ComfyUI  >  Nodes  >  tiled_ksampler >  Circular VAEDecode

ComfyUI Node: Circular VAEDecode

Class Name

Circular VAEDecode

FlyingFireCo (Account age: 641 days)
Latest Updated
Github Stars

How to Install tiled_ksampler

Install this extension via the ComfyUI Manager by searching for  tiled_ksampler
  • 1. Click the Manager button in the main menu
  • 2. Select Custom Nodes Manager button
  • 3. Enter tiled_ksampler in the search bar
After installation, click the  Restart button to restart ComfyUI. Then, manually refresh your browser to clear the cache and access the updated list of nodes.

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Circular VAEDecode Description

Circular VAEDecode node decodes latent representations into images with circular padding for seamless wrapping and tiling textures.

Circular VAEDecode:

The Circular VAEDecode node is designed to decode latent representations into images using a Variational Autoencoder (VAE) with a unique twist: it modifies the padding mode of convolutional layers to circular. This adjustment ensures that the edges of the decoded images are seamlessly wrapped around, which can be particularly beneficial for generating textures or patterns that need to tile seamlessly. By leveraging the circular padding mode, this node helps in avoiding visible seams or discontinuities at the borders of the generated images, making it an essential tool for AI artists working on projects that require seamless textures or continuous patterns.

Circular VAEDecode Input Parameters:


This parameter represents the latent representations that need to be decoded into images. Latent representations are typically the compressed and encoded form of images, capturing essential features in a lower-dimensional space. The samples parameter is crucial as it serves as the input data that the VAE will decode back into a full-resolution image. The quality and characteristics of the output image heavily depend on the information contained within these latent samples.


The vae parameter refers to the Variational Autoencoder model used for decoding the latent representations. A VAE is a type of neural network that learns to encode images into a latent space and then decode them back into images. In this node, the VAE's convolutional layers are modified to use circular padding, which helps in generating seamless images. The choice of VAE can impact the style, quality, and fidelity of the decoded images, making it a critical component of the node's functionality.

Circular VAEDecode Output Parameters:


The output of the Circular VAEDecode node is an IMAGE, which is the result of decoding the provided latent representations using the specified VAE. This image is generated with circular padding applied to the convolutional layers, ensuring that the edges wrap around seamlessly. This output is particularly useful for creating textures or patterns that need to tile without visible seams, enhancing the visual quality and continuity of the generated content.

Circular VAEDecode Usage Tips:

  • To achieve seamless textures, ensure that the latent samples provided to the node are representative of the desired texture or pattern.
  • Experiment with different VAEs to find the one that best suits your artistic style and the specific requirements of your project.
  • Use this node in conjunction with other texture generation tools to create complex and visually appealing seamless patterns.

Circular VAEDecode Common Errors and Solutions:

"Invalid latent samples provided"

  • Explanation: This error occurs when the latent samples input does not match the expected format or dimensions required by the VAE.
  • Solution: Ensure that the latent samples are correctly generated and formatted according to the VAE's requirements. Verify the dimensions and data type of the samples.

"VAE model not found"

  • Explanation: This error indicates that the specified VAE model could not be located or loaded.
  • Solution: Check that the VAE model is correctly specified and available in the expected directory. Ensure that the model file is not corrupted and is compatible with the node.

"Decoding failed due to incompatible VAE"

  • Explanation: This error suggests that the VAE model provided is not compatible with the circular padding modification.
  • Solution: Use a VAE model that supports circular padding or modify the existing VAE to ensure compatibility. Verify that the VAE's convolutional layers can be adjusted to use circular padding.

Circular VAEDecode Related Nodes

Go back to the extension to check out more related nodes.

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