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comfy_extras/nodes_differential_diffusion.py

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+# code adapted from https://github.com/exx8/differential-diffusion
+
+import torch
+
+class DifferentialDiffusion():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL", ),
+                            }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply"
+    CATEGORY = "_for_testing"
+    INIT = False
+
+    def apply(self, model):
+        model = model.clone()
+        model.set_model_denoise_mask_function(self.forward)
+        return (model,)
+
+    def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict):
+        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)
+
+        return (denoise_mask >= threshold).to(denoise_mask.dtype)
+
+
+NODE_CLASS_MAPPINGS = {
+    "DifferentialDiffusion": DifferentialDiffusion,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DifferentialDiffusion": "Differential Diffusion",
+}