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.