ComfyUI Node: CFG-less Negative Prompt

CFG-less Negative Prompt:

The CFGlessNegativePrompt node is designed to refine and enhance the conditioning of a given prompt by utilizing a negative prompt without relying on a traditional Classifier-Free Guidance (CFG) scale. This node is particularly useful for AI artists who want to subtly influence the output of their models by specifying what should be avoided in the generated content. By encoding both the negative prompt and an empty prompt using a CLIP model, the node calculates a conditioning delta, which is then subtracted from the original conditioning. This process allows for a nuanced adjustment of the model's output, ensuring that certain undesired elements are minimized or excluded, thereby enhancing the overall quality and relevance of the generated content.

CFG-less Negative Prompt Input Parameters:

conditioning

This parameter represents the initial conditioning input that guides the model's output. It is a crucial component as it sets the baseline for the content generation process. The conditioning is adjusted by the node to incorporate the effects of the negative prompt, ensuring that the final output aligns more closely with the user's intent.

clip

The CLIP model used for encoding the text prompts. This parameter is essential as it determines how the text is transformed into a format that the model can process. The choice of CLIP model can significantly impact the effectiveness of the negative prompt in altering the conditioning.

negative_prompt

A text input that specifies what should be avoided in the generated content. This parameter allows users to guide the model away from certain elements or themes, enhancing the relevance and quality of the output. The negative prompt is encoded and used to calculate the conditioning delta.

negative_prompt_strength

A floating-point value that determines the intensity of the negative prompt's influence on the conditioning. The default value is 0.6, with a range from -100.0 to 100.0. This parameter allows users to fine-tune the degree to which the negative prompt affects the final output, providing flexibility in achieving the desired result.

CFG-less Negative Prompt Output Parameters:

conditioning

The output is a modified version of the initial conditioning, adjusted to account for the influence of the negative prompt. This refined conditioning guides the model to produce content that aligns more closely with the user's specifications, minimizing the presence of undesired elements.

CFG-less Negative Prompt Usage Tips:

• Experiment with different negative prompts to see how they influence the output. This can help you understand the impact of specific words or phrases on the generated content.
• Adjust the negative_prompt_strength parameter to find the right balance between the original conditioning and the influence of the negative prompt. A higher strength may lead to more pronounced changes, while a lower strength might result in subtler adjustments.

CFG-less Negative Prompt Common Errors and Solutions:

"Cannot execute because a node is missing the class_type property."

• Explanation: This error occurs when a node in the prompt does not have the required class_type property, which is necessary for execution.
• Solution: Ensure that all nodes in your prompt are correctly defined with the class_type property. Check the node definitions and update any missing properties.

"Cannot execute because node <class_type> does not exist."

• Explanation: This error indicates that a node referenced in the prompt does not exist in the current configuration or setup.
• Solution: Verify that all nodes used in your prompt are available and correctly mapped in the system. If a node is missing, you may need to install or define it properly.

"Prompt has no outputs"

• Explanation: This error suggests that the prompt does not produce any outputs, which is necessary for successful execution.
• Solution: Review your prompt setup to ensure that it includes nodes that generate outputs. Adjust the configuration to include output-generating nodes if necessary.