Fix channel padding with new Comfy core API (#106)

* Fix channel padding with new Comfy core API

* nit

layered_diffusion.py

@@ -1,7 +1,6 @@
 import os
 from enum import Enum
 import torch
-import functools
 import copy
 from typing import Optional, List
 from dataclasses import dataclass
@@ -31,73 +30,6 @@ else:
 load_layer_model_state_dict = load_torch_file
 
 
-# ------------ Start patching ComfyUI ------------
-def calculate_weight_adjust_channel(func):
-    """Patches ComfyUI's LoRA weight application to accept multi-channel inputs."""
-
-    @functools.wraps(func)
-    def calculate_weight(
-        self: ModelPatcher, patches, weight: torch.Tensor, key: str
-    ) -> torch.Tensor:
-        weight = func(self, patches, weight, key)
-
-        for p in patches:
-            alpha = p[0]
-            v = p[1]
-
-            # The recursion call should be handled in the main func call.
-            if isinstance(v, list):
-                continue
-
-            if len(v) == 1:
-                patch_type = "diff"
-            elif len(v) == 2:
-                patch_type = v[0]
-                v = v[1]
-
-            if patch_type == "diff":
-                w1 = v[0]
-                if all(
-                    (
-                        alpha != 0.0,
-                        w1.shape != weight.shape,
-                        w1.ndim == weight.ndim == 4,
-                    )
-                ):
-                    new_shape = [max(n, m) for n, m in zip(weight.shape, w1.shape)]
-                    print(
-                        f"Merged with {key} channel changed from {weight.shape} to {new_shape}"
-                    )
-                    new_diff = alpha * comfy.model_management.cast_to_device(
-                        w1, weight.device, weight.dtype
-                    )
-                    new_weight = torch.zeros(size=new_shape).to(weight)
-                    new_weight[
-                        : weight.shape[0],
-                        : weight.shape[1],
-                        : weight.shape[2],
-                        : weight.shape[3],
-                    ] = weight
-                    new_weight[
-                        : new_diff.shape[0],
-                        : new_diff.shape[1],
-                        : new_diff.shape[2],
-                        : new_diff.shape[3],
-                    ] += new_diff
-                    new_weight = new_weight.contiguous().clone()
-                    weight = new_weight
-        return weight
-
-    return calculate_weight
-
-
-ModelPatcher.calculate_weight = calculate_weight_adjust_channel(
-    ModelPatcher.calculate_weight
-)
-
-# ------------ End patching ComfyUI ------------
-
-
 class LayeredDiffusionDecode:
     """
     Decode alpha channel value from pixel value.
@@ -323,8 +255,19 @@ class LayeredDiffusionBase:
             model_dir=layer_model_root,
             file_name=self.model_file_name,
         )
+        def pad_diff_weight(v):
+            if len(v) == 1:
+                return ("diff", [v[0], {"pad_weight": True}])
+            elif len(v) == 2 and v[0] == "diff":
+                return ("diff", [v[1][0], {"pad_weight": True}])
+            else:
+                return v
+
         layer_lora_state_dict = load_layer_model_state_dict(model_path)
-        layer_lora_patch_dict = to_lora_patch_dict(layer_lora_state_dict)
+        layer_lora_patch_dict = {
+            k: pad_diff_weight(v)
+            for k, v in to_lora_patch_dict(layer_lora_state_dict).items()
+        }
         work_model = model.clone()
         work_model.add_patches(layer_lora_patch_dict, weight)
         return (work_model,)