ComfyUI  >  Nodes  >  ComfyUI Impact Pack >  RegionalSampler

ComfyUI Node: RegionalSampler

Class Name

RegionalSampler

Category
ImpactPack/Regional
Author
Dr.Lt.Data (Account age: 458 days)
Extension
ComfyUI Impact Pack
Latest Updated
6/19/2024
Github Stars
1.4K

How to Install ComfyUI Impact Pack

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

Enhance image sampling precision in specific regions for AI artists.

RegionalSampler:

The RegionalSampler node is designed to provide advanced sampling capabilities within specific regions of an image. This node allows you to focus on particular areas, enabling more precise and controlled sampling processes. By leveraging this node, you can achieve higher quality results in targeted regions, which is particularly useful for tasks that require detailed attention to specific parts of an image. The main goal of the RegionalSampler is to enhance the flexibility and accuracy of your sampling operations, making it an essential tool for AI artists looking to refine their work with greater precision.

RegionalSampler Input Parameters:

model

This parameter specifies the model to be used for the sampling process. It is crucial as it determines the underlying architecture and capabilities that will be applied to the image regions. The model parameter ensures that the appropriate computational framework is utilized for the task at hand.

seed

The seed parameter is an integer value that initializes the random number generator used in the sampling process. By setting a specific seed, you can ensure reproducibility of the results. The default value is 0, with a minimum of 0 and a maximum of 0xffffffffffffffff.

steps

This parameter defines the number of steps to be taken during the sampling process. More steps generally lead to higher quality results but require more computational resources. The default value is 20, with a minimum of 1 and a maximum of 10000.

cfg

The cfg parameter stands for "configuration" and is a floating-point value that influences the behavior of the sampling algorithm. It allows you to fine-tune the process to achieve the desired balance between quality and performance. The default value is 8.0, with a range from 0.0 to 100.0, adjustable in steps of 0.1.

sampler_name

This parameter specifies the name of the sampler to be used. Different samplers have unique characteristics and can produce varying results. Choosing the right sampler is essential for achieving the desired output.

scheduler

The scheduler parameter determines the scheduling strategy for the sampling process. It affects how the steps are distributed and can influence the overall quality and efficiency of the sampling.

positive

This parameter represents the positive conditioning to be applied during the sampling process. It helps guide the sampling towards desired features or characteristics in the image.

negative

The negative parameter is used to apply negative conditioning, which helps steer the sampling away from unwanted features or characteristics.

latent_image

This parameter specifies the latent image to be used as the starting point for the sampling process. It serves as the initial input that the sampling algorithm will refine.

denoise

The denoise parameter is a floating-point value that controls the amount of denoising applied during the sampling process. It helps reduce noise and improve the clarity of the final output. The default value is 1.0, with a range from 0.0 to 1.0, adjustable in steps of 0.01.

RegionalSampler Output Parameters:

LATENT

The output of the RegionalSampler node is a latent representation of the sampled image. This latent output can be further processed or decoded to obtain the final image. The latent representation is crucial as it encapsulates the refined details and characteristics achieved through the regional sampling process.

RegionalSampler Usage Tips:

  • To achieve consistent results, always set a specific seed value.
  • Experiment with different sampler names and schedulers to find the best combination for your specific task.
  • Adjust the number of steps based on the desired quality and available computational resources.
  • Use the denoise parameter to fine-tune the clarity of the output, especially in regions with high noise levels.

RegionalSampler Common Errors and Solutions:

"Invalid model parameter"

  • Explanation: The model parameter provided is not recognized or supported.
  • Solution: Ensure that you are using a valid and compatible model for the sampling process.

"Seed value out of range"

  • Explanation: The seed value provided is outside the acceptable range.
  • Solution: Set the seed value within the range of 0 to 0xffffffffffffffff.

"Steps value too high"

  • Explanation: The number of steps specified exceeds the maximum allowed value.
  • Solution: Reduce the steps value to be within the range of 1 to 10000.

"Invalid sampler name"

  • Explanation: The sampler name provided does not match any available samplers.
  • Solution: Verify the sampler name and ensure it matches one of the supported samplers.

"Denoise value out of range"

  • Explanation: The denoise value is outside the acceptable range.
  • Solution: Adjust the denoise value to be within the range of 0.0 to 1.0.

RegionalSampler Related Nodes

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