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Enhance AI art sampling with adjustable sharpness for improved visual quality and control.
The Fooocus_KSampler node is designed to enhance the sampling process in AI art generation by incorporating a sharpness parameter. This node builds upon the standard KSampler, adding the ability to adjust the sharpness of the generated images, which can significantly impact the visual quality and detail of the output. By allowing you to fine-tune the sharpness, this node provides greater control over the artistic style and clarity of the generated images, making it a valuable tool for artists looking to achieve specific visual effects or improve the overall quality of their AI-generated artwork.
This parameter specifies the model to be used for sampling. It is a required input and determines the underlying AI model that will generate the images.
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. Changing the seed will produce different variations of the generated images.
This integer parameter defines the number of sampling steps to be performed. The default value is 20, with a minimum of 1 and a maximum of 10000. Increasing the number of steps can improve the quality of the generated images but will also increase the computation time.
The cfg (classifier-free guidance) parameter is a float that controls the strength of the guidance during sampling. The default value is 8.0, with a minimum of 0.0 and a maximum of 100.0, adjustable in steps of 0.1. Higher values can lead to more pronounced features in the generated images.
This parameter allows you to select the specific sampler to be used from a predefined list of samplers. The choice of sampler can affect the style and quality of the generated images.
The scheduler parameter specifies the scheduling method to be used during sampling. Different schedulers can impact the convergence and quality of the generated images.
This parameter provides positive conditioning to guide the sampling process. It is used to influence the generated images towards desired features or styles.
The negative parameter provides negative conditioning, which helps to steer the sampling process away from undesired features or styles.
This parameter represents the latent image to be used as the starting point for sampling. It is a required input and serves as the initial state from which the final image will be generated.
The denoise parameter is a float that controls the amount of noise reduction applied during sampling. The default value is 1.0, with a minimum of 0.0 and a maximum of 1.0, adjustable in steps of 0.01. Lower values can retain more noise, leading to more abstract results, while higher values produce cleaner images.
This optional float parameter allows you to adjust the sharpness of the generated images. The default value is 2.0, with a minimum of 0.0 and a maximum of 100.0, adjustable in steps of 0.01. Increasing the sharpness can enhance the detail and clarity of the images.
The output parameter is a latent representation of the generated image. This latent output can be further processed or decoded to obtain the final image. It encapsulates the visual information generated by the sampling process, influenced by the input parameters.
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