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. 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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