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