43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
# code adapted from https://github.com/exx8/differential-diffusion
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import torch
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class DifferentialDiffusion():
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"model": ("MODEL", ),
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}}
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "apply"
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CATEGORY = "_for_testing"
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INIT = False
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def apply(self, model):
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model = model.clone()
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model.set_model_denoise_mask_function(self.forward)
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return (model,)
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def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict):
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model = extra_options["model"]
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step_sigmas = extra_options["sigmas"]
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sigma_to = model.inner_model.model_sampling.sigma_min
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if step_sigmas[-1] > sigma_to:
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sigma_to = step_sigmas[-1]
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sigma_from = step_sigmas[0]
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ts_from = model.inner_model.model_sampling.timestep(sigma_from)
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ts_to = model.inner_model.model_sampling.timestep(sigma_to)
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current_ts = model.inner_model.model_sampling.timestep(sigma)
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threshold = (current_ts - ts_to) / (ts_from - ts_to)
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return (denoise_mask >= threshold).to(denoise_mask.dtype)
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NODE_CLASS_MAPPINGS = {
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"DifferentialDiffusion": DifferentialDiffusion,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"DifferentialDiffusion": "Differential Diffusion",
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}
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