Juggernaut Lightning Flux

Identifier for the type of task being performed

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

Image output type.

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

• `JPG`: Best for photorealistic images with smaller file sizes (no transparency).
• `PNG`: Lossless compression, supports high quality and transparency (alpha channel).
• `WEBP`: Modern format providing superior compression and transparency support.

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 image 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.

Advanced caching mechanisms to speed up generation.

Image used as a starting point for the generation (UUID, URL, Data URI, or Base64).

Image used to specify which areas of the seed image should be edited (UUID, URL, Data URI, or Base64).

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.

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.

Strength of the transformation. Lower values result in more influence from the original input.

Extra context pixels around the masked region during inpainting. The model zooms into the masked area with these additional pixels for better integration.

Syntax used for prompt weighting.

Pixel extensions for each boundary direction of the source image. At least one direction is required.

Configuration for Low-Rank Adaptation models.

Configuration for identifying and applying ControlNet models.

Configuration for IP-Adapter image-prompted generation.

True Classifier-Free Guidance scale. Higher values increase prompt adherence at the cost of quality.

Configuration object for Ultralytics face enhancement during generation. This feature uses face detection and inpainting to improve facial details in the same generation step, without requiring post-processing.

Two-stage generation for improved resolution and detail. The model generates at a lower resolution first, then upscales and refines the result in a second pass. Can be enabled with true for default settings, or configured as an object for fine-grained control over the upscaling model, steps, and strength.

When using the object form, the model parameter is required. Available upscaling models:

"hiresFix": true

"hiresFix": {
  "model": "runware:esrgan@ultrasharp",
  "steps": 15,
  "strength": 0.6
}