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Decode latent representations into images with circular tiling effects for seamless, tileable textures and patterns.
The CircularVAEDecode
node is designed to decode latent representations into images while optionally applying circular tiling effects. This node is particularly useful for generating seamless, tileable images from latent space representations, which can be beneficial for creating textures or patterns that need to repeat without visible seams. By leveraging a Variational Autoencoder (VAE), this node can decode latent samples into high-quality images, and the tiling options allow for flexible control over how the tiling is applied, whether it be in both directions, only horizontally, only vertically, or not at all. This makes it a versatile tool for AI artists looking to create seamless textures or other tileable visual content.
This parameter represents the latent samples that need to be decoded into images. These samples are typically generated by an encoder or another process that outputs latent space representations. The quality and characteristics of the decoded image will depend on the content of these latent samples.
This parameter is the Variational Autoencoder (VAE) model used for decoding the latent samples into images. The VAE is a crucial component as it defines the decoding process and the quality of the resulting images. The VAE model should be pre-trained and compatible with the latent samples provided.
This parameter controls the tiling effect applied during the decoding process. It has four options: "enable", "x_only", "y_only", and "disable".
The output is the decoded image generated from the latent samples using the specified VAE model. If tiling is enabled, the image will be seamless according to the selected tiling option. This image can be used directly in various applications, such as textures for 3D models, backgrounds, or any other visual content requiring seamless patterns.
tiling
parameter to "enable" to ensure the image is seamless in both directions.AttributeError: 'NoneType' object has no attribute 'first_stage_model'
None
.None
and is compatible with the latent samples.TypeError: 'NoneType' object is not subscriptable
samples
parameter is None
or not properly formatted.samples
parameter is a valid dictionary with the key "samples" containing the latent data.ValueError: Invalid tiling option
tiling
parameter.tiling
parameter is set to one of the valid options: "enable", "x_only", "y_only", or "disable". Double-check the input to avoid typos or incorrect values.© Copyright 2024 RunComfy. All Rights Reserved.