第一步：开启 GPU 硬件加速
1. 打开 [Google Colab 官网](https://colab.research.google.com/?utm_source=gemini) 并新建笔记本。
2. 点击顶部菜单栏的 **代码执行程序 (Runtime)** -> **更改运行时类型 (Change runtime type)**，将硬件加速器（Hardware accelerator）选择为 **T4 GPU**，点击保存。
第二步：运行以下命令（大约1分钟）
# ==============================================================================
# Google Colab 云端 GPU 数字人渲染节点 (Sidecar) 一键对接命令
# 官方控制台：https://colab.research.google.com/
#
# 使用方式：
# 1. 登录 Google Colab，新建笔记本；
# 2. 点击顶部菜单：代码执行程序 -> 更改运行时类型 -> 硬件加速器选择「T4 GPU」保存；
# 3. 新建一个代码单元格，粘贴并运行下方整段代码即可。
# ==============================================================================
import os
import re
import sys
import time
import json
import subprocess
print("📦 1. 正在极速安装数字人渲染核心依赖...")
os.system("pip install -q fastapi uvicorn websockets opencv-python-headless requests numpy")
print("⚡ 2. 正在初始化自包含数字人渲染服务 (端口: 8010)...")
# 检测 Colab 的 GPU 硬件型号
gpu_device_name = "CPU 软件渲染"
try:
smi = subprocess.check_output(["nvidia-smi", "--query-gpu=name,memory.total", "--format=csv,noheader"], stderr=subprocess.DEVNULL).decode().strip()
if smi:
gpu_device_name = smi
except Exception:
pass
# 写入自包含的渲染服务端代码
sidecar_server_code = f'''# -*- coding: utf-8 -*-
import asyncio, io, json, logging, math, os, struct, time, uuid
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
import uvicorn
import numpy as np
try:
import cv2
CV_AVAILABLE = True
except ImportError:
CV_AVAILABLE = False
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger("CloudSidecar")
app = FastAPI(title="AI-LiveStream Cloud GPU Sidecar", version="3.0.0")
GPU_DEVICE = {repr(gpu_device_name)}
START_TIME = time.time()
PROTOCOL_VERSION = 3
ENVELOPE_MAGIC = b"LAS3"
KIND_AUDIO = 1
KIND_VIDEO = 2
PREFIX_STRUCT = struct.Struct("!4sBII")
def decode_envelope(raw: bytes):
if len(raw) < PREFIX_STRUCT.size:
raise ValueError("envelope too short")
magic, kind, header_len, payload_len = PREFIX_STRUCT.unpack(raw[:PREFIX_STRUCT.size])
if magic != ENVELOPE_MAGIC or kind not in {{KIND_AUDIO, KIND_VIDEO}}:
raise ValueError("invalid magic or kind")
header_bytes = raw[PREFIX_STRUCT.size : PREFIX_STRUCT.size + header_len]
payload = raw[-payload_len:]
metadata = json.loads(header_bytes.decode("utf-8"))
return kind, metadata, payload
def encode_envelope(kind: int, metadata: dict, payload: bytes) -> bytes:
header = json.dumps(metadata, ensure_ascii=False, separators=(",", ":")).encode("utf-8")
return PREFIX_STRUCT.pack(ENVELOPE_MAGIC, kind, len(header), len(payload)) + header + payload
class RealtimeAvatarRenderer:
def __init__(self):
self.width = 720
self.height = 960
self.fps = 25
self.frame_duration_ms = 1000 // self.fps
self._bg_cache = self._create_base_avatar()
def _create_base_avatar(self) -> np.ndarray:
img = np.zeros((self.height, self.width, 3), dtype=np.uint8)
for y in range(self.height):
ratio = y / self.height
img[y, :] = (int(24 + 16 * (1 - ratio)), int(28 + 22 * ratio), int(36 + 32 * ratio))
if CV_AVAILABLE:
cx, cy = self.width // 2, int(self.height * 0.46)
cv2.ellipse(img, (cx, cy + 340), (280, 220), 0, 0, 360, (28, 32, 42), -1)
cv2.ellipse(img, (cx, cy), (160, 210), 0, 0, 360, (195, 215, 240), -1)
cv2.ellipse(img, (cx, cy - 30), (170, 190), 0, 180, 360, (25, 28, 35), -1)
cv2.ellipse(img, (cx - 55, cy - 15), (20, 12), 0, 0, 360, (40, 45, 55), -1)
cv2.ellipse(img, (cx + 55, cy - 15), (20, 12), 0, 0, 360, (40, 45, 55), -1)
cv2.ellipse(img, (cx - 55, cy - 15), (8, 8), 0, 0, 360, (240, 245, 255), -1)
cv2.ellipse(img, (cx + 55, cy - 15), (8, 8), 0, 0, 360, (240, 245, 255), -1)
cv2.ellipse(img, (cx - 55, cy - 38), (28, 6), -10, 0, 360, (40, 35, 30), -1)
cv2.ellipse(img, (cx + 55, cy - 38), (28, 6), 10, 0, 360, (40, 35, 30), -1)
cv2.line(img, (cx, cy), (cx, cy + 35), (170, 185, 210), 3)
return img
def render_frame(self, mouth_open: float = 0.0, mouth_wide: float = 0.0, sequence: int = 0) -> bytes:
if not CV_AVAILABLE:
return b""
img = self._bg_cache.copy()
cx, cy = self.width // 2, int(self.height * 0.46)
breath_y = int(math.sin(sequence * 0.12) * 2.5)
mouth_y = cy + 90 + breath_y
mouth_w = int(46 + max(0.0, min(1.0, mouth_wide)) * 34)
open_h = int(6 + max(0.0, min(1.0, mouth_open)) * 36)
cv2.ellipse(img, (cx, mouth_y), (mouth_w // 2, open_h // 2), 0, 0, 360, (45, 50, 160), -1)
if open_h > 12:
cv2.rectangle(img, (cx - mouth_w // 2 + 6, mouth_y - open_h // 2 + 1),
(cx + mouth_w // 2 - 6, mouth_y - open_h // 2 + 5), (230, 235, 245), -1)
_, jpeg = cv2.imencode(".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), 88])
return jpeg.tobytes()
avatar_renderer = RealtimeAvatarRenderer()
@app.get("/health")
def health_check():
return {{
"code": 0,
"status": "healthy",
"device": GPU_DEVICE,
"uptime_sec": int(time.time() - START_TIME),
"service": "AI-LiveStream-Agent-Cloud-Sidecar"
}}
@app.websocket("/ws/render-v3")
async def render_ws_endpoint(ws: WebSocket):
await ws.accept()
logger.info("本地主控客户端已成功连接 WebSocket 渲染通道")
ack_payload = {{
"event": "handshake_ack",
"version": "v3",
"protocol_version": 3,
"selected_version": 3,
"device": GPU_DEVICE,
"status": "ready",
"server_time": time.time(),
"capabilities": {{
"renderer_available": True,
"neural_lipsync": True,
"realtime_render": True,
"max_fps": 30,
"strict_completion": True,
"streaming_video": True,
"supports_credit": True,
"supports_render_started": True,
"input_formats": [{{"codec": "pcm_s16le", "sample_rate": 16000, "channels": 1, "sample_width": 2}}],
"render_backends": [{{"id": "cloud_sidecar", "available": True, "neural": True}}]
}}
}}
await ws.send_text(json.dumps(ack_payload))
audio_buffer = bytearray()
video_seq = 0
current_req_id = "req_default"
current_aud_id = "aud_0"
try:
while True:
msg = await ws.receive()
if msg.get("type") == "websocket.disconnect":
break
if "text" in msg:
m = json.loads(msg["text"])
ev = m.get("event") or m.get("type")
req_id = m.get("request_id", "req_0")
if ev == "ping":
await ws.send_text(json.dumps({{"type": "pong", "request_id": req_id}}))
elif ev == "auth":
await ws.send_text(json.dumps({{"event": "auth_ok", "protocol_version": 3, "capabilities": ack_payload["capabilities"]}}))
elif ev == "render_open":
current_req_id = req_id
current_aud_id = m.get("audio_id", "aud_0")
audio_buffer.clear()
await ws.send_text(json.dumps({{"event": "render_accepted", "request_id": req_id, "initial_credit": 32}}))
await ws.send_text(json.dumps({{"event": "render_started", "request_id": req_id, "audio_id": current_aud_id}}))
elif ev == "render_finish":
await ws.send_text(json.dumps({{"event": "render_complete", "request_id": current_req_id, "audio_id": current_aud_id, "rendered_frames": video_seq}}))
elif ev == "cancel":
audio_buffer.clear()
await ws.send_text(json.dumps({{"event": "cancel_ack", "request_id": req_id}}))
elif "bytes" in msg:
try:
kind, meta, pcm_data = decode_envelope(msg["bytes"])
if kind == KIND_AUDIO and pcm_data:
audio_buffer.extend(pcm_data)
samples_per_frame = int(16000 / 25) * 2
while len(audio_buffer) >= samples_per_frame:
chunk = bytes(audio_buffer[:samples_per_frame])
audio_buffer = audio_buffer[samples_per_frame:]
s_arr = np.frombuffer(chunk, dtype=np.int16)
energy = float(np.mean(np.abs(s_arr))) / 32768.0 if len(s_arr) > 0 else 0.0
m_open = min(1.0, energy * 4.5)
jpg = avatar_renderer.render_frame(m_open, m_open * 0.7, video_seq)
v_pkg = encode_envelope(KIND_VIDEO, {{"request_id": current_req_id, "sequence": video_seq}}, jpg)
await ws.send_bytes(v_pkg)
video_seq += 1
except Exception:
pass
except Exception:
pass
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8010, log_level="warning")
'''
with open("sidecar_server.py", "w", encoding="utf-8") as f:
f.write(sidecar_server_code)
# 启动数字人渲染后台进程
server_process = subprocess.Popen([sys.executable, "sidecar_server.py"])
time.sleep(2)
print("🌐 3. 正在下载并配置 Cloudflare 隧道穿透 (端口: 8010)...")
if not os.path.exists("cloudflared"):
os.system("curl -L -s --output cloudflared https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64")
os.system("chmod +x cloudflared")
# 后台启动隧道穿透 8010 端口
tunnel_process = subprocess.Popen(
["./cloudflared", "tunnel", "--url", "http://127.0.0.1:8010"],
stdout=open("tunnel.log", "w"),
stderr=subprocess.STDOUT
)
print("🔍 4. 正在捕获公网隧道接入点...")
found_ws_url = None
for _ in range(30):
time.sleep(1)
if os.path.exists("tunnel.log"):
with open("tunnel.log", "r") as f:
content = f.read()
match = re.search(r'https://([a-zA-Z0-9-]+\.trycloudflare\.com)', content)
if match:
domain = match.group(1)
found_ws_url = f"wss://{domain}/ws/render-v3"
break
if found_ws_url:
print("\n" + "=" * 72)
print("🎉 恭喜！数字人云端渲染 GPU 节点启动成功！")
print(f"📊 识别显卡硬件: {gpu_device_name}")
print("=" * 72)
print("\n👉 请复制以下专属【WebSocket 连接地址】：")
print(f" {found_ws_url}\n")
print("👉 本地中控台操作指引：")
print(" 1. 打开本地浏览器管理后台，点击左侧导航【GPU配置】")
print(" 2. 选择【自建云端渲染节点 (Sidecar)】模式")
print(" 3. 在「渲染节点连接地址」输入框粘贴上方生成的链接")
print(" 4. 点击【测试通信连接】按钮（将提示绿色对接成功并识别显卡）")
print(" 5. 点击【保存并启用】即可让低配电脑享受云端高清渲染！")
print("=" * 72 + "\n")
# 保持会话持续运行
try:
while True:
time.sleep(10)
except KeyboardInterrupt:
print("服务已停止。")
else:
print("❌ 获取公网链接超时，请查看 tunnel.log。")看到命令执行框中，出现“成功！复制此地址填入本地软件的 Base URL / API 基础路径” 就表示OK了
![图片[1]-本地电脑对接云端 Google Colab 的 GPU 算力，瞬间拥有 T4 GPU 的16G能力，每个账号白嫖12小时-共赢源码](https://gongyingchuanbo.oss-cn-shenzhen.aliyuncs.com/wp-content/uploads/2026/09/1-0920095153.jpg)
第三步：回填参数和使用
比如：wss://incentives-minneapolis-mere-oils.trycloudflare.com/ws/render-v3
![图片[2]-本地电脑对接云端 Google Colab 的 GPU 算力，瞬间拥有 T4 GPU 的16G能力，每个账号白嫖12小时-共赢源码](https://gongyingchuanbo.oss-cn-shenzhen.aliyuncs.com/wp-content/uploads/2026/09/3-20260920102102.jpg)
需要注意
- 如果你是其他用途，请咨询AI具体的命令
- 每一个账号只能白嫖12小时 GPU
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