| |
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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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| |
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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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| |
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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[0])
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| |
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| | threshold = (current_ts - ts_to) / (ts_from - ts_to)
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| |
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| | return (denoise_mask >= threshold).to(denoise_mask.dtype)
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| |
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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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| |
|
