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Upscale latent image representations for AI-generated artwork with various methods for enhanced resolution control.
The LatentUpscaleBy
node is designed to upscale latent representations of images, allowing you to enhance the resolution of your AI-generated artwork. This node provides a variety of upscaling methods to choose from, ensuring that you can achieve the desired level of detail and quality in your images. By adjusting the scale factor, you can precisely control the degree of upscaling, making it a versatile tool for refining and improving the visual output of your AI models. The primary goal of this node is to offer a straightforward and efficient way to increase the resolution of latent images, which can be particularly useful for creating high-quality, detailed artwork.
This parameter represents the latent image data that you want to upscale. It is the core input for the node, containing the image information in a latent space format. The samples
parameter is essential for the node to perform its upscaling function.
This parameter allows you to select the method used for upscaling the latent image. The available options are "nearest-exact", "bilinear", "area", "bicubic", and "bislerp". Each method has its own characteristics and can affect the quality and appearance of the upscaled image. For example, "bilinear" and "bicubic" methods are known for producing smoother results, while "nearest-exact" can preserve sharp edges. Choosing the right method depends on the specific requirements of your project.
This parameter determines the factor by which the latent image will be upscaled. It is a floating-point value with a default of 1.5, a minimum of 0.01, and a maximum of 8.0. Adjusting this value allows you to control the extent of the upscaling, with higher values resulting in larger images. The scale_by
parameter provides fine-grained control over the upscaling process, enabling you to achieve the desired resolution for your artwork.
The output of the LatentUpscaleBy
node is a latent representation of the upscaled image. This output retains the structure of the input latent data but with increased resolution as specified by the scale_by
parameter. The upscaled latent image can then be further processed or decoded into a higher-resolution image, making it a crucial step in enhancing the quality of AI-generated artwork.
upscale_method
options to find the one that best suits your artistic needs. Each method can produce different visual effects, so testing them can help you achieve the desired outcome.scale_by
parameter to fine-tune the resolution of your upscaled images. Start with the default value and adjust incrementally to see how it affects the final result.samples
are of good quality, as the upscaling process can amplify any imperfections present in the original latent image.scale_by
value is set outside the allowed range of 0.01 to 8.0.scale_by
parameter to be within the valid range. Use values between 0.01 and 8.0.samples
are not in the expected latent format.samples
are correctly formatted latent representations before passing them to the node.© Copyright 2024 RunComfy. All Rights Reserved.