ComfyUI Node: 🔧 KSampler Stochastic Variations

🔧 KSampler Stochastic Variations+:

KSamplerVariationsStochastic+ is a powerful node designed to enhance the sampling process by introducing stochastic variations. This node is particularly useful for AI artists looking to generate diverse and unique outputs from a given model. By leveraging stochastic methods, it allows for the creation of variations in the generated images, providing a broader range of artistic possibilities. The primary goal of this node is to add controlled randomness to the sampling process, which can help in exploring different creative directions and achieving more dynamic results. This node is ideal for those who want to experiment with different styles and effects without having to manually tweak the parameters for each variation.

🔧 KSampler Stochastic Variations+ Input Parameters:

model

This parameter specifies the model to be used for sampling. It is a required input and determines the base capabilities and characteristics of the generated output.

seed

The seed parameter is an integer that initializes the random number generator. It ensures reproducibility of the results. The default value is 0, with a minimum of 0 and a maximum of 0xffffffffffffffff.

steps

This integer parameter defines the number of steps for the sampling process. More steps generally lead to higher quality outputs but take longer to compute. The default value is 20, with a minimum of 1 and a maximum of 10000.

cfg

The cfg (Classifier-Free Guidance) parameter is a float that controls the strength of the guidance. Higher values make the output more closely follow the conditioning, while lower values allow for more creativity. 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. It determines the algorithm that will guide the sampling process.

scheduler

The scheduler parameter defines the scheduling method for the sampling steps. It influences how the steps are distributed over the sampling process.

positive

This parameter provides the positive conditioning for the model, guiding it towards desired features in the output.

negative

The negative parameter provides the negative conditioning, helping to steer the model away from unwanted features.

latent_image

This parameter is the latent representation of the image to be sampled. It serves as the starting point for the sampling process.

denoise

The denoise parameter is a float that controls the amount of denoising applied during the sampling process. The default value is 1.0, with a range from 0.0 to 1.0, adjustable in steps of 0.01.

noise_seed

This integer parameter initializes the random number generator for noise. It ensures reproducibility of the noise patterns.

variation_seed

The variation_seed parameter is an integer that initializes the random number generator for variations. It ensures reproducibility of the variations.

variation_strength

This float parameter controls the strength of the variations introduced. Higher values result in more pronounced variations.

cfg_scale

The cfg_scale parameter is a float that scales the cfg value for the variation stage. It ensures that the variations are guided appropriately.

variation_sampler

This parameter specifies the sampler to be used for the variation stage. The default value is "dpmpp_2m_sde".

🔧 KSampler Stochastic Variations+ Output Parameters:

LATENT

The output of this node is a latent representation of the image, which can be further processed or decoded into a final image. This latent output contains the variations introduced during the sampling process, providing a diverse range of possible images.

🔧 KSampler Stochastic Variations+ Usage Tips:

• Experiment with different seeds to explore a wide range of variations and find the most appealing results.
• Adjust the variation_strength parameter to control the extent of the variations. Higher values can lead to more creative and unexpected outputs.
• Use the cfg_scale parameter to fine-tune the guidance during the variation stage, ensuring that the variations still align with your artistic vision.

🔧 KSampler Stochastic Variations+ Common Errors and Solutions:

"Invalid seed value"

• Explanation: The seed value provided is outside the acceptable range.
• Solution: Ensure that the seed value is between 0 and 0xffffffffffffffff.

"Steps value out of range"

• Explanation: The number of steps specified is either too low or too high.
• Solution: Set the steps parameter to a value between 1 and 10000.

"Model not specified"

• Explanation: The model parameter is missing or invalid.
• Solution: Provide a valid model for the sampling process.

"Invalid variation_strength value"

• Explanation: The variation_strength parameter is set to an invalid value.
• Solution: Ensure that the variation_strength is set to a valid float value that makes sense for your use case.