Make custom noise more extensible (internal change)

py/nodes.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import abc
 import inspect
 from typing import Any, Callable
 
@@ -70,7 +71,11 @@ class NoisyLatentLikeNode:
         return ({"samples": result},)
 
 
-class SonarCustomNoiseNode:
+class SonarCustomNoiseNodeBase(abc.ABC):
+    @abc.abstractmethod
+    def get_item_class(self):
+        raise NotImplementedError
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -95,13 +100,6 @@ class SonarCustomNoiseNode:
                         "round": False,
                     },
                 ),
-                "noise_type": (
-                    tuple(
-                        t.name.lower()
-                        for t in noise.NoiseType
-                        if t is not noise.NoiseType.BROWNIAN
-                    ),
-                ),
             },
             "optional": {
                 "sonar_custom_noise_opt": ("SONAR_CUSTOM_NOISE",),
@@ -112,17 +110,42 @@ class SonarCustomNoiseNode:
     CATEGORY = "advanced/noise"
     FUNCTION = "go"
 
-    def go(self, factor, rescale, noise_type, sonar_custom_noise_opt=None):
+    def go(
+        self,
+        factor,
+        rescale,
+        sonar_custom_noise_opt=None,
+        **kwargs: dict[str, Any],
+    ):
         nis = (
             sonar_custom_noise_opt.clone()
             if sonar_custom_noise_opt
             else noise.CustomNoiseChain()
         )
         if factor != 0:
-            nis.add(noise.CustomNoiseItem(factor, noise_type))
+            nis.add(self.get_item_class()(factor, **kwargs))
         return (nis if rescale == 0 else nis.rescaled(rescale),)
 
 
+class SonarCustomNoiseNode(SonarCustomNoiseNodeBase):
+    @classmethod
+    def INPUT_TYPES(cls):
+        result = super().INPUT_TYPES()
+        result["required"] |= {
+            "noise_type": (
+                tuple(
+                    t.name.lower()
+                    for t in noise.NoiseType
+                    if t is not noise.NoiseType.BROWNIAN
+                ),
+            ),
+        }
+        return result
+
+    def get_item_class(self):
+        return noise.CustomNoiseItem
+
+
 class GuidanceConfigNode:
     @classmethod
     def INPUT_TYPES(cls):

py/noise.py

@@ -1,6 +1,7 @@
 # Noise generation functions shamelessly yoinked from https://github.com/Clybius/ComfyUI-Extra-Samplers
 from __future__ import annotations
 
+import abc
 import functools as fun
 import math
 import operator as op
@@ -16,7 +17,6 @@ from torch import FloatTensor, Generator, Tensor
 
 def scale_noise(noise, factor=1.0):
     mean, std = noise.mean(), noise.std()
-    # print(f"NOISE * {factor:.3}: std={std:.3}, mean={mean:.3}")
     return (noise - mean).div_(std).mul_(factor)
 
 
@@ -42,10 +42,56 @@ class NoiseError(Exception):
     pass
 
 
-class CustomNoiseItem:
-    def __init__(self, factor, noise_type):
+class CustomNoiseItemBase(abc.ABC):
+    def __init__(self, factor, **kwargs):
         self.factor = factor
-        self.noise_type = noise_type
+        self.keys = set(kwargs.keys())
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+
+    def clone(self):
+        return self.__class__(self.factor, **{k: getattr(self, k) for k in self.keys})
+
+    def set_factor(self, factor):
+        self.factor = factor
+        return self
+
+    @abc.abstractmethod
+    def make_noise_sampler(
+        self,
+        x: Tensor,
+        sigma_min=None,
+        sigma_max=None,
+        seed=None,
+        cpu=True,
+    ):
+        raise NotImplementedError
+
+
+class CustomNoiseItem(CustomNoiseItemBase):
+    def __init__(self, factor, **kwargs):
+        super().__init__(factor, **kwargs)
+        if getattr(self, "noise_type", None) is None:
+            raise ValueError("Noise type required!")
+
+    @torch.no_grad()
+    def make_noise_sampler(
+        self,
+        x: Tensor,
+        sigma_min=None,
+        sigma_max=None,
+        seed=None,
+        cpu=True,
+    ):
+        return get_noise_sampler(
+            self.noise_type,
+            x,
+            sigma_min,
+            sigma_max,
+            seed=seed,
+            cpu=cpu,
+            factor=self.factor,
+        )
 
 
 class CustomNoiseChain:
@@ -54,7 +100,7 @@ class CustomNoiseChain:
 
     def clone(self):
         return CustomNoiseChain(
-            [CustomNoiseItem(i.factor, i.noise_type) for i in self.items],
+            [i.clone() for i in self.items],
         )
 
     def add(self, item):
@@ -65,20 +111,9 @@ class CustomNoiseChain:
         divisor = total / scale
         divisor = divisor if divisor != 0 else 1.0
         return CustomNoiseChain(
-            [CustomNoiseItem(i.factor / divisor, i.noise_type) for i in self.items],
+            [i.clone().set_factor(i.factor / divisor) for i in self.items],
         )
 
-    def __call__(
-        self,
-        x: Tensor,
-        sigma_min=None,
-        sigma_max=None,
-        seed=None,
-        transform=lambda x: x,
-        cpu=True,
-    ):
-        pass
-
     @torch.no_grad()
     def make_noise_sampler(
         self,
@@ -89,14 +124,12 @@ class CustomNoiseChain:
         cpu=True,
     ) -> Callable:
         noise_samplers = tuple(
-            get_noise_sampler(
-                i.noise_type,
+            i.make_noise_sampler(
                 x,
                 sigma_min,
                 sigma_max,
                 seed=seed,
                 cpu=cpu,
-                factor=i.factor,
             )
             for i in self.items
         )