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Enhance AI model performance with self-attention mechanisms for improved focus and accuracy in outputs.
SelfAttentionGuidance is a specialized node designed to enhance the performance of AI models by incorporating self-attention mechanisms. This node leverages the power of self-attention to improve the model's ability to focus on relevant parts of the input data, thereby enhancing the quality and accuracy of the generated outputs. Self-attention mechanisms are particularly beneficial in tasks that require understanding complex dependencies within the data, such as image generation, text processing, and more. By integrating SelfAttentionGuidance into your workflow, you can achieve more refined and contextually aware results, making it an invaluable tool for AI artists looking to push the boundaries of their creative projects.
This parameter specifies the AI model that will be enhanced with self-attention guidance. The model should be compatible with the self-attention mechanisms provided by this node. The input type is MODEL
.
The scale parameter controls the intensity of the self-attention guidance applied to the model. A higher scale value increases the influence of self-attention, potentially leading to more focused and contextually relevant outputs. The default value is 3.0, with a minimum of 0.0 and a maximum of 100.0. Adjusting this parameter allows you to fine-tune the balance between the original model's behavior and the enhanced self-attention guidance.
The output is the enhanced AI model with integrated self-attention guidance. This model is now capable of producing more refined and contextually aware results, leveraging the benefits of self-attention mechanisms to improve the quality and accuracy of its outputs.
ValueError: Incompatible model type
TypeError: Scale parameter out of range
RuntimeError: Model patching failed
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