ComfyUI Node: Pre CFG flip flop

Pre CFG flip flop:

The Pre CFG flip flop node is designed to enhance the flexibility and control of your AI model's conditioning process. This node allows you to swap the conditional and unconditional outputs of your model before the classifier-free guidance (CFG) is applied. This can be particularly useful in scenarios where you want to experiment with different conditioning strategies or need to ensure that certain conditions are met before proceeding with further processing. By enabling this node, you can dynamically alter the behavior of your model based on the presence of unconditional outputs, providing a powerful tool for fine-tuning and optimizing your AI-generated art.

Pre CFG flip flop Input Parameters:

model

This parameter represents the AI model that you are working with. It is a required input and ensures that the node has the necessary model to apply the flip flop operation. The model parameter does not have specific minimum or maximum values as it is a reference to the model object itself.

enabled

This is a boolean parameter that determines whether the flip flop operation should be applied. When set to True, the node will perform the swap of conditional and unconditional outputs if an unconditional output is detected. The default value is True, meaning the flip flop operation is enabled by default. This parameter allows you to easily toggle the functionality on or off without removing the node from your workflow.

Pre CFG flip flop Output Parameters:

model

The output of this node is the modified model with the flip flop operation applied. This output model will have its conditional and unconditional outputs swapped if the conditions are met and the operation is enabled. This allows for further processing or generation steps to utilize the altered conditioning, potentially leading to different and interesting results in your AI-generated art.

Pre CFG flip flop Usage Tips:

• Use the enabled parameter to quickly test the impact of the flip flop operation on your model's output without needing to reconfigure your entire workflow.
• Experiment with enabling and disabling this node in different parts of your pipeline to see how it affects the final output, especially in complex workflows with multiple conditioning steps.
• Combine this node with other Pre CFG nodes to create unique conditioning strategies that can enhance the creativity and variability of your AI-generated art.

Pre CFG flip flop Common Errors and Solutions:

"WRONG CHANNEL SELECTED. THE LATENT SPACE ONLY HAS {len(conds_out[1][b])} CHANNELS"

• Explanation: This error occurs when the selected channel for the unconditional output does not exist in the latent space of the model.
• Solution: Ensure that the channel number specified in the node's parameters is within the valid range of channels available in the model's latent space. Adjust the channel number accordingly to match the model's configuration.

"No unconditional output detected"

• Explanation: This error indicates that the node did not find any unconditional output to swap with the conditional output.
• Solution: Verify that your model is configured to produce both conditional and unconditional outputs. Ensure that the unconditional output is correctly generated and passed to the node.

"Model object not provided"

• Explanation: This error occurs when the model parameter is not supplied to the node.
• Solution: Make sure to connect a valid model object to the node's model input parameter. This is necessary for the node to perform the flip flop operation.