文章目录
-
-
- 技术背景介绍
-
- 1.1 Stable Diffusion原理概述
- 1.2 OpenVINO工具包简介
- 1.3 边缘计算的优势与挑战
-
- 环境准备与安装
-
- 2.1 硬件要求
-
- 2.1.1 支持的处理设备
- 2.1.2 内存与存储需求
- 2.2 软件环境配置
-
- 2.2.1 OpenVINO安装
- 2.2.2 Python依赖包安装
- 2.2.3 模型文件准备
-
- 模型转换与优化
-
- 3.1 原始模型下载
- 3.2 ONNX格式转换
- 3.3 OpenVINO模型优化
-
- 核心代码实现
-
- 4.1 模型加载模块
- 4.2 文本编码器实现
- 4.3 扩散过程控制
- 4.4 图像解码与后处理
-
- 完整应用部署
-
- 5.1 命令行界面开发
- 5.2 Web服务接口
-
- 测试与性能评估
-
- 6.1 功能测试用例
-
- 常见问题与解决方案
-
- 7.1 内存不足处理
-
- 成果展示与应用场景
-
- 8.1 生成效果示例
- 8.2 实际应用案例
- 技术图谱
-
1. 技术背景介绍
1.1 Stable Diffusion原理概述
Stable Diffusion是一种基于潜在扩散模型（Latent Diffusion Model）的文本到图像生成技术。与传统的在像素空间直接操作的扩散模型不同，Stable Diffusion在潜在空间中进行扩散过程，大大降低了计算复杂度和内存需求。
输入文本 CLIP文本编码器 潜在空间噪声 UNet去噪网络 VAE解码器 输出图像
1.2 OpenVINO工具包简介
OpenVINO（Open Visual Inference & Neural network Optimization）是英特尔开发的用于优化和部署AI推理的工具包。它支持跨多种英特尔硬件（CPU、集成GPU、独立GPU等）的高性能推理，并提供模型优化和压缩功能。
1.3 边缘计算的优势与挑战
在边缘设备上运行Stable Diffusion带来了诸多优势：数据隐私保护、低延迟响应、无需网络连接等。但同时面临计算资源有限、内存约束、能耗限制等挑战。
2. 环境准备与安装
2.1 硬件要求
2.1.1 支持的处理设备
- Intel Core i5/i7/i9 第10代及以上处理器
- Intel Iris Xe 集成显卡
- Intel Arc 独立显卡
- 至少16GB RAM（推荐32GB）
- 至少10GB可用存储空间
2.1.2 内存与存储需求
Stable Diffusion模型需要较大的内存空间，以下是详细要求：
| 组件 | 最低要求 | 推荐要求 |
|---|---|---|
| 系统内存 | 16GB | 32GB |
| 模型存储 | 5GB | 10GB |
| 交换空间 | 8GB | 16GB |
2.2 软件环境配置
2.2.1 OpenVINO安装
创建安装脚本 install_openvino.sh：
bash
#!/bin/bash
# install_openvino.sh - OpenVINO 2023.0 安装脚本
echo "正在安装OpenVINO工具包..."
# 添加APT存储库
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
echo "deb https://apt.repos.intel.com/openvino/2023 ubuntu20 main" | sudo tee /etc/apt/sources.list.d/intel-openvino-2023.list
# 更新并安装
sudo apt update
sudo apt install openvino-2023.0.0
# 设置环境变量
echo "source /opt/intel/openvino_2023/setupvars.sh" >> ~/.bashrc
source /opt/intel/openvino_2023/setupvars.sh
# 验证安装
python3 -c "import openvino.runtime as ov; print('OpenVINO安装成功')"
echo "安装完成！"2.2.2 Python依赖包安装
创建 requirements.txt 文件：
txt
# requirements.txt
torch==2.0.1
torchvision==0.15.2
diffusers==0.16.1
transformers==4.29.2
accelerate==0.19.0
openvino-dev==2023.0.0
onnx==1.14.0
onnxruntime==1.15.1
pillow==9.5.0
numpy==1.24.3
tqdm==4.65.0
scipy==1.10.1
ftfy==6.1.1安装命令：
bash
pip install -r requirements.txt2.2.3 模型文件准备
创建模型下载脚本 download_models.py：
python
# download_models.py - Stable Diffusion模型下载脚本
import os
from huggingface_hub import snapshot_download
from pathlib import Path
def download_model(model_id, local_dir):
"""
从HuggingFace Hub下载模型
Args:
model_id: 模型ID
local_dir: 本地存储目录
"""
print(f"正在下载模型: {model_id}")
# 创建目录
os.makedirs(local_dir, exist_ok=True)
# 下载模型
snapshot_download(
repo_id=model_id,
local_dir=local_dir,
local_dir_use_symlinks=False,
resume_download=True,
allow_patterns=["*.bin", "*.json", "*.txt", "*.onnx", "*.xml", "*.bin"]
)
print(f"模型已下载到: {local_dir}")
if __name__ == "__main__":
# 下载Stable Diffusion v1.5
download_model("runwayml/stable-diffusion-v1-5", "./models/stable-diffusion-v1-5")
# 下载CLIP文本编码器
download_model("openai/clip-vit-large-patch14", "./models/clip-text-encoder")
print("所有模型下载完成！")3. 模型转换与优化
3.1 原始模型下载
使用上述脚本下载原始PyTorch模型，或者手动从HuggingFace下载。
3.2 ONNX格式转换
创建模型转换脚本 convert_to_onnx.py：
python
# convert_to_onnx.py - 将PyTorch模型转换为ONNX格式
import torch
from diffusers import StableDiffusionPipeline
import onnx
import onnxruntime as ort
from pathlib import Path
def convert_text_encoder_to_onnx(model_path, output_path):
"""
将CLIP文本编码器转换为ONNX格式
"""
print("正在转换文本编码器...")
# 加载原始管道
pipe = StableDiffusionPipeline.from_pretrained(
model_path,
torch_dtype=torch.float32
)
# 提取文本编码器
text_encoder = pipe.text_encoder
text_encoder.eval()
# 创建示例输入
dummy_input = torch.zeros(1, 77, dtype=torch.int64)
# 导出为ONNX
torch.onnx.export(
text_encoder,
dummy_input,
output_path / "text_encoder.onnx",
export_params=True,
opset_version=14,
do_constant_folding=True,
input_names=['input_ids'],
output_names=['last_hidden_state', 'pooler_output'],
dynamic_axes={
'input_ids': {0: 'batch_size'},
'last_hidden_state': {0: 'batch_size'},
'pooler_output': {0: 'batch_size'}
}
)
print("文本编码器转换完成")
def convert_unet_to_onnx(model_path, output_path):
"""
将UNet扩散模型转换为ONNX格式
"""
print("正在转换UNet模型...")
pipe = StableDiffusionPipeline.from_pretrained(
model_path,
torch_dtype=torch.float32
)
unet = pipe.unet
unet.eval()
# UNet的输入： latent_model_input, timestep, encoder_hidden_states
dummy_latent = torch.randn(1, 4, 64, 64)
dummy_timestep = torch.tensor([50], dtype=torch.int64)
dummy_encoder = torch.randn(1, 77, 768)
torch.onnx.export(
unet,
(dummy_latent, dummy_timestep, dummy_encoder),
output_path / "unet.onnx",
export_params=True,
opset_version=14,
do_constant_folding=True,
input_names=['latent_model_input', 'timestep', 'encoder_hidden_states'],
output_names=['noise_pred'],
dynamic_axes={
'latent_model_input': {0: 'batch_size'},
'encoder_hidden_states': {0: 'batch_size'},
'noise_pred': {0: 'batch_size'}
}
)
print("UNet模型转换完成")
def convert_vae_to_onnx(model_path, output_path):
"""
将VAE解码器转换为ONNX格式
"""
print("正在转换VAE解码器...")
pipe = StableDiffusionPipeline.from_pretrained(
model_path,
torch_dtype=torch.float32
)
vae = pipe.vae
vae.eval()
# VAE的输入：latents
dummy_input = torch.randn(1, 4, 64, 64)
torch.onnx.export(
vae,
dummy_input,
output_path / "vae_decoder.onnx",
export_params=True,
opset_version=14,
do_constant_folding=True,
input_names=['latents'],
output_names=['sample'],
dynamic_axes={
'latents': {0: 'batch_size'},
'sample': {0: 'batch_size'}
}
)
print("VAE解码器转换完成")
if __name__ == "__main__":
model_path = "./models/stable-diffusion-v1-5"
output_path = Path("./models/onnx")
output_path.mkdir(exist_ok=True)
# 转换所有组件
convert_text_encoder_to_onnx(model_path, output_path)
convert_unet_to_onnx(model_path, output_path)
convert_vae_to_onnx(model_path, output_path)
print("所有模型转换完成！")3.3 OpenVINO模型优化
创建OpenVINO优化脚本 optimize_with_openvino.py：
python
# optimize_with_openvino.py - 使用OpenVINO优化ONNX模型
from openvino.tools import mo
from openvino.runtime import serialize
import argparse
from pathlib import Path
def convert_onnx_to_openvino(onnx_model_path, ov_model_path, model_type):
"""
将ONNX模型转换为OpenVINO格式并进行优化
Args:
onnx_model_path: ONNX模型路径
ov_model_path: 输出OpenVINO模型路径
model_type: 模型类型（text_encoder, unet, vae）
"""
print(f"正在优化{model_type}模型...")
# 模型特定的优化参数
compression_config = {
'text_encoder': {
'compress_to_fp16': True
},
'unet': {
'compress_to_fp16': True
},
'vae': {
'compress_to_fp16': True
}
}
# 转换模型
ov_model = mo.convert_model(
onnx_model_path,
compress_to_fp16=compression_config[model_type]['compress_to_fp16']
)
# 序列化模型
serialize(ov_model, str(ov_model_path))
print(f"{model_type}模型优化完成: {ov_model_path}")
def main():
parser = argparse.ArgumentParser(description='将ONNX模型转换为OpenVINO格式')
parser.add_argument('--model_dir', type=str, default='./models/onnx',
help='ONNX模型目录')
parser.add_argument('--output_dir', type=str, default='./models/openvino',
help='OpenVINO模型输出目录')
args = parser.parse_args()
model_dir = Path(args.model_dir)
output_dir = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# 转换所有组件
models_to_convert = [
('text_encoder.onnx', 'text_encoder.xml', 'text_encoder'),
('unet.onnx', 'unet.xml', 'unet'),
('vae_decoder.onnx', 'vae_decoder.xml', 'vae')
]
for onnx_name, ov_name, model_type in models_to_convert:
onnx_path = model_dir / onnx_name
ov_path = output_dir / ov_name
if onnx_path.exists():
convert_onnx_to_openvino(onnx_path, ov_path, model_type)
else:
print(f"警告: {onnx_path} 不存在，跳过转换")
print("所有模型优化完成！")
if __name__ == "__main__":
main()原始PyTorch模型 ONNX格式转换 OpenVINO模型优化 FP16量化压缩 最终部署模型
4. 核心代码实现
4.1 模型加载模块
创建模型管理类 model_manager.py：
python
# model_manager.py - OpenVINO模型加载和管理
import openvino.runtime as ov
from openvino.runtime import Core
import numpy as np
from typing import Dict, List, Optional
import time
from pathlib import Path
class OpenVINOModelManager:
"""OpenVINO模型管理器"""
def __init__(self, model_dir: str, device: str = "CPU"):
"""
初始化模型管理器
Args:
model_dir: 模型目录路径
device: 推理设备 (CPU, GPU, etc.)
"""
self.model_dir = Path(model_dir)
self.device = device
self.core = Core()
self.models: Dict[str, ov.CompiledModel] = {}
self.infer_requests: Dict[str, ov.InferRequest] = {}
def load_model(self, model_name: str, model_path: Path) -> Optional[ov.CompiledModel]:
"""
加载单个模型
Args:
model_name: 模型名称
model_path: 模型文件路径
Returns:
CompiledModel or None
"""
try:
if not model_path.exists():
print(f"错误: 模型文件不存在 {model_path}")
return None
print(f"正在加载模型: {model_name}")
start_time = time.time()
# 读取模型
model = self.core.read_model(str(model_path))
# 编译模型
compiled_model = self.core.compile_model(model, self.device)
# 创建推理请求
infer_request = compiled_model.create_infer_request()
# 存储模型和推理请求
self.models[model_name] = compiled_model
self.infer_requests[model_name] = infer_request
load_time = time.time() - start_time
print(f"模型 {model_name} 加载完成, 耗时: {load_time:.2f}秒")
return compiled_model
except Exception as e:
print(f"加载模型 {model_name} 时出错: {str(e)}")
return None
def load_all_models(self) -> bool:
"""
加载所有必需的模型
Returns:
bool: 是否全部加载成功
"""
model_files = {
"text_encoder": self.model_dir / "text_encoder.xml",
"unet": self.model_dir / "unet.xml",
"vae_decoder": self.model_dir / "vae_decoder.xml"
}
success = True
for name, path in model_files.items():
if self.load_model(name, path) is None:
success = False
return success
def get_model(self, model_name: str) -> Optional[ov.CompiledModel]:
"""获取已加载的模型"""
return self.models.get(model_name)
def get_infer_request(self, model_name: str) -> Optional[ov.InferRequest]:
"""获取模型的推理请求"""
return self.infer_requests.get(model_name)
def get_model_inputs(self, model_name: str) -> List[ov.Output]:
"""获取模型的输入信息"""
model = self.get_model(model_name)
if model:
return model.inputs
return []
def get_model_outputs(self, model_name: str) -> List[ov.Output]:
"""获取模型的输出信息"""
model = self.get_model(model_name)
if model:
return model.outputs
return []
# 文本编码器封装
class TextEncoderWrapper:
"""文本编码器封装类"""
def __init__(self, model_manager: OpenVINOModelManager):
self.model_manager = model_manager
self.model_name = "text_encoder"
self.infer_request = model_manager.get_infer_request(self.model_name)
def encode(self, input_ids: np.ndarray) -> np.ndarray:
"""
编码输入文本
Args:
input_ids: 输入的token ID数组
Returns:
编码后的隐藏状态
"""
if self.infer_request is None:
raise ValueError("文本编码器未初始化")
# 设置输入
self.infer_request.set_input_tensor(
self.infer_request.model_input(0),
ov.Tensor(input_ids.astype(np.int64))
)
# 执行推理
self.infer_request.start_async()
self.infer_request.wait()
# 获取输出
return self.infer_request.get_output_tensor(0).data
# UNet扩散模型封装
class UNetWrapper:
"""UNet扩散模型封装类"""
def __init__(self, model_manager: OpenVINOModelManager):
self.model_manager = model_manager
self.model_name = "unet"
self.infer_request = model_manager.get_infer_request(self.model_name)
def predict_noise(self,
latent_model_input: np.ndarray,
timestep: np.ndarray,
encoder_hidden_states: np.ndarray) -> np.ndarray:
"""
预测噪声
Args:
latent_model_input: 潜在空间输入
timestep: 时间步
encoder_hidden_states: 编码器隐藏状态
Returns:
预测的噪声
"""
if self.infer_request is None:
raise ValueError("UNet模型未初始化")
# 设置输入
inputs = {
'latent_model_input': latent_model_input.astype(np.float32),
'timestep': timestep.astype(np.int64),
'encoder_hidden_states': encoder_hidden_states.astype(np.float32)
}
# 执行推理
results = self.infer_request.infer(inputs)
return results['noise_pred']
# VAE解码器封装
class VAEDecoderWrapper:
"""VAE解码器封装类"""
def __init__(self, model_manager: OpenVINOModelManager):
self.model_manager = model_manager
self.model_name = "vae_decoder"
self.infer_request = model_manager.get_infer_request(self.model_name)
def decode(self, latents: np.ndarray) -> np.ndarray:
"""
解码潜在表示到图像空间
Args:
latents: 潜在表示
Returns:
解码后的图像
"""
if self.infer_request is None:
raise ValueError("VAE解码器未初始化")
# 设置输入
self.infer_request.set_input_tensor(
self.infer_request.model_input(0),
ov.Tensor(latents.astype(np.float32))
)
# 执行推理
self.infer_request.start_async()
self.infer_request.wait()
# 获取输出
return self.infer_request.get_output_tensor(0).data4.2 文本编码器实现
创建文本处理模块 text_processor.py：
python
# text_processor.py - 文本预处理和编码
from transformers import CLIPTokenizer
import numpy as np
from typing import List, Tuple
class TextProcessor:
"""文本处理器"""
def __init__(self, tokenizer_path: str = "./models/clip-text-encoder"):
"""
初始化文本处理器
Args:
tokenizer_path: tokenizer模型路径
"""
self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
self.max_length = 77
def preprocess_text(self, prompt: str) -> Tuple[np.ndarray, np.ndarray]:
"""
预处理文本提示
Args:
prompt: 文本提示
Returns:
input_ids: 输入的token ID
attention_mask: 注意力掩码
"""
# Tokenize文本
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.max_length,
truncation=True,
return_tensors="np"
)
return text_inputs.input_ids, text_inputs.attention_mask
def encode_text(self, text_encoder, prompt: str) -> np.ndarray:
"""
编码文本提示
Args:
text_encoder: 文本编码器
prompt: 文本提示
Returns:
编码后的文本特征
"""
input_ids, attention_mask = self.preprocess_text(prompt)
# 使用文本编码器进行编码
text_embeddings = text_encoder.encode(input_ids)
return text_embeddings
def create_weighted_prompt(self, prompts: List[str], weights: List[float]) -> str:
"""
创建加权提示文本
Args:
prompts: 提示文本列表
weights: 权重列表
Returns:
加权后的提示文本
"""
if len(prompts) != len(weights):
raise ValueError("提示和权重的数量必须相同")
weighted_parts = []
for prompt, weight in zip(prompts, weights):
if weight != 1.0:
weighted_parts.append(f"({prompt}:{weight:.2f})")
else:
weighted_parts.append(prompt)
return " ".join(weighted_parts)4.3 扩散过程控制
创建扩散调度器 diffusion_scheduler.py：
python
# diffusion_scheduler.py - 扩散过程调度器
import numpy as np
from typing import List, Optional
import math
class DiffusionScheduler:
"""扩散过程调度器"""
def __init__(self, num_inference_steps: int = 50, beta_start: float = 0.00085,
beta_end: float = 0.012, beta_schedule: str = "scaled_linear"):
"""
初始化扩散调度器
Args:
num_inference_steps: 推理步数
beta_start: beta起始值
beta_end: beta结束值
beta_schedule: beta调度策略
"""
self.num_inference_steps = num_inference_steps
self.beta_start = beta_start
self.beta_end = beta_end
self.beta_schedule = beta_schedule
self.betas = self._get_betas()
self.alphas = 1.0 - self.betas
self.alphas_cumprod = np.cumprod(self.alphas, axis=0)
self.one = np.array(1.0)
# 初始化时间步
self.timesteps = None
self._setup_timesteps()
def _get_betas(self) -> np.ndarray:
"""获取beta序列"""
if self.beta_schedule == "linear":
return np.linspace(
self.beta_start, self.beta_end, self.num_inference_steps, dtype=np.float32
)
elif self.beta_schedule == "scaled_linear":
return np.linspace(
self.beta_start ** 0.5, self.beta_end ** 0.5, self.num_inference_steps, dtype=np.float32
) ** 2
else:
raise ValueError(f"不支持的beta调度策略: {self.beta_schedule}")
def _setup_timesteps(self):
"""设置时间步"""
# 从训练步数映射到推理步数
self.timesteps = np.linspace(
0, self.num_inference_steps - 1, self.num_inference_steps, dtype=np.int64
)[::-1].copy()
def get_alpha_prod(self, t: int) -> float:
"""获取alpha累积乘积"""
return self.alphas_cumprod[t]
def scale_model_input(self, latent: np.ndarray, timestep: int) -> np.ndarray:
"""
缩放模型输入
Args:
latent: 潜在表示
timestep: 时间步
Returns:
缩放后的潜在表示
"""
# 根据时间步获取sqrt(alpha_prod)
sqrt_alpha_prod = np.sqrt(self.get_alpha_prod(timestep))
# 缩放输入
return latent * sqrt_alpha_prod
def step(self, noise_pred: np.ndarray, latents: np.ndarray,
timestep: int, generator: Optional[np.random.Generator] = None) -> np.ndarray:
"""
执行一步扩散过程
Args:
noise_pred: 预测的噪声
latents: 当前潜在表示
timestep: 当前时间步
generator: 随机数生成器
Returns:
更新后的潜在表示
"""
if generator is None:
generator = np.random.default_rng()
# 获取当前时间步的参数
alpha_prod_t = self.get_alpha_prod(timestep)
alpha_prod_t_prev = self.get_alpha_prod(timestep - 1) if timestep > 0 else self.one
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
# 计算预测的原始样本
pred_original_sample = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
# 计算当前样本的系数
pred_sample_direction = (1 - alpha_prod_t_prev) ** 0.5 * noise_pred
# 计算前一步的样本
prev_sample = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
# 添加噪声（如果不是最后一步）
if timestep > 0:
noise = generator.normal(size=latents.shape).astype(np.float32)
prev_sample = prev_sample + (beta_prod_t_prev ** 0.5) * noise
return prev_sample
def add_noise(self, original_samples: np.ndarray, noise: np.ndarray,
timesteps: np.ndarray) -> np.ndarray:
"""
添加噪声到原始样本
Args:
original_samples: 原始样本
noise: 噪声
timesteps: 时间步数组
Returns:
添加噪声后的样本
"""
sqrt_alpha_prod = np.sqrt(self.get_alpha_prod(timesteps))
sqrt_one_minus_alpha_prod = np.sqrt(1 - self.get_alpha_prod(timesteps))
noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples4.4 图像解码与后处理
创建图像处理模块 image_processor.py：
python
# image_processor.py - 图像后处理和解码
import numpy as np
from PIL import Image
import cv2
from typing import List, Optional
class ImageProcessor:
"""图像处理器"""
def __init__(self, image_size: int = 512):
"""
初始化图像处理器
Args:
image_size: 图像尺寸
"""
self.image_size = image_size
self.mean = np.array([0.485, 0.456, 0.406])
self.std = np.array([0.229, 0.224, 0.225])
def decode_latents(self, vae_decoder, latents: np.ndarray) -> np.ndarray:
"""
解码潜在表示到图像
Args:
vae_decoder: VAE解码器
latents: 潜在表示
Returns:
解码后的图像
"""
# 使用VAE解码器进行解码
images = vae_decoder.decode(latents)
# 从CHW转换为HWC
images = np.transpose(images, (0, 2, 3, 1))
# 反归一化
images = (images / 2 + 0.5).clip(0, 1)
# 转换为0-255范围
images = (images * 255).astype(np.uint8)
return images
def numpy_to_pil(self, images: np.ndarray) -> List[Image.Image]:
"""
numpy数组转换为PIL图像
Args:
images: numpy图像数组
Returns:
PIL图像列表
"""
if images.ndim == 3:
images = images[np.newaxis, ...]
pil_images = []
for image in images:
pil_image = Image.fromarray(image)
pil_images.append(pil_image)
return pil_images
def resize_and_crop(self, image: Image.Image, size: int) -> Image.Image:
"""
调整大小和裁剪图像
Args:
image: 输入图像
size: 目标尺寸
Returns:
处理后的图像
"""
# 保持宽高比调整大小
width, height = image.size
if width > height:
new_width = int(width * size / height)
new_height = size
else:
new_width = size
new_height = int(height * size / width)
resized_image = image.resize((new_width, new_height), Image.LANCZOS)
# 中心裁剪
left = (new_width - size) / 2
top = (new_height - size) / 2
right = (new_width + size) / 2
bottom = (new_height + size) / 2
cropped_image = resized_image.crop((left, top, right, bottom))
return cropped_image
def apply_watermark(self, image: Image.Image) -> Image.Image:
"""
添加水印
Args:
image: 输入图像
Returns:
带水印的图像
"""
# 创建水印文本
watermark_text = "Generated with OpenVINO"
# 转换为OpenCV格式
cv_image = np.array(image)
if cv_image.shape[2] == 4: # RGBA
cv_image = cv2.cvtColor(cv_image, cv2.COLOR_RGBA2RGB)
# 添加文本水印
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.5
font_color = (255, 255, 255)
thickness = 1
# 获取文本尺寸
text_size = cv2.getTextSize(watermark_text, font, font_scale, thickness)[0]
# 计算文本位置（右下角）
text_x = image.width - text_size[0] - 10
text_y = image.height - 10
# 添加文本背景
cv2.rectangle(cv_image,
(text_x - 5, text_y - text_size[1] - 5),
(text_x + text_size[0] + 5, text_y + 5),
(0, 0, 0), -1)
# 添加文本
cv2.putText(cv_image, watermark_text, (text_x, text_y),
font, font_scale, font_color, thickness)
# 转换回PIL格式
return Image.fromarray(cv_image)
def create_grid(self, images: List[Image.Image], grid_size: Optional[tuple] = None) -> Image.Image:
"""
创建图像网格
Args:
images: 图像列表
grid_size: 网格尺寸 (rows, cols)
Returns:
网格图像
"""
if grid_size is None:
grid_size = (int(np.ceil(np.sqrt(len(images)))),
int(np.ceil(np.sqrt(len(images)))))
rows, cols = grid_size
width, height = images[0].size
# 创建空白网格图像
grid_image = Image.new('RGB', (cols * width, rows * height))
# 填充网格
for i, image in enumerate(images):
row = i // cols
col = i % cols
grid_image.paste(image, (col * width, row * height))
return grid_image5. 完整应用部署
5.1 命令行界面开发
创建主应用程序 main.py：
python
# main.py - Stable Diffusion文生图主应用程序
import argparse
import numpy as np
from pathlib import Path
from typing import List, Optional
import time
from PIL import Image
from model_manager import OpenVINOModelManager, TextEncoderWrapper, UNetWrapper, VAEDecoderWrapper
from text_processor import TextProcessor
from diffusion_scheduler import DiffusionScheduler
from image_processor import ImageProcessor
class StableDiffusionApp:
"""Stable Diffusion应用程序"""
def __init__(self, model_dir: str = "./models/openvino", device: str = "CPU"):
"""
初始化应用程序
Args:
model_dir: 模型目录
device: 推理设备
"""
self.model_dir = model_dir
self.device = device
# 初始化组件
self.model_manager = None
self.text_encoder = None
self.unet = None
self.vae_decoder = None
self.text_processor = None
self.scheduler = None
self.image_processor = None
# 状态标志
self.is_initialized = False
def initialize(self):
"""初始化所有组件"""
print("正在初始化Stable Diffusion应用程序...")
start_time = time.time()
try:
# 初始化模型管理器
self.model_manager = OpenVINOModelManager(self.model_dir, self.device)
if not self.model_manager.load_all_models():
raise RuntimeError("模型加载失败")
# 初始化模型封装器
self.text_encoder = TextEncoderWrapper(self.model_manager)
self.unet = UNetWrapper(self.model_manager)
self.vae_decoder = VAEDecoderWrapper(self.model_manager)
# 初始化处理器
self.text_processor = TextProcessor()
self.scheduler = DiffusionScheduler(num_inference_steps=20) # 减少步数以加快速度
self.image_processor = ImageProcessor()
self.is_initialized = True
init_time = time.time() - start_time
print(f"应用程序初始化完成，耗时: {init_time:.2f}秒")
except Exception as e:
print(f"初始化失败: {str(e)}")
self.is_initialized = False
def generate_image(self, prompt: str, negative_prompt: Optional[str] = None,
num_images: int = 1, seed: Optional[int] = None,
guidance_scale: float = 7.5) -> List[Image.Image]:
"""
生成图像
Args:
prompt: 正向提示
negative_prompt: 负向提示
num_images: 生成图像数量
seed: 随机种子
guidance_scale: 引导尺度
Returns:
生成的图像列表
"""
if not self.is_initialized:
raise RuntimeError("应用程序未初始化")
print(f"正在生成图像: {prompt}")
start_time = time.time()
# 设置随机种子
if seed is not None:
np.random.seed(seed)
generator = np.random.default_rng(seed)
try:
# 编码文本提示
print("编码文本提示...")
prompt_embeds = self.text_processor.encode_text(self.text_encoder, prompt)
if negative_prompt:
negative_embeds = self.text_processor.encode_text(self.text_encoder, negative_prompt)
# 合并正向和负向提示
text_embeddings = np.concatenate([negative_embeds, prompt_embeds], axis=0)
else:
text_embeddings = prompt_embeds
# 初始化潜在噪声
print("初始化潜在噪声...")
batch_size = num_images
height = 512 // 8 # VAE缩放因子
width = 512 // 8
latents = generator.normal(
size=(batch_size, 4, height, width)
).astype(np.float32)
# 扩散过程
print("开始扩散过程...")
self.scheduler.timesteps = self.scheduler.timesteps
for i, t in enumerate(self.scheduler.timesteps):
print(f"扩散步数: {i+1}/{len(self.scheduler.timesteps)}")
# 扩展潜在噪声以匹配批大小
latent_model_input = np.concatenate([latents] * 2) if negative_prompt else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# 预测噪声
noise_pred = self.unet.predict_noise(
latent_model_input,
np.array([t], dtype=np.int64),
text_embeddings
)
# 执行引导
if negative_prompt:
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# 更新潜在表示
latents = self.scheduler.step(noise_pred, latents, t, generator)
# 解码图像
print("解码图像...")
images = self.image_processor.decode_latents(self.vae_decoder, latents)
pil_images = self.image_processor.numpy_to_pil(images)
# 添加水印
pil_images = [self.image_processor.apply_watermark(img) for img in pil_images]
gen_time = time.time() - start_time
print(f"图像生成完成，耗时: {gen_time:.2f}秒")
return pil_images
except Exception as e:
print(f"图像生成失败: {str(e)}")
raise
def save_images(self, images: List[Image.Image], output_dir: str,
base_name: str = "generated"):
"""
保存生成的图像
Args:
images: 图像列表
output_dir: 输出目录
base_name: 基础文件名
"""
output_path = Path(output_dir)
output_path.mkdir(exist_ok=True)
saved_paths = []
for i, image in enumerate(images):
filename = f"{base_name}_{i+1:03d}.png"
filepath = output_path / filename
image.save(filepath)
saved_paths.append(filepath)
print(f"已保存: {filepath}")
# 创建网格图像
if len(images) > 1:
grid_image = self.image_processor.create_grid(images)
grid_path = output_path / f"{base_name}_grid.png"
grid_image.save(grid_path)
print(f"已保存网格: {grid_path}")
saved_paths.append(grid_path)
return saved_paths
def main():
"""主函数"""
parser = argparse.ArgumentParser(description='Stable Diffusion文生图应用程序')
parser.add_argument('--prompt', type=str, required=True,
help='文本提示')
parser.add_argument('--negative-prompt', type=str, default="",
help='负向提示')
parser.add_argument('--num-images', type=int, default=1,
help='生成图像数量')
parser.add_argument('--seed', type=int, default=None,
help='随机种子')
parser.add_argument('--guidance-scale', type=float, default=7.5,
help='引导尺度')
parser.add_argument('--output-dir', type=str, default="./output",
help='输出目录')
parser.add_argument('--model-dir', type=str, default="./models/openvino",
help='模型目录')
parser.add_argument('--device', type=str, default="CPU",
help='推理设备 (CPU, GPU, etc.)')
args = parser.parse_args()
# 创建应用程序实例
app = StableDiffusionApp(args.model_dir, args.device)
# 初始化
app.initialize()
if not app.is_initialized:
print("应用程序初始化失败")
return
# 生成图像
try:
images = app.generate_image(
prompt=args.prompt,
negative_prompt=args.negative_prompt if args.negative_prompt else None,
num_images=args.num_images,
seed=args.seed,
guidance_scale=args.guidance_scale
)
# 保存图像
saved_paths = app.save_images(images, args.output_dir)
print(f"生成完成！共保存 {len(saved_paths)} 个文件")
except Exception as e:
print(f"生成过程中出错: {str(e)}")
if __name__ == "__main__":
main()5.2 Web服务接口
创建Web服务 web_app.py：
python
# web_app.py - Stable Diffusion Web服务
from flask import Flask, request, jsonify, send_file
import io
import base64
from PIL import Image
import numpy as np
import threading
import time
from typing import Dict, Any
from main import StableDiffusionApp
app = Flask(__name__)
sd_app = None
request_queue = []
request_lock = threading.Lock()
processing = False
def initialize_app():
"""初始化应用程序"""
global sd_app
print("正在初始化Stable Diffusion Web服务...")
sd_app = StableDiffusionApp()
sd_app.initialize()
print("Web服务初始化完成")
@app.before_first_request
def before_first_request():
"""在第一个请求前初始化"""
init_thread = threading.Thread(target=initialize_app)
init_thread.start()
@app.route('/generate', methods=['POST'])
def generate_image():
"""生成图像API"""
if not sd_app or not sd_app.is_initialized:
return jsonify({'error': '服务未就绪'}), 503
try:
data = request.get_json()
prompt = data.get('prompt', '')
negative_prompt = data.get('negative_prompt', '')
num_images = data.get('num_images', 1)
seed = data.get('seed', None)
guidance_scale = data.get('guidance_scale', 7.5)
if not prompt:
return jsonify({'error': '提示不能为空'}), 400
# 生成图像
images = sd_app.generate_image(
prompt=prompt,
negative_prompt=negative_prompt if negative_prompt else None,
num_images=num_images,
seed=seed,
guidance_scale=guidance_scale
)
# 准备响应
response_data = {
'prompt': prompt,
'num_generated': len(images),
'images': []
}
# 转换图像为base64
for i, img in enumerate(images):
img_io = io.BytesIO()
img.save(img_io, 'PNG')
img_io.seek(0)
img_base64 = base64.b64encode(img_io.getvalue()).decode('utf-8')
response_data['images'].append({
'index': i + 1,
'data': f"data:image/png;base64,{img_base64}"
})
return jsonify(response_data)
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/status', methods=['GET'])
def get_status():
"""获取服务状态"""
status = {
'initialized': sd_app is not None and sd_app.is_initialized,
'device': sd_app.device if sd_app else 'unknown',
'model_loaded': sd_app is not None
}
return jsonify(status)
@app.route('/health', methods=['GET'])
def health_check():
"""健康检查"""
return jsonify({'status': 'ok'})
if __name__ == '__main__':
# 先初始化应用程序
initialize_app()
# 启动Web服务
app.run(host='0.0.0.0', port=5000, threaded=True)用户请求 Web服务器 认证与验证 文本编码 扩散过程 图像解码 后处理 返回结果
6. 测试与性能评估
6.1 功能测试用例
创建测试脚本 test_sd.py：
python
# test_sd.py - 功能测试脚本
import unittest
import numpy as np
from PIL import Image
import os
from pathlib import Path
from main import StableDiffusionApp
from model_manager import OpenVINOModelManager
from text_processor import TextProcessor
class TestStableDiffusion(unittest.TestCase):
"""Stable Diffusion功能测试"""
@classmethod
def setUpClass(cls):
"""测试类设置"""
cls.app = StableDiffusionApp()
cls.app.initialize()
def test_text_encoding(self):
"""测试文本编码"""
print("测试文本编码...")
# 测试文本预处理
text_processor = TextProcessor()
input_ids, attention_mask = text_processor.preprocess_text("a beautiful landscape")
self.assertEqual(input_ids.shape, (1, 77))
self.assertEqual(attention_mask.shape, (1, 77))
# 测试文本编码
embeddings = self.app.text_encoder.encode(input_ids)
self.assertEqual(embeddings.shape, (1, 77, 768))
print("文本编码测试通过")
def test_latent_generation(self):
"""测试潜在噪声生成"""
print("测试潜在噪声生成...")
generator = np.random.default_rng(42)
latents = generator.normal(size=(1, 4, 64, 64)).astype(np.float32)
self.assertEqual(latents.shape, (1, 4, 64, 64))
self.assertAlmostEqual(np.mean(latents), 0.0, delta=0.1)
print("潜在噪声生成测试通过")
def test_image_generation(self):
"""测试图像生成"""
print("测试图像生成...")
# 生成简单图像
try:
images = self.app.generate_image(
prompt="a simple test image",
num_images=1,
seed=42,
guidance_scale=7.5
)
self.assertEqual(len(images), 1)
self.assertIsInstance(images[0], Image.Image)
self.assertEqual(images[0].size, (512, 512))
# 保存测试图像
test_dir = Path("./test_output")
test_dir.mkdir(exist_ok=True)
images[0].save(test_dir / "test_image.png")
print("图像生成测试通过")
except Exception as e:
self.fail(f"图像生成失败: {str(e)}")
def test_batch_generation(self):
"""测试批量生成"""
print("测试批量生成...")
try:
images = self.app.generate_image(
prompt="a beautiful sunset",
num_images=4,
seed=123,
guidance_scale=7.5
)
self.assertEqual(len(images), 4)
for img in images:
self.assertIsInstance(img, Image.Image)
self.assertEqual(img.size, (512, 512))
print("批量生成测试通过")
except Exception as e:
self.fail(f"批量生成失败: {str(e)}")
def run_performance_test():
"""运行性能测试"""
print("运行性能测试...")
app = StableDiffusionApp()
app.initialize()
# 测试不同提示的性能
test_prompts = [
"a cat sitting on a chair",
"a beautiful landscape with mountains and lakes",
"a futuristic city with flying cars"
]
results = []
for prompt in test_prompts:
print(f"测试提示: {prompt}")
start_time = time.time()
images = app.generate_image(
prompt=prompt,
num_images=1,
seed=42,
guidance_scale=7.5
)
end_time = time.time()
generation_time = end_time - start_time
results.append({
'prompt': prompt,
'time': generation_time,
'success': len(images) > 0
})
print(f"生成时间: {generation_time:.2f}秒")
# 输出性能报告
print("\n性能测试结果:")
print("=" * 50)
for result in results:
status = "成功" if result['success'] else "失败"
print(f"{result['prompt']}: {result['time']:.2f}秒 ({status})")
avg_time = sum(r['time'] for r in results) / len(results)
print(f"\n平均生成时间: {avg_time:.2f}秒")
if __name__ == '__main__':
# 运行单元测试
print("运行单元测试...")
unittest.main(exit=False)
# 运行性能测试
run_performance_test()7. 常见问题与解决方案
7.1 内存不足处理
问题描述: 在内存有限的设备上运行时出现内存不足错误。
解决方案:
- 使用模型量化减少内存占用
- 启用内存交换
- 减少批处理大小
创建内存优化脚本 memory_optimizer.py：
python
# memory_optimizer.py - 内存优化工具
import psutil
import gc
import numpy as np
from typing import Optional
class MemoryOptimizer:
"""内存优化器"""
def __init__(self, max_memory_usage: float = 0.8):
"""
初始化内存优化器
Args:
max_memory_usage: 最大内存使用比例
"""
self.max_memory_usage = max_memory_usage
self.total_memory = psutil.virtual_memory().total
def get_memory_usage(self) -> float:
"""获取当前内存使用率"""
return psutil.virtual_memory().percent / 100
def is_memory_available(self, required_mb: int) -> bool:
"""
检查是否有足够内存
Args:
required_mb: 需要的内存(MB)
Returns:
是否有足够内存
"""
available_memory = psutil.virtual_memory().available
return available_memory >= required_mb * 1024 * 1024
def optimize_memory(self):
"""优化内存使用"""
print("优化内存使用...")
# 强制执行垃圾回收
gc.collect()
# 清空numpy缓存
try:
np.clear_cache()
except AttributeError:
pass
# 检查当前内存使用
current_usage = self.get_memory_usage()
print(f"当前内存使用率: {current_usage:.2%}")
if current_usage > self.max_memory_usage:
print("内存使用过高，建议减少批处理大小或使用模型量化")
def calculate_batch_size(self, model_size_mb: int, available_memory_mb: int) -> int:
"""
计算合适的批处理大小
Args:
model_size_mb: 模型大小(MB)
available_memory_mb: 可用内存(MB)
Returns:
推荐的批处理大小
"""
# 估计每个样本所需内存
per_sample_memory = model_size_mb * 2 # 保守估计
# 计算最大批处理大小
max_batch_size = max(1, available_memory_mb // per_sample_memory)
print(f"模型大小: {model_size_mb}MB")
print(f"可用内存: {available_memory_mb}MB")
print(f"推荐批处理大小: {max_batch_size}")
return max_batch_size
# 使用示例
if __name__ == '__main__':
optimizer = MemoryOptimizer()
# 检查内存状态
optimizer.optimize_memory()
# 计算合适的批处理大小
model_size = 500 # MB
available_memory = psutil.virtual_memory().available / (1024 * 1024)
batch_size = optimizer.calculate_batch_size(model_size, available_memory)
print(f"建议批处理大小: {batch_size}")8. 成果展示与应用场景
8.1 生成效果示例
以下是使用本教程实现的Stable Diffusion在边缘设备上生成的一些示例图像：
示例1 : "一座被雪山环绕的美丽湖泊，晨光照射"
示例2 : "未来城市景观，飞行汽车，霓虹灯"
示例3: "梵高风格的星空下的咖啡馆"
8.2 实际应用案例
- 智能相册生成: 在手机或平板电脑上离线生成个性化图像
- 教育辅助: 在教室环境中实时生成教学插图
- 设计原型: 快速生成设计概念和原型图像
- 艺术创作: 数字艺术家在移动设备上进行创作
技术图谱
OpenVINO SDK 模型优化 硬件加速 跨平台部署 量化压缩 图优化 层融合 CPU优化 GPU加速 VPU支持 Windows Linux macOS 边缘设备 FP16量化 INT8量化 常量折叠 死代码消除 Conv+ReLU融合 BatchNorm融合
通过本教程，您已经学会了如何在边缘设备上使用OpenVINO部署和运行Stable Diffusion模型，实现完全离线的文本到图像生成功能。这个解决方案不仅保护了数据隐私，还提供了低延迟的图像生成体验，非常适合在各种边缘计算场景中应用。