Vertex AI 影片生成（Veo）
LiteLLM 支援 Vertex AI 的 Veo 影片生成模型，使用統一的 OpenAI 影片 API 介面。
| 屬性 | 詳細資訊 |
|---|---|
| 說明 | Google Cloud Vertex AI Veo 影片生成模型 |
| LiteLLM 上的提供者路由 | vertex_ai/ |
| 支援的模型 | veo-2.0-generate-001, veo-3.0-generate-preview, veo-3.0-fast-generate-preview, veo-3.1-generate-preview, veo-3.1-fast-generate-preview |
| 成本追蹤 | ✅ 依期間計費 |
| 記錄支援 | ✅ 完整的請求/回應記錄 |
| 代理伺服器支援 | ✅ 完整的代理整合，支援虛擬金鑰 |
| 支出管理 | ✅ 預算追蹤與速率限制 |
| 提供者文件連結 | Vertex AI Veo 文件 ↗ |
快速開始
必要的環境設定
import json
import os
os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
os.environ["VERTEXAI_LOCATION"] = "us-central1"
# Option 1: Point to a service account file
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/service_account.json"
# Option 2: Store the service account JSON directly
with open("/path/to/service_account.json", "r", encoding="utf-8") as f:
os.environ["VERTEXAI_CREDENTIALS"] = f.read()
基本使用方式
from litellm import video_generation, video_status, video_content
import json
import os
import time
with open("/path/to/service_account.json", "r", encoding="utf-8") as f:
vertex_credentials = f.read()
response = video_generation(
model="vertex_ai/veo-3.0-generate-preview",
prompt="A cat playing with a ball of yarn in a sunny garden",
vertex_project="your-gcp-project-id",
vertex_location="us-central1",
vertex_credentials=vertex_credentials,
seconds="8",
size="1280x720",
)
print(f"Video ID: {response.id}")
print(f"Initial Status: {response.status}")
# Poll for completion
while True:
status = video_status(
video_id=response.id,
vertex_project="your-gcp-project-id",
vertex_location="us-central1",
vertex_credentials=vertex_credentials,
)
print(f"Current Status: {status.status}")
if status.status == "completed":
break
if status.status == "failed":
raise RuntimeError("Video generation failed")
time.sleep(10)
# Download the rendered video
video_bytes = video_content(
video_id=response.id,
vertex_project="your-gcp-project-id",
vertex_location="us-central1",
vertex_credentials=vertex_credentials,
)
with open("generated_video.mp4", "wb") as f:
f.write(video_bytes)
支援的模型
| 模型名稱 | 說明 | 最長時間 | 狀態 |
|---|---|---|---|
| veo-2.0-generate-001 | Veo 2.0 影片生成 | 5 秒 | GA |
| veo-3.0-generate-preview | Veo 3.0 高品質 | 8 秒 | 預覽 |
| veo-3.0-fast-generate-preview | Veo 3.0 快速生成 | 8 秒 | 預覽 |
| veo-3.1-generate-preview | Veo 3.1 高品質 | 10 秒 | 預覽 |
| veo-3.1-fast-generate-preview | Veo 3.1 快速 | 10 秒 | 預覽 |
影片生成參數
LiteLLM 會自動將 OpenAI 風格的參數轉換為 Veo 的 API 格式：
| OpenAI 參數 | Vertex AI 參數 | 說明 | 範例 |
|---|---|---|---|
prompt | instances[].prompt | 影片的文字描述 | "A cat playing" |
size | parameters.aspectRatio | 轉換為 16:9 或 9:16 | "1280x720" → 16:9 |
seconds | parameters.durationSeconds | 片長（秒） | "8" → 8 |
input_reference | instances[].image | 用於動畫的參考圖片 | open("image.jpg", "rb") |
| 特定於提供者的參數 | extra_body | 轉送至 Vertex API | {"negativePrompt": "blurry"} |
尺寸到長寬比對應
1280x720,1920x1080→16:9720x1280,1080x1920→9:16- 未知尺寸預設為
16:9
非同步使用方式
from litellm import avideo_generation, avideo_status, avideo_content
import asyncio
import json
with open("/path/to/service_account.json", "r", encoding="utf-8") as f:
vertex_credentials = f.read()
async def workflow():
response = await avideo_generation(
model="vertex_ai/veo-3.1-generate-preview",
prompt="Slow motion water droplets splashing into a pool",
seconds="10",
vertex_project="your-gcp-project-id",
vertex_location="us-central1",
vertex_credentials=vertex_credentials,
)
while True:
status = await avideo_status(
video_id=response.id,
vertex_project="your-gcp-project-id",
vertex_location="us-central1",
vertex_credentials=vertex_credentials,
)
if status.status == "completed":
break
if status.status == "failed":
raise RuntimeError("Video generation failed")
await asyncio.sleep(10)
video_bytes = await avideo_content(
video_id=response.id,
vertex_project="your-gcp-project-id",
vertex_location="us-central1",
vertex_credentials=vertex_credentials,
)
with open("veo_water.mp4", "wb") as f:
f.write(video_bytes)
asyncio.run(workflow())
LiteLLM 代理使用方式
將 Veo 模型加入您的 config.yaml：
model_list:
- model_name: veo-3
litellm_params:
model: vertex_ai/veo-3.0-generate-preview
vertex_project: os.environ/VERTEXAI_PROJECT
vertex_location: os.environ/VERTEXAI_LOCATION
vertex_credentials: os.environ/VERTEXAI_CREDENTIALS
啟動代理並發出請求：
- Curl
- Python SDK
# Step 1: Generate video
curl --location 'http://0.0.0.0:4000/videos' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer sk-1234' \
--data '{
"model": "veo-3",
"prompt": "Aerial shot over a futuristic city at sunrise",
"seconds": "8"
}'
# Step 2: Poll status
curl --location 'http://localhost:4000/v1/videos/{video_id}' \
--header 'x-litellm-api-key: sk-1234'
# Step 3: Download video
curl --location 'http://localhost:4000/v1/videos/{video_id}/content' \
--header 'x-litellm-api-key: sk-1234' \
--output video.mp4
import litellm
litellm.api_base = "http://0.0.0.0:4000"
litellm.api_key = "sk-1234"
response = litellm.video_generation(
model="veo-3",
prompt="Aerial shot over a futuristic city at sunrise",
)
status = litellm.video_status(video_id=response.id)
while status.status not in ["completed", "failed"]:
status = litellm.video_status(video_id=response.id)
if status.status == "completed":
content = litellm.video_content(video_id=response.id)
with open("veo_city.mp4", "wb") as f:
f.write(content)
成本追蹤
LiteLLM 會記錄 Veo 回傳的期間，讓您可以套用依期間計費。
with open("/path/to/service_account.json", "r", encoding="utf-8") as f:
vertex_credentials = f.read()
response = video_generation(
model="vertex_ai/veo-2.0-generate-001",
prompt="Flowers blooming in fast forward",
seconds="5",
vertex_project="your-gcp-project-id",
vertex_location="us-central1",
vertex_credentials=vertex_credentials,
)
print(response.usage) # {"duration_seconds": 5.0}
疑難排解
vertex_project is required：設定VERTEXAI_PROJECT環境變數，或在請求中傳入vertex_project。Permission denied：請確認服務帳戶具有Vertex AI User角色，且已啟用正確的區域。- 影片卡在
processing：Veo 作業屬於長時間執行。請持續每 10–15 秒輪詢一次，最長約 10 分鐘。