ComfyUI Node: 🔧 Images Batch Multiple

🔧 Images Batch Multiple+:

The ImageBatchMultiple+ node is designed to streamline the process of combining multiple images into a single batch, making it easier to manage and process large sets of images simultaneously. This node is particularly useful for AI artists who need to handle multiple images in their workflows, such as for batch processing, applying consistent transformations, or preparing datasets for training machine learning models. By leveraging this node, you can efficiently concatenate images along the batch dimension, ensuring that all images are uniformly sized and ready for further processing. This node simplifies the task of managing image batches, saving you time and effort while maintaining the quality and consistency of your image data.

🔧 Images Batch Multiple+ Input Parameters:

image1

The first image to be included in the batch. This parameter expects an image tensor and serves as the primary reference for the batch. The dimensions of this image will determine the target size for any additional images being concatenated. Ensuring that this image is of the desired size and quality is crucial, as it sets the standard for the entire batch.

image2

The second image to be included in the batch. This parameter also expects an image tensor. If the dimensions of this image do not match those of image1, it will be automatically resized to match the dimensions of image1 using a bilinear interpolation method. This ensures that all images in the batch are uniformly sized, which is essential for consistent processing and analysis.

🔧 Images Batch Multiple+ Output Parameters:

IMAGE

The output is a single image tensor that represents the concatenated batch of images. This tensor combines the input images along the batch dimension, resulting in a multi-image tensor that can be easily processed in subsequent steps. The output maintains the quality and dimensions of the input images, ensuring that the batch is ready for further manipulation or analysis.

🔧 Images Batch Multiple+ Usage Tips:

• Ensure that the first image (image1) is of the desired size and quality, as it sets the standard for the entire batch.
• Use this node to prepare datasets for machine learning models by combining multiple images into a single batch, making it easier to manage and process.
• If you have images of varying sizes, this node will automatically resize them to match the dimensions of image1, ensuring uniformity across the batch.

🔧 Images Batch Multiple+ Common Errors and Solutions:

"Dimension mismatch between images"

• Explanation: This error occurs when the input images have different dimensions and cannot be concatenated without resizing.
• Solution: Ensure that all input images are of the same dimensions or allow the node to automatically resize them by providing a reference image (image1) with the desired dimensions.

"Invalid image tensor"

• Explanation: This error occurs when the input provided is not a valid image tensor.
• Solution: Verify that the inputs are correctly formatted image tensors and that they are properly loaded into the node.

"Insufficient memory"

• Explanation: This error occurs when the system runs out of memory while processing large batches of images.
• Solution: Reduce the number of images in the batch or downscale the images to a smaller size to fit within the available memory.