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.