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Initialize workspace for AI model training with Kohya-ss framework, streamlining setup for efficient model training.
The MZ_KohyaSSInitWorkspace
node is designed to initialize a workspace for training AI models using the Kohya-ss framework. This node facilitates the setup process by cloning the necessary repositories and configuring the workspace environment based on the provided parameters. It streamlines the initial setup, allowing you to focus on the training process without worrying about the underlying infrastructure. This node is particularly useful for AI artists who want to leverage the Kohya-ss tools for their projects, providing a seamless and efficient way to get started with model training.
This parameter specifies the name of the LoRA (Low-Rank Adaptation) model you wish to use or create within the workspace. It is a string value, and the default is an empty string. The lora_name
helps in organizing and identifying different models within your workspace.
This parameter defines the specific branch of the repository to be used for setting up the workspace. It is a string value with a default of "71e2c91330a9d866ec05cdd10584bbb962896a99". The branch parameter allows you to select a particular version or state of the repository, ensuring compatibility and stability for your training environment.
This parameter indicates the source from which the repository should be cloned. It offers multiple options: "github", "githubfast", "521github", and "kkgithub", with "github" being the default. The source parameter provides flexibility in choosing the most suitable or fastest repository source based on your network conditions and preferences.
This parameter sets the seed value for random number generation, which can affect the reproducibility of your training results. It is an integer value with a default of 0. By setting the seed, you can ensure that your training process is consistent and repeatable, which is crucial for debugging and comparing different training runs.
The output parameter workspace_config
is of type MZ_TT_SS_WorkspaceConfig
. This configuration object contains all the necessary settings and paths required for the initialized workspace. It serves as a reference for subsequent nodes and processes, ensuring that the workspace is correctly set up and ready for training.
lora_name
is unique and descriptive to avoid confusion when managing multiple models within the same workspace.branch
parameter to select a stable and compatible version of the repository, especially if you encounter issues with the default branch.source
option that provides the fastest and most reliable access to the repository, particularly if you experience slow download speeds.seed
parameter to a fixed value if you need to reproduce your training results for comparison or debugging purposes.<workspace_dir>
"<detailed_error_message>
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