ComfyUI Node: Primere Lycoris Keyword Merger

Primere Lycoris Keyword Merger:

The PrimereLycorisKeywordMerger node is designed to streamline the process of merging keywords from various sources related to Lycoris models. This node is particularly useful for AI artists who work with different Lycoris models and need to consolidate keywords from multiple sources such as SD, SDXL, and tag loaders. By using this node, you can efficiently gather and merge keywords, ensuring that your model has the most relevant and comprehensive set of keywords for optimal performance. The main goal of this node is to simplify the keyword management process, making it easier for you to focus on your creative tasks without worrying about the technical details of keyword merging.

Primere Lycoris Keyword Merger Input Parameters:

lycoris_keyword_SD

This parameter accepts a list of keywords related to the Lycoris model for SD (Stable Diffusion). It filters out any None values and expects exactly two elements: the model keyword and its placement. If provided, it will be used to set the model keyword and placement for the Lycoris model. This parameter helps in specifying the keywords that are directly associated with the SD version of the Lycoris model.

lycoris_keyword_SDXL

This parameter accepts a list of keywords related to the Lycoris model for SDXL (Stable Diffusion XL). Similar to lycoris_keyword_SD, it filters out any None values and expects exactly two elements: the model keyword and its placement. If provided, it will be used to set the model keyword and placement for the Lycoris model in the SDXL context. This parameter is crucial for managing keywords specific to the SDXL version of the Lycoris model.

lycoris_keyword_tagloader

This parameter accepts a list of keywords from a tag loader related to the Lycoris model. It filters out any None values and expects exactly two elements: the model keyword and its placement. If provided, it will be used to set the model keyword and placement for the Lycoris model based on the tag loader's input. This parameter is useful for incorporating keywords that are dynamically loaded from external tag sources.

Primere Lycoris Keyword Merger Output Parameters:

model_keyword

The output parameter model_keyword is a tuple containing two elements: the model keyword and its placement. This output provides the consolidated keyword and its designated placement, which can be used in further processing or model configuration. The output ensures that you have a unified keyword set that is ready for use with your Lycoris model, enhancing the model's performance and relevance.

Primere Lycoris Keyword Merger Usage Tips:

• Ensure that each input parameter list contains exactly two elements: the model keyword and its placement. This will help the node function correctly and provide accurate outputs.
• Use this node to merge keywords from different sources to create a comprehensive keyword set for your Lycoris model, which can improve the model's performance and relevance in your projects.
• If you are working with multiple versions of Lycoris models (SD and SDXL), make sure to provide keywords for both lycoris_keyword_SD and lycoris_keyword_SDXL to get the most out of this node.

Primere Lycoris Keyword Merger Common Errors and Solutions:

Input list does not contain exactly two elements

• Explanation: The input list provided to one of the parameters does not contain exactly two elements, which is required for the node to function correctly.
• Solution: Ensure that each input list (lycoris_keyword_SD, lycoris_keyword_SDXL, lycoris_keyword_tagloader) contains exactly two elements: the model keyword and its placement.

NoneType object is not iterable

• Explanation: One of the input parameters is None, and the node is attempting to iterate over it.
• Solution: Make sure to provide a valid list for each input parameter. If a particular keyword source is not available, you can pass an empty list instead of None.

Keyword merging did not produce expected results

• Explanation: The merged keywords do not match the expected output, possibly due to incorrect input values or placements.
• Solution: Double-check the input lists to ensure they contain the correct keywords and placements. Verify that the keywords are relevant and correctly formatted for the Lycoris model you are working with.