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` |

> This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! [Edit on GitHub](https://github.com/Comfy-Org/embedded-docs/blob/main/comfyui_embedded_docs/docs/ModelSamplingDiscrete/en.md)