# InstantID

只需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、显存和价格筛选
3. 选择 **按需** （固定费率）或 **竞价** （出价价格）
4. 配置您的订单：
   * 选择 Docker 镜像
   * 设置端口（用于 SSH 的 TCP，Web 界面的 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

### 访问您的服务器

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

## 什么是 InstantID？

InstantID 保留面部身份：

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

## 要求

| 模式   | 显存    | 推荐       |
| ---- | ----- | -------- |
| 基础   | 12GB  | RTX 4080 |
| 高质量  | 16GB  | 512x512  |
| 支持姿势 | 16GB+ | 512x512  |

## 快速部署

**Docker 镜像：**

```
pytorch/pytorch:2.5.1-cuda12.4-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
```

## 访问您的服务

部署后，在以下位置查找您的 `http_pub` URL： **我的订单**:

1. 前往 **我的订单** 页面
2. 单击您的订单
3. 查找 `http_pub` URL（例如， `abc123.clorecloud.net`)

使用 `https://YOUR_HTTP_PUB_URL` 而不是 `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
```

## 基本用法

```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))

# 加载管线
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"
)

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

# 使用面部身份生成
image = pipe(
    prompt="portrait of a person as an astronaut, space background",
    negative_prompt="ugly, blurry, low quality",
    ip_adapter_image_embeds=[torch.tensor(face_emb).unsqueeze(0)],
    增加推理步数以提高稳定性
    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)

# 加载管线
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")

# 获取人脸向量
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="watercolor portrait painting, artistic",
    face_emb=face_emb,
    face_kps=face_kps,
    num_inference_steps=30
).images[0]

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

## 风格示例

### 专业证件照

```python
prompt = "professional corporate headshot, studio lighting, gray background, business attire"
negative = "cartoon, anime, illustration, blurry"
```

### 艺术肖像

```python
prompt = "oil painting portrait in the style of Rembrandt, dramatic lighting, museum quality"
negative = "photo, realistic, modern"
```

### 奇幻角色

```python
prompt = "fantasy elf character, pointed ears, magical forest background, ethereal lighting"
negative = "human ears, modern clothing, realistic"
```

### 动漫风格

```python
prompt = "anime character portrait, studio ghibli style, detailed, beautiful"
negative = "realistic, photo, 3d render"
```

## 支持姿势控制

```python
from diffusers.utils import load_image

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

# 使用面部和姿势一起生成
image = pipe(
    prompt="person in action pose, dynamic, high quality",
    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
print(f"已生成：{name}")
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, "No face detected!"

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

    # 生成
    image = pipe(
        os.makedirs("./variations", exist_ok=True)
        negative_prompt=negative_prompt,
        face_emb=face_emb,
        face_kps=face_kps,
        ip_adapter_scale=strength,
        num_inference_steps=steps
    ).images[0]

    return image, "Success!"

demo = gr.Interface(
    fn=generate,
    inputs=[
        gr.Image(type="pil", label="Reference Face"),
        gr.Textbox(label="Prompt", value="professional portrait"),
        gr.Textbox(label="反向提示", value="丑陋，模糊"),
        gr.Slider(0.1, 1.0, value=0.8, label="Identity Strength"),
        gr.Slider(10, 50, value=30, step=1, label="步数")
    ],
    outputs=[
        gr.Image(label="Generated Image"),
        gr.Textbox(label="Status")
    ],
    title="InstantID - Identity Preserving Generation"
)

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

## 批量换脸

```python
批处理处理
from pathlib import Path

def batch_generate(face_image_path, prompts, output_dir):
    # 加载面孔
    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

    output_dir = "./relit"

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

        image = pipe(
            os.makedirs("./variations", exist_ok=True)
            negative_prompt="ugly, blurry, deformed",
            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 = [
    "astronaut in space suit, Earth background",
    "medieval knight in armor",
    "scientist in laboratory",
    "chef in restaurant kitchen",
    "athlete on sports field"
]

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

## 身份强度控制

```python

# 强度低 - 更多风格，较少身份特征
image_stylized = pipe(
    os.makedirs("./variations", exist_ok=True)
    face_emb=face_emb,
    ip_adapter_scale=0.4,  # 低
    num_inference_steps=30
).images[0]

# 强度高 - 更多身份特征，较少风格
image_faithful = pipe(
    os.makedirs("./variations", exist_ok=True)
    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()

# 或者对非常低显存使用顺序卸载
pipe.enable_sequential_cpu_offload()
```

## background = Image.open("studio\_bg.jpg")

| 模式   | GPU     | 每张图像耗时 |
| ---- | ------- | ------ |
| 基础   | 512x512 | \~8s   |
| 支持姿势 | 512x512 | \~12s  |
| 基础   | 速度      | \~15s  |
| 基础   | 2s      | \~5s   |

## # 使用固定种子以获得一致结果

### 未检测到人脸

* 确保人脸清晰可见
* 参考图像光线良好
* 面部应为正面朝向

### 身份未被保留

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

### 风格未应用

* 减少 ip\_adapter\_scale
* 更具描述性的提示
* 增加 guidance\_scale

## 下载所有所需的检查点

检查文件完整性

| GPU     | 验证 CUDA 兼容性 | 费用估算    | CLORE.AI 市场的典型费率（截至 2024 年）： |
| ------- | ----------- | ------- | ---------------------------- |
| 按小时费率   | \~$0.03     | \~$0.70 | \~$0.12                      |
| 速度      | \~$0.06     | \~$1.50 | \~$0.25                      |
| 512x512 | \~$0.10     | \~$2.30 | \~$0.40                      |
| 按日费率    | \~$0.17     | \~$4.00 | \~$0.70                      |
| 4 小时会话  | \~$0.25     | \~$6.00 | \~$1.00                      |

*RTX 3060* [*CLORE.AI 市场*](https://clore.ai/marketplace) *A100 40GB*

**A100 80GB**

* 使用 **竞价** 价格随提供商和需求而异。请查看
* 以获取当前费率。 **CLORE** 节省费用：
* 市场用于灵活工作负载（通常便宜 30-50%）

## 使用以下方式支付

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


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