter=True, counter_start=1, counter_padding=4,
|
|
||||||
jpeg_quality=95, webp_quality=90, webp_lossless=False, png_compression=6,
|
|
||||||
embed_metadata=True, embed_workflow=False,
|
|
||||||
overwrite_existing=False, create_backup=False, optimize_size=False,
|
|
||||||
prompt=None, extra_pnginfo=None):
|
|
||||||
"""保存图片主函数"""
|
|
||||||
|
|
||||||
# 验证输入
|
|
||||||
if not self.validator.validate_file_format(file_format):
|
|
||||||
print(f"[SmartImageSaver] 不支持的文件格式: {file_format}")
|
|
||||||
file_format = "png"
|
|
||||||
|
|
||||||
if not self.validator.validate_quality_value(jpeg_quality):
|
|
||||||
jpeg_quality = 95
|
|
||||||
|
|
||||||
if not self.validator.validate_quality_value(webp_quality):
|
|
||||||
webp_quality = 90
|
|
||||||
|
|
||||||
if not self.validator.validate_counter_settings(counter_start, counter_padding):
|
|
||||||
counter_start, counter_padding = 1, 4
|
|
||||||
|
|
||||||
# 清理输入
|
|
||||||
filename_prefix = self.validator.sanitize_input_string(filename_prefix, 100)
|
|
||||||
|
|
||||||
# 处理预览模式
|
|
||||||
if preview_mode == "preview_only":
|
|
||||||
# 只预览，不保存
|
|
||||||
try:
|
|
||||||
result = nodes.PreviewImage().save_images(
|
|
||||||
images,
|
|
||||||
filename_prefix=filename_prefix or "preview"
|
|
||||||
)
|
|
||||||
return {"ui": result.get("ui", {}), "result": (images,)}
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[SmartImageSaver] 预览失败: {e}")
|
|
||||||
return {"ui": {}, "result": (images,)}
|
|
||||||
|
|
||||||
print(f"[SmartImageSaver] 接收到的folder_path: '{folder_path}' (类型: {type(folder_path)})")
|
|
||||||
folder_path = InputValidator.secure_path_join(folder_path or "", "")
|
|
||||||
if not os.path.exists(folder_path):
|
|
||||||
try:
|
|
||||||
os.makedirs(folder_path, exist_ok=True)
|
|
||||||
except Exception as e:
|
|
||||||
folder_path = folder_paths.get_output_directory()
|
|
||||||
|
|
||||||
# 解析元数据
|
|
||||||
metadata_dict = self.parse_metadata_json(metadata_json)
|
|
||||||
workflow_metadata = metadata_dict.get("workflow_metadata", {})
|
|
||||||
|
|
||||||
# 获取图片尺寸
|
|
||||||
if len(images) > 0:
|
|
||||||
width, height = self.image_processor.get_image_info(images[0])
|
|
||||||
workflow_metadata.update({"width": width, "height": height})
|
|
||||||
|
|
||||||
# 构建元数据文本
|
|
||||||
metadata_text = None
|
|
||||||
if embed_metadata:
|
|
||||||
metadata_text = self.metadata_builder.build_parameters_text(workflow_metadata)
|
|
||||||
|
|
||||||
# 准备质量设置
|
|
||||||
quality_settings = {
|
|
||||||
"jpeg_quality": jpeg_quality,
|
|
||||||
"webp_quality": webp_quality,
|
|
||||||
"webp_lossless": webp_lossless,
|
|
||||||
"png_compression": png_compression,
|
|
||||||
"optimize_size": optimize_size,
|
|
||||||
}
|
|
||||||
|
|
||||||
# 准备工作流数据
|
|
||||||
workflow_data = None
|
|
||||||
if embed_workflow and extra_pnginfo and "workflow" in extra_pnginfo:
|
|
||||||
workflow_data = extra_pnginfo["workflow"]
|
|
||||||
|
|
||||||
# 保存图片
|
|
||||||
saved_images = []
|
|
||||||
|
|
||||||
for i, image in enumerate(images):
|
|
||||||
# 生成文件名
|
|
||||||
filename = self.generate_filename(
|
|
||||||
filename_prefix, i, add_timestamp, add_counter,
|
|
||||||
counter_start, counter_padding, file_format
|
|
||||||
)
|
|
||||||
|
|
||||||
# 处理文件名冲突
|
|
||||||
if not overwrite_existing:
|
|
||||||
filename = self.path_manager.generate_unique_filename(
|
|
||||||
folder_path,
|
|
||||||
os.path.splitext(filename)[0],
|
|
||||||
os.path.splitext(filename)[1],
|
|
||||||
overwrite_existing
|
|
||||||
)
|
|
||||||
|
|
||||||
filepath_candidate = os.path.realpath(os.path.join(folder_path, filename))
|
|
||||||
base_real = os.path.realpath(folder_path)
|
|
||||||
try:
|
|
||||||
if os.path.commonpath([base_real, filepath_candidate]) == base_real:
|
|
||||||
filepath = filepath_candidate
|
|
||||||
else:
|
|
||||||
name_root, name_ext = os.path.splitext(filename)
|
|
||||||
safe_name = self.path_manager.sanitize_filename(os.path.basename(name_root)) + name_ext
|
|
||||||
filepath = os.path.realpath(os.path.join(base_real, safe_name))
|
|
||||||
except ValueError:
|
|
||||||
name_root, name_ext = os.path.splitext(filename)
|
|
||||||
safe_name = self.path_manager.sanitize_filename(os.path.basename(name_root)) + name_ext
|
|
||||||
filepath = os.path.realpath(os.path.join(folder_paths.get_output_directory(), safe_name))
|
|
||||||
|
|
||||||
# 创建备份
|
|
||||||
if overwrite_existing and create_backup and os.path.exists(filepath):
|
|
||||||
self.image_processor.create_backup(filepath)
|
|
||||||
|
|
||||||
# 保存图片
|
|
||||||
try:
|
|
||||||
success = self.image_processor.save_image(
|
|
||||||
image, filepath, file_format, quality_settings,
|
|
||||||
metadata_text, embed_workflow, workflow_data
|
|
||||||
)
|
|
||||||
|
|
||||||
if success:
|
|
||||||
# 获取相对路径用于UI显示
|
|
||||||
try:
|
|
||||||
rel_folder = os.path.relpath(folder_path, folder_paths.get_output_directory())
|
|
||||||
if rel_folder == ".":
|
|
||||||
rel_folder = ""
|
|
||||||
except ValueError:
|
|
||||||
# 如果是绝对路径且不在输出目录下
|
|
||||||
rel_folder = os.path.basename(folder_path)
|
|
||||||
|
|
||||||
saved_images.append({
|
|
||||||
"filename": filename,
|
|
||||||
"subfolder": rel_folder,
|
|
||||||
"type": "output"
|
|
||||||
})
|
|
||||||
print(f"[SmartImageSaver] 保存成功: {filepath}")
|
|
||||||
else:
|
|
||||||
print(f"[SmartImageSaver] 保存失败: {filepath}")
|
|
||||||
|
|
||||||
except Exception as e:
|
|
||||||
print(f"[SmartImageSaver] 保存图片时出错: {e}")
|
|
||||||
|
|
||||||
# 返回结果
|
|
||||||
result = {"result": (images,)}
|
|
||||||
|
|
||||||
if preview_mode == "save_and_preview" and saved_images:
|
|
||||||
# 保存并预览
|
|
||||||
result["ui"] = {"images": saved_images}
|
|
||||||
elif preview_mode == "save_only":
|
|
||||||
# 仅保存，不显示预览 - 不返回images避免显示预览
|
|
||||||
result["ui"] = {}
|
|
||||||
|
|
||||||
return result
|
|
||||||
@@ -0,0 +1,605 @@
|
|||||||
|
"""SmartSaveImage —— 规则可视化、目录可预览、模型读取健壮的图片保存节点。
|
||||||
|
|
||||||
|
设计目标（对应用户诉求）：
|
||||||
|
1. 所见即所得：保存规则（目录 / 文件名模板）在节点里清晰展示，配合前端实时预览，
|
||||||
|
不需要保存完再去猜文件去哪了。
|
||||||
|
2. 目录规则强大：支持根目录选择、多级目录模板、丰富的占位符（日期 / 模型 / lora /
|
||||||
|
采样器 / 尺寸 / 提示词 / 计数器等），并做安全清洗。
|
||||||
|
3. 模型读取健壮：从工作流 prompt 中广度扫描各种加载器、各种输入键、其它插件的自定义
|
||||||
|
节点，尽最大努力拿到 checkpoint / unet / lora / vae 名称，且支持手动覆盖。
|
||||||
|
|
||||||
|
本文件不依赖任何旧的 core/utils 模块，方便独立维护。
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import json
|
||||||
|
import os
|
||||||
|
import re
|
||||||
|
import tempfile
|
||||||
|
from datetime import datetime
|
||||||
|
from pathlib import Path
|
||||||
|
from typing import Any
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from PIL import Image
|
||||||
|
from PIL.PngImagePlugin import PngInfo
|
||||||
|
|
||||||
|
import folder_paths
|
||||||
|
import nodes as comfy_nodes
|
||||||
|
|
||||||
|
try:
|
||||||
|
from comfy.cli_args import args
|
||||||
|
except Exception: # pragma: no cover - 测试环境可能没有
|
||||||
|
args = None
|
||||||
|
|
||||||
|
|
||||||
|
# --------------------------------------------------------------------------- #
|
||||||
|
# 常量
|
||||||
|
# --------------------------------------------------------------------------- #
|
||||||
|
|
||||||
|
_INVALID_NAME_CHARS = re.compile(r'[<>:"/\\|?*\x00-\x1f]')
|
||||||
|
_DATE_TOKEN = re.compile(r"%date(?::([^%]+))?%", re.IGNORECASE)
|
||||||
|
_WINDOWS_RESERVED = {
|
||||||
|
"CON", "PRN", "AUX", "NUL",
|
||||||
|
*(f"COM{i}" for i in range(1, 10)),
|
||||||
|
*(f"LPT{i}" for i in range(1, 10)),
|
||||||
|
}
|
||||||
|
|
||||||
|
MODEL_EXTS = (".safetensors", ".ckpt", ".pt", ".pth", ".sft", ".bin", ".gguf", ".onnx")
|
||||||
|
|
||||||
|
# 各类模型输入键（尽量覆盖官方 + 常见第三方节点）
|
||||||
|
_CKPT_KEYS = ("ckpt_name", "checkpoint", "ckpt", "base_ckpt_name", "model_path")
|
||||||
|
_UNET_KEYS = ("unet_name", "diffusion_model_name", "model_name")
|
||||||
|
_LORA_KEYS = ("lora_name", "lora", "lora_name_1", "lora_1", "lora_0")
|
||||||
|
_VAE_KEYS = ("vae_name", "vae")
|
||||||
|
|
||||||
|
_SAMPLER_SEED_KEYS = ("seed", "noise_seed")
|
||||||
|
|
||||||
|
|
||||||
|
# --------------------------------------------------------------------------- #
|
||||||
|
# 上下文提取（模型 / 种子 / 提示词 / 采样参数）
|
||||||
|
# --------------------------------------------------------------------------- #
|
||||||
|
|
||||||
|
def _is_link(value: Any) -> bool:
|
||||||
|
"""ComfyUI 中，连线输入会表现为 [node_id, output_index]。"""
|
||||||
|
return isinstance(value, list) and len(value) == 2 and isinstance(value[0], (str, int))
|
||||||
|
|
||||||
|
|
||||||
|
def _looks_like_model(value: Any) -> bool:
|
||||||
|
return isinstance(value, str) and value.strip().lower().endswith(MODEL_EXTS)
|
||||||
|
|
||||||
|
|
||||||
|
def _iter_prompt_nodes(prompt: Any):
|
||||||
|
"""遍历 prompt（{node_id: {class_type, inputs}}）里的每个节点。"""
|
||||||
|
if not isinstance(prompt, dict):
|
||||||
|
return
|
||||||
|
for node_data in prompt.values():
|
||||||
|
if isinstance(node_data, dict) and "class_type" in node_data:
|
||||||
|
inputs = node_data.get("inputs")
|
||||||
|
yield str(node_data.get("class_type", "")), inputs if isinstance(inputs, dict) else {}
|
||||||
|
|
||||||
|
|
||||||
|
def extract_context(prompt: Any, width: int = 0, height: int = 0) -> dict[str, Any]:
|
||||||
|
"""从工作流 prompt 中健壮地提取用于命名的上下文信息。
|
||||||
|
|
||||||
|
返回值一定包含所有键，缺失时给出合理的占位值，绝不抛异常。
|
||||||
|
"""
|
||||||
|
ctx: dict[str, Any] = {
|
||||||
|
"model": "",
|
||||||
|
"model_full": "",
|
||||||
|
"unet": "",
|
||||||
|
"lora": "",
|
||||||
|
"loras": [],
|
||||||
|
"vae": "",
|
||||||
|
"seed": "",
|
||||||
|
"steps": "",
|
||||||
|
"cfg": "",
|
||||||
|
"sampler": "",
|
||||||
|
"scheduler": "",
|
||||||
|
"positive": "",
|
||||||
|
"negative": "",
|
||||||
|
"width": str(int(width)) if width else "",
|
||||||
|
"height": str(int(height)) if height else "",
|
||||||
|
}
|
||||||
|
|
||||||
|
checkpoint = ""
|
||||||
|
unet = ""
|
||||||
|
vae = ""
|
||||||
|
loras: list[str] = []
|
||||||
|
text_candidates: list[str] = []
|
||||||
|
|
||||||
|
for class_type, inputs in _iter_prompt_nodes(prompt):
|
||||||
|
cls_lower = class_type.lower()
|
||||||
|
|
||||||
|
# ---- 模型：checkpoint / unet ----
|
||||||
|
for key, value in inputs.items():
|
||||||
|
if _is_link(value):
|
||||||
|
continue
|
||||||
|
key_l = key.lower()
|
||||||
|
if not checkpoint and (key_l in _CKPT_KEYS or (_looks_like_model(value) and "ckpt" in key_l)):
|
||||||
|
if isinstance(value, str) and value.strip():
|
||||||
|
checkpoint = value.strip()
|
||||||
|
if not unet and key_l in _UNET_KEYS and isinstance(value, str) and value.strip():
|
||||||
|
unet = value.strip()
|
||||||
|
if not vae and key_l in _VAE_KEYS and isinstance(value, str) and value.strip() and value != "taesd":
|
||||||
|
vae = value.strip()
|
||||||
|
# lora 可能有多个键（lora_name / lora_1 / lora_02 ...）
|
||||||
|
if isinstance(value, str) and value.strip() and ("lora" in key_l) and _looks_like_model(value):
|
||||||
|
loras.append(value.strip())
|
||||||
|
|
||||||
|
# 兜底：某些加载器把模型放在不常见的键里，但类名里带 Checkpoint/Loader
|
||||||
|
if not checkpoint and ("checkpoint" in cls_lower or "ckpt" in cls_lower):
|
||||||
|
for value in inputs.values():
|
||||||
|
if not _is_link(value) and _looks_like_model(value):
|
||||||
|
checkpoint = value.strip()
|
||||||
|
break
|
||||||
|
|
||||||
|
# ---- 采样参数 ----
|
||||||
|
if "sampler" in cls_lower or "ksampler" in cls_lower:
|
||||||
|
for k in _SAMPLER_SEED_KEYS:
|
||||||
|
v = inputs.get(k)
|
||||||
|
if not ctx["seed"] and isinstance(v, (int, float, str)) and not _is_link(v):
|
||||||
|
ctx["seed"] = str(int(v)) if isinstance(v, float) else str(v)
|
||||||
|
for field in ("steps", "cfg", "sampler_name", "scheduler"):
|
||||||
|
v = inputs.get(field)
|
||||||
|
if _is_link(v):
|
||||||
|
continue
|
||||||
|
target = {"steps": "steps", "cfg": "cfg",
|
||||||
|
"sampler_name": "sampler", "scheduler": "scheduler"}[field]
|
||||||
|
if not ctx[target] and isinstance(v, (int, float, str)) and str(v) != "":
|
||||||
|
ctx[target] = str(v)
|
||||||
|
|
||||||
|
# ---- 提示词文本 ----
|
||||||
|
if "textencode" in cls_lower or "cliptextencode" in cls_lower or "text" in cls_lower:
|
||||||
|
for tkey in ("text", "text_g", "text_l", "prompt", "positive"):
|
||||||
|
v = inputs.get(tkey)
|
||||||
|
if isinstance(v, str) and v.strip() and not _is_link(v):
|
||||||
|
text_candidates.append(v.strip())
|
||||||
|
|
||||||
|
# ---- 尺寸兜底（预览时没有真实图片，从 EmptyLatentImage 等读取）----
|
||||||
|
if not ctx["width"]:
|
||||||
|
w = inputs.get("width")
|
||||||
|
if isinstance(w, (int, float)) and not _is_link(w):
|
||||||
|
ctx["width"] = str(int(w))
|
||||||
|
if not ctx["height"]:
|
||||||
|
h = inputs.get("height")
|
||||||
|
if isinstance(h, (int, float)) and not _is_link(h):
|
||||||
|
ctx["height"] = str(int(h))
|
||||||
|
|
||||||
|
# 模型主名：优先 checkpoint，其次 unet
|
||||||
|
model_full = checkpoint or unet
|
||||||
|
ctx["model_full"] = model_full
|
||||||
|
ctx["model"] = Path(model_full).stem if model_full else ""
|
||||||
|
ctx["unet"] = Path(unet).stem if unet else ""
|
||||||
|
ctx["vae"] = Path(vae).stem if vae else ""
|
||||||
|
# 去重保序
|
||||||
|
seen = set()
|
||||||
|
uniq_loras = []
|
||||||
|
for l in loras:
|
||||||
|
stem = Path(l).stem
|
||||||
|
if stem not in seen:
|
||||||
|
seen.add(stem)
|
||||||
|
uniq_loras.append(stem)
|
||||||
|
ctx["loras"] = uniq_loras
|
||||||
|
ctx["lora"] = uniq_loras[0] if uniq_loras else ""
|
||||||
|
|
||||||
|
if text_candidates:
|
||||||
|
ctx["positive"] = text_candidates[0]
|
||||||
|
if len(text_candidates) > 1:
|
||||||
|
ctx["negative"] = text_candidates[1]
|
||||||
|
|
||||||
|
return ctx
|
||||||
|
|
||||||
|
|
||||||
|
# --------------------------------------------------------------------------- #
|
||||||
|
# 模板引擎
|
||||||
|
# --------------------------------------------------------------------------- #
|
||||||
|
|
||||||
|
def _strftime_format(value: str) -> str:
|
||||||
|
result = value
|
||||||
|
for token, repl in (
|
||||||
|
("yyyy", "%Y"), ("yy", "%y"), ("MM", "%m"), ("dd", "%d"),
|
||||||
|
("HH", "%H"), ("hh", "%H"), ("mm", "%M"), ("ss", "%S"),
|
||||||
|
):
|
||||||
|
result = result.replace(token, repl)
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
def sanitize_segment(value: str, fallback: str = "", max_length: int = 80) -> str:
|
||||||
|
value = _INVALID_NAME_CHARS.sub("_", str(value)).strip().strip(".")
|
||||||
|
value = re.sub(r"\s+", " ", value)
|
||||||
|
if value.upper() in _WINDOWS_RESERVED:
|
||||||
|
value = f"_{value}"
|
||||||
|
value = value[:max_length].rstrip()
|
||||||
|
return value or fallback
|
||||||
|
|
||||||
|
|
||||||
|
def expand_template(template: str, ctx: dict[str, Any], batch_index: int = 0,
|
||||||
|
prompt_max_len: int = 60) -> str:
|
||||||
|
"""把模板字符串中的占位符替换为真实值（不做路径分段清洗）。"""
|
||||||
|
now = datetime.now()
|
||||||
|
|
||||||
|
def replace_date(match: re.Match[str]) -> str:
|
||||||
|
fmt = match.group(1) or "yyyy-MM-dd"
|
||||||
|
try:
|
||||||
|
return now.strftime(_strftime_format(fmt))
|
||||||
|
except ValueError:
|
||||||
|
return now.strftime("%Y-%m-%d")
|
||||||
|
|
||||||
|
value = _DATE_TOKEN.sub(replace_date, template or "")
|
||||||
|
|
||||||
|
positive = re.sub(r"\s+", " ", ctx.get("positive", "")).strip()[:prompt_max_len]
|
||||||
|
|
||||||
|
# Only literal separators typed in the template may create directories.
|
||||||
|
# Values coming from models/prompts often contain slashes and must stay in one segment.
|
||||||
|
model = sanitize_segment(ctx.get("model") or "unknown_model", "unknown_model", 100)
|
||||||
|
model_full = sanitize_segment(ctx.get("model_full") or "unknown_model", "unknown_model", 140)
|
||||||
|
unet = sanitize_segment(ctx.get("unet") or "", "", 100)
|
||||||
|
lora = sanitize_segment(ctx.get("lora") or "no_lora", "no_lora", 100)
|
||||||
|
vae = sanitize_segment(ctx.get("vae") or "", "", 100)
|
||||||
|
prompt_text = sanitize_segment(positive or "untitled", "untitled", prompt_max_len)
|
||||||
|
|
||||||
|
tokens = {
|
||||||
|
"%year%": now.strftime("%Y"),
|
||||||
|
"%month%": now.strftime("%m"),
|
||||||
|
"%day%": now.strftime("%d"),
|
||||||
|
"%hour%": now.strftime("%H"),
|
||||||
|
"%minute%": now.strftime("%M"),
|
||||||
|
"%second%": now.strftime("%S"),
|
||||||
|
"%model%": model,
|
||||||
|
"%model_full%": model_full,
|
||||||
|
"%unet%": unet,
|
||||||
|
"%lora%": lora,
|
||||||
|
"%vae%": vae,
|
||||||
|
"%seed%": str(ctx.get("seed") or "0"),
|
||||||
|
"%steps%": str(ctx.get("steps") or ""),
|
||||||
|
"%cfg%": str(ctx.get("cfg") or ""),
|
||||||
|
"%sampler%": ctx.get("sampler") or "",
|
||||||
|
"%scheduler%": ctx.get("scheduler") or "",
|
||||||
|
"%width%": str(ctx.get("width") or "0"),
|
||||||
|
"%height%": str(ctx.get("height") or "0"),
|
||||||
|
"%prompt%": prompt_text,
|
||||||
|
"%batch%": f"{batch_index:02d}",
|
||||||
|
}
|
||||||
|
for token, repl in tokens.items():
|
||||||
|
value = value.replace(token, str(repl))
|
||||||
|
return value
|
||||||
|
|
||||||
|
|
||||||
|
def build_subfolder(template: str, ctx: dict[str, Any], prompt_max_len: int = 60) -> str:
|
||||||
|
"""把目录模板展开成安全的相对子目录（保留多级）。"""
|
||||||
|
expanded = expand_template(template, ctx, 0, prompt_max_len)
|
||||||
|
parts = re.split(r"[\\/]+", expanded)
|
||||||
|
safe = [sanitize_segment(p) for p in parts if p.strip() not in {"", ".", ".."}]
|
||||||
|
safe = [p for p in safe if p]
|
||||||
|
return os.path.join(*safe) if safe else ""
|
||||||
|
|
||||||
|
|
||||||
|
def build_filename_base(template: str, ctx: dict[str, Any], batch_index: int,
|
||||||
|
batch_size: int, counter_digits: int) -> str:
|
||||||
|
"""Build one safe filename stem using the same rules for preview and saving."""
|
||||||
|
expanded = expand_template(template or "image", ctx, batch_index)
|
||||||
|
base = sanitize_segment(expanded, fallback="image", max_length=160)
|
||||||
|
if batch_size > 1 and "%batch%" not in (template or ""):
|
||||||
|
width = max(int(counter_digits), 1)
|
||||||
|
base = f"{base}_{batch_index:0{width}d}"
|
||||||
|
return base
|
||||||
|
|
||||||
|
|
||||||
|
# --------------------------------------------------------------------------- #
|
||||||
|
# 根目录解析
|
||||||
|
# --------------------------------------------------------------------------- #
|
||||||
|
|
||||||
|
def _known_roots() -> dict[str, str]:
|
||||||
|
roots = {
|
||||||
|
"output": folder_paths.get_output_directory(),
|
||||||
|
"input": folder_paths.get_input_directory(),
|
||||||
|
"temp": folder_paths.get_temp_directory(),
|
||||||
|
}
|
||||||
|
return {k: os.path.realpath(v) for k, v in roots.items()}
|
||||||
|
|
||||||
|
|
||||||
|
def resolve_root(root_mode: str, custom_root: str) -> str:
|
||||||
|
roots = _known_roots()
|
||||||
|
if root_mode in roots:
|
||||||
|
return roots[root_mode]
|
||||||
|
# custom
|
||||||
|
output_root = roots["output"]
|
||||||
|
if not custom_root or not custom_root.strip():
|
||||||
|
return output_root
|
||||||
|
requested = os.path.expandvars(os.path.expanduser(custom_root.strip()))
|
||||||
|
if not os.path.isabs(requested):
|
||||||
|
requested = os.path.join(output_root, requested)
|
||||||
|
return os.path.realpath(requested)
|
||||||
|
|
||||||
|
|
||||||
|
def resolve_target(root_mode: str, custom_root: str, folder_template: str,
|
||||||
|
ctx: dict[str, Any], prompt_max_len: int = 60) -> tuple[str, str]:
|
||||||
|
"""返回 (根目录, 完整目标目录)。"""
|
||||||
|
root = resolve_root(root_mode, custom_root)
|
||||||
|
subfolder = build_subfolder(folder_template, ctx, prompt_max_len)
|
||||||
|
target = os.path.realpath(os.path.join(root, subfolder))
|
||||||
|
return root, target
|
||||||
|
|
||||||
|
|
||||||
|
# --------------------------------------------------------------------------- #
|
||||||
|
# 节点主体
|
||||||
|
# --------------------------------------------------------------------------- #
|
||||||
|
|
||||||
|
class SmartSaveImage:
|
||||||
|
"""规则可视化的智能图片保存节点。"""
|
||||||
|
|
||||||
|
CATEGORY = "image/save"
|
||||||
|
DESCRIPTION = "所见即所得的图片保存：目录/文件名规则清晰可见，支持实时目录预览与健壮的模型读取。"
|
||||||
|
SEARCH_ALIASES = ["save image", "smart save", "保存图片", "智能保存"]
|
||||||
|
OUTPUT_NODE = True
|
||||||
|
RETURN_TYPES = ()
|
||||||
|
FUNCTION = "save_images"
|
||||||
|
|
||||||
|
ROOT_MODES = ["output", "custom", "input", "temp"]
|
||||||
|
FORMATS = ["png", "jpeg", "webp"]
|
||||||
|
COLLISION_INCREMENT = "increment"
|
||||||
|
COLLISION_OVERWRITE = "overwrite"
|
||||||
|
MODE_SAVE_PREVIEW = "save_and_preview"
|
||||||
|
MODE_SAVE_ONLY = "save_only"
|
||||||
|
MODE_PREVIEW_ONLY = "preview_only"
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def INPUT_TYPES(cls):
|
||||||
|
try:
|
||||||
|
ckpts = folder_paths.get_filename_list("checkpoints")
|
||||||
|
except Exception:
|
||||||
|
ckpts = []
|
||||||
|
model_choices = ["auto"] + ckpts
|
||||||
|
|
||||||
|
return {
|
||||||
|
"required": {
|
||||||
|
"images": ("IMAGE", {"tooltip": "需要保存的图片或图片批次。"}),
|
||||||
|
"root_mode": (cls.ROOT_MODES, {
|
||||||
|
"default": "output",
|
||||||
|
"tooltip": "根目录：output=ComfyUI 输出目录；custom=自定义绝对/相对路径；input/temp 为内置目录。",
|
||||||
|
}),
|
||||||
|
"custom_root": ("STRING", {
|
||||||
|
"default": "",
|
||||||
|
"multiline": False,
|
||||||
|
"tooltip": "仅当 root_mode=custom 生效；可用绝对路径，留空回退到 output。",
|
||||||
|
}),
|
||||||
|
"folder_template": ("STRING", {
|
||||||
|
"default": "%date:yyyy-MM-dd%/%model%",
|
||||||
|
"multiline": False,
|
||||||
|
"tooltip": "子目录模板，支持多级与占位符，如 %date%/%model%/%sampler%。",
|
||||||
|
}),
|
||||||
|
"filename_template": ("STRING", {
|
||||||
|
"default": "%model%_%seed%",
|
||||||
|
"multiline": False,
|
||||||
|
"tooltip": "文件名模板（不含扩展名）。批量时自动追加序号，或用 %batch% 自定义位置。",
|
||||||
|
}),
|
||||||
|
"file_format": (cls.FORMATS, {"default": "png"}),
|
||||||
|
"quality": ("INT", {
|
||||||
|
"default": 95, "min": 1, "max": 100, "step": 1,
|
||||||
|
"tooltip": "旧工作流兼容字段，不在新版界面中显示。",
|
||||||
|
}),
|
||||||
|
"collision_mode": ([cls.COLLISION_INCREMENT, cls.COLLISION_OVERWRITE], {
|
||||||
|
"default": cls.COLLISION_INCREMENT,
|
||||||
|
"tooltip": "increment=自动编号避免覆盖；overwrite=覆盖同名文件。",
|
||||||
|
}),
|
||||||
|
"save_mode": ([cls.MODE_SAVE_PREVIEW, cls.MODE_SAVE_ONLY, cls.MODE_PREVIEW_ONLY], {
|
||||||
|
"default": cls.MODE_SAVE_PREVIEW,
|
||||||
|
}),
|
||||||
|
},
|
||||||
|
"optional": {
|
||||||
|
"manual_model": (model_choices, {
|
||||||
|
"default": "auto",
|
||||||
|
"tooltip": "手动指定模型名以覆盖自动读取（auto=自动从工作流读取）。",
|
||||||
|
}),
|
||||||
|
"embed_workflow": ("BOOLEAN", {
|
||||||
|
"default": True,
|
||||||
|
"tooltip": "PNG 写入 ComfyUI 工作流元数据；JPEG/WebP 写入 EXIF。",
|
||||||
|
}),
|
||||||
|
"counter_digits": ("INT", {
|
||||||
|
"default": 3, "min": 0, "max": 8,
|
||||||
|
"tooltip": "批量/防冲突序号位数，0 表示尽量不加序号。",
|
||||||
|
}),
|
||||||
|
"png_compression": ("INT", {
|
||||||
|
"default": 4, "min": 0, "max": 9, "step": 1,
|
||||||
|
"tooltip": "PNG 无损压缩等级；4 与 ComfyUI 自带保存节点一致。",
|
||||||
|
}),
|
||||||
|
},
|
||||||
|
"hidden": {
|
||||||
|
"prompt": "PROMPT",
|
||||||
|
"extra_pnginfo": "EXTRA_PNGINFO",
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
# --------------------------- 元数据 --------------------------- #
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _safe_json(value: Any) -> str:
|
||||||
|
return json.dumps(value, ensure_ascii=False, separators=(",", ":"), default=str)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _metadata_disabled() -> bool:
|
||||||
|
return args is not None and getattr(args, "disable_metadata", False)
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def _png_metadata(cls, prompt, extra_pnginfo, ctx, enabled: bool) -> PngInfo | None:
|
||||||
|
if not enabled or cls._metadata_disabled():
|
||||||
|
return None
|
||||||
|
meta = PngInfo()
|
||||||
|
# A1111 风格 parameters，方便第三方查看器识别
|
||||||
|
params = cls._build_parameters_text(ctx)
|
||||||
|
if params:
|
||||||
|
meta.add_text("parameters", params)
|
||||||
|
if prompt is not None:
|
||||||
|
meta.add_text("prompt", cls._safe_json(prompt))
|
||||||
|
if isinstance(extra_pnginfo, dict):
|
||||||
|
for key, value in extra_pnginfo.items():
|
||||||
|
meta.add_text(str(key), cls._safe_json(value))
|
||||||
|
return meta
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def _exif_metadata(cls, image: Image.Image, prompt, extra_pnginfo, enabled: bool):
|
||||||
|
if not enabled or cls._metadata_disabled():
|
||||||
|
return None
|
||||||
|
payload = {"prompt": prompt, "extra_pnginfo": extra_pnginfo}
|
||||||
|
exif = image.getexif()
|
||||||
|
exif[0x9286] = cls._safe_json(payload) # UserComment
|
||||||
|
return exif
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _build_parameters_text(ctx: dict[str, Any]) -> str:
|
||||||
|
parts: list[str] = []
|
||||||
|
if ctx.get("positive"):
|
||||||
|
parts.append(ctx["positive"])
|
||||||
|
if ctx.get("negative"):
|
||||||
|
parts.append(f"Negative prompt: {ctx['negative']}")
|
||||||
|
extra = []
|
||||||
|
if ctx.get("steps"):
|
||||||
|
extra.append(f"Steps: {ctx['steps']}")
|
||||||
|
if ctx.get("sampler"):
|
||||||
|
sch = ctx.get("scheduler")
|
||||||
|
extra.append(f"Sampler: {ctx['sampler']}" + (f" {sch}" if sch and sch != "normal" else ""))
|
||||||
|
if ctx.get("cfg"):
|
||||||
|
extra.append(f"CFG scale: {ctx['cfg']}")
|
||||||
|
if ctx.get("seed"):
|
||||||
|
extra.append(f"Seed: {ctx['seed']}")
|
||||||
|
if ctx.get("width") and ctx.get("height"):
|
||||||
|
extra.append(f"Size: {ctx['width']}x{ctx['height']}")
|
||||||
|
if ctx.get("model"):
|
||||||
|
extra.append(f"Model: {ctx['model']}")
|
||||||
|
if extra:
|
||||||
|
parts.append(", ".join(extra))
|
||||||
|
return "\n".join(parts)
|
||||||
|
|
||||||
|
# --------------------------- 图片写盘 --------------------------- #
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _extension(file_format: str) -> str:
|
||||||
|
return {"png": ".png", "jpeg": ".jpg", "webp": ".webp"}.get(file_format, ".png")
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _tensor_to_image(image) -> Image.Image:
|
||||||
|
array = image.detach().cpu().numpy() if hasattr(image, "detach") else np.asarray(image)
|
||||||
|
array = np.clip(array * 255.0, 0, 255).astype(np.uint8)
|
||||||
|
return Image.fromarray(array)
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def _save_one(cls, image, path, file_format, quality, png_compression,
|
||||||
|
prompt, extra_pnginfo, ctx, embed):
|
||||||
|
pil = cls._tensor_to_image(image)
|
||||||
|
pil_format = {"png": "PNG", "jpeg": "JPEG", "webp": "WEBP"}.get(file_format, "PNG")
|
||||||
|
kwargs: dict[str, Any] = {}
|
||||||
|
if file_format == "png":
|
||||||
|
kwargs["pnginfo"] = cls._png_metadata(prompt, extra_pnginfo, ctx, embed)
|
||||||
|
kwargs["compress_level"] = max(0, min(int(png_compression), 9))
|
||||||
|
else:
|
||||||
|
if pil.mode not in {"RGB", "L"}:
|
||||||
|
pil = pil.convert("RGB")
|
||||||
|
kwargs["exif"] = cls._exif_metadata(pil, prompt, extra_pnginfo, embed)
|
||||||
|
if file_format == "webp":
|
||||||
|
kwargs["lossless"] = True
|
||||||
|
else:
|
||||||
|
kwargs["quality"] = max(1, min(int(quality), 100))
|
||||||
|
kwargs["subsampling"] = 0
|
||||||
|
|
||||||
|
os.makedirs(os.path.dirname(path), exist_ok=True)
|
||||||
|
temp_path = ""
|
||||||
|
try:
|
||||||
|
with tempfile.NamedTemporaryFile(prefix=".smartsave-", suffix=cls._extension(file_format),
|
||||||
|
dir=os.path.dirname(path), delete=False) as handle:
|
||||||
|
temp_path = handle.name
|
||||||
|
pil.save(temp_path, format=pil_format,
|
||||||
|
**{k: v for k, v in kwargs.items() if v is not None})
|
||||||
|
os.replace(temp_path, path)
|
||||||
|
temp_path = ""
|
||||||
|
finally:
|
||||||
|
if temp_path and os.path.exists(temp_path):
|
||||||
|
os.remove(temp_path)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _unique_path(path: str, overwrite: bool, digits: int) -> str:
|
||||||
|
if overwrite or not os.path.exists(path):
|
||||||
|
return path
|
||||||
|
root, ext = os.path.splitext(path)
|
||||||
|
width = max(digits, 1)
|
||||||
|
counter = 1
|
||||||
|
while counter <= 9_999_999:
|
||||||
|
candidate = f"{root}_{counter:0{width}d}{ext}"
|
||||||
|
if not os.path.exists(candidate):
|
||||||
|
return candidate
|
||||||
|
counter += 1
|
||||||
|
raise RuntimeError(f"无法为文件生成可用编号: {path}")
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _is_inside(path: str, root: str) -> bool:
|
||||||
|
try:
|
||||||
|
return os.path.commonpath([os.path.realpath(path), os.path.realpath(root)]) == os.path.realpath(root)
|
||||||
|
except ValueError:
|
||||||
|
return False
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def _ui_image(cls, path: str) -> dict[str, str] | None:
|
||||||
|
roots = (
|
||||||
|
("output", folder_paths.get_output_directory()),
|
||||||
|
("input", folder_paths.get_input_directory()),
|
||||||
|
("temp", folder_paths.get_temp_directory()),
|
||||||
|
)
|
||||||
|
for image_type, root in roots:
|
||||||
|
if not cls._is_inside(path, root):
|
||||||
|
continue
|
||||||
|
subfolder = os.path.relpath(os.path.dirname(path), root)
|
||||||
|
return {
|
||||||
|
"filename": os.path.basename(path),
|
||||||
|
"subfolder": "" if subfolder == "." else subfolder.replace("\\", "/"),
|
||||||
|
"type": image_type,
|
||||||
|
}
|
||||||
|
return None
|
||||||
|
|
||||||
|
# --------------------------- 主函数 --------------------------- #
|
||||||
|
|
||||||
|
def save_images(self, images, root_mode, custom_root, folder_template, filename_template,
|
||||||
|
file_format, quality, collision_mode, save_mode,
|
||||||
|
manual_model="auto", embed_workflow=True, counter_digits=3, png_compression=4,
|
||||||
|
prompt=None, extra_pnginfo=None):
|
||||||
|
|
||||||
|
if save_mode == self.MODE_PREVIEW_ONLY:
|
||||||
|
return comfy_nodes.PreviewImage().save_images(
|
||||||
|
images, filename_prefix="SmartSavePreview",
|
||||||
|
prompt=prompt, extra_pnginfo=extra_pnginfo)
|
||||||
|
|
||||||
|
height = int(images[0].shape[0]) if len(images) else 0
|
||||||
|
width = int(images[0].shape[1]) if len(images) else 0
|
||||||
|
ctx = extract_context(prompt, width, height)
|
||||||
|
if manual_model and manual_model != "auto":
|
||||||
|
ctx["model_full"] = manual_model
|
||||||
|
ctx["model"] = Path(manual_model).stem
|
||||||
|
|
||||||
|
root, target_folder = resolve_target(root_mode, custom_root, folder_template, ctx)
|
||||||
|
os.makedirs(target_folder, exist_ok=True)
|
||||||
|
|
||||||
|
overwrite = collision_mode == self.COLLISION_OVERWRITE
|
||||||
|
saved_ui: list[dict[str, str]] = []
|
||||||
|
|
||||||
|
for batch_index, image in enumerate(images):
|
||||||
|
base = build_filename_base(
|
||||||
|
filename_template, ctx, batch_index, len(images), counter_digits)
|
||||||
|
filename = base + self._extension(file_format)
|
||||||
|
path = self._unique_path(os.path.join(target_folder, filename), overwrite, counter_digits)
|
||||||
|
|
||||||
|
self._save_one(
|
||||||
|
image, path, file_format, quality, png_compression,
|
||||||
|
prompt, extra_pnginfo, ctx, embed_workflow)
|
||||||
|
|
||||||
|
ui_image = self._ui_image(path)
|
||||||
|
if ui_image:
|
||||||
|
saved_ui.append(ui_image)
|
||||||
|
|
||||||
|
if save_mode == self.MODE_SAVE_ONLY:
|
||||||
|
return {"ui": {}}
|
||||||
|
if saved_ui:
|
||||||
|
return {"ui": {"images": saved_ui}}
|
||||||
|
# 保存到 output 之外时无法通过 /view 直接预览，用临时预览兜底
|
||||||
|
return comfy_nodes.PreviewImage().save_images(
|
||||||
|
images, filename_prefix="SmartSavePreview",
|
||||||
|
prompt=prompt, extra_pnginfo=extra_pnginfo)
|
||||||
@@ -0,0 +1,125 @@
|
|||||||
|
"""为 SmartSaveImage 提供实时目录预览的后端路由。
|
||||||
|
|
||||||
|
前端把当前工作流 prompt 和模板发过来，这里用与保存时完全相同的解析逻辑算出：
|
||||||
|
- 解析到的上下文（model / seed / sampler ...），让用户一眼看清占位符会变成什么；
|
||||||
|
- 最终目标目录、示例文件名；
|
||||||
|
- 目录是否已存在、已有多少张图片。
|
||||||
|
|
||||||
|
这样用户在保存前就能确认规则，而不是保存完再去猜。
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import annotations
|
||||||
|
|
||||||
|
import os
|
||||||
|
|
||||||
|
from .nodes.smart_save import (
|
||||||
|
SmartSaveImage,
|
||||||
|
build_filename_base,
|
||||||
|
build_subfolder,
|
||||||
|
extract_context,
|
||||||
|
resolve_root,
|
||||||
|
)
|
||||||
|
|
||||||
|
_IMAGE_EXTS = (".png", ".jpg", ".jpeg", ".webp", ".bmp", ".tiff", ".gif")
|
||||||
|
|
||||||
|
|
||||||
|
def _count_images(folder: str) -> int:
|
||||||
|
try:
|
||||||
|
return sum(
|
||||||
|
1 for name in os.listdir(folder)
|
||||||
|
if name.lower().endswith(_IMAGE_EXTS) and os.path.isfile(os.path.join(folder, name))
|
||||||
|
)
|
||||||
|
except Exception:
|
||||||
|
return 0
|
||||||
|
|
||||||
|
|
||||||
|
def compute_preview(data: dict) -> dict:
|
||||||
|
prompt = data.get("prompt") or {}
|
||||||
|
root_mode = data.get("root_mode", "output")
|
||||||
|
custom_root = data.get("custom_root", "")
|
||||||
|
folder_template = data.get("folder_template", "")
|
||||||
|
filename_template = data.get("filename_template", "image")
|
||||||
|
file_format = data.get("file_format", "png")
|
||||||
|
manual_model = data.get("manual_model", "auto")
|
||||||
|
counter_digits = min(max(int(data.get("counter_digits", 3) or 0), 0), 8)
|
||||||
|
batch_size = min(max(int(data.get("batch_size", 1) or 1), 1), 1000)
|
||||||
|
collision_mode = data.get("collision_mode", SmartSaveImage.COLLISION_INCREMENT)
|
||||||
|
|
||||||
|
ctx = extract_context(prompt)
|
||||||
|
if manual_model and manual_model != "auto":
|
||||||
|
from pathlib import Path
|
||||||
|
ctx["model_full"] = manual_model
|
||||||
|
ctx["model"] = Path(manual_model).stem
|
||||||
|
|
||||||
|
root = resolve_root(root_mode, custom_root)
|
||||||
|
subfolder = build_subfolder(folder_template, ctx)
|
||||||
|
target = os.path.realpath(os.path.join(root, subfolder))
|
||||||
|
|
||||||
|
ext = SmartSaveImage._extension(file_format)
|
||||||
|
example_names: list[str] = []
|
||||||
|
preview_count = min(max(batch_size, 1), 3)
|
||||||
|
for i in range(preview_count):
|
||||||
|
base = build_filename_base(filename_template, ctx, i, batch_size, counter_digits)
|
||||||
|
desired_path = os.path.join(target, base + ext)
|
||||||
|
preview_path = SmartSaveImage._unique_path(
|
||||||
|
desired_path,
|
||||||
|
collision_mode == SmartSaveImage.COLLISION_OVERWRITE,
|
||||||
|
counter_digits,
|
||||||
|
)
|
||||||
|
example_names.append(os.path.basename(preview_path))
|
||||||
|
|
||||||
|
exists = os.path.isdir(target)
|
||||||
|
return {
|
||||||
|
"ok": True,
|
||||||
|
"root": root,
|
||||||
|
"subfolder": subfolder,
|
||||||
|
"target": target,
|
||||||
|
"exists": exists,
|
||||||
|
"existing_count": _count_images(target) if exists else 0,
|
||||||
|
"example_filenames": example_names,
|
||||||
|
"context": {
|
||||||
|
"model": ctx.get("model") or "",
|
||||||
|
"model_full": ctx.get("model_full") or "",
|
||||||
|
"lora": ctx.get("lora") or "",
|
||||||
|
"loras": ctx.get("loras", []),
|
||||||
|
"vae": ctx.get("vae") or "",
|
||||||
|
"seed": ctx.get("seed") or "",
|
||||||
|
"steps": ctx.get("steps") or "",
|
||||||
|
"cfg": ctx.get("cfg") or "",
|
||||||
|
"sampler": ctx.get("sampler") or "",
|
||||||
|
"scheduler": ctx.get("scheduler") or "",
|
||||||
|
"width": ctx.get("width") or "",
|
||||||
|
"height": ctx.get("height") or "",
|
||||||
|
"positive": (ctx.get("positive") or "")[:120],
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def register_routes() -> None:
|
||||||
|
try:
|
||||||
|
from server import PromptServer
|
||||||
|
from aiohttp import web
|
||||||
|
except Exception:
|
||||||
|
return
|
||||||
|
|
||||||
|
instance = getattr(PromptServer, "instance", None)
|
||||||
|
if instance is None or not hasattr(instance, "routes"):
|
||||||
|
return
|
||||||
|
|
||||||
|
# 避免重复注册
|
||||||
|
if getattr(instance, "_smartsave_routes_registered", False):
|
||||||
|
return
|
||||||
|
|
||||||
|
@instance.routes.post("/smartsave/preview")
|
||||||
|
async def smartsave_preview(request): # noqa: ANN001
|
||||||
|
try:
|
||||||
|
data = await request.json()
|
||||||
|
except Exception:
|
||||||
|
data = {}
|
||||||
|
try:
|
||||||
|
result = compute_preview(data or {})
|
||||||
|
except Exception as exc: # 预览失败不影响使用
|
||||||
|
result = {"ok": False, "error": str(exc)}
|
||||||
|
return web.json_response(result)
|
||||||
|
|
||||||
|
instance._smartsave_routes_registered = True
|
||||||
@@ -1,7 +0,0 @@
|
|||||||
"""SmartSaveImage 工具模块"""
|
|
||||||
|
|
||||||
from .validators import InputValidator
|
|
||||||
|
|
||||||
__all__ = [
|
|
||||||
"InputValidator",
|
|
||||||
]
|
|
||||||
@@ -1,147 +0,0 @@
|
|||||||
"""输入验证工具"""
|
|
||||||
|
|
||||||
import os
|
|
||||||
import re
|
|
||||||
from typing import Any, List, Optional, Union
|
|
||||||
|
|
||||||
|
|
||||||
class InputValidator:
|
|
||||||
"""输入验证器"""
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def validate_folder_path(path: str, allowed_base_paths: list = None) -> bool:
|
|
||||||
"""验证文件夹路径是否有效且安全"""
|
|
||||||
if not path:
|
|
||||||
return False
|
|
||||||
|
|
||||||
# 检查路径长度
|
|
||||||
if len(path) > 260: # Windows路径长度限制
|
|
||||||
return False
|
|
||||||
|
|
||||||
# 检查非法字符
|
|
||||||
illegal_chars = r'[<>"|?*]'
|
|
||||||
if re.search(illegal_chars, path):
|
|
||||||
return False
|
|
||||||
|
|
||||||
# 检查路径遍历攻击
|
|
||||||
if '..' in path or path.startswith('/') or '~' in path:
|
|
||||||
return False
|
|
||||||
|
|
||||||
return True
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def secure_path_join(base_path: str, user_path: str = "") -> str:
|
|
||||||
"""安全地连接路径，防止路径遍历攻击 - 使用GitHub建议的方法"""
|
|
||||||
import folder_paths
|
|
||||||
|
|
||||||
# 获取ComfyUI的输出目录作为安全基础路径
|
|
||||||
safe_base = folder_paths.get_output_directory()
|
|
||||||
|
|
||||||
try:
|
|
||||||
# 使用realpath解析安全基础路径
|
|
||||||
resolved_safe_base = os.path.realpath(safe_base)
|
|
||||||
|
|
||||||
# 处理base_path
|
|
||||||
if os.path.isabs(base_path):
|
|
||||||
# 绝对路径：直接解析
|
|
||||||
candidate_path = os.path.realpath(base_path)
|
|
||||||
else:
|
|
||||||
# 相对路径：相对于安全基础路径
|
|
||||||
candidate_path = os.path.realpath(os.path.join(safe_base, base_path))
|
|
||||||
|
|
||||||
# 如果有用户路径，继续连接
|
|
||||||
if user_path:
|
|
||||||
candidate_path = os.path.realpath(os.path.join(candidate_path, user_path))
|
|
||||||
|
|
||||||
# 使用commonpath确保路径在安全范围内
|
|
||||||
try:
|
|
||||||
common = os.path.commonpath([resolved_safe_base, candidate_path])
|
|
||||||
# 检查公共路径是否就是安全基础路径
|
|
||||||
if os.path.realpath(common) == resolved_safe_base:
|
|
||||||
return candidate_path
|
|
||||||
else:
|
|
||||||
# 路径逃逸，返回安全基础路径
|
|
||||||
return resolved_safe_base
|
|
||||||
except ValueError:
|
|
||||||
# commonpath失败（通常是不同驱动器），返回安全基础路径
|
|
||||||
return resolved_safe_base
|
|
||||||
|
|
||||||
except (OSError, ValueError) as e:
|
|
||||||
# 任何路径操作失败，返回安全基础路径
|
|
||||||
return safe_base
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def validate_filename(filename: str) -> bool:
|
|
||||||
"""验证文件名是否有效"""
|
|
||||||
if not filename:
|
|
||||||
return False
|
|
||||||
|
|
||||||
# 检查长度
|
|
||||||
if len(filename) > 255:
|
|
||||||
return False
|
|
||||||
|
|
||||||
# 检查非法字符
|
|
||||||
illegal_chars = r'[<>:"/\\|?*]'
|
|
||||||
if re.search(illegal_chars, filename):
|
|
||||||
return False
|
|
||||||
|
|
||||||
# 检查保留名称（Windows）
|
|
||||||
reserved_names = [
|
|
||||||
'CON', 'PRN', 'AUX', 'NUL',
|
|
||||||
'COM1', 'COM2', 'COM3', 'COM4', 'COM5', 'COM6', 'COM7', 'COM8', 'COM9',
|
|
||||||
'LPT1', 'LPT2', 'LPT3', 'LPT4', 'LPT5', 'LPT6', 'LPT7', 'LPT8', 'LPT9'
|
|
||||||
]
|
|
||||||
|
|
||||||
name_without_ext = os.path.splitext(filename)[0].upper()
|
|
||||||
if name_without_ext in reserved_names:
|
|
||||||
return False
|
|
||||||
|
|
||||||
return True
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def validate_date_format(date_format: str) -> bool:
|
|
||||||
"""验证日期格式字符串"""
|
|
||||||
if not date_format:
|
|
||||||
return False
|
|
||||||
|
|
||||||
# 检查是否包含有效的日期格式标记
|
|
||||||
valid_tokens = ['yyyy', 'yy', 'MM', 'dd', 'hh', 'mm', 'ss']
|
|
||||||
has_valid_token = any(token in date_format for token in valid_tokens)
|
|
||||||
|
|
||||||
return has_valid_token
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def validate_quality_value(quality: int, min_val: int = 1, max_val: int = 100) -> bool:
|
|
||||||
"""验证质量值范围"""
|
|
||||||
return isinstance(quality, int) and min_val <= quality <= max_val
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def validate_file_format(file_format: str) -> bool:
|
|
||||||
"""验证文件格式"""
|
|
||||||
supported_formats = ['png', 'jpeg', 'webp', 'bmp', 'tiff']
|
|
||||||
return file_format.lower() in supported_formats
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def sanitize_input_string(input_str: str, max_length: int = 1000) -> str:
|
|
||||||
"""清理输入字符串"""
|
|
||||||
if not isinstance(input_str, str):
|
|
||||||
input_str = str(input_str)
|
|
||||||
|
|
||||||
# 移除控制字符
|
|
||||||
input_str = re.sub(r'[\x00-\x1f\x7f-\x9f]', '', input_str)
|
|
||||||
|
|
||||||
# 限制长度
|
|
||||||
if len(input_str) > max_length:
|
|
||||||
input_str = input_str[:max_length]
|
|
||||||
|
|
||||||
return input_str.strip()
|
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def validate_counter_settings(counter_start: int, counter_padding: int) -> bool:
|
|
||||||
"""验证计数器设置"""
|
|
||||||
return (
|
|
||||||
isinstance(counter_start, int) and
|
|
||||||
isinstance(counter_padding, int) and
|
|
||||||
0 <= counter_start <= 99999 and
|
|
||||||
1 <= counter_padding <= 10
|
|
||||||
)
|
|
||||||
+3
-3
@@ -1,4 +1,4 @@
|
|||||||
[pytest]
|
[pytest]
|
||||||
testpaths = . # Run tests in the current directory
|
testpaths = .
|
||||||
python_files = test_*.py # Run tests in files that start with "test_"
|
python_files = test_*.py
|
||||||
norecursedirs = .. # Don't run tests in the parent directory
|
norecursedirs = __pycache__
|
||||||
|
|||||||
+185
-15
@@ -1,21 +1,191 @@
|
|||||||
#!/usr/bin/env python
|
"""SmartSaveImage 节点回归测试。"""
|
||||||
|
|
||||||
"""Tests for `SmartSaveImage` package."""
|
from pathlib import Path
|
||||||
|
|
||||||
import pytest
|
import pytest
|
||||||
from src.SmartSaveImage.nodes import Example
|
import torch
|
||||||
|
from PIL import Image
|
||||||
|
|
||||||
@pytest.fixture
|
pytest.importorskip("folder_paths")
|
||||||
def example_node():
|
|
||||||
"""Fixture to create an Example node instance."""
|
|
||||||
return Example()
|
|
||||||
|
|
||||||
def test_example_node_initialization(example_node):
|
from src.SmartSaveImage.nodes import NODE_CLASS_MAPPINGS, SmartSaveImage
|
||||||
"""Test that the node can be instantiated."""
|
from src.SmartSaveImage.nodes.smart_save import (
|
||||||
assert isinstance(example_node, Example)
|
build_subfolder,
|
||||||
|
expand_template,
|
||||||
|
extract_context,
|
||||||
|
)
|
||||||
|
from src.SmartSaveImage.server_routes import compute_preview
|
||||||
|
|
||||||
def test_return_types():
|
|
||||||
"""Test the node's metadata."""
|
def test_node_is_registered():
|
||||||
assert Example.RETURN_TYPES == ("IMAGE",)
|
assert NODE_CLASS_MAPPINGS["SmartSaveImage"] is SmartSaveImage
|
||||||
assert Example.FUNCTION == "test"
|
assert SmartSaveImage.OUTPUT_NODE is True
|
||||||
assert Example.CATEGORY == "Example"
|
assert SmartSaveImage.RETURN_TYPES == ()
|
||||||
|
|
||||||
|
|
||||||
|
def test_template_expansion_and_sanitizing():
|
||||||
|
context = {
|
||||||
|
"model": "model:name",
|
||||||
|
"seed": "42",
|
||||||
|
"positive": "portrait / studio",
|
||||||
|
"width": "1024",
|
||||||
|
"height": "768",
|
||||||
|
}
|
||||||
|
|
||||||
|
folder = build_subfolder("%model%/%seed%/%prompt%", context)
|
||||||
|
|
||||||
|
assert Path(folder).parts == ("model_name", "42", "portrait _ studio")
|
||||||
|
|
||||||
|
|
||||||
|
def test_extracts_model_name_from_unet_loader():
|
||||||
|
prompt = {
|
||||||
|
"1": {
|
||||||
|
"class_type": "UNETLoader",
|
||||||
|
"inputs": {"unet_name": "Krea/Krea2_fp8.safetensors"},
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
context = extract_context(prompt)
|
||||||
|
|
||||||
|
assert context["model"] == "Krea2_fp8"
|
||||||
|
|
||||||
|
|
||||||
|
def test_extract_context_ignores_links_and_reads_sampler():
|
||||||
|
prompt = {
|
||||||
|
"1": {"class_type": "CheckpointLoaderSimple",
|
||||||
|
"inputs": {"ckpt_name": "sdxl/dreamShaperXL.safetensors"}},
|
||||||
|
"2": {"class_type": "LoraLoader",
|
||||||
|
"inputs": {"lora_name": "add_detail.safetensors", "model": ["1", 0]}},
|
||||||
|
"3": {"class_type": "KSampler",
|
||||||
|
"inputs": {"seed": 999, "steps": 30, "cfg": 6.0,
|
||||||
|
"sampler_name": "euler", "scheduler": "normal", "model": ["2", 0]}},
|
||||||
|
}
|
||||||
|
|
||||||
|
ctx = extract_context(prompt)
|
||||||
|
|
||||||
|
assert ctx["model"] == "dreamShaperXL"
|
||||||
|
assert ctx["lora"] == "add_detail"
|
||||||
|
assert ctx["seed"] == "999"
|
||||||
|
assert ctx["sampler"] == "euler"
|
||||||
|
|
||||||
|
|
||||||
|
def test_collision_mode_adds_counter(tmp_path):
|
||||||
|
original = tmp_path / "image.png"
|
||||||
|
original.write_bytes(b"existing")
|
||||||
|
|
||||||
|
result = SmartSaveImage._unique_path(str(original), overwrite=False, digits=3)
|
||||||
|
|
||||||
|
assert Path(result).name == "image_001.png"
|
||||||
|
assert original.read_bytes() == b"existing"
|
||||||
|
|
||||||
|
|
||||||
|
def test_preview_uses_next_available_filename(tmp_path):
|
||||||
|
(tmp_path / "image.png").write_bytes(b"existing")
|
||||||
|
|
||||||
|
preview = compute_preview({
|
||||||
|
"root_mode": "custom",
|
||||||
|
"custom_root": str(tmp_path),
|
||||||
|
"folder_template": "",
|
||||||
|
"filename_template": "image",
|
||||||
|
"file_format": "png",
|
||||||
|
"collision_mode": "increment",
|
||||||
|
"counter_digits": 3,
|
||||||
|
})
|
||||||
|
|
||||||
|
assert preview["example_filenames"] == ["image_001.png"]
|
||||||
|
|
||||||
|
|
||||||
|
def test_batch_token_in_filename():
|
||||||
|
ctx = extract_context({})
|
||||||
|
assert expand_template("img_%batch%", ctx, 5) == "img_05"
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.mark.parametrize("file_format,pil_format", [
|
||||||
|
("png", "PNG"),
|
||||||
|
("jpeg", "JPEG"),
|
||||||
|
("webp", "WEBP"),
|
||||||
|
])
|
||||||
|
def test_saves_real_image_batches(tmp_path, file_format, pil_format):
|
||||||
|
images = torch.rand((2, 16, 20, 3))
|
||||||
|
node = SmartSaveImage()
|
||||||
|
|
||||||
|
node.save_images(
|
||||||
|
images=images,
|
||||||
|
root_mode="custom",
|
||||||
|
custom_root=str(tmp_path),
|
||||||
|
folder_template="case_%model%",
|
||||||
|
filename_template="sample_%batch%",
|
||||||
|
file_format=file_format,
|
||||||
|
quality=91,
|
||||||
|
collision_mode=SmartSaveImage.COLLISION_INCREMENT,
|
||||||
|
save_mode=SmartSaveImage.MODE_SAVE_ONLY,
|
||||||
|
manual_model="auto",
|
||||||
|
embed_workflow=True,
|
||||||
|
counter_digits=3,
|
||||||
|
prompt={
|
||||||
|
"1": {
|
||||||
|
"class_type": "CheckpointLoaderSimple",
|
||||||
|
"inputs": {"ckpt_name": "models/demo.safetensors"},
|
||||||
|
}
|
||||||
|
},
|
||||||
|
extra_pnginfo={"workflow": {"nodes": []}},
|
||||||
|
)
|
||||||
|
|
||||||
|
ext = SmartSaveImage._extension(file_format).lstrip(".")
|
||||||
|
saved = sorted(tmp_path.rglob(f"*.{ext}"))
|
||||||
|
assert [path.stem for path in saved] == ["sample_00", "sample_01"]
|
||||||
|
assert {Image.open(path).format for path in saved} == {pil_format}
|
||||||
|
|
||||||
|
|
||||||
|
def test_overwrite_batch_with_zero_digits_keeps_every_image(tmp_path):
|
||||||
|
images = torch.rand((2, 8, 8, 3))
|
||||||
|
|
||||||
|
SmartSaveImage().save_images(
|
||||||
|
images=images,
|
||||||
|
root_mode="custom",
|
||||||
|
custom_root=str(tmp_path),
|
||||||
|
folder_template="",
|
||||||
|
filename_template="image",
|
||||||
|
file_format="png",
|
||||||
|
quality=95,
|
||||||
|
collision_mode=SmartSaveImage.COLLISION_OVERWRITE,
|
||||||
|
save_mode=SmartSaveImage.MODE_SAVE_ONLY,
|
||||||
|
manual_model="auto",
|
||||||
|
embed_workflow=False,
|
||||||
|
counter_digits=0,
|
||||||
|
)
|
||||||
|
|
||||||
|
assert sorted(path.name for path in tmp_path.glob("*.png")) == ["image_0.png", "image_1.png"]
|
||||||
|
|
||||||
|
|
||||||
|
def test_png_compression_defaults_to_comfyui_save_image_level():
|
||||||
|
config = SmartSaveImage.INPUT_TYPES()["optional"]["png_compression"][1]
|
||||||
|
|
||||||
|
assert config["default"] == 4
|
||||||
|
assert config["min"] == 0
|
||||||
|
assert config["max"] == 9
|
||||||
|
|
||||||
|
|
||||||
|
def test_png_compression_changes_size_without_changing_pixels(tmp_path):
|
||||||
|
image = torch.zeros((1, 64, 64, 3))
|
||||||
|
node = SmartSaveImage()
|
||||||
|
common = {
|
||||||
|
"images": image,
|
||||||
|
"root_mode": "custom",
|
||||||
|
"custom_root": str(tmp_path),
|
||||||
|
"folder_template": "",
|
||||||
|
"file_format": "png",
|
||||||
|
"quality": 95,
|
||||||
|
"collision_mode": SmartSaveImage.COLLISION_OVERWRITE,
|
||||||
|
"save_mode": SmartSaveImage.MODE_SAVE_ONLY,
|
||||||
|
"embed_workflow": False,
|
||||||
|
}
|
||||||
|
|
||||||
|
node.save_images(filename_template="level_0", png_compression=0, **common)
|
||||||
|
node.save_images(filename_template="level_4", png_compression=4, **common)
|
||||||
|
|
||||||
|
level_0 = tmp_path / "level_0.png"
|
||||||
|
level_4 = tmp_path / "level_4.png"
|
||||||
|
with Image.open(level_0) as first, Image.open(level_4) as second:
|
||||||
|
assert first.tobytes() == second.tobytes()
|
||||||
|
assert level_4.stat().st_size < level_0.stat().st_size
|
||||||
|
|||||||
@@ -0,0 +1,270 @@
|
|||||||
|
.ssi-panel {
|
||||||
|
display: flex;
|
||||||
|
flex-direction: column;
|
||||||
|
gap: 9px;
|
||||||
|
padding: 6px 8px 10px;
|
||||||
|
box-sizing: border-box;
|
||||||
|
width: 100%;
|
||||||
|
min-width: 0;
|
||||||
|
font-size: 12px;
|
||||||
|
color: var(--input-text, #e6e6e6);
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-row {
|
||||||
|
display: flex;
|
||||||
|
align-items: center;
|
||||||
|
gap: 8px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-field {
|
||||||
|
display: flex;
|
||||||
|
flex-direction: column;
|
||||||
|
gap: 3px;
|
||||||
|
min-width: 0;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-label {
|
||||||
|
font-weight: 600;
|
||||||
|
color: var(--descrip-text, #b8c0cc);
|
||||||
|
min-width: 60px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-input,
|
||||||
|
.ssi-select {
|
||||||
|
flex: 1;
|
||||||
|
width: 100%;
|
||||||
|
min-width: 0;
|
||||||
|
height: 30px;
|
||||||
|
box-sizing: border-box;
|
||||||
|
background: var(--comfy-input-bg, #222);
|
||||||
|
color: var(--input-text, #e6e6e6);
|
||||||
|
border: 1px solid var(--border-color, #444);
|
||||||
|
border-radius: 5px;
|
||||||
|
padding: 5px 8px;
|
||||||
|
outline: none;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-input:focus,
|
||||||
|
.ssi-select:focus {
|
||||||
|
border-color: #5b9dff;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-mono {
|
||||||
|
font-family: "Consolas", "Menlo", monospace;
|
||||||
|
letter-spacing: 0.2px;
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 根目录分段按钮 */
|
||||||
|
.ssi-location {
|
||||||
|
align-items: stretch;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-seg {
|
||||||
|
display: grid;
|
||||||
|
grid-template-columns: repeat(4, minmax(0, 1fr));
|
||||||
|
gap: 3px;
|
||||||
|
width: 100%;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-seg-btn {
|
||||||
|
min-width: 0;
|
||||||
|
height: 30px;
|
||||||
|
padding: 4px 3px;
|
||||||
|
border: 1px solid var(--border-color, #444);
|
||||||
|
border-radius: 5px;
|
||||||
|
background: var(--comfy-input-bg, #222);
|
||||||
|
color: var(--descrip-text, #b8c0cc);
|
||||||
|
cursor: pointer;
|
||||||
|
transition: all 0.12s ease;
|
||||||
|
overflow: hidden;
|
||||||
|
text-overflow: ellipsis;
|
||||||
|
white-space: nowrap;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-seg-btn:hover {
|
||||||
|
border-color: #5b9dff;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-seg-active {
|
||||||
|
background: #3a6df0;
|
||||||
|
color: #fff;
|
||||||
|
border-color: #3a6df0;
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 占位符调色板 */
|
||||||
|
.ssi-palette {
|
||||||
|
padding: 7px 8px;
|
||||||
|
background: rgba(255, 255, 255, 0.03);
|
||||||
|
border: 1px solid var(--border-color, #444);
|
||||||
|
border-radius: 6px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-palette-summary {
|
||||||
|
cursor: pointer;
|
||||||
|
color: var(--descrip-text, #b8c0cc);
|
||||||
|
font-weight: 600;
|
||||||
|
user-select: none;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-palette[open] .ssi-palette-summary {
|
||||||
|
margin-bottom: 7px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-palette .ssi-hint,
|
||||||
|
.ssi-palette .ssi-token-group {
|
||||||
|
margin-top: 6px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-hint {
|
||||||
|
color: var(--descrip-text, #8a93a2);
|
||||||
|
font-size: 11px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-token-group {
|
||||||
|
display: flex;
|
||||||
|
flex-wrap: wrap;
|
||||||
|
align-items: center;
|
||||||
|
gap: 4px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-group-name {
|
||||||
|
color: #8a93a2;
|
||||||
|
font-size: 11px;
|
||||||
|
margin-right: 4px;
|
||||||
|
min-width: 28px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-chip {
|
||||||
|
padding: 3px 7px;
|
||||||
|
border: 1px solid var(--border-color, #444);
|
||||||
|
border-radius: 5px;
|
||||||
|
background: var(--comfy-input-bg, #2a2a2a);
|
||||||
|
color: #cdd6e0;
|
||||||
|
cursor: pointer;
|
||||||
|
font-size: 11px;
|
||||||
|
transition: all 0.12s ease;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-chip:hover {
|
||||||
|
background: #3a6df0;
|
||||||
|
color: #fff;
|
||||||
|
border-color: #3a6df0;
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 选项网格 */
|
||||||
|
.ssi-grid {
|
||||||
|
display: grid;
|
||||||
|
grid-template-columns: repeat(3, minmax(0, 1fr));
|
||||||
|
gap: 7px;
|
||||||
|
align-items: end;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-cell {
|
||||||
|
display: flex;
|
||||||
|
flex-direction: column;
|
||||||
|
gap: 3px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-cell-label {
|
||||||
|
font-size: 11px;
|
||||||
|
color: var(--descrip-text, #8a93a2);
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-check {
|
||||||
|
display: flex;
|
||||||
|
align-items: center;
|
||||||
|
gap: 6px;
|
||||||
|
font-size: 12px;
|
||||||
|
min-height: 30px;
|
||||||
|
}
|
||||||
|
|
||||||
|
/* 预览区 */
|
||||||
|
.ssi-preview {
|
||||||
|
display: flex;
|
||||||
|
flex-direction: column;
|
||||||
|
gap: 5px;
|
||||||
|
padding: 8px;
|
||||||
|
border-radius: 6px;
|
||||||
|
background: rgba(255, 255, 255, 0.035);
|
||||||
|
border: 1px solid var(--border-color, #444);
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-preview-head {
|
||||||
|
display: flex;
|
||||||
|
align-items: center;
|
||||||
|
justify-content: space-between;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-preview-title {
|
||||||
|
font-weight: 600;
|
||||||
|
color: var(--input-text, #e6e6e6);
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-refresh {
|
||||||
|
padding: 3px 8px;
|
||||||
|
border: 1px solid var(--border-color, #555);
|
||||||
|
border-radius: 5px;
|
||||||
|
background: transparent;
|
||||||
|
color: var(--descrip-text, #b8c0cc);
|
||||||
|
cursor: pointer;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-refresh:hover {
|
||||||
|
background: #3a6df0;
|
||||||
|
color: #fff;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-path {
|
||||||
|
font-family: "Consolas", "Menlo", monospace;
|
||||||
|
overflow-wrap: anywhere;
|
||||||
|
color: var(--input-text, #e6e6e6);
|
||||||
|
background: rgba(0, 0, 0, 0.25);
|
||||||
|
padding: 5px 7px;
|
||||||
|
border-radius: 4px;
|
||||||
|
max-height: 38px;
|
||||||
|
overflow: auto;
|
||||||
|
font-size: 11px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-file {
|
||||||
|
color: #cdd6e0;
|
||||||
|
overflow-wrap: anywhere;
|
||||||
|
font-size: 11px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-context {
|
||||||
|
display: flex;
|
||||||
|
flex-wrap: wrap;
|
||||||
|
gap: 6px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-context:empty,
|
||||||
|
.ssi-file:empty {
|
||||||
|
display: none;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-ctx-tag {
|
||||||
|
background: rgba(255, 255, 255, 0.08);
|
||||||
|
border-radius: 4px;
|
||||||
|
padding: 2px 8px;
|
||||||
|
font-size: 11px;
|
||||||
|
color: #dfe6ee;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-ctx-tag b {
|
||||||
|
color: #9fc3ff;
|
||||||
|
font-weight: 600;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-status {
|
||||||
|
font-size: 11px;
|
||||||
|
color: #8a93a2;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-status-ok {
|
||||||
|
color: #6bd08a;
|
||||||
|
}
|
||||||
|
|
||||||
|
.ssi-status-warn {
|
||||||
|
color: #f0a35b;
|
||||||
|
}
|
||||||
@@ -0,0 +1,445 @@
|
|||||||
|
import { app } from "../../scripts/app.js";
|
||||||
|
import { api } from "../../scripts/api.js";
|
||||||
|
|
||||||
|
const NODE_NAME = "SmartSaveImage";
|
||||||
|
const CSS_HREF = "extensions/SmartSaveImage/smart_save.css";
|
||||||
|
|
||||||
|
// 需要隐藏并由自定义面板托管的原生 widget
|
||||||
|
const MANAGED_WIDGETS = [
|
||||||
|
"root_mode",
|
||||||
|
"custom_root",
|
||||||
|
"folder_template",
|
||||||
|
"filename_template",
|
||||||
|
"file_format",
|
||||||
|
"quality",
|
||||||
|
"collision_mode",
|
||||||
|
"save_mode",
|
||||||
|
"manual_model",
|
||||||
|
"embed_workflow",
|
||||||
|
"counter_digits",
|
||||||
|
"png_compression",
|
||||||
|
];
|
||||||
|
|
||||||
|
// 占位符说明（分组展示，点击插入到当前聚焦的模板输入框）
|
||||||
|
const TOKENS = [
|
||||||
|
{ group: "时间", items: [
|
||||||
|
{ t: "%date:yyyy-MM-dd%", d: "日期(可自定义格式)" },
|
||||||
|
{ t: "%year%", d: "年" }, { t: "%month%", d: "月" }, { t: "%day%", d: "日" },
|
||||||
|
{ t: "%hour%", d: "时" }, { t: "%minute%", d: "分" }, { t: "%second%", d: "秒" },
|
||||||
|
]},
|
||||||
|
{ group: "模型", items: [
|
||||||
|
{ t: "%model%", d: "模型名(去扩展名)" },
|
||||||
|
{ t: "%model_full%", d: "模型完整名" },
|
||||||
|
{ t: "%unet%", d: "UNet/扩散模型" },
|
||||||
|
{ t: "%lora%", d: "首个 LoRA" },
|
||||||
|
{ t: "%vae%", d: "VAE" },
|
||||||
|
]},
|
||||||
|
{ group: "采样", items: [
|
||||||
|
{ t: "%seed%", d: "种子" }, { t: "%steps%", d: "步数" }, { t: "%cfg%", d: "CFG" },
|
||||||
|
{ t: "%sampler%", d: "采样器" }, { t: "%scheduler%", d: "调度器" },
|
||||||
|
]},
|
||||||
|
{ group: "图片", items: [
|
||||||
|
{ t: "%width%", d: "宽" }, { t: "%height%", d: "高" },
|
||||||
|
{ t: "%prompt%", d: "正向提示词" }, { t: "%batch%", d: "批次序号(仅文件名)" },
|
||||||
|
]},
|
||||||
|
];
|
||||||
|
|
||||||
|
function ensureStyles() {
|
||||||
|
const id = "smart-save-image-css";
|
||||||
|
if (document.getElementById(id)) return;
|
||||||
|
const link = document.createElement("link");
|
||||||
|
link.id = id;
|
||||||
|
link.rel = "stylesheet";
|
||||||
|
link.href = CSS_HREF;
|
||||||
|
document.head.append(link);
|
||||||
|
}
|
||||||
|
|
||||||
|
function el(tag, className, text) {
|
||||||
|
const node = document.createElement(tag);
|
||||||
|
if (className) node.className = className;
|
||||||
|
if (text != null) node.textContent = text;
|
||||||
|
return node;
|
||||||
|
}
|
||||||
|
|
||||||
|
function getWidget(node, name) {
|
||||||
|
return node.widgets?.find((w) => w.name === name);
|
||||||
|
}
|
||||||
|
|
||||||
|
function widgetValue(node, name, fallback) {
|
||||||
|
const w = getWidget(node, name);
|
||||||
|
return w ? w.value : fallback;
|
||||||
|
}
|
||||||
|
|
||||||
|
function setWidget(node, name, value) {
|
||||||
|
const w = getWidget(node, name);
|
||||||
|
if (!w) return;
|
||||||
|
w.value = value;
|
||||||
|
w.callback?.(value);
|
||||||
|
}
|
||||||
|
|
||||||
|
function hideWidget(node, name) {
|
||||||
|
const w = getWidget(node, name);
|
||||||
|
if (!w) return;
|
||||||
|
w.hidden = true;
|
||||||
|
w.computeSize = () => [0, -4];
|
||||||
|
if (!w.options) w.options = {};
|
||||||
|
w.options.serialize = true;
|
||||||
|
}
|
||||||
|
|
||||||
|
function buildPanel(node) {
|
||||||
|
let debounceTimer = null;
|
||||||
|
let previewRequest = 0;
|
||||||
|
|
||||||
|
node.serialize_widgets = true;
|
||||||
|
for (const name of MANAGED_WIDGETS) hideWidget(node, name);
|
||||||
|
|
||||||
|
const modelWidget = getWidget(node, "manual_model");
|
||||||
|
const modelOptions = modelWidget?.options?.values || ["auto"];
|
||||||
|
|
||||||
|
const root = el("div", "ssi-panel");
|
||||||
|
|
||||||
|
// ---- 根目录 ----
|
||||||
|
const rootRow = el("div", "ssi-field ssi-location");
|
||||||
|
rootRow.append(el("label", "ssi-label", "保存位置"));
|
||||||
|
const rootSeg = el("div", "ssi-seg");
|
||||||
|
const ROOTS = [
|
||||||
|
{ v: "output", label: "输出目录" },
|
||||||
|
{ v: "custom", label: "自定义" },
|
||||||
|
{ v: "input", label: "输入" },
|
||||||
|
{ v: "temp", label: "临时" },
|
||||||
|
];
|
||||||
|
const rootButtons = {};
|
||||||
|
for (const r of ROOTS) {
|
||||||
|
const b = el("button", "ssi-seg-btn", r.label);
|
||||||
|
b.type = "button";
|
||||||
|
b.onclick = () => { setWidget(node, "root_mode", r.v); syncRoot(); schedulePreview(); };
|
||||||
|
rootButtons[r.v] = b;
|
||||||
|
rootSeg.append(b);
|
||||||
|
}
|
||||||
|
rootRow.append(rootSeg);
|
||||||
|
|
||||||
|
const customRoot = el("input", "ssi-input");
|
||||||
|
customRoot.placeholder = "例如 D:\\AI\\output（留空使用输出目录）";
|
||||||
|
customRoot.value = widgetValue(node, "custom_root", "");
|
||||||
|
customRoot.oninput = () => { setWidget(node, "custom_root", customRoot.value); schedulePreview(); };
|
||||||
|
const customRootWrap = el("div", "ssi-field ssi-custom-root");
|
||||||
|
customRootWrap.append(el("label", "ssi-cell-label", "自定义路径"), customRoot);
|
||||||
|
|
||||||
|
// ---- 目录模板 ----
|
||||||
|
const folderRow = el("div", "ssi-field");
|
||||||
|
folderRow.append(el("label", "ssi-label", "子目录规则"));
|
||||||
|
const folderInput = el("input", "ssi-input ssi-mono");
|
||||||
|
folderInput.placeholder = "例如 %date:yyyy-MM-dd%/%model%";
|
||||||
|
folderInput.value = widgetValue(node, "folder_template", "");
|
||||||
|
folderInput.oninput = () => { setWidget(node, "folder_template", folderInput.value); schedulePreview(); };
|
||||||
|
folderInput.onfocus = () => { activeField = folderInput; };
|
||||||
|
folderRow.append(folderInput);
|
||||||
|
|
||||||
|
// ---- 文件名模板 ----
|
||||||
|
const nameRow = el("div", "ssi-field");
|
||||||
|
nameRow.append(el("label", "ssi-label", "文件名规则"));
|
||||||
|
const nameInput = el("input", "ssi-input ssi-mono");
|
||||||
|
nameInput.placeholder = "例如 %model%_%seed%";
|
||||||
|
nameInput.value = widgetValue(node, "filename_template", "");
|
||||||
|
nameInput.oninput = () => { setWidget(node, "filename_template", nameInput.value); schedulePreview(); };
|
||||||
|
nameInput.onfocus = () => { activeField = nameInput; };
|
||||||
|
nameRow.append(nameInput);
|
||||||
|
|
||||||
|
let activeField = folderInput;
|
||||||
|
|
||||||
|
// ---- 占位符调色板 ----
|
||||||
|
const palette = el("details", "ssi-palette");
|
||||||
|
palette.append(el("summary", "ssi-palette-summary", "模板变量"));
|
||||||
|
palette.append(el("div", "ssi-hint", "先选中目录或文件名输入框，再点击变量插入。"));
|
||||||
|
for (const grp of TOKENS) {
|
||||||
|
const gwrap = el("div", "ssi-token-group");
|
||||||
|
gwrap.append(el("span", "ssi-group-name", grp.group));
|
||||||
|
for (const it of grp.items) {
|
||||||
|
const chip = el("button", "ssi-chip", it.t.replace(/%/g, ""));
|
||||||
|
chip.type = "button";
|
||||||
|
chip.title = `${it.t} — ${it.d}`;
|
||||||
|
chip.onclick = () => insertToken(it.t);
|
||||||
|
gwrap.append(chip);
|
||||||
|
}
|
||||||
|
palette.append(gwrap);
|
||||||
|
}
|
||||||
|
|
||||||
|
function insertToken(token) {
|
||||||
|
const field = activeField || folderInput;
|
||||||
|
const start = field.selectionStart ?? field.value.length;
|
||||||
|
const end = field.selectionEnd ?? field.value.length;
|
||||||
|
field.value = field.value.slice(0, start) + token + field.value.slice(end);
|
||||||
|
const pos = start + token.length;
|
||||||
|
field.setSelectionRange(pos, pos);
|
||||||
|
field.focus();
|
||||||
|
field.dispatchEvent(new Event("input"));
|
||||||
|
}
|
||||||
|
|
||||||
|
// ---- 模型来源 ----
|
||||||
|
const modelRow = el("div", "ssi-row");
|
||||||
|
modelRow.append(el("label", "ssi-label", "模型来源"));
|
||||||
|
const modelSelect = el("select", "ssi-select");
|
||||||
|
for (const opt of modelOptions) {
|
||||||
|
const o = el("option", null, opt === "auto" ? "自动读取工作流" : opt);
|
||||||
|
o.value = opt;
|
||||||
|
modelSelect.append(o);
|
||||||
|
}
|
||||||
|
modelSelect.value = widgetValue(node, "manual_model", "auto");
|
||||||
|
modelSelect.onchange = () => { setWidget(node, "manual_model", modelSelect.value); schedulePreview(); };
|
||||||
|
modelRow.append(modelSelect);
|
||||||
|
|
||||||
|
// ---- 格式 / 压缩 / 冲突 / 模式 ----
|
||||||
|
const optGrid = el("div", "ssi-grid");
|
||||||
|
|
||||||
|
const fmtSelect = makeSelect(["png", "jpeg", "webp"], widgetValue(node, "file_format", "png"),
|
||||||
|
(v) => { setWidget(node, "file_format", v); syncFormat(); schedulePreview(); });
|
||||||
|
optGrid.append(labeled("格式", fmtSelect));
|
||||||
|
|
||||||
|
const compressionInput = el("input", "ssi-input");
|
||||||
|
compressionInput.type = "number";
|
||||||
|
compressionInput.min = "0"; compressionInput.max = "9";
|
||||||
|
compressionInput.value = widgetValue(node, "png_compression", 4);
|
||||||
|
compressionInput.title = "0 最快且文件最大，9 最慢；所有等级均为无损";
|
||||||
|
compressionInput.oninput = () => {
|
||||||
|
const value = Math.max(0, Math.min(parseInt(compressionInput.value || "0", 10), 9));
|
||||||
|
setWidget(node, "png_compression", value);
|
||||||
|
};
|
||||||
|
const compressionWrap = labeled("PNG 压缩", compressionInput);
|
||||||
|
optGrid.append(compressionWrap);
|
||||||
|
|
||||||
|
const collisionSelect = makeSelect(
|
||||||
|
[["increment", "自动编号"], ["overwrite", "覆盖"]],
|
||||||
|
widgetValue(node, "collision_mode", "increment"),
|
||||||
|
(v) => { setWidget(node, "collision_mode", v); schedulePreview(); });
|
||||||
|
optGrid.append(labeled("同名冲突", collisionSelect));
|
||||||
|
|
||||||
|
const modeSelect = makeSelect(
|
||||||
|
[["save_and_preview", "保存并预览"], ["save_only", "仅保存"], ["preview_only", "仅预览"]],
|
||||||
|
widgetValue(node, "save_mode", "save_and_preview"),
|
||||||
|
(v) => setWidget(node, "save_mode", v));
|
||||||
|
optGrid.append(labeled("保存模式", modeSelect));
|
||||||
|
|
||||||
|
const digitsInput = el("input", "ssi-input");
|
||||||
|
digitsInput.type = "number";
|
||||||
|
digitsInput.min = "0"; digitsInput.max = "8";
|
||||||
|
digitsInput.value = widgetValue(node, "counter_digits", 3);
|
||||||
|
digitsInput.oninput = () => { setWidget(node, "counter_digits", parseInt(digitsInput.value || "0", 10)); schedulePreview(); };
|
||||||
|
optGrid.append(labeled("序号位数", digitsInput));
|
||||||
|
|
||||||
|
const embedLabel = el("label", "ssi-check");
|
||||||
|
const embedBox = el("input");
|
||||||
|
embedBox.type = "checkbox";
|
||||||
|
embedBox.checked = widgetValue(node, "embed_workflow", true) !== false;
|
||||||
|
embedBox.onchange = () => setWidget(node, "embed_workflow", embedBox.checked);
|
||||||
|
embedLabel.append(embedBox, document.createTextNode(" 嵌入工作流"));
|
||||||
|
optGrid.append(embedLabel);
|
||||||
|
|
||||||
|
// ---- 预览区 ----
|
||||||
|
const previewBox = el("div", "ssi-preview");
|
||||||
|
const previewHead = el("div", "ssi-preview-head");
|
||||||
|
const previewTitle = el("span", "ssi-preview-title", "保存结果预览");
|
||||||
|
const refreshBtn = el("button", "ssi-refresh", "刷新");
|
||||||
|
refreshBtn.type = "button";
|
||||||
|
refreshBtn.onclick = () => runPreview();
|
||||||
|
previewHead.append(previewTitle, refreshBtn);
|
||||||
|
const pathLine = el("div", "ssi-path", "—");
|
||||||
|
const fileLine = el("div", "ssi-file", "");
|
||||||
|
const ctxLine = el("div", "ssi-context", "");
|
||||||
|
const statusLine = el("div", "ssi-status", "");
|
||||||
|
previewBox.append(previewHead, pathLine, fileLine, ctxLine, statusLine);
|
||||||
|
|
||||||
|
root.append(rootRow, customRootWrap, folderRow, nameRow, previewBox, palette, modelRow, optGrid);
|
||||||
|
|
||||||
|
function syncFormat() {
|
||||||
|
compressionWrap.style.display = fmtSelect.value === "png" ? "flex" : "none";
|
||||||
|
}
|
||||||
|
|
||||||
|
function syncRoot() {
|
||||||
|
const mode = widgetValue(node, "root_mode", "output");
|
||||||
|
for (const [v, b] of Object.entries(rootButtons)) {
|
||||||
|
b.classList.toggle("ssi-seg-active", v === mode);
|
||||||
|
}
|
||||||
|
customRootWrap.style.display = mode === "custom" ? "flex" : "none";
|
||||||
|
}
|
||||||
|
|
||||||
|
function syncFromWidgets() {
|
||||||
|
customRoot.value = widgetValue(node, "custom_root", "");
|
||||||
|
folderInput.value = widgetValue(node, "folder_template", "");
|
||||||
|
nameInput.value = widgetValue(node, "filename_template", "");
|
||||||
|
const modelValue = widgetValue(node, "manual_model", "auto");
|
||||||
|
if (![...modelSelect.options].some((option) => option.value === modelValue)) {
|
||||||
|
const missingModel = el("option", null, `${modelValue}（当前不可用）`);
|
||||||
|
missingModel.value = modelValue;
|
||||||
|
modelSelect.append(missingModel);
|
||||||
|
}
|
||||||
|
modelSelect.value = modelValue;
|
||||||
|
fmtSelect.value = widgetValue(node, "file_format", "png");
|
||||||
|
compressionInput.value = widgetValue(node, "png_compression", 4);
|
||||||
|
collisionSelect.value = widgetValue(node, "collision_mode", "increment");
|
||||||
|
modeSelect.value = widgetValue(node, "save_mode", "save_and_preview");
|
||||||
|
digitsInput.value = widgetValue(node, "counter_digits", 3);
|
||||||
|
embedBox.checked = widgetValue(node, "embed_workflow", true) !== false;
|
||||||
|
syncFormat();
|
||||||
|
syncRoot();
|
||||||
|
}
|
||||||
|
|
||||||
|
// ---- 调用后端计算预览 ----
|
||||||
|
async function runPreview() {
|
||||||
|
const requestId = ++previewRequest;
|
||||||
|
statusLine.textContent = "正在计算…";
|
||||||
|
statusLine.className = "ssi-status";
|
||||||
|
try {
|
||||||
|
const graph = await app.graphToPrompt();
|
||||||
|
const payload = {
|
||||||
|
prompt: graph?.output || {},
|
||||||
|
root_mode: widgetValue(node, "root_mode", "output"),
|
||||||
|
custom_root: widgetValue(node, "custom_root", ""),
|
||||||
|
folder_template: widgetValue(node, "folder_template", ""),
|
||||||
|
filename_template: widgetValue(node, "filename_template", "image"),
|
||||||
|
file_format: widgetValue(node, "file_format", "png"),
|
||||||
|
manual_model: widgetValue(node, "manual_model", "auto"),
|
||||||
|
counter_digits: widgetValue(node, "counter_digits", 3),
|
||||||
|
collision_mode: widgetValue(node, "collision_mode", "increment"),
|
||||||
|
batch_size: 1,
|
||||||
|
};
|
||||||
|
const resp = await api.fetchApi("/smartsave/preview", {
|
||||||
|
method: "POST",
|
||||||
|
headers: { "Content-Type": "application/json" },
|
||||||
|
body: JSON.stringify(payload),
|
||||||
|
});
|
||||||
|
if (!resp.ok) throw new Error(`HTTP ${resp.status}`);
|
||||||
|
const data = await resp.json();
|
||||||
|
if (requestId !== previewRequest) return;
|
||||||
|
if (!data.ok) {
|
||||||
|
statusLine.textContent = "预览失败：" + (data.error || "未知错误");
|
||||||
|
statusLine.className = "ssi-status ssi-status-warn";
|
||||||
|
return;
|
||||||
|
}
|
||||||
|
pathLine.textContent = data.target;
|
||||||
|
pathLine.title = data.target;
|
||||||
|
const examples = (data.example_filenames || []).join(" 、 ");
|
||||||
|
fileLine.textContent = "示例文件：" + examples;
|
||||||
|
const c = data.context || {};
|
||||||
|
ctxLine.innerHTML = "";
|
||||||
|
const chips = [
|
||||||
|
["模型", c.model], ["LoRA", c.lora], ["种子", c.seed],
|
||||||
|
["采样器", c.sampler], ["尺寸", c.width && c.height ? `${c.width}x${c.height}` : ""],
|
||||||
|
];
|
||||||
|
for (const [k, v] of chips) {
|
||||||
|
if (!v) continue;
|
||||||
|
const tag = el("span", "ssi-ctx-tag");
|
||||||
|
tag.append(el("b", null, k + "："), document.createTextNode(v));
|
||||||
|
ctxLine.append(tag);
|
||||||
|
}
|
||||||
|
if (data.exists) {
|
||||||
|
statusLine.textContent = `目录已存在，已有 ${data.existing_count} 张图片`;
|
||||||
|
statusLine.className = "ssi-status ssi-status-ok";
|
||||||
|
} else {
|
||||||
|
statusLine.textContent = "目录尚不存在，保存时将自动创建";
|
||||||
|
statusLine.className = "ssi-status";
|
||||||
|
}
|
||||||
|
} catch (err) {
|
||||||
|
if (requestId !== previewRequest) return;
|
||||||
|
statusLine.textContent = "预览异常：" + err;
|
||||||
|
statusLine.className = "ssi-status ssi-status-warn";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
function schedulePreview() {
|
||||||
|
clearTimeout(debounceTimer);
|
||||||
|
debounceTimer = setTimeout(runPreview, 350);
|
||||||
|
}
|
||||||
|
|
||||||
|
node.__ssi_refresh = () => {
|
||||||
|
syncFromWidgets();
|
||||||
|
runPreview();
|
||||||
|
};
|
||||||
|
|
||||||
|
function panelContentHeight() {
|
||||||
|
const style = getComputedStyle(root);
|
||||||
|
const visibleChildren = [...root.children].filter((child) => child.offsetHeight > 0);
|
||||||
|
const gap = parseFloat(style.rowGap || style.gap || "0") || 0;
|
||||||
|
const padding = (parseFloat(style.paddingTop) || 0) + (parseFloat(style.paddingBottom) || 0);
|
||||||
|
const childrenHeight = visibleChildren.reduce((sum, child) => sum + child.offsetHeight, 0);
|
||||||
|
return Math.ceil(padding + childrenHeight + gap * Math.max(visibleChildren.length - 1, 0));
|
||||||
|
}
|
||||||
|
|
||||||
|
const widget = node.addDOMWidget("smart_save_panel", "smart-save", root, {
|
||||||
|
serialize: false,
|
||||||
|
getMinHeight() { return Math.max(360, panelContentHeight() + 8); },
|
||||||
|
getMaxHeight() { return 900; },
|
||||||
|
});
|
||||||
|
widget.serialize = false;
|
||||||
|
|
||||||
|
function fitNodeToPanel() {
|
||||||
|
const host = root.parentElement;
|
||||||
|
if (!host) return;
|
||||||
|
const desiredHeight = Math.ceil(panelContentHeight() + 50);
|
||||||
|
const desiredWidth = Math.max(node.size[0], 420);
|
||||||
|
if (Math.abs(node.size[1] - desiredHeight) > 2 || node.size[0] < 420) {
|
||||||
|
node.setSize([desiredWidth, desiredHeight]);
|
||||||
|
app.graph.setDirtyCanvas(true, true);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const panelObserver = new ResizeObserver(() => requestAnimationFrame(fitNodeToPanel));
|
||||||
|
panelObserver.observe(root);
|
||||||
|
palette.addEventListener("toggle", () => requestAnimationFrame(fitNodeToPanel));
|
||||||
|
|
||||||
|
const onRemoved = node.onRemoved;
|
||||||
|
node.onRemoved = function () {
|
||||||
|
clearTimeout(debounceTimer);
|
||||||
|
panelObserver.disconnect();
|
||||||
|
onRemoved?.apply(this, arguments);
|
||||||
|
};
|
||||||
|
|
||||||
|
syncFromWidgets();
|
||||||
|
requestAnimationFrame(() => {
|
||||||
|
node.setSize([Math.max(node.size[0], 420), Math.max(node.size[1], 500)]);
|
||||||
|
requestAnimationFrame(fitNodeToPanel);
|
||||||
|
runPreview();
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
// 辅助：带标签的字段
|
||||||
|
function labeled(text, control) {
|
||||||
|
const wrap = el("div", "ssi-cell");
|
||||||
|
wrap.append(el("label", "ssi-cell-label", text), control);
|
||||||
|
return wrap;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 辅助：下拉框（支持 [value, label] 或纯字符串）
|
||||||
|
function makeSelect(options, value, onChange) {
|
||||||
|
const sel = el("select", "ssi-select");
|
||||||
|
for (const opt of options) {
|
||||||
|
const [v, label] = Array.isArray(opt) ? opt : [opt, opt];
|
||||||
|
const o = el("option", null, label);
|
||||||
|
o.value = v;
|
||||||
|
sel.append(o);
|
||||||
|
}
|
||||||
|
sel.value = value;
|
||||||
|
sel.onchange = () => onChange(sel.value);
|
||||||
|
return sel;
|
||||||
|
}
|
||||||
|
|
||||||
|
app.registerExtension({
|
||||||
|
name: "Comfy.SmartSaveImage",
|
||||||
|
init() {
|
||||||
|
ensureStyles();
|
||||||
|
},
|
||||||
|
async beforeRegisterNodeDef(nodeType, nodeData) {
|
||||||
|
if (nodeData.name !== NODE_NAME) return;
|
||||||
|
|
||||||
|
const onNodeCreated = nodeType.prototype.onNodeCreated;
|
||||||
|
nodeType.prototype.onNodeCreated = function () {
|
||||||
|
onNodeCreated?.apply(this, arguments);
|
||||||
|
buildPanel(this);
|
||||||
|
};
|
||||||
|
|
||||||
|
const onConfigure = nodeType.prototype.onConfigure;
|
||||||
|
nodeType.prototype.onConfigure = function () {
|
||||||
|
onConfigure?.apply(this, arguments);
|
||||||
|
requestAnimationFrame(() => this.__ssi_refresh?.());
|
||||||
|
};
|
||||||
|
},
|
||||||
|
});
|
||||||
Reference in New Issue
Block a user