ComfyUI Node: Tiled KSampler

Tiled KSampler:

The Tiled KSampler node is designed to enhance the sampling process by enabling tiling, which is particularly useful for generating high-resolution images without running into memory limitations. This node allows you to apply circular padding to convolutional layers, ensuring seamless tiling and reducing artifacts at the tile boundaries. By leveraging this method, you can achieve more consistent and higher-quality results in your AI-generated art. The Tiled KSampler is ideal for artists looking to create large-scale images or those who want to maintain high detail and quality across their entire artwork.

Tiled KSampler Input Parameters:

model

This parameter specifies the model to be used for sampling. It is a required input and should be a pre-trained model compatible with the Tiled KSampler node.

seed

The seed parameter is an integer that initializes the random number generator, ensuring reproducibility of the results. The default value is 0, with a minimum of 0 and a maximum of 0xffffffffffffffff. Using different seeds will produce different variations of the generated image.

tiling

This integer parameter enables or disables tiling. A value of 1 enables tiling, while 0 disables it. The default value is 1. Enabling tiling is useful for generating high-resolution images by dividing the image into smaller tiles.

steps

The steps parameter defines the number of sampling steps to be performed. It is an integer with a default value of 20, a minimum of 1, and a maximum of 10000. More steps generally lead to higher quality images but will take longer to process.

cfg

This float parameter, known as the classifier-free guidance scale, controls the strength of the guidance. The default value is 8.0, with a range from 0.0 to 100.0. Higher values result in images that more closely follow the provided conditioning.

sampler_name

This parameter specifies the name of the sampler to be used. It should be one of the available samplers in comfy.samplers.KSampler.SAMPLERS. The choice of sampler can affect the style and quality of the generated image.

scheduler

The scheduler parameter determines the scheduling strategy for the sampling process. It should be one of the available schedulers in comfy.samplers.KSampler.SCHEDULERS. Different schedulers can impact the convergence and quality of the results.

positive

This parameter provides the positive conditioning for the sampling process. It is a required input and should be a conditioning object that guides the model towards desired features in the generated image.

negative

The negative parameter provides the negative conditioning, which guides the model away from undesired features. It is a required input and should be a conditioning object.

latent_image

This parameter specifies the latent image to be used as the starting point for sampling. It is a required input and should be a latent representation of an image.

denoise

The denoise parameter is a float that controls the amount of noise to be added during the sampling process. The default value is 1.0, with a range from 0.0 to 1.0 and a step size of 0.01. Lower values result in less noise and potentially smoother images.

Tiled KSampler Output Parameters:

LATENT

The output of the Tiled KSampler node is a latent representation of the sampled image. This latent output can be further processed or decoded into an actual image using a VAE (Variational Autoencoder) decoder. The latent output is crucial for generating high-quality images, as it contains the compressed information of the sampled image.

Tiled KSampler Usage Tips:

• To achieve high-resolution images, enable the tiling parameter and ensure your model supports circular padding.
• Experiment with different seeds to generate diverse variations of your artwork.
• Adjust the steps parameter to balance between processing time and image quality; more steps generally yield better results.
• Use the cfg parameter to fine-tune the guidance strength; higher values make the image more closely follow the conditioning.
• Choose the appropriate sampler and scheduler based on the desired style and quality of the output.

Tiled KSampler Common Errors and Solutions:

"Model not specified"

• Explanation: The model parameter is missing or not provided.
• Solution: Ensure that you specify a valid pre-trained model compatible with the Tiled KSampler node.

"Invalid seed value"

• Explanation: The seed parameter is out of the acceptable range.
• Solution: Provide a seed value between 0 and 0xffffffffffffffff.

"Tiling parameter out of range"

• Explanation: The tiling parameter is set to a value other than 0 or 1. - Solution: Set the tiling parameter to either 0 (disable) or 1 (enable).

"Steps parameter out of range"

• Explanation: The steps parameter is set to a value outside the range of 1 to 10000.
• Solution: Adjust the steps parameter to a value within the specified range.

"CFG value out of range"

• Explanation: The cfg parameter is set to a value outside the range of 0.0 to 100.0.
• Solution: Set the cfg parameter to a value within the specified range.

"Denoise value out of range"

• Explanation: The denoise parameter is set to a value outside the range of 0.0 to 1.0.
• Solution: Adjust the denoise parameter to a value within the specified range.