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Advanced configuration for precise AI art generation control.
The KSampler Config (rgthree) node is designed to provide advanced configuration options for the KSampler, a key component in AI art generation workflows. This node allows you to fine-tune various parameters that control the sampling process, enabling you to achieve more precise and desired outcomes in your generated images. By adjusting settings such as the number of steps, the configuration scale (cfg), and the denoise level, you can influence the quality, style, and fidelity of the output. The KSampler Config (rgthree) node is essential for artists looking to have greater control over the sampling process, ensuring that the generated art aligns closely with their creative vision.
This parameter specifies the model to be used for sampling. It is a required input and ensures that the sampling process is aligned with the chosen model's capabilities and characteristics.
The seed parameter is an integer that initializes the random number generator, ensuring reproducibility of results. The default value is 0, with a minimum of 0 and a maximum of 0xffffffffffffffff. Changing the seed will produce different variations of the generated image.
This integer parameter defines the number of steps to be taken during the sampling process. The default value is 20, with a minimum of 1 and a maximum of 10000. More steps generally lead to higher quality images but increase computation time.
The cfg (configuration scale) parameter is a float that controls the strength of the conditioning. The default value is 8.0, with a range from 0.0 to 100.0, adjustable in steps of 0.1 and rounded to 0.01. Higher values make the output more closely follow the conditioning inputs.
This parameter allows you to select the sampler to be used from a predefined list of samplers. The choice of sampler can significantly affect the style and quality of the generated image.
The scheduler parameter specifies the scheduling algorithm to be used during sampling. Different schedulers can impact the efficiency and outcome of the sampling process.
This parameter takes conditioning inputs that positively influence the generated image, guiding the model towards desired features and styles.
This parameter takes conditioning inputs that negatively influence the generated image, helping to avoid unwanted features and styles.
The latent_image parameter provides the initial latent space representation of the image to be sampled. This serves as the starting point for the sampling process.
The denoise parameter is a float that controls the level of denoising applied during sampling. The default value is 1.0, with a range from 0.0 to 1.0, adjustable in steps of 0.01. Lower values retain more noise, which can be useful for certain artistic effects.
The output parameter LATENT represents the final latent space representation of the generated image after the sampling process. This output can be further processed or directly converted into an image, depending on the workflow.
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