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

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
}