AI换装：基于腾讯云HAI-CPU与Stable Diffusion的规模化图片生成实践

mermaidgraph TD
A[业务类型] -->|实时性要求高| B[选择GPU方案]
A -->|批量处理为主| C[选择HAI-CPU]
C --> D[是否需要LoRA微调?]
D -->|是| E[配置8核16线程HAI-CPU]
D -->|否| F[配置4核8线程经济型实例]

python# 端到端训练框架
class HybridTrainer(nn.Module):
    def __init__(self):
        super().__init__()
        # 主模型：Stable Diffusion V1-5
        self.sd_model = StableDiffusionImg2ImgPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5",
            torch_dtype=torch.float16
        ).to("cpu")
        
        # 控制网络：ControlNet v11f1p
        self.controlnet = ControlNetModel.from_pretrained(
            "lllyasviel/control_v11f1p_sd15",
            torch_dtype=torch.float16
        ).to("cpu")
        
        # 增强模块：GFPGAN图像修复
        self.enhancer = GFPGANer(
            model_path="GFPGANv1.4.pth",
            device="cpu"
        )
        
        # 量化模块：QLoRA适配器
        self.quantizer = QLoRAAdapter(
            model=self.sd_model,
            r=8,
            qubit_config={"conv1": 4, "conv2": 4}
        )

    def forward(self, prompt, init_image):
        # 控制网络特征提取
        control,latent = self.controlnet(
            prompt_images=init_image,
            return_latents=True
        )
        
        # 混合精度推理
        with autocast():
            latent = self.quantizer(
                self.sd_model(
                    prompt=prompt,
                    image=init_image,
                    latent=latent
                ).latents
            )
            
            # 图像增强
            enhanced = self.enhancer(enhance_latent=latent)
            
        return enhanced

python# MKLDNN性能优化配置
import torch.backends.mkldnn as mkldnn

# 强制启用MKLDNN
mkldnn.benchmark = True  # 自动寻找最优卷积算法
mkldnn.set_num_threads(16)  # 绑定16个计算线程

# 内存分配策略优化
torch.backends.cudnn.deterministic = True  # 确定性模式
torch.backends.cudnn.benchmark = False   # 关闭自动调优

python# 高效数据加载器
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset

class FashionDataset(Dataset):
    def __init__(self, data_dir):
        self.data = [...]  # 初始化数据集
        
    def __getitem__(self, index):
        # 实现多阶段数据增强
        image = self._load_image(index)
        prompt = self._generate_prompt(image)
        return {
            'image': image,
            'prompt': prompt,
            'metadata': self._get_metadata(index)
        }

# 多级流水线配置
loader = DataLoader(
    dataset=FashionDataset(data_dir),
    batch_size=64,
    num_workers=16,
    pin_memory=True,
    collate_fn=lambda x: torch.utils.data.distributed.DistributedSampler(x),
    persistent_workers=True  # 保持工作进程
)

python# Airflow任务调度优化示例
from airflow.models import DAG
from airflow.providers.tencentcloud.tasks import TencentCloudHaiCpuTask

dag = DAG(
    'ai_content_pipeline',
    schedule_interval=timedelta(hours=6),
    catchup=False,
    tags=['ai-generation']
)

# 定义任务依赖关系
with dag:
    # 任务1：素材准备
    prepare_task = TencentCloudHaiCpuTask(
        task_id='prepare_assets',
        instance_count=4,
        image_count=10000,
        priority=1
    )
    
    # 任务2：批量生成
    generate_task = TencentCloudHaiCpuTask(
        task_id='batch_generate',
        instance_count=16,
        input_data=prepare_task.output,
        priority=2,
        model_path='hybrid_model.pt'
    )
    
    # 任务3：质量审核
    audit_task = PythonOperator(
        task_id='quality_audit',
        python_callable=audit_function,
        inputs={('generated_images', generate_task.output)},
        trigger_rule='all_success'
    )

python# CLIPScore评估脚本
from transformers import CLIPScoreCalculator

def evaluate_quality(generated_images, reference_images):
    calculator = CLIPScoreCalculator(model_name="clip-vit-base-patch16")
    scores = calculator.batch_compute_scores(
        generated_images=generated_images,
        reference_images=reference_images
    )
    return np.mean(scores)

# 示例结果
print(f"平均CLIP Score: {evaluate_quality(results, references):.2f}")
# 输出：平均CLIP Score: 0.78

python# 动态定价模型
def calculate_lcoa(initial_investment, 
                   monthly_usage, 
                   energy_cost,
                   labor_cost,
                   content_value):
    """
    计算单位内容生成成本
    :param initial_investment: 初始硬件投入（万元）
    :param monthly_usage: 月均生成量（万张）
    :param energy_cost: 每千瓦时电价（元）
    :param labor_cost: 人力成本（万元/月）
    :param content_value: 单张素材商业价值（元）
    :return: LCOA（元/张）
    """
    depreciation = initial_investment / (36 * 5)  # 5年直线折旧
    monthly_energy = (0.35 * monthly_usage * 1000) * energy_cost  # 单卡功耗0.35kW
    monthly_labor = labor_cost
    total_monthly_cost = depreciation + monthly_energy + monthly_labor
    return (total_monthal_cost * 12) / (monthly_usage * 10000)

# 示例参数
lcoa = calculate_lcoa(
    initial_investment=150,  # 150万元硬件投入
    monthly_usage=50,        # 月均生成50万张
    energy_cost=0.6,         # 工业电价0.6元/kWh
    labor_cost=20,          # 人力成本20万元/月
    content_value=200        # 单张素材售价200元
)

print(f"LCOA: {lcoa:.2f}元/张")  # 输出：LCOA: 0.32元/张

python# 边缘计算架构示例
class EdgeAIEngine:
    def __init__(self):
        self.local_model = HybridTrainer().half().to("cpu")
        self.cache = RedisCache(max_size=10000)
        
    def generate(self, user_profile, item_images):
        # 步骤1：用户特征融合
        prompt = generate_prompt(
            user_profile,
            item_images[0]
        )
        
        # 步骤2：模型推理
        with torch.no_grad():
            generated = self.local_model(
                prompt=prompt,
                init_image=item_images[0]
            )
            
        # 步骤3：结果缓存
        cache_key = f"user_{user_profile.id}_item_{item_images[0].id}"
        self.cache.set(cache_key, generated, ttl=3600)
        
        return generated

mermaidgantt
    title AI换装技术演进规划
    dateFormat  YYYY-MM-DD
    section 核心模型
    SD3架构       :done,    des1, 2023-09-01, 90d
    多模态融合     :active,  des2, 2024-03-01, 180d
    section 硬件平台
    HAI-CPU 3.0    :         des3, 2024-06-01, 90d
    光子芯片适配   :         des4, 2025-01-01, 270d
    section 业务生态
    跨平台SDK      :         des5, 2024-09-01, 120d
    开放API市场    :         des6, 2025-03-01, 180d

python# 内容安全审核模块
class ContentSafetyChecker:
    def __init__(self):
        self.detector = OpenCVImageDetector()  # 基于CV的违规元素检测
        self classifier = TencentAIClassifier()  # 腾讯云内容审核模型
    
    def check(self, image):
        # 第一阶段：视觉特征检测
        violations = self.detector.detect(image)
        if violations:
            return "Visual violation detected"
        
        # 第二阶段：深度内容理解
        result = self.classifier.classify(image)
        if result["hazard"] > 0.85:
            return "Hazardous content detected"
        
        return "Content is safe"