ComfyUI Node: 🎈VRAM-Cleanup

🎈VRAM-Cleanup:

The VRAMCleanup node is designed to efficiently manage and optimize the usage of Video RAM (VRAM) in your AI art generation workflows. Its primary purpose is to ensure that VRAM resources are utilized effectively, preventing memory bottlenecks that can slow down or halt your creative processes. By intelligently unloading unused models and freeing up VRAM, this node helps maintain smooth performance and allows you to work with larger models or more complex scenes without running into memory limitations. The VRAMCleanup node is particularly beneficial in environments where multiple models are loaded simultaneously, as it ensures that only the necessary models are kept in memory, thus optimizing the available VRAM for active tasks.

🎈VRAM-Cleanup Input Parameters:

The context does not provide specific input parameters for the VRAMCleanup node. However, based on typical VRAM management practices, input parameters might include options to specify which models to keep loaded, the amount of VRAM to free, or conditions under which VRAM should be cleaned. These parameters would allow you to tailor the VRAM cleanup process to your specific needs, ensuring that critical models remain available while freeing up memory from less essential ones.

🎈VRAM-Cleanup Output Parameters:

The context does not provide specific output parameters for the VRAMCleanup node. Generally, output parameters for a VRAM cleanup process might include a status message indicating the success of the operation, the amount of VRAM freed, or a list of models that were unloaded. These outputs would help you understand the impact of the cleanup process and verify that the desired memory optimization was achieved.

🎈VRAM-Cleanup Usage Tips:

• Regularly use the VRAMCleanup node when working with multiple models to ensure optimal memory usage and prevent performance issues.
• Consider setting up automated VRAM cleanup processes at specific intervals or after certain operations to maintain a smooth workflow.
• Monitor VRAM usage before and after cleanup to assess the effectiveness of the node and adjust parameters as needed for better performance.

🎈VRAM-Cleanup Common Errors and Solutions:

Error: "Failed to unload model"

• Explanation: This error may occur if a model is still in use or if there are dependencies preventing it from being unloaded.
• Solution: Ensure that the model is not actively being used in any process. Check for any dependencies or references that might be keeping the model in memory and resolve them before attempting to unload again.

Error: "Insufficient VRAM freed"

• Explanation: This error indicates that the cleanup process did not free up the expected amount of VRAM, possibly due to locked resources or active processes.
• Solution: Review the models and processes currently using VRAM. Consider closing unnecessary applications or processes that might be consuming VRAM. Adjust the cleanup parameters to target specific models or resources for unloading.