--- title: "RunwayML - 影片生成" url: "/docs/providers/runwayml/videos" canonical_url: "https://docs.litellm.ai/docs/providers/runwayml/videos" type: "docs" last_updated: "2026-10-08" related: - "/docs/providers/runwayml/images" - "/docs/providers/s3_vectors" --- # RunwayML - 影片生成 > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt LiteLLM 支援 RunwayML 的 Gen-4 影片生成 API，讓您可以從文字提示與圖片生成影片。 ## 快速開始 {#quick-start} ```python showLineNumbers title="Basic Video Generation" from litellm import video_generation import os os.environ["RUNWAYML_API_KEY"] = "your-api-key" # Generate video from text and image response = video_generation( model="runwayml/gen4_turbo", prompt="A high quality demo video of litellm ai gateway", input_reference="https://media.licdn.com/dms/image/v2/D4D0BAQFqOrIAJEgtLw/company-logo_200_200/company-logo_200_200/0/1714076049190/berri_ai_logo?e=2147483647&v=beta&t=7tG_KRZZ4MPGc7Iin79PcFcrpvf5Hu6rBM4ptHGU1DY", seconds=5, size="1280x720" ) print(f"Video ID: {response.id}") print(f"Status: {response.status}") ``` ## 驗證 {#authentication} 設定您的 RunwayML API 金鑰： ```python showLineNumbers title="Set API Key" import os os.environ["RUNWAYML_API_KEY"] = "your-api-key" ``` ## 支援的參數 {#supported-parameters} | 參數 | 類型 | 必填 | 說明 | |-----------|------|----------|-------------| | `model` | string | 是 | 要使用的模型（例如 `runwayml/gen4_turbo`） | | `prompt` | string | 是 | 影片的文字描述 | | `input_reference` | string/file | 是 | 參考圖片的 URL 或檔案路徑 | | `seconds` | int | 否 | 影片長度（5 或 10 秒） | | `size` | string | 否 | 影片尺寸（`1280x720` 或 `720x1280`）。也可使用 `ratio` 格式（`1280:720`） | ## 完整工作流程 {#complete-workflow} ```python showLineNumbers title="Complete Video Generation Workflow" from litellm import video_generation, video_status, video_content import os import time os.environ["RUNWAYML_API_KEY"] = "your-api-key" # 1. Generate video response = video_generation( model="runwayml/gen4_turbo", prompt="A high quality demo video of litellm ai gateway", input_reference="https://media.licdn.com/dms/image/v2/D4D0BAQFqOrIAJEgtLw/company-logo_200_200/company-logo_200_200/0/1714076049190/berri_ai_logo?e=2147483647&v=beta&t=7tG_KRZZ4MPGc7Iin79PcFcrpvf5Hu6rBM4ptHGU1DY", seconds=5, size="1280x720" ) video_id = response.id print(f"Video generation started: {video_id}") # 2. Check status until completed while True: status_response = video_status(video_id=video_id) print(f"Status: {status_response.status}") if status_response.status == "completed": print("Video generation completed!") break elif status_response.status == "failed": print("Video generation failed") break time.sleep(10) # Wait 10 seconds before checking again # 3. Download video content video_bytes = video_content(video_id=video_id) # 4. Save to file with open("generated_video.mp4", "wb") as f: f.write(video_bytes) print("Video saved successfully!") ``` ## 非同步使用 {#async-usage} ```python showLineNumbers title="Async Video Generation" from litellm import avideo_generation, avideo_status, avideo_content import os import asyncio os.environ["RUNWAYML_API_KEY"] = "your-api-key" async def generate_video(): # Generate video response = await avideo_generation( model="runwayml/gen4_turbo", prompt="A serene lake with mountains in the background", input_reference="https://example.com/lake.jpg", seconds=5, size="1280x720" ) video_id = response.id print(f"Video generation started: {video_id}") # Poll for completion while True: status_response = await avideo_status(video_id=video_id) print(f"Status: {status_response.status}") if status_response.status == "completed": break elif status_response.status == "failed": print("Video generation failed") return await asyncio.sleep(10) # Download video video_bytes = await avideo_content(video_id=video_id) # Save to file with open("generated_video.mp4", "wb") as f: f.write(video_bytes) print("Video saved successfully!") asyncio.run(generate_video()) ``` ## LiteLLM Proxy 使用方式 {#litellm-proxy-usage} 將 RunwayML 新增至您的 proxy 設定： ```yaml showLineNumbers title="config.yaml" model_list: - model_name: gen4-turbo litellm_params: model: runwayml/gen4_turbo api_key: os.environ/RUNWAYML_API_KEY ``` 啟動 proxy： ```bash litellm --config /path/to/config.yaml ``` 透過 proxy 生成影片： ```bash showLineNumbers title="Proxy Request" curl --location 'http://localhost:4000/v1/videos' \ --header 'Content-Type: application/json' \ --header 'x-litellm-api-key: sk-' \ --data '{ "model": "runwayml/gen4_turbo", "prompt": "A high quality demo video of litellm ai gateway", "input_reference": "https://media.licdn.com/dms/image/v2/D4D0BAQFqOrIAJEgtLw/company-logo_200_200/company-logo_200_200/0/1714076049190/berri_ai_logo?e=2147483647&v=beta&t=7tG_KRZZ4MPGc7Iin79PcFcrpvf5Hu6rBM4ptHGU1DY", "ratio": "1280:720" }' ``` 檢查影片狀態： ```bash showLineNumbers title="Check Status" curl --location 'http://localhost:4000/v1/videos/{video_id}' \ --header 'x-litellm-api-key: sk-' ``` 下載影片內容： ```bash showLineNumbers title="Download Video" curl --location 'http://localhost:4000/v1/videos/{video_id}/content' \ --header 'x-litellm-api-key: sk-' \ --output video.mp4 ``` ## 支援的模型 {#supported-models} | 模型 | 說明 | 長度 | 長寬比 | |-------|-------------|----------|---------------| | `runwayml/gen4_turbo` | 快速影片生成 | 5-10 秒 | 1280x720, 720x1280 | ## 錯誤處理 {#error-handling} ```python showLineNumbers title="Error Handling" from litellm import video_generation, video_status import time try: response = video_generation( model="runwayml/gen4_turbo", prompt="A scenic mountain view", input_reference="https://example.com/mountain.jpg", seconds=5 ) # Poll for completion max_attempts = 60 # 10 minutes max attempts = 0 while attempts < max_attempts: status_response = video_status(video_id=response.id) if status_response.status == "completed": print("Video generation completed!") break elif status_response.status == "failed": error = status_response.error or {} print(f"Video generation failed: {error.get('message', 'Unknown error')}") break time.sleep(10) attempts += 1 if attempts >= max_attempts: print("Video generation timed out") except Exception as e: print(f"Error: {str(e)}") ``` ## 成本追蹤 {#cost-tracking} LiteLLM 會自動追蹤 RunwayML 影片生成成本： ```python showLineNumbers title="Cost Tracking" from litellm import video_generation, completion_cost response = video_generation( model="runwayml/gen4_turbo", prompt="A high quality demo video of litellm ai gateway", input_reference="https://media.licdn.com/dms/image/v2/D4D0BAQFqOrIAJEgtLw/company-logo_200_200/company-logo_200_200/0/1714076049190/berri_ai_logo?e=2147483647&v=beta&t=7tG_KRZZ4MPGc7Iin79PcFcrpvf5Hu6rBM4ptHGU1DY", seconds=5, size="1280x720" ) # Calculate cost cost = completion_cost(completion_response=response) print(f"Video generation cost: ${cost}") ``` ## API 參考 {#api-reference} 如需完整 API 詳情，請參閱 LiteLLM 所遵循的 [OpenAI Video Generation API specification](https://platform.openai.com/docs/guides/video-generation)。 ## 支援的功能 {#supported-features} | 功能 | 支援 | |---------|-----------| | 影片生成 | ✅ | | 圖片轉影片 | ✅ | | 狀態檢查 | ✅ | | 內容下載 | ✅ | | 成本追蹤 | ✅ | | 記錄 | ✅ | | 備援 | ✅ | | 負載平衡 | ✅ | ## Related pages - [RunwayML - 圖像生成](https://docs.litellm.ai/docs/providers/runwayml/images.md) - [AWS S3 Vectors](https://docs.litellm.ai/docs/providers/s3_vectors.md)