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

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
}