ModelSamplingDiscrete - ComfyUI Built-in Node Documentation

This node is designed to modify the sampling behavior of a model by applying a discrete sampling strategy. It allows for the selection of different sampling methods, such as epsilon, v_prediction, lcm, or x0, and optionally adjusts the model’s noise reduction strategy based on the zero-shot noise ratio (zsnr) setting.

Inputs

Parameter	Description	Data Type	Python dtype
`model`	The model to which the discrete sampling strategy will be applied. This parameter is crucial as it defines the base model that will undergo modification.	MODEL	`torch.nn.Module`
`sampling`	Specifies the discrete sampling method to be applied to the model. The choice of method affects how the model generates samples, offering different strategies for sampling.	COMBO[STRING]	`str`
`zsnr`	A boolean flag that, when enabled, adjusts the model’s noise reduction strategy based on the zero-shot noise ratio. This can influence the quality and characteristics of the generated samples.	`BOOLEAN`	`bool`

Outputs

Parameter	Description	Data Type	Python dtype
`model`	The modified model with the applied discrete sampling strategy. This model is now equipped to generate samples using the specified method and adjustments.	MODEL	`torch.nn.Module`

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