Initial commit: Comfyui_o1key v1.10.0
This commit is contained in:
o1key
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@@ -0,0 +1,634 @@
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# Comfyui_o1key 开发指南
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## 对话原则
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始终使用中文进行对话。
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## 项目概述
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这是一个 ComfyUI 自定义节点插件，通过 api.o1key.com 调用 AI 模型进行图像生成。
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### 技术栈
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- Python 3.7+
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- ComfyUI 框架
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- aiohttp (异步 HTTP)
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- Pillow (图像处理)
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- PyTorch (张量处理)
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---
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## 目录结构
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```
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Comfyui_o1key/
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├── __init__.py # 节点注册入口
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├── models_config.py # 模型配置中心 ⭐ 管理所有支持的模型
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├── version.txt # 版本号文件
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├── update.bat # Windows 自动更新脚本
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├── update.sh # Linux/Mac 自动更新脚本
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├── nodes/ # 节点模块
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│ ├── __init__.py
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│ ├── nano_banana_pro.py # NanoBananaPro 节点
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│ └── batch_nano_banana_pro.py # 批量节点
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├── utils/ # 工具模块
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│ ├── __init__.py
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│ ├── image_utils.py # 图像转换工具
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│ ├── config.py # 配置管理
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│ └── update_checker.py # 更新检查器
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├── clients/ # API 客户端
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│ ├── __init__.py
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│ ├── base_client.py # 客户端基类
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│ └── gemini_client.py # Gemini API 客户端
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├── .config # API 配置文件（不提交）
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├── .config.example # 配置示例
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├── requirements.txt # 依赖包
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└── README.md # 用户文档
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```
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---
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## 模型管理系统
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### 概述
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所有 Nano Banana Pro 支持的模型都在 `models_config.py` 中统一管理。要添加新模型或临时关闭某个模型，只需编辑这个文件即可。
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### 模型配置文件 (models_config.py)
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#### 配置结构
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```python
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GEMINI_MODELS = [
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{
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"id": "gemini-3-pro-image-preview-url",
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"description": "URL 模式,根据分辨率自动选择端点 (1K/2K/4K)",
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"enabled": True,
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"endpoint_type": "dynamic",
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"endpoint": None # 动态端点，由代码根据分辨率选择
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},
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{
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"id": "gemini-3-pro-image-preview",
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"description": "标准模式,固定端点",
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"enabled": True,
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"endpoint_type": "standard",
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"endpoint": "/v1beta/models/gemini-3-pro-image-preview:generateContent"
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},
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# 更多模型...
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]
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```
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#### 字段说明
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| 字段 | 类型 | 必需 | 说明 |
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|------|------|------|------|
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| `id` | string | 是 | 模型标识符，用于 API 调用 |
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| `description` | string | 是 | 模型描述，说明特点和适用场景 |
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| `enabled` | boolean | 是 | 是否启用该模型（false 则在节点中隐藏） |
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| `endpoint_type` | string | 是 | 端点类型："dynamic", "standard", "flatfee" |
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| `endpoint` | string | 是 | API 端点路径（动态端点设为 None） |
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#### 端点类型说明
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- **dynamic**: 根据分辨率动态选择端点（如 gemini-3-pro-image-preview-url）
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- **standard**: 使用固定端点（如 gemini-3-pro-image-preview）
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- **flatfee**: 固定费用模式端点（如 gemini-3-pro-image-preview-flatfee）
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### 常见操作
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#### 1. 添加新模型
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在 `GEMINI_MODELS` 列表末尾添加新模型：
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```python
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GEMINI_MODELS = [
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# ... 现有模型 ...
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{
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"id": "gemini-新模型名称",
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"description": "新模型的描述和特点",
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"enabled": True,
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"endpoint_type": "standard", # 根据实际情况选择
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"endpoint": "/v1beta/models/gemini-新模型名称:generateContent" # 配置端点
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}
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]
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```
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**注意**：
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- **固定端点模型**：直接在 `endpoint` 字段填写完整的端点路径即可，无需修改代码
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- **动态端点模型**：如果模型需要根据分辨率动态选择端点，设置 `endpoint_type: "dynamic"` 和 `endpoint: None`，并在 `gemini_client.py` 的 `get_endpoint()` 方法中添加对应逻辑
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#### 2. 临时关闭模型
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将模型的 `enabled` 字段设为 `False`：
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```python
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{
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"id": "gemini-3-pro-image-preview-url",
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"description": "URL 模式",
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"enabled": False, # 临时关闭
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"endpoint_type": "dynamic"
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}
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```
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关闭后，该模型将不会出现在 ComfyUI 节点的下拉列表中。
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#### 3. 重新启用模型
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将 `enabled` 改回 `True`：
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```python
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{
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"id": "gemini-3-pro-image-preview-url",
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"enabled": True, # 重新启用
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# ...
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}
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```
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#### 4. 修改模型描述
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直接编辑 `description` 字段：
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```python
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{
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"id": "gemini-3-pro-image-preview",
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"description": "标准模式,固定端点,适用于常规图像生成", # 更新描述
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# ...
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}
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```
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### 工具函数
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`models_config.py` 提供了一些工具函数，可在代码中使用：
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```python
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from ..models_config import (
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get_enabled_models, # 获取启用的模型列表
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get_all_models, # 获取所有模型（包括禁用的）
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get_model_config, # 获取指定模型的完整配置
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is_model_enabled, # 检查模型是否启用
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get_model_description, # 获取模型描述
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get_endpoint_type, # 获取端点类型
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get_model_endpoint # 获取模型端点
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)
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# 示例：获取启用的模型
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enabled = get_enabled_models()
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# ['gemini-3-pro-image-preview-url', 'gemini-3-pro-image-preview', ...]
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# 示例：获取模型配置
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config = get_model_config("gemini-3-pro-image-preview-url")
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# {'id': '...', 'description': '...', 'enabled': True, 'endpoint_type': 'dynamic', 'endpoint': None}
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# 示例：获取模型端点
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endpoint = get_model_endpoint("gemini-3-pro-image-preview")
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# '/v1beta/models/gemini-3-pro-image-preview:generateContent'
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```
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### 节点集成
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所有使用模型列表的节点都会自动从 `models_config.py` 加载：
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```python
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from ..models_config import get_enabled_models
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class NanoBananaPro:
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@classmethod
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def INPUT_TYPES(cls):
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# 自动从配置加载启用的模型
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enabled_models = get_enabled_models()
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return {
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"required": {
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"模型": (enabled_models, {
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"default": enabled_models[0]
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}),
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# ...
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}
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}
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```
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### 配置验证
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`models_config.py` 在加载时会自动验证配置：
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- 检查每个模型是否有必需字段（id, description, enabled, endpoint_type, endpoint）
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- 检查 `endpoint_type` 是否合法（dynamic, standard, flatfee）
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- 检查非动态端点模型必须配置有效的 `endpoint`
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- 检查端点格式是否正确（应以 `/v1beta/models/` 开头）
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- 确保至少有一个模型是启用的
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如果配置不合法，会在终端打印警告信息。
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### 最佳实践
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1. **添加新模型前**：
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- 确认模型使用 Gemini 原生接口格式
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- 确认端点规则（dynamic/standard/flatfee）
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- 编写清晰的描述说明
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2. **临时测试**：
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- 关闭其他模型，只启用测试模型
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- 验证功能后再重新启用其他模型
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3. **版本控制**：
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- `models_config.py` 应纳入版本控制
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- 重大模型变更应记录在 `CHANGELOG.md` 中
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4. **文档更新**：
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- 添加新模型后，更新 `README.md` 中的模型列表
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- 如有特殊使用说明，添加到文档中
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---
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## 开发新节点流程
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### 1. 创建节点文件
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在 `nodes/` 目录下创建新的 Python 文件：
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```python
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# nodes/my_new_node.py
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from typing import Optional, Tuple
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import torch
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from ..utils.image_utils import tensor_to_pil, pil_to_tensor
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from ..clients.gemini_client import GeminiAPIClient
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class MyNewNode:
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"""节点描述"""
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def __init__(self):
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self.client = None
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"prompt": ("STRING", {"default": "", "multiline": True}),
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# 更多参数...
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},
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"optional": {
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"images": ("IMAGE",)
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "execute"
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CATEGORY = "image/generation"
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def execute(self, prompt: str, images: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor]:
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# 实现逻辑
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pass
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```
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### 2. 注册节点
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在 `nodes/__init__.py` 中添加导出：
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```python
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from .my_new_node import MyNewNode
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__all__ = ['NanoBananaPro', 'MyNewNode']
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```
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在根 `__init__.py` 中注册：
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```python
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from .nodes import NanoBananaPro, MyNewNode
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NODE_CLASS_MAPPINGS = {
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"NanoBananaPro": NanoBananaPro,
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"MyNewNode": MyNewNode
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"NanoBananaPro": "Nano Banana Pro",
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"MyNewNode": "My New Node"
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}
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```
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### 3. 更新 CHANGELOG.md
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记录新增功能。
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---
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## ComfyUI 节点规范
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### INPUT_TYPES 参数类型
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| 类型 | 格式 | 示例 |
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|------|------|------|
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| 字符串 | `("STRING", {...})` | `("STRING", {"default": "", "multiline": True})` |
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| 整数 | `("INT", {...})` | `("INT", {"default": 1, "min": 1, "max": 100})` |
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| 浮点数 | `("FLOAT", {...})` | `("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.1})` |
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| 下拉选项 | `([...], {...})` | `(["option1", "option2"], {"default": "option1"})` |
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| 图像 | `("IMAGE",)` | 放在 optional 中 |
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### 返回值规范
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```python
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RETURN_TYPES = ("IMAGE", "MASK", "STRING") # 类型元组
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RETURN_NAMES = ("images", "mask", "text") # 名称元组
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```
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### 必须的类属性
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```python
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FUNCTION = "execute" # 执行函数名
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CATEGORY = "image/generation" # 节点分类路径
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```
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---
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## 工具模块使用
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### 图像转换 (utils/image_utils.py)
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```python
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from ..utils.image_utils import tensor_to_pil, pil_to_tensor
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# ComfyUI Tensor → PIL Image 列表
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pil_images = tensor_to_pil(tensor) # tensor: [B, H, W, C], range [0, 1]
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# PIL Image 列表 → ComfyUI Tensor
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tensor = pil_to_tensor(pil_images) # 返回 [B, H, W, C], range [0, 1]
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# PIL → Base64
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from ..utils.image_utils import encode_image_to_base64
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b64_str = encode_image_to_base64(pil_image)
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# Base64 → PIL
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from ..utils.image_utils import decode_base64_to_pil
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pil_image = decode_base64_to_pil(b64_str)
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```
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### 配置管理 (utils/config.py)
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```python
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from ..utils.config import get_api_key, get_api_key_or_raise, load_config
|
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# 获取 API 密钥（返回 None 如果未找到）
|
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api_key = get_api_key("O1KEY_API_KEY")
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# 获取 API 密钥（抛出异常如果未找到）
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api_key = get_api_key_or_raise("O1KEY_API_KEY")
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# 加载完整配置
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config = load_config()
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```
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||||
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||||
---
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||||
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||||
## API 客户端使用
|
||||
|
||||
### 使用 GeminiAPIClient
|
||||
|
||||
```python
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from ..clients.gemini_client import GeminiAPIClient
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||||
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||||
# 初始化（自动读取配置）
|
||||
client = GeminiAPIClient()
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||||
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||||
# 同步生成（用于 ComfyUI 节点）
|
||||
images = client.generate_sync(
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prompt="描述文字",
|
||||
model="gemini-3-pro-image-preview-url",
|
||||
resolution="2K",
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||||
aspect_ratio="1:1",
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batch_size=1,
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images=None, # 可选：输入图像列表
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progress_callback=None
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||||
)
|
||||
```
|
||||
|
||||
### 创建新的 API 客户端
|
||||
|
||||
继承 `BaseAPIClient` 并实现抽象方法：
|
||||
|
||||
```python
|
||||
from ..clients.base_client import BaseAPIClient
|
||||
|
||||
class MyAPIClient(BaseAPIClient):
|
||||
def __init__(self):
|
||||
super().__init__(
|
||||
base_url="https://api.example.com",
|
||||
api_key=get_api_key_or_raise("MY_API_KEY"),
|
||||
max_request_size=20 * 1024 * 1024
|
||||
)
|
||||
|
||||
def get_endpoint(self, **kwargs) -> str:
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||||
return "/v1/generate"
|
||||
|
||||
def build_request_body(self, **kwargs) -> dict:
|
||||
return {"prompt": kwargs.get("prompt", "")}
|
||||
|
||||
def parse_response(self, response: dict) -> Any:
|
||||
return response.get("result")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## API 端点说明
|
||||
|
||||
### Gemini 模型端点
|
||||
|
||||
**gemini-3-pro-image-preview-url** (根据分辨率动态选择):
|
||||
- 1K: `/v1beta/models/gemini-3-pro-image-preview-url:generateContent`
|
||||
- 2K: `/v1beta/models/gemini-3-pro-image-preview-2k-url:generateContent`
|
||||
- 4K: `/v1beta/models/gemini-3-pro-image-preview-4k-url:generateContent`
|
||||
|
||||
**gemini-3-pro-image-preview** (固定端点):
|
||||
- `/v1beta/models/gemini-3-pro-image-preview:generateContent`
|
||||
|
||||
**gemini-3-pro-image-preview-flatfee** (固定端点):
|
||||
- `/v1beta/models/gemini-3-pro-image-preview-flatfee:generateContent`
|
||||
|
||||
### 请求格式
|
||||
|
||||
```json
|
||||
{
|
||||
"contents": [{
|
||||
"role": "user",
|
||||
"parts": [
|
||||
{"text": "提示词"},
|
||||
{"inline_data": {"mime_type": "image/png", "data": "base64..."}}
|
||||
]
|
||||
}],
|
||||
"generationConfig": {
|
||||
"responseModalities": ["TEXT", "IMAGE"],
|
||||
"imageConfig": {
|
||||
"aspectRatio": "1:1",
|
||||
"imageSize": "2K"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 代码规范
|
||||
|
||||
### 命名约定
|
||||
|
||||
- 类名：PascalCase（如 `NanoBananaPro`）
|
||||
- 函数/方法：snake_case（如 `tensor_to_pil`）
|
||||
- 常量：UPPER_CASE（如 `API_BASE_URL`）
|
||||
- 私有方法：前缀下划线（如 `_load_config`）
|
||||
|
||||
### 类型注解
|
||||
|
||||
所有公开函数必须有类型注解：
|
||||
|
||||
```python
|
||||
def function_name(param1: str, param2: Optional[int] = None) -> List[Image.Image]:
|
||||
pass
|
||||
```
|
||||
|
||||
### 文档字符串
|
||||
|
||||
使用 Google 风格的 docstring：
|
||||
|
||||
```python
|
||||
def function_name(param1: str, param2: int) -> bool:
|
||||
"""
|
||||
函数简短描述
|
||||
|
||||
Args:
|
||||
param1: 参数1说明
|
||||
param2: 参数2说明
|
||||
|
||||
Returns:
|
||||
返回值说明
|
||||
|
||||
Raises:
|
||||
ValueError: 异常情况说明
|
||||
|
||||
Example:
|
||||
>>> result = function_name("test", 42)
|
||||
>>> print(result)
|
||||
True
|
||||
"""
|
||||
pass
|
||||
```
|
||||
|
||||
### 错误处理
|
||||
|
||||
```python
|
||||
try:
|
||||
# 业务逻辑
|
||||
pass
|
||||
except ValueError as e:
|
||||
# 用户输入错误
|
||||
print(f"节点名: 输入错误 - {str(e)}")
|
||||
raise
|
||||
except RuntimeError as e:
|
||||
# API 或网络错误
|
||||
print(f"节点名: API 错误 - {str(e)}")
|
||||
raise
|
||||
except Exception as e:
|
||||
# 未知错误
|
||||
print(f"节点名: 未知错误 - {str(e)}")
|
||||
raise
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 限制与约束
|
||||
|
||||
| 限制项 | 值 | 说明 |
|
||||
|--------|-----|------|
|
||||
| 请求体大小 | 20MB | 超过会报错 |
|
||||
| 输入图像数量 | 14张 | 图生图模式限制 |
|
||||
| 批次大小 | 1-1000 | 并发生成数量 |
|
||||
| 支持的分辨率 | 1K/2K/4K | API 限制 |
|
||||
|
||||
---
|
||||
|
||||
## 测试检查清单
|
||||
|
||||
新节点开发完成后，验证以下场景：
|
||||
|
||||
- [ ] 文生图基础功能
|
||||
- [ ] 图生图功能（如支持）
|
||||
- [ ] 不同分辨率（1K/2K/4K）
|
||||
- [ ] 不同宽高比
|
||||
- [ ] 批量生成
|
||||
- [ ] 错误处理（无 API 密钥、网络错误等）
|
||||
- [ ] 边界条件（最大图像数、最大批次）
|
||||
|
||||
---
|
||||
|
||||
## 更新日志
|
||||
|
||||
修改代码后，更新 `CHANGELOG.md` 记录变更。
|
||||
|
||||
格式：
|
||||
```markdown
|
||||
## [版本号] - 日期
|
||||
|
||||
### Added
|
||||
- 新增功能
|
||||
|
||||
### Changed
|
||||
- 变更内容
|
||||
|
||||
### Fixed
|
||||
- 修复问题
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 版本发布流程
|
||||
|
||||
### 1. 准备发布
|
||||
|
||||
发布新版本前确认以下事项：
|
||||
|
||||
- [ ] 所有功能测试通过
|
||||
- [ ] 更新 `CHANGELOG.md`（记录本次变更）
|
||||
- [ ] 更新 `version.txt`（更新版本号）
|
||||
- [ ] 更新 `README.md`（如有新功能需要说明）
|
||||
|
||||
### 2. 版本号规范
|
||||
|
||||
遵循语义化版本 (Semantic Versioning)：
|
||||
|
||||
- **主版本号** (Major): 重大架构变更、不兼容的 API 修改
|
||||
- **次版本号** (Minor): 新增功能、向后兼容
|
||||
- **修订号** (Patch): Bug 修复、小改进
|
||||
|
||||
示例：`v1.10.2` → Major.Minor.Patch
|
||||
|
||||
### 3. 发布步骤
|
||||
|
||||
```bash
|
||||
# 1. 更新版本号
|
||||
echo "v1.11.0" > version.txt
|
||||
|
||||
# 2. 提交变更
|
||||
git add .
|
||||
git commit -m "Release v1.11.0: 添加新功能描述"
|
||||
|
||||
# 3. 创建标签
|
||||
git tag v1.11.0
|
||||
|
||||
# 4. 推送到远程
|
||||
git push origin main --tags
|
||||
```
|
||||
|
||||
### 4. 用户更新
|
||||
|
||||
用户运行更新脚本即可获取最新版本：
|
||||
|
||||
- **Windows**: 双击 `update.bat`
|
||||
- **Linux/Mac**: 运行 `./update.sh`
|
||||
|
||||
更新脚本会自动：
|
||||
- 检查远程更新
|
||||
- 备份配置文件
|
||||
- 拉取最新代码
|
||||
- 更新依赖包
|
||||
- 显示更新日志
|
||||
|
||||
---
|
||||
+26
@@ -0,0 +1,26 @@
|
||||
# 隐私文件（已弃用配置文件，改用环境变量）
|
||||
# .config
|
||||
|
||||
# Python 缓存
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
*.so
|
||||
.Python
|
||||
|
||||
# 环境
|
||||
.env
|
||||
.venv
|
||||
env/
|
||||
venv/
|
||||
|
||||
# IDE
|
||||
.vscode/
|
||||
.idea/
|
||||
*.swp
|
||||
*.swo
|
||||
*~
|
||||
|
||||
# OS
|
||||
.DS_Store
|
||||
Thumbs.db
|
||||
+610
@@ -0,0 +1,610 @@
|
||||
# Changelog
|
||||
|
||||
本项目的所有重要变更都将记录在此文件中。
|
||||
|
||||
格式基于 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.0.0/)。
|
||||
|
||||
---
|
||||
|
||||
## [1.10.0] - 2026-02-06
|
||||
|
||||
### Added ⭐
|
||||
- **自动更新系统** - 让用户轻松更新插件到最新版本
|
||||
- 新增 `update.bat` - Windows 自动更新脚本
|
||||
- 新增 `update.sh` - Linux/Mac 自动更新脚本
|
||||
- 新增 `version.txt` - 版本号管理文件
|
||||
- 新增 `utils/update_checker.py` - 启动时自动检查更新
|
||||
- 新增更新检查功能：每次启动 ComfyUI 时自动检测是否有新版本
|
||||
|
||||
- **更新脚本功能**：
|
||||
- ✅ 自动检查远程更新
|
||||
- ✅ 自动备份和恢复 `.config` 配置文件
|
||||
- ✅ 自动拉取最新代码
|
||||
- ✅ 自动更新 Python 依赖包
|
||||
- ✅ 显示版本变更信息
|
||||
- ✅ 显示最近更新日志（前 20 行）
|
||||
- ✅ 友好的彩色终端输出（Linux/Mac）
|
||||
- ✅ 完善的错误处理和提示
|
||||
|
||||
### Changed
|
||||
- **插件启动流程** (`__init__.py`)
|
||||
- 集成更新检查模块
|
||||
- 启动时自动检查是否有新版本
|
||||
- 如有更新，终端显示友好的更新提示
|
||||
- 静默失败机制，不影响插件正常加载
|
||||
|
||||
- **文档更新** (`README.md`)
|
||||
- 新增"🔄 更新插件"章节
|
||||
- 提供两种更新方法：自动更新（推荐）和手动更新
|
||||
- 详细的跨平台更新说明
|
||||
- 更新提示和注意事项
|
||||
|
||||
### Benefits
|
||||
- 🎯 **用户友好** - 一键更新，无需手动操作 Git
|
||||
- 🔒 **配置安全** - 自动备份恢复配置，不会丢失设置
|
||||
- ⚡ **依赖同步** - 自动更新 Python 包，确保兼容性
|
||||
- 📋 **信息透明** - 显示版本变更和更新日志
|
||||
- 🌍 **跨平台** - 支持 Windows/Linux/Mac
|
||||
- 🛡️ **稳定可靠** - 完善的错误处理，不影响插件运行
|
||||
|
||||
---
|
||||
|
||||
## [1.9.1] - 2026-02-05
|
||||
|
||||
### Security 🔐
|
||||
- **错误信息伪装 - 极致隐蔽性**
|
||||
- 未配置情况下的错误信息伪装成商业授权提示
|
||||
- 前端弹框显示：`未授权！`
|
||||
- 控制台日志显示：`请联系作者授权后方可使用！`
|
||||
- 完全隐藏真实的配置方式（环境变量）
|
||||
- 让外部用户以为这是需要购买授权的商业插件
|
||||
|
||||
### Changed
|
||||
- **配置模块** (`utils/config.py`)
|
||||
- 将未找到 API 密钥的错误消息改为简洁的"未授权！"
|
||||
- 移除所有暴露配置方式的信息
|
||||
|
||||
- **节点异常处理** (所有节点)
|
||||
- `NanoBananaPro` - 添加授权错误检测，特殊处理授权失败日志
|
||||
- `BatchNanoBananaPro` - 添加授权错误检测，特殊处理授权失败日志
|
||||
- `GoogleGemini` - 添加授权错误检测，特殊处理授权失败日志
|
||||
- 其他 ValueError 类型仍正常显示详细错误信息
|
||||
|
||||
### Benefits
|
||||
- ✅ **极致隐蔽** - 完全看不出需要配置环境变量
|
||||
- ✅ **伪装商业** - 外部用户以为需要付费授权
|
||||
- ✅ **信息安全** - 不泄露任何配置相关信息
|
||||
- ✅ **功能完整** - 其他错误类型仍正常提示
|
||||
- ✅ **向后兼容** - 已配置用户完全不受影响
|
||||
|
||||
---
|
||||
|
||||
## [1.9.0] - 2026-02-05
|
||||
|
||||
### Security 🔐
|
||||
- **配置方式优化 - 提升隐蔽性和安全性**
|
||||
- 移除所有配置相关文件（`setup_config.ps1`, `.config`, `.config.example`）
|
||||
- 改为使用系统环境变量存储 API 密钥
|
||||
- 避免在项目中留下配置痕迹，提高隐蔽性
|
||||
- 配置方式对外部用户完全不可见
|
||||
|
||||
### Changed
|
||||
- **配置管理模块重构** (`utils/config.py`)
|
||||
- 调整读取优先级：环境变量优先 > .config 文件（向后兼容）
|
||||
- 简化错误提示：仅提示设置环境变量，不再提及配置文件
|
||||
- 更新模块说明：从"处理 .config 文件"改为"处理环境变量"
|
||||
|
||||
- **文档更新**
|
||||
- `README.md` - 简化配置说明，仅保留环境变量设置方法
|
||||
- `批量节点使用指南.md` - 更新常见问题中的配置说明
|
||||
- `.gitignore` - 注释配置文件规则（已弃用）
|
||||
|
||||
### Benefits
|
||||
- ✅ **高隐蔽性** - 项目中无任何配置相关文件
|
||||
- ✅ **高安全性** - 敏感信息存储在系统级别，不在项目目录
|
||||
- ✅ **简化维护** - 一行命令创建/更新/删除配置
|
||||
- ✅ **多项目共享** - 环境变量可被其他项目复用
|
||||
- ✅ **向后兼容** - 仍支持从 .config 文件读取（如果存在）
|
||||
|
||||
---
|
||||
|
||||
## [1.8.0] - 2026-02-04
|
||||
|
||||
### Added
|
||||
- **集成 ComfyUI 原生进度条** 🎉
|
||||
- `NanoBananaPro` 节点现在支持 UI 绿色进度条显示
|
||||
- `BatchNanoBananaPro` 节点现在支持 UI 绿色进度条显示
|
||||
- 使用 `comfy.utils.ProgressBar` 实现实时进度更新
|
||||
- 进度条在节点上方显示，从 0% 平滑更新到 100%
|
||||
- 兼容性检查：如果 ProgressBar 不可用，自动降级到终端进度显示
|
||||
|
||||
### Improved
|
||||
- **用户体验提升**
|
||||
- 生图过程中可视化进度反馈更直观
|
||||
- 节点运行时自动显示绿色边框（ComfyUI 原生）
|
||||
- 保留详细的终端进度日志，方便调试
|
||||
- 批量提示词模式下进度条总数自动调整
|
||||
|
||||
### Technical
|
||||
- 在 `nodes/nano_banana_pro.py` 中集成 ProgressBar
|
||||
- 创建进度条实例：`ProgressBar(生图数量)`
|
||||
- 在 `progress_callback` 中调用 `pbar.update(1)`
|
||||
- 批量提示词模式重新创建进度条以匹配实际总数
|
||||
- 在 `nodes/batch_nano_banana_pro.py` 中集成 ProgressBar
|
||||
- 将 `pbar` 参数传递给 `_process_batch_async` 方法
|
||||
- 每完成一个任务立即更新进度条
|
||||
- 支持大批量任务的实时进度显示
|
||||
- 添加 `PROGRESS_BAR_AVAILABLE` 标志进行兼容性检测
|
||||
|
||||
### Impact
|
||||
- ✅ 所有图像生成节点现在都有 UI 进度条
|
||||
- ✅ 后续新增的视频生成节点可直接复用此实现
|
||||
- ✅ 不影响现有功能，完全向后兼容
|
||||
|
||||
---
|
||||
|
||||
## [1.7.0] - 2026-02-03
|
||||
|
||||
### Added
|
||||
- **新增 Google Gemini 节点** (`GoogleGemini`)
|
||||
- 用于调用 Gemini 3 Flash 模型进行多模态文本生成
|
||||
- **输入支持**：
|
||||
- 提示词（必填）：用户提示词，支持多行
|
||||
- 系统指令（可选）：系统级指令，引导模型行为
|
||||
- 思考深度：不思考（默认）/ 高
|
||||
- 图片（可选）：支持 ComfyUI IMAGE 类型输入
|
||||
- 视频（可选）：支持 ComfyUI VIDEO 类型输入
|
||||
- **输出**：文本内容（STRING 类型）
|
||||
- **支持的视频格式**：mp4, mpeg, mov, avi, flv, webm, wmv, 3gpp
|
||||
- **端点映射**：
|
||||
- 不思考 → `/v1beta/models/gemini-3-flash-preview-nothinking:generateContent`
|
||||
- 高 → `/v1beta/models/gemini-3-flash-preview-high:generateContent`
|
||||
|
||||
- **新增 Gemini Flash API 客户端** (`clients/gemini_flash_client.py`)
|
||||
- 继承 `BaseAPIClient` 基类
|
||||
- 支持系统指令配置
|
||||
- 支持图片和视频的 base64 编码发送
|
||||
- 智能超时设置（视频请求 5 分钟，其他 3 分钟）
|
||||
|
||||
### Technical
|
||||
- 新增 `GeminiFlashClient` 类处理 Gemini Flash 模型 API 调用
|
||||
- 新增 `GoogleGemini` 节点类实现多模态文本生成
|
||||
- 支持自动检测视频 MIME 类型
|
||||
- 视频文件大小限制 20MB
|
||||
|
||||
---
|
||||
|
||||
## [1.6.2] - 2026-02-02
|
||||
|
||||
### Added
|
||||
- **模型端点配置化** (`models_config.py`)
|
||||
- 在模型配置中新增 `endpoint` 字段，集中管理每个模型的 API 端点
|
||||
- 新增 `get_model_endpoint()` 工具函数，用于获取模型端点
|
||||
- 添加新模型时只需在配置文件中填写端点，无需修改代码
|
||||
|
||||
### Changed
|
||||
- **简化端点获取逻辑** (`clients/gemini_client.py`)
|
||||
- 重构 `get_endpoint()` 方法，从配置文件读取端点而非硬编码
|
||||
- 保留动态端点模型（gemini-3-pro-image-preview-url）的特殊处理逻辑
|
||||
- 其他模型自动从 `models_config.py` 读取端点配置
|
||||
|
||||
### Improved
|
||||
- **增强配置验证** (`models_config.py`)
|
||||
- 验证非动态端点模型必须配置有效的 `endpoint`
|
||||
- 验证端点格式是否正确（应以 `/v1beta/models/` 开头）
|
||||
- 更新必需字段列表，包含 `endpoint` 字段
|
||||
|
||||
- **更新开发文档** (`.cursorrules`)
|
||||
- 更新模型配置示例，说明 `endpoint` 字段的使用方法
|
||||
- 更新添加新模型的指南，强调配置端点的方式
|
||||
- 更新工具函数列表，添加 `get_model_endpoint()` 说明
|
||||
- 更新配置验证规则说明
|
||||
|
||||
### Benefits
|
||||
- 降低维护成本：添加新模型只需修改配置文件
|
||||
- 提高可读性：端点集中管理，一目了然
|
||||
- 减少错误：配置验证确保端点格式正确
|
||||
- 保持灵活性：动态端点模型仍可使用代码逻辑
|
||||
|
||||
---
|
||||
|
||||
## [1.6.1] - 2026-02-02
|
||||
|
||||
### Fixed
|
||||
- **修复 gemini-3-pro-image-preview-flatfee 模型 504 错误**
|
||||
- 暂时禁用 `gemini-3-pro-image-preview-flatfee` 模型（端点返回 504 Gateway Timeout）
|
||||
- 更新模型描述标注"暂时不可用-504错误"
|
||||
- 建议用户使用其他可用模型（如 gemini-3-pro-image-preview 或 gemini-3-pro-image-preview-url）
|
||||
|
||||
### Improved
|
||||
- **增强 HTTP 错误处理** (`clients/base_client.py`)
|
||||
- 新增针对 504 Gateway Timeout 的友好错误提示
|
||||
- 说明原因：服务器响应超时或端点暂时不可用
|
||||
- 提供解决建议：尝试其他模型、稍后重试、降低分辨率等
|
||||
- 新增针对 503 Service Unavailable 的错误提示
|
||||
- 说明原因：模型服务过载或维护中
|
||||
- 提供解决建议：稍后重试或尝试其他模型
|
||||
- 新增针对 429 Too Many Requests 的错误提示
|
||||
- 说明原因：API 配额用尽或请求过于频繁
|
||||
- 提供解决建议：等待后重试或检查配额
|
||||
- 新增针对 404 Not Found 的错误提示
|
||||
- 说明原因：端点路径错误或模型不存在
|
||||
- 提供解决建议：检查模型名称或使用其他模型
|
||||
- 统一错误信息格式：错误类型 + 原因 + 建议
|
||||
- 同时优化 POST 和 GET 请求的错误处理
|
||||
|
||||
### Technical
|
||||
- 在 `request_async()` 和 `request_get_async()` 中添加状态码判断逻辑
|
||||
- 提供更详细的错误诊断信息，帮助用户快速定位和解决问题
|
||||
|
||||
---
|
||||
|
||||
## [1.6.0] - 2026-02-02
|
||||
|
||||
### Added
|
||||
- **模型管理系统** (`models_config.py`)
|
||||
- 创建集中式模型配置文件，所有 Nano Banana Pro 支持的模型统一管理
|
||||
- 支持快速添加新模型、临时关闭或启用模型
|
||||
- 模型配置包含：模型ID、描述、启用状态、端点类型
|
||||
- 提供工具函数：
|
||||
- `get_enabled_models()` - 获取启用的模型列表
|
||||
- `get_all_models()` - 获取所有模型（包括禁用的）
|
||||
- `get_model_config()` - 获取指定模型的完整配置
|
||||
- `is_model_enabled()` - 检查模型是否启用
|
||||
- `get_model_description()` - 获取模型描述
|
||||
- `get_endpoint_type()` - 获取端点类型
|
||||
- 自动配置验证，确保配置完整性和合法性
|
||||
|
||||
### Changed
|
||||
- **NanoBananaPro 节点重构**
|
||||
- 移除硬编码的 `MODELS` 列表
|
||||
- 改为从 `models_config.py` 动态加载模型列表
|
||||
- 节点在 ComfyUI 中显示的模型列表自动同步配置文件
|
||||
|
||||
- **BatchNanoBananaPro 节点重构**
|
||||
- 移除硬编码的 `MODELS` 列表
|
||||
- 改为从 `models_config.py` 动态加载模型列表
|
||||
- 保持与 NanoBananaPro 节点的模型列表一致性
|
||||
|
||||
### Improved
|
||||
- **开发指南更新** (`.cursorrules`)
|
||||
- 新增"模型管理系统"章节
|
||||
- 详细说明模型配置文件结构和字段含义
|
||||
- 提供添加新模型、关闭/启用模型、修改描述等常见操作指南
|
||||
- 新增工具函数使用示例和节点集成说明
|
||||
- 补充配置验证机制和最佳实践建议
|
||||
|
||||
- **目录结构更新**
|
||||
- 在开发指南中添加 `models_config.py` 文件说明
|
||||
- 标记为模型配置中心 ⭐
|
||||
|
||||
### Benefits
|
||||
- ✅ **集中管理** - 所有模型定义在一个文件，易于维护
|
||||
- ✅ **易于扩展** - 添加新模型只需在配置文件中添加一个字典
|
||||
- ✅ **快速开关** - 修改 `enabled` 字段即可临时关闭或启用模型
|
||||
- ✅ **文档化** - 每个模型都有 `description` 说明特点和适用场景
|
||||
- ✅ **类型安全** - Python 文件支持代码提示和类型检查
|
||||
- ✅ **自动同步** - 所有节点自动使用最新的模型配置
|
||||
|
||||
---
|
||||
|
||||
## [1.5.7] - 2026-02-02
|
||||
|
||||
### Performance 🚀
|
||||
- **极致性能优化：彻底解决异步阻塞问题**
|
||||
- 将 `parse_response()` 改为 `parse_response_async()`，实现完全异步的图片下载
|
||||
- 使用 `aiohttp` 替代同步的 `requests.get()` 下载图片
|
||||
- **问题**：之前在异步事件循环中使用同步 HTTP 请求会阻塞整个事件循环
|
||||
- **影响**：虽然 API 请求是并发的，但图片下载变成了串行操作
|
||||
- **效果**：现在图片下载也是完全并发的，真正实现端到端的异步性能
|
||||
|
||||
- **性能提升幅度**：
|
||||
- 使用 `gemini-3-pro-image-preview-url` 模型时提升最明显
|
||||
- 批量生成 4 张图时，从"串行下载 4 张"变为"并发下载 4 张"
|
||||
- 预计性能提升 2-4 倍（取决于网络延迟和图片大小）
|
||||
|
||||
### Changed
|
||||
- `GeminiAPIClient.parse_response()` → `parse_response_async()`
|
||||
- 新增 `session` 参数，用于复用 aiohttp 会话
|
||||
- 支持并发下载多个图片 URL
|
||||
- 自动管理 session 生命周期
|
||||
- `GeminiAPIClient.generate_single_async()` 现在调用异步解析方法
|
||||
- 移除 `requests` 依赖，统一使用 `aiohttp`
|
||||
|
||||
### Impact
|
||||
- 所有节点自动受益：
|
||||
- ✅ `NanoBananaPro` - 批量生成速度显著提升
|
||||
- ✅ `BatchNanoBananaPro` - 大批量任务性能大幅改善
|
||||
- ✅ 所有使用 `gemini-3-pro-image-preview-url` 模型的场景
|
||||
|
||||
---
|
||||
|
||||
## [1.5.6] - 2026-02-02
|
||||
|
||||
### Improved
|
||||
- **优化批量生成实时进度显示** (`Nano Banana Pro`)
|
||||
- 使用 `asyncio.as_completed` 替代 `asyncio.gather`，实现真正的实时进度
|
||||
- 每完成一个请求立即显示进度，而非等待所有请求完成后批量显示
|
||||
- 进度信息包含成功/失败状态：
|
||||
- 成功：`✓ [1/4] 第 1 张生成成功`
|
||||
- 失败：`✗ [2/4] 生成失败 - 错误原因`
|
||||
- 最终统计显示成功和失败数量
|
||||
|
||||
### Fixed
|
||||
- **修复批量生成失败信息丢失问题**
|
||||
- 之前：失败的请求被静默忽略，用户不知道哪些请求失败
|
||||
- 现在：每个失败的请求都会显示错误原因，方便排查问题
|
||||
|
||||
### Technical
|
||||
- 更新 `generate_batch_async()` 和 `generate_multi_prompts_async()` 方法
|
||||
- 进度回调签名变更：`(current, total)` → `(current, total, success, error_msg)`
|
||||
- 错误信息自动截取第一行，避免过长输出
|
||||
|
||||
---
|
||||
|
||||
## [1.5.5] - 2026-02-02
|
||||
|
||||
### Added
|
||||
- **增强 API 错误处理机制**
|
||||
- 新增 `candidatesTokenCount = 0` 检测（最高优先级）
|
||||
- 自动检测内容审核拒绝情况
|
||||
- 提供明确的拒绝原因和改进建议
|
||||
- 新增 `finishReason` 异常检测（次优先级）
|
||||
- 支持检测 `PROHIBITED_CONTENT`（违禁内容）
|
||||
- 支持检测 `SAFETY`（安全过滤器）
|
||||
- 支持检测 `RECITATION`（版权问题）
|
||||
- 支持检测 `MAX_TOKENS`（Token 超限）
|
||||
- 针对每种错误类型提供具体的解决建议
|
||||
- 新增 API 文本响应拒绝检测
|
||||
- 当 API 返回文本而非图像时，自动提取拒绝说明
|
||||
- 直接展示 API 的拒绝理由给用户
|
||||
|
||||
### Improved
|
||||
- **优化错误提示格式** (`clients/gemini_client.py`)
|
||||
- 统一错误信息格式：错误类型 + 原因 + 建议
|
||||
- 所有错误以 `RuntimeError` 抛出，便于节点层面捕获
|
||||
- 提供清晰的多行格式化错误信息
|
||||
- 包含针对性的操作建议，帮助用户快速解决问题
|
||||
|
||||
### Technical
|
||||
- 在 `GeminiAPIClient.parse_response()` 方法中实现三层错误检测
|
||||
- 错误检测按优先级顺序执行，确保最重要的问题优先报告
|
||||
- 保持向后兼容，不影响正常的图像生成流程
|
||||
|
||||
---
|
||||
|
||||
## [1.5.4] - 2026-02-01
|
||||
|
||||
### Added
|
||||
- **新增余额查询功能**
|
||||
- 在每次图像生成请求完成后自动查询并显示用户余额
|
||||
- 支持查询 API 名称和当前可用余额
|
||||
- 余额格式：`当前余额：$XX.XX | API：xxx`
|
||||
- 查询失败时显示警告信息，不影响主流程
|
||||
- 适用于 `NanoBananaPro` 和 `BatchNanoBananaPro` 节点
|
||||
|
||||
### Changed
|
||||
- **扩展 API 客户端功能** (`clients/base_client.py`)
|
||||
- 新增 `request_get_async()` 方法支持 GET 请求
|
||||
- 扩展 `get_headers()` 方法支持 Bearer Token 认证
|
||||
- 兼容现有的 x-goog-api-key 认证方式
|
||||
|
||||
- **增强 Gemini 客户端** (`clients/gemini_client.py`)
|
||||
- 新增 `query_balance_async()` 异步查询余额方法
|
||||
- 新增 `query_balance_sync()` 同步查询余额方法（用于节点）
|
||||
- 新增 `format_balance_info()` 格式化余额信息方法
|
||||
- 余额转换公式：实际显示 = total_available / 500000
|
||||
|
||||
---
|
||||
|
||||
## [1.5.3] - 2026-02-01
|
||||
|
||||
### Changed
|
||||
- **重大更新新手使用指南** (`GUIDE.md`)
|
||||
- 新增批量提示词功能详细说明（节点详细说明部分）
|
||||
- 新增场景 5：批量提示词生成（多提示词并发）
|
||||
- 新增场景 6：批量提示词 + 图生图（共享参考图）
|
||||
- 新增场景 7：批量提示词精准匹配规则详解
|
||||
- 单提示词模式与批量提示词模式对比
|
||||
- 详细的行为示例（纯文生图、图生图、多参考图）
|
||||
- 常见误区与正确用法对照
|
||||
- 使用决策树帮助用户选择合适的节点和模式
|
||||
- 新增节点功能对比表和提示词模式对比表
|
||||
- 新增 Q6-Q7：批量提示词相关常见问题
|
||||
- 新增技巧 7：批量提示词最佳实践（4个子技巧）
|
||||
- 更新场景编号（原场景 6-8 → 新场景 8）
|
||||
- 修正批量提示词的描述（所有提示词共享输入图像，而非 1:1 匹配）
|
||||
- 强化对 `---` 分隔符格式要求的说明
|
||||
|
||||
---
|
||||
|
||||
## [1.5.2] - 2026-02-01
|
||||
|
||||
### Added
|
||||
- **新增新手使用指南** (`GUIDE.md`)
|
||||
- 详细的安装配置步骤
|
||||
- 三个核心节点的完整文档和参数说明
|
||||
- 六种常见使用场景的工作流示例
|
||||
- 常见问题解答和解决方案
|
||||
- 六个进阶使用技巧
|
||||
- 面向初次使用插件的用户，提供从零到一的完整指导
|
||||
|
||||
### Changed
|
||||
- **更新开发规范** (`.cursorrules`)
|
||||
- 新增"新手指南维护规则"章节
|
||||
- 规定每次新增或变更节点时必须同步更新 `GUIDE.md`
|
||||
- 提供节点文档和使用场景的标准模板
|
||||
- 明确文档维护的五大原则（用户视角、实用性、完整性、同步性、可读性）
|
||||
|
||||
---
|
||||
|
||||
## [1.5.1] - 2026-02-01
|
||||
|
||||
### Fixed
|
||||
- **修复批量 Nano Banana Pro 节点事件循环冲突**
|
||||
- 修复 `RuntimeError: Cannot run the event loop while another loop is running` 错误
|
||||
- 使用 `ThreadPoolExecutor` 在独立线程中运行异步事件循环
|
||||
- 避免与 ComfyUI 主事件循环冲突
|
||||
- 确保批量处理任务稳定执行
|
||||
|
||||
---
|
||||
|
||||
## [1.5.0] - 2026-02-01
|
||||
|
||||
### Changed
|
||||
- **批量 Nano Banana Pro 节点重构** (`BatchNanoBananaPro`)
|
||||
- 参数重命名：
|
||||
- `手动参考图` → `加载参考图`
|
||||
- `预览图像` → `输出图像`
|
||||
- `配对模式` → `图片配对模式`
|
||||
- 配对模式选项值重命名：
|
||||
- `1:1索引配对` → `1:1`
|
||||
- `笛卡尔积` → `1*N`
|
||||
- 参数顺序调整：`文件夹2-4` 移到 `文件夹1` 下方（从 optional 移至 required）
|
||||
- 固定并发控制：移除 `最大并发数` 参数，默认自动管理（每批最多 100 并发）
|
||||
- 固定生成数量：移除 `每组生成数量` 参数，每组配对固定生成 1 张图
|
||||
|
||||
### Added
|
||||
- **批量 Nano Banana Pro 节点新增像素缩放功能**
|
||||
- 新增 `像素缩放` 参数（BOOLEAN，默认 True）
|
||||
- 新增 `分辨率像素` 参数（FLOAT，默认 1.0，范围 0.1-100.0）
|
||||
- 支持对文件夹加载的图片和手动参考图进行缩放
|
||||
- 使用 Lanczos 重采样算法保持图片质量
|
||||
- 缩放在发送 API 前应用
|
||||
|
||||
### Removed
|
||||
- **批量 Nano Banana Pro 节点移除处理报告功能**
|
||||
- 移除返回值中的 `处理报告` 字符串输出
|
||||
- 简化返回类型为单一 `IMAGE` 输出
|
||||
- 统计信息仍通过控制台打印输出
|
||||
|
||||
### Improved
|
||||
- **输出图像优化**
|
||||
- `输出图像` 现在返回所有生成的图片（而非仅预览最后几张）
|
||||
- 提供完整的批处理结果输出
|
||||
|
||||
---
|
||||
|
||||
## [1.4.0] - 2026-02-01
|
||||
|
||||
### Added
|
||||
- **批量 Nano Banana Pro 节点** (`BatchNanoBananaPro`)
|
||||
- 支持从 1-4 个文件夹批量加载图片
|
||||
- 两种配对模式：1:1 索引配对 / 笛卡尔积
|
||||
- 支持手动参考图输入（可与文件夹图片混合使用）
|
||||
- 智能命名保存（保留原始文件名 + 自动后缀）
|
||||
- 并发控制（默认最大 100，超过自动分批）
|
||||
- 完整的处理报告输出
|
||||
- 预览最后生成的图片
|
||||
|
||||
- **文件处理工具模块** (`utils/file_utils.py`)
|
||||
- `load_images_from_folder()` - 从文件夹加载图片
|
||||
- `pair_images_indexed()` - 1:1 索引配对
|
||||
- `pair_images_cartesian()` - 笛卡尔积配对
|
||||
- `generate_output_filename()` - 智能输出文件名生成
|
||||
- `save_image()` - 保存图片到指定路径
|
||||
|
||||
### Technical
|
||||
- 新增 `ImageInfo` 命名元组，携带图片元数据
|
||||
- 支持 jpg/jpeg/png/webp/bmp/gif 图片格式
|
||||
- 文件名按字母顺序排序，确保配对顺序一致
|
||||
|
||||
---
|
||||
|
||||
## [1.3.0] - 2026-02-01
|
||||
|
||||
### Added
|
||||
- **批量提示词功能（Nano Banana Pro）**
|
||||
- 支持使用单行 `---` 分隔符同时提交多个不同提示词
|
||||
- 所有提示词并发生成，提高效率
|
||||
- 每个提示词可生成指定数量的图像（提示词数量 × 生图数量）
|
||||
- 示例：3个提示词 × 2张/提示词 = 6张图
|
||||
- 触发条件：`---` 必须单独占据一行
|
||||
- 自动过滤空提示词
|
||||
|
||||
### Changed
|
||||
- 优化 Nano Banana Pro 节点日志输出
|
||||
- 批量提示词模式显示 "X个提示词 × Y张/提示词 = Z张图"
|
||||
- 单提示词模式保持原有输出格式
|
||||
|
||||
### Technical
|
||||
- 新增 `parse_batch_prompts()` 工具函数（utils/image_utils.py）
|
||||
- 新增 `generate_multi_prompts_async()` 方法（clients/gemini_client.py）
|
||||
- 新增 `generate_multi_prompts_sync()` 方法（clients/gemini_client.py）
|
||||
|
||||
---
|
||||
|
||||
## [1.2.1] - 2026-02-01
|
||||
|
||||
### Changed
|
||||
- **加载批次图像（Nano Banana Pro）** 节点优化
|
||||
- 节点显示名称改为 "加载批次图像（Nano Banana Pro）"
|
||||
- 移除 "参考图数量" 参数，改为自动检测所有有效输入图像数量
|
||||
- "开启像素缩放" 改名为 "像素缩放"，类型改为 BOOLEAN 开关（默认开启）
|
||||
- "目标像素数_百万" 改名为 "分辨率像素"
|
||||
|
||||
### Improved
|
||||
- 简化用户操作流程，无需手动设置图像数量
|
||||
- 支持灵活的图像输入方式（1-14 张任意数量）
|
||||
|
||||
---
|
||||
|
||||
## [1.2.0] - 2026-02-01
|
||||
|
||||
### Added
|
||||
- **加载批次图像** 节点 (`BatchImageLoader`)
|
||||
- 支持动态输入数量（1-14 张图像）
|
||||
- 可选的像素缩放功能（保持纵横比）
|
||||
- 使用 Lanczos 重采样算法进行高质量缩放
|
||||
- 支持设置目标像素数（0.1-100 百万像素）
|
||||
- 可与原生"加载图像"节点连接使用
|
||||
- 输出批次张量供其他节点使用
|
||||
|
||||
---
|
||||
|
||||
## [1.1.1] - 2026-02-01
|
||||
|
||||
### Changed
|
||||
- 将 Nano Banana Pro 节点的随机种子参数改为 ComfyUI 原生格式
|
||||
- `随机种子` → `seed` (参数名符合 ComfyUI 标准)
|
||||
- 移除 `-1` 自动随机逻辑
|
||||
- 种子默认值改为 `0`，取值范围 `[0, 2^64-1]`
|
||||
|
||||
### Removed
|
||||
- 移除 `control_after_generate` 参数（简化节点参数）
|
||||
|
||||
---
|
||||
|
||||
## [1.1.0] - 2026-02-01
|
||||
|
||||
### Changed
|
||||
- 重构项目结构，模块化设计
|
||||
- 新增 `nodes/` 目录存放节点实现
|
||||
- 新增 `utils/` 目录存放工具函数
|
||||
- 新增 `clients/` 目录存放 API 客户端
|
||||
- 封装图像转换工具到 `utils/image_utils.py`
|
||||
- 封装配置管理到 `utils/config.py`
|
||||
- 重构 API 客户端，拆分为基类和具体实现
|
||||
- 精简文档结构，整合为 README.md + CHANGELOG.md + .cursorrules
|
||||
|
||||
### Added
|
||||
- `BaseAPIClient` 抽象基类，支持快速开发新 API 客户端
|
||||
- `.cursorrules` 开发指导文档，统一开发规范
|
||||
- `get_api_key_or_raise()` 函数，简化密钥获取逻辑
|
||||
|
||||
---
|
||||
|
||||
## [1.0.0] - 2026-02-01
|
||||
|
||||
### Added
|
||||
- 初始版本发布
|
||||
- Nano Banana Pro 节点
|
||||
- 文生图功能
|
||||
- 图生图功能（最多 14 张输入图像）
|
||||
- 批量并发生成（最多 1000 张）
|
||||
- 多分辨率支持（1K / 2K / 4K）
|
||||
- 10 种宽高比选项
|
||||
- 可控随机种子
|
||||
- 通过 api.o1key.com 调用 Gemini 3 Pro 模型
|
||||
- 支持 .config 文件和环境变量配置 API 密钥
|
||||
- 完善的错误处理和日志输出
|
||||
@@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2026 Comfyui_o1key
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -0,0 +1,12 @@
|
||||
# Comfyui_o1key
|
||||
|
||||
通过 `api.o1key.com` 调用 AI 模型的 ComfyUI 自定义节点集合。
|
||||
|
||||
## 功能特性
|
||||
|
||||
- 🎨 文生图 / 图生图
|
||||
- 🔄 批量并发生成（最多 1000 张）
|
||||
- 📐 10 种宽高比
|
||||
- 🎯 3 种分辨率（1K / 2K / 4K）
|
||||
- 🌱 可控随机种子
|
||||
|
||||
+37
@@ -0,0 +1,37 @@
|
||||
"""
|
||||
Comfyui_o1key - ComfyUI 自定义节点集合
|
||||
通过 api.o1key.com 调用 AI 模型进行图像生成和文本生成
|
||||
|
||||
项目结构:
|
||||
├── nodes/ # 节点实现
|
||||
├── utils/ # 工具模块
|
||||
├── clients/ # API 客户端
|
||||
└── __init__.py # 节点注册入口
|
||||
"""
|
||||
|
||||
# 检查更新（仅在启动时检查一次）
|
||||
try:
|
||||
from .utils.update_checker import check_for_updates, notify_update_available
|
||||
|
||||
if check_for_updates():
|
||||
notify_update_available()
|
||||
except Exception:
|
||||
# 静默失败，不影响插件加载
|
||||
pass
|
||||
|
||||
from .nodes import NanoBananaPro, BatchNanoBananaPro, GoogleGemini
|
||||
|
||||
# ComfyUI 节点注册
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"NanoBananaPro": NanoBananaPro,
|
||||
"BatchNanoBananaPro": BatchNanoBananaPro,
|
||||
"GoogleGemini": GoogleGemini
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"NanoBananaPro": "Nano Banana Pro",
|
||||
"BatchNanoBananaPro": "批量 Nano Banana Pro",
|
||||
"GoogleGemini": "Google Gemini"
|
||||
}
|
||||
|
||||
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
|
||||
@@ -0,0 +1,10 @@
|
||||
"""
|
||||
API 客户端模块
|
||||
包含与外部 API 通信的客户端实现
|
||||
"""
|
||||
|
||||
from .base_client import BaseAPIClient
|
||||
from .gemini_client import GeminiAPIClient
|
||||
from .gemini_flash_client import GeminiFlashClient
|
||||
|
||||
__all__ = ['BaseAPIClient', 'GeminiAPIClient', 'GeminiFlashClient']
|
||||
@@ -0,0 +1,368 @@
|
||||
"""
|
||||
API 客户端基类
|
||||
提供通用的 HTTP 请求、响应解析和错误处理功能
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import threading
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Callable, Dict, List, Optional
|
||||
|
||||
import aiohttp
|
||||
|
||||
|
||||
class BaseAPIClient(ABC):
|
||||
"""
|
||||
API 客户端抽象基类
|
||||
|
||||
子类需要实现以下方法：
|
||||
- get_endpoint(): 获取 API 端点
|
||||
- build_request_body(): 构建请求体
|
||||
- parse_response(): 解析响应
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_url: str,
|
||||
api_key: str,
|
||||
max_request_size: int = 20 * 1024 * 1024
|
||||
):
|
||||
"""
|
||||
初始化客户端
|
||||
|
||||
Args:
|
||||
base_url: API 基础 URL
|
||||
api_key: API 密钥
|
||||
max_request_size: 最大请求体大小（字节），默认 20MB
|
||||
"""
|
||||
self.base_url = base_url
|
||||
self.api_key = api_key
|
||||
self.max_request_size = max_request_size
|
||||
|
||||
@abstractmethod
|
||||
def get_endpoint(self, **kwargs) -> str:
|
||||
"""
|
||||
获取 API 端点路径
|
||||
|
||||
Args:
|
||||
**kwargs: 额外参数（如模型名、分辨率等）
|
||||
|
||||
Returns:
|
||||
端点路径字符串
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def build_request_body(self, **kwargs) -> Dict[str, Any]:
|
||||
"""
|
||||
构建 API 请求体
|
||||
|
||||
Args:
|
||||
**kwargs: 请求参数
|
||||
|
||||
Returns:
|
||||
请求体字典
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def parse_response(self, response: Dict[str, Any]) -> Any:
|
||||
"""
|
||||
解析 API 响应
|
||||
|
||||
Args:
|
||||
response: API 响应字典
|
||||
|
||||
Returns:
|
||||
解析后的结果
|
||||
"""
|
||||
pass
|
||||
|
||||
def get_headers(self, use_bearer_token: bool = False) -> Dict[str, str]:
|
||||
"""
|
||||
获取请求头
|
||||
|
||||
Args:
|
||||
use_bearer_token: 是否使用 Bearer Token 认证（默认为 False）
|
||||
|
||||
Returns:
|
||||
请求头字典
|
||||
"""
|
||||
if use_bearer_token:
|
||||
return {
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"x-goog-api-key": self.api_key,
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
def check_request_size(self, request_body: Dict[str, Any]) -> None:
|
||||
"""
|
||||
检查请求体大小是否超过限制
|
||||
|
||||
Args:
|
||||
request_body: 请求体字典
|
||||
|
||||
Raises:
|
||||
ValueError: 如果请求体超过限制
|
||||
"""
|
||||
request_json = json.dumps(request_body)
|
||||
request_size = len(request_json.encode('utf-8'))
|
||||
|
||||
if request_size > self.max_request_size:
|
||||
size_mb = request_size / 1024 / 1024
|
||||
limit_mb = self.max_request_size / 1024 / 1024
|
||||
raise ValueError(
|
||||
f"请求体大小 {size_mb:.2f}MB 超过限制 {limit_mb:.0f}MB，"
|
||||
"请降低分辨率或减少图片数量"
|
||||
)
|
||||
|
||||
async def request_async(
|
||||
self,
|
||||
endpoint: str,
|
||||
request_body: Dict[str, Any],
|
||||
session: Optional[aiohttp.ClientSession] = None,
|
||||
use_bearer_token: bool = False,
|
||||
timeout: Optional[int] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
发送异步 HTTP 请求
|
||||
|
||||
Args:
|
||||
endpoint: API 端点
|
||||
request_body: 请求体
|
||||
session: aiohttp 会话（可选）
|
||||
use_bearer_token: 是否使用 Bearer Token 认证
|
||||
|
||||
Returns:
|
||||
响应 JSON
|
||||
|
||||
Raises:
|
||||
RuntimeError: 请求失败时
|
||||
"""
|
||||
url = f"{self.base_url}{endpoint}"
|
||||
headers = self.get_headers(use_bearer_token)
|
||||
|
||||
# 检查请求大小
|
||||
self.check_request_size(request_body)
|
||||
|
||||
close_session = False
|
||||
if session is None:
|
||||
session = aiohttp.ClientSession()
|
||||
close_session = True
|
||||
|
||||
try:
|
||||
# 设置超时
|
||||
timeout_obj = aiohttp.ClientTimeout(total=timeout) if timeout else None
|
||||
async with session.post(url, json=request_body, headers=headers, timeout=timeout_obj) as response:
|
||||
if response.status != 200:
|
||||
error_text = await response.text()
|
||||
|
||||
# 针对常见错误状态码提供友好提示
|
||||
if response.status == 504:
|
||||
raise RuntimeError(
|
||||
f"API 请求超时 (504 Gateway Timeout)\n"
|
||||
f"原因：服务器响应超时或该端点暂时不可用\n"
|
||||
f"建议：\n"
|
||||
f" - 尝试使用其他模型\n"
|
||||
f" - 稍后重试\n"
|
||||
f" - 降低分辨率或减少输入图像数量\n"
|
||||
f"详细错误: {error_text[:200]}"
|
||||
)
|
||||
elif response.status == 503:
|
||||
raise RuntimeError(
|
||||
f"服务暂时不可用 (503 Service Unavailable)\n"
|
||||
f"原因：模型服务过载或维护中\n"
|
||||
f"建议：\n"
|
||||
f" - 稍后重试\n"
|
||||
f" - 尝试使用其他模型"
|
||||
)
|
||||
elif response.status == 429:
|
||||
raise RuntimeError(
|
||||
f"请求频率超限 (429 Too Many Requests)\n"
|
||||
f"原因：API 配额用尽或请求过于频繁\n"
|
||||
f"建议：\n"
|
||||
f" - 等待一段时间后重试\n"
|
||||
f" - 检查 API 配额是否充足"
|
||||
)
|
||||
elif response.status == 404:
|
||||
raise RuntimeError(
|
||||
f"端点不存在 (404 Not Found)\n"
|
||||
f"原因：API 端点路径错误或模型不存在\n"
|
||||
f"建议：\n"
|
||||
f" - 检查模型名称是否正确\n"
|
||||
f" - 使用其他可用模型"
|
||||
)
|
||||
else:
|
||||
raise RuntimeError(
|
||||
f"API 请求失败 (状态码: {response.status}): {error_text}"
|
||||
)
|
||||
|
||||
return await response.json()
|
||||
|
||||
finally:
|
||||
if close_session:
|
||||
await session.close()
|
||||
|
||||
async def request_get_async(
|
||||
self,
|
||||
endpoint: str,
|
||||
session: Optional[aiohttp.ClientSession] = None,
|
||||
use_bearer_token: bool = True,
|
||||
timeout: Optional[int] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
发送异步 HTTP GET 请求
|
||||
|
||||
Args:
|
||||
endpoint: API 端点
|
||||
session: aiohttp 会话（可选）
|
||||
use_bearer_token: 是否使用 Bearer Token 认证（默认为 True）
|
||||
|
||||
Returns:
|
||||
响应 JSON
|
||||
|
||||
Raises:
|
||||
RuntimeError: 请求失败时
|
||||
"""
|
||||
url = f"{self.base_url}{endpoint}"
|
||||
headers = self.get_headers(use_bearer_token)
|
||||
|
||||
close_session = False
|
||||
if session is None:
|
||||
session = aiohttp.ClientSession()
|
||||
close_session = True
|
||||
|
||||
try:
|
||||
# 设置超时
|
||||
timeout_obj = aiohttp.ClientTimeout(total=timeout) if timeout else None
|
||||
async with session.get(url, headers=headers, timeout=timeout_obj) as response:
|
||||
if response.status != 200:
|
||||
error_text = await response.text()
|
||||
|
||||
# 针对常见错误状态码提供友好提示
|
||||
if response.status == 504:
|
||||
raise RuntimeError(
|
||||
f"API 请求超时 (504 Gateway Timeout)\n"
|
||||
f"原因：服务器响应超时或该端点暂时不可用\n"
|
||||
f"建议：稍后重试"
|
||||
)
|
||||
elif response.status == 503:
|
||||
raise RuntimeError(
|
||||
f"服务暂时不可用 (503 Service Unavailable)\n"
|
||||
f"原因：服务过载或维护中\n"
|
||||
f"建议：稍后重试"
|
||||
)
|
||||
elif response.status == 429:
|
||||
raise RuntimeError(
|
||||
f"请求频率超限 (429 Too Many Requests)\n"
|
||||
f"原因：API 配额用尽或请求过于频繁\n"
|
||||
f"建议：等待一段时间后重试"
|
||||
)
|
||||
else:
|
||||
raise RuntimeError(
|
||||
f"API 请求失败 (状态码: {response.status}): {error_text}"
|
||||
)
|
||||
|
||||
return await response.json()
|
||||
|
||||
finally:
|
||||
if close_session:
|
||||
await session.close()
|
||||
|
||||
async def batch_request_async(
|
||||
self,
|
||||
requests: List[Dict[str, Any]],
|
||||
progress_callback: Optional[Callable[[int, int], None]] = None
|
||||
) -> List[Any]:
|
||||
"""
|
||||
批量并发请求
|
||||
|
||||
Args:
|
||||
requests: 请求列表，每个元素包含 endpoint 和 request_body
|
||||
progress_callback: 进度回调函数 (current, total)
|
||||
|
||||
Returns:
|
||||
响应结果列表
|
||||
"""
|
||||
results = []
|
||||
completed = 0
|
||||
total = len(requests)
|
||||
|
||||
# 创建无限制的连接器
|
||||
connector = aiohttp.TCPConnector(limit=0, limit_per_host=0)
|
||||
|
||||
async with aiohttp.ClientSession(connector=connector) as session:
|
||||
tasks = []
|
||||
|
||||
for req in requests:
|
||||
task = self.request_async(
|
||||
endpoint=req['endpoint'],
|
||||
request_body=req['request_body'],
|
||||
session=session
|
||||
)
|
||||
tasks.append(task)
|
||||
|
||||
# 并发执行
|
||||
responses = await asyncio.gather(*tasks, return_exceptions=True)
|
||||
|
||||
for i, resp in enumerate(responses):
|
||||
if isinstance(resp, Exception):
|
||||
print(f"⚠️ 第 {i+1} 个请求失败: {str(resp)}")
|
||||
continue
|
||||
|
||||
try:
|
||||
parsed = self.parse_response(resp)
|
||||
results.append(parsed)
|
||||
completed += 1
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(completed, total)
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ 第 {i+1} 个响应解析失败: {str(e)}")
|
||||
|
||||
return results
|
||||
|
||||
def run_async_in_thread(self, coro) -> Any:
|
||||
"""
|
||||
在独立线程中运行异步代码（用于 ComfyUI 同步接口）
|
||||
|
||||
Args:
|
||||
coro: 协程对象
|
||||
|
||||
Returns:
|
||||
协程执行结果
|
||||
"""
|
||||
result_container = []
|
||||
error_container = []
|
||||
|
||||
def run_in_thread():
|
||||
try:
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
|
||||
try:
|
||||
result = loop.run_until_complete(coro)
|
||||
result_container.append(result)
|
||||
finally:
|
||||
loop.close()
|
||||
|
||||
except Exception as e:
|
||||
error_container.append(e)
|
||||
|
||||
thread = threading.Thread(target=run_in_thread)
|
||||
thread.start()
|
||||
thread.join()
|
||||
|
||||
if error_container:
|
||||
raise error_container[0]
|
||||
|
||||
if not result_container:
|
||||
raise RuntimeError("异步任务未返回结果")
|
||||
|
||||
return result_container[0]
|
||||
@@ -0,0 +1,728 @@
|
||||
"""
|
||||
Gemini API 客户端
|
||||
处理与 api.o1key.com 的通信，用于图像生成
|
||||
"""
|
||||
|
||||
import re
|
||||
import time
|
||||
from io import BytesIO
|
||||
from typing import Any, Callable, Dict, List, Optional
|
||||
|
||||
import aiohttp
|
||||
from PIL import Image
|
||||
|
||||
from ..utils.image_utils import encode_image_to_base64, decode_base64_to_pil
|
||||
from ..utils.config import get_api_key_or_raise
|
||||
from .base_client import BaseAPIClient
|
||||
|
||||
|
||||
# API 基础配置
|
||||
API_BASE_URL = "https://api.o1key.com"
|
||||
|
||||
|
||||
class GeminiAPIClient(BaseAPIClient):
|
||||
"""
|
||||
Gemini API 客户端
|
||||
用于调用 Gemini 3 Pro 模型进行图像生成
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def get_timeout_by_resolution(resolution: str) -> int:
|
||||
"""
|
||||
根据分辨率获取超时时间
|
||||
|
||||
Args:
|
||||
resolution: 分辨率（1K, 2K, 4K）
|
||||
|
||||
Returns:
|
||||
超时时间（秒）
|
||||
"""
|
||||
timeout_map = {
|
||||
"1K": 180, # 3 分钟
|
||||
"2K": 300, # 5 分钟
|
||||
"4K": 360 # 6 分钟
|
||||
}
|
||||
return timeout_map.get(resolution, 300) # 默认 5 分钟
|
||||
|
||||
def __init__(self, api_key: Optional[str] = None):
|
||||
"""
|
||||
初始化客户端
|
||||
|
||||
Args:
|
||||
api_key: API 密钥，如果为 None 则从配置文件或环境变量读取
|
||||
"""
|
||||
if api_key is None:
|
||||
api_key = get_api_key_or_raise("O1KEY_API_KEY")
|
||||
|
||||
super().__init__(
|
||||
base_url=API_BASE_URL,
|
||||
api_key=api_key,
|
||||
max_request_size=20 * 1024 * 1024
|
||||
)
|
||||
|
||||
def get_endpoint(self, model: str = "", resolution: str = "2K", **kwargs) -> str:
|
||||
"""
|
||||
根据模型和分辨率获取 API 端点
|
||||
|
||||
Args:
|
||||
model: 模型名称
|
||||
resolution: 分辨率（1K, 2K, 4K）
|
||||
|
||||
Returns:
|
||||
API 端点路径
|
||||
"""
|
||||
from ..models_config import get_model_endpoint
|
||||
|
||||
# 特殊处理：动态端点模型（根据分辨率选择）
|
||||
if model == "nano-banana-pro":
|
||||
if resolution == "1K":
|
||||
return "/v1beta/models/nano-banana-pro:generateContent"
|
||||
elif resolution == "2K":
|
||||
return "/v1beta/models/nano-banana-pro-2k:generateContent"
|
||||
elif resolution == "4K":
|
||||
return "/v1beta/models/nano-banana-pro-4k:generateContent"
|
||||
else:
|
||||
return "/v1beta/models/nano-banana-pro-2k:generateContent"
|
||||
|
||||
elif model == "gemini-3-pro-image-preview-url":
|
||||
if resolution == "1K":
|
||||
return "/v1beta/models/gemini-3-pro-image-preview-url:generateContent"
|
||||
elif resolution == "2K":
|
||||
return "/v1beta/models/gemini-3-pro-image-preview-2k-url:generateContent"
|
||||
elif resolution == "4K":
|
||||
return "/v1beta/models/gemini-3-pro-image-preview-4k-url:generateContent"
|
||||
else:
|
||||
return "/v1beta/models/gemini-3-pro-image-preview-2k-url:generateContent"
|
||||
|
||||
# 其他模型：从配置文件读取端点
|
||||
endpoint = get_model_endpoint(model)
|
||||
if endpoint:
|
||||
return endpoint
|
||||
|
||||
# 兜底：使用标准模式端点
|
||||
return "/v1beta/models/gemini-3-pro-image-preview:generateContent"
|
||||
|
||||
def build_request_body(
|
||||
self,
|
||||
prompt: str = "",
|
||||
images: Optional[List[Image.Image]] = None,
|
||||
aspect_ratio: str = "1:1",
|
||||
resolution: str = "2K",
|
||||
**kwargs
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
构建 API 请求体
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
images: 输入图像列表（可选）
|
||||
aspect_ratio: 宽高比
|
||||
resolution: 分辨率
|
||||
|
||||
Returns:
|
||||
请求体字典
|
||||
"""
|
||||
parts = []
|
||||
|
||||
# 添加文本部分
|
||||
parts.append({"text": prompt})
|
||||
|
||||
# 添加图像部分（如果有）
|
||||
if images:
|
||||
for img in images:
|
||||
img_base64 = encode_image_to_base64(img)
|
||||
parts.append({
|
||||
"inline_data": {
|
||||
"mime_type": "image/png",
|
||||
"data": img_base64
|
||||
}
|
||||
})
|
||||
|
||||
# 构建请求体
|
||||
request_body = {
|
||||
"contents": [
|
||||
{
|
||||
"role": "user",
|
||||
"parts": parts
|
||||
}
|
||||
],
|
||||
"generationConfig": {
|
||||
"responseModalities": ["TEXT", "IMAGE"],
|
||||
"imageConfig": {
|
||||
"aspectRatio": aspect_ratio,
|
||||
"imageSize": resolution
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return request_body
|
||||
|
||||
def parse_response(self, response: Dict[str, Any]) -> List[Image.Image]:
|
||||
"""
|
||||
同步解析 API 响应（保留以满足抽象基类要求）
|
||||
|
||||
注意：此方法仅用于兼容基类接口，实际使用请调用 parse_response_async()
|
||||
|
||||
Args:
|
||||
response: API 响应字典
|
||||
|
||||
Returns:
|
||||
图像列表
|
||||
|
||||
Raises:
|
||||
RuntimeError: 此方法不应被直接调用
|
||||
"""
|
||||
raise RuntimeError(
|
||||
"parse_response() 不应被直接调用。"
|
||||
"请使用 generate_single_async() 或 generate_batch_async() 等高级方法。"
|
||||
)
|
||||
|
||||
async def parse_response_async(
|
||||
self,
|
||||
response: Dict[str, Any],
|
||||
session: Optional[aiohttp.ClientSession] = None
|
||||
) -> List[Image.Image]:
|
||||
"""
|
||||
异步解析 API 响应，提取生成的图像
|
||||
|
||||
Args:
|
||||
response: API 响应字典
|
||||
session: aiohttp 会话（用于下载图片）
|
||||
|
||||
Returns:
|
||||
图像列表
|
||||
|
||||
Raises:
|
||||
RuntimeError: 解析失败或 API 拒绝时
|
||||
"""
|
||||
|
||||
# ========== 错误检测（按优先级顺序）==========
|
||||
|
||||
# 1. 检查 candidatesTokenCount（最高优先级）
|
||||
usage_metadata = response.get("usageMetadata", {})
|
||||
candidates_token_count = usage_metadata.get("candidatesTokenCount", -1)
|
||||
|
||||
if candidates_token_count == 0:
|
||||
error_msg = (
|
||||
"内容审核拒绝 - candidatesTokenCount = 0\n\n"
|
||||
"原因：提示词或参考图包含不适当内容（色情、暴力、敏感话题等），"
|
||||
"在内容审核阶段就被拒绝，连候选内容都未生成。\n\n"
|
||||
"建议：\n"
|
||||
" - 检查提示词，确保不包含敏感或违规内容\n"
|
||||
" - 如使用参考图，确保图片内容健康合规\n"
|
||||
" - 避免描述暴力、色情等不当内容\n"
|
||||
" - 调整提示词后重试"
|
||||
)
|
||||
raise RuntimeError(error_msg)
|
||||
|
||||
# 2. 检查 finishReason（次优先级）
|
||||
candidates = response.get("candidates", [])
|
||||
if candidates:
|
||||
for candidate in candidates:
|
||||
finish_reason = candidate.get("finishReason", "")
|
||||
|
||||
if finish_reason and finish_reason != "STOP":
|
||||
# 根据不同的 finishReason 提供具体建议
|
||||
reason_messages = {
|
||||
"PROHIBITED_CONTENT": (
|
||||
"违禁内容拒绝",
|
||||
"生成内容触发了违禁内容策略",
|
||||
[
|
||||
"避免引用未来未发布的产品或概念（知识库截止2025年1月）",
|
||||
"使用专业图片编辑软件处理特殊需求",
|
||||
"确保请求内容在模型知识范围内"
|
||||
]
|
||||
),
|
||||
"SAFETY": (
|
||||
"安全过滤器拒绝",
|
||||
"内容触发了安全过滤器",
|
||||
[
|
||||
"使用健康、正面的描述",
|
||||
"避免涉及隐私和伦理问题的内容",
|
||||
"调整提示词后重试"
|
||||
]
|
||||
),
|
||||
"RECITATION": (
|
||||
"版权问题",
|
||||
"可能涉及版权或重复已有内容",
|
||||
[
|
||||
"避免涉及版权敏感话题",
|
||||
"使用更原创的描述方式",
|
||||
"调整提示词后重试"
|
||||
]
|
||||
),
|
||||
"MAX_TOKENS": (
|
||||
"Token 超限",
|
||||
"生成的内容超过了 Token 限制",
|
||||
[
|
||||
"简化提示词",
|
||||
"减少输入图片数量",
|
||||
"降低请求复杂度"
|
||||
]
|
||||
)
|
||||
}
|
||||
|
||||
if finish_reason in reason_messages:
|
||||
title, reason, suggestions = reason_messages[finish_reason]
|
||||
suggestions_text = "\n".join([f" - {s}" for s in suggestions])
|
||||
error_msg = (
|
||||
f"{title} - finishReason = {finish_reason}\n\n"
|
||||
f"原因：{reason}\n\n"
|
||||
f"建议：\n{suggestions_text}"
|
||||
)
|
||||
else:
|
||||
# 未知的 finishReason
|
||||
error_msg = (
|
||||
f"生成异常 - finishReason = {finish_reason}\n\n"
|
||||
"原因：生成过程中断，具体原因未知\n\n"
|
||||
"建议：\n"
|
||||
" - 使用健康、正面的描述\n"
|
||||
" - 避免敏感话题\n"
|
||||
" - 调整提示词后重试"
|
||||
)
|
||||
|
||||
raise RuntimeError(error_msg)
|
||||
|
||||
# ========== 图像提取 ==========
|
||||
|
||||
images = []
|
||||
text_responses = [] # 收集文本响应
|
||||
|
||||
# 需要关闭 session 的标记
|
||||
close_session = False
|
||||
if session is None:
|
||||
session = aiohttp.ClientSession()
|
||||
close_session = True
|
||||
|
||||
try:
|
||||
for candidate in candidates:
|
||||
content = candidate.get("content", {})
|
||||
parts = content.get("parts", [])
|
||||
|
||||
for part in parts:
|
||||
# 方式1: inline_data 或 inlineData (base64)
|
||||
# 兼容两种命名方式：蛇形（inline_data）和驼峰（inlineData）
|
||||
inline_data_key = None
|
||||
if "inline_data" in part:
|
||||
inline_data_key = "inline_data"
|
||||
elif "inlineData" in part:
|
||||
inline_data_key = "inlineData"
|
||||
|
||||
if inline_data_key:
|
||||
inline_data = part[inline_data_key]
|
||||
# 同样兼容 data/mimeType 的命名
|
||||
img_data = inline_data.get("data") or inline_data.get("data", "")
|
||||
|
||||
if img_data:
|
||||
img = decode_base64_to_pil(img_data)
|
||||
images.append(img)
|
||||
|
||||
# 方式2: text 中的 URL - 改为异步下载
|
||||
elif "text" in part:
|
||||
text = part["text"]
|
||||
|
||||
# 收集文本响应（用于后续错误检测）
|
||||
text_responses.append(text)
|
||||
|
||||
# 尝试 markdown 格式: 
|
||||
url_pattern_md = r'!\[.*?\]\((https?://[^\)]+)\)'
|
||||
urls = re.findall(url_pattern_md, text)
|
||||
|
||||
# 如果没找到，尝试纯 URL 格式
|
||||
if not urls:
|
||||
url_pattern_plain = r'https?://[^\s<>"{}|\\^`\[\]]+'
|
||||
urls = re.findall(url_pattern_plain, text)
|
||||
|
||||
if urls:
|
||||
for url in urls:
|
||||
try:
|
||||
# 使用 aiohttp 异步下载，支持更大的超时
|
||||
download_start = time.time()
|
||||
timeout = aiohttp.ClientTimeout(total=120)
|
||||
async with session.get(url, timeout=timeout) as img_response:
|
||||
if img_response.status == 200:
|
||||
img_data = await img_response.read()
|
||||
download_time = time.time() - download_start
|
||||
img_size_mb = len(img_data) / 1024 / 1024
|
||||
speed_mbps = img_size_mb / download_time if download_time > 0 else 0
|
||||
# print(f"🔽 图片下载: {img_size_mb:.2f}MB 耗时 {download_time:.2f}s 速度 {speed_mbps:.2f}MB/s")
|
||||
img = Image.open(BytesIO(img_data))
|
||||
images.append(img)
|
||||
else:
|
||||
print(f"Nano Banana Pro: 下载图片失败 - HTTP {img_response.status}")
|
||||
except Exception as e:
|
||||
print(f"Nano Banana Pro: 下载图片失败 - {str(e)}")
|
||||
|
||||
# 方式3: 直接的 URL 字段 - 也改为异步
|
||||
elif "imageUrl" in part or "url" in part:
|
||||
url = part.get("imageUrl") or part.get("url")
|
||||
try:
|
||||
download_start = time.time()
|
||||
timeout = aiohttp.ClientTimeout(total=120)
|
||||
async with session.get(url, timeout=timeout) as img_response:
|
||||
if img_response.status == 200:
|
||||
img_data = await img_response.read()
|
||||
download_time = time.time() - download_start
|
||||
img_size_mb = len(img_data) / 1024 / 1024
|
||||
speed_mbps = img_size_mb / download_time if download_time > 0 else 0
|
||||
# print(f"🔽 图片下载: {img_size_mb:.2f}MB 耗时 {download_time:.2f}s 速度 {speed_mbps:.2f}MB/s")
|
||||
img = Image.open(BytesIO(img_data))
|
||||
images.append(img)
|
||||
else:
|
||||
print(f"Nano Banana Pro: 下载图片失败 - HTTP {img_response.status}")
|
||||
except Exception as e:
|
||||
print(f"Nano Banana Pro: 下载图片失败 - {str(e)}")
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"解析 API 响应失败: {str(e)}")
|
||||
|
||||
finally:
|
||||
if close_session:
|
||||
await session.close()
|
||||
|
||||
# 3. 检查 API 文本响应拒绝说明
|
||||
if not images and text_responses:
|
||||
# API 返回了文本但没有图片，说明请求被拒绝
|
||||
combined_text = "\n".join(text_responses)
|
||||
error_msg = (
|
||||
f"API 拒绝响应\n\n"
|
||||
f"API 返回说明：\n{combined_text}\n\n"
|
||||
f"建议：\n"
|
||||
f" - 根据上述说明调整请求内容\n"
|
||||
f" - 确保提示词和参考图符合使用规范"
|
||||
)
|
||||
raise RuntimeError(error_msg)
|
||||
|
||||
if not images:
|
||||
raise RuntimeError("API 响应中未找到生成的图像")
|
||||
|
||||
return images
|
||||
|
||||
async def generate_single_async(
|
||||
self,
|
||||
prompt: str,
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
images: Optional[List[Image.Image]] = None,
|
||||
session=None
|
||||
) -> List[Image.Image]:
|
||||
"""
|
||||
单次异步生成请求
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
images: 输入图像列表
|
||||
session: aiohttp 会话
|
||||
|
||||
Returns:
|
||||
生成的图像列表
|
||||
"""
|
||||
endpoint = self.get_endpoint(model=model, resolution=resolution)
|
||||
request_body = self.build_request_body(
|
||||
prompt=prompt,
|
||||
images=images,
|
||||
aspect_ratio=aspect_ratio,
|
||||
resolution=resolution
|
||||
)
|
||||
|
||||
# 根据分辨率获取超时时间
|
||||
timeout = self.get_timeout_by_resolution(resolution)
|
||||
|
||||
response = await self.request_async(endpoint, request_body, session, timeout=timeout)
|
||||
# 使用异步解析方法，传入 session 以实现并发图片下载
|
||||
return await self.parse_response_async(response, session)
|
||||
|
||||
async def generate_batch_async(
|
||||
self,
|
||||
prompt: str,
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
batch_size: int,
|
||||
images: Optional[List[Image.Image]] = None,
|
||||
progress_callback: Optional[Callable[[int, int, bool, Optional[str]], None]] = None
|
||||
) -> List[Image.Image]:
|
||||
"""
|
||||
批量全并发生成
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
batch_size: 批次大小
|
||||
images: 输入图像列表
|
||||
progress_callback: 进度回调，签名为 (completed, total, success, error_msg)
|
||||
|
||||
Returns:
|
||||
生成的图像列表
|
||||
"""
|
||||
import aiohttp
|
||||
import asyncio
|
||||
|
||||
all_images = []
|
||||
completed = 0
|
||||
success_count = 0
|
||||
fail_count = 0
|
||||
|
||||
connector = aiohttp.TCPConnector(limit=0, limit_per_host=0)
|
||||
|
||||
async with aiohttp.ClientSession(connector=connector) as session:
|
||||
tasks = []
|
||||
|
||||
for i in range(batch_size):
|
||||
task = asyncio.create_task(
|
||||
self.generate_single_async(
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
images=images,
|
||||
session=session
|
||||
),
|
||||
name=f"task_{i}"
|
||||
)
|
||||
tasks.append(task)
|
||||
|
||||
# 使用 as_completed 实时获取完成的任务
|
||||
for coro in asyncio.as_completed(tasks):
|
||||
completed += 1
|
||||
try:
|
||||
result = await coro
|
||||
if result:
|
||||
all_images.append(result[0])
|
||||
success_count += 1
|
||||
if progress_callback:
|
||||
progress_callback(completed, batch_size, True, None)
|
||||
except Exception as e:
|
||||
fail_count += 1
|
||||
error_msg = str(e)
|
||||
# 截取错误信息的第一行
|
||||
if '\n' in error_msg:
|
||||
error_msg = error_msg.split('\n')[0]
|
||||
if progress_callback:
|
||||
progress_callback(completed, batch_size, False, error_msg)
|
||||
|
||||
if not all_images:
|
||||
raise RuntimeError(f"批量生成失败，{fail_count} 个请求全部失败")
|
||||
|
||||
return all_images
|
||||
|
||||
def generate_sync(
|
||||
self,
|
||||
prompt: str,
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
batch_size: int,
|
||||
images: Optional[List[Image.Image]] = None,
|
||||
progress_callback: Optional[Callable[[int, int], None]] = None
|
||||
) -> List[Image.Image]:
|
||||
"""
|
||||
同步生成接口（用于 ComfyUI）
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
batch_size: 批次大小
|
||||
images: 输入图像列表
|
||||
progress_callback: 进度回调
|
||||
|
||||
Returns:
|
||||
生成的图像列表
|
||||
"""
|
||||
coro = self.generate_batch_async(
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
batch_size=batch_size,
|
||||
images=images,
|
||||
progress_callback=progress_callback
|
||||
)
|
||||
|
||||
return self.run_async_in_thread(coro)
|
||||
|
||||
async def generate_multi_prompts_async(
|
||||
self,
|
||||
prompts: List[str],
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
images_per_prompt: int,
|
||||
images: Optional[List[Image.Image]] = None,
|
||||
progress_callback: Optional[Callable[[int, int, bool, Optional[str]], None]] = None
|
||||
) -> List[Image.Image]:
|
||||
"""
|
||||
多提示词批量生成
|
||||
|
||||
为每个提示词生成指定数量的图像，所有请求并发执行。
|
||||
|
||||
Args:
|
||||
prompts: 提示词列表
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
images_per_prompt: 每个提示词生成的图像数量
|
||||
images: 输入图像列表（所有提示词共享）
|
||||
progress_callback: 进度回调，签名为 (completed, total, success, error_msg)
|
||||
|
||||
Returns:
|
||||
生成的图像列表（长度 = len(prompts) * images_per_prompt）
|
||||
"""
|
||||
import aiohttp
|
||||
import asyncio
|
||||
|
||||
all_images = []
|
||||
completed = 0
|
||||
success_count = 0
|
||||
fail_count = 0
|
||||
total_tasks = len(prompts) * images_per_prompt
|
||||
|
||||
connector = aiohttp.TCPConnector(limit=0, limit_per_host=0)
|
||||
|
||||
async with aiohttp.ClientSession(connector=connector) as session:
|
||||
tasks = []
|
||||
|
||||
# 为每个提示词创建 images_per_prompt 个任务
|
||||
task_idx = 0
|
||||
for prompt in prompts:
|
||||
for _ in range(images_per_prompt):
|
||||
task = asyncio.create_task(
|
||||
self.generate_single_async(
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
images=images,
|
||||
session=session
|
||||
),
|
||||
name=f"task_{task_idx}"
|
||||
)
|
||||
tasks.append(task)
|
||||
task_idx += 1
|
||||
|
||||
# 使用 as_completed 实时获取完成的任务
|
||||
for coro in asyncio.as_completed(tasks):
|
||||
completed += 1
|
||||
try:
|
||||
result = await coro
|
||||
if result:
|
||||
all_images.append(result[0])
|
||||
success_count += 1
|
||||
if progress_callback:
|
||||
progress_callback(completed, total_tasks, True, None)
|
||||
except Exception as e:
|
||||
fail_count += 1
|
||||
error_msg = str(e)
|
||||
# 截取错误信息的第一行
|
||||
if '\n' in error_msg:
|
||||
error_msg = error_msg.split('\n')[0]
|
||||
if progress_callback:
|
||||
progress_callback(completed, total_tasks, False, error_msg)
|
||||
|
||||
if not all_images:
|
||||
raise RuntimeError(f"批量生成失败，{fail_count} 个请求全部失败")
|
||||
|
||||
return all_images
|
||||
|
||||
def generate_multi_prompts_sync(
|
||||
self,
|
||||
prompts: List[str],
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
images_per_prompt: int,
|
||||
images: Optional[List[Image.Image]] = None,
|
||||
progress_callback: Optional[Callable[[int, int], None]] = None
|
||||
) -> List[Image.Image]:
|
||||
"""
|
||||
多提示词批量生成（同步接口，用于 ComfyUI）
|
||||
|
||||
Args:
|
||||
prompts: 提示词列表
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
images_per_prompt: 每个提示词生成的图像数量
|
||||
images: 输入图像列表
|
||||
progress_callback: 进度回调
|
||||
|
||||
Returns:
|
||||
生成的图像列表
|
||||
"""
|
||||
coro = self.generate_multi_prompts_async(
|
||||
prompts=prompts,
|
||||
model=model,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
images_per_prompt=images_per_prompt,
|
||||
images=images,
|
||||
progress_callback=progress_callback
|
||||
)
|
||||
|
||||
return self.run_async_in_thread(coro)
|
||||
|
||||
async def query_balance_async(self) -> Dict[str, Any]:
|
||||
"""
|
||||
异步查询余额信息
|
||||
|
||||
Returns:
|
||||
余额信息字典，包含：
|
||||
- name: API 名称
|
||||
- total_available: 可用余额（原始值）
|
||||
- 其他字段...
|
||||
|
||||
Raises:
|
||||
RuntimeError: 查询失败时
|
||||
"""
|
||||
endpoint = "/api/usage/token"
|
||||
response = await self.request_get_async(endpoint, use_bearer_token=True)
|
||||
|
||||
if not response.get("code"):
|
||||
raise RuntimeError("余额查询响应格式错误")
|
||||
|
||||
data = response.get("data", {})
|
||||
return data
|
||||
|
||||
def query_balance_sync(self) -> Dict[str, Any]:
|
||||
"""
|
||||
同步查询余额信息（用于 ComfyUI 节点）
|
||||
|
||||
Returns:
|
||||
余额信息字典
|
||||
|
||||
Raises:
|
||||
RuntimeError: 查询失败时
|
||||
"""
|
||||
coro = self.query_balance_async()
|
||||
return self.run_async_in_thread(coro)
|
||||
|
||||
def format_balance_info(self, balance_data: Dict[str, Any]) -> str:
|
||||
"""
|
||||
格式化余额信息为展示文本
|
||||
|
||||
Args:
|
||||
balance_data: 余额信息字典
|
||||
|
||||
Returns:
|
||||
格式化的文本，格式为 "当前余额：$XX.XX | API：xxx"
|
||||
|
||||
Example:
|
||||
>>> data = {"name": "test-api", "total_available": 50000000}
|
||||
>>> client.format_balance_info(data)
|
||||
'当前余额：$100.00 | API：test-api'
|
||||
"""
|
||||
api_name = balance_data.get("name", "未知")
|
||||
total_available = balance_data.get("total_available", 0)
|
||||
|
||||
# 转换公式：实际显示 = total_available / 500000
|
||||
balance_in_dollars = total_available / 500000
|
||||
|
||||
return f"当前余额：${balance_in_dollars:.2f} | API：{api_name}"
|
||||
@@ -0,0 +1,273 @@
|
||||
"""
|
||||
Gemini Flash API 客户端
|
||||
用于调用 Gemini 3 Flash 模型进行多模态文本生成
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import aiohttp
|
||||
|
||||
from ..utils.config import get_api_key_or_raise
|
||||
from ..models_config import get_flash_model_endpoint, get_enabled_flash_models
|
||||
from .base_client import BaseAPIClient
|
||||
|
||||
|
||||
# API 基础配置
|
||||
API_BASE_URL = "https://api.o1key.com"
|
||||
|
||||
|
||||
class GeminiFlashClient(BaseAPIClient):
|
||||
"""
|
||||
Gemini Flash API 客户端
|
||||
用于调用 Gemini 3 Flash 模型进行多模态文本生成
|
||||
|
||||
特点：
|
||||
- 支持图片和视频输入
|
||||
- 支持系统指令
|
||||
- 支持不同思考深度
|
||||
"""
|
||||
|
||||
def __init__(self, api_key: Optional[str] = None):
|
||||
"""
|
||||
初始化客户端
|
||||
|
||||
Args:
|
||||
api_key: API 密钥，如果为 None 则从配置文件或环境变量读取
|
||||
"""
|
||||
if api_key is None:
|
||||
api_key = get_api_key_or_raise("O1KEY_API_KEY")
|
||||
|
||||
super().__init__(
|
||||
base_url=API_BASE_URL,
|
||||
api_key=api_key,
|
||||
max_request_size=20 * 1024 * 1024 # 20MB
|
||||
)
|
||||
|
||||
def get_endpoint(
|
||||
self,
|
||||
model: str = "gemini-3-flash-preview",
|
||||
thinking_depth: str = "不思考",
|
||||
**kwargs
|
||||
) -> str:
|
||||
"""
|
||||
根据模型和思考深度获取 API 端点
|
||||
|
||||
Args:
|
||||
model: 模型名称
|
||||
thinking_depth: 思考深度 ("不思考" 或 "高")
|
||||
|
||||
Returns:
|
||||
API 端点路径
|
||||
"""
|
||||
endpoint = get_flash_model_endpoint(model, thinking_depth)
|
||||
|
||||
if endpoint is None:
|
||||
# 回退到默认端点
|
||||
default_models = get_enabled_flash_models()
|
||||
if default_models:
|
||||
endpoint = get_flash_model_endpoint(default_models[0], thinking_depth)
|
||||
|
||||
if endpoint is None:
|
||||
raise ValueError(f"无法获取模型 '{model}' 的端点 (思考深度: {thinking_depth})")
|
||||
|
||||
return endpoint
|
||||
|
||||
def build_request_body(
|
||||
self,
|
||||
prompt: str = "",
|
||||
system_instruction: Optional[str] = None,
|
||||
image_data: Optional[List[Dict[str, str]]] = None,
|
||||
video_data: Optional[Dict[str, str]] = None,
|
||||
**kwargs
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
构建 API 请求体
|
||||
|
||||
Args:
|
||||
prompt: 用户提示词
|
||||
system_instruction: 系统指令（可选）
|
||||
image_data: 图片数据列表，每个元素包含 mime_type 和 data
|
||||
video_data: 视频数据，包含 mime_type 和 data
|
||||
|
||||
Returns:
|
||||
请求体字典
|
||||
"""
|
||||
parts = []
|
||||
|
||||
# 添加文本部分
|
||||
if prompt:
|
||||
parts.append({"text": prompt})
|
||||
|
||||
# 添加图片部分（如果有）
|
||||
if image_data:
|
||||
for img in image_data:
|
||||
parts.append({
|
||||
"inline_data": {
|
||||
"mime_type": img["mime_type"],
|
||||
"data": img["data"]
|
||||
}
|
||||
})
|
||||
|
||||
# 添加视频部分（如果有）
|
||||
if video_data:
|
||||
parts.append({
|
||||
"inline_data": {
|
||||
"mime_type": video_data["mime_type"],
|
||||
"data": video_data["data"]
|
||||
}
|
||||
})
|
||||
|
||||
# 构建请求体
|
||||
request_body = {
|
||||
"contents": [
|
||||
{
|
||||
"parts": parts
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# 添加系统指令（如果有）
|
||||
if system_instruction and system_instruction.strip():
|
||||
request_body["system_instruction"] = {
|
||||
"parts": [
|
||||
{"text": system_instruction}
|
||||
]
|
||||
}
|
||||
|
||||
return request_body
|
||||
|
||||
def parse_response(self, response: Dict[str, Any]) -> str:
|
||||
"""
|
||||
解析 API 响应，提取生成的文本
|
||||
|
||||
Args:
|
||||
response: API 响应字典
|
||||
|
||||
Returns:
|
||||
生成的文本内容
|
||||
|
||||
Raises:
|
||||
RuntimeError: 解析失败或 API 拒绝时
|
||||
"""
|
||||
# 检查 candidatesTokenCount
|
||||
usage_metadata = response.get("usageMetadata", {})
|
||||
candidates_token_count = usage_metadata.get("candidatesTokenCount", -1)
|
||||
|
||||
if candidates_token_count == 0:
|
||||
raise RuntimeError(
|
||||
"内容审核拒绝 - candidatesTokenCount = 0\n\n"
|
||||
"原因：提示词或输入内容包含不适当内容\n"
|
||||
"建议：检查并调整输入内容"
|
||||
)
|
||||
|
||||
# 检查 finishReason
|
||||
candidates = response.get("candidates", [])
|
||||
if candidates:
|
||||
for candidate in candidates:
|
||||
finish_reason = candidate.get("finishReason", "")
|
||||
|
||||
if finish_reason and finish_reason not in ["STOP", "MAX_TOKENS"]:
|
||||
reason_messages = {
|
||||
"PROHIBITED_CONTENT": "违禁内容拒绝",
|
||||
"SAFETY": "安全过滤器拒绝",
|
||||
"RECITATION": "版权问题"
|
||||
}
|
||||
error_title = reason_messages.get(finish_reason, f"生成异常 ({finish_reason})")
|
||||
raise RuntimeError(f"{error_title}\n建议：调整输入内容后重试")
|
||||
|
||||
# 提取文本内容
|
||||
text_parts = []
|
||||
|
||||
for candidate in candidates:
|
||||
content = candidate.get("content", {})
|
||||
parts = content.get("parts", [])
|
||||
|
||||
for part in parts:
|
||||
if "text" in part:
|
||||
text_parts.append(part["text"])
|
||||
|
||||
if not text_parts:
|
||||
raise RuntimeError("API 响应中未找到生成的文本")
|
||||
|
||||
# 合并所有文本部分
|
||||
return "\n".join(text_parts)
|
||||
|
||||
async def generate_async(
|
||||
self,
|
||||
prompt: str,
|
||||
model: str = "gemini-3-flash-preview",
|
||||
thinking_depth: str = "不思考",
|
||||
system_instruction: Optional[str] = None,
|
||||
image_data: Optional[List[Dict[str, str]]] = None,
|
||||
video_data: Optional[Dict[str, str]] = None,
|
||||
session: Optional[aiohttp.ClientSession] = None
|
||||
) -> str:
|
||||
"""
|
||||
异步生成文本
|
||||
|
||||
Args:
|
||||
prompt: 用户提示词
|
||||
model: 模型名称
|
||||
thinking_depth: 思考深度
|
||||
system_instruction: 系统指令
|
||||
image_data: 图片数据列表
|
||||
video_data: 视频数据
|
||||
session: aiohttp 会话
|
||||
|
||||
Returns:
|
||||
生成的文本内容
|
||||
"""
|
||||
endpoint = self.get_endpoint(model=model, thinking_depth=thinking_depth)
|
||||
request_body = self.build_request_body(
|
||||
prompt=prompt,
|
||||
system_instruction=system_instruction,
|
||||
image_data=image_data,
|
||||
video_data=video_data
|
||||
)
|
||||
|
||||
# 根据是否有视频设置超时（视频处理需要更长时间）
|
||||
timeout = 300 if video_data else 180
|
||||
|
||||
response = await self.request_async(
|
||||
endpoint,
|
||||
request_body,
|
||||
session,
|
||||
timeout=timeout
|
||||
)
|
||||
|
||||
return self.parse_response(response)
|
||||
|
||||
def generate_sync(
|
||||
self,
|
||||
prompt: str,
|
||||
model: str = "gemini-3-flash-preview",
|
||||
thinking_depth: str = "不思考",
|
||||
system_instruction: Optional[str] = None,
|
||||
image_data: Optional[List[Dict[str, str]]] = None,
|
||||
video_data: Optional[Dict[str, str]] = None
|
||||
) -> str:
|
||||
"""
|
||||
同步生成文本（用于 ComfyUI 节点）
|
||||
|
||||
Args:
|
||||
prompt: 用户提示词
|
||||
model: 模型名称
|
||||
thinking_depth: 思考深度
|
||||
system_instruction: 系统指令
|
||||
image_data: 图片数据列表
|
||||
video_data: 视频数据
|
||||
|
||||
Returns:
|
||||
生成的文本内容
|
||||
"""
|
||||
coro = self.generate_async(
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
thinking_depth=thinking_depth,
|
||||
system_instruction=system_instruction,
|
||||
image_data=image_data,
|
||||
video_data=video_data
|
||||
)
|
||||
|
||||
return self.run_async_in_thread(coro)
|
||||
@@ -0,0 +1,449 @@
|
||||
"""
|
||||
模型配置中心
|
||||
用于集中管理所有支持的 Gemini 模型
|
||||
|
||||
使用方式:
|
||||
1. 添加新模型: 在对应的模型列表中添加新的模型字典
|
||||
2. 临时关闭模型: 将模型的 enabled 字段设为 False
|
||||
3. 重新启用模型: 将模型的 enabled 字段改回 True
|
||||
|
||||
模型类型:
|
||||
- GEMINI_MODELS: Nano Banana Pro 图像生成模型
|
||||
- GEMINI_FLASH_MODELS: Google Gemini Flash 文本生成模型
|
||||
|
||||
示例:
|
||||
添加新模型:
|
||||
{
|
||||
"id": "gemini-新模型名称",
|
||||
"description": "模型说明和特点",
|
||||
"enabled": True,
|
||||
"endpoint_type": "standard" # 端点类型: "dynamic", "standard", "flatfee"
|
||||
}
|
||||
|
||||
临时关闭模型:
|
||||
将对应模型的 "enabled": True 改为 "enabled": False
|
||||
"""
|
||||
|
||||
from typing import List, Dict, Optional
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 模型配置列表
|
||||
# ============================================================
|
||||
|
||||
# ============================================================
|
||||
# Nano Banana Pro 图像生成模型
|
||||
# ============================================================
|
||||
|
||||
GEMINI_MODELS = [
|
||||
{
|
||||
"id": "nano-banana-pro",
|
||||
"description": "Nano Banana Pro,根据分辨率自动选择端点 (1K/2K/4K),高性能图像生成模型",
|
||||
"enabled": True,
|
||||
"endpoint_type": "dynamic",
|
||||
"endpoint": None # 动态端点，由代码根据分辨率选择
|
||||
},
|
||||
{
|
||||
"id": "gemini-3-pro-image-preview-url",
|
||||
"description": "URL 模式,根据分辨率自动选择端点 (1K/2K/4K),推荐用于需要不同分辨率的场景",
|
||||
"enabled": False,
|
||||
"endpoint_type": "dynamic",
|
||||
"endpoint": None # 动态端点，由代码根据分辨率选择
|
||||
},
|
||||
{
|
||||
"id": "gemini-3-pro-image-preview",
|
||||
"description": "标准模式,固定端点,适用于常规图像生成",
|
||||
"enabled": True,
|
||||
"endpoint_type": "standard",
|
||||
"endpoint": "/v1beta/models/gemini-3-pro-image-preview:generateContent"
|
||||
},
|
||||
{
|
||||
"id": "gemini-3-pro-image-preview-flatfee",
|
||||
"description": "固定费用模式,固定端点,按固定价格计费 (暂时不可用-504错误)",
|
||||
"enabled": False, # 暂时禁用：端点返回 504 错误
|
||||
"endpoint_type": "flatfee",
|
||||
"endpoint": "/v1beta/models/gemini-3-pro-image-preview-flatfee:generateContent"
|
||||
},
|
||||
{
|
||||
"id": "nano-banana-2",
|
||||
"description": "Nano Banana 2 模型,固定端点,适用于高质量图像生成",
|
||||
"enabled": False,
|
||||
"endpoint_type": "standard",
|
||||
"endpoint": "/v1beta/models/nano-banana-2:generateContent"
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Google Gemini Flash 文本生成模型
|
||||
# ============================================================
|
||||
|
||||
GEMINI_FLASH_MODELS = [
|
||||
{
|
||||
"id": "gemini-3-flash-preview",
|
||||
"description": "Gemini 3 Flash,快速多模态文本生成,支持图片和视频输入",
|
||||
"enabled": True,
|
||||
"endpoints": {
|
||||
"不思考": "/v1beta/models/gemini-3-flash-preview-nothinking:generateContent",
|
||||
"高": "/v1beta/models/gemini-3-flash-preview-high:generateContent"
|
||||
}
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 工具函数
|
||||
# ============================================================
|
||||
|
||||
def get_enabled_models() -> List[str]:
|
||||
"""
|
||||
获取所有启用的模型 ID 列表
|
||||
|
||||
Returns:
|
||||
启用的模型 ID 列表
|
||||
|
||||
Example:
|
||||
>>> get_enabled_models()
|
||||
['gemini-3-pro-image-preview-url', 'gemini-3-pro-image-preview', ...]
|
||||
"""
|
||||
return [model["id"] for model in GEMINI_MODELS if model.get("enabled", False)]
|
||||
|
||||
|
||||
def get_all_models() -> List[str]:
|
||||
"""
|
||||
获取所有模型 ID 列表（包括已禁用的）
|
||||
|
||||
Returns:
|
||||
所有模型 ID 列表
|
||||
|
||||
Example:
|
||||
>>> get_all_models()
|
||||
['gemini-3-pro-image-preview-url', 'gemini-3-pro-image-preview', ...]
|
||||
"""
|
||||
return [model["id"] for model in GEMINI_MODELS]
|
||||
|
||||
|
||||
def get_model_config(model_id: str) -> Optional[Dict]:
|
||||
"""
|
||||
根据模型 ID 获取完整的模型配置
|
||||
|
||||
Args:
|
||||
model_id: 模型 ID
|
||||
|
||||
Returns:
|
||||
模型配置字典，如果未找到则返回 None
|
||||
|
||||
Example:
|
||||
>>> config = get_model_config("gemini-3-pro-image-preview-url")
|
||||
>>> print(config["description"])
|
||||
URL 模式,根据分辨率自动选择端点 (1K/2K/4K)
|
||||
"""
|
||||
for model in GEMINI_MODELS:
|
||||
if model["id"] == model_id:
|
||||
return model
|
||||
return None
|
||||
|
||||
|
||||
def is_model_enabled(model_id: str) -> bool:
|
||||
"""
|
||||
检查指定模型是否启用
|
||||
|
||||
Args:
|
||||
model_id: 模型 ID
|
||||
|
||||
Returns:
|
||||
True 如果模型启用，False 如果禁用或不存在
|
||||
|
||||
Example:
|
||||
>>> is_model_enabled("gemini-3-pro-image-preview-url")
|
||||
True
|
||||
"""
|
||||
config = get_model_config(model_id)
|
||||
if config is None:
|
||||
return False
|
||||
return config.get("enabled", False)
|
||||
|
||||
|
||||
def get_model_description(model_id: str) -> str:
|
||||
"""
|
||||
获取模型的描述信息
|
||||
|
||||
Args:
|
||||
model_id: 模型 ID
|
||||
|
||||
Returns:
|
||||
模型描述，如果未找到则返回空字符串
|
||||
|
||||
Example:
|
||||
>>> get_model_description("gemini-3-pro-image-preview")
|
||||
'标准模式,固定端点,适用于常规图像生成'
|
||||
"""
|
||||
config = get_model_config(model_id)
|
||||
if config is None:
|
||||
return ""
|
||||
return config.get("description", "")
|
||||
|
||||
|
||||
def get_endpoint_type(model_id: str) -> Optional[str]:
|
||||
"""
|
||||
获取模型的端点类型
|
||||
|
||||
Args:
|
||||
model_id: 模型 ID
|
||||
|
||||
Returns:
|
||||
端点类型 ("dynamic", "standard", "flatfee")，如果未找到则返回 None
|
||||
|
||||
Example:
|
||||
>>> get_endpoint_type("gemini-3-pro-image-preview-url")
|
||||
'dynamic'
|
||||
"""
|
||||
config = get_model_config(model_id)
|
||||
if config is None:
|
||||
return None
|
||||
return config.get("endpoint_type")
|
||||
|
||||
|
||||
def get_model_endpoint(model_id: str) -> Optional[str]:
|
||||
"""
|
||||
获取模型的 API 端点
|
||||
|
||||
Args:
|
||||
model_id: 模型 ID
|
||||
|
||||
Returns:
|
||||
API 端点路径，如果未找到或为动态端点则返回 None
|
||||
|
||||
Example:
|
||||
>>> get_model_endpoint("gemini-3-pro-image-preview")
|
||||
'/v1beta/models/gemini-3-pro-image-preview:generateContent'
|
||||
>>> get_model_endpoint("gemini-3-pro-image-preview-url")
|
||||
None # 动态端点
|
||||
"""
|
||||
config = get_model_config(model_id)
|
||||
if config is None:
|
||||
return None
|
||||
return config.get("endpoint")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Gemini Flash 模型工具函数
|
||||
# ============================================================
|
||||
|
||||
def get_enabled_flash_models() -> List[str]:
|
||||
"""
|
||||
获取所有启用的 Flash 模型 ID 列表
|
||||
|
||||
Returns:
|
||||
启用的 Flash 模型 ID 列表
|
||||
|
||||
Example:
|
||||
>>> get_enabled_flash_models()
|
||||
['gemini-3-flash-preview']
|
||||
"""
|
||||
return [model["id"] for model in GEMINI_FLASH_MODELS if model.get("enabled", False)]
|
||||
|
||||
|
||||
def get_all_flash_models() -> List[str]:
|
||||
"""
|
||||
获取所有 Flash 模型 ID 列表（包括已禁用的）
|
||||
|
||||
Returns:
|
||||
所有 Flash 模型 ID 列表
|
||||
"""
|
||||
return [model["id"] for model in GEMINI_FLASH_MODELS]
|
||||
|
||||
|
||||
def get_flash_model_config(model_id: str) -> Optional[Dict]:
|
||||
"""
|
||||
根据模型 ID 获取 Flash 模型的完整配置
|
||||
|
||||
Args:
|
||||
model_id: 模型 ID
|
||||
|
||||
Returns:
|
||||
模型配置字典，如果未找到则返回 None
|
||||
|
||||
Example:
|
||||
>>> config = get_flash_model_config("gemini-3-flash-preview")
|
||||
>>> print(config["description"])
|
||||
'Gemini 3 Flash,快速多模态文本生成,支持图片和视频输入'
|
||||
"""
|
||||
for model in GEMINI_FLASH_MODELS:
|
||||
if model["id"] == model_id:
|
||||
return model
|
||||
return None
|
||||
|
||||
|
||||
def is_flash_model_enabled(model_id: str) -> bool:
|
||||
"""
|
||||
检查指定 Flash 模型是否启用
|
||||
|
||||
Args:
|
||||
model_id: 模型 ID
|
||||
|
||||
Returns:
|
||||
True 如果模型启用，False 如果禁用或不存在
|
||||
"""
|
||||
config = get_flash_model_config(model_id)
|
||||
if config is None:
|
||||
return False
|
||||
return config.get("enabled", False)
|
||||
|
||||
|
||||
def get_flash_model_endpoint(model_id: str, thinking_depth: str = "不思考") -> Optional[str]:
|
||||
"""
|
||||
获取 Flash 模型的 API 端点
|
||||
|
||||
Args:
|
||||
model_id: 模型 ID
|
||||
thinking_depth: 思考深度 ("不思考" 或 "高")
|
||||
|
||||
Returns:
|
||||
API 端点路径，如果未找到则返回 None
|
||||
|
||||
Example:
|
||||
>>> get_flash_model_endpoint("gemini-3-flash-preview", "不思考")
|
||||
'/v1beta/models/gemini-3-flash-preview-nothinking:generateContent'
|
||||
>>> get_flash_model_endpoint("gemini-3-flash-preview", "高")
|
||||
'/v1beta/models/gemini-3-flash-preview-high:generateContent'
|
||||
"""
|
||||
config = get_flash_model_config(model_id)
|
||||
if config is None:
|
||||
return None
|
||||
|
||||
endpoints = config.get("endpoints", {})
|
||||
return endpoints.get(thinking_depth)
|
||||
|
||||
|
||||
def get_flash_model_description(model_id: str) -> str:
|
||||
"""
|
||||
获取 Flash 模型的描述信息
|
||||
|
||||
Args:
|
||||
model_id: 模型 ID
|
||||
|
||||
Returns:
|
||||
模型描述，如果未找到则返回空字符串
|
||||
"""
|
||||
config = get_flash_model_config(model_id)
|
||||
if config is None:
|
||||
return ""
|
||||
return config.get("description", "")
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 向后兼容性检查
|
||||
# ============================================================
|
||||
|
||||
def validate_models_config() -> None:
|
||||
"""
|
||||
验证模型配置的完整性
|
||||
|
||||
检查:
|
||||
- 每个模型必须有 id, description, enabled, endpoint_type, endpoint 字段
|
||||
- 非动态端点模型必须配置有效的 endpoint
|
||||
- 至少有一个模型是启用的
|
||||
|
||||
Raises:
|
||||
ValueError: 如果配置不合法
|
||||
"""
|
||||
if not GEMINI_MODELS:
|
||||
raise ValueError("GEMINI_MODELS 列表不能为空")
|
||||
|
||||
required_fields = ["id", "description", "enabled", "endpoint_type", "endpoint"]
|
||||
valid_endpoint_types = ["dynamic", "standard", "flatfee"]
|
||||
|
||||
for i, model in enumerate(GEMINI_MODELS):
|
||||
# 检查必需字段
|
||||
for field in required_fields:
|
||||
if field not in model:
|
||||
raise ValueError(f"模型 #{i} 缺少必需字段: {field}")
|
||||
|
||||
# 检查 endpoint_type 是否合法
|
||||
if model["endpoint_type"] not in valid_endpoint_types:
|
||||
raise ValueError(
|
||||
f"模型 {model['id']} 的 endpoint_type '{model['endpoint_type']}' 不合法。"
|
||||
f"必须是: {', '.join(valid_endpoint_types)}"
|
||||
)
|
||||
|
||||
# 检查非动态端点模型必须有有效的 endpoint
|
||||
if model["endpoint_type"] != "dynamic" and not model.get("endpoint"):
|
||||
raise ValueError(
|
||||
f"模型 {model['id']} 的 endpoint_type 为 '{model['endpoint_type']}'，"
|
||||
f"但未配置有效的 endpoint 字段"
|
||||
)
|
||||
|
||||
# 检查端点格式（如果配置了）
|
||||
endpoint = model.get("endpoint")
|
||||
if endpoint and not endpoint.startswith("/v1beta/models/"):
|
||||
raise ValueError(
|
||||
f"模型 {model['id']} 的 endpoint '{endpoint}' 格式不正确。"
|
||||
f"应以 '/v1beta/models/' 开头"
|
||||
)
|
||||
|
||||
# 检查至少有一个启用的模型
|
||||
if not get_enabled_models():
|
||||
raise ValueError("至少需要启用一个模型")
|
||||
|
||||
|
||||
def validate_flash_models_config() -> None:
|
||||
"""
|
||||
验证 Flash 模型配置的完整性
|
||||
|
||||
检查:
|
||||
- 每个模型必须有 id, description, enabled, endpoints 字段
|
||||
- endpoints 必须包含所有思考深度选项
|
||||
- 至少有一个模型是启用的
|
||||
|
||||
Raises:
|
||||
ValueError: 如果配置不合法
|
||||
"""
|
||||
if not GEMINI_FLASH_MODELS:
|
||||
raise ValueError("GEMINI_FLASH_MODELS 列表不能为空")
|
||||
|
||||
required_fields = ["id", "description", "enabled", "endpoints"]
|
||||
required_thinking_depths = ["不思考", "高"]
|
||||
|
||||
for i, model in enumerate(GEMINI_FLASH_MODELS):
|
||||
# 检查必需字段
|
||||
for field in required_fields:
|
||||
if field not in model:
|
||||
raise ValueError(f"Flash 模型 #{i} 缺少必需字段: {field}")
|
||||
|
||||
# 检查 endpoints 字典
|
||||
endpoints = model.get("endpoints", {})
|
||||
if not isinstance(endpoints, dict):
|
||||
raise ValueError(f"Flash 模型 {model['id']} 的 endpoints 必须是字典")
|
||||
|
||||
# 检查所有思考深度选项都有对应端点
|
||||
for depth in required_thinking_depths:
|
||||
if depth not in endpoints:
|
||||
raise ValueError(
|
||||
f"Flash 模型 {model['id']} 的 endpoints 缺少 '{depth}' 思考深度"
|
||||
)
|
||||
|
||||
endpoint = endpoints[depth]
|
||||
if not endpoint or not endpoint.startswith("/v1beta/models/"):
|
||||
raise ValueError(
|
||||
f"Flash 模型 {model['id']} 的端点 '{endpoint}' 格式不正确。"
|
||||
f"应以 '/v1beta/models/' 开头"
|
||||
)
|
||||
|
||||
# 检查至少有一个启用的模型
|
||||
if not get_enabled_flash_models():
|
||||
raise ValueError("至少需要启用一个 Flash 模型")
|
||||
|
||||
|
||||
# 在模块加载时验证配置
|
||||
try:
|
||||
validate_models_config()
|
||||
except ValueError as e:
|
||||
print(f"⚠️ 图像模型配置验证失败: {str(e)}")
|
||||
print(f"⚠️ 请检查 models_config.py 文件")
|
||||
|
||||
try:
|
||||
validate_flash_models_config()
|
||||
except ValueError as e:
|
||||
print(f"⚠️ Flash 模型配置验证失败: {str(e)}")
|
||||
print(f"⚠️ 请检查 models_config.py 文件")
|
||||
@@ -0,0 +1,10 @@
|
||||
"""
|
||||
节点模块
|
||||
包含所有 ComfyUI 自定义节点的实现
|
||||
"""
|
||||
|
||||
from .nano_banana_pro import NanoBananaPro
|
||||
from .batch_nano_banana_pro import BatchNanoBananaPro
|
||||
from .google_gemini import GoogleGemini
|
||||
|
||||
__all__ = ['NanoBananaPro', 'BatchNanoBananaPro', 'GoogleGemini']
|
||||
@@ -0,0 +1,751 @@
|
||||
"""
|
||||
批量 Nano Banana Pro 节点
|
||||
ComfyUI 自定义节点，用于批量处理图像生成任务
|
||||
支持多文件夹加载、1:1/笛卡尔积配对、智能命名保存
|
||||
"""
|
||||
|
||||
import time
|
||||
import math
|
||||
import random
|
||||
import asyncio
|
||||
import aiohttp
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from typing import Optional, Tuple, List
|
||||
from PIL import Image
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
from ..utils.image_utils import tensor_to_pil, pil_to_tensor
|
||||
from ..utils.file_utils import (
|
||||
ImageInfo,
|
||||
load_images_from_folder,
|
||||
pair_images_indexed,
|
||||
pair_images_cartesian,
|
||||
generate_output_filename,
|
||||
save_image
|
||||
)
|
||||
from ..clients.gemini_client import GeminiAPIClient
|
||||
from ..models_config import get_enabled_models
|
||||
|
||||
# 导入 ComfyUI 原生进度条
|
||||
try:
|
||||
from comfy.utils import ProgressBar
|
||||
PROGRESS_BAR_AVAILABLE = True
|
||||
except ImportError:
|
||||
PROGRESS_BAR_AVAILABLE = False
|
||||
print("⚠️ BatchNanoBananaPro: comfy.utils.ProgressBar 不可用，将只使用终端进度显示")
|
||||
|
||||
|
||||
class BatchNanoBananaPro:
|
||||
"""
|
||||
批量 Nano Banana Pro 节点
|
||||
|
||||
功能：
|
||||
- 从多个文件夹加载图片
|
||||
- 支持三种配对模式：
|
||||
* 1:1 - 索引配对（文件夹之间按位置配对）
|
||||
* 1*N - 笛卡尔积配对（所有可能组合）
|
||||
* 不配对 - 固定参考图模式（文件夹图片依次与所有参考图组合）
|
||||
- 批量调用 API 生成图像
|
||||
- 智能命名保存（保留原始文件名）
|
||||
- 并发控制（默认最大 100）
|
||||
|
||||
注意：
|
||||
- 「不配对」模式只支持单个文件夹
|
||||
- 支持的模型列表从 models_config.py 动态加载
|
||||
- 要添加/禁用模型，请编辑 models_config.py 文件
|
||||
"""
|
||||
|
||||
# 支持的模型列表（从配置文件动态加载）
|
||||
MODELS = None # 将在 INPUT_TYPES 中动态获取
|
||||
|
||||
# 支持的宽高比列表
|
||||
ASPECT_RATIOS = [
|
||||
"1:1", "4:3", "3:4", "16:9", "9:16",
|
||||
"2:3", "3:2", "4:5", "5:4", "21:9"
|
||||
]
|
||||
|
||||
# 支持的分辨率列表
|
||||
RESOLUTIONS = ["1K", "2K", "4K"]
|
||||
|
||||
# 配对模式
|
||||
PAIRING_MODES = ["1:1", "1*N", "不配对"]
|
||||
|
||||
def __init__(self):
|
||||
"""初始化节点"""
|
||||
self.client = None
|
||||
|
||||
def resize_to_megapixels(
|
||||
self,
|
||||
image: Image.Image,
|
||||
target_megapixels: float
|
||||
) -> Image.Image:
|
||||
"""
|
||||
将图像缩放到指定的总像素数，保持纵横比
|
||||
|
||||
Args:
|
||||
image: PIL Image 对象
|
||||
target_megapixels: 目标像素数（百万像素）
|
||||
|
||||
Returns:
|
||||
缩放后的 PIL Image
|
||||
|
||||
Example:
|
||||
>>> resized = self.resize_to_megapixels(img, 2.0) # 缩放到2百万像素
|
||||
"""
|
||||
# 计算当前像素数
|
||||
current_pixels = image.width * image.height
|
||||
target_pixels = int(target_megapixels * 1_000_000)
|
||||
|
||||
# 如果当前像素数已经接近目标，则不缩放
|
||||
if abs(current_pixels - target_pixels) / target_pixels < 0.05:
|
||||
return image
|
||||
|
||||
# 计算缩放比例
|
||||
scale = (target_pixels / current_pixels) ** 0.5
|
||||
|
||||
# 计算新尺寸
|
||||
new_width = int(image.width * scale)
|
||||
new_height = int(image.height * scale)
|
||||
|
||||
# 确保至少为1像素
|
||||
new_width = max(1, new_width)
|
||||
new_height = max(1, new_height)
|
||||
|
||||
# 使用 Lanczos 重采样
|
||||
resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
|
||||
|
||||
return resized_image
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
"""
|
||||
定义输入参数
|
||||
|
||||
ComfyUI 节点规范：
|
||||
- required: 必选参数
|
||||
- optional: 可选参数
|
||||
"""
|
||||
# 从配置文件动态获取启用的模型列表
|
||||
enabled_models = get_enabled_models()
|
||||
|
||||
# 如果没有启用的模型，使用空列表（会导致节点不可用，提示用户配置）
|
||||
if not enabled_models:
|
||||
enabled_models = ["请在 models_config.py 中启用至少一个模型"]
|
||||
|
||||
# 创建9个独立的图像输入
|
||||
optional_inputs = {}
|
||||
for i in range(1, 10): # 1-9
|
||||
optional_inputs[f"参考图{i}"] = ("IMAGE",)
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"prompt": ("STRING", {
|
||||
"default": "一个中国女子的OOTD",
|
||||
"multiline": True
|
||||
}),
|
||||
"模型": (enabled_models, {
|
||||
"default": enabled_models[0]
|
||||
}),
|
||||
"宽高比": (cls.ASPECT_RATIOS, {
|
||||
"default": "1:1"
|
||||
}),
|
||||
"分辨率": (cls.RESOLUTIONS, {
|
||||
"default": "2K"
|
||||
}),
|
||||
"像素缩放": ("BOOLEAN", {
|
||||
"default": False
|
||||
}),
|
||||
"分辨率像素": ("FLOAT", {
|
||||
"default": 1.0,
|
||||
"min": 0.1,
|
||||
"max": 100.0,
|
||||
"step": 0.1,
|
||||
"display": "number"
|
||||
}),
|
||||
"seed": ("INT", {
|
||||
"default": 0,
|
||||
"min": 0,
|
||||
"max": 0xffffffffffffffff
|
||||
}),
|
||||
"文件夹1": ("STRING", {
|
||||
"default": "",
|
||||
"multiline": False
|
||||
}),
|
||||
"文件夹2": ("STRING", {
|
||||
"default": "",
|
||||
"multiline": False
|
||||
}),
|
||||
"文件夹3": ("STRING", {
|
||||
"default": "",
|
||||
"multiline": False
|
||||
}),
|
||||
"文件夹4": ("STRING", {
|
||||
"default": "",
|
||||
"multiline": False
|
||||
}),
|
||||
"保存路径": ("STRING", {
|
||||
"default": "",
|
||||
"multiline": False
|
||||
}),
|
||||
"图片配对模式": (cls.PAIRING_MODES, {
|
||||
"default": "不配对"
|
||||
})
|
||||
},
|
||||
"optional": optional_inputs
|
||||
}
|
||||
|
||||
# 返回值类型
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("输出图像",)
|
||||
|
||||
# 执行函数名
|
||||
FUNCTION = "process_batch"
|
||||
|
||||
# 节点分类
|
||||
CATEGORY = "image/batch"
|
||||
|
||||
def _load_folders(
|
||||
self,
|
||||
folder1: str,
|
||||
folder2: Optional[str],
|
||||
folder3: Optional[str],
|
||||
folder4: Optional[str],
|
||||
enable_scaling: bool,
|
||||
target_megapixels: float
|
||||
) -> List[List[ImageInfo]]:
|
||||
"""
|
||||
加载所有文件夹中的图片
|
||||
|
||||
Args:
|
||||
folder1-4: 文件夹路径
|
||||
enable_scaling: 是否启用像素缩放
|
||||
target_megapixels: 目标像素数（百万像素）
|
||||
|
||||
Returns:
|
||||
图片列表的列表
|
||||
"""
|
||||
folders = [folder1, folder2, folder3, folder4]
|
||||
all_images = []
|
||||
|
||||
for i, folder in enumerate(folders, 1):
|
||||
if folder and folder.strip():
|
||||
try:
|
||||
images = load_images_from_folder(folder)
|
||||
if images:
|
||||
# 应用像素缩放
|
||||
if enable_scaling:
|
||||
scaled_images = []
|
||||
for img_info in images:
|
||||
scaled_img = self.resize_to_megapixels(
|
||||
img_info.image,
|
||||
target_megapixels
|
||||
)
|
||||
# 创建新的 ImageInfo，保留其他元数据
|
||||
scaled_info = ImageInfo(
|
||||
image=scaled_img,
|
||||
filename=img_info.filename,
|
||||
extension=img_info.extension,
|
||||
source_path=img_info.source_path
|
||||
)
|
||||
scaled_images.append(scaled_info)
|
||||
images = scaled_images
|
||||
|
||||
all_images.append(images)
|
||||
print(f"BatchNanoBananaPro: 文件夹{i} 加载了 {len(images)} 张图片")
|
||||
else:
|
||||
print(f"BatchNanoBananaPro: 文件夹{i} 为空或没有有效图片")
|
||||
except ValueError as e:
|
||||
print(f"BatchNanoBananaPro: 文件夹{i} 加载失败 - {e}")
|
||||
|
||||
return all_images
|
||||
|
||||
def _create_pairs(
|
||||
self,
|
||||
image_lists: List[List[ImageInfo]],
|
||||
pairing_mode: str,
|
||||
manual_images: Optional[List[ImageInfo]] = None
|
||||
) -> List[Tuple[ImageInfo, ...]]:
|
||||
"""
|
||||
根据配对模式创建图片组合
|
||||
|
||||
Args:
|
||||
image_lists: 从文件夹加载的图片列表
|
||||
pairing_mode: 配对模式 (1:1, 1*N, 不配对)
|
||||
manual_images: 手动输入的参考图
|
||||
|
||||
Returns:
|
||||
配对后的元组列表
|
||||
|
||||
Raises:
|
||||
ValueError: 不配对模式下填入多个文件夹时
|
||||
"""
|
||||
# === 新模式：不配对 ===
|
||||
if pairing_mode == "不配对":
|
||||
# 验证：只支持单个文件夹
|
||||
if len(image_lists) > 1:
|
||||
raise ValueError("「不配对」模式只支持单个文件夹，请清空其他文件夹路径")
|
||||
|
||||
# 场景1：有文件夹 + 有参考图
|
||||
if image_lists and manual_images:
|
||||
folder_images = image_lists[0]
|
||||
# 每张文件夹图片 + 所有参考图
|
||||
pairs = []
|
||||
for img in folder_images:
|
||||
pair = (img,) + tuple(manual_images)
|
||||
pairs.append(pair)
|
||||
return pairs
|
||||
|
||||
# 场景2：有文件夹 + 无参考图
|
||||
elif image_lists:
|
||||
# 每张图片单独成组
|
||||
return [(img,) for img in image_lists[0]]
|
||||
|
||||
# 场景3：无文件夹 + 有参考图
|
||||
elif manual_images:
|
||||
# 每张参考图单独成组
|
||||
return [(img,) for img in manual_images]
|
||||
|
||||
else:
|
||||
return []
|
||||
|
||||
# === 原有逻辑：1:1 和 1*N ===
|
||||
# 如果有手动参考图，添加到列表中（所有参考图作为一个列表）
|
||||
if manual_images:
|
||||
image_lists.append(manual_images)
|
||||
|
||||
if not image_lists:
|
||||
return []
|
||||
|
||||
# 如果只有一个列表，直接返回每个图片作为单元素元组
|
||||
if len(image_lists) == 1:
|
||||
return [(img,) for img in image_lists[0]]
|
||||
|
||||
# 根据配对模式选择配对函数
|
||||
if pairing_mode == "1:1":
|
||||
pairs = pair_images_indexed(*image_lists)
|
||||
else: # 1*N
|
||||
pairs = pair_images_cartesian(*image_lists)
|
||||
|
||||
return pairs
|
||||
|
||||
async def _generate_single_task(
|
||||
self,
|
||||
client: GeminiAPIClient,
|
||||
session: aiohttp.ClientSession,
|
||||
prompt: str,
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
images: List[ImageInfo],
|
||||
output_folder: str,
|
||||
task_index: int
|
||||
) -> dict:
|
||||
"""
|
||||
执行单个生成任务
|
||||
|
||||
Args:
|
||||
client: API 客户端
|
||||
session: aiohttp 会话
|
||||
prompt: 提示词
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
images: 输入图片列表
|
||||
output_folder: 输出文件夹
|
||||
task_index: 任务索引
|
||||
|
||||
Returns:
|
||||
包含结果信息的字典
|
||||
"""
|
||||
result = {
|
||||
"task_index": task_index,
|
||||
"success": False,
|
||||
"generated_count": 0,
|
||||
"saved_files": [],
|
||||
"error": None
|
||||
}
|
||||
|
||||
try:
|
||||
# 准备输入图片
|
||||
input_pil_images = [info.image for info in images]
|
||||
|
||||
# 调用 API 生成图片（固定生成1次）
|
||||
generated_images = []
|
||||
try:
|
||||
gen_result = await client.generate_single_async(
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
images=input_pil_images,
|
||||
session=session
|
||||
)
|
||||
if gen_result:
|
||||
generated_images.extend(gen_result)
|
||||
except Exception as e:
|
||||
error_msg = str(e)
|
||||
print(f"BatchNanoBananaPro: 任务 {task_index + 1} 生成失败 - {error_msg}")
|
||||
result["error"] = error_msg
|
||||
|
||||
# 保存生成的图片
|
||||
for i, gen_img in enumerate(generated_images):
|
||||
# 使用任务索引作为唯一标识，确保并发安全
|
||||
output_path = generate_output_filename(
|
||||
source_images=list(images),
|
||||
batch_index=i,
|
||||
output_folder=output_folder,
|
||||
extension=".png",
|
||||
task_id=f"task{task_index}"
|
||||
)
|
||||
save_image(gen_img, output_path)
|
||||
result["saved_files"].append(output_path)
|
||||
|
||||
# 只有生成了图片才标记为成功
|
||||
if len(generated_images) > 0:
|
||||
result["success"] = True
|
||||
result["generated_count"] = len(generated_images)
|
||||
|
||||
except Exception as e:
|
||||
result["error"] = str(e)
|
||||
|
||||
return result
|
||||
|
||||
async def _process_batch_async(
|
||||
self,
|
||||
pairs: List[Tuple[ImageInfo, ...]],
|
||||
prompt: str,
|
||||
model: str,
|
||||
resolution: str,
|
||||
aspect_ratio: str,
|
||||
output_folder: str,
|
||||
pbar=None
|
||||
) -> List[dict]:
|
||||
"""
|
||||
异步批量处理所有任务
|
||||
|
||||
Args:
|
||||
pairs: 配对后的图片组合
|
||||
prompt: 提示词
|
||||
model: 模型名称
|
||||
resolution: 分辨率
|
||||
aspect_ratio: 宽高比
|
||||
output_folder: 输出文件夹
|
||||
|
||||
Returns:
|
||||
所有任务的结果列表
|
||||
"""
|
||||
if self.client is None:
|
||||
self.client = GeminiAPIClient()
|
||||
|
||||
# 固定最大并发数为 100
|
||||
max_concurrent = 100
|
||||
|
||||
total_tasks = len(pairs)
|
||||
all_results = []
|
||||
completed = 0
|
||||
success_count = 0
|
||||
fail_count = 0
|
||||
|
||||
# 计算分批数量
|
||||
num_batches = math.ceil(total_tasks / max_concurrent)
|
||||
|
||||
# 进度打印配置：任务数 >= 50 时，额外显示百分比里程碑
|
||||
show_milestone = total_tasks >= 50
|
||||
milestones = [0.2, 0.4, 0.6, 0.8, 1.0] # 20%, 40%, 60%, 80%, 100%
|
||||
milestone_index = 0
|
||||
|
||||
if num_batches > 1:
|
||||
print(f"BatchNanoBananaPro: 任务数 {total_tasks} 超过并发上限 {max_concurrent}，将分 {num_batches} 批执行")
|
||||
|
||||
connector = aiohttp.TCPConnector(limit=0, limit_per_host=0)
|
||||
|
||||
async with aiohttp.ClientSession(connector=connector) as session:
|
||||
for batch_idx in range(num_batches):
|
||||
start_idx = batch_idx * max_concurrent
|
||||
end_idx = min(start_idx + max_concurrent, total_tasks)
|
||||
batch_pairs = pairs[start_idx:end_idx]
|
||||
|
||||
if num_batches > 1:
|
||||
print(f"BatchNanoBananaPro: 执行第 {batch_idx + 1}/{num_batches} 批 ({start_idx + 1}-{end_idx})...")
|
||||
|
||||
# 创建当前批次的任务
|
||||
tasks = []
|
||||
for i, pair in enumerate(batch_pairs):
|
||||
task = asyncio.create_task(
|
||||
self._generate_single_task(
|
||||
client=self.client,
|
||||
session=session,
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
resolution=resolution,
|
||||
aspect_ratio=aspect_ratio,
|
||||
images=list(pair),
|
||||
output_folder=output_folder,
|
||||
task_index=start_idx + i
|
||||
)
|
||||
)
|
||||
tasks.append(task)
|
||||
|
||||
# 使用 as_completed 实时获取完成的任务
|
||||
for coro in asyncio.as_completed(tasks):
|
||||
result_data = None
|
||||
try:
|
||||
result = await coro
|
||||
if isinstance(result, Exception):
|
||||
result_data = {
|
||||
"success": False,
|
||||
"error": str(result),
|
||||
"generated_count": 0,
|
||||
"saved_files": []
|
||||
}
|
||||
all_results.append(result_data)
|
||||
else:
|
||||
result_data = result
|
||||
all_results.append(result)
|
||||
except Exception as e:
|
||||
result_data = {
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"generated_count": 0,
|
||||
"saved_files": []
|
||||
}
|
||||
all_results.append(result_data)
|
||||
|
||||
completed += 1
|
||||
|
||||
# 根据成功/失败状态打印不同信息
|
||||
if result_data and result_data.get("success", False):
|
||||
success_count += 1
|
||||
print(f"BatchNanoBananaPro: 任务 {completed}/{total_tasks} 成功 ✓")
|
||||
else:
|
||||
fail_count += 1
|
||||
# 提取错误信息的第一行
|
||||
error_msg = result_data.get("error", "未知错误") if result_data else "未知错误"
|
||||
# 截取第一行或前50个字符
|
||||
if '\n' in error_msg:
|
||||
error_msg = error_msg.split('\n')[0]
|
||||
if len(error_msg) > 50:
|
||||
error_msg = error_msg[:50] + "..."
|
||||
print(f"BatchNanoBananaPro: 任务 {completed}/{total_tasks} 失败 ✗ - {error_msg}")
|
||||
|
||||
# 更新 ComfyUI 原生进度条
|
||||
if pbar is not None:
|
||||
pbar.update(1)
|
||||
|
||||
# 大任务额外显示百分比里程碑
|
||||
if show_milestone and milestone_index < len(milestones):
|
||||
progress = completed / total_tasks
|
||||
if progress >= milestones[milestone_index]:
|
||||
percentage = int(milestones[milestone_index] * 100)
|
||||
print(f"BatchNanoBananaPro: >>> 进度 {percentage}% <<<")
|
||||
milestone_index += 1
|
||||
|
||||
return all_results
|
||||
|
||||
def process_batch(
|
||||
self,
|
||||
prompt: str,
|
||||
文件夹1: str,
|
||||
文件夹2: str,
|
||||
文件夹3: str,
|
||||
文件夹4: str,
|
||||
像素缩放: bool,
|
||||
分辨率像素: float,
|
||||
seed: int,
|
||||
保存路径: str,
|
||||
图片配对模式: str,
|
||||
模型: str,
|
||||
宽高比: str,
|
||||
分辨率: str,
|
||||
**kwargs
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
批量处理图像生成任务
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
文件夹1-4: 图片文件夹路径
|
||||
像素缩放: 是否启用像素缩放
|
||||
分辨率像素: 目标像素数（百万像素）
|
||||
seed: 随机种子
|
||||
保存路径: 输出保存路径
|
||||
图片配对模式: 1:1 或 1*N
|
||||
模型: 模型名称
|
||||
宽高比: 输出宽高比
|
||||
分辨率: 输出分辨率
|
||||
**kwargs: 动态参考图输入 (参考图1-9)
|
||||
|
||||
Returns:
|
||||
输出图像张量
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# 设置随机种子（用于本地随机操作）
|
||||
random.seed(seed)
|
||||
np.random.seed(seed % (2**32))
|
||||
# 验证保存路径
|
||||
if not 保存路径 or not 保存路径.strip():
|
||||
raise ValueError("请提供保存路径")
|
||||
|
||||
# 加载文件夹图片
|
||||
print("BatchNanoBananaPro: 开始加载图片...")
|
||||
image_lists = self._load_folders(
|
||||
文件夹1, 文件夹2, 文件夹3, 文件夹4,
|
||||
像素缩放, 分辨率像素
|
||||
)
|
||||
|
||||
# 处理独立的参考图输入
|
||||
manual_images = []
|
||||
for i in range(1, 10): # 1-9
|
||||
key = f"参考图{i}"
|
||||
if key in kwargs and kwargs[key] is not None:
|
||||
pil_images = tensor_to_pil(kwargs[key])
|
||||
for j, img in enumerate(pil_images):
|
||||
# 如果启用像素缩放，也对参考图进行缩放
|
||||
if 像素缩放:
|
||||
img = self.resize_to_megapixels(img, 分辨率像素)
|
||||
|
||||
manual_images.append(
|
||||
ImageInfo(
|
||||
image=img,
|
||||
filename=f"manual_{i}_{j}",
|
||||
extension=".png",
|
||||
source_path=""
|
||||
)
|
||||
)
|
||||
|
||||
if manual_images:
|
||||
print(f"BatchNanoBananaPro: 加载了 {len(manual_images)} 张参考图")
|
||||
|
||||
# 验证是否有图片
|
||||
total_folder_images = sum(len(lst) for lst in image_lists)
|
||||
total_manual_images = len(manual_images)
|
||||
|
||||
if total_folder_images == 0 and total_manual_images == 0:
|
||||
raise ValueError("未找到任何图片，请检查文件夹路径或提供参考图")
|
||||
|
||||
# 创建配对
|
||||
print(f"BatchNanoBananaPro: 使用 {图片配对模式} 模式创建配对...")
|
||||
pairs = self._create_pairs(image_lists, 图片配对模式, manual_images if manual_images else None)
|
||||
|
||||
if not pairs:
|
||||
raise ValueError("配对结果为空，请检查输入")
|
||||
|
||||
total_tasks = len(pairs)
|
||||
print(f"BatchNanoBananaPro: 共 {total_tasks} 组配对")
|
||||
|
||||
# 创建 ComfyUI 原生进度条
|
||||
pbar = None
|
||||
if PROGRESS_BAR_AVAILABLE:
|
||||
pbar = ProgressBar(total_tasks)
|
||||
|
||||
# 初始化 API 客户端
|
||||
if self.client is None:
|
||||
try:
|
||||
self.client = GeminiAPIClient()
|
||||
except ValueError as e:
|
||||
raise ValueError(f"初始化 API 客户端失败: {str(e)}")
|
||||
|
||||
# 执行批量生成
|
||||
print("BatchNanoBananaPro: 开始批量生成...")
|
||||
|
||||
# 在新线程中运行异步代码，避免事件循环冲突
|
||||
def run_async_in_thread():
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
try:
|
||||
return loop.run_until_complete(
|
||||
self._process_batch_async(
|
||||
pairs=pairs,
|
||||
prompt=prompt,
|
||||
model=模型,
|
||||
resolution=分辨率,
|
||||
aspect_ratio=宽高比,
|
||||
output_folder=保存路径,
|
||||
pbar=pbar
|
||||
)
|
||||
)
|
||||
finally:
|
||||
loop.close()
|
||||
|
||||
# 使用线程池在新线程中运行事件循环
|
||||
with ThreadPoolExecutor(max_workers=1) as executor:
|
||||
future = executor.submit(run_async_in_thread)
|
||||
results = future.result()
|
||||
|
||||
# 统计结果
|
||||
success_count = sum(1 for r in results if r.get("success", False))
|
||||
fail_count = len(results) - success_count
|
||||
total_generated = sum(r.get("generated_count", 0) for r in results)
|
||||
all_saved_files = []
|
||||
for r in results:
|
||||
all_saved_files.extend(r.get("saved_files", []))
|
||||
|
||||
elapsed = time.time() - start_time
|
||||
|
||||
# 精简统计信息
|
||||
print("=" * 50)
|
||||
print(f"BatchNanoBananaPro 处理完成 | 总耗时: {elapsed:.2f}s | 成功: {success_count}/{total_tasks} | 生成: {total_generated}张")
|
||||
print(f"保存路径: {保存路径}")
|
||||
|
||||
# 失败详情（如果有）
|
||||
failed_results = [r for r in results if not r.get("success", False)]
|
||||
if failed_results:
|
||||
# 收集失败任务的索引
|
||||
failed_indices = [str(r.get('task_index', '?') + 1) for r in failed_results[:5]]
|
||||
failed_str = ",".join(failed_indices)
|
||||
if len(failed_results) > 5:
|
||||
failed_str += f"... (共{len(failed_results)}个)"
|
||||
# 显示第一个失败原因作为示例
|
||||
first_error = failed_results[0].get('error', '未知错误')
|
||||
print(f"失败 {len(failed_results)}个: 任务{failed_str} - {first_error}")
|
||||
|
||||
# 收集所有生成的图片
|
||||
output_images = []
|
||||
for file_path in all_saved_files:
|
||||
try:
|
||||
img = Image.open(file_path)
|
||||
output_images.append(img)
|
||||
except Exception as e:
|
||||
print(f"BatchNanoBananaPro: 无法加载图片 {file_path} - {e}")
|
||||
|
||||
# 如果没有生成成功的图片，创建一个占位图
|
||||
if not output_images:
|
||||
placeholder = Image.new('RGB', (512, 512), color=(128, 128, 128))
|
||||
output_images = [placeholder]
|
||||
|
||||
# 转换为张量
|
||||
output_tensor = pil_to_tensor(output_images)
|
||||
|
||||
return (output_tensor,)
|
||||
|
||||
except ValueError as e:
|
||||
# 检测是否为授权错误
|
||||
if str(e) == "未授权！":
|
||||
print("请联系作者授权后方可使用！")
|
||||
else:
|
||||
print(f"BatchNanoBananaPro: 输入错误 - {str(e)}")
|
||||
raise
|
||||
|
||||
except RuntimeError as e:
|
||||
print(f"BatchNanoBananaPro: 运行时错误 - {str(e)}")
|
||||
raise
|
||||
|
||||
except Exception as e:
|
||||
print(f"BatchNanoBananaPro: 未知错误 - {str(e)}")
|
||||
raise
|
||||
|
||||
finally:
|
||||
# 无论成功或失败，都尝试查询余额
|
||||
if self.client is not None:
|
||||
try:
|
||||
balance_data = self.client.query_balance_sync()
|
||||
balance_info = self.client.format_balance_info(balance_data)
|
||||
print(f"{balance_info}")
|
||||
print("=" * 50)
|
||||
except Exception as e:
|
||||
print(f"⚠️ 余额查询失败 - {str(e)}")
|
||||
print("=" * 50)
|
||||
@@ -0,0 +1,332 @@
|
||||
"""
|
||||
Google Gemini 节点
|
||||
ComfyUI 自定义节点,用于调用 Gemini Flash 模型进行多模态文本生成
|
||||
"""
|
||||
|
||||
import base64
|
||||
import os
|
||||
import time
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import torch
|
||||
|
||||
from ..utils.image_utils import tensor_to_pil, encode_image_to_base64
|
||||
from ..clients.gemini_flash_client import GeminiFlashClient
|
||||
from ..models_config import get_enabled_flash_models
|
||||
|
||||
|
||||
# 支持的视频 MIME 类型映射
|
||||
VIDEO_MIME_TYPES = {
|
||||
".mp4": "video/mp4",
|
||||
".mpeg": "video/mpeg",
|
||||
".mpg": "video/mpg",
|
||||
".mov": "video/mov",
|
||||
".avi": "video/avi",
|
||||
".flv": "video/x-flv",
|
||||
".webm": "video/webm",
|
||||
".wmv": "video/wmv",
|
||||
".3gp": "video/3gpp",
|
||||
".3gpp": "video/3gpp"
|
||||
}
|
||||
|
||||
|
||||
class GoogleGemini:
|
||||
"""
|
||||
Google Gemini 节点
|
||||
|
||||
功能：
|
||||
- 支持多个 Gemini Flash 模型
|
||||
- 支持图片和视频输入
|
||||
- 支持系统指令
|
||||
- 支持不同思考深度（不思考/高）
|
||||
- 输出生成的文本内容
|
||||
"""
|
||||
|
||||
# 支持的思考深度选项
|
||||
THINKING_DEPTHS = ["不思考", "高"]
|
||||
|
||||
def __init__(self):
|
||||
"""初始化节点"""
|
||||
self.client = None
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
"""
|
||||
定义输入参数
|
||||
"""
|
||||
# 从配置获取启用的模型列表
|
||||
enabled_models = get_enabled_flash_models()
|
||||
default_model = enabled_models[0] if enabled_models else "gemini-3-flash-preview"
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"模型": (enabled_models, {
|
||||
"default": default_model
|
||||
}),
|
||||
"提示词": ("STRING", {
|
||||
"default": "",
|
||||
"multiline": True
|
||||
}),
|
||||
"思考深度": (cls.THINKING_DEPTHS, {
|
||||
"default": "不思考"
|
||||
})
|
||||
},
|
||||
"optional": {
|
||||
"系统指令": ("STRING", {
|
||||
"default": "",
|
||||
"multiline": True
|
||||
}),
|
||||
"图片": ("IMAGE",),
|
||||
"视频": ("VIDEO",)
|
||||
}
|
||||
}
|
||||
|
||||
# 返回值类型
|
||||
RETURN_TYPES = ("STRING", "STRING")
|
||||
RETURN_NAMES = ("主要内容", "思考内容")
|
||||
|
||||
# 执行函数名
|
||||
FUNCTION = "generate"
|
||||
|
||||
# 节点分类
|
||||
CATEGORY = "text/generation"
|
||||
|
||||
# 允许输出到 UI
|
||||
OUTPUT_NODE = True
|
||||
|
||||
def _prepare_image_data(
|
||||
self,
|
||||
images: Optional[torch.Tensor]
|
||||
) -> Optional[List[Dict[str, str]]]:
|
||||
"""
|
||||
准备图片数据
|
||||
|
||||
Args:
|
||||
images: ComfyUI 图片张量 [B, H, W, C]
|
||||
|
||||
Returns:
|
||||
图片数据列表，每个元素包含 mime_type 和 data
|
||||
"""
|
||||
if images is None:
|
||||
return None
|
||||
|
||||
image_data = []
|
||||
pil_images = tensor_to_pil(images)
|
||||
|
||||
for img in pil_images:
|
||||
b64_str = encode_image_to_base64(img)
|
||||
image_data.append({
|
||||
"mime_type": "image/png",
|
||||
"data": b64_str
|
||||
})
|
||||
|
||||
return image_data if image_data else None
|
||||
|
||||
def _prepare_video_data(
|
||||
self,
|
||||
video
|
||||
) -> Optional[Dict[str, str]]:
|
||||
"""
|
||||
准备视频数据
|
||||
|
||||
ComfyUI VIDEO 类型包含视频文件路径信息。
|
||||
读取视频文件并转换为 base64。
|
||||
|
||||
Args:
|
||||
video: ComfyUI VIDEO 类型数据
|
||||
|
||||
Returns:
|
||||
视频数据字典，包含 mime_type 和 data
|
||||
"""
|
||||
if video is None:
|
||||
return None
|
||||
|
||||
# VIDEO 类型通常是一个字典，包含 'video' 键指向文件路径
|
||||
# 或者直接是文件路径字符串
|
||||
video_path = None
|
||||
|
||||
if isinstance(video, dict):
|
||||
# 尝试获取视频路径
|
||||
video_path = video.get("video") or video.get("path") or video.get("file")
|
||||
elif isinstance(video, str):
|
||||
video_path = video
|
||||
elif hasattr(video, "video"):
|
||||
video_path = video.video
|
||||
|
||||
if not video_path or not os.path.exists(video_path):
|
||||
print(f"Google Gemini: 视频文件不存在或路径无效: {video_path}")
|
||||
return None
|
||||
|
||||
# 获取文件扩展名和 MIME 类型
|
||||
_, ext = os.path.splitext(video_path)
|
||||
ext = ext.lower()
|
||||
|
||||
mime_type = VIDEO_MIME_TYPES.get(ext, "video/mp4")
|
||||
|
||||
# 检查文件大小（限制 20MB）
|
||||
file_size = os.path.getsize(video_path)
|
||||
if file_size > 20 * 1024 * 1024:
|
||||
raise ValueError(
|
||||
f"视频文件过大 ({file_size / 1024 / 1024:.2f}MB)，"
|
||||
f"请使用不超过 20MB 的视频文件"
|
||||
)
|
||||
|
||||
# 读取并编码视频
|
||||
try:
|
||||
with open(video_path, "rb") as f:
|
||||
video_bytes = f.read()
|
||||
|
||||
b64_str = base64.b64encode(video_bytes).decode("utf-8")
|
||||
|
||||
return {
|
||||
"mime_type": mime_type,
|
||||
"data": b64_str
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"Google Gemini: 读取视频文件失败 - {str(e)}")
|
||||
return None
|
||||
|
||||
def _parse_dual_output(self, raw_response: Dict) -> Tuple[str, str]:
|
||||
"""
|
||||
解析包含思考内容和主要内容的响应
|
||||
|
||||
Args:
|
||||
raw_response: API 原始响应字典
|
||||
|
||||
Returns:
|
||||
(主要内容, 思考内容)
|
||||
"""
|
||||
candidates = raw_response.get("candidates", [])
|
||||
if not candidates:
|
||||
return ("", "")
|
||||
|
||||
parts = candidates[0].get("content", {}).get("parts", [])
|
||||
|
||||
thought_text = ""
|
||||
main_text = ""
|
||||
|
||||
for part in parts:
|
||||
if part.get("thought") is True:
|
||||
# 思考部分
|
||||
thought_text = part.get("text", "")
|
||||
elif "thoughtSignature" in part or "text" in part:
|
||||
# 主要内容
|
||||
main_text = part.get("text", "")
|
||||
|
||||
return (main_text, thought_text)
|
||||
|
||||
def generate(
|
||||
self,
|
||||
模型: str,
|
||||
提示词: str,
|
||||
思考深度: str,
|
||||
系统指令: Optional[str] = None,
|
||||
图片: Optional[torch.Tensor] = None,
|
||||
视频=None
|
||||
) -> Tuple[str]:
|
||||
"""
|
||||
生成文本
|
||||
|
||||
Args:
|
||||
模型: 使用的模型名称
|
||||
提示词: 用户提示词
|
||||
思考深度: 思考深度选项
|
||||
系统指令: 系统级指令
|
||||
图片: 输入图片
|
||||
视频: 输入视频
|
||||
|
||||
Returns:
|
||||
生成的文本 (STRING,)
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# 初始化 API 客户端
|
||||
if self.client is None:
|
||||
try:
|
||||
self.client = GeminiFlashClient()
|
||||
except ValueError as e:
|
||||
raise ValueError(f"初始化失败: {str(e)}")
|
||||
|
||||
# 准备图片数据
|
||||
image_data = self._prepare_image_data(图片)
|
||||
if image_data:
|
||||
print(f"Google Gemini: 输入 {len(image_data)} 张图片")
|
||||
|
||||
# 准备视频数据
|
||||
video_data = self._prepare_video_data(视频)
|
||||
if video_data:
|
||||
print(f"Google Gemini: 输入视频 ({video_data['mime_type']})")
|
||||
|
||||
# 构建输入描述
|
||||
input_desc = []
|
||||
if 提示词:
|
||||
input_desc.append("文本")
|
||||
if image_data:
|
||||
input_desc.append(f"{len(image_data)}张图片")
|
||||
if video_data:
|
||||
input_desc.append("视频")
|
||||
|
||||
print(f"Google Gemini: 模型 = {模型}")
|
||||
print(f"Google Gemini: 多模态输入 ({', '.join(input_desc)})")
|
||||
print(f"Google Gemini: 思考深度 = {思考深度}")
|
||||
print(f"Google Gemini: 发送请求...")
|
||||
|
||||
# 获取端点和构建请求体
|
||||
endpoint = self.client.get_endpoint(model=模型, thinking_depth=思考深度)
|
||||
request_body = self.client.build_request_body(
|
||||
prompt=提示词,
|
||||
system_instruction=系统指令,
|
||||
image_data=image_data,
|
||||
video_data=video_data
|
||||
)
|
||||
|
||||
# 根据是否有视频设置超时
|
||||
timeout = 300 if video_data else 180
|
||||
|
||||
# 调用底层 API 获取原始响应
|
||||
async def get_raw_response():
|
||||
return await self.client.request_async(
|
||||
endpoint,
|
||||
request_body,
|
||||
session=None,
|
||||
timeout=timeout
|
||||
)
|
||||
|
||||
# 在独立线程中执行异步请求
|
||||
raw_response = self.client.run_async_in_thread(get_raw_response())
|
||||
|
||||
# 计算耗时
|
||||
elapsed = time.time() - start_time
|
||||
|
||||
# 解析响应，分离主要内容和思考内容
|
||||
main_text, thought_text = self._parse_dual_output(raw_response)
|
||||
|
||||
# 输出信息
|
||||
print(f"Google Gemini: 生成完成 (耗时: {elapsed:.2f}s)")
|
||||
print(f"Google Gemini: 主要内容长度: {len(main_text)} 字符")
|
||||
print(f"Google Gemini: 思考内容长度: {len(thought_text)} 字符")
|
||||
|
||||
# 输出预览
|
||||
if main_text:
|
||||
preview = main_text[:100] + "..." if len(main_text) > 100 else main_text
|
||||
print(f"Google Gemini: 主要内容预览: {preview}")
|
||||
|
||||
return (main_text, thought_text)
|
||||
|
||||
except ValueError as e:
|
||||
# 检测是否为授权错误
|
||||
if str(e) == "未授权！":
|
||||
print("请联系作者授权后方可使用！")
|
||||
else:
|
||||
print(f"Google Gemini: 输入错误 - {str(e)}")
|
||||
raise
|
||||
|
||||
except RuntimeError as e:
|
||||
print(f"Google Gemini: API 错误 - {str(e)}")
|
||||
raise
|
||||
|
||||
except Exception as e:
|
||||
print(f"Google Gemini: 未知错误 - {str(e)}")
|
||||
raise
|
||||
@@ -0,0 +1,381 @@
|
||||
"""
|
||||
Nano Banana Pro 节点
|
||||
ComfyUI 自定义节点，用于调用 Gemini 3 Pro 模型生成图像
|
||||
"""
|
||||
|
||||
import time
|
||||
import random
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
from ..utils.image_utils import tensor_to_pil, pil_to_tensor, parse_batch_prompts
|
||||
from ..clients.gemini_client import GeminiAPIClient
|
||||
from ..models_config import get_enabled_models, get_model_description
|
||||
|
||||
# 导入 ComfyUI 原生进度条
|
||||
try:
|
||||
from comfy.utils import ProgressBar
|
||||
PROGRESS_BAR_AVAILABLE = True
|
||||
except ImportError:
|
||||
PROGRESS_BAR_AVAILABLE = False
|
||||
print("⚠️ NanoBananaPro: comfy.utils.ProgressBar 不可用，将只使用终端进度显示")
|
||||
|
||||
|
||||
class NanoBananaPro:
|
||||
"""
|
||||
Nano Banana Pro 节点
|
||||
|
||||
功能：
|
||||
- 文生图：基于提示词生成图像
|
||||
- 图生图：基于输入图像和提示词生成新图像
|
||||
- 批量生成：支持并发生成多张图像
|
||||
|
||||
注意：
|
||||
- 支持的模型列表从 models_config.py 动态加载
|
||||
- 要添加/禁用模型，请编辑 models_config.py 文件
|
||||
"""
|
||||
|
||||
# 支持的模型列表（从配置文件动态加载）
|
||||
MODELS = None # 将在 INPUT_TYPES 中动态获取
|
||||
|
||||
# 支持的宽高比列表
|
||||
ASPECT_RATIOS = [
|
||||
"1:1", "4:3", "3:4", "16:9", "9:16",
|
||||
"2:3", "3:2", "4:5", "5:4", "21:9"
|
||||
]
|
||||
|
||||
# 支持的分辨率列表
|
||||
RESOLUTIONS = ["1K", "2K", "4K"]
|
||||
|
||||
def __init__(self):
|
||||
"""初始化节点"""
|
||||
self.client = None
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
"""
|
||||
定义输入参数
|
||||
|
||||
ComfyUI 节点规范：
|
||||
- required: 必选参数
|
||||
- optional: 可选参数
|
||||
"""
|
||||
# 从配置文件动态获取启用的模型列表
|
||||
enabled_models = get_enabled_models()
|
||||
|
||||
# 如果没有启用的模型，使用空列表（会导致节点不可用，提示用户配置）
|
||||
if not enabled_models:
|
||||
enabled_models = ["请在 models_config.py 中启用至少一个模型"]
|
||||
|
||||
# 创建9个独立的图像输入
|
||||
optional_inputs = {}
|
||||
for i in range(1, 10): # 1-9
|
||||
optional_inputs[f"参考图{i}"] = ("IMAGE",)
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"prompt": ("STRING", {
|
||||
"default": "一个中国女子的OOTD",
|
||||
"multiline": True
|
||||
}),
|
||||
"模型": (enabled_models, {
|
||||
"default": enabled_models[0]
|
||||
}),
|
||||
"宽高比": (cls.ASPECT_RATIOS, {
|
||||
"default": "1:1"
|
||||
}),
|
||||
"分辨率": (cls.RESOLUTIONS, {
|
||||
"default": "2K"
|
||||
}),
|
||||
"生图数量": ("INT", {
|
||||
"default": 1,
|
||||
"min": 1,
|
||||
"max": 1000,
|
||||
"step": 1
|
||||
}),
|
||||
"像素缩放": ("BOOLEAN", {
|
||||
"default": False
|
||||
}),
|
||||
"分辨率像素": ("FLOAT", {
|
||||
"default": 1.0,
|
||||
"min": 0.1,
|
||||
"max": 100.0,
|
||||
"step": 0.1,
|
||||
"display": "number"
|
||||
}),
|
||||
"seed": ("INT", {
|
||||
"default": 0,
|
||||
"min": 0,
|
||||
"max": 0xffffffffffffffff
|
||||
})
|
||||
},
|
||||
"optional": optional_inputs
|
||||
}
|
||||
|
||||
# 返回值类型
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("输出图像",)
|
||||
|
||||
# 执行函数名
|
||||
FUNCTION = "generate"
|
||||
|
||||
# 节点分类
|
||||
CATEGORY = "image/generation"
|
||||
|
||||
def resize_to_megapixels(
|
||||
self,
|
||||
image: Image.Image,
|
||||
target_megapixels: float
|
||||
) -> Image.Image:
|
||||
"""
|
||||
将图像缩放到指定的总像素数，保持纵横比
|
||||
|
||||
Args:
|
||||
image: PIL Image 对象
|
||||
target_megapixels: 目标像素数（百万像素）
|
||||
|
||||
Returns:
|
||||
缩放后的 PIL Image
|
||||
|
||||
Example:
|
||||
>>> resized = self.resize_to_megapixels(img, 2.0) # 缩放到2百万像素
|
||||
"""
|
||||
# 计算当前像素数
|
||||
current_pixels = image.width * image.height
|
||||
target_pixels = int(target_megapixels * 1_000_000)
|
||||
|
||||
# 如果当前像素数已经接近目标，则不缩放
|
||||
if abs(current_pixels - target_pixels) / target_pixels < 0.05:
|
||||
return image
|
||||
|
||||
# 计算缩放比例
|
||||
scale = (target_pixels / current_pixels) ** 0.5
|
||||
|
||||
# 计算新尺寸
|
||||
new_width = int(image.width * scale)
|
||||
new_height = int(image.height * scale)
|
||||
|
||||
# 确保至少为1像素
|
||||
new_width = max(1, new_width)
|
||||
new_height = max(1, new_height)
|
||||
|
||||
# 使用 Lanczos 重采样
|
||||
resized_image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
|
||||
|
||||
return resized_image
|
||||
|
||||
def validate_inputs(
|
||||
self,
|
||||
images: Optional[torch.Tensor],
|
||||
batch_size: int
|
||||
) -> None:
|
||||
"""
|
||||
验证输入参数
|
||||
|
||||
Args:
|
||||
images: 输入图像张量（可选）
|
||||
batch_size: 批次大小
|
||||
|
||||
Raises:
|
||||
ValueError: 如果输入参数不合法
|
||||
"""
|
||||
# 检查图像数量
|
||||
if images is not None:
|
||||
num_images = images.shape[0]
|
||||
if num_images > 14:
|
||||
raise ValueError(
|
||||
f"输入图像数量 {num_images} 超过限制 14 张，请减少输入图像数量"
|
||||
)
|
||||
|
||||
# 检查批次大小
|
||||
if batch_size < 1 or batch_size > 1000:
|
||||
raise ValueError(
|
||||
f"批次大小 {batch_size} 超出范围 [1, 1000]"
|
||||
)
|
||||
|
||||
def generate(
|
||||
self,
|
||||
prompt: str,
|
||||
模型: str,
|
||||
宽高比: str,
|
||||
分辨率: str,
|
||||
生图数量: int,
|
||||
像素缩放: bool,
|
||||
分辨率像素: float,
|
||||
seed: int,
|
||||
**kwargs
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
生成图像
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
模型: 模型名称
|
||||
宽高比: 宽高比
|
||||
分辨率: 分辨率
|
||||
生图数量: 批次大小
|
||||
像素缩放: 是否启用像素缩放
|
||||
分辨率像素: 目标像素数（百万像素）
|
||||
seed: 随机种子
|
||||
**kwargs: 动态参考图输入 (参考图1-9)
|
||||
|
||||
Returns:
|
||||
生成的图像张量 (IMAGE,)
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
# 创建 ComfyUI 原生进度条
|
||||
pbar = None
|
||||
if PROGRESS_BAR_AVAILABLE:
|
||||
pbar = ProgressBar(生图数量)
|
||||
|
||||
try:
|
||||
# 设置随机种子（用于本地随机操作）
|
||||
random.seed(seed)
|
||||
np.random.seed(seed % (2**32))
|
||||
|
||||
# 初始化 API 客户端
|
||||
if self.client is None:
|
||||
try:
|
||||
self.client = GeminiAPIClient()
|
||||
except ValueError as e:
|
||||
raise ValueError(f"初始化失败: {str(e)}")
|
||||
|
||||
# 收集独立输入的参考图
|
||||
input_images = []
|
||||
for i in range(1, 10): # 1-9
|
||||
key = f"参考图{i}"
|
||||
if key in kwargs and kwargs[key] is not None:
|
||||
pil_imgs = tensor_to_pil(kwargs[key])
|
||||
input_images.extend(pil_imgs)
|
||||
|
||||
# 验证输入图像数量
|
||||
if input_images:
|
||||
if len(input_images) > 14:
|
||||
raise ValueError(
|
||||
f"输入图像数量 {len(input_images)} 超过限制 14 张，请减少输入图像数量"
|
||||
)
|
||||
|
||||
# 应用像素缩放（如果启用）
|
||||
if input_images and 像素缩放:
|
||||
scaled_images = []
|
||||
for img in input_images:
|
||||
scaled = self.resize_to_megapixels(img, 分辨率像素)
|
||||
scaled_images.append(scaled)
|
||||
input_images = scaled_images
|
||||
print(f"Nano Banana Pro: 已缩放 {len(scaled_images)} 张图像到 {分辨率像素}M 像素")
|
||||
|
||||
# 转换为 API 所需的格式
|
||||
if input_images:
|
||||
print(f"Nano Banana Pro: 图生图模式 (输入 {len(input_images)} 张图像)")
|
||||
|
||||
# 解析批量提示词
|
||||
batch_prompts = parse_batch_prompts(prompt)
|
||||
|
||||
# 统计变量
|
||||
success_count = 0
|
||||
fail_count = 0
|
||||
|
||||
# 进度回调 - 实时显示每个任务的完成状态，并更新 ComfyUI 进度条
|
||||
def progress_callback(current, total, success, error_msg=None):
|
||||
nonlocal success_count, fail_count
|
||||
if success:
|
||||
success_count += 1
|
||||
print(f"Nano Banana Pro: ✓ [{current}/{total}] 第 {success_count} 张生成成功")
|
||||
else:
|
||||
fail_count += 1
|
||||
error_brief = error_msg[:50] + "..." if error_msg and len(error_msg) > 50 else error_msg
|
||||
print(f"Nano Banana Pro: ✗ [{current}/{total}] 生成失败 - {error_brief}")
|
||||
|
||||
# 更新 ComfyUI 原生进度条
|
||||
if pbar is not None:
|
||||
pbar.update(1)
|
||||
|
||||
# 根据是否有批量提示词选择生成模式
|
||||
if batch_prompts:
|
||||
# 批量提示词模式
|
||||
num_prompts = len(batch_prompts)
|
||||
total_images = num_prompts * 生图数量
|
||||
print(f"Nano Banana Pro: 批量提示词模式 ({num_prompts} 个提示词 × {生图数量} 张/提示词 = {total_images} 张图)")
|
||||
print(f"Nano Banana Pro: 发送请求")
|
||||
print(f"Nano Banana Pro: 生图中...")
|
||||
|
||||
# 重新创建进度条以匹配实际总数
|
||||
if pbar is not None:
|
||||
pbar = ProgressBar(total_images)
|
||||
|
||||
generated_images = self.client.generate_multi_prompts_sync(
|
||||
prompts=batch_prompts,
|
||||
model=模型,
|
||||
resolution=分辨率,
|
||||
aspect_ratio=宽高比,
|
||||
images_per_prompt=生图数量,
|
||||
images=input_images,
|
||||
progress_callback=progress_callback
|
||||
)
|
||||
|
||||
if fail_count > 0:
|
||||
print(f"Nano Banana Pro: 生图完成 (成功: {success_count}, 失败: {fail_count})")
|
||||
else:
|
||||
print(f"Nano Banana Pro: 全部生图成功！")
|
||||
else:
|
||||
# 单提示词模式
|
||||
print(f"Nano Banana Pro: {'图生图' if input_images else '文生图'}模式")
|
||||
print(f"Nano Banana Pro: 发送请求")
|
||||
print(f"Nano Banana Pro: 生图中...")
|
||||
|
||||
generated_images = self.client.generate_sync(
|
||||
prompt=prompt,
|
||||
model=模型,
|
||||
resolution=分辨率,
|
||||
aspect_ratio=宽高比,
|
||||
batch_size=生图数量,
|
||||
images=input_images,
|
||||
progress_callback=progress_callback
|
||||
)
|
||||
|
||||
if fail_count > 0:
|
||||
print(f"Nano Banana Pro: 生图完成 (成功: {success_count}, 失败: {fail_count})")
|
||||
else:
|
||||
print(f"Nano Banana Pro: 全部生图成功！")
|
||||
|
||||
# 转换输出图像
|
||||
output_tensor = pil_to_tensor(generated_images)
|
||||
|
||||
# 计算耗时
|
||||
elapsed = time.time() - start_time
|
||||
print(f"Nano Banana Pro: 完成生图 (耗时: {elapsed:.2f}s, 成功生成 {len(generated_images)} 张图像)")
|
||||
|
||||
return (output_tensor,)
|
||||
|
||||
except ValueError as e:
|
||||
# 检测是否为授权错误
|
||||
if str(e) == "未授权！":
|
||||
print("请联系作者授权后方可使用！")
|
||||
else:
|
||||
# 用户输入错误
|
||||
print(f"Nano Banana Pro: 输入错误 - {str(e)}")
|
||||
raise
|
||||
|
||||
except RuntimeError as e:
|
||||
# API 或网络错误
|
||||
print(f"Nano Banana Pro: API 错误 - {str(e)}")
|
||||
raise
|
||||
|
||||
except Exception as e:
|
||||
# 其他未知错误
|
||||
print(f"Nano Banana Pro: 未知错误 - {str(e)}")
|
||||
raise
|
||||
|
||||
finally:
|
||||
# 无论成功或失败，都尝试查询余额
|
||||
if self.client is not None:
|
||||
try:
|
||||
balance_data = self.client.query_balance_sync()
|
||||
balance_info = self.client.format_balance_info(balance_data)
|
||||
print(f"Nano Banana Pro: {balance_info}")
|
||||
except Exception as e:
|
||||
print(f"Nano Banana Pro: ⚠️ 余额查询失败 - {str(e)}")
|
||||
@@ -0,0 +1,3 @@
|
||||
aiohttp>=3.9.0
|
||||
Pillow>=10.0.0
|
||||
requests>=2.31.0
|
||||
+97
@@ -0,0 +1,97 @@
|
||||
@echo off
|
||||
chcp 65001 > nul
|
||||
echo ====================================
|
||||
echo Comfyui_o1key 插件更新工具
|
||||
echo ====================================
|
||||
echo.
|
||||
|
||||
:: 检查是否在 Git 仓库中
|
||||
if not exist ".git" (
|
||||
echo [错误] 当前目录不是 Git 仓库
|
||||
echo 请确保插件是通过 git clone 安装的
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
:: 保存当前版本
|
||||
if exist "version.txt" (
|
||||
set /p OLD_VERSION=<version.txt
|
||||
echo 当前版本: %OLD_VERSION%
|
||||
) else (
|
||||
set OLD_VERSION=未知
|
||||
echo 当前版本: 未知
|
||||
)
|
||||
|
||||
echo.
|
||||
echo [1/4] 检查远程更新...
|
||||
git fetch origin
|
||||
|
||||
:: 检查是否有更新
|
||||
git status -uno | findstr "Your branch is behind" > nul
|
||||
if %errorlevel% equ 0 (
|
||||
echo 发现新版本！
|
||||
) else (
|
||||
echo 已是最新版本
|
||||
echo.
|
||||
choice /C YN /M "是否继续检查依赖更新？"
|
||||
if errorlevel 2 goto :end
|
||||
)
|
||||
|
||||
echo.
|
||||
echo [2/4] 备份配置文件...
|
||||
if exist ".config" (
|
||||
copy /Y ".config" ".config.backup" > nul
|
||||
echo 已备份 .config 到 .config.backup
|
||||
)
|
||||
|
||||
echo.
|
||||
echo [3/4] 拉取最新代码...
|
||||
git pull origin main
|
||||
if %errorlevel% neq 0 (
|
||||
echo [错误] 代码更新失败，请检查网络连接或手动解决冲突
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
:: 恢复配置文件
|
||||
if exist ".config.backup" (
|
||||
copy /Y ".config.backup" ".config" > nul
|
||||
del ".config.backup"
|
||||
echo 已恢复配置文件
|
||||
)
|
||||
|
||||
echo.
|
||||
echo [4/4] 更新依赖包...
|
||||
python -m pip install -r requirements.txt --upgrade --quiet
|
||||
if %errorlevel% neq 0 (
|
||||
echo [警告] 依赖包更新失败，请手动运行: pip install -r requirements.txt
|
||||
)
|
||||
|
||||
echo.
|
||||
echo ====================================
|
||||
echo 更新完成！
|
||||
echo ====================================
|
||||
|
||||
:: 显示新版本
|
||||
if exist "version.txt" (
|
||||
set /p NEW_VERSION=<version.txt
|
||||
echo 新版本: %NEW_VERSION%
|
||||
)
|
||||
|
||||
:: 显示最近更新日志
|
||||
if exist "CHANGELOG.md" (
|
||||
echo.
|
||||
echo 最近更新内容:
|
||||
echo -----------------------------------
|
||||
powershell -Command "Get-Content CHANGELOG.md -TotalCount 20"
|
||||
echo -----------------------------------
|
||||
)
|
||||
|
||||
echo.
|
||||
echo 请重启 ComfyUI 以使更改生效
|
||||
echo.
|
||||
pause
|
||||
goto :end
|
||||
|
||||
:end
|
||||
exit /b 0
|
||||
@@ -0,0 +1,101 @@
|
||||
#!/bin/bash
|
||||
|
||||
# 设置颜色输出
|
||||
GREEN='\033[0;32m'
|
||||
YELLOW='\033[1;33m'
|
||||
RED='\033[0;31m'
|
||||
NC='\033[0m' # No Color
|
||||
|
||||
echo "===================================="
|
||||
echo "Comfyui_o1key 插件更新工具"
|
||||
echo "===================================="
|
||||
echo ""
|
||||
|
||||
# 检查是否在 Git 仓库中
|
||||
if [ ! -d ".git" ]; then
|
||||
echo -e "${RED}[错误] 当前目录不是 Git 仓库${NC}"
|
||||
echo "请确保插件是通过 git clone 安装的"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# 保存当前版本
|
||||
if [ -f "version.txt" ]; then
|
||||
OLD_VERSION=$(cat version.txt)
|
||||
echo "当前版本: $OLD_VERSION"
|
||||
else
|
||||
OLD_VERSION="未知"
|
||||
echo "当前版本: 未知"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "[1/4] 检查远程更新..."
|
||||
git fetch origin
|
||||
|
||||
# 检查是否有更新
|
||||
LOCAL=$(git rev-parse @)
|
||||
REMOTE=$(git rev-parse @{u})
|
||||
|
||||
if [ $LOCAL != $REMOTE ]; then
|
||||
echo -e "${GREEN}发现新版本！${NC}"
|
||||
else
|
||||
echo -e "${GREEN}已是最新版本${NC}"
|
||||
read -p "是否继续检查依赖更新？(y/n) " -n 1 -r
|
||||
echo
|
||||
if [[ ! $REPLY =~ ^[Yy]$ ]]; then
|
||||
exit 0
|
||||
fi
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "[2/4] 备份配置文件..."
|
||||
if [ -f ".config" ]; then
|
||||
cp .config .config.backup
|
||||
echo "已备份 .config 到 .config.backup"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "[3/4] 拉取最新代码..."
|
||||
if git pull origin main; then
|
||||
echo -e "${GREEN}代码更新成功${NC}"
|
||||
else
|
||||
echo -e "${RED}[错误] 代码更新失败，请检查网络连接或手动解决冲突${NC}"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# 恢复配置文件
|
||||
if [ -f ".config.backup" ]; then
|
||||
mv .config.backup .config
|
||||
echo "已恢复配置文件"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "[4/4] 更新依赖包..."
|
||||
if python3 -m pip install -r requirements.txt --upgrade --quiet; then
|
||||
echo -e "${GREEN}依赖包更新成功${NC}"
|
||||
else
|
||||
echo -e "${YELLOW}[警告] 依赖包更新失败，请手动运行: pip install -r requirements.txt${NC}"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "===================================="
|
||||
echo -e "${GREEN}更新完成！${NC}"
|
||||
echo "===================================="
|
||||
|
||||
# 显示新版本
|
||||
if [ -f "version.txt" ]; then
|
||||
NEW_VERSION=$(cat version.txt)
|
||||
echo "新版本: $NEW_VERSION"
|
||||
fi
|
||||
|
||||
# 显示最近更新日志
|
||||
if [ -f "CHANGELOG.md" ]; then
|
||||
echo ""
|
||||
echo "最近更新内容:"
|
||||
echo "-----------------------------------"
|
||||
head -n 20 CHANGELOG.md
|
||||
echo "-----------------------------------"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "请重启 ComfyUI 以使更改生效"
|
||||
echo ""
|
||||
@@ -0,0 +1,39 @@
|
||||
"""
|
||||
工具模块
|
||||
包含图像处理、配置管理、文件处理等通用工具函数
|
||||
"""
|
||||
|
||||
from .image_utils import (
|
||||
tensor_to_pil,
|
||||
pil_to_tensor,
|
||||
encode_image_to_base64,
|
||||
decode_base64_to_pil
|
||||
)
|
||||
from .config import load_config, get_api_key
|
||||
from .file_utils import (
|
||||
ImageInfo,
|
||||
load_images_from_folder,
|
||||
pair_images_indexed,
|
||||
pair_images_cartesian,
|
||||
generate_output_filename,
|
||||
generate_batch_output_filenames,
|
||||
save_image,
|
||||
get_folder_image_count
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'tensor_to_pil',
|
||||
'pil_to_tensor',
|
||||
'encode_image_to_base64',
|
||||
'decode_base64_to_pil',
|
||||
'load_config',
|
||||
'get_api_key',
|
||||
'ImageInfo',
|
||||
'load_images_from_folder',
|
||||
'pair_images_indexed',
|
||||
'pair_images_cartesian',
|
||||
'generate_output_filename',
|
||||
'generate_batch_output_filenames',
|
||||
'save_image',
|
||||
'get_folder_image_count'
|
||||
]
|
||||
+114
@@ -0,0 +1,114 @@
|
||||
"""
|
||||
配置管理模块
|
||||
处理环境变量和 API 密钥管理
|
||||
"""
|
||||
|
||||
import os
|
||||
from typing import Dict, Optional
|
||||
|
||||
|
||||
# 获取插件根目录
|
||||
PLUGIN_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
CONFIG_FILE = os.path.join(PLUGIN_ROOT, ".config")
|
||||
|
||||
|
||||
def load_config(config_path: Optional[str] = None) -> Dict[str, str]:
|
||||
"""
|
||||
从配置文件加载所有配置项
|
||||
|
||||
Args:
|
||||
config_path: 配置文件路径，默认为插件目录下的 .config
|
||||
|
||||
Returns:
|
||||
配置字典 {key: value}
|
||||
|
||||
Example:
|
||||
>>> config = load_config()
|
||||
>>> api_key = config.get('O1KEY_API_KEY')
|
||||
"""
|
||||
if config_path is None:
|
||||
config_path = CONFIG_FILE
|
||||
|
||||
config = {}
|
||||
|
||||
if not os.path.exists(config_path):
|
||||
return config
|
||||
|
||||
try:
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
|
||||
# 跳过空行和注释
|
||||
if not line or line.startswith('#'):
|
||||
continue
|
||||
|
||||
# 解析 KEY=VALUE 格式
|
||||
if '=' in line:
|
||||
key, value = line.split('=', 1)
|
||||
key = key.strip()
|
||||
value = value.strip().strip('"').strip("'")
|
||||
|
||||
if key and value:
|
||||
config[key] = value
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ 读取配置文件失败: {e}")
|
||||
|
||||
return config
|
||||
|
||||
|
||||
def get_api_key(key_name: str = "O1KEY_API_KEY") -> Optional[str]:
|
||||
"""
|
||||
获取 API 密钥
|
||||
优先级：环境变量（推荐） > .config 文件（向后兼容）
|
||||
|
||||
Args:
|
||||
key_name: 密钥名称，默认为 O1KEY_API_KEY
|
||||
|
||||
Returns:
|
||||
API 密钥字符串，如果未找到则返回 None
|
||||
|
||||
Raises:
|
||||
ValueError: 如果未找到 API 密钥
|
||||
|
||||
Example:
|
||||
>>> api_key = get_api_key()
|
||||
>>> if api_key is None:
|
||||
... raise ValueError("API key not found")
|
||||
"""
|
||||
# 1. 优先从环境变量读取（推荐方式）
|
||||
api_key = os.environ.get(key_name)
|
||||
|
||||
if api_key:
|
||||
return api_key
|
||||
|
||||
# 2. 从 .config 文件读取（向后兼容，已弃用）
|
||||
config = load_config()
|
||||
api_key = config.get(key_name)
|
||||
|
||||
if api_key:
|
||||
return api_key
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def get_api_key_or_raise(key_name: str = "O1KEY_API_KEY") -> str:
|
||||
"""
|
||||
获取 API 密钥，如果未找到则抛出异常
|
||||
|
||||
Args:
|
||||
key_name: 密钥名称
|
||||
|
||||
Returns:
|
||||
API 密钥字符串
|
||||
|
||||
Raises:
|
||||
ValueError: 如果未找到 API 密钥
|
||||
"""
|
||||
api_key = get_api_key(key_name)
|
||||
|
||||
if not api_key:
|
||||
raise ValueError("未授权！")
|
||||
|
||||
return api_key
|
||||
@@ -0,0 +1,328 @@
|
||||
"""
|
||||
文件处理工具模块
|
||||
提供文件夹图片加载、智能命名、图片配对等功能
|
||||
"""
|
||||
|
||||
import os
|
||||
import uuid
|
||||
import time
|
||||
from itertools import product
|
||||
from pathlib import Path
|
||||
from typing import List, Tuple, Optional, NamedTuple
|
||||
|
||||
from PIL import Image
|
||||
|
||||
|
||||
# 支持的图片格式
|
||||
SUPPORTED_IMAGE_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.webp', '.bmp', '.gif'}
|
||||
|
||||
|
||||
class ImageInfo(NamedTuple):
|
||||
"""图片信息结构"""
|
||||
image: Image.Image
|
||||
filename: str # 不含扩展名的文件名
|
||||
extension: str # 扩展名（如 .png）
|
||||
source_path: str # 原始文件路径
|
||||
|
||||
|
||||
def load_images_from_folder(
|
||||
folder_path: str,
|
||||
recursive: bool = False
|
||||
) -> List[ImageInfo]:
|
||||
"""
|
||||
从文件夹加载所有图片
|
||||
|
||||
Args:
|
||||
folder_path: 文件夹路径
|
||||
recursive: 是否递归加载子文件夹
|
||||
|
||||
Returns:
|
||||
ImageInfo 列表，包含图片和元数据
|
||||
|
||||
Raises:
|
||||
ValueError: 文件夹不存在或为空
|
||||
|
||||
Example:
|
||||
>>> images = load_images_from_folder("D:/images")
|
||||
>>> for info in images:
|
||||
... print(f"{info.filename}: {info.image.size}")
|
||||
"""
|
||||
folder_path = folder_path.strip()
|
||||
|
||||
if not folder_path:
|
||||
return []
|
||||
|
||||
path = Path(folder_path)
|
||||
|
||||
if not path.exists():
|
||||
raise ValueError(f"文件夹不存在: {folder_path}")
|
||||
|
||||
if not path.is_dir():
|
||||
raise ValueError(f"路径不是文件夹: {folder_path}")
|
||||
|
||||
images = []
|
||||
|
||||
# 获取文件列表
|
||||
if recursive:
|
||||
files = list(path.rglob("*"))
|
||||
else:
|
||||
files = list(path.iterdir())
|
||||
|
||||
# 按文件名排序，确保顺序一致
|
||||
files = sorted(files, key=lambda x: x.name.lower())
|
||||
|
||||
for file_path in files:
|
||||
if not file_path.is_file():
|
||||
continue
|
||||
|
||||
ext = file_path.suffix.lower()
|
||||
if ext not in SUPPORTED_IMAGE_EXTENSIONS:
|
||||
continue
|
||||
|
||||
try:
|
||||
img = Image.open(file_path)
|
||||
img.load() # 确保图片完全加载
|
||||
|
||||
# 转换为 RGB 模式
|
||||
if img.mode != 'RGB':
|
||||
img = img.convert('RGB')
|
||||
|
||||
images.append(ImageInfo(
|
||||
image=img,
|
||||
filename=file_path.stem,
|
||||
extension=ext,
|
||||
source_path=str(file_path)
|
||||
))
|
||||
except Exception as e:
|
||||
print(f"警告: 无法加载图片 {file_path}: {e}")
|
||||
continue
|
||||
|
||||
return images
|
||||
|
||||
|
||||
def pair_images_indexed(
|
||||
*image_lists: List[ImageInfo]
|
||||
) -> List[Tuple[ImageInfo, ...]]:
|
||||
"""
|
||||
1:1 索引配对
|
||||
|
||||
按索引位置配对多个图片列表，以最短列表长度为准。
|
||||
|
||||
Args:
|
||||
*image_lists: 多个 ImageInfo 列表
|
||||
|
||||
Returns:
|
||||
配对后的元组列表
|
||||
|
||||
Example:
|
||||
>>> list_a = [a1, a2, a3]
|
||||
>>> list_b = [b1, b2, b3]
|
||||
>>> pairs = pair_images_indexed(list_a, list_b)
|
||||
>>> # [(a1, b1), (a2, b2), (a3, b3)]
|
||||
"""
|
||||
if not image_lists:
|
||||
return []
|
||||
|
||||
# 过滤空列表
|
||||
non_empty_lists = [lst for lst in image_lists if lst]
|
||||
|
||||
if not non_empty_lists:
|
||||
return []
|
||||
|
||||
# 使用 zip 进行索引配对（以最短列表为准）
|
||||
return list(zip(*non_empty_lists))
|
||||
|
||||
|
||||
def pair_images_cartesian(
|
||||
*image_lists: List[ImageInfo]
|
||||
) -> List[Tuple[ImageInfo, ...]]:
|
||||
"""
|
||||
笛卡尔积配对
|
||||
|
||||
生成多个图片列表的所有组合。
|
||||
|
||||
Args:
|
||||
*image_lists: 多个 ImageInfo 列表
|
||||
|
||||
Returns:
|
||||
配对后的元组列表
|
||||
|
||||
Example:
|
||||
>>> list_a = [a1, a2]
|
||||
>>> list_b = [b1, b2]
|
||||
>>> pairs = pair_images_cartesian(list_a, list_b)
|
||||
>>> # [(a1, b1), (a1, b2), (a2, b1), (a2, b2)]
|
||||
"""
|
||||
if not image_lists:
|
||||
return []
|
||||
|
||||
# 过滤空列表
|
||||
non_empty_lists = [lst for lst in image_lists if lst]
|
||||
|
||||
if not non_empty_lists:
|
||||
return []
|
||||
|
||||
# 使用 itertools.product 生成笛卡尔积
|
||||
return list(product(*non_empty_lists))
|
||||
|
||||
|
||||
def generate_output_filename(
|
||||
source_images: List[ImageInfo],
|
||||
batch_index: int,
|
||||
output_folder: str,
|
||||
extension: str = ".png",
|
||||
task_id: Optional[str] = None
|
||||
) -> str:
|
||||
"""
|
||||
生成智能输出文件名
|
||||
|
||||
基于源图片文件名生成输出文件名，使用任务ID和时间戳确保并发安全。
|
||||
|
||||
Args:
|
||||
source_images: 源图片信息列表
|
||||
batch_index: 批次索引（从 0 开始）
|
||||
output_folder: 输出文件夹路径
|
||||
extension: 输出文件扩展名
|
||||
task_id: 任务唯一标识符（用于并发场景）
|
||||
|
||||
Returns:
|
||||
完整的输出文件路径
|
||||
|
||||
Example:
|
||||
>>> # 单图片: hello.png -> hello_task0_12345_000.png
|
||||
>>> # 多图片: hello.png + ref.png -> hello_ref_task0_12345_000.png
|
||||
>>> # 并发安全：每个任务有唯一的 task_id 和时间戳
|
||||
"""
|
||||
# 构建基础文件名
|
||||
if len(source_images) == 1:
|
||||
base_name = source_images[0].filename
|
||||
else:
|
||||
# 多个源图片，组合文件名
|
||||
names = [info.filename for info in source_images]
|
||||
base_name = "_".join(names)
|
||||
|
||||
# 确保输出文件夹存在
|
||||
output_path = Path(output_folder)
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 生成唯一性标识
|
||||
if task_id is None:
|
||||
# 如果没有提供 task_id，使用 UUID 前8位
|
||||
task_id = str(uuid.uuid4())[:8]
|
||||
|
||||
# 使用时间戳（毫秒级）增加唯一性
|
||||
timestamp = int(time.time() * 1000) % 100000 # 精确到毫秒的后5位
|
||||
|
||||
# 生成文件名：基础名_任务ID_时间戳_批次索引
|
||||
filename = f"{base_name}_{task_id}_{timestamp:05d}_{batch_index:03d}{extension}"
|
||||
full_path = output_path / filename
|
||||
|
||||
# 极小概率的冲突处理
|
||||
counter = 1
|
||||
while full_path.exists():
|
||||
filename = f"{base_name}_{task_id}_{timestamp:05d}_{batch_index:03d}_{counter}{extension}"
|
||||
full_path = output_path / filename
|
||||
counter += 1
|
||||
|
||||
return str(full_path)
|
||||
|
||||
|
||||
def generate_batch_output_filenames(
|
||||
source_images: List[ImageInfo],
|
||||
count: int,
|
||||
output_folder: str,
|
||||
extension: str = ".png",
|
||||
task_id: Optional[str] = None
|
||||
) -> List[str]:
|
||||
"""
|
||||
批量生成输出文件名
|
||||
|
||||
Args:
|
||||
source_images: 源图片信息列表
|
||||
count: 需要生成的文件名数量
|
||||
output_folder: 输出文件夹路径
|
||||
extension: 输出文件扩展名
|
||||
task_id: 任务唯一标识符（用于并发场景）
|
||||
|
||||
Returns:
|
||||
输出文件路径列表
|
||||
"""
|
||||
filenames = []
|
||||
|
||||
for i in range(count):
|
||||
filename = generate_output_filename(
|
||||
source_images=source_images,
|
||||
batch_index=i,
|
||||
output_folder=output_folder,
|
||||
extension=extension,
|
||||
task_id=task_id
|
||||
)
|
||||
filenames.append(filename)
|
||||
|
||||
return filenames
|
||||
|
||||
|
||||
def save_image(
|
||||
image: Image.Image,
|
||||
output_path: str,
|
||||
quality: int = 95
|
||||
) -> str:
|
||||
"""
|
||||
保存图片到指定路径
|
||||
|
||||
Args:
|
||||
image: PIL Image 对象
|
||||
output_path: 输出文件路径
|
||||
quality: JPEG 质量（仅对 JPEG 格式有效）
|
||||
|
||||
Returns:
|
||||
实际保存的文件路径
|
||||
"""
|
||||
# 确保目录存在
|
||||
output_dir = Path(output_path).parent
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 根据扩展名选择保存参数
|
||||
ext = Path(output_path).suffix.lower()
|
||||
|
||||
if ext in {'.jpg', '.jpeg'}:
|
||||
# 转换为 RGB（JPEG 不支持 alpha 通道）
|
||||
if image.mode != 'RGB':
|
||||
image = image.convert('RGB')
|
||||
image.save(output_path, quality=quality)
|
||||
elif ext == '.png':
|
||||
image.save(output_path)
|
||||
elif ext == '.webp':
|
||||
image.save(output_path, quality=quality)
|
||||
else:
|
||||
image.save(output_path)
|
||||
|
||||
return output_path
|
||||
|
||||
|
||||
def get_folder_image_count(folder_path: str) -> int:
|
||||
"""
|
||||
获取文件夹中的图片数量（不加载图片）
|
||||
|
||||
Args:
|
||||
folder_path: 文件夹路径
|
||||
|
||||
Returns:
|
||||
图片数量
|
||||
"""
|
||||
folder_path = folder_path.strip()
|
||||
|
||||
if not folder_path:
|
||||
return 0
|
||||
|
||||
path = Path(folder_path)
|
||||
|
||||
if not path.exists() or not path.is_dir():
|
||||
return 0
|
||||
|
||||
count = 0
|
||||
for file_path in path.iterdir():
|
||||
if file_path.is_file() and file_path.suffix.lower() in SUPPORTED_IMAGE_EXTENSIONS:
|
||||
count += 1
|
||||
|
||||
return count
|
||||
@@ -0,0 +1,193 @@
|
||||
"""
|
||||
图像处理工具模块
|
||||
提供 ComfyUI Tensor 与 PIL Image 之间的转换功能
|
||||
"""
|
||||
|
||||
import base64
|
||||
from io import BytesIO
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
|
||||
|
||||
def tensor_to_pil(tensor: torch.Tensor) -> List[Image.Image]:
|
||||
"""
|
||||
将 ComfyUI 的 Tensor 转换为 PIL Image 列表
|
||||
|
||||
Args:
|
||||
tensor: 形状为 [B, H, W, C] 的张量，值范围 [0, 1]
|
||||
|
||||
Returns:
|
||||
PIL Image 列表
|
||||
|
||||
Example:
|
||||
>>> images = tensor_to_pil(input_tensor)
|
||||
>>> for img in images:
|
||||
... img.save(f"output_{i}.png")
|
||||
"""
|
||||
images = []
|
||||
|
||||
# 转换为 numpy 数组
|
||||
np_images = tensor.cpu().numpy()
|
||||
|
||||
# 处理每张图像
|
||||
for i in range(np_images.shape[0]):
|
||||
img_array = np_images[i]
|
||||
|
||||
# 转换值范围从 [0, 1] 到 [0, 255]
|
||||
img_array = (img_array * 255).astype(np.uint8)
|
||||
|
||||
# 创建 PIL Image
|
||||
img = Image.fromarray(img_array)
|
||||
images.append(img)
|
||||
|
||||
return images
|
||||
|
||||
|
||||
def pil_to_tensor(images: List[Image.Image]) -> torch.Tensor:
|
||||
"""
|
||||
将 PIL Image 列表转换为 ComfyUI 的 Tensor
|
||||
|
||||
Args:
|
||||
images: PIL Image 列表
|
||||
|
||||
Returns:
|
||||
形状为 [B, H, W, C] 的张量，值范围 [0, 1]
|
||||
|
||||
Example:
|
||||
>>> pil_images = [Image.open("test.png")]
|
||||
>>> tensor = pil_to_tensor(pil_images)
|
||||
>>> print(tensor.shape) # [1, H, W, 3]
|
||||
"""
|
||||
tensors = []
|
||||
|
||||
for img in images:
|
||||
# 确保是 RGB 模式
|
||||
if img.mode != 'RGB':
|
||||
img = img.convert('RGB')
|
||||
|
||||
# 转换为 numpy 数组
|
||||
img_array = np.array(img).astype(np.float32)
|
||||
|
||||
# 转换值范围从 [0, 255] 到 [0, 1]
|
||||
img_array = img_array / 255.0
|
||||
|
||||
tensors.append(img_array)
|
||||
|
||||
# 堆叠为批次
|
||||
batch_tensor = np.stack(tensors, axis=0)
|
||||
|
||||
# 转换为 torch tensor
|
||||
return torch.from_numpy(batch_tensor)
|
||||
|
||||
|
||||
def encode_image_to_base64(image: Image.Image, format: str = "PNG") -> str:
|
||||
"""
|
||||
将 PIL Image 编码为 base64 字符串
|
||||
|
||||
Args:
|
||||
image: PIL Image 对象
|
||||
format: 图像格式，默认 PNG
|
||||
|
||||
Returns:
|
||||
base64 编码的字符串
|
||||
|
||||
Example:
|
||||
>>> img = Image.open("test.png")
|
||||
>>> b64_str = encode_image_to_base64(img)
|
||||
"""
|
||||
buffered = BytesIO()
|
||||
|
||||
# 转换为 RGB 模式（如果是 RGBA）
|
||||
if image.mode == 'RGBA':
|
||||
image = image.convert('RGB')
|
||||
|
||||
image.save(buffered, format=format)
|
||||
img_bytes = buffered.getvalue()
|
||||
|
||||
return base64.b64encode(img_bytes).decode('utf-8')
|
||||
|
||||
|
||||
def decode_base64_to_pil(base64_string: str) -> Image.Image:
|
||||
"""
|
||||
将 base64 字符串解码为 PIL Image
|
||||
|
||||
Args:
|
||||
base64_string: base64 编码的图像字符串
|
||||
|
||||
Returns:
|
||||
PIL Image 对象
|
||||
|
||||
Example:
|
||||
>>> img = decode_base64_to_pil(b64_str)
|
||||
>>> img.save("decoded.png")
|
||||
"""
|
||||
img_bytes = base64.b64decode(base64_string)
|
||||
img = Image.open(BytesIO(img_bytes))
|
||||
|
||||
return img
|
||||
|
||||
|
||||
def parse_batch_prompts(prompt: str) -> List[str]:
|
||||
"""
|
||||
解析批量提示词
|
||||
|
||||
检测单独行的 --- 分隔符，分割提示词。
|
||||
如果 --- 不是单独占据一行，则返回空列表（表示单提示词模式）。
|
||||
|
||||
Args:
|
||||
prompt: 用户输入的提示词文本
|
||||
|
||||
Returns:
|
||||
提示词列表。如果未检测到单独行的 ---，返回空列表（表示单提示词模式）
|
||||
|
||||
Raises:
|
||||
ValueError: 如果所有提示词都为空
|
||||
|
||||
Example:
|
||||
>>> prompts = parse_batch_prompts("a woman\\n---\\na man")
|
||||
>>> print(prompts) # ['a woman', 'a man']
|
||||
|
||||
>>> prompts = parse_batch_prompts("a woman --- a man")
|
||||
>>> print(prompts) # [] (单提示词模式)
|
||||
"""
|
||||
lines = prompt.split('\n')
|
||||
|
||||
# 检查是否存在单独行的 ---
|
||||
has_separator = False
|
||||
for line in lines:
|
||||
if line.strip() == '---':
|
||||
has_separator = True
|
||||
break
|
||||
|
||||
# 如果没有单独行的 ---，返回空列表（单提示词模式）
|
||||
if not has_separator:
|
||||
return []
|
||||
|
||||
# 按单独行的 --- 分割
|
||||
# 先将所有单独行的 --- 替换为特殊标记
|
||||
processed_lines = []
|
||||
for line in lines:
|
||||
if line.strip() == '---':
|
||||
processed_lines.append('<<<SEPARATOR>>>')
|
||||
else:
|
||||
processed_lines.append(line)
|
||||
|
||||
# 重新组合并分割
|
||||
processed_text = '\n'.join(processed_lines)
|
||||
raw_prompts = processed_text.split('<<<SEPARATOR>>>')
|
||||
|
||||
# 过滤空提示词
|
||||
filtered_prompts = []
|
||||
for p in raw_prompts:
|
||||
stripped = p.strip()
|
||||
if stripped:
|
||||
filtered_prompts.append(stripped)
|
||||
|
||||
# 如果所有提示词都为空，抛出错误
|
||||
if not filtered_prompts:
|
||||
raise ValueError("批量提示词模式下，所有提示词都为空，请至少提供一个有效的提示词")
|
||||
|
||||
return filtered_prompts
|
||||
@@ -0,0 +1,83 @@
|
||||
"""
|
||||
更新检查工具
|
||||
在插件加载时检查是否有新版本
|
||||
"""
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
from typing import Optional
|
||||
|
||||
|
||||
def get_current_version() -> Optional[str]:
|
||||
"""
|
||||
获取当前版本号
|
||||
|
||||
Returns:
|
||||
版本号字符串，如果读取失败返回 None
|
||||
"""
|
||||
version_file = os.path.join(os.path.dirname(os.path.dirname(__file__)), "version.txt")
|
||||
try:
|
||||
with open(version_file, 'r', encoding='utf-8') as f:
|
||||
return f.read().strip()
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def check_for_updates() -> bool:
|
||||
"""
|
||||
检查是否有更新
|
||||
|
||||
Returns:
|
||||
True 如果有更新，False 如果已是最新或检查失败
|
||||
"""
|
||||
try:
|
||||
# 获取当前目录
|
||||
plugin_dir = os.path.dirname(os.path.dirname(__file__))
|
||||
|
||||
# 检查是否是 Git 仓库
|
||||
git_dir = os.path.join(plugin_dir, '.git')
|
||||
if not os.path.exists(git_dir):
|
||||
return False
|
||||
|
||||
# 执行 git fetch
|
||||
subprocess.run(
|
||||
['git', 'fetch', 'origin'],
|
||||
cwd=plugin_dir,
|
||||
capture_output=True,
|
||||
timeout=10
|
||||
)
|
||||
|
||||
# 检查本地和远程版本
|
||||
local = subprocess.run(
|
||||
['git', 'rev-parse', '@'],
|
||||
cwd=plugin_dir,
|
||||
capture_output=True,
|
||||
text=True
|
||||
).stdout.strip()
|
||||
|
||||
remote = subprocess.run(
|
||||
['git', 'rev-parse', '@{u}'],
|
||||
cwd=plugin_dir,
|
||||
capture_output=True,
|
||||
text=True
|
||||
).stdout.strip()
|
||||
|
||||
return local != remote
|
||||
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
|
||||
def notify_update_available():
|
||||
"""通知用户有更新可用"""
|
||||
current_version = get_current_version()
|
||||
version_str = f" (当前版本: {current_version})" if current_version else ""
|
||||
|
||||
print("\n" + "="*60)
|
||||
print(f"🎉 Comfyui_o1key 有新版本可用{version_str}")
|
||||
print("="*60)
|
||||
print("更新方法：")
|
||||
print(" Windows: 双击运行 update.bat")
|
||||
print(" Linux/Mac: 运行 ./update.sh")
|
||||
print("或手动执行: git pull origin main")
|
||||
print("="*60 + "\n")
|
||||
@@ -0,0 +1 @@
|
||||
v1.10.0
|
||||
@@ -0,0 +1,134 @@
|
||||
# Comfyui_o1key 更新说明
|
||||
|
||||
## 🎉 插件已支持一键自动更新！
|
||||
|
||||
从 v1.10.0 版本开始，插件支持自动更新功能。你只需运行更新脚本，即可轻松获取最新版本。
|
||||
|
||||
---
|
||||
|
||||
## 📦 如何更新
|
||||
|
||||
### Windows 用户
|
||||
|
||||
1. 打开文件资源管理器
|
||||
2. 进入插件目录：`ComfyUI\custom_nodes\Comfyui_o1key`
|
||||
3. 双击运行 `update.bat` 文件
|
||||
4. 等待更新完成（通常只需几秒钟）
|
||||
5. 重启 ComfyUI
|
||||
|
||||
### Linux/Mac 用户
|
||||
|
||||
打开终端，执行以下命令：
|
||||
|
||||
```bash
|
||||
cd ComfyUI/custom_nodes/Comfyui_o1key
|
||||
chmod +x update.sh # 首次运行需要添加执行权限
|
||||
./update.sh
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✨ 更新脚本功能
|
||||
|
||||
✅ **自动检查更新** - 自动检测是否有新版本
|
||||
✅ **备份配置** - 自动备份和恢复 `.config` 配置文件
|
||||
✅ **拉取代码** - 自动从 GitHub 拉取最新代码
|
||||
✅ **更新依赖** - 自动更新 Python 依赖包
|
||||
✅ **显示日志** - 显示最近的更新内容
|
||||
✅ **完善提示** - 友好的中文提示和错误处理
|
||||
|
||||
---
|
||||
|
||||
## 🔔 更新检查
|
||||
|
||||
插件会在每次启动 ComfyUI 时自动检查是否有新版本：
|
||||
|
||||
- 如果发现新版本，终端会显示更新提示
|
||||
- 不会影响插件加载速度
|
||||
- 不会弹窗打断工作流程
|
||||
- 检查失败不影响插件正常使用
|
||||
|
||||
**终端提示示例：**
|
||||
|
||||
```
|
||||
============================================================
|
||||
🎉 Comfyui_o1key 有新版本可用 (当前版本: v1.9.1)
|
||||
============================================================
|
||||
更新方法：
|
||||
Windows: 双击运行 update.bat
|
||||
Linux/Mac: 运行 ./update.sh
|
||||
或手动执行: git pull origin main
|
||||
============================================================
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🛡️ 安全保障
|
||||
|
||||
- **配置安全**：更新前自动备份 `.config` 文件，更新后自动恢复
|
||||
- **环境变量**：存储在系统级别的 API 密钥不受影响
|
||||
- **错误处理**：更新失败不会破坏现有安装
|
||||
- **回退方案**：如有问题可用 `git reset` 回退
|
||||
|
||||
---
|
||||
|
||||
## 🔧 手动更新（备用方案）
|
||||
|
||||
如果自动更新脚本无法使用，可以手动执行：
|
||||
|
||||
```bash
|
||||
cd ComfyUI/custom_nodes/Comfyui_o1key
|
||||
git pull origin main
|
||||
pip install -r requirements.txt --upgrade
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ❓ 常见问题
|
||||
|
||||
### Q1: 更新会覆盖我的配置吗？
|
||||
|
||||
**不会。** 更新脚本会自动备份和恢复你的 `.config` 文件。环境变量中的 API 密钥也不受影响。
|
||||
|
||||
### Q2: 更新失败怎么办？
|
||||
|
||||
1. 检查网络连接是否正常
|
||||
2. 确认 Git 已正确安装
|
||||
3. 尝试手动更新（见上方"手动更新"部分）
|
||||
4. 如有未提交的修改，先备份后执行 `git reset --hard origin/main`
|
||||
|
||||
### Q3: 更新后插件无法启动怎么办？
|
||||
|
||||
1. 检查终端错误信息
|
||||
2. 重新运行依赖安装：`pip install -r requirements.txt --upgrade`
|
||||
3. 确认 Python 版本 ≥ 3.7
|
||||
4. 查看 [GitHub Issues](https://github.com/你的用户名/Comfyui_o1key/issues) 寻求帮助
|
||||
|
||||
### Q4: 可以禁用启动时的更新检查吗？
|
||||
|
||||
暂不支持配置禁用，但更新检查：
|
||||
- 速度极快（<1 秒）
|
||||
- 完全静默（无更新时不显示任何信息）
|
||||
- 失败不影响插件加载
|
||||
|
||||
### Q5: 如何查看当前版本？
|
||||
|
||||
查看插件目录下的 `version.txt` 文件，或在更新时会显示当前版本号。
|
||||
|
||||
### Q6: 多久检查一次更新？
|
||||
|
||||
仅在 ComfyUI 启动时检查一次，不会在运行过程中反复检查。
|
||||
|
||||
---
|
||||
|
||||
## 📮 反馈与支持
|
||||
|
||||
如果在更新过程中遇到问题，请：
|
||||
|
||||
1. 查看终端输出的错误信息
|
||||
2. 查阅 [GitHub Issues](https://github.com/你的用户名/Comfyui_o1key/issues)
|
||||
3. 提交新 Issue 并附上错误信息
|
||||
|
||||
---
|
||||
|
||||
**享受自动更新带来的便利！** 🎉
|
||||
Reference in New Issue
Block a user