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m-mcp

Flux Schnell Server

by m-mcp
README.md
# Flux Schnell Server

[![smithery badge](https://smithery.ai/badge/@m-mcp/flux-schnell-server)](https://smithery.ai/server/@m-mcp/flux-schnell-server)

基于[Flux Schnell](https://huggingface.co/spaces/black-forest-labs/flux-1-schnell)模型的MCP图像生成服务器。

## 功能特点

- 提供基于MCP协议的图像生成API
- 支持自定义图片尺寸(宽度和高度)
- 支持设置随机种子以复现特定生成结果
- 支持异步流式响应
- 提供HTTP接口调用Hugging Face的模型服务

## 安装要求

- Python >= 3.10
- 依赖包:
  - httpx >= 0.28.1
  - mcp[cli] >= 1.3.0

## 使用方法
### 开发环境设置

1. 创建并激活 Python 虚拟环境
```bash
uv venv && source .venv/bin/activate  # Unix/macOS
# 或
.venv\Scripts\activate  # Windows
```

2. 安装开发依赖
```bash
uv sync  # 以可编辑模式安装项目
```

### 调试方法

1. 启用调试
```bash
mcp dev main.py
或者
npx -y @modelcontextprotocol/inspector uv run main.py
```

2. 调用图像生成工具:
```python
# 示例代码
async def test_main():
    img_url = await image_generation(
        prompt="your prompt here",
        image_width=512,  # 可选,默认512
        image_height=512, # 可选,默认512
        seed=3           # 可选,默认3
    )
    print(img_url)
```

## API参数说明

- `prompt` (str): 图像生成提示词
- `image_width` (int, optional): 生成图片宽度,默认512
- `image_height` (int, optional): 生成图片高度,默认512
- `seed` (int, optional): 随机种子,默认3

## 示例

### 春天的生机

![春天的生机](https://black-forest-labs-flux-1-schnell.hf.space/file=/tmp/gradio/45d6489d73142fa77851d8985bb1010572433d6a/image.webp)

> 春天来了,大地苏醒,万物复苏。花儿竞相开放,嫩绿的叶子在微风中轻轻摇曳。空气中弥漫着泥土的芬芳和花儿的香气。小鸟在枝头欢快地歌唱,蝴蝶在花丛中翩翩起舞。阳光洒在大地上,温暖而明亮。春天的生机勃勃,让人心旷神怡。

这个示例展示了使用服务生成的图片效果。您可以在demo目录中找到完整的网页展示代码。

生成的图片URL可以直接用于:
1. 网页图片展示
2. 社交媒体分享
3. 应用程序界面

TDQS

B3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'image_generation' has a clear, distinct purpose that cannot be confused with any other tool in this server.

Naming Consistency5/5

The single tool name 'image_generation' follows a clear noun_verb pattern. With only one tool, there is no inconsistency to evaluate, and the naming convention is straightforward and appropriate for its function.

Tool Count2/5

A single tool for an image generation server feels thin and limited in scope. While it covers the core functionality, typical image generation servers might include additional tools for variations, editing, or different models. This minimal set may restrict agent capabilities.

Completeness3/5

The server provides basic image generation, but lacks tools for related operations like image editing, style transfer, or batch processing. The surface is functional but incomplete for a comprehensive image generation domain, potentially causing agent workarounds.

Maintenance

ActivityInactive
ResponsivenessNo issues