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Enhance AI models with nuanced conditioning control using FABRIC technique for improved performance and precise outputs.
The FABRICPatchModelAdv node is designed to enhance your AI model by integrating the FABRIC (Feedback-Aware Backpropagation for Improved Conditioning) technique. This advanced node allows you to fine-tune your model's conditioning by applying positive and negative weights to specific conditioning inputs. The primary goal of this node is to improve the model's performance by leveraging feedback mechanisms, which can be particularly useful in scenarios where nuanced control over the model's behavior is required. By using this node, you can achieve more precise and controlled outputs, making it a valuable tool for AI artists looking to refine their models.
This parameter represents the AI model you wish to patch using the FABRIC technique. It is a required input and ensures that the node has a model to apply the conditioning adjustments to.
This parameter is a conditioning input that serves as a baseline for positive conditioning. It is required and helps the node understand what the default positive conditioning should be.
This parameter is a conditioning input that serves as a baseline for negative conditioning. It is required and helps the node understand what the default negative conditioning should be.
This parameter is a floating-point value that determines the weight of the positive conditioning. It ranges from 0.0 to 1.0, with a default value of 1.0. Adjusting this weight allows you to control the influence of positive conditioning on the model.
This parameter is a floating-point value that determines the weight of the negative conditioning. It ranges from 0.0 to 1.0, with a default value of 1.0. Adjusting this weight allows you to control the influence of negative conditioning on the model.
This optional parameter represents the latent variables for positive conditioning. Providing these latents can help the node apply more specific positive conditioning to the model.
This optional parameter represents the latent variables for negative conditioning. Providing these latents can help the node apply more specific negative conditioning to the model.
The output of this node is the patched model. This model has been adjusted using the FABRIC technique, incorporating the specified positive and negative conditioning weights and latents. The patched model is expected to perform better and provide more controlled outputs based on the conditioning inputs.
pos_weight
and neg_weight
parameters to fine-tune the influence of positive and negative conditioning on your model.pos_latents
and neg_latents
parameters to enhance the model's performance.pos_weight
or neg_weight
parameters are set outside the valid range of 0.0 to 1.0.pos_weight
and neg_weight
parameters to be within the valid range of 0.0 to 1.0.model
parameter is not provided or is invalid.© Copyright 2024 RunComfy. All Rights Reserved.