ComfyUI Node: Free Memory (Latent)

Free Memory (Latent):

The FreeMemoryLatent node is designed to optimize memory usage within your AI art generation workflow by freeing up system RAM and GPU VRAM. This node is particularly useful when working with latent representations, which are intermediate data structures used in machine learning models. By managing memory efficiently, the node helps prevent memory bottlenecks, allowing for smoother and more efficient processing of large datasets or complex models. The node can operate in a standard mode or an aggressive mode, where it attempts to free up more memory by clearing caches and unloading models. This functionality is crucial for maintaining performance and stability, especially when working with resource-intensive tasks.

Free Memory (Latent) Input Parameters:

latent

The latent parameter represents the latent data structure that you are working with. This is a required input and serves as the primary data that the node will process. The latent data is typically an intermediate representation used in machine learning models, and it is crucial for the node to manage memory effectively while handling this data.

aggressive

The aggressive parameter is a boolean option that determines the level of memory freeing actions the node will take. When set to True, the node will perform more aggressive memory clearing operations, such as unloading models and clearing system caches, to maximize the amount of freed memory. The default value is False, which means the node will perform standard memory freeing operations. This parameter allows you to control the balance between memory usage and processing speed, depending on your specific needs.

Free Memory (Latent) Output Parameters:

latent

The latent output parameter returns the same latent data structure that was input into the node. This ensures that the data you are working with remains unchanged while the node performs memory management tasks. The primary purpose of this output is to allow the node to be seamlessly integrated into a larger workflow, where the latent data can continue to be used in subsequent processing steps.

Free Memory (Latent) Usage Tips:

• Use the aggressive mode when working with very large datasets or models that are causing memory issues, as this can help free up additional resources.
• Integrate the FreeMemoryLatent node into your workflow at points where memory usage is highest, such as after loading large models or datasets, to maintain optimal performance.

Free Memory (Latent) Common Errors and Solutions:

CUDA is not available. No GPU VRAM to free.

• Explanation: This error occurs when the node attempts to free GPU VRAM, but CUDA is not available on your system.
• Solution: Ensure that your system has a compatible NVIDIA GPU and that CUDA is properly installed and configured. If you do not have a CUDA-capable GPU, consider using CPU-based processing instead.

Failed to clear system caches on Linux: <error_message>

• Explanation: This error indicates that the node attempted to clear system caches on a Linux system, but encountered an issue.
• Solution: Check that you have the necessary permissions to write to /proc/sys/vm/drop_caches. You may need to run your application with elevated privileges or adjust system settings to allow cache clearing.