Stable Cascade Stage C.

comfy/ldm/cascade/common.py

@@ -0,0 +1,161 @@
+"""
+    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 * (x * Nx) + self.beta + 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