ComfyUI Node: KSampler FABRIC

KSampler FABRIC:

KSamplerFABRICAdv is an advanced node designed to enhance the capabilities of the regular KSampler by integrating additional inputs specific to the FABRIC framework. This node is particularly useful for AI artists looking to fine-tune their generative models with more nuanced control over conditioning and feedback mechanisms. By leveraging the advanced features of FABRIC, KSamplerFABRICAdv allows for more precise adjustments in the sampling process, leading to higher quality and more tailored outputs. The node is designed to be user-friendly, making it accessible even to those without a deep technical background, while still offering powerful customization options for more experienced users.

KSampler FABRIC Input Parameters:

null_pos

This parameter accepts a conditioning input that serves as a null or baseline positive condition. It helps in defining the starting point for positive conditioning, ensuring that the model has a reference for generating positive samples.

null_neg

This parameter accepts a conditioning input that serves as a null or baseline negative condition. It helps in defining the starting point for negative conditioning, ensuring that the model has a reference for generating negative samples.

pos_weight

This floating-point parameter adjusts the weight of the positive conditioning. It ranges from 0.0 to 1.0, with a default value of 1.0. Increasing this weight will make the positive conditioning more influential in the sampling process.

neg_weight

This floating-point parameter adjusts the weight of the negative conditioning. It ranges from 0.0 to 1.0, with a default value of 1.0. Increasing this weight will make the negative conditioning more influential in the sampling process.

feedback_start

This integer parameter specifies the starting step for feedback during the sampling process. It ranges from 0 to 10000, with a default value of 0. Setting this parameter allows you to control when feedback mechanisms begin to influence the sampling.

feedback_end

This integer parameter specifies the ending step for feedback during the sampling process. It ranges from 0 to 10000, with a default value of 10000. Setting this parameter allows you to control when feedback mechanisms stop influencing the sampling.

pos_latents

This optional parameter accepts latent inputs for positive conditioning. If provided, these latents will be used to guide the sampling process towards more positive outcomes.

neg_latents

This optional parameter accepts latent inputs for negative conditioning. If provided, these latents will be used to guide the sampling process towards more negative outcomes.

KSampler FABRIC Output Parameters:

LATENT

The output of this node is a latent representation that has been sampled using the advanced FABRIC inputs. This latent can be further processed or decoded to generate the final output, providing a more refined and controlled result based on the specified conditioning and feedback parameters.

KSampler FABRIC Usage Tips:

• To achieve more nuanced results, experiment with different values for pos_weight and neg_weight to see how they influence the output.
• Utilize the feedback_start and feedback_end parameters to fine-tune when feedback mechanisms should be active, which can help in achieving more stable and desired results.
• If you have specific latent representations that you want to guide the sampling process, make sure to provide them through the pos_latents and neg_latents parameters.

KSampler FABRIC Common Errors and Solutions:

"No reference latents found. Defaulting to regular KSampler."

• Explanation: This error occurs when both pos_latents and neg_latents are not provided or are empty.
• Solution: Ensure that you provide valid latent inputs for either pos_latents or neg_latents to utilize the advanced FABRIC features. If you do not have specific latents, consider using the default KSampler instead.

"Invalid feedback range."

• Explanation: This error occurs when the feedback_start parameter is greater than the feedback_end parameter.
• Solution: Make sure that the feedback_start value is less than or equal to the feedback_end value to define a valid feedback range.

"Conditioning input missing."

• Explanation: This error occurs when either null_pos or null_neg conditioning inputs are not provided.
• Solution: Ensure that you provide valid conditioning inputs for both null_pos and null_neg parameters to guide the sampling process effectively.