mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-03-15 05:57:20 +00:00
Add add_weight_wrapper function to model patcher.
Functions can now easily be added to wrap/modify model weights.
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@ -96,8 +96,28 @@ def wipe_lowvram_weight(m):
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if hasattr(m, "prev_comfy_cast_weights"):
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m.comfy_cast_weights = m.prev_comfy_cast_weights
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del m.prev_comfy_cast_weights
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m.weight_function = None
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m.bias_function = None
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if hasattr(m, "weight_function"):
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m.weight_function = []
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if hasattr(m, "bias_function"):
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m.bias_function = []
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def move_weight_functions(m, device):
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if device is None:
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return 0
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memory = 0
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if hasattr(m, "weight_function"):
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for f in m.weight_function:
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if hasattr(f, "move_to"):
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memory += f.move_to(device=device)
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if hasattr(m, "bias_function"):
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for f in m.bias_function:
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if hasattr(f, "move_to"):
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memory += f.move_to(device=device)
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return memory
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class LowVramPatch:
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def __init__(self, key, patches):
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@ -192,6 +212,7 @@ class ModelPatcher:
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self.backup = {}
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self.object_patches = {}
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self.object_patches_backup = {}
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self.weight_wrapper_patches = {}
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self.model_options = {"transformer_options":{}}
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self.model_size()
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self.load_device = load_device
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@ -250,6 +271,7 @@ class ModelPatcher:
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n.patches_uuid = self.patches_uuid
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n.object_patches = self.object_patches.copy()
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n.weight_wrapper_patches = self.weight_wrapper_patches.copy()
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n.model_options = copy.deepcopy(self.model_options)
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n.backup = self.backup
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n.object_patches_backup = self.object_patches_backup
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@ -402,6 +424,10 @@ class ModelPatcher:
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def add_object_patch(self, name, obj):
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self.object_patches[name] = obj
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def add_weight_wrapper(self, name, function):
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self.weight_wrapper_patches[name] = self.weight_wrapper_patches.get(name, []) + [function]
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self.patches_uuid = uuid.uuid4()
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def get_model_object(self, name: str) -> torch.nn.Module:
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"""Retrieves a nested attribute from an object using dot notation considering
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object patches.
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@ -566,6 +592,9 @@ class ModelPatcher:
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lowvram_weight = False
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weight_key = "{}.weight".format(n)
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bias_key = "{}.bias".format(n)
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if not full_load and hasattr(m, "comfy_cast_weights"):
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if mem_counter + module_mem >= lowvram_model_memory:
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lowvram_weight = True
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@ -573,34 +602,42 @@ class ModelPatcher:
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if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed
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continue
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weight_key = "{}.weight".format(n)
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bias_key = "{}.bias".format(n)
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if lowvram_weight:
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if hasattr(m, "comfy_cast_weights"):
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m.weight_function = []
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m.bias_function = []
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if weight_key in self.patches:
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if force_patch_weights:
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self.patch_weight_to_device(weight_key)
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else:
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m.weight_function = LowVramPatch(weight_key, self.patches)
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m.weight_function = [LowVramPatch(weight_key, self.patches)]
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patch_counter += 1
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if bias_key in self.patches:
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if force_patch_weights:
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self.patch_weight_to_device(bias_key)
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else:
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m.bias_function = LowVramPatch(bias_key, self.patches)
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m.bias_function = [LowVramPatch(bias_key, self.patches)]
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patch_counter += 1
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m.prev_comfy_cast_weights = m.comfy_cast_weights
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m.comfy_cast_weights = True
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else:
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if hasattr(m, "comfy_cast_weights"):
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if m.comfy_cast_weights:
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wipe_lowvram_weight(m)
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if full_load or mem_counter + module_mem < lowvram_model_memory:
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mem_counter += module_mem
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load_completely.append((module_mem, n, m, params))
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if weight_key in self.weight_wrapper_patches:
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m.weight_function.extend(self.weight_wrapper_patches[weight_key])
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if bias_key in self.weight_wrapper_patches:
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m.bias_function.extend(self.weight_wrapper_patches[bias_key])
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mem_counter += move_weight_functions(m, device_to)
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load_completely.sort(reverse=True)
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for x in load_completely:
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n = x[1]
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@ -662,6 +699,7 @@ class ModelPatcher:
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self.unpatch_hooks()
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if self.model.model_lowvram:
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for m in self.model.modules():
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move_weight_functions(m, device_to)
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wipe_lowvram_weight(m)
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self.model.model_lowvram = False
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@ -729,12 +767,13 @@ class ModelPatcher:
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bias_key = "{}.bias".format(n)
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if move_weight:
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m.to(device_to)
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module_mem += move_weight_functions(m, device_to)
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if lowvram_possible:
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if weight_key in self.patches:
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m.weight_function = LowVramPatch(weight_key, self.patches)
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m.weight_function.append(LowVramPatch(weight_key, self.patches))
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patch_counter += 1
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if bias_key in self.patches:
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m.bias_function = LowVramPatch(bias_key, self.patches)
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m.bias_function.append(LowVramPatch(bias_key, self.patches))
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patch_counter += 1
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m.prev_comfy_cast_weights = m.comfy_cast_weights
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33
comfy/ops.py
33
comfy/ops.py
@ -38,21 +38,23 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
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bias = None
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non_blocking = comfy.model_management.device_supports_non_blocking(device)
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if s.bias is not None:
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has_function = s.bias_function is not None
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has_function = len(s.bias_function) > 0
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bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function)
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if has_function:
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bias = s.bias_function(bias)
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for f in s.bias_function:
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bias = f(bias)
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has_function = s.weight_function is not None
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has_function = len(s.weight_function) > 0
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weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function)
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if has_function:
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weight = s.weight_function(weight)
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for f in s.weight_function:
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weight = f(weight)
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return weight, bias
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class CastWeightBiasOp:
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comfy_cast_weights = False
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weight_function = None
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bias_function = None
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weight_function = []
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bias_function = []
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class disable_weight_init:
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class Linear(torch.nn.Linear, CastWeightBiasOp):
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@ -64,7 +66,7 @@ class disable_weight_init:
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return torch.nn.functional.linear(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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@ -78,7 +80,7 @@ class disable_weight_init:
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return self._conv_forward(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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@ -92,7 +94,7 @@ class disable_weight_init:
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return self._conv_forward(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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@ -106,7 +108,7 @@ class disable_weight_init:
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return self._conv_forward(input, weight, bias)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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@ -120,12 +122,11 @@ class disable_weight_init:
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return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
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def reset_parameters(self):
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return None
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@ -139,7 +140,7 @@ class disable_weight_init:
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return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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@ -160,7 +161,7 @@ class disable_weight_init:
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output_padding, self.groups, self.dilation)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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@ -181,7 +182,7 @@ class disable_weight_init:
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output_padding, self.groups, self.dilation)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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return super().forward(*args, **kwargs)
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@ -199,7 +200,7 @@ class disable_weight_init:
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return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
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def forward(self, *args, **kwargs):
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if self.comfy_cast_weights:
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if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
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return self.forward_comfy_cast_weights(*args, **kwargs)
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else:
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if "out_dtype" in kwargs:
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