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controlnet: adopt new cast_bias_weight synchronization scheme

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This is nessacary for safe async weight offloading.
This commit is contained in:
Rattus 2025-10-27 18:53:19 +10:00
parent 2d69bdfabe
commit 432a5a9d82
1 changed files with 10 additions and 7 deletions
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17
comfy/controlnet.py
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@ -310,11 +310,13 @@ class ControlLoraOps:
self.bias = None self.bias = None
def forward(self, input): def forward(self, input):
weight, bias = comfy.ops.cast_bias_weight(self, input) weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)
if self.up is not None: if self.up is not None:
return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) x = torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias)
else: else:
return torch.nn.functional.linear(input, weight, bias) x = torch.nn.functional.linear(input, weight, bias)
comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)
return x
class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp): class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
def __init__( def __init__(
@ -350,12 +352,13 @@ class ControlLoraOps:
def forward(self, input): def forward(self, input):
weight, bias = comfy.ops.cast_bias_weight(self, input) weight, bias, offload_stream = comfy.ops.cast_bias_weight(self, input, offloadable=True)
if self.up is not None: if self.up is not None:
return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) x = torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups)
else: else:
return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) x = torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
comfy.ops.uncast_bias_weight(self, weight, bias, offload_stream)
return x
class ControlLora(ControlNet): class ControlLora(ControlNet):
def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options def __init__(self, control_weights, global_average_pooling=False, model_options={}): #TODO? model_options