Model Sampling Continuous EDM
Documentation
- Class name:
ModelSamplingContinuousEDM - Category:
advanced/model - Output node:
False
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.
Input types
| Parameter | Comfy dtype | Python dtype | Description |
|---|---|---|---|
model | MODEL | torch.nn.Module | The model to be enhanced with continuous EDM sampling capabilities. It serves as the foundation for applying the advanced sampling techniques. |
sampling | COMBO[STRING] | str | 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. |
sigma_max | FLOAT | float | The maximum sigma value for noise level, allowing for upper bound control in the noise injection process during sampling. |
sigma_min | FLOAT | float | The minimum sigma value for noise level, setting the lower limit for noise injection, thus affecting the model's sampling precision. |
Output types
| Parameter | Comfy dtype | Python dtype | Description |
|---|---|---|---|
model | MODEL | torch.nn.Module | The enhanced model with integrated continuous EDM sampling capabilities, ready for further use in generation tasks. |