ModelSamplingContinuousEDM - ComfyUI Built-in Node Documentation

This node is designed to enhance a model’s sampling capabilities by integrating continuous EDM (Energy-based Diffusion Models) sampling techniques.

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

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