ComfyUI Node: ImageAssistedCFGGuider

ImageAssistedCFGGuider:

The ImageAssistedCFGGuider node is designed to enhance the capabilities of AI models by integrating image-based guidance into the Conditional Function Guidance (CFG) process. This node allows you to leverage the power of images to influence the model's output, providing a more nuanced and controlled generation process. By incorporating image guidance, you can achieve more precise and contextually relevant results, making it an invaluable tool for AI artists looking to fine-tune their creations. The node works by setting conditions and strengths based on both textual and image inputs, ensuring that the generated content aligns closely with the desired artistic vision.

ImageAssistedCFGGuider Input Parameters:

model

This parameter specifies the AI model to be used for the generation process. The model serves as the foundation upon which the guidance and conditioning are applied.

positive

This parameter represents the positive conditioning input, which is used to guide the model towards desired features or characteristics in the generated output. It helps in emphasizing certain aspects that you want to be prominent in the final result.

negative

This parameter represents the negative conditioning input, which is used to guide the model away from undesired features or characteristics. It helps in suppressing elements that you do not want to appear in the final result.

cfg

This parameter stands for Conditional Function Guidance strength. It controls the overall influence of the conditioning inputs on the model's output. The default value is 8.0, with a minimum of 0.0 and a maximum of 100.0, adjustable in steps of 0.1 and rounded to 0.01.

image_cfg

This parameter controls the strength of the image-based guidance. It determines how much influence the provided image has on the model's output. The default value is 1.0, with a range from -100.0 to 100.0, adjustable in steps of 0.01 and rounded to 0.001.

image_weighting

This parameter allows you to select the weighting method for the image guidance. The available options are "flat," "linear down," and "cosine down." Each method affects how the image guidance is applied across the generation process.

weight_scaling

This parameter controls the scaling of the weights applied to the image guidance. It allows for fine-tuning the impact of the image on the model's output. The default value is 1.0, with a range from 0.01 to 100.0, adjustable in steps of 0.01 and rounded to 0.001.

latent_image

This parameter represents the latent image input, which is used as a reference for the image-based guidance. It helps in aligning the generated output with the visual characteristics of the provided image.

ImageAssistedCFGGuider Output Parameters:

GUIDER

The output of this node is a GUIDER object, which encapsulates the configured guidance settings. This object is used to influence the model's generation process, ensuring that the output aligns with the specified textual and image-based conditions.

ImageAssistedCFGGuider Usage Tips:

• Experiment with different values for cfg and image_cfg to find the optimal balance between textual and image-based guidance for your specific use case.
• Use the image_weighting parameter to adjust how the image guidance is applied throughout the generation process. For instance, "linear down" can gradually reduce the influence of the image, while "cosine down" can provide a smoother transition.
• Fine-tune the weight_scaling parameter to control the overall impact of the image guidance. Higher values can make the image influence more pronounced, while lower values can make it more subtle.

ImageAssistedCFGGuider Common Errors and Solutions:

"Invalid model input"

• Explanation: This error occurs when the specified model is not recognized or is incompatible with the node.
• Solution: Ensure that you are using a valid and compatible model for the ImageAssistedCFGGuider node.

"Positive or negative conditioning input missing"

• Explanation: This error occurs when either the positive or negative conditioning input is not provided.
• Solution: Make sure to provide both positive and negative conditioning inputs to guide the model effectively.

"Invalid range for cfg or image_cfg"

• Explanation: This error occurs when the values for cfg or image_cfg are outside the allowed range.
• Solution: Ensure that the values for cfg are between 0.0 and 100.0, and for image_cfg between -100.0 and 100.0.

"Latent image input missing"

• Explanation: This error occurs when the latent image input is not provided.
• Solution: Provide a valid latent image input to use the image-based guidance feature effectively.