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"""
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This file is part of ComfyUI.
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Copyright (C) 2024 Stability AI
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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"""
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import torch
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import torchvision
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from torch import nn
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from .common import LayerNorm2d_op
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class CNetResBlock(nn.Module):
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def __init__(self, c, dtype=None, device=None, operations=None):
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super().__init__()
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self.blocks = nn.Sequential(
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LayerNorm2d_op(operations)(c, dtype=dtype, device=device),
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nn.GELU(),
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operations.Conv2d(c, c, kernel_size=3, padding=1),
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LayerNorm2d_op(operations)(c, dtype=dtype, device=device),
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nn.GELU(),
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operations.Conv2d(c, c, kernel_size=3, padding=1),
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)
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def forward(self, x):
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return x + self.blocks(x)
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class ControlNet(nn.Module):
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def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn):
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super().__init__()
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if bottleneck_mode is None:
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bottleneck_mode = 'effnet'
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self.proj_blocks = proj_blocks
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if bottleneck_mode == 'effnet':
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embd_channels = 1280
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self.backbone = torchvision.models.efficientnet_v2_s().features.eval()
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if c_in != 3:
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in_weights = self.backbone[0][0].weight.data
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self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device)
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if c_in > 3:
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self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone()
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else:
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self.backbone[0][0].weight.data = in_weights[:, :c_in].clone()
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elif bottleneck_mode == 'simple':
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embd_channels = c_in
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self.backbone = nn.Sequential(
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operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device),
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nn.LeakyReLU(0.2, inplace=True),
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operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device),
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)
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elif bottleneck_mode == 'large':
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self.backbone = nn.Sequential(
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operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device),
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nn.LeakyReLU(0.2, inplace=True),
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operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device),
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*[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)],
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operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device),
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)
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embd_channels = 1280
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else:
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raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}')
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self.projections = nn.ModuleList()
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for _ in range(len(proj_blocks)):
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self.projections.append(nn.Sequential(
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operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device),
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nn.LeakyReLU(0.2, inplace=True),
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operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device),
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))
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self.xl = False
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self.input_channels = c_in
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self.unshuffle_amount = 8
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def forward(self, x):
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x = self.backbone(x)
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proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)]
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for i, idx in enumerate(self.proj_blocks):
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proj_outputs[idx] = self.projections[i](x)
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return {"input": proj_outputs[::-1]}
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