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Streamline stacking multiple control nets in AI art projects for efficient management and application.
The Control Net Stacker node is designed to streamline and enhance the process of stacking multiple control nets in your AI art projects. This node allows you to combine various control nets with specific images and parameters, creating a stack that can be applied collectively. By using this node, you can efficiently manage and apply multiple control nets, ensuring that your artistic creations are both complex and cohesive. The primary goal of the Control Net Stacker is to simplify the workflow for artists by providing a structured way to handle multiple control nets, thereby saving time and effort while maintaining high-quality results.
This parameter represents the control net that you want to stack. A control net is a neural network that guides the generation process, helping to achieve specific artistic effects or styles. By stacking multiple control nets, you can create more intricate and detailed artworks.
The image parameter is the specific image that you want to associate with the control net. This image serves as the input for the control net, allowing it to apply its effects based on the visual content of the image.
The strength parameter determines the intensity of the control net's effect on the image. It is a floating-point value with a default of 1.0, a minimum of 0.0, and a maximum of 10.0, adjustable in steps of 0.01. Higher values result in stronger effects, while lower values produce subtler changes.
This parameter specifies the starting point of the control net's effect as a percentage of the image's progress. It is a floating-point value with a default of 0.0, a minimum of 0.0, and a maximum of 1.0, adjustable in steps of 0.001. This allows you to control when the effect begins during the image generation process.
The end_percent parameter defines the ending point of the control net's effect as a percentage of the image's progress. It is a floating-point value with a default of 1.0, a minimum of 0.0, and a maximum of 1.0, adjustable in steps of 0.001. This parameter helps you control when the effect stops during the image generation process.
This optional parameter allows you to provide an existing stack of control nets. If not provided, the node will initialize an empty stack. This parameter is useful for combining new control nets with previously stacked ones, enabling more complex and layered effects.
The output parameter CNET_STACK is a list that contains tuples of the control nets, images, and their associated parameters (strength, start_percent, end_percent). This stack can be used in subsequent nodes to apply the combined effects of all the control nets in the stack, ensuring a cohesive and well-integrated result.
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