ComfyUI Node: Mask Sequence From Mask

Mask Sequence From Mask:

The JWMaskSequenceFromMask node is designed to transform a single mask into a sequence of masks, which can be particularly useful in scenarios where batch processing is required. This node takes an individual mask and replicates it across a specified batch size, creating a sequence of identical masks. This functionality is essential for tasks that involve applying the same mask across multiple samples or frames, ensuring consistency and efficiency in processing. By converting a single mask into a sequence, you can streamline workflows that require repetitive masking operations, making it easier to manage and apply masks in batch processing environments.

Mask Sequence From Mask Input Parameters:

mask

The mask parameter is the input mask that you want to convert into a sequence. It should be a 2D tensor representing the mask. This mask will be replicated across the batch size to create the mask sequence. The mask must be a tensor with two dimensions, typically representing the height and width of the mask.

batch_size

The batch_size parameter specifies the number of times the input mask should be replicated to form the mask sequence. It is an integer value with a minimum of 1, ensuring that at least one mask is present in the sequence. The default value is 1, meaning the mask will be replicated once if no other value is provided. This parameter allows you to control the size of the mask sequence, making it adaptable to different batch processing needs.

Mask Sequence From Mask Output Parameters:

MASK_SEQUENCE

The MASK_SEQUENCE output is the resulting sequence of masks created by replicating the input mask according to the specified batch size. This output is a tensor where each element in the sequence is identical to the input mask. The mask sequence can then be used in subsequent operations that require batch processing of masks, ensuring uniform application across multiple samples or frames.

Mask Sequence From Mask Usage Tips:

• To ensure consistent masking across multiple samples, set the batch_size to match the number of samples you are processing.
• Use this node when you need to apply the same mask to a batch of images or data points, simplifying the workflow and reducing the need for repetitive operations.

Mask Sequence From Mask Common Errors and Solutions:

AssertionError: mask must be a 2D tensor

• Explanation: This error occurs when the input mask is not a 2D tensor.
• Solution: Ensure that the input mask is a tensor with exactly two dimensions, typically representing the height and width of the mask.

AssertionError: batch_size must be an integer

• Explanation: This error occurs when the batch_size parameter is not an integer.
• Solution: Verify that the batch_size is provided as an integer value. If you are passing a variable, ensure it is correctly cast to an integer.

ValueError: batch_size must be at least 1

• Explanation: This error occurs when the batch_size is set to a value less than 1.
• Solution: Set the batch_size to a value of 1 or higher to ensure that at least one mask is present in the sequence.