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Facilitates AI art sampling with efficient management of techniques for unique results.
The List sampler 🪴 node is designed to facilitate the sampling process in AI art generation by providing a structured and efficient way to manage and execute various sampling techniques. This node is particularly useful for artists who want to experiment with different sampling methods to achieve unique and high-quality results. By leveraging the capabilities of the List sampler 🪴, you can easily switch between different samplers, adjust parameters, and fine-tune the sampling process to suit your creative needs. This node simplifies the complex task of sampling, making it more accessible and manageable for users without a deep technical background.
The model
parameter specifies the AI model to be used for sampling. This is a required parameter and ensures that the sampling process is aligned with the capabilities and characteristics of the chosen model.
The seed
parameter is an integer that sets the random seed for the sampling process. This allows for reproducibility of results. The default value is 0, with a minimum of 0 and a maximum of 0xffffffffffffffff.
The steps
parameter defines the number of steps to be taken during the sampling process. It controls the granularity and detail of the generated output. The default value is 20, with a minimum of 1 and a maximum of 10000.
The cfg
parameter stands for "configuration" and is a floating-point value that influences the behavior of the sampling process. It has a default value of 8.0, with a range from 0.0 to 100.0, and can be adjusted in steps of 0.1, rounded to 0.01.
The sampler_name
parameter allows you to select the specific sampler to be used from a predefined list of samplers. This enables you to experiment with different sampling techniques to achieve the desired artistic effect.
The scheduler
parameter specifies the scheduling method to be used during the sampling process. Different schedulers can impact the timing and sequence of the sampling steps, affecting the final output.
The positive
parameter is used to provide positive conditioning to the sampling process. This can guide the model towards generating outputs that align with the desired positive attributes.
The negative
parameter is used to provide negative conditioning to the sampling process. This can help in steering the model away from undesired attributes in the generated output.
The latent_image
parameter represents the latent image to be used as the starting point for the sampling process. This is a crucial input that influences the initial state of the sampling.
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 range from 0.0 to 1.0, adjustable in steps of 0.01.
The LATENT
output parameter represents the latent space representation of the sampled image. This output is crucial for further processing or for generating the final image from the latent representation.
sampler_name
and scheduler
combinations to find the best sampling technique for your specific artistic needs.steps
parameter to control the level of detail in the generated output. More steps can lead to finer details but may increase processing time.seed
parameter to reproduce specific results, which is useful for iterative experimentation and refinement.cfg
and denoise
parameters to balance between creativity and noise reduction in the generated output.© Copyright 2024 RunComfy. All Rights Reserved.