ComfyUI/comfy/ldm/cascade/controlnet.py

"""
 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 proj_outputs