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Facilitates creation of attention regions within AI models for precise conditioning and prioritization.
The AttentionCoupleRegion
node is designed to facilitate the creation of attention regions within an AI model, allowing you to specify areas of focus and their respective importance. This node is particularly useful for tasks that require fine-grained control over different regions of an image or other data types, enabling more precise and targeted conditioning. By defining specific regions and their associated weights, you can influence the model's attention mechanism to prioritize certain areas over others, enhancing the overall quality and relevance of the generated output.
The cond
parameter represents the conditioning input for the attention region. This input is crucial as it provides the contextual information that the model will use to focus on the specified region. The conditioning input can be any form of data that the model uses to guide its attention, such as text prompts or other relevant features.
The mask
parameter is a binary or grayscale mask that defines the specific area of the region within the input data. This mask helps the model to identify which parts of the input should be given attention based on the conditioning input. The mask should be of the same dimensions as the input data to ensure accurate region specification.
The weight
parameter determines the importance of the specified region relative to other regions. It is a floating-point value that ranges from 0.01 to 1.0, with a default value of 1.0. A higher weight means that the region will receive more attention from the model, while a lower weight will reduce its importance. This parameter allows you to fine-tune the focus on different regions to achieve the desired output.
The region
output is a dictionary containing the conditioning input, mask, and weight for the specified attention region. This output is used by other nodes or processes to apply the defined attention mechanism to the input data. The dictionary format ensures that all relevant information for the attention region is encapsulated and easily accessible for further processing.
weight
parameter to balance the attention between multiple regions, giving more importance to critical areas.AttentionCoupleRegion
nodes to create complex attention patterns for intricate tasks.regions
parameter is not provided as a list.regions
parameter is passed as a list, even if it contains only one region.© Copyright 2024 RunComfy. All Rights Reserved.