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:
- Takes a list of images and output dimensions (width and height) as input
- 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)
- Divides the output canvas into equal-sized cells based on the grid dimensions
- Resizes each input image to fit within its assigned cell while maintaining aspect ratio (images are scaled to fit the cell dimensions without distortion)
- Places images in the grid starting from the top-left corner, filling left-to-right and top-to-bottom (row-major order)
- Centers each resized image within its cell, creating evenly spaced grid layout
- 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.. | โ |
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.. | โ |
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.. | โ |
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:
Detections List Roll-Up,Buffer,Google Gemini,SIFT Comparison,Template Matching,Florence-2 Model,Motion Detection,MoonshotAI Kimi,Camera Focus,Qwen-VL,MoonshotAI Kimi,Google Gemma API,Line Counter,Qwen 3.5 API,Anthropic Claude,Line Counter,Anthropic Claude,Dimension Collapse,Qwen 3.6 API,Florence-2 Model,OpenAI,Image Contours,Google Gemini,Google Gemma,Detection Event Log,Llama 3.2 Vision,Size Measurement,OpenAI,Clip Comparison,Pixel Color Count,Dynamic Zone,Google Gemini,Llama 3.2 Vision,OpenRouter,Distance Measurement,Image Stack,SIFT Comparison,Clip Comparison,Anthropic Claude,OpenAI,Perspective Correction - outputs:
Stability AI Outpainting,Multi-Label Classification Model,CLIP Embedding Model,SAM 3,Motion Detection,Contrast Enhancement,Camera Focus,Image Preprocessing,Seg Preview,Ellipse Visualization,Corner Visualization,Roboflow Vision Events,Object Detection Model,Heatmap Visualization,Trace Visualization,OC-SORT Tracker,VLM As Classifier,Time in Zone,OpenAI,Byte Tracker,Keypoint Visualization,Model Comparison Visualization,YOLO-World Model,Polygon Zone Visualization,Single-Label Classification Model,Dynamic Crop,Polygon Visualization,GLM-OCR,Stitch Images,OpenRouter,Semantic Segmentation Model,Image Blur,Clip Comparison,Detections Stitch,Segment Anything 2 Model,Instance Segmentation Model,Google Gemini,Buffer,Pixelate Visualization,EasyOCR,SIFT,Contrast Equalization,Image Threshold,Instance Segmentation Model,Polygon Visualization,Anthropic Claude,Halo Visualization,Qwen2.5-VL,Keypoint Detection Model,Florence-2 Model,Icon Visualization,Single-Label Classification Model,Image Contours,OpenAI,SAM2 Video Tracker,SAM 3,ByteTrack Tracker,VLM As Detector,Barcode Detection,Multi-Label Classification Model,Object Detection Model,LMM,Image Convert Grayscale,Reference Path Visualization,Dominant Color,Keypoint Detection Model,SIFT Comparison,Roboflow Dataset Upload,BoT-SORT Tracker,Qwen3-VL,Object Detection Model,Morphological Transformation,Crop Visualization,Blur Visualization,Qwen-VL,Mask Visualization,Stability AI Image Generation,Google Gemma API,Qwen3.5,Image Slicer,Qwen 3.5 API,Perception Encoder Embedding Model,Background Color Visualization,Anthropic Claude,Qwen 3.6 API,Color Visualization,Bounding Box Visualization,Google Gemma,Relative Static Crop,Llama 3.2 Vision,CogVLM,Instance Segmentation Model,Qwen3.5-VL,Instance Segmentation Model,Google Vision OCR,Camera Focus,Google Gemini,Llama 3.2 Vision,SAM 3,Single-Label Classification Model,SORT Tracker,SmolVLM2,Detections Stabilizer,Moondream2,Anthropic Claude,Image Slicer,OpenAI,Depth Estimation,Multi-Label Classification Model,Gaze Detection,Template Matching,Classification Label Visualization,Florence-2 Model,MoonshotAI Kimi,MoonshotAI Kimi,Dot Visualization,Keypoint Detection Model,Background Subtraction,Roboflow Dataset Upload,Stability AI Inpainting,Semantic Segmentation Model,QR Code Detection,Label Visualization,Absolute Static Crop,Google Gemini,VLM As Classifier,Halo Visualization,Email Notification,Camera Calibration,OpenAI,Clip Comparison,Pixel Color Count,LMM For Classification,Text Display,Line Counter Visualization,Circle Visualization,OCR Model,VLM As Detector,Image Stack,Morphological Transformation,Twilio SMS/MMS Notification,Mask Edge Snap,Triangle Visualization,Perspective Correction
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
}