Support for Control Loras.

Control loras are controlnets where some of the weights are stored in
"lora" format: an up and a down low rank matrice that when multiplied
together and added to the unet weight give the controlnet weight.

This allows a much smaller memory footprint depending on the rank of the
matrices.

These controlnets are used just like regular ones.

comfy/cldm/cldm.py

@@ -6,8 +6,6 @@ import torch as th
 import torch.nn as nn
 
 from ..ldm.modules.diffusionmodules.util import (
-    conv_nd,
-    linear,
     zero_module,
     timestep_embedding,
 )
@@ -15,7 +13,7 @@ from ..ldm.modules.diffusionmodules.util import (
 from ..ldm.modules.attention import SpatialTransformer
 from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
 from ..ldm.util import exists
-
+import comfy.ops
 
 class ControlledUnetModel(UNetModel):
     #implemented in the ldm unet
@@ -55,6 +53,8 @@ class ControlNet(nn.Module):
         use_linear_in_transformer=False,
         adm_in_channels=None,
         transformer_depth_middle=None,
+        device=None,
+        operations=comfy.ops,
     ):
         super().__init__()
         assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
@@ -117,9 +117,9 @@ class ControlNet(nn.Module):
 
         time_embed_dim = model_channels * 4
         self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
+            operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
             nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
+            operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
         )
 
         if self.num_classes is not None:
@@ -132,9 +132,9 @@ class ControlNet(nn.Module):
                 assert adm_in_channels is not None
                 self.label_emb = nn.Sequential(
                     nn.Sequential(
-                        linear(adm_in_channels, time_embed_dim),
+                        operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
                         nn.SiLU(),
-                        linear(time_embed_dim, time_embed_dim),
+                        operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
                     )
                 )
             else:
@@ -143,28 +143,28 @@ class ControlNet(nn.Module):
         self.input_blocks = nn.ModuleList(
             [
                 TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                    operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
                 )
             ]
         )
-        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
+        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations)])
 
         self.input_hint_block = TimestepEmbedSequential(
-                    conv_nd(dims, hint_channels, 16, 3, padding=1),
+                    operations.conv_nd(dims, hint_channels, 16, 3, padding=1),
                     nn.SiLU(),
-                    conv_nd(dims, 16, 16, 3, padding=1),
+                    operations.conv_nd(dims, 16, 16, 3, padding=1),
                     nn.SiLU(),
-                    conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+                    operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2),
                     nn.SiLU(),
-                    conv_nd(dims, 32, 32, 3, padding=1),
+                    operations.conv_nd(dims, 32, 32, 3, padding=1),
                     nn.SiLU(),
-                    conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+                    operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2),
                     nn.SiLU(),
-                    conv_nd(dims, 96, 96, 3, padding=1),
+                    operations.conv_nd(dims, 96, 96, 3, padding=1),
                     nn.SiLU(),
-                    conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+                    operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2),
                     nn.SiLU(),
-                    zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
+                    zero_module(operations.conv_nd(dims, 256, model_channels, 3, padding=1))
         )
 
         self._feature_size = model_channels
@@ -182,6 +182,7 @@ class ControlNet(nn.Module):
                         dims=dims,
                         use_checkpoint=use_checkpoint,
                         use_scale_shift_norm=use_scale_shift_norm,
+                        operations=operations
                     )
                 ]
                 ch = mult * model_channels
@@ -204,11 +205,11 @@ class ControlNet(nn.Module):
                             SpatialTransformer(
                                 ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
                                 disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint
+                                use_checkpoint=use_checkpoint, operations=operations
                             )
                         )
                 self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self.zero_convs.append(self.make_zero_conv(ch))
+                self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
                 self._feature_size += ch
                 input_block_chans.append(ch)
             if level != len(channel_mult) - 1:
@@ -224,16 +225,17 @@ class ControlNet(nn.Module):
                             use_checkpoint=use_checkpoint,
                             use_scale_shift_norm=use_scale_shift_norm,
                             down=True,
+                            operations=operations
                         )
                         if resblock_updown
                         else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
+                            ch, conv_resample, dims=dims, out_channels=out_ch, operations=operations
                         )
                     )
                 )
                 ch = out_ch
                 input_block_chans.append(ch)
-                self.zero_convs.append(self.make_zero_conv(ch))
+                self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
                 ds *= 2
                 self._feature_size += ch
 
@@ -253,11 +255,12 @@ class ControlNet(nn.Module):
                 dims=dims,
                 use_checkpoint=use_checkpoint,
                 use_scale_shift_norm=use_scale_shift_norm,
+                operations=operations
             ),
             SpatialTransformer(  # always uses a self-attn
                             ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
                             disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
-                            use_checkpoint=use_checkpoint
+                            use_checkpoint=use_checkpoint, operations=operations
                         ),
             ResBlock(
                 ch,
@@ -266,13 +269,14 @@ class ControlNet(nn.Module):
                 dims=dims,
                 use_checkpoint=use_checkpoint,
                 use_scale_shift_norm=use_scale_shift_norm,
+                operations=operations
             ),
         )
-        self.middle_block_out = self.make_zero_conv(ch)
+        self.middle_block_out = self.make_zero_conv(ch, operations=operations)
         self._feature_size += ch
 
-    def make_zero_conv(self, channels):
-        return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
+    def make_zero_conv(self, channels, operations=None):
+        return TimestepEmbedSequential(zero_module(operations.conv_nd(self.dims, channels, channels, 1, padding=0)))
 
     def forward(self, x, hint, timesteps, context, y=None, **kwargs):
         t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)

comfy/ldm/modules/attention.py

@@ -10,7 +10,6 @@ from .diffusionmodules.util import checkpoint
 from .sub_quadratic_attention import efficient_dot_product_attention
 
 from comfy import model_management
-import comfy.ops
 
 if model_management.xformers_enabled():
     import xformers
@@ -52,9 +51,9 @@ def init_(tensor):
 
 # feedforward
 class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out, dtype=None, device=None):
+    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):
         super().__init__()
-        self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
+        self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
 
     def forward(self, x):
         x, gate = self.proj(x).chunk(2, dim=-1)
@@ -62,19 +61,19 @@ class GEGLU(nn.Module):
 
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None):
         super().__init__()
         inner_dim = int(dim * mult)
         dim_out = default(dim_out, dim)
         project_in = nn.Sequential(
-            comfy.ops.Linear(dim, inner_dim, dtype=dtype, device=device),
+            operations.Linear(dim, inner_dim, dtype=dtype, device=device),
             nn.GELU()
-        ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device)
+        ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations)
 
         self.net = nn.Sequential(
             project_in,
             nn.Dropout(dropout),
-            comfy.ops.Linear(inner_dim, dim_out, dtype=dtype, device=device)
+            operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
         )
 
     def forward(self, x):
@@ -148,7 +147,7 @@ class SpatialSelfAttention(nn.Module):
 
 
 class CrossAttentionBirchSan(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
@@ -156,12 +155,12 @@ class CrossAttentionBirchSan(nn.Module):
         self.scale = dim_head ** -0.5
         self.heads = heads
 
-        self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
 
         self.to_out = nn.Sequential(
-            comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
+            operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
             nn.Dropout(dropout)
         )
 
@@ -245,7 +244,7 @@ class CrossAttentionBirchSan(nn.Module):
 
 
 class CrossAttentionDoggettx(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
@@ -253,12 +252,12 @@ class CrossAttentionDoggettx(nn.Module):
         self.scale = dim_head ** -0.5
         self.heads = heads
 
-        self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
 
         self.to_out = nn.Sequential(
-            comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
+            operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
             nn.Dropout(dropout)
         )
 
@@ -343,7 +342,7 @@ class CrossAttentionDoggettx(nn.Module):
         return self.to_out(r2)
 
 class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
@@ -351,12 +350,12 @@ class CrossAttention(nn.Module):
         self.scale = dim_head ** -0.5
         self.heads = heads
 
-        self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
 
         self.to_out = nn.Sequential(
-            comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
+            operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
             nn.Dropout(dropout)
         )
 
@@ -399,7 +398,7 @@ class CrossAttention(nn.Module):
 
 class MemoryEfficientCrossAttention(nn.Module):
     # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=None):
         super().__init__()
         print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
               f"{heads} heads.")
@@ -409,11 +408,11 @@ class MemoryEfficientCrossAttention(nn.Module):
         self.heads = heads
         self.dim_head = dim_head
 
-        self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
 
-        self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
         self.attention_op: Optional[Any] = None
 
     def forward(self, x, context=None, value=None, mask=None):
@@ -450,7 +449,7 @@ class MemoryEfficientCrossAttention(nn.Module):
         return self.to_out(out)
 
 class CrossAttentionPytorch(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
@@ -458,11 +457,11 @@ class CrossAttentionPytorch(nn.Module):
         self.heads = heads
         self.dim_head = dim_head
 
-        self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
-        self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
 
-        self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
         self.attention_op: Optional[Any] = None
 
     def forward(self, x, context=None, value=None, mask=None):
@@ -508,14 +507,14 @@ else:
 
 class BasicTransformerBlock(nn.Module):
     def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
-                 disable_self_attn=False, dtype=None, device=None):
+                 disable_self_attn=False, dtype=None, device=None, operations=None):
         super().__init__()
         self.disable_self_attn = disable_self_attn
         self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
-                              context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device)  # is a self-attention if not self.disable_self_attn
-        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device)
+                              context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations)  # is a self-attention if not self.disable_self_attn
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
         self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
-                              heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device)  # is self-attn if context is none
+                              heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
         self.norm1 = nn.LayerNorm(dim, dtype=dtype, device=device)
         self.norm2 = nn.LayerNorm(dim, dtype=dtype, device=device)
         self.norm3 = nn.LayerNorm(dim, dtype=dtype, device=device)
@@ -648,7 +647,7 @@ class SpatialTransformer(nn.Module):
     def __init__(self, in_channels, n_heads, d_head,
                  depth=1, dropout=0., context_dim=None,
                  disable_self_attn=False, use_linear=False,
-                 use_checkpoint=True, dtype=None, device=None):
+                 use_checkpoint=True, dtype=None, device=None, operations=None):
         super().__init__()
         if exists(context_dim) and not isinstance(context_dim, list):
             context_dim = [context_dim] * depth
@@ -656,26 +655,26 @@ class SpatialTransformer(nn.Module):
         inner_dim = n_heads * d_head
         self.norm = Normalize(in_channels, dtype=dtype, device=device)
         if not use_linear:
-            self.proj_in = nn.Conv2d(in_channels,
+            self.proj_in = operations.Conv2d(in_channels,
                                      inner_dim,
                                      kernel_size=1,
                                      stride=1,
                                      padding=0, dtype=dtype, device=device)
         else:
-            self.proj_in = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device)
+            self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
 
         self.transformer_blocks = nn.ModuleList(
             [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
-                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device)
+                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations)
                 for d in range(depth)]
         )
         if not use_linear:
-            self.proj_out = nn.Conv2d(inner_dim,in_channels,
+            self.proj_out = operations.Conv2d(inner_dim,in_channels,
                                                   kernel_size=1,
                                                   stride=1,
                                                   padding=0, dtype=dtype, device=device)
         else:
-            self.proj_out = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device)
+            self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
         self.use_linear = use_linear
 
     def forward(self, x, context=None, transformer_options={}):

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -8,8 +8,6 @@ import torch.nn.functional as F
 
 from .util import (
     checkpoint,
-    conv_nd,
-    linear,
     avg_pool_nd,
     zero_module,
     normalization,
@@ -17,7 +15,7 @@ from .util import (
 )
 from ..attention import SpatialTransformer
 from comfy.ldm.util import exists
-
+import comfy.ops
 
 class TimestepBlock(nn.Module):
     """
@@ -72,14 +70,14 @@ class Upsample(nn.Module):
                  upsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None):
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=None):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
         self.use_conv = use_conv
         self.dims = dims
         if use_conv:
-            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
+            self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
 
     def forward(self, x, output_shape=None):
         assert x.shape[1] == self.channels
@@ -108,7 +106,7 @@ class Downsample(nn.Module):
                  downsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None):
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=None):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
@@ -116,7 +114,7 @@ class Downsample(nn.Module):
         self.dims = dims
         stride = 2 if dims != 3 else (1, 2, 2)
         if use_conv:
-            self.op = conv_nd(
+            self.op = operations.conv_nd(
                 dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device
             )
         else:
@@ -158,6 +156,7 @@ class ResBlock(TimestepBlock):
         down=False,
         dtype=None,
         device=None,
+        operations=None
     ):
         super().__init__()
         self.channels = channels
@@ -171,7 +170,7 @@ class ResBlock(TimestepBlock):
         self.in_layers = nn.Sequential(
             nn.GroupNorm(32, channels, dtype=dtype, device=device),
             nn.SiLU(),
-            conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
+            operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
         )
 
         self.updown = up or down
@@ -187,7 +186,7 @@ class ResBlock(TimestepBlock):
 
         self.emb_layers = nn.Sequential(
             nn.SiLU(),
-            linear(
+            operations.Linear(
                 emb_channels,
                 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
             ),
@@ -197,18 +196,18 @@ class ResBlock(TimestepBlock):
             nn.SiLU(),
             nn.Dropout(p=dropout),
             zero_module(
-                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
+                operations.conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
             ),
         )
 
         if self.out_channels == channels:
             self.skip_connection = nn.Identity()
         elif use_conv:
-            self.skip_connection = conv_nd(
+            self.skip_connection = operations.conv_nd(
                 dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device
             )
         else:
-            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
+            self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
 
     def forward(self, x, emb):
         """
@@ -317,6 +316,7 @@ class UNetModel(nn.Module):
         adm_in_channels=None,
         transformer_depth_middle=None,
         device=None,
+        operations=comfy.ops,
     ):
         super().__init__()
         assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
@@ -379,9 +379,9 @@ class UNetModel(nn.Module):
 
         time_embed_dim = model_channels * 4
         self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
+            operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
             nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
+            operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
         )
 
         if self.num_classes is not None:
@@ -394,9 +394,9 @@ class UNetModel(nn.Module):
                 assert adm_in_channels is not None
                 self.label_emb = nn.Sequential(
                     nn.Sequential(
-                        linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
+                        operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
                         nn.SiLU(),
-                        linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
+                        operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
                     )
                 )
             else:
@@ -405,7 +405,7 @@ class UNetModel(nn.Module):
         self.input_blocks = nn.ModuleList(
             [
                 TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
+                    operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
                 )
             ]
         )
@@ -426,6 +426,7 @@ class UNetModel(nn.Module):
                         use_scale_shift_norm=use_scale_shift_norm,
                         dtype=self.dtype,
                         device=device,
+                        operations=operations,
                     )
                 ]
                 ch = mult * model_channels
@@ -447,7 +448,7 @@ class UNetModel(nn.Module):
                         layers.append(SpatialTransformer(
                                 ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
                                 disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device
+                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
                             )
                         )
                 self.input_blocks.append(TimestepEmbedSequential(*layers))
@@ -468,10 +469,11 @@ class UNetModel(nn.Module):
                             down=True,
                             dtype=self.dtype,
                             device=device,
+                            operations=operations
                         )
                         if resblock_updown
                         else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device
+                            ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
                         )
                     )
                 )
@@ -498,11 +500,12 @@ class UNetModel(nn.Module):
                 use_scale_shift_norm=use_scale_shift_norm,
                 dtype=self.dtype,
                 device=device,
+                operations=operations
             ),
             SpatialTransformer(  # always uses a self-attn
                             ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
                             disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
-                            use_checkpoint=use_checkpoint, dtype=self.dtype, device=device
+                            use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
                         ),
             ResBlock(
                 ch,
@@ -513,6 +516,7 @@ class UNetModel(nn.Module):
                 use_scale_shift_norm=use_scale_shift_norm,
                 dtype=self.dtype,
                 device=device,
+                operations=operations
             ),
         )
         self._feature_size += ch
@@ -532,6 +536,7 @@ class UNetModel(nn.Module):
                         use_scale_shift_norm=use_scale_shift_norm,
                         dtype=self.dtype,
                         device=device,
+                        operations=operations
                     )
                 ]
                 ch = model_channels * mult
@@ -554,7 +559,7 @@ class UNetModel(nn.Module):
                             SpatialTransformer(
                                 ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
                                 disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device
+                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
                             )
                         )
                 if level and i == self.num_res_blocks[level]:
@@ -571,9 +576,10 @@ class UNetModel(nn.Module):
                             up=True,
                             dtype=self.dtype,
                             device=device,
+                            operations=operations
                         )
                         if resblock_updown
-                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device)
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)
                     )
                     ds //= 2
                 self.output_blocks.append(TimestepEmbedSequential(*layers))
@@ -582,12 +588,12 @@ class UNetModel(nn.Module):
         self.out = nn.Sequential(
             nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
             nn.SiLU(),
-            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
+            zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
         )
         if self.predict_codebook_ids:
             self.id_predictor = nn.Sequential(
             nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
-            conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
+            operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
             #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
         )
 

comfy/ops.py

@@ -21,6 +21,11 @@ class Conv2d(torch.nn.Conv2d):
     def reset_parameters(self):
         return None
 
+def conv_nd(dims, *args, **kwargs):
+    if dims == 2:
+        return Conv2d(*args, **kwargs)
+    else:
+        raise ValueError(f"unsupported dimensions: {dims}")
 
 @contextmanager
 def use_comfy_ops(): # Kind of an ugly hack but I can't think of a better way

comfy/samplers.py

@@ -478,7 +478,7 @@ def pre_run_control(model, conds):
         timestep_end = None
         percent_to_timestep_function = lambda a: model.sigma_to_t(model.t_to_sigma(torch.tensor(a) * 999.0))
         if 'control' in x[1]:
-            x[1]['control'].pre_run(model.inner_model, percent_to_timestep_function)
+            x[1]['control'].pre_run(model.inner_model.inner_model, percent_to_timestep_function)
 
 def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
     cond_cnets = []

comfy/sd.py

@@ -844,9 +844,119 @@ class ControlNet(ControlBase):
         out.append(self.control_model_wrapped)
         return out
 
+class ControlLoraOps:
+    class Linear(torch.nn.Module):
+        def __init__(self, in_features: int, out_features: int, bias: bool = True,
+                    device=None, dtype=None) -> None:
+            factory_kwargs = {'device': device, 'dtype': dtype}
+            super().__init__()
+            self.in_features = in_features
+            self.out_features = out_features
+            self.weight = None
+            self.up = None
+            self.down = None
+            self.bias = None
+
+        def forward(self, input):
+            if self.up is not None:
+                return torch.nn.functional.linear(input, self.weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(self.weight.dtype), self.bias)
+            else:
+                return torch.nn.functional.linear(input, self.weight, self.bias)
+
+    class Conv2d(torch.nn.Module):
+        def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            padding=0,
+            dilation=1,
+            groups=1,
+            bias=True,
+            padding_mode='zeros',
+            device=None,
+            dtype=None
+        ):
+            super().__init__()
+            self.in_channels = in_channels
+            self.out_channels = out_channels
+            self.kernel_size = kernel_size
+            self.stride = stride
+            self.padding = padding
+            self.dilation = dilation
+            self.transposed = False
+            self.output_padding = 0
+            self.groups = groups
+            self.padding_mode = padding_mode
+
+            self.weight = None
+            self.bias = None
+            self.up = None
+            self.down = None
+
+
+        def forward(self, input):
+            if self.up is not None:
+                return torch.nn.functional.conv2d(input, self.weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(self.weight.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
+            else:
+                return torch.nn.functional.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
+
+    def conv_nd(self, dims, *args, **kwargs):
+        if dims == 2:
+            return self.Conv2d(*args, **kwargs)
+        else:
+            raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class ControlLora(ControlNet):
+    def __init__(self, control_weights, global_average_pooling=False, device=None):
+        ControlBase.__init__(self, device)
+        self.control_weights = control_weights
+        self.global_average_pooling = global_average_pooling
+
+    def pre_run(self, model, percent_to_timestep_function):
+        super().pre_run(model, percent_to_timestep_function)
+        controlnet_config = model.model_config.unet_config.copy()
+        controlnet_config.pop("out_channels")
+        controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
+        controlnet_config["operations"] = ControlLoraOps()
+        self.control_model = cldm.ControlNet(**controlnet_config)
+        if model_management.should_use_fp16():
+            self.control_model.half()
+        self.control_model.to(model_management.get_torch_device())
+        diffusion_model = model.diffusion_model
+        sd = diffusion_model.state_dict()
+        cm = self.control_model.state_dict()
+
+        for k in sd:
+            try:
+                set_attr(self.control_model, k, sd[k])
+            except:
+                pass
+
+        for k in self.control_weights:
+            if k not in {"lora_controlnet"}:
+                set_attr(self.control_model, k, self.control_weights[k].to(model_management.get_torch_device()))
+
+    def copy(self):
+        c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
+        self.copy_to(c)
+        return c
+
+    def cleanup(self):
+        del self.control_model
+        self.control_model = None
+        super().cleanup()
+
+    def get_models(self):
+        out = ControlBase.get_models(self)
+        return out
 
 def load_controlnet(ckpt_path, model=None):
     controlnet_data = utils.load_torch_file(ckpt_path, safe_load=True)
+    if "lora_controlnet" in controlnet_data:
+        return ControlLora(controlnet_data)
 
     controlnet_config = None
     if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format