Grid Visualization

Class: GridVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.grid.v1.GridVisualizationBlockV1

The GridVisualization block displays an array of images in a grid. It will automatically resize the images to in the specified width and height. The first image will be in the top left corner, and the rest will be added to the right of the previous image until the row is full.

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
width	int	Width of the output image..	✅
height	int	Height of the output image..	✅

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 Grid Visualization in version v1.

• inputs: Clip Comparison, Distance Measurement, OpenAI, Template Matching, Dimension Collapse, Line Counter, Clip Comparison, Florence-2 Model, Perspective Correction, Size Measurement, SIFT Comparison, Anthropic Claude, Google Gemini, OpenAI, Florence-2 Model, Dynamic Zone, Buffer, Line Counter, Llama 3.2 Vision, Image Contours, Pixel Color Count, SIFT Comparison
• outputs: CLIP Embedding Model, Detections Stitch, Circle Visualization, Time in Zone, Template Matching, Roboflow Dataset Upload, Image Slicer, Dot Visualization, Gaze Detection, Perception Encoder Embedding Model, Single-Label Classification Model, Blur Visualization, Clip Comparison, Perspective Correction, Roboflow Dataset Upload, Anthropic Claude, Background Color Visualization, OpenAI, Florence-2 Model, Object Detection Model, Keypoint Detection Model, Llama 3.2 Vision, Dynamic Crop, Crop Visualization, Barcode Detection, OCR Model, EasyOCR, Trace Visualization, Keypoint Detection Model, Image Threshold, Triangle Visualization, Reference Path Visualization, QR Code Detection, Model Comparison Visualization, Polygon Visualization, Corner Visualization, Image Slicer, Florence-2 Model, Image Blur, SmolVLM2, SIFT Comparison, Moondream2, Bounding Box Visualization, Buffer, Keypoint Visualization, Multi-Label Classification Model, Image Convert Grayscale, Byte Tracker, YOLO-World Model, Clip Comparison, Line Counter Visualization, SIFT, Icon Visualization, Stability AI Inpainting, VLM as Detector, Google Vision OCR, Polygon Zone Visualization, OpenAI, Instance Segmentation Model, CogVLM, Camera Calibration, Mask Visualization, Camera Focus, Stability AI Outpainting, Classification Label Visualization, Multi-Label Classification Model, LMM, Image Preprocessing, Morphological Transformation, Color Visualization, Depth Estimation, LMM For Classification, Detections Stabilizer, Instance Segmentation Model, Dominant Color, Ellipse Visualization, Stability AI Image Generation, Segment Anything 2 Model, Qwen2.5-VL, VLM as Classifier, VLM as Detector, Stitch Images, Halo Visualization, Absolute Static Crop, Google Gemini, VLM as Classifier, Object Detection Model, Pixelate Visualization, Image Contours, Pixel Color Count, Label Visualization, OpenAI, Single-Label Classification Model, Contrast Equalization, Relative Static Crop

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Grid Visualization in version v1 has.

Bindings

• input

  • images (list_of_values): Images to visualize.
  • width (integer): Width of the output image..
  • height (integer): Height of the output image..

• output

  • image (image): Image in workflows.

Example JSON definition of step Grid Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/grid_visualization@v1",
    "images": "$steps.buffer.output",
    "width": 2560,
    "height": 1440
}