Simplify differential diffusion code.

comfy/model_patcher.py

@@ -67,6 +67,9 @@ class ModelPatcher:
     def set_model_unet_function_wrapper(self, unet_wrapper_function):
         self.model_options["model_function_wrapper"] = unet_wrapper_function
 
+    def set_model_denoise_mask_function(self, denoise_mask_function):
+        self.model_options["denoise_mask_function"] = denoise_mask_function
+
     def set_model_patch(self, patch, name):
         to = self.model_options["transformer_options"]
         if "patches" not in to:

comfy/samplers.py

@@ -272,13 +272,14 @@ class CFGNoisePredictor(torch.nn.Module):
         return self.apply_model(*args, **kwargs)
 
 class KSamplerX0Inpaint(torch.nn.Module):
-    def __init__(self, model):
+    def __init__(self, model, sigmas):
         super().__init__()
         self.inner_model = model
+        self.sigmas = sigmas
     def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None):
         if denoise_mask is not None:
             if "denoise_mask_function" in model_options:
-                denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask)
+                denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
             latent_mask = 1. - denoise_mask
             x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask
         out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed)
@@ -528,7 +529,7 @@ class KSAMPLER(Sampler):
 
     def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
         extra_args["denoise_mask"] = denoise_mask
-        model_k = KSamplerX0Inpaint(model_wrap)
+        model_k = KSamplerX0Inpaint(model_wrap, sigmas)
         model_k.latent_image = latent_image
         if self.inpaint_options.get("random", False): #TODO: Should this be the default?
             generator = torch.manual_seed(extra_args.get("seed", 41) + 1)