ComfyUI  >  Nodes  >  demofusion-comfyui >  Batch Unsampler

ComfyUI Node: Batch Unsampler

Class Name

Batch Unsampler

deroberon (Account age: 5297 days)
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How to Install demofusion-comfyui

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

Reverse order and sample latent images in batches for improved quality and noise handling in AI art creation.

Batch Unsampler:

The Batch Unsampler node is designed to process batches of latent images by reversing their order and then applying a sampling method to generate new images. This node is particularly useful when dealing with batches of images that have varying levels of noise, as it expects the input batch to be ordered from most to least noisy. By reversing the batch, the node ensures that the sampling process can be applied correctly, leading to more accurate and visually appealing results. The primary goal of the Batch Unsampler is to facilitate the generation of high-quality images from latent representations, making it an essential tool for AI artists looking to refine their creations.

Batch Unsampler Input Parameters:


This parameter specifies the model to be used for the sampling process. It is essential for defining the architecture and weights that will guide the generation of new images from the latent representations.


The seed parameter is an integer value used to initialize the random number generator, ensuring reproducibility of the results. The default value is 0, with a minimum of 0 and a maximum of 0xffffffffffffffff.


The cfg parameter is a floating-point value that controls the classifier-free guidance scale. It influences the strength of the guidance applied during sampling, with a default value of 8.0, a minimum of 0.0, and a maximum of 100.0.


This parameter specifies the name of the sampler to be used. It determines the algorithm that will be applied for sampling the latent images.


The scheduler parameter defines the scheduling strategy for the sampling process. It helps in managing the progression of the sampling steps.


This parameter controls the increment of steps during the sampling process. It is crucial for defining the granularity of the sampling iterations.


The positive parameter provides conditioning information that positively influences the sampling process, guiding the generation towards desired features.


The negative parameter provides conditioning information that negatively influences the sampling process, helping to avoid undesired features in the generated images.


This parameter contains the batch of latent images to be processed. It includes the latent representations that will be sampled to generate new images.


The denoise parameter is a floating-point value that controls the level of denoising applied during the sampling process. The default value is 1.0, with a minimum of 0.0 and a maximum of 1.0.


The alpha_1 parameter is a floating-point value that influences the sampling process. It has a default value of 3.0 and plays a role in the overall behavior of the sampling algorithm.


This boolean parameter determines whether the latent image batch should be reversed before sampling. The default value is True, ensuring that the batch is processed from most to least noisy.

Batch Unsampler Output Parameters:


The output of the Batch Unsampler node is a batch of latent images that have been processed and sampled. These latent images can be further used to generate high-quality visual outputs, making them a crucial component in the image generation pipeline.

Batch Unsampler Usage Tips:

  • Ensure that the latent image batch is ordered from least to most noisy before inputting it into the node, as the node expects to reverse this order for optimal results.
  • Experiment with different values of the cfg parameter to find the right balance of guidance strength for your specific use case.
  • Use a fixed seed value if you need reproducible results, especially when fine-tuning the sampling process.

Batch Unsampler Common Errors and Solutions:

"Invalid latent image batch format"

  • Explanation: This error occurs when the input latent image batch does not conform to the expected format.
  • Solution: Ensure that the latent image batch is correctly structured and contains the necessary fields such as "samples" and optionally "noise_mask".

"Model not specified"

  • Explanation: This error indicates that the model parameter has not been provided.
  • Solution: Specify a valid model to be used for the sampling process.

"Seed value out of range"

  • Explanation: The seed parameter value is outside the acceptable range.
  • Solution: Provide a seed value within the range of 0 to 0xffffffffffffffff.

"Invalid cfg value"

  • Explanation: The cfg parameter value is not within the acceptable range.
  • Solution: Ensure that the cfg value is between 0.0 and 100.0.

"Sampler name not recognized"

  • Explanation: The specified sampler name is not valid.
  • Solution: Check the available sampler names and provide a valid one.

"Scheduler not recognized"

  • Explanation: The specified scheduler is not valid.
  • Solution: Check the available schedulers and provide a valid one.

Batch Unsampler Related Nodes

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

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