Wan2.2 A14B Turbo

Identifier for the type of task being performed

UUID v4 identifier for tracking tasks and matching async responses. Must be unique per task.

Video output type.

Specifies the file format of the generated output. The available values depend on the task type and the specific model's capabilities.

• `MP4`: Widely supported video container (H.264), recommended for general use.
• `WEBM`: Optimized for web delivery.
• `MOV`: QuickTime format, common in professional workflows (Apple ecosystem).

Compression quality of the output. Higher values preserve quality but increase file size.

Specifies a webhook URL where JSON responses will be sent via HTTP POST when generation tasks complete. For batch requests with multiple results, each completed item triggers a separate webhook call as it becomes available.

Determines how the API delivers task results.

Specifies a URL where the generated content will be automatically uploaded using the HTTP PUT method. The raw binary data of the media file is sent directly as the request body. For secure uploads to cloud storage, use presigned URLs that include temporary authentication credentials.

Common use cases:

• Cloud storage: Upload directly to S3 buckets, Google Cloud Storage, or Azure Blob Storage using presigned URLs.
• CDN integration: Upload to content delivery networks for immediate distribution.

// S3 presigned URL for secure upload
https://your-bucket.s3.amazonaws.com/generated/content.mp4?X-Amz-Signature=abc123&X-Amz-Expires=3600

// Google Cloud Storage presigned URL
https://storage.googleapis.com/your-bucket/content.jpg?X-Goog-Signature=xyz789

// Custom storage endpoint
https://storage.example.com/uploads/generated-image.jpg

The content data will be sent as the request body to the specified URL when generation is complete.

Content safety checking configuration for video generation.

Time-to-live (TTL) in seconds for generated content. Only applies when outputType is URL.

Include task cost in the response.

Number of results to generate. Each result uses a different seed, producing variations of the same parameters.

Optimization level.

Advanced caching mechanisms to speed up generation.

An array of objects that define key frames to guide video generation. Each object specifies an input image and optionally its position within the video timeline.

The frameImages parameter allows you to constrain specific frames within the video sequence, ensuring that particular visual content appears at designated points. This is different from referenceImages, which provide overall visual guidance without constraining specific timeline positions.

When the frame parameter is omitted from objects, automatic distribution rules apply:

• 1 image: Used as the first frame.
• 2 images: First and last frames.

"frameImages": [
  {
    "image": "aac49721-1964-481a-ae78-8a4e29b91402"
  }
]

"frameImages": [
  {
    "image": "aac49721-1964-481a-ae78-8a4e29b91402",
    "frame": "first"
  },
  {
    "image": "3ad204c3-a9de-4963-8a1a-c3911e3afafe",
    "frame": "last"
  }
]

Identifier of the model to use for generation.

Text prompt describing elements to include in the generated output.

Prompt to guide what to exclude from generation. Ignored when guidance is disabled (CFGScale ≤ 1).

Width of the generated media in pixels.

Height of the generated media in pixels.

Resolution preset for the output. When used with input media, automatically matches the aspect ratio from the input.

Frames per second for video generation. Higher values create smoother motion but require more processing time.

Random seed for reproducible generation. When not provided, a random seed is generated in the unsigned 32-bit range.

Total number of denoising steps. Higher values generally produce more detailed results but take longer.

Scheduler to use for the diffusion process.

Guidance scale representing how closely the output will resemble the prompt. Higher values produce results more aligned with the prompt.

resources:

• https://runware.ai/docs/guides/text-to-image#loras-style-and-subject-adapters

With LoRA (Low-Rank Adaptation), you can adapt a model to specific styles or features by emphasizing particular aspects of the data. This technique enhances the quality and relevance of generated content and can be especially useful when the output needs to adhere to a specific artistic style or follow particular guidelines.

Multiple LoRA models can be used simultaneously to achieve different adaptation goals.

"lora": [
  {
    "model": "<lora-model-air>",
    "weight": 0.8
  }
]