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.

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