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Running
on
Zero
| """ | |
| 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 torch | |
| 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: | |
| # nn.init.constant_(self.backbone[0][0].weight, 0) | |
| 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), | |
| )) | |
| # nn.init.constant_(self.projections[-1][-1].weight, 0) # zero output projection | |
| 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]} | |