ComfyUI Node: Attn2 SVD Prompt Injection

Attn2 SVD Prompt Injection:

The SVDPromptInjection node is designed to enhance the capabilities of AI models by injecting specific prompts into the attention mechanism of the model using Singular Value Decomposition (SVD). This technique allows for more precise control over the model's focus during the generation process, enabling the creation of more detailed and contextually relevant outputs. By leveraging SVD, this node can effectively blend additional context into the model's attention layers, resulting in improved performance and more nuanced outputs. This is particularly beneficial for tasks that require high levels of detail and specificity, such as generating complex images or text.

Attn2 SVD Prompt Injection Input Parameters:

model

This parameter specifies the AI model that will be used for the prompt injection. It is a required parameter and ensures that the node knows which model to apply the SVD-based prompt injection to.

block

This optional parameter allows you to specify which block of the model to target for the prompt injection. The available options include various input, middle, and output blocks such as "input:4", "input:5", "input:7", "input:8", "middle:0", "output:0", "output:1", "output:2", "output:3", "output:4", and "output:5". This flexibility enables you to fine-tune which part of the model's architecture will be influenced by the injected prompt.

conditioning

This optional parameter provides the conditioning context that will be injected into the model. It allows you to specify additional information or context that the model should consider during the generation process, enhancing the relevance and quality of the output.

weight

This optional parameter controls the influence of the injected prompt on the model's attention mechanism. It accepts a floating-point value with a default of 1.0, a minimum of -2.0, and a maximum of 5.0, with a step of 0.05. Adjusting this weight allows you to balance the impact of the prompt injection, making it more or less prominent in the model's output.

start_at

This optional parameter defines the starting point for the prompt injection in terms of the model's sigma value. It accepts a floating-point value with a default of 0.0, a minimum of 0.0, and a maximum of 1.0, with a step of 0.001. This parameter helps in controlling when the prompt injection should begin during the model's processing.

end_at

This optional parameter specifies the ending point for the prompt injection in terms of the model's sigma value. It accepts a floating-point value with a default of 1.0, a minimum of 0.0, and a maximum of 1.0, with a step of 0.001. This parameter allows you to control when the prompt injection should cease, providing fine-grained control over the duration of the injection.

Attn2 SVD Prompt Injection Output Parameters:

model

The output parameter is the modified AI model with the SVD-based prompt injection applied. This model will now incorporate the additional context provided by the prompt injection, resulting in potentially more detailed and contextually relevant outputs.

Attn2 SVD Prompt Injection Usage Tips:

• To achieve the best results, experiment with different blocks and weights to find the optimal configuration for your specific task.
• Use the start_at and end_at parameters to control the timing of the prompt injection, ensuring it aligns with the most critical parts of the model's processing.
• Combine the SVDPromptInjection node with other nodes to create complex and highly customized AI generation pipelines.

Attn2 SVD Prompt Injection Common Errors and Solutions:

"Invalid block specified"

• Explanation: The block parameter provided does not match any of the available options.
• Solution: Ensure that the block parameter is set to one of the valid options such as "input:4", "input:5", "input:7", "input:8", "middle:0", "output:0", "output:1", "output:2", "output:3", "output:4", or "output:5".

"Conditioning context is None"

• Explanation: The conditioning parameter is not provided or is set to None.
• Solution: Provide a valid conditioning context to ensure the prompt injection can be applied effectively.

"Weight value out of range"

• Explanation: The weight parameter is set to a value outside the allowed range.
• Solution: Adjust the weight parameter to a value within the range of -2.0 to 5.0.

"Start_at or end_at value out of range"

• Explanation: The start_at or end_at parameter is set to a value outside the allowed range.
• Solution: Ensure that the start_at and end_at parameters are set to values within the range of 0.0 to 1.0.