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Minimalist latent generator creating empty latent space for AI artists to start creative processes efficiently.
The DD-SimpleLatent node is a minimalist latent generator designed to create an empty latent space of specified dimensions. This node is particularly useful for AI artists who need a blank canvas in the latent space to start their creative processes. By generating a latent space filled with zeros, it provides a neutral starting point that can be further manipulated or transformed using other nodes in the pipeline. The main goal of this node is to offer a straightforward and efficient way to initialize a latent space, ensuring that the dimensions are compatible with subsequent operations that may require specific size constraints. This node is part of the DD series, which is known for its simplicity and effectiveness in handling latent spaces.
The 宽度
parameter specifies the width of the latent space to be generated. It determines how wide the latent space will be, which can impact the resolution and detail of any subsequent operations performed on this space. The width must be a multiple of 8, and the node will automatically adjust the input to the nearest valid value if necessary. The minimum value for this parameter is 64, the maximum is 8192, and the default is set to 512. Adjusting this parameter allows you to control the horizontal dimension of the latent space, which can be crucial for certain artistic or computational requirements.
The 高度
parameter defines the height of the latent space to be generated. Similar to the width, this parameter affects the vertical dimension of the latent space and must also be a multiple of 8. The node ensures that the input height is adjusted to the nearest valid value if it does not meet this requirement. The minimum value for this parameter is 64, the maximum is 8192, and the default is 512. By modifying this parameter, you can influence the vertical resolution and detail of the latent space, which is important for achieving the desired artistic effects or computational outcomes.
The 潜空间
output parameter represents the generated latent space, which is a tensor filled with zeros. This output serves as a blank slate in the latent space, ready for further manipulation or transformation. The latent space is structured as a four-dimensional tensor, with dimensions corresponding to batch size, channels, height, and width. This structure is essential for compatibility with other nodes and operations that may be applied to the latent space. The zero-filled nature of this output ensures that it does not introduce any bias or pre-existing patterns, making it ideal for starting new creative processes or experiments.
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