workflow-templates-broken-zap-2026-06-08/site/knowledge/nodes/custom/DifferentialDiffusion.md

DifferentialDiffusion

Category: _for_testing

Display Name: Differential Diffusion

Also known as: inpaint gradient, variable denoise strength

Description

This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHub

The Differential Diffusion node modifies the denoising process by applying a binary mask based on timestep thresholds. It creates a mask that blends between the original denoise mask and a threshold-based binary mask, allowing controlled adjustment of the diffusion process strength.

Inputs

Parameter	Type	Default	Description
model	Model	—
strength	Float	1.0

Outputs

Output	Type
)],
is_experimental=True,
)

@classmethod
def execute(cls, model, strength=1.0) -> io.NodeOutput:
    model = model.clone()
    model.set_model_denoise_mask_function(lambda *args, **kwargs: cls.forward(*args, **kwargs, strength=strength))
    return io.NodeOutput(model)

@classmethod
def forward(cls, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict, strength: float | Model |

Source: comfy_extras/nodes_differential_diffusion.py

Used in 1 template(s)