ComfyUI/comfy/ldm/cascade/common.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 torch.nn as nn
from comfy.ldm.modules.attention import optimized_attention

class Linear(torch.nn.Linear):
 def reset_parameters(self):
 return None

class Conv2d(torch.nn.Conv2d):
 def reset_parameters(self):
 return None

class OptimizedAttention(nn.Module):
 def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
 super().__init__()
 self.heads = nhead

 self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
 self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
 self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device)

 self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)

 def forward(self, q, k, v):
 q = self.to_q(q)
 k = self.to_k(k)
 v = self.to_v(v)

 out = optimized_attention(q, k, v, self.heads)

 return self.out_proj(out)

class Attention2D(nn.Module):
 def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
 super().__init__()
 self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations)
 # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device)

 def forward(self, x, kv, self_attn=False):
 orig_shape = x.shape
 x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1) # Bx4xHxW -> Bx(HxW)x4
 if self_attn:
 kv = torch.cat([x, kv], dim=1)
 # x = self.attn(x, kv, kv, need_weights=False)[0]
 x = self.attn(x, kv, kv)
 x = x.permute(0, 2, 1).view(*orig_shape)
 return x


def LayerNorm2d_op(operations):
 class LayerNorm2d(operations.LayerNorm):
 def __init__(self, *args, **kwargs):
 super().__init__(*args, **kwargs)

 def forward(self, x):
 return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
 return LayerNorm2d

class GlobalResponseNorm(nn.Module):
 "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105"
 def __init__(self, dim, dtype=None, device=None):
 super().__init__()
 self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device))
 self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device))

 def forward(self, x):
 Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
 Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
 return self.gamma.to(device=x.device, dtype=x.dtype) * (x * Nx) + self.beta.to(device=x.device, dtype=x.dtype) + x


class ResBlock(nn.Module):
 def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None): # , num_heads=4, expansion=2):
 super().__init__()
 self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c, dtype=dtype, device=device)
 # self.depthwise = SAMBlock(c, num_heads, expansion)
 self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
 self.channelwise = nn.Sequential(
 operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device),
 nn.GELU(),
 GlobalResponseNorm(c * 4, dtype=dtype, device=device),
 nn.Dropout(dropout),
 operations.Linear(c * 4, c, dtype=dtype, device=device)
 )

 def forward(self, x, x_skip=None):
 x_res = x
 x = self.norm(self.depthwise(x))
 if x_skip is not None:
 x = torch.cat([x, x_skip], dim=1)
 x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
 return x + x_res


class AttnBlock(nn.Module):
 def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None):
 super().__init__()
 self.self_attn = self_attn
 self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
 self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations)
 self.kv_mapper = nn.Sequential(
 nn.SiLU(),
 operations.Linear(c_cond, c, dtype=dtype, device=device)
 )

 def forward(self, x, kv):
 kv = self.kv_mapper(kv)
 x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn)
 return x


class FeedForwardBlock(nn.Module):
 def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None):
 super().__init__()
 self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
 self.channelwise = nn.Sequential(
 operations.Linear(c, c * 4, dtype=dtype, device=device),
 nn.GELU(),
 GlobalResponseNorm(c * 4, dtype=dtype, device=device),
 nn.Dropout(dropout),
 operations.Linear(c * 4, c, dtype=dtype, device=device)
 )

 def forward(self, x):
 x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
 return x


class TimestepBlock(nn.Module):
 def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None):
 super().__init__()
 self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)
 self.conds = conds
 for cname in conds:
 setattr(self, f"mapper_{cname}", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device))

 def forward(self, x, t):
 t = t.chunk(len(self.conds) + 1, dim=1)
 a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1)
 for i, c in enumerate(self.conds):
 ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1)
 a, b = a + ac, b + bc
 return x * (1 + a) + b