""" 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 . """ import torch import torch.nn as nn from comfy.ldm.modules.attention import optimized_attention import comfy.ops 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.empty(1, 1, 1, dim, dtype=dtype, device=device)) self.beta = nn.Parameter(torch.empty(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 comfy.ops.cast_to_input(self.gamma, x) * (x * Nx) + comfy.ops.cast_to_input(self.beta, x) + 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