> 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/ren-lian-yu-shen-fen/instantid.md).

# InstantID

从一张参考照片生成具有任意人脸身份的图像

仅使用一张参考照片即可生成任意面部身份的图像。

{% 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 InstantID？

InstantID 保留面部身份：

* 使用任意参考人脸
* 零样本 - 无需训练
* 适用于任何风格/提示词
* 优于 LoRA 训练

## 需求

| 模式  | 显存    | 推荐       |
| --- | ----- | -------- |
| 基础  | 12GB  | RTX 4080 |
| 高质量 | 16GB  | RTX 4090 |
| 带姿势 | 16GB+ | RTX 4090 |

## 快速部署

**Docker 镜像：**

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

**端口：**

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

**命令：**

```bash
pip install diffusers transformers accelerate opencv-python insightface onnxruntime-gpu && \
huggingface-cli download InstantX/InstantID --local-dir ./checkpoints && \
python instantid_app.py
```

## 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。

## 安装

```bash
pip install diffusers transformers accelerate
pip install opencv-python insightface onnxruntime-gpu
pip install huggingface_hub

# 下载模型
huggingface-cli download InstantX/InstantID --local-dir ./checkpoints
```

## 基础用法

```python
import torch
import cv2
import numpy as np
from PIL import Image
from diffusers import StableDiffusionXLPipeline, DDIMScheduler
from insightface.app import FaceAnalysis

# 初始化人脸分析器
app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))

# 加载管道
pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16
).to("cuda")

# 加载 InstantID 组件
from diffusers import ControlNetModel

controlnet = ControlNetModel.from_pretrained(
    "./checkpoints/ControlNetModel",
    torch_dtype=torch.float16
)

# 为人脸加载 IP-Adapter
pipe.load_ip_adapter(
    "./checkpoints",
    subfolder="",
    weight_name="ip-adapter.bin"
)

# 处理参考人脸
face_image = cv2.imread("reference_face.jpg")
faces = app.get(face_image)
face_emb = faces[0].normed_embedding

# 使用人脸身份生成
image = pipe(
    prompt="宇航员肖像，太空背景",
    negative_prompt="丑陋、模糊、低质量",
    ip_adapter_image_embeds=[torch.tensor(face_emb).unsqueeze(0)],
    num_inference_steps=30,
    guidance_scale=7.5
).images[0]

image.save("output.png")
```

## 使用 Diffusers 管道

```python
from diffusers import StableDiffusionXLInstantIDPipeline, DDIMScheduler
from insightface.app import FaceAnalysis
import torch
import cv2

# 加载人脸分析器
app = FaceAnalysis(name='antelopev2', providers=['CUDAExecutionProvider'])
app.prepare(ctx_id=0)

# 加载管道
pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet="./checkpoints/ControlNetModel",
    torch_dtype=torch.float16
).to("cuda")

pipe.load_ip_adapter_instantid("./checkpoints/ip-adapter.bin")

# 获取人脸嵌入
face_image = cv2.imread("face.jpg")
face_info = app.get(face_image)[0]

face_emb = face_info.normed_embedding
face_kps = face_info.kps

# 生成
image = pipe(
    prompt="水彩肖像画，艺术风格",
    face_emb=face_emb,
    face_kps=face_kps,
    num_inference_steps=30
).images[0]

image.save("portrait.png")
```

## 风格示例

### 专业头像照

```python
prompt = "专业商务头像照，影棚灯光，灰色背景，商务着装"
negative = "卡通、动漫、插画、模糊"
```

### 艺术肖像

```python
prompt = "伦勃朗风格的油画肖像，戏剧性灯光，博物馆级品质"
negative = "照片、写实、现代"
```

### 奇幻角色

```python
prompt = "奇幻精灵角色，尖耳朵，魔法森林背景，空灵光线"
negative = "人类耳朵、现代服装、写实"
```

### 动漫风格

```python
prompt = "动漫角色肖像，吉卜力风格，细节丰富，精美"
negative = "写实、照片、3D 渲染"
```

## 带姿势控制

```python
from diffusers.utils import load_image

# 加载姿势参考
pose_image = load_image("pose_reference.jpg")

# 使用人脸和姿势生成
image = pipe(
    prompt="动作姿势的人物，动感，高质量",
    face_emb=face_emb,
    face_kps=face_kps,
    image=pose_image,  # 姿势参考
    controlnet_conditioning_scale=0.8,
    num_inference_steps=30
).images[0]
```

## Gradio 界面

```python
import gradio as gr
import torch
import cv2
import numpy as np
from diffusers import StableDiffusionXLInstantIDPipeline
from insightface.app import FaceAnalysis

app = FaceAnalysis(name='antelopev2', providers=['CUDAExecutionProvider'])
app.prepare(ctx_id=0)

pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet="./checkpoints/ControlNetModel",
    torch_dtype=torch.float16
).to("cuda")

pipe.load_ip_adapter_instantid("./checkpoints/ip-adapter.bin")

def generate(face_image, prompt, negative_prompt, strength, steps):
    # 转换为 cv2 格式
    face_cv = cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR)

    # 获取人脸信息
    faces = app.get(face_cv)
    if len(faces) == 0:
        return None, "未检测到人脸！"

    face_info = faces[0]
    face_emb = face_info.normed_embedding
    face_kps = face_info.kps

    # 生成
    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        face_emb=face_emb,
        face_kps=face_kps,
        ip_adapter_scale=strength,
        num_inference_steps=steps
    ).images[0]

    return image, "成功！"

demo = gr.Interface(
    fn=generate,
    inputs=[
        gr.Image(type="pil", label="参考人脸"),
        gr.Textbox(label="提示词", value="专业肖像"),
        gr.Textbox(label="负面提示词", value="丑陋，模糊"),
        gr.Slider(0.1, 1.0, value=0.8, label="身份强度"),
        gr.Slider(10, 50, value=30, step=1, label="步数")
    ],
    outputs=[
        gr.Image(label="生成的图像"),
        gr.Textbox(label="状态")
    ],
    title="InstantID - 身份保留生成"
)

demo.launch(server_name="0.0.0.0", server_port=7860)
```

## 批量换脸

```python
import os
from pathlib import Path

def batch_generate(face_image_path, prompts, output_dir):
    # 加载人脸
    face_cv = cv2.imread(face_image_path)
    face_info = app.get(face_cv)[0]
    face_emb = face_info.normed_embedding
    face_kps = face_info.kps

    os.makedirs(output_dir, exist_ok=True)

    for i, prompt in enumerate(prompts):
        print(f"正在生成 {i+1}/{len(prompts)}: {prompt[:50]}...")

        image = pipe(
            prompt=prompt,
            negative_prompt="丑陋、模糊、变形",
            face_emb=face_emb,
            face_kps=face_kps,
            num_inference_steps=30
        ).images[0]

        image.save(f"{output_dir}/output_{i:03d}.png")

# 用法
prompts = [
    "太空服宇航员，地球背景",
    "身穿盔甲的中世纪骑士",
    "实验室里的科学家",
    "餐厅厨房里的厨师",
    "运动场上的运动员"
]

batch_generate("my_face.jpg", prompts, "./outputs")
```

## 身份强度控制

```python

# 低强度 - 更多风格，更少身份
image_stylized = pipe(
    prompt=prompt,
    face_emb=face_emb,
    ip_adapter_scale=0.4,  # 低
    num_inference_steps=30
).images[0]

# 高强度 - 更多身份，更少风格
image_faithful = pipe(
    prompt=prompt,
    face_emb=face_emb,
    ip_adapter_scale=0.9,  # 高
    num_inference_steps=30
).images[0]
```

## 内存优化

```python

# 启用优化
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()

# 或者在极低显存下使用顺序卸载
pipe.enable_sequential_cpu_offload()
```

## 性能

| 模式  | GPU      | 每张图像耗时 |
| --- | -------- | ------ |
| 基础  | RTX 4090 | \~8s   |
| 带姿势 | RTX 4090 | \~12 秒 |
| 基础  | RTX 3090 | \~15s  |
| 基础  | A100     | 约 5 秒  |

## 故障排查

### 未检测到人脸

* 确保人脸清晰可见
* 参考图像光线良好
* 人脸应正面朝向

### 身份未保留

* 提高 ip\_adapter\_scale
* 使用更清晰的参考照片
* 避免极端角度

### 未应用风格

* 降低 ip\_adapter\_scale
* 更具描述性的提示词
* 提高 guidance\_scale

## 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

* [IP-Adapter](/guides/guides_v2-zh/ren-lian-yu-shen-fen/ip-adapter.md) - 图像提示
* Stable Diffusion WebUI - InstantID 扩展
* [ControlNet](/guides/guides_v2-zh/tu-xiang-chu-li/controlnet-advanced.md) - 姿势控制


---

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