> For the complete documentation index, see [llms.txt](https://docs.clore.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.clore.ai/guides/guides_v2-zh/tu-xiang-chu-li/gfpgan-face-restore.md).

# GFPGAN 人脸修复

在 Clore.ai 上使用 GFPGAN 修复并增强照片中的人脸

使用 GFPGAN 恢复并增强照片中的人脸。

{% hint style="success" %}
所有示例都可以在通过以下方式租用的 GPU 服务器上运行 [CLORE.AI 市场](https://clore.ai/marketplace).
{% endhint %}

## 在 CLORE.AI 上租用

1. 访问 [CLORE.AI 市场](https://clore.ai/marketplace)
2. 按 GPU 类型、VRAM 和价格筛选
3. 选择 **按需** （固定费率）或 **竞价** （出价）
4. 配置你的订单：
   * 选择 Docker 镜像
   * 设置端口（SSH 用 TCP，Web UI 用 HTTP）
   * 如有需要，添加环境变量
   * 输入启动命令
5. 选择支付方式： **CLORE**, **BTC**，或 **USDT/USDC**
6. 创建订单并等待部署

### 访问你的服务器

* 在以下位置查找连接信息 **我的订单**
* Web 界面：使用 HTTP 端口 URL
* SSH： `ssh -p <port> root@<proxy-address>`

## 什么是 GFPGAN？

GFPGAN（生成式人脸先验 GAN）专注于：

* 修复旧照片/受损照片
* 增强模糊的人脸
* 改进 AI 生成的人脸
* 修复低分辨率肖像

## 快速部署

**Docker 镜像：**

```
pytorch/pytorch:2.11.0-cuda12.8-cudnn9-runtime
```

**端口：**

```
22/tcp
7860/http
```

**命令：**

```bash
pip install gfpgan gradio && \\
python -c "
import gradio as gr
from gfpgan import GFPGANer
import cv2
import numpy as np

restorer = GFPGANer(
    model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth',
    upscale=2,
    arch='clean',
    channel_multiplier=2,
    bg_upsampler=None
)

def restore(image):
    img = np.array(image)
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    _, _, output = restorer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
    output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
    return output

demo = gr.Interface(fn=restore, inputs=gr.Image(), outputs=gr.Image(), title='GFPGAN 人脸修复器')
demo.launch(server_name='0.0.0.0', server_port=7860)
"
```

## 访问你的服务

部署后，找到你的 `http_pub` URL 在 **我的订单**:

1. 前往 **我的订单** 页面
2. 点击你的订单
3. 找到 `http_pub` URL（例如， `abc123.clorecloud.net`)

使用 `https://YOUR_HTTP_PUB_URL` 替代 `localhost` 在下面的示例中。

## CLI 用法

### 安装

```bash
pip install gfpgan
```

### 下载模型

```bash

# 下载人脸修复模型
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth -P ./models

# 下载检测模型
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/detection_Resnet50_Final.pth -P ./models

# 下载解析模型
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/parsing_parsenet.pth -P ./models
```

### 基础用法

```bash

# 修复单张图片
python inference_gfpgan.py -i input.jpg -o results -v 1.4 -s 2

# 修复文件夹
python inference_gfpgan.py -i ./inputs -o ./results -v 1.4 -s 2
```

### 选项

```bash
python inference_gfpgan.py \\
    -i input.jpg \\      # 输入图片/文件夹
    -o results \\        # 输出文件夹
    -v 1.4 \\            # GFPGAN 版本（1.2、1.3、1.4）
    -s 2 \\              # 放大倍数
    --bg_upsampler realesrgan \\  # 背景超分模型
    --only_center_face  # 仅修复中心人脸
```

## Python API

### 基础人脸修复

```python
from gfpgan import GFPGANer
import cv2

# 初始化
restorer = GFPGANer(
    model_path='GFPGANv1.4.pth',
    upscale=2,
    arch='clean',
    channel_multiplier=2,
    bg_upsampler=None
)

# 加载图像
img = cv2.imread('photo.jpg')

# 修复人脸
cropped_faces, restored_faces, restored_img = restorer.enhance(
    img,
    has_aligned=False,
    only_center_face=False,
    paste_back=True
)

# 保存结果
cv2.imwrite('restored.jpg', restored_img)
```

### 开启背景增强

```python
from gfpgan import GFPGANer
from realesrgan import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet
import cv2

# 设置背景超分模型
bg_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
bg_upsampler = RealESRGANer(
    scale=2,
    model_path='RealESRGAN_x2plus.pth',
    model=bg_model,
    half=True
)

# 设置带背景增强的人脸修复器
restorer = GFPGANer(
    model_path='GFPGANv1.4.pth',
    upscale=2,
    arch='clean',
    channel_multiplier=2,
    bg_upsampler=bg_upsampler
)

# 处理
img = cv2.imread('old_photo.jpg')
_, _, output = restorer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
cv2.imwrite('enhanced.jpg', output)
```

### 仅处理人脸（不贴回原图）

```python

# 获取单独修复后的人脸
cropped_faces, restored_faces, _ = restorer.enhance(
    img,
    has_aligned=False,
    only_center_face=False,
    paste_back=False
)

# 分别保存每张人脸
for i, face in enumerate(restored_faces):
    cv2.imwrite(f'face_{i}.jpg', face)
```

## 批量处理

```python
import os
from gfpgan import GFPGANer
import cv2
from tqdm import tqdm

restorer = GFPGANer(
    model_path='GFPGANv1.4.pth',
    upscale=2,
    arch='clean',
    channel_multiplier=2
)

input_dir = './old_photos'
output_dir = './restored'
os.makedirs(output_dir, exist_ok=True)

for filename in tqdm(os.listdir(input_dir)):
    if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
        img = cv2.imread(os.path.join(input_dir, filename))

        try:
            _, _, output = restorer.enhance(
                img,
                has_aligned=False,
                only_center_face=False,
                paste_back=True
            )
            cv2.imwrite(os.path.join(output_dir, filename), output)
        except Exception as e:
            print(f"失败: {filename} - {e}")
```

## CodeFormer（替代方案）

CodeFormer 也是另一个很出色的人脸修复工具：

```python

# 安装
pip install codeformer-pip

# 用法
from codeformer import CodeFormer
import cv2

restorer = CodeFormer()
img = cv2.imread('blurry_face.jpg')
result = restorer.restore(img)
cv2.imwrite('restored.jpg', result)
```

## 视频人脸修复

```python
import cv2
from gfpgan import GFPGANer
from tqdm import tqdm

restorer = GFPGANer(
    model_path='GFPGANv1.4.pth',
    upscale=1,  # 视频保持原始尺寸
    arch='clean',
    channel_multiplier=2
)

cap = cv2.VideoCapture('video.mp4')
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

out = cv2.VideoWriter('restored_video.mp4', cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))

for _ in tqdm(range(total)):
    ret, frame = cap.read()
    if not ret:
        break

    try:
        _, _, restored = restorer.enhance(frame, paste_back=True)
        out.write(restored)
    except:
        out.write(frame)  # 修复失败时保留原始帧

cap.release()
out.release()
```

## API 服务器

```python
from fastapi import FastAPI, UploadFile
from fastapi.responses import Response
from gfpgan import GFPGANer
import cv2
import numpy as np

app = FastAPI()

restorer = GFPGANer(
    model_path='GFPGANv1.4.pth',
    upscale=2,
    arch='clean',
    channel_multiplier=2
)

@app.post("/restore")
async def restore_face(file: UploadFile, upscale: int = 2):
    contents = await file.read()
    nparr = np.frombuffer(contents, np.uint8)
    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

    _, _, output = restorer.enhance(img, paste_back=True)

    _, encoded = cv2.imencode('.jpg', output)
    return Response(content=encoded.tobytes(), media_type="image/jpeg")

# 运行：uvicorn server:app --host 0.0.0.0 --port 8000
```

## 模型版本

| 版本   | 质量 | 速度 | 备注   |
| ---- | -- | -- | ---- |
| v1.4 | 最佳 | 中等 | 推荐   |
| v1.3 | 很高 | 快  | 效果平衡 |
| v1.2 | 好  | 最快 | 旧版本  |

## 应用场景

### 旧照片修复

```python

# 旧照片的最佳设置
restorer = GFPGANer(
    model_path='GFPGANv1.4.pth',
    upscale=4,  # 旧的低分辨率照片使用更高的放大倍数
    bg_upsampler=bg_upsampler
)
```

### AI 艺术增强

```python

# 适用于带有人脸瑕疵的 AI 生成图像
restorer = GFPGANer(
    model_path='GFPGANv1.4.pth',
    upscale=1,  # 保持原始尺寸
    only_center_face=True  # 聚焦主脸
)
```

### 合照

```python

# 处理合照中的所有人脸
restorer = GFPGANer(
    model_path='GFPGANv1.4.pth',
    upscale=2,
    only_center_face=False  # 处理所有人脸
)
```

## 性能

| 图像尺寸      | 面部 | GPU      | 时间      |
| --------- | -- | -------- | ------- |
| 512x512   | 1  | RTX 3090 | \~0.2秒  |
| 1024x1024 | 1  | RTX 3090 | \~0.3 秒 |
| 1024x1024 | 5  | RTX 3090 | \~0.8秒  |
| 2048x2048 | 1  | RTX 4090 | \~0.3 秒 |

## 故障排查

### 未检测到人脸

```python

# 降低检测阈值
from gfpgan.utils import GFPGANer

# 或先手动裁剪人脸区域
```

### 过度平滑的结果

* 使用较低保真度权重的 CodeFormer
* 使用 alpha 合成与原图融合

### 显存问题

```python

# 使用 CPU 进行人脸检测
import torch
torch.cuda.empty_cache()

# 一次处理一张人脸
only_center_face=True
```

## 成本估算

CLORE.AI 市场常见费率（截至 2024 年）：

| GPU       | 小时费率    | 日费率     | 4 小时会话  |
| --------- | ------- | ------- | ------- |
| RTX 3060  | \~$0.03 | \~$0.70 | \~$0.12 |
| RTX 3090  | \~$0.06 | \~$1.50 | \~$0.25 |
| RTX 4090  | \~$0.10 | \~$2.30 | \~$0.40 |
| A100 40GB | \~$0.17 | \~$4.00 | \~$0.70 |
| A100 80GB | \~$0.25 | \~$6.00 | \~$1.00 |

*价格因提供商和需求而异。请查看* [*CLORE.AI 市场*](https://clore.ai/marketplace) *以获取当前费率。*

**节省费用：**

* 使用 **竞价** 可中断工作市场——约三分之一的服务器将现货价格定得低于按需价格（中位数约优惠 13%），其余则与按需价格持平
* 使用 **CLORE** 代币支付
* 比较不同提供商的价格

## 下一步

* [Real-ESRGAN 超分辨率放大](/guides/guides_v2-zh/tu-xiang-chu-li/real-esrgan-upscaling.md)
* Stable Diffusion WebUI
* [AI 视频生成](/guides/guides_v2-zh/shi-pin-sheng-cheng/ai-video-generation.md)


---

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