Stability AI Image Generation

Class: StabilityAIImageGenBlockV1

Source: inference.core.workflows.core_steps.models.foundation.stability_ai.image_gen.v1.StabilityAIImageGenBlockV1

The block wraps Stability AI image generation API and let users generate new images from text, or create variations of existing images.

Type identifier

Use the following identifier in step "type" field: roboflow_core/stability_ai_image_gen@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
strength	float	controls how much influence the image parameter has on the generated image. A value of 0 would yield an image that is identical to the input. A value of 1 would be as if you passed in no image at all..	✅
prompt	str	Prompt to generate new images from text (what you wish to see).	✅
negative_prompt	str	Negative prompt to image generation model (what you do not wish to see).	✅
model	str	choose one of {'core', 'ultra', 'sd3'}. Default 'core' .	✅
api_key	str	Your Stability AI API key.	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Stability AI Image Generation in version v1.

• inputs: Keypoint Detection Model, Halo Visualization, OpenAI, Stability AI Image Generation, Model Comparison Visualization, Keypoint Visualization, Crop Visualization, Image Blur, Bounding Box Visualization, Roboflow Dataset Upload, Mask Visualization, Image Slicer, Instance Segmentation Model, Ellipse Visualization, Email Notification, OpenAI, Slack Notification, VLM as Detector, Twilio SMS Notification, Webhook Sink, Label Visualization, Google Vision OCR, Stability AI Outpainting, CSV Formatter, Single-Label Classification Model, Polygon Visualization, Identify Changes, Model Monitoring Inference Aggregator, Roboflow Custom Metadata, Reference Path Visualization, Florence-2 Model, OCR Model, Anthropic Claude, Camera Calibration, Image Preprocessing, Image Contours, Line Counter Visualization, Florence-2 Model, LMM For Classification, Corner Visualization, Clip Comparison, Stitch Images, Depth Estimation, SIFT, Blur Visualization, Image Convert Grayscale, Background Color Visualization, Image Slicer, Dynamic Crop, Local File Sink, Perspective Correction, Stitch OCR Detections, Circle Visualization, Triangle Visualization, Dot Visualization, CogVLM, Object Detection Model, VLM as Classifier, LMM, Color Visualization, Stability AI Inpainting, Classification Label Visualization, OpenAI, Absolute Static Crop, Image Threshold, Detections Consensus, Llama 3.2 Vision, Pixelate Visualization, Trace Visualization, Camera Focus, Roboflow Dataset Upload, Grid Visualization, Google Gemini, SIFT Comparison, Multi-Label Classification Model, Relative Static Crop, Polygon Zone Visualization, Identify Outliers
• outputs: Keypoint Detection Model, Gaze Detection, Halo Visualization, OpenAI, Stability AI Image Generation, Model Comparison Visualization, Keypoint Visualization, Crop Visualization, Image Blur, Bounding Box Visualization, Roboflow Dataset Upload, Template Matching, Mask Visualization, SmolVLM2, VLM as Classifier, Image Slicer, Instance Segmentation Model, Instance Segmentation Model, QR Code Detection, CLIP Embedding Model, Ellipse Visualization, OpenAI, VLM as Detector, Qwen2.5-VL, Label Visualization, Google Vision OCR, Stability AI Outpainting, Single-Label Classification Model, Buffer, Polygon Visualization, Barcode Detection, Dominant Color, Reference Path Visualization, Florence-2 Model, OCR Model, VLM as Detector, Anthropic Claude, Image Preprocessing, Line Counter Visualization, Florence-2 Model, Image Contours, Camera Calibration, Single-Label Classification Model, LMM For Classification, Clip Comparison, Corner Visualization, Pixel Color Count, Stitch Images, SIFT, Depth Estimation, Time in Zone, Perception Encoder Embedding Model, Blur Visualization, Image Convert Grayscale, Image Slicer, Background Color Visualization, Dynamic Crop, Perspective Correction, Circle Visualization, Triangle Visualization, Dot Visualization, CogVLM, Multi-Label Classification Model, Object Detection Model, Segment Anything 2 Model, VLM as Classifier, LMM, Color Visualization, OpenAI, Classification Label Visualization, Stability AI Inpainting, Byte Tracker, Moondream2, Absolute Static Crop, Image Threshold, Keypoint Detection Model, Llama 3.2 Vision, Pixelate Visualization, Trace Visualization, Detections Stabilizer, Camera Focus, Roboflow Dataset Upload, Clip Comparison, YOLO-World Model, Google Gemini, SIFT Comparison, Multi-Label Classification Model, Relative Static Crop, Detections Stitch, Object Detection Model, Polygon Zone Visualization

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Stability AI Image Generation in version v1 has.

Bindings

• input

  • image (image): The image to use as the starting point for the generation..
  • strength (float_zero_to_one): controls how much influence the image parameter has on the generated image. A value of 0 would yield an image that is identical to the input. A value of 1 would be as if you passed in no image at all..
  • prompt (string): Prompt to generate new images from text (what you wish to see).
  • negative_prompt (string): Negative prompt to image generation model (what you do not wish to see).
  • model (string): choose one of {'core', 'ultra', 'sd3'}. Default 'core' .
  • api_key (Union[secret, string]): Your Stability AI API key.

• output

  • image (image): Image in workflows.

Example JSON definition of step Stability AI Image Generation in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/stability_ai_image_gen@v1",
    "image": "$inputs.image",
    "strength": 0.3,
    "prompt": "my prompt",
    "negative_prompt": "my prompt",
    "model": "my prompt",
    "api_key": "xxx-xxx"
}