ComfyUI Node: multi pass

multi pass:

The aegisflow Multi_Pass node is designed to facilitate the seamless integration and processing of multiple data passes within your AI art projects. This node is particularly useful for handling complex workflows where multiple stages of data transformation or processing are required. By using this node, you can efficiently manage and pass through various types of data, ensuring that each stage of your workflow receives the necessary inputs and produces the desired outputs. The primary goal of the aegisflow Multi_Pass node is to streamline the data handling process, making it easier for you to focus on the creative aspects of your project without getting bogged down by technical details.

multi pass Input Parameters:

model

This optional parameter accepts a model input, which can be any model type used in your workflow. The model input is passed through the node, allowing it to be used in subsequent stages of your project. If no model is provided, the node will generate an empty model placeholder. This ensures that your workflow remains functional even if a model input is not available. There are no specific minimum, maximum, or default values for this parameter, as it depends on the models you are working with.

latent

This optional parameter accepts a latent input, which represents the latent space data used in your AI art project. Similar to the model input, the latent data is passed through the node for use in later stages. If no latent data is provided, the node will generate an empty latent placeholder. This helps maintain the integrity of your workflow by ensuring that latent data is always available when needed. There are no specific minimum, maximum, or default values for this parameter.

clip

This optional parameter accepts a CLIP input, which is used for text-to-image or image-to-text transformations in your project. The CLIP data is passed through the node, allowing it to be utilized in subsequent stages. If no CLIP data is provided, the node will generate an empty CLIP placeholder. This ensures that your workflow can continue to function even if CLIP data is not available. There are no specific minimum, maximum, or default values for this parameter.

mask

This optional parameter accepts a mask input, which is used for masking specific areas of an image in your project. The mask data is passed through the node, enabling it to be used in later stages. If no mask data is provided, the node will generate an empty mask placeholder. This helps ensure that your workflow remains operational even if mask data is not available. There are no specific minimum, maximum, or default values for this parameter.

positive

This optional parameter accepts a positive conditioning input, which is used for conditioning the AI model with positive examples. The positive conditioning data is passed through the node, allowing it to be used in subsequent stages. If no positive conditioning data is provided, the node will generate an empty conditioning placeholder. This ensures that your workflow can continue to function even if positive conditioning data is not available. There are no specific minimum, maximum, or default values for this parameter.

negative

This optional parameter accepts a negative conditioning input, which is used for conditioning the AI model with negative examples. The negative conditioning data is passed through the node, enabling it to be used in later stages. If no negative conditioning data is provided, the node will generate an empty conditioning placeholder. This helps maintain the integrity of your workflow by ensuring that negative conditioning data is always available when needed. There are no specific minimum, maximum, or default values for this parameter.

multi pass Output Parameters:

model

The model output parameter returns the model data that was passed through the node. This output is essential for ensuring that the model data is available for use in subsequent stages of your workflow. If no model input was provided, the output will be an empty model placeholder, ensuring that your workflow remains functional.

latent

The latent output parameter returns the latent data that was passed through the node. This output is crucial for maintaining the flow of latent space data in your project. If no latent input was provided, the output will be an empty latent placeholder, ensuring that your workflow can continue to operate.

clip

The clip output parameter returns the CLIP data that was passed through the node. This output is important for enabling text-to-image or image-to-text transformations in your project. If no CLIP input was provided, the output will be an empty CLIP placeholder, ensuring that your workflow remains operational.

mask

The mask output parameter returns the mask data that was passed through the node. This output is vital for ensuring that mask data is available for use in subsequent stages of your project. If no mask input was provided, the output will be an empty mask placeholder, ensuring that your workflow can continue to function.

positive

The positive output parameter returns the positive conditioning data that was passed through the node. This output is essential for conditioning the AI model with positive examples in later stages of your workflow. If no positive conditioning input was provided, the output will be an empty conditioning placeholder, ensuring that your workflow remains functional.

negative

The negative output parameter returns the negative conditioning data that was passed through the node. This output is crucial for conditioning the AI model with negative examples in subsequent stages of your project. If no negative conditioning input was provided, the output will be an empty conditioning placeholder, ensuring that your workflow can continue to operate.

multi pass Usage Tips:

• Ensure that you provide the necessary inputs for each stage of your workflow to avoid generating empty placeholders.
• Use the node to manage complex workflows with multiple data passes, simplifying the data handling process and allowing you to focus on the creative aspects of your project.

multi pass Common Errors and Solutions:

Missing model input

• Explanation: The model input parameter was not provided.
• Solution: Ensure that you provide a valid model input or handle the empty model placeholder in your workflow.

Missing latent input

• Explanation: The latent input parameter was not provided.
• Solution: Ensure that you provide valid latent data or handle the empty latent placeholder in your workflow.

Missing clip input

• Explanation: The clip input parameter was not provided.
• Solution: Ensure that you provide valid CLIP data or handle the empty CLIP placeholder in your workflow.

Missing mask input

• Explanation: The mask input parameter was not provided.
• Solution: Ensure that you provide valid mask data or handle the empty mask placeholder in your workflow.

Missing positive conditioning input

• Explanation: The positive conditioning input parameter was not provided.
• Solution: Ensure that you provide valid positive conditioning data or handle the empty conditioning placeholder in your workflow.

Missing negative conditioning input

• Explanation: The negative conditioning input parameter was not provided.
• Solution: Ensure that you provide valid negative conditioning data or handle the empty conditioning placeholder in your workflow.