ComfyUI  >  Nodes  >  Jags_VectorMagic >  Jags-CircularVAEDecode

ComfyUI Node: Jags-CircularVAEDecode

Class Name


jags111 (Account age: 3879 days)
Latest Updated
Github Stars

How to Install Jags_VectorMagic

Install this extension via the ComfyUI Manager by searching for  Jags_VectorMagic
  • 1. Click the Manager button in the main menu
  • 2. Select Custom Nodes Manager button
  • 3. Enter Jags_VectorMagic 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.

Visit ComfyUI Online for ready-to-use ComfyUI environment

  • Free trial available
  • High-speed GPU machines
  • 200+ preloaded models/nodes
  • Freedom to upload custom models/nodes
  • 50+ ready-to-run workflows
  • 100% private workspace with up to 200GB storage
  • Dedicated Support

Run ComfyUI Online

Jags-CircularVAEDecode Description

CircularVAEDecode node decodes latent representations into seamless images using circular padding for cohesive visual output.


The CircularVAEDecode 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 can help in generating seamless and continuous images, particularly useful in applications where edge artifacts are undesirable. By leveraging the circular padding mode, this node ensures that the edges of the decoded images wrap around, creating a more natural and cohesive visual output. This feature is particularly beneficial for AI artists looking to create tiling textures or seamless patterns.

Jags-CircularVAEDecode Input Parameters:


This parameter represents the latent representations that need to be decoded into images. It is a crucial input as it contains the encoded data that the VAE will transform back into a visual format. The latent representations are typically the result of a previous encoding process and serve as the compressed form of the original image data.


This parameter is the Variational Autoencoder (VAE) model used for decoding the latent representations. The VAE is responsible for transforming the latent data back into an image. By modifying the convolutional layers' padding mode to circular, the VAE ensures that the decoded images have seamless edges, which is particularly useful for creating tiling textures or continuous patterns.

Jags-CircularVAEDecode Output Parameters:


The output of this node is an image that has been decoded from the provided latent representations. The image is generated by the VAE model, which transforms the compressed latent data back into a visual format. The use of circular padding ensures that the edges of the image wrap around seamlessly, making it ideal for applications requiring continuous or tiling visuals.

Jags-CircularVAEDecode Usage Tips:

  • To create seamless textures or patterns, ensure that the latent representations provided to the node are well-suited for tiling. This will enhance the effectiveness of the circular padding mode.
  • Experiment with different VAE models to see how the circular padding affects the output. Some models may produce more visually appealing results than others.
  • Use this node in conjunction with other nodes that generate or manipulate latent representations to create a complete workflow for generating seamless images.

Jags-CircularVAEDecode Common Errors and Solutions:

AttributeError: 'module' object has no attribute 'Conv2d'

  • Explanation: This error occurs if the torch library is not properly imported or if there is an issue with the version of torch being used.
  • Solution: Ensure that the torch library is correctly installed and imported in your environment. You may need to update torch to a compatible version.

KeyError: 'samples'

  • Explanation: This error indicates that the samples key is missing from the input dictionary.
  • Solution: Verify that the input dictionary contains the samples key with the appropriate latent representations. Ensure that the data passed to the node is correctly formatted.

TypeError: 'NoneType' object is not iterable

  • Explanation: This error can occur if the VAE model or the latent representations are not properly initialized or passed to the node.
  • Solution: Check that both the VAE model and the latent representations are correctly initialized and passed to the node. Ensure that the VAE model is properly trained and capable of decoding the latent data.

Jags-CircularVAEDecode Related Nodes

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

© Copyright 2024 RunComfy. All Rights Reserved.

RunComfy is the premier ComfyUI platform, offering ComfyUI online environment and services, along with ComfyUI workflows featuring stunning visuals.