CodeCow/stable-diffusion-webui-forge
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Haoming 2026-04-20 10:36:41 +08:00
parent 6e6eb77c44
commit 55c084bfe8
2 changed files with 8 additions and 1 deletions
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8
backend/nn/cnets/cldm.py
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@ -17,6 +17,7 @@ class ControlNet(nn.Module):
in_channels,
model_channels,
hint_channels,
hint_width,
num_res_blocks,
dropout=0,
channel_mult=(1, 2, 4, 8),
@ -118,7 +119,12 @@ class ControlNet(nn.Module):
self.input_blocks = nn.ModuleList([TimestepEmbedSequential(nn.Conv2d(in_channels, model_channels, 3, padding=1))])
self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
self.input_hint_block = TimestepEmbedSequential(conv_nd(dims, hint_channels, 16, 3, padding=1), nn.SiLU(), conv_nd(dims, 16, 16, 3, padding=1), nn.SiLU(), conv_nd(dims, 16, 32, 3, padding=1, stride=2), nn.SiLU(), conv_nd(dims, 32, 32, 3, padding=1), nn.SiLU(), conv_nd(dims, 32, 96, 3, padding=1, stride=2), nn.SiLU(), conv_nd(dims, 96, 96, 3, padding=1), nn.SiLU(), conv_nd(dims, 96, 256, 3, padding=1, stride=2), nn.SiLU(), conv_nd(dims, 256, model_channels, 3, padding=1))
if hint_width == 48:
c0, c1, c2, c3 = 48, 96, 192, 384
else:
c0, c1, c2, c3 = 16, 32, 96, 256
self.input_hint_block = TimestepEmbedSequential(conv_nd(dims, hint_channels, c0, 3, padding=1), nn.SiLU(), conv_nd(dims, c0, c0, 3, padding=1), nn.SiLU(), conv_nd(dims, c0, c1, 3, padding=1, stride=2), nn.SiLU(), conv_nd(dims, c1, c1, 3, padding=1), nn.SiLU(), conv_nd(dims, c1, c2, 3, padding=1, stride=2), nn.SiLU(), conv_nd(dims, c2, c2, 3, padding=1), nn.SiLU(), conv_nd(dims, c2, c3, 3, padding=1, stride=2), nn.SiLU(), conv_nd(dims, c3, model_channels, 3, padding=1))
self._feature_size = model_channels
input_block_chans = [model_channels]

1
modules_forge/supported_controlnet.py
View File

@ -128,6 +128,7 @@ class ControlNetPatcher(ControlModelPatcher):
computation_dtype = shared.sd_model.forge_objects.unet.model.computation_dtype
controlnet_config.pop("out_channels")
controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
controlnet_config["hint_width"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[0]
with using_forge_operations(dtype=unet_dtype, manual_cast_enabled=computation_dtype != unet_dtype):
control_model = cldm.ControlNet(**controlnet_config).to(dtype=unet_dtype)