ComfyUI Node: Empty Latent Image Presets

Empty Latent Image Presets:

The EmptyLatentImagePresets node is designed to generate latent images with predefined dimensions, making it easier for you to create images with specific sizes without manually setting the width and height each time. This node is particularly useful for AI artists who need to work with standard image sizes frequently. By providing a set of common dimensions, it simplifies the process of generating latent images, allowing you to focus more on your creative work rather than technical details. Additionally, the node offers options to invert the dimensions and set the batch size, giving you flexibility in how you generate your latent images.

Empty Latent Image Presets Input Parameters:

dimensions

The dimensions parameter allows you to select from a list of predefined image sizes. This makes it convenient to generate images with standard dimensions without manually entering the width and height. The available options are 512 x 512, 768 x 512, 960 x 512, 1024 x 512, 1536 x 640, 1344 x 768, 1216 x 832, 1152 x 896, and 1024 x 1024. The default value is 512 x 512. This parameter ensures that you can quickly select a common image size, streamlining your workflow.

invert

The invert parameter is a boolean option that allows you to swap the width and height of the selected dimensions. If set to True, the width and height values are inverted. The default value is False. This parameter provides additional flexibility, enabling you to easily switch the orientation of your image dimensions without manually adjusting the values.

batch_size

The batch_size parameter specifies the number of latent images to generate in a single batch. It accepts integer values with a minimum of 1 and a maximum of 4096. The default value is 1. This parameter is useful for generating multiple images at once, which can be particularly beneficial for tasks that require a large number of images, such as training machine learning models or creating image datasets.

Empty Latent Image Presets Output Parameters:

Latent

The Latent output is a tensor representing the generated latent image(s). This tensor contains the pixel data for the image(s) and is used as the basis for further image processing or generation tasks. The latent image is essential for various AI art applications, as it serves as the starting point for generating final images.

Width

The Width output provides the width of the generated latent image(s) in pixels. This value is derived from the selected dimensions and is crucial for understanding the size and aspect ratio of the generated images. Knowing the width helps you plan how the images will be used or displayed in your projects.

Height

The Height output provides the height of the generated latent image(s) in pixels. Similar to the Width output, this value is derived from the selected dimensions and is important for understanding the size and aspect ratio of the generated images. Knowing the height ensures that you can effectively use or display the images in your projects.

Empty Latent Image Presets Usage Tips:

• Use the dimensions parameter to quickly select common image sizes, saving time and reducing the chance of errors from manual entry.
• Set the invert parameter to True if you need to switch the orientation of your image dimensions without manually adjusting the width and height.
• Adjust the batch_size parameter to generate multiple images at once, which can be useful for creating datasets or training models.

Empty Latent Image Presets Common Errors and Solutions:

Invalid dimension format

• Explanation: The selected dimension format is not recognized.
• Solution: Ensure that you select a dimension from the provided list of options.

Batch size out of range

• Explanation: The batch_size value is outside the allowed range (1 to 4096).
• Solution: Set the batch_size to a value within the allowed range.

Inversion error

• Explanation: An error occurred while inverting the dimensions.
• Solution: Double-check the invert parameter and ensure it is set correctly. If the issue persists, try selecting a different dimension and then reapplying the inversion.