Visit ComfyUI Online for ready-to-use ComfyUI environment
Adjust weights across various parameters in Animate Diff framework for enhanced model performance and artistic control.
The ADE_AdjustWeightAllMult node is designed to provide a comprehensive adjustment mechanism for various weights within the Animate Diff framework. This node allows you to apply multiplicative adjustments to a wide range of parameters, including positional encoding (pe), attention mechanisms (attn), and other related weights. By using this node, you can fine-tune the behavior of your models, ensuring that specific aspects of the model's performance are enhanced or modified according to your artistic needs. The primary goal of this node is to offer a flexible and powerful tool for adjusting weights, making it easier to achieve the desired effects in your AI-generated art.
This parameter controls the multiplicative adjustment for positional encoding weights. It allows you to scale the positional encoding, which can impact how the model interprets spatial information. The value ranges from 0.0 to 2.0, with a default of 1.0.
This parameter adjusts the overall attention weights multiplicatively. By scaling the attention weights, you can influence how the model focuses on different parts of the input. The value ranges from 0.0 to 2.0, with a default of 1.0.
This parameter specifically adjusts the query weights in the attention mechanism. It allows you to fine-tune how the model queries information from the input. The value ranges from 0.0 to 2.0, with a default of 1.0.
This parameter adjusts the key weights in the attention mechanism. By scaling the key weights, you can modify how the model matches keys to queries. The value ranges from 0.0 to 2.0, with a default of 1.0.
This parameter adjusts the value weights in the attention mechanism. It allows you to scale the values that are retrieved based on the attention scores. The value ranges from 0.0 to 2.0, with a default of 1.0.
This parameter adjusts the output weights of the attention mechanism. By scaling these weights, you can influence the final output of the attention layer. The value ranges from 0.0 to 2.0, with a default of 1.0.
This parameter adjusts the bias weights in the attention mechanism's output. It allows you to fine-tune the bias applied to the attention output. The value ranges from 0.0 to 2.0, with a default of 1.0.
This parameter provides a multiplicative adjustment for other weights that do not fall into the specific categories mentioned above. It offers a general scaling mechanism for additional weights. The value ranges from 0.0 to 2.0, with a default of 1.0.
This boolean parameter controls whether the adjustments made by the node are printed out. It is useful for debugging and understanding the impact of the adjustments. The default value is False.
This optional parameter allows you to pass in a previous weight adjustment group. If provided, the new adjustments will be added to this group. If not provided, a new adjustment group will be created.
The output of this node is a weight adjustment group that contains all the adjustments made by the node. This group can be used in subsequent nodes to apply the adjustments to the model's weights, ensuring that the desired modifications are implemented.
print_adjustment
parameter to monitor the changes being made, which can help in fine-tuning the adjustments.prev_weight_adjust
parameter is not an instance of the AdjustGroup
class.prev_weight_adjust
parameter, if provided, is a valid AdjustGroup
object.AdjustGroup
class implementation and ensure it supports cloning.ยฉ Copyright 2024 RunComfy. All Rights Reserved.