ComfyUI Node: 🔍 CR Apply Multi Upscale

🔍 CR Apply Multi Upscale:

The CR Apply Multi Upscale node is designed to enhance the resolution of images by applying multiple upscaling models sequentially. This node is particularly useful for AI artists who want to improve the quality and detail of their images without losing the original essence. By leveraging a stack of upscaling models, this node allows for a more refined and controlled upscaling process, ensuring that each model contributes to the final output. The primary goal of this node is to provide a flexible and powerful tool for image enhancement, making it easier to achieve high-quality results with minimal effort.

🔍 CR Apply Multi Upscale Input Parameters:

image

This parameter represents the input image that you want to upscale. It is the starting point for the upscaling process and will be transformed by the various models in the upscale stack.

resampling_method

This parameter determines the method used for resampling the image during the upscaling process. Common resampling methods include "nearest-exact", "bilinear", "area", "bicubic", and "lanczos". The choice of resampling method can affect the quality and smoothness of the upscaled image.

supersample

This boolean parameter indicates whether supersampling should be applied during the upscaling process. Supersampling can help to reduce aliasing and improve the overall quality of the upscaled image. The default value is 'true'.

rounding_modulus

This parameter is used to ensure that the dimensions of the upscaled image are rounded to a specific modulus. This can be useful for maintaining compatibility with certain display or processing requirements. The default value is 8.

upscale_stack

This parameter is a list of tuples, where each tuple contains an upscale model and a rescale factor. The upscale models are applied sequentially to the input image, with each model contributing to the final upscaled result. The rescale factor determines the amount by which the image is rescaled at each step.

🔍 CR Apply Multi Upscale Output Parameters:

image

The output parameter represents the final upscaled image after all the models in the upscale stack have been applied. This image will have enhanced resolution and quality, reflecting the combined effects of the various upscaling models.

🔍 CR Apply Multi Upscale Usage Tips:

• Experiment with different combinations of upscale models in the upscale stack to achieve the best results for your specific image.
• Use the resampling_method parameter to fine-tune the quality of the upscaled image. Different methods can produce varying levels of sharpness and smoothness.
• Enable supersample to reduce aliasing and improve the overall quality of the upscaled image, especially for images with fine details.
• Adjust the rounding_modulus parameter to ensure that the dimensions of the upscaled image meet specific requirements or constraints.

🔍 CR Apply Multi Upscale Common Errors and Solutions:

"Model not found"

• Explanation: This error occurs when the specified upscale model cannot be found in the designated directory.
• Solution: Ensure that the model name is correct and that the model file is located in the appropriate directory.

"Out of memory"

• Explanation: This error occurs when the system runs out of memory during the upscaling process.
• Solution: Reduce the tile size or overlap in the upscale_with_model function to lower memory usage. If the problem persists, consider using a machine with more memory.

"Invalid rescale factor"

• Explanation: This error occurs when the rescale factor is set to an invalid value.
• Solution: Ensure that the rescale factor is a positive number and within a reasonable range for the upscaling process.