This node is designed to enhance a model's sampling capabilities by integrating continuous EDM (Energy-based Diffusion Models) sampling techniques. It allows for the dynamic adjustment of the noise levels within the model's sampling process, offering a more refined control over the generation quality and diversity.

## Inputs

| Parameter | Description | Data Type | Python dtype |
| --- | --- | --- | --- |
| `model` | The model to be enhanced with continuous EDM sampling capabilities. It serves as the foundation for applying the advanced sampling techniques. | `MODEL` | `torch.nn.Module` |
| `sampling` | Specifies the type of sampling to be applied, either 'eps' for epsilon sampling or 'v_prediction' for velocity prediction, influencing the model's behavior during the sampling process. | COMBO[STRING] | `str` |
| `sigma_max` | The maximum sigma value for noise level, allowing for upper bound control in the noise injection process during sampling. | `FLOAT` | `float` |
| `sigma_min` | The minimum sigma value for noise level, setting the lower limit for noise injection, thus affecting the model's sampling precision. | `FLOAT` | `float` |

## Outputs

| Parameter | Description | Data Type | Python dtype |
| --- | --- | --- | --- |
| `model` | The enhanced model with integrated continuous EDM sampling capabilities, ready for further use in generation tasks. | 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/ModelSamplingContinuousEDM/en.md)