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93 lines
4.2 KiB
93 lines
4.2 KiB
""" |
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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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# nn.init.constant_(self.backbone[0][0].weight, 0) |
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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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# nn.init.constant_(self.projections[-1][-1].weight, 0) # zero output projection |
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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 proj_outputs
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