OpenAI 影片生成

LiteLLM 支援 OpenAI 的影片生成模型，包括 Sora。

快速入門

所需 API 金鑰

import os 
os.environ["OPENAI_API_KEY"] = "your-api-key"

基本用法

from litellm import video_generation, video_content
import os

os.environ["OPENAI_API_KEY"] = "your-api-key"

# Generate a video
response = video_generation(
    prompt="A cat playing with a ball of yarn in a sunny garden",
    model="sora-2",
    seconds="8",
    size="720x1280"
)

print(f"Video ID: {response.id}")
print(f"Status: {response.status}")

# Download video content when ready
video_bytes = video_content(
    video_id=response.id,
)

# Save to file
with open("generated_video.mp4", "wb") as f:
    f.write(video_bytes)

LiteLLM Proxy 用法

LiteLLM 提供與 OpenAI API 相容的影片端點，涵蓋完整的影片生成工作流程：

• /videos/generations - 生成新影片
• /videos/remix - 使用參考圖片編輯既有影片
• /videos/status - 檢查影片生成狀態
• /videos/retrieval - 下載完成的影片

設定

將以下內容加入您的 litellm proxy config.yaml

model_list:
  - model_name: sora-2
    litellm_params:
      model: openai/sora-2
      api_key: os.environ/OPENAI_API_KEY

啟動 litellm

litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

測試影片生成請求

curl --location 'http://localhost:4000/v1/videos' \
--header 'Content-Type: application/json' \
--header 'x-litellm-api-key: sk-1234' \
--data '{
    "model": "sora-2",
    "prompt": "A beautiful sunset over the ocean"
}'

測試影片狀態請求

# Using custom-llm-provider header
curl --location 'http://localhost:4000/v1/videos/video_id' \
--header 'Accept: application/json' \
--header 'x-litellm-api-key: sk-1234' \
--header 'custom-llm-provider: openai'

測試影片擷取請求

# Using custom-llm-provider header
curl --location 'http://localhost:4000/v1/videos/video_id/content' \
--header 'Accept: application/json' \
--header 'x-litellm-api-key: sk-1234' \
--header 'custom-llm-provider: openai' \
--output video.mp4

# Or using query parameter
curl --location 'http://localhost:4000/v1/videos/video_id/content?custom_llm_provider=openai' \
--header 'Accept: application/json' \
--header 'x-litellm-api-key: sk-1234' \
--output video.mp4

測試影片 remix 請求

# Using custom_llm_provider in request body
curl --location --request POST 'http://localhost:4000/v1/videos/video_id/remix' \
--header 'Accept: application/json' \
--header 'Content-Type: application/json' \
--header 'x-litellm-api-key: sk-1234' \
--data '{
    "prompt": "New remix instructions",
    "custom_llm_provider": "openai"
}'

# Or using custom-llm-provider header
curl --location --request POST 'http://localhost:4000/v1/videos/video_id/remix' \
--header 'Accept: application/json' \
--header 'Content-Type: application/json' \
--header 'x-litellm-api-key: sk-1234' \
--header 'custom-llm-provider: openai' \
--data '{
    "prompt": "New remix instructions"
}'

Character、Edit 與 Extension 路由

LiteLLM proxy 支援的 OpenAI 影片路由：

• POST /v1/videos/characters
• GET /v1/videos/characters/{character_id}
• POST /v1/videos/edits
• POST /v1/videos/extensions

target_model_names 在建立 character 時的支援

POST /v1/videos/characters 支援 target_model_names，用於基於模型的路由（與 video create 的行為相同）。

curl --location 'http://localhost:4000/v1/videos/characters' \
--header 'Authorization: Bearer sk-1234' \
-F 'name=hero' \
-F 'target_model_names=gpt-4' \
-F 'video=@/path/to/character.mp4'

當使用 target_model_names 時，LiteLLM 會回傳一個已編碼的 character ID：

{
  "id": "character_...",
  "object": "character",
  "created_at": 1712697600,
  "name": "hero"
}

在 get 時直接使用該已編碼 ID：

curl --location 'http://localhost:4000/v1/videos/characters/character_...' \
--header 'Authorization: Bearer sk-1234'

edit/extension 的已編碼與未編碼影片 ID

這兩個路由都接受純文字或已編碼的 video.id：

• POST /v1/videos/edits
• POST /v1/videos/extensions

curl --location 'http://localhost:4000/v1/videos/edits' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
  "prompt": "Make this brighter",
  "video": { "id": "video_..." }
}'

curl --location 'http://localhost:4000/v1/videos/extensions' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
  "prompt": "Continue this scene",
  "seconds": "4",
  "video": { "id": "video_..." }
}'

custom_llm_provider 輸入來源

對於這些路由，custom_llm_provider 可透過以下方式提供：

• 標頭：custom-llm-provider
• 查詢：?custom_llm_provider=...
• 主體：custom_llm_provider（以及 extra_body.custom_llm_provider，若支援）

測試 OpenAI 影片生成請求

curl http://localhost:4000/v1/videos \
  -H "Authorization: Bearer sk-1234" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sora-2",
    "prompt": "A cat playing with a ball of yarn in a sunny garden",
    "seconds": "8",
    "size": "720x1280"
  }'

支援的模型

模型名稱	說明	最長持續時間	支援尺寸
sora-2	OpenAI 最新的影片生成模型	8 秒	720x1280, 1280x720

影片生成參數

• prompt（必填）：所需影片的文字描述
• model（選填）：要使用的模型，預設為 "sora-2"
• seconds（選填）：影片長度（秒）（例如："8"、"16"）
• size（選填）：影片尺寸（例如："720x1280"、"1280x720"）
• input_reference（選填）：用於影片編輯的參考圖片
• user（選填）：用於追蹤的使用者識別碼

影片內容擷取

# Download video content
video_bytes = video_content(
    video_id="video_1234567890"
)

# Save to file
with open("video.mp4", "wb") as f:
    f.write(video_bytes)

完整工作流程

import litellm
import time

def generate_and_download_video(prompt):
    # Step 1: Generate video
    response = litellm.video_generation(
        prompt=prompt,
        model="sora-2",
        seconds="8",
        size="720x1280"
    )
    
    video_id = response.id
    print(f"Video ID: {video_id}")
    
    # Step 2: Wait for processing (in practice, poll status)
    time.sleep(30)
    
    # Step 3: Download video
    video_bytes = litellm.video_content(
        video_id=video_id
    )
    
    # Step 4: Save to file
    with open(f"video_{video_id}.mp4", "wb") as f:
        f.write(video_bytes)
    
    return f"video_{video_id}.mp4"

# Usage
video_file = generate_and_download_video(
    "A cat playing with a ball of yarn in a sunny garden"
)

使用參考圖片進行影片編輯

# Video editing with reference image
response = litellm.video_generation(
    prompt="Make the cat jump higher",
    input_reference=open("path/to/image.jpg", "rb"),  # Reference image
    model="sora-2",
    seconds="8"
)

print(f"Video ID: {response.id}")

錯誤處理

from litellm.exceptions import BadRequestError, AuthenticationError

try:
    response = video_generation(
        prompt="A cat playing with a ball of yarn"
    )
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except BadRequestError as e:
    print(f"Bad request: {e}")