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. |