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Initialize workspace for AI model training with KohyaSS framework for KohakuBlueleaf repository, streamlining setup process.
The MZ_KohyaSS_KohakuBlueleaf_HYHiDInitWorkspace
node is designed to initialize a workspace for training AI models using the KohyaSS framework, specifically tailored for the KohakuBlueleaf repository. This node facilitates the setup process by configuring the necessary environment and parameters, ensuring a smooth start for your training tasks. It is particularly useful for AI artists who want to leverage the KohyaSS tools without delving into the technical intricacies of environment setup. By automating the initialization process, this node helps you focus more on the creative aspects of AI model training.
This parameter specifies the name of the LoRA (Low-Rank Adaptation) model you wish to use or create. It is a string value and can be left empty if you do not have a specific LoRA model in mind. The default value is an empty string ""
.
This parameter determines the specific branch of the KohakuBlueleaf repository to be used. It is a string value that defaults to "0dc79edc01f2000de1dad5ad6d20d8b099bfafe2"
. This allows you to specify a particular version or state of the repository, ensuring compatibility and stability for your training tasks.
This parameter allows you to choose the source from which the repository will be cloned. It offers multiple options: "github"
, "githubfast"
, "521github"
, and "kkgithub"
. The default value is "github"
. Selecting the appropriate source can impact the speed and reliability of the cloning process.
This parameter sets the seed for random number generation, which is crucial for reproducibility in training tasks. It is an integer value with a default of 0
. By setting a specific seed, you can ensure that your training results are consistent and reproducible.
The output of this node is a configuration object of type MZ_TT_SS_WorkspaceConfig
. This configuration encapsulates all the necessary settings and parameters required to initialize and manage the training workspace. It serves as a blueprint for the training environment, ensuring that all components are correctly set up and ready for use.
{workspace_dir}
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