ComfyUI  >  Nodes  >  KJNodes for ComfyUI >  CheckpointPerturbWeights

ComfyUI Node: CheckpointPerturbWeights

Class Name


kijai (Account age: 2192 days)
KJNodes for ComfyUI
Latest Updated
Github Stars

How to Install KJNodes for ComfyUI

Install this extension via the ComfyUI Manager by searching for  KJNodes for ComfyUI
  • 1. Click the Manager button in the main menu
  • 2. Select Custom Nodes Manager button
  • 3. Enter KJNodes for ComfyUI in the search bar
After installation, click the  Restart button to restart ComfyUI. Then, manually refresh your browser to clear the cache and access the updated list of nodes.

Visit ComfyUI Online for ready-to-use ComfyUI environment

  • Free trial available
  • High-speed GPU machines
  • 200+ preloaded models/nodes
  • Freedom to upload custom models/nodes
  • 50+ ready-to-run workflows
  • 100% private workspace with up to 200GB storage
  • Dedicated Support

Run ComfyUI Online

CheckpointPerturbWeights Description

Modify neural network weights at checkpoints for model optimization and experimentation.


The CheckpointPerturbWeights node is designed to modify the weights of a neural network model at specific checkpoints during its training or inference process. This node allows you to apply perturbations to the model's weights, which can be useful for various purposes such as model fine-tuning, experimentation with different weight configurations, or enhancing the robustness of the model. By adjusting the weights at certain checkpoints, you can explore how different weight configurations impact the model's performance and behavior. This node is particularly beneficial for AI artists and researchers who want to experiment with and optimize their models without delving deep into the technical intricacies of weight manipulation.

CheckpointPerturbWeights Input Parameters:


This parameter represents the specific modifications or perturbations to be applied to the model's weights. It is a dictionary where the keys are the names of the weights to be modified, and the values are the perturbation values or functions. The patches parameter allows you to precisely control which weights are altered and how they are modified, providing flexibility in experimenting with different weight configurations. There are no strict minimum or maximum values for this parameter, as it depends on the specific use case and the model being used.


The weight parameter specifies the base weight value to be used in the perturbation process. This value serves as the starting point for applying the patches. If an old weight value is available, it will be used instead. The weight parameter is crucial for determining the initial state of the weights before any perturbations are applied. The default value for this parameter is typically the current weight value of the model.


The key parameter is a string that identifies the specific weight or set of weights to be perturbed. It is used to locate the corresponding weights in the model and apply the specified patches. The key parameter ensures that the correct weights are targeted for modification, allowing for precise and controlled perturbations. There are no strict minimum or maximum values for this parameter, as it depends on the specific weights being targeted.

CheckpointPerturbWeights Output Parameters:


The weight output parameter represents the modified weight value after the perturbations have been applied. This value reflects the changes made to the original weight based on the specified patches. The modified weight is crucial for understanding the impact of the perturbations on the model's performance and behavior. By analyzing the output weight, you can gain insights into how different weight configurations affect the model and make informed decisions about further adjustments.

CheckpointPerturbWeights Usage Tips:

  • Experiment with different patches to explore how various perturbations impact your model's performance. This can help you identify optimal weight configurations for specific tasks.
  • Use the key parameter to target specific weights in your model, allowing for precise and controlled modifications.
  • Monitor the output weight to understand the effects of the perturbations and make data-driven decisions about further adjustments.

CheckpointPerturbWeights Common Errors and Solutions:

"Invalid key specified"

  • Explanation: The key parameter does not match any weights in the model.
  • Solution: Ensure that the key parameter correctly identifies the weights you want to modify. Double-check the weight names in your model.

"Patches parameter is empty"

  • Explanation: No patches were provided for modifying the weights.
  • Solution: Provide a dictionary of patches with the appropriate perturbation values or functions to apply to the weights.

"Weight parameter is None"

  • Explanation: The weight parameter was not specified or is set to None.
  • Solution: Ensure that the weight parameter is provided and contains a valid base weight value for the perturbation process.

CheckpointPerturbWeights Related Nodes

Go back to the extension to check out more related nodes.
KJNodes for ComfyUI

© Copyright 2024 RunComfy. All Rights Reserved.

RunComfy is the premier ComfyUI platform, offering ComfyUI online environment and services, along with ComfyUI workflows featuring stunning visuals.