Model Sampling Discrete

Documentation

• Class name: ModelSamplingDiscrete

• Category: advanced/model

• Output node: False

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.

Input types

Parameter	Comfy dtype	Python dtype	Description
model	MODEL	torch.nn.Module	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.
sampling	COMBO[STRING]	str	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.
zsnr	BOOLEAN	bool	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.

Output types

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