ComfyUI Node: HYPIR Advanced Restoration

HYPIR Advanced Restoration:

The HYPIRAdvancedRestoration node is a sophisticated tool designed for advanced image restoration within the ComfyUI framework. It provides users with enhanced control over the restoration process, allowing for the refinement of images to achieve high-quality, detailed results. This node leverages the HYPIR model, which is optimized for performance on both CPU and GPU, to restore images by adjusting various parameters such as upscale factors and model configurations. The primary goal of this node is to offer a flexible and powerful solution for image enhancement, making it an invaluable asset for AI artists seeking to improve the quality and detail of their digital artworks.

HYPIR Advanced Restoration Input Parameters:

image

This parameter represents the input image that you wish to restore. It is the primary subject of the restoration process, and the node will apply its enhancement techniques to this image to improve its quality and detail.

prompt

The prompt is a string input that guides the restoration process. It typically contains descriptive keywords like "high quality" or "detailed" to influence the output style. The default value is "high quality, detailed," and it supports multiline input for more complex descriptions.

upscale_factor

This integer parameter determines the factor by which the image will be upscaled during restoration. It ranges from 1 to 8, with a default value of 1. Increasing this factor can enhance the image resolution, but may also increase processing time.

seed

The seed is an integer used to initialize the random number generator for the restoration process. It allows for reproducibility of results. The default value is -1, and it can range from -1 to 0xffffffffffffffff. Setting a specific seed ensures consistent output across multiple runs.

model_name

This parameter specifies the name of the model to be used for restoration. Currently, the available option is "HYPIR_sd2." This choice determines the underlying algorithms and techniques applied during the restoration process.

base_model_path

The base_model_path parameter allows you to select from available base models as defined in the HYPIR configuration. This selection influences the foundational model architecture used in the restoration process.

model_t

An integer parameter that controls the model's temporal configuration, ranging from 1 to 1000, with a default set by the HYPIR configuration. It affects the model's behavior and performance during restoration.

coeff_t

This integer parameter, ranging from 1 to 1000, with a default from the HYPIR configuration, adjusts the coefficient settings of the model. It influences the intensity and style of the restoration effects.

lora_rank

The lora_rank parameter is an integer that ranges from 1 to 512, with a default value specified in the HYPIR configuration. It determines the rank of the LoRA (Low-Rank Adaptation) modules, affecting the model's adaptability and performance.

patch_size

This integer parameter defines the size of the patches used during the restoration process, ranging from 256 to 1024, with a default value from the HYPIR configuration. It impacts the granularity of the restoration, with larger patches potentially improving processing speed.

encode_patch_size

Similar to patch_size, this parameter specifies the size of patches during the encoding phase, ranging from 256 to 1024, with a default of 512. It affects how the image is processed and encoded for restoration.

decode_patch_size

This parameter sets the size of patches during the decoding phase, ranging from 256 to 1024, with a default of 512. It influences the final output quality and detail level of the restored image.

batch_size

The batch_size parameter determines the number of images processed simultaneously, ranging from 1 to 8, with a default of 1. Increasing the batch size can improve processing efficiency but may require more computational resources.

unload_model_after

A boolean parameter that, when set to true, unloads the model from memory after the restoration process is complete. The default value is false. This can help manage memory usage, especially on systems with limited resources.

HYPIR Advanced Restoration Output Parameters:

IMAGE

The IMAGE output is the restored version of the input image, enhanced according to the specified parameters. It reflects the improvements in quality and detail achieved through the restoration process.

STRING

This output provides a status message detailing the restoration process. It includes information about the parameters used and any errors encountered, offering insights into the operation's success and any adjustments that may be needed.

HYPIR Advanced Restoration Usage Tips:

• Experiment with different upscale_factor values to find the optimal balance between image resolution and processing time for your specific project.
• Use the prompt parameter to guide the restoration style, incorporating descriptive keywords that align with your artistic vision.
• Adjust the lora_rank and patch_size parameters to fine-tune the model's adaptability and processing granularity, which can significantly impact the final image quality.

HYPIR Advanced Restoration Common Errors and Solutions:

Error loading model: <error_message>

• Explanation: This error occurs when the model fails to load due to incorrect configurations or missing files.
• Solution: Verify that the model_name and base_model_path are correctly specified and that all necessary files are accessible.

HYPIR advanced restoration error: <error_message>

• Explanation: This error indicates a problem during the restoration process, possibly due to incompatible parameter settings or resource limitations.
• Solution: Check the input parameters for any inconsistencies and ensure that your system meets the necessary resource requirements for the specified batch size and patch sizes.

Error unloading HYPIR model: <error_message>

• Explanation: This error arises when the model fails to unload from memory, potentially due to ongoing processes or resource locks.
• Solution: Ensure that no other processes are using the model and try setting unload_model_after to true to manage memory usage effectively.