Grid Visualization

Class: GridVisualizationBlockV1

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

Arrange multiple images in a grid layout, automatically organizing a list of images into a square grid pattern with automatic resizing and cell-based positioning for side-by-side comparison, thumbnail displays, or batch visualization.

How This Block Works

This block takes a list of images and arranges them into a grid layout within a single output image. The block:

1. Takes a list of images and output dimensions (width and height) as input
2. Calculates the grid size based on the number of images (creates a square grid with dimensions equal to the square root of the image count, rounded up)
3. Divides the output canvas into equal-sized cells based on the grid dimensions
4. Resizes each input image to fit within its assigned cell while maintaining aspect ratio (images are scaled to fit the cell dimensions without distortion)
5. Places images in the grid starting from the top-left corner, filling left-to-right and top-to-bottom (row-major order)
6. Centers each resized image within its cell, creating evenly spaced grid layout
7. Returns a single output image containing all input images arranged in the grid

The block automatically organizes multiple images into a grid for easy comparison or batch viewing. Each image is resized to fit its grid cell while preserving aspect ratio, and images are centered within their cells. The grid dimensions are automatically calculated to create a roughly square grid (e.g., 4 images = 2x2, 9 images = 3x3, 10 images = 4x4). This creates a compact, organized layout ideal for comparing multiple images, displaying thumbnails, or creating batch visualization outputs. The block uses caching to optimize performance when the same images are reused.

Common Use Cases

• Batch Image Comparison: Arrange multiple images side-by-side in a grid for easy comparison, allowing you to visualize results from different models, time periods, or processing steps simultaneously
• Thumbnail Gallery Creation: Create thumbnail grids from collections of images for gallery displays, image browsers, or preview interfaces where multiple images need to be shown in a compact layout
• Multi-Image Workflow Results: Display results from multi-image workflows (like batch processing, image slicer outputs, or buffer collections) in an organized grid format for overview visualization
• Before/After Comparisons: Arrange before and after images, original and processed versions, or multiple workflow outputs in a grid for comparison and validation workflows
• Time-Series Visualization: Display images from different time points, frames, or snapshots in a grid to visualize temporal changes, sequences, or progression over time
• Quality Control and Review: Create grid layouts for quality control workflows, batch review, or inspection processes where multiple images need to be viewed together for evaluation or validation

Connecting to Other Blocks

The grid output image from this block can be connected to:

• Image processing blocks (e.g., Buffer, Image Slicer, Dynamic Crop) to receive lists of images that are arranged into grid layouts
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save grid images for documentation, reporting, or batch review purposes
• Webhook blocks to send grid visualizations to external systems, APIs, or web applications for display in dashboards, galleries, or batch viewing interfaces
• Notification blocks (e.g., Email Notification, Slack Notification) to send grid images as visual evidence in alerts or reports containing multiple images
• Video output blocks to create video streams or recordings with grid layouts for live multi-image monitoring or batch visualization workflows
• Other visualization blocks that can accept single images, allowing grid outputs to be further processed or combined with additional annotations

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 grid image in pixels. Controls the total width of the canvas where the image grid will be arranged. The width is divided into equal-sized cells based on the grid dimensions. Typical values range from 1280 to 3840 pixels depending on desired output size and number of images..	✅
height	int	Height of the output grid image in pixels. Controls the total height of the canvas where the image grid will be arranged. The height is divided into equal-sized cells based on the grid dimensions. Typical values range from 720 to 2160 pixels depending on desired output size and number of images..	✅

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: Anthropic Claude, Clip Comparison, Llama 3.2 Vision, Image Contours, Dimension Collapse, Anthropic Claude, Distance Measurement, OpenAI, Camera Focus, Line Counter, Detection Event Log, Google Gemini, Florence-2 Model, Pixel Color Count, Florence-2 Model, Size Measurement, Google Gemini, Line Counter, SIFT Comparison, OpenAI, Dynamic Zone, Detections List Roll-Up, Buffer, OpenAI, Anthropic Claude, SIFT Comparison, Perspective Correction, Template Matching, Motion Detection, Clip Comparison, Google Gemini
• outputs: Anthropic Claude, Clip Comparison, Qwen3-VL, Halo Visualization, Image Blur, Email Notification, CLIP Embedding Model, Camera Focus, Text Display, Contrast Equalization, Object Detection Model, Segment Anything 2 Model, Blur Visualization, VLM As Detector, Detections Stabilizer, Roboflow Dataset Upload, Dynamic Crop, Corner Visualization, Classification Label Visualization, Relative Static Crop, Keypoint Detection Model, Trace Visualization, Multi-Label Classification Model, SAM 3, Qwen2.5-VL, Mask Visualization, Camera Focus, SIFT, VLM As Classifier, YOLO-World Model, Background Color Visualization, Polygon Visualization, Moondream2, Barcode Detection, Florence-2 Model, Ellipse Visualization, SIFT Comparison, Pixelate Visualization, Single-Label Classification Model, LMM, SAM 3, OpenAI, Dot Visualization, Circle Visualization, Byte Tracker, Absolute Static Crop, Template Matching, Morphological Transformation, Dominant Color, Crop Visualization, Single-Label Classification Model, Google Gemini, Keypoint Visualization, Polygon Visualization, Google Vision OCR, Multi-Label Classification Model, Icon Visualization, Llama 3.2 Vision, Gaze Detection, OpenAI, Image Contours, Stitch Images, VLM As Classifier, Color Visualization, LMM For Classification, VLM As Detector, Anthropic Claude, SmolVLM2, OpenAI, Triangle Visualization, Bounding Box Visualization, Background Subtraction, Reference Path Visualization, EasyOCR, Instance Segmentation Model, Line Counter Visualization, Model Comparison Visualization, OCR Model, Halo Visualization, Time in Zone, Stability AI Inpainting, Image Slicer, Google Gemini, Florence-2 Model, Image Convert Grayscale, Stability AI Image Generation, Heatmap Visualization, Pixel Color Count, Seg Preview, Polygon Zone Visualization, Image Slicer, Label Visualization, Google Gemini, Depth Estimation, Image Preprocessing, Detections Stitch, OpenAI, Object Detection Model, Stability AI Outpainting, Buffer, QR Code Detection, Image Threshold, Anthropic Claude, Instance Segmentation Model, CogVLM, Perception Encoder Embedding Model, Camera Calibration, Perspective Correction, SAM 3, Motion Detection, Keypoint Detection Model, Twilio SMS/MMS Notification, Roboflow Dataset Upload, Clip Comparison

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): List of images to arrange in a grid layout. Can be a list of image outputs from blocks like Buffer, Image Slicer, Dynamic Crop, or other blocks that output multiple images. Images will be automatically arranged in a square grid (calculated from the number of images) and resized to fit their grid cells while maintaining aspect ratio..
  • width (integer): Width of the output grid image in pixels. Controls the total width of the canvas where the image grid will be arranged. The width is divided into equal-sized cells based on the grid dimensions. Typical values range from 1280 to 3840 pixels depending on desired output size and number of images..
  • height (integer): Height of the output grid image in pixels. Controls the total height of the canvas where the image grid will be arranged. The height is divided into equal-sized cells based on the grid dimensions. Typical values range from 720 to 2160 pixels depending on desired output size and number of images..

• 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
}