ComfyUI > Nodes > ComfyUI > SplitSigmasDenoise

ComfyUI Node: SplitSigmasDenoise

Class Name

SplitSigmasDenoise

Category
sampling/custom_sampling/sigmas
Author
ComfyAnonymous (Account age: 598days)
Extension
ComfyUI
Latest Updated
2024-08-12
Github Stars
45.85K

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

Facilitates denoising in AI art by splitting sigma values based on denoise factor for precise control.

SplitSigmasDenoise:

The SplitSigmasDenoise node is designed to facilitate the denoising process in AI art generation by splitting a sequence of sigma values into two distinct parts based on a specified denoise factor. This node is particularly useful in custom sampling techniques where controlling the denoising steps can significantly impact the quality and style of the generated artwork. By adjusting the denoise parameter, you can fine-tune the balance between high and low sigma values, allowing for more precise control over the denoising process. This can help in achieving smoother transitions and more refined details in the final output.

SplitSigmasDenoise Input Parameters:

sigmas

The sigmas parameter represents a sequence of sigma values used in the denoising process. These values are crucial in determining the noise levels at different stages of the denoising algorithm. The sequence of sigma values is typically provided by the sampling algorithm and is used to guide the denoising steps.

denoise

The denoise parameter is a floating-point value that controls the extent of denoising applied to the sigma sequence. It ranges from 0.0 to 1.0, with a default value of 1.0. A value of 1.0 means full denoising, where the maximum number of steps will be used, while a value of 0.0 means no denoising. Adjusting this parameter allows you to control the balance between high and low sigma values, thereby influencing the smoothness and detail of the final output.

SplitSigmasDenoise Output Parameters:

high_sigmas

The high_sigmas output represents the portion of the sigma sequence that corresponds to higher noise levels. This part of the sequence is used in the initial stages of the denoising process, where more noise is present, and helps in gradually reducing the noise as the process progresses.

low_sigmas

The low_sigmas output represents the portion of the sigma sequence that corresponds to lower noise levels. This part of the sequence is used in the later stages of the denoising process, where less noise is present, and helps in refining the details and achieving a smoother final output.

SplitSigmasDenoise Usage Tips:

  • To achieve a smoother final output with finer details, set the denoise parameter closer to 1.0. This will ensure that more steps are used in the denoising process.
  • If you want to retain more texture and noise in your artwork, set the denoise parameter to a lower value. This will reduce the number of denoising steps and preserve more of the original noise.

SplitSigmasDenoise Common Errors and Solutions:

"IndexError: index out of range"

  • Explanation: This error occurs when the denoise parameter is set too high, resulting in an attempt to access indices outside the range of the sigma sequence.
  • Solution: Ensure that the denoise parameter is set within a reasonable range (0.0 to 1.0) and that the sigma sequence has enough values to accommodate the specified denoise factor.

"TypeError: expected Tensor as input"

  • Explanation: This error occurs when the sigmas parameter is not provided as a tensor.
  • Solution: Ensure that the sigmas parameter is passed as a tensor. If you are using a different data type, convert it to a tensor before passing it to the node.

SplitSigmasDenoise Related Nodes

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