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# code adapted from https://github.com/exx8/differential-diffusion
from typing_extensions import override
import torch
from comfy_api.latest import ComfyExtension, io
class DifferentialDiffusion(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="DifferentialDiffusion",
display_name="Differential Diffusion",
category="_for_testing",
inputs=[
io.Model.Input("model"),
io.Float.Input(
"strength",
default=1.0,
min=0.0,
max=1.0,
step=0.01,
optional=True,
),
],
outputs=[io.Model.Output()],
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 = extra_options["model"]
step_sigmas = extra_options["sigmas"]
sigma_to = model.inner_model.model_sampling.sigma_min
if step_sigmas[-1] > sigma_to:
sigma_to = step_sigmas[-1]
sigma_from = step_sigmas[0]
ts_from = model.inner_model.model_sampling.timestep(sigma_from)
ts_to = model.inner_model.model_sampling.timestep(sigma_to)
current_ts = model.inner_model.model_sampling.timestep(sigma[0])
threshold = (current_ts - ts_to) / (ts_from - ts_to)
# Generate the binary mask based on the threshold
binary_mask = (denoise_mask >= threshold).to(denoise_mask.dtype)
# Blend binary mask with the original denoise_mask using strength
if strength and strength < 1:
blended_mask = strength * binary_mask + (1 - strength) * denoise_mask
return blended_mask
else:
return binary_mask
class DifferentialDiffusionExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
DifferentialDiffusion,
]
async def comfy_entrypoint() -> DifferentialDiffusionExtension:
return DifferentialDiffusionExtension()
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