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Initialize workspace for HYDiT model training, automating setup for streamlined model preparation.
The MZ_HYDiTInitWorkspace
node is designed to initialize a workspace for training models using the HYDiT framework. This node sets up the necessary directory structure and configurations required for a training session, ensuring that all essential components are in place. By automating the workspace initialization process, it helps streamline the setup phase, allowing you to focus more on the training and fine-tuning of your models. This node is particularly useful for AI artists who want to quickly and efficiently prepare their environment for model training without delving into the technical intricacies of directory management and configuration settings.
This parameter specifies the name of the training session. It is a string value that will be used to create and identify the workspace directory. The train_name
is crucial as it ensures that the workspace is uniquely named and easily accessible. If this parameter is not provided or is left empty, the node will raise an exception. There is no default value for this parameter, and it must be explicitly set by the user.
This parameter defines the specific branch of the repository to be used for the training session. It is a string value with a default of "5657364143e44ac90f72aeb47b81bd505a95665d". The branch parameter allows you to specify which version of the codebase you want to use, which can be important for compatibility and feature reasons.
This parameter indicates the source from which the repository will be cloned. It is a selection from a predefined list of options: "github", "githubfast", "521github", and "kkgithub". The default value is "github". This parameter helps in choosing the most suitable source based on your network conditions and preferences.
This parameter sets the seed for random number generation, which can be important for reproducibility of training results. It is an integer value with a default of 0. By setting the seed, you ensure that the training process can be replicated exactly, which is useful for debugging and comparing different training runs.
The output of this node is a configuration object named workspace_config
. This object contains all the necessary information about the initialized workspace, including the directory paths and any other relevant settings. This configuration is essential for subsequent nodes in the training pipeline, as it provides the context and environment setup required for further processing.
train_name
parameter is unique and descriptive to avoid conflicts and make it easier to identify different training sessions.branch
parameter to specify the exact version of the repository you want to use, especially if you are working with multiple versions or need specific features.source
option that best suits your network conditions to ensure a smooth and fast cloning process.seed
parameter if you need to reproduce your training results exactly, which can be helpful for debugging and comparison purposes.train_name
parameter is not provided or is left empty.train_name
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