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HunyuanVideo-Foley部署案例：Kubernetes集群中HunyuanVideo-Foley服务编排

news 2026/4/19 5:06:48

HunyuanVideo-Foley部署案例：Kubernetes集群中HunyuanVideo-Foley服务编排

1. 镜像概述与核心特性

HunyuanVideo-Foley是一款专为视频生成与音效生成任务优化的私有部署镜像，基于RTX 4090D 24GB显存显卡和CUDA 12.4深度优化。该镜像内置完整的运行环境和加速库，提供开箱即用的视频与音效生成能力。

核心优化特性：

采用xFormers和FlashAttention加速技术，推理速度提升30%+
专为24GB显存设计的显存调度策略
低内存占用模型加载方案
预装所有依赖项，避免环境冲突
支持WebUI可视化界面和API服务两种部署方式

2. 环境准备与Kubernetes配置

2.1 硬件要求

在Kubernetes集群中部署HunyuanVideo-Foley服务前，需确保节点满足以下硬件配置：

GPU节点：至少1个RTX 4090D/4090显卡（24GB显存）
CPU：10核以上
内存：120GB以上
存储：
- 系统盘：50GB
- 数据盘：40GB（用于模型存储）

2.2 Kubernetes集群配置

# gpu-node.yaml apiVersion: v1 kind: Node metadata: labels: accelerator: nvidia-gpu spec: taints: - key: nvidia.com/gpu effect: NoSchedule

确保已安装NVIDIA设备插件：

kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.14.1/nvidia-device-plugin.yml

3. Kubernetes部署方案

3.1 创建持久化存储

# pvc.yaml apiVersion: v1 kind: PersistentVolumeClaim metadata: name: hunyuan-pvc spec: accessModes: - ReadWriteOnce resources: requests: storage: 40Gi

3.2 部署HunyuanVideo-Foley服务

# deployment.yaml apiVersion: apps/v1 kind: Deployment metadata: name: hunyuan-video spec: replicas: 1 selector: matchLabels: app: hunyuan template: metadata: labels: app: hunyuan spec: containers: - name: hunyuan-container image: hunyuan-video-foley:latest resources: limits: nvidia.com/gpu: "1" cpu: "10" memory: 120Gi requests: nvidia.com/gpu: "1" cpu: "10" memory: 120Gi volumeMounts: - mountPath: /workspace/output name: output-volume volumes: - name: output-volume persistentVolumeClaim: claimName: hunyuan-pvc tolerations: - key: "nvidia.com/gpu" operator: "Exists" effect: "NoSchedule"

3.3 暴露服务

# service.yaml apiVersion: v1 kind: Service metadata: name: hunyuan-service spec: type: NodePort ports: - port: 7860 targetPort: 7860 name: webui - port: 8000 targetPort: 8000 name: api selector: app: hunyuan

4. 服务访问与使用

4.1 访问WebUI界面

部署完成后，可通过以下方式访问WebUI界面：

kubectl port-forward svc/hunyuan-service 7860:7860

然后在浏览器中访问：http://localhost:7860

4.2 API调用示例

import requests url = "http://<cluster-ip>:8000/generate" payload = { "prompt": "生成一段雨林环境的音效", "duration": 10, "sample_rate": 44100 } response = requests.post(url, json=payload) with open("output.wav", "wb") as f: f.write(response.content)

5. 性能优化与监控

5.1 资源监控配置

# hpa.yaml apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: hunyuan-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: hunyuan-video minReplicas: 1 maxReplicas: 3 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70