ComfyUI Node: 🖼🔍 Image Details

🖼🔍 Image Details:

The Bjornulf_ImageDetails node is designed to extract and present detailed information about images, making it an invaluable tool for AI artists who need to understand the characteristics of their visual inputs. This node processes images to determine their dimensions, transparency, orientation, and more, providing a comprehensive overview of each image's properties. By analyzing both tensor and byte-based image inputs, it ensures compatibility with a wide range of image formats and sources. The node's ability to summarize these details into a cohesive string makes it easy for users to quickly grasp the essential attributes of their images, aiding in tasks such as image selection, categorization, and further processing.

🖼🔍 Image Details Input Parameters:

image_input

The image_input parameter is the primary input for the node, accepting either a tensor or a bytes-like object representing the image(s) to be analyzed. When provided as a tensor, the image data should be in a format compatible with PyTorch, typically in a batch format with dimensions indicating batch size, channels, height, and width. For bytes-like objects, the input should be a list of image data in byte format. This parameter is crucial as it determines the source and format of the image data that the node will process. There are no explicit minimum, maximum, or default values, but the input must be a valid image representation in one of the supported formats.

🖼🔍 Image Details Output Parameters:

all_widths

This output parameter provides a list of the widths of the processed images. It is essential for understanding the horizontal dimensions of each image, which can be critical for layout and design considerations.

all_heights

Similar to all_widths, this parameter outputs a list of the heights of the images. Knowing the vertical dimensions helps in assessing the overall size and aspect ratio of the images.

all_transparencies

This parameter outputs a list indicating whether each image contains transparency. This information is vital for tasks that require compositing or layering images, as transparency can affect how images are combined.

all_orientations

The all_orientations parameter provides a list of the orientations of the images, categorizing them as landscape, portrait, or square. This classification helps in organizing images based on their visual layout.

combined_details

This output is a single string that combines all the detailed information about the images, including type, dimensions, transparency, mode, and orientation. It serves as a quick reference for users to understand the key attributes of their images in one glance.

🖼🔍 Image Details Usage Tips:

• Ensure that your image inputs are in the correct format (tensor or bytes) to avoid processing errors and to make full use of the node's capabilities.
• Use the combined_details output to quickly review the essential characteristics of your images, which can be particularly useful when working with large batches of images.

🖼🔍 Image Details Common Errors and Solutions:

Invalid image file

• Explanation: This error occurs when the input image file is not recognized or cannot be processed by the node.
• Solution: Verify that the image file is correctly formatted and accessible. Ensure that the file path is correct and that the image is not corrupted.

Unsupported image format

• Explanation: The node may not support certain image formats, leading to processing failures.
• Solution: Convert the image to a supported format such as PNG or JPEG before inputting it into the node.

Tensor shape mismatch

• Explanation: This error arises when the input tensor does not have the expected dimensions or format.
• Solution: Ensure that the tensor is in the correct shape, typically (batch, channels, height, width), and adjust the dimensions if necessary.