ComfyUI Node: Load Latent (Numpy)

Load Latent (Numpy):

The LoadLatentNumpy node is designed to load latent representations from various file formats, including .latent, .safetensors, .npy, and .npz. This node is particularly useful for AI artists who work with latent spaces in generative models, as it allows for the seamless integration of pre-saved latent data into their workflows. By supporting multiple file formats, LoadLatentNumpy ensures flexibility and compatibility with different tools and datasets. The node automatically handles the loading process, converting the latent data into a format that can be readily used in subsequent nodes or processes. This capability is essential for tasks that involve manipulating or analyzing latent spaces, such as image generation, style transfer, or other creative AI applications.

Load Latent (Numpy) Input Parameters:

latent

The latent parameter specifies the file name of the latent data to be loaded. This file can be in one of several supported formats: .latent, .safetensors, .npy, or .npz. The node will automatically detect the file format based on the extension and apply the appropriate loading method. This parameter is crucial as it determines the source of the latent data that will be processed. Ensure that the file exists in the specified directory and has the correct format to avoid errors.

Load Latent (Numpy) Output Parameters:

samples

The samples output parameter contains the loaded latent data in a format that can be used by other nodes or processes. The data is converted to a torch.float32 tensor, ensuring compatibility with PyTorch-based operations. This output is essential for further manipulation or analysis of the latent data, enabling a wide range of creative and technical applications.

Load Latent (Numpy) Usage Tips:

• Ensure that the latent file is in one of the supported formats (.latent, .safetensors, .npy, .npz) and is located in the correct directory to avoid loading errors.
• Use the samples output in conjunction with other nodes that accept latent data to create complex workflows and achieve desired artistic effects.
• If you encounter issues with specific file formats, try converting the latent data to a different supported format and reloading it.

Load Latent (Numpy) Common Errors and Solutions:

Unknown latent extension '<ext>'

• Explanation: This error occurs when the file extension of the latent data is not recognized by the node.
• Solution: Ensure that the file has one of the supported extensions: .latent, .safetensors, .npy, or .npz. Rename the file if necessary.

Invalid latent file '<latent>'

• Explanation: This error indicates that the specified latent file does not exist in the expected directory.
• Solution: Verify that the file is present in the correct directory and that the file name is spelled correctly. Check the directory path and file extension.

Error loading numpy file

• Explanation: This error occurs when there is an issue loading a .npy file, possibly due to file corruption or incompatible data format.
• Solution: Check the integrity of the .npy file and ensure it is not corrupted. Try re-saving the file in the correct format and reloading it.

Error loading koyha file

• Explanation: This error occurs when there is an issue loading a .npz file, possibly due to file corruption or incompatible data format.
• Solution: Verify the contents of the .npz file and ensure it contains the expected data structure. Re-save the file if necessary and try loading it again.