ComfyAssets-ComfyUI-KikoTools/examples/documentation/display_any.md

Display Any

The Display Any node is a debugging and inspection tool that can display any type of input value in ComfyUI. It's particularly useful for understanding data structures and tensor shapes during workflow development.

Features

• Universal Input: Accepts any type of input data (tensors, strings, numbers, lists, dictionaries, etc.)
• Two Display Modes:
  • Raw Value: Shows the string representation of the input
  • Tensor Shape: Extracts and displays the shapes of any tensors found in the input
• Nested Structure Support: Can find tensors within nested dictionaries and lists
• UI Output: Displays results directly in the ComfyUI interface

Inputs

• input (*): Any value you want to display or inspect
• mode (DROPDOWN): Display mode selection
  • raw value: Shows the complete string representation of the input
  • tensor shape: Extracts and shows shapes of any tensors in the input

Outputs

• display_text (STRING): The formatted display text

Usage Examples

1. Display Simple Values

Connect any output to see its raw value:

String Input: "Hello, ComfyUI!"
Mode: raw value
Output: "Hello, ComfyUI!"

2. Inspect Tensor Shapes

Great for debugging image processing pipelines:

Image Tensor: [1, 3, 512, 512]
Mode: tensor shape
Output: "[[1, 3, 512, 512]]"

3. Debug Complex Data Structures

View nested data structures with multiple tensors:

Input: {
    "images": tensor([1, 3, 256, 256]),
    "masks": [tensor([256, 256]), tensor([256, 256, 1])],
    "config": {"steps": 20}
}
Mode: tensor shape
Output: "[[1, 3, 256, 256], [256, 256], [256, 256, 1]]"

4. Workflow Debugging

Use Display Any nodes at various points in your workflow to understand data flow:

• After loading images to verify dimensions
• Before/after processing nodes to track shape changes
• To inspect conditioning or latent data structures
• To view metadata or configuration dictionaries

Use Cases

Image Pipeline Debugging

Place Display Any nodes after image loading and processing nodes to track dimension changes:

Load Image → Display Any (tensor shape) → Resize → Display Any (tensor shape)

Latent Space Inspection

Understand latent dimensions in your workflows:

VAE Encode → Display Any (tensor shape) → KSampler → Display Any (raw value)

Configuration Verification

Display complex configuration objects to ensure correct settings:

Config Node → Display Any (raw value) → Processing Node

Tips

1. Multiple Display Nodes: You can use multiple Display Any nodes in a single workflow to track data at different stages

2. Tensor Shape Mode: Particularly useful when working with:

   • Image batches to verify batch size
   • Latent tensors to understand dimensions
   • Mask arrays to check compatibility

3. Raw Value Mode: Best for:

   • String prompts and text
   • Configuration dictionaries
   • Debugging node outputs
   • Understanding data structure

4. No Tensors Found: If you see "No tensors found in input" in tensor shape mode, the input doesn't contain any tensor-like objects (numpy arrays, torch tensors, etc.)

Technical Notes

• The node uses str() for raw value display, providing Python's string representation
• Tensor shape detection works with any object that has a shape attribute
• Nested structure traversal supports dictionaries, lists, and tuples
• The output is both displayed in the UI and available as a string output for further processing

Example Workflow Integration

[Load Image] → [Image Processing] → [Display Any (tensor shape)]
                                           ↓
                                    "[[1, 3, 512, 512]]"
                                           ↓
[Text Multiline] ← [Concatenate] ← "Image dimensions: "

This creates a text output showing the current image dimensions that can be used elsewhere in your workflow.