Refactor comfy.ops

comfy.ops -> comfy.ops.disable_weight_init

This should make it more clear what they actually do.

Some unused code has also been removed.

comfy/cldm/cldm.py

@@ -53,7 +53,7 @@ class ControlNet(nn.Module):
         transformer_depth_middle=None,
         transformer_depth_output=None,
         device=None,
-        operations=comfy.ops,
+        operations=comfy.ops.disable_weight_init,
         **kwargs,
     ):
         super().__init__()

comfy/clip_vision.py

@@ -38,7 +38,7 @@ class ClipVisionModel():
         if comfy.model_management.should_use_fp16(self.load_device, prioritize_performance=False):
             self.dtype = torch.float16
 
-        self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops)
+        self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops.disable_weight_init)
 
         self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
     def load_sd(self, sd):

comfy/controlnet.py

@@ -208,9 +208,9 @@ class ControlLoraOps:
 
         def forward(self, input):
             if self.up is not None:
-                return torch.nn.functional.linear(input, self.weight.to(input.dtype).to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
+                return torch.nn.functional.linear(input, self.weight.to(dtype=input.dtype, device=input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
             else:
-                return torch.nn.functional.linear(input, self.weight.to(input.device), self.bias)
+                return torch.nn.functional.linear(input, self.weight.to(dtype=input.dtype, device=input.device), self.bias)
 
     class Conv2d(torch.nn.Module):
         def __init__(
@@ -247,24 +247,9 @@ class ControlLoraOps:
 
         def forward(self, input):
             if self.up is not None:
-                return torch.nn.functional.conv2d(input, self.weight.to(input.dtype).to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
+                return torch.nn.functional.conv2d(input, self.weight.to(dtype=input.dtype, device=input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
             else:
-                return torch.nn.functional.conv2d(input, self.weight.to(input.device), 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 Conv3d(comfy.ops.Conv3d):
-        pass
-
-    class GroupNorm(comfy.ops.GroupNorm):
-        pass
-
-    class LayerNorm(comfy.ops.LayerNorm):
-        pass
+                return torch.nn.functional.conv2d(input, self.weight.to(dtype=input.dtype, device=input.device), self.bias, self.stride, self.padding, self.dilation, self.groups)
 
 
 class ControlLora(ControlNet):
@@ -278,7 +263,9 @@ class ControlLora(ControlNet):
         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()
+        class control_lora_ops(ControlLoraOps, comfy.ops.disable_weight_init):
+            pass
+        controlnet_config["operations"] = control_lora_ops
         self.control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
         dtype = model.get_dtype()
         self.control_model.to(dtype)

comfy/ldm/modules/attention.py

@@ -19,6 +19,7 @@ if model_management.xformers_enabled():
 
 from comfy.cli_args import args
 import comfy.ops
+ops = comfy.ops.disable_weight_init
 
 # CrossAttn precision handling
 if args.dont_upcast_attention:
@@ -55,7 +56,7 @@ def init_(tensor):
 
 # feedforward
 class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=comfy.ops):
+    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops):
         super().__init__()
         self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
 
@@ -65,7 +66,7 @@ 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, operations=comfy.ops):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops):
         super().__init__()
         inner_dim = int(dim * mult)
         dim_out = default(dim_out, dim)
@@ -356,7 +357,7 @@ def optimized_attention_for_device(device, mask=False):
 
 
 class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
@@ -389,7 +390,7 @@ class CrossAttention(nn.Module):
 
 class BasicTransformerBlock(nn.Module):
     def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None,
-                 disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, dtype=None, device=None, operations=comfy.ops):
+                 disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, dtype=None, device=None, operations=ops):
         super().__init__()
 
         self.ff_in = ff_in or inner_dim is not None
@@ -558,7 +559,7 @@ 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, operations=comfy.ops):
+                 use_checkpoint=True, dtype=None, device=None, operations=ops):
         super().__init__()
         if exists(context_dim) and not isinstance(context_dim, list):
             context_dim = [context_dim] * depth
@@ -632,7 +633,7 @@ class SpatialVideoTransformer(SpatialTransformer):
         disable_self_attn=False,
         disable_temporal_crossattention=False,
         max_time_embed_period: int = 10000,
-        dtype=None, device=None, operations=comfy.ops
+        dtype=None, device=None, operations=ops
     ):
         super().__init__(
             in_channels,

comfy/ldm/modules/diffusionmodules/model.py

@@ -8,6 +8,7 @@ from typing import Optional, Any
 
 from comfy import model_management
 import comfy.ops
+ops = comfy.ops.disable_weight_init
 
 if model_management.xformers_enabled_vae():
     import xformers
@@ -48,7 +49,7 @@ class Upsample(nn.Module):
         super().__init__()
         self.with_conv = with_conv
         if self.with_conv:
-            self.conv = comfy.ops.Conv2d(in_channels,
+            self.conv = ops.Conv2d(in_channels,
                                         in_channels,
                                         kernel_size=3,
                                         stride=1,
@@ -78,7 +79,7 @@ class Downsample(nn.Module):
         self.with_conv = with_conv
         if self.with_conv:
             # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = comfy.ops.Conv2d(in_channels,
+            self.conv = ops.Conv2d(in_channels,
                                         in_channels,
                                         kernel_size=3,
                                         stride=2,
@@ -105,30 +106,30 @@ class ResnetBlock(nn.Module):
 
         self.swish = torch.nn.SiLU(inplace=True)
         self.norm1 = Normalize(in_channels)
-        self.conv1 = comfy.ops.Conv2d(in_channels,
+        self.conv1 = ops.Conv2d(in_channels,
                                      out_channels,
                                      kernel_size=3,
                                      stride=1,
                                      padding=1)
         if temb_channels > 0:
-            self.temb_proj = comfy.ops.Linear(temb_channels,
+            self.temb_proj = ops.Linear(temb_channels,
                                              out_channels)
         self.norm2 = Normalize(out_channels)
         self.dropout = torch.nn.Dropout(dropout, inplace=True)
-        self.conv2 = comfy.ops.Conv2d(out_channels,
+        self.conv2 = ops.Conv2d(out_channels,
                                      out_channels,
                                      kernel_size=3,
                                      stride=1,
                                      padding=1)
         if self.in_channels != self.out_channels:
             if self.use_conv_shortcut:
-                self.conv_shortcut = comfy.ops.Conv2d(in_channels,
+                self.conv_shortcut = ops.Conv2d(in_channels,
                                                      out_channels,
                                                      kernel_size=3,
                                                      stride=1,
                                                      padding=1)
             else:
-                self.nin_shortcut = comfy.ops.Conv2d(in_channels,
+                self.nin_shortcut = ops.Conv2d(in_channels,
                                                     out_channels,
                                                     kernel_size=1,
                                                     stride=1,
@@ -245,22 +246,22 @@ class AttnBlock(nn.Module):
         self.in_channels = in_channels
 
         self.norm = Normalize(in_channels)
-        self.q = comfy.ops.Conv2d(in_channels,
+        self.q = ops.Conv2d(in_channels,
                                  in_channels,
                                  kernel_size=1,
                                  stride=1,
                                  padding=0)
-        self.k = comfy.ops.Conv2d(in_channels,
+        self.k = ops.Conv2d(in_channels,
                                  in_channels,
                                  kernel_size=1,
                                  stride=1,
                                  padding=0)
-        self.v = comfy.ops.Conv2d(in_channels,
+        self.v = ops.Conv2d(in_channels,
                                  in_channels,
                                  kernel_size=1,
                                  stride=1,
                                  padding=0)
-        self.proj_out = comfy.ops.Conv2d(in_channels,
+        self.proj_out = ops.Conv2d(in_channels,
                                         in_channels,
                                         kernel_size=1,
                                         stride=1,
@@ -312,14 +313,14 @@ class Model(nn.Module):
             # timestep embedding
             self.temb = nn.Module()
             self.temb.dense = nn.ModuleList([
-                comfy.ops.Linear(self.ch,
+                ops.Linear(self.ch,
                                 self.temb_ch),
-                comfy.ops.Linear(self.temb_ch,
+                ops.Linear(self.temb_ch,
                                 self.temb_ch),
             ])
 
         # downsampling
-        self.conv_in = comfy.ops.Conv2d(in_channels,
+        self.conv_in = ops.Conv2d(in_channels,
                                        self.ch,
                                        kernel_size=3,
                                        stride=1,
@@ -388,7 +389,7 @@ class Model(nn.Module):
 
         # end
         self.norm_out = Normalize(block_in)
-        self.conv_out = comfy.ops.Conv2d(block_in,
+        self.conv_out = ops.Conv2d(block_in,
                                         out_ch,
                                         kernel_size=3,
                                         stride=1,
@@ -461,7 +462,7 @@ class Encoder(nn.Module):
         self.in_channels = in_channels
 
         # downsampling
-        self.conv_in = comfy.ops.Conv2d(in_channels,
+        self.conv_in = ops.Conv2d(in_channels,
                                        self.ch,
                                        kernel_size=3,
                                        stride=1,
@@ -506,7 +507,7 @@ class Encoder(nn.Module):
 
         # end
         self.norm_out = Normalize(block_in)
-        self.conv_out = comfy.ops.Conv2d(block_in,
+        self.conv_out = ops.Conv2d(block_in,
                                         2*z_channels if double_z else z_channels,
                                         kernel_size=3,
                                         stride=1,
@@ -541,7 +542,7 @@ class Decoder(nn.Module):
     def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
                  attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
                  resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
-                 conv_out_op=comfy.ops.Conv2d,
+                 conv_out_op=ops.Conv2d,
                  resnet_op=ResnetBlock,
                  attn_op=AttnBlock,
                 **ignorekwargs):
@@ -565,7 +566,7 @@ class Decoder(nn.Module):
             self.z_shape, np.prod(self.z_shape)))
 
         # z to block_in
-        self.conv_in = comfy.ops.Conv2d(z_channels,
+        self.conv_in = ops.Conv2d(z_channels,
                                        block_in,
                                        kernel_size=3,
                                        stride=1,

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -12,13 +12,13 @@ from .util import (
     checkpoint,
     avg_pool_nd,
     zero_module,
-    normalization,
     timestep_embedding,
     AlphaBlender,
 )
 from ..attention import SpatialTransformer, SpatialVideoTransformer, default
 from comfy.ldm.util import exists
 import comfy.ops
+ops = comfy.ops.disable_weight_init
 
 class TimestepBlock(nn.Module):
     """
@@ -70,7 +70,7 @@ 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, operations=comfy.ops):
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
@@ -106,7 +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, operations=comfy.ops):
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
@@ -159,7 +159,7 @@ class ResBlock(TimestepBlock):
         skip_t_emb=False,
         dtype=None,
         device=None,
-        operations=comfy.ops
+        operations=ops
     ):
         super().__init__()
         self.channels = channels
@@ -284,7 +284,7 @@ class VideoResBlock(ResBlock):
         down: bool = False,
         dtype=None,
         device=None,
-        operations=comfy.ops
+        operations=ops
     ):
         super().__init__(
             channels,
@@ -434,7 +434,7 @@ class UNetModel(nn.Module):
         disable_temporal_crossattention=False,
         max_ddpm_temb_period=10000,
         device=None,
-        operations=comfy.ops,
+        operations=ops,
     ):
         super().__init__()
         assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
@@ -581,7 +581,7 @@ class UNetModel(nn.Module):
             up=False,
             dtype=None,
             device=None,
-            operations=comfy.ops
+            operations=ops
         ):
             if self.use_temporal_resblocks:
                 return VideoResBlock(

comfy/ldm/modules/diffusionmodules/util.py

@@ -16,7 +16,6 @@ import numpy as np
 from einops import repeat, rearrange
 
 from comfy.ldm.util import instantiate_from_config
-import comfy.ops
 
 class AlphaBlender(nn.Module):
     strategies = ["learned", "fixed", "learned_with_images"]
@@ -273,46 +272,6 @@ def mean_flat(tensor):
     return tensor.mean(dim=list(range(1, len(tensor.shape))))
 
 
-def normalization(channels, dtype=None):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels, dtype=dtype)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return comfy.ops.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return comfy.ops.Linear(*args, **kwargs)
-
-
 def avg_pool_nd(dims, *args, **kwargs):
     """
     Create a 1D, 2D, or 3D average pooling module.

comfy/ldm/modules/temporal_ae.py

@@ -5,6 +5,7 @@ import torch
 from einops import rearrange, repeat
 
 import comfy.ops
+ops = comfy.ops.disable_weight_init
 
 from .diffusionmodules.model import (
     AttnBlock,
@@ -130,9 +131,9 @@ class AttnVideoBlock(AttnBlock):
 
         time_embed_dim = self.in_channels * 4
         self.video_time_embed = torch.nn.Sequential(
-            comfy.ops.Linear(self.in_channels, time_embed_dim),
+            ops.Linear(self.in_channels, time_embed_dim),
             torch.nn.SiLU(),
-            comfy.ops.Linear(time_embed_dim, self.in_channels),
+            ops.Linear(time_embed_dim, self.in_channels),
         )
 
         self.merge_strategy = merge_strategy

comfy/model_base.py

@@ -48,7 +48,7 @@ class BaseModel(torch.nn.Module):
             if self.manual_cast_dtype is not None:
                 operations = comfy.ops.manual_cast
             else:
-                operations = comfy.ops
+                operations = comfy.ops.disable_weight_init
             self.diffusion_model = UNetModel(**unet_config, device=device, operations=operations)
         self.model_type = model_type
         self.model_sampling = model_sampling(model_config, model_type)

comfy/ops.py

 import torch
 from contextlib import contextmanager
 
-class Linear(torch.nn.Linear):
-    def reset_parameters(self):
-        return None
+class disable_weight_init:
+    class Linear(torch.nn.Linear):
+        def reset_parameters(self):
+            return None
 
-class Conv2d(torch.nn.Conv2d):
-    def reset_parameters(self):
-        return None
+    class Conv2d(torch.nn.Conv2d):
+        def reset_parameters(self):
+            return None
 
-class Conv3d(torch.nn.Conv3d):
-    def reset_parameters(self):
-        return None
+    class Conv3d(torch.nn.Conv3d):
+        def reset_parameters(self):
+            return None
 
-class GroupNorm(torch.nn.GroupNorm):
-    def reset_parameters(self):
-        return None
+    class GroupNorm(torch.nn.GroupNorm):
+        def reset_parameters(self):
+            return None
 
-class LayerNorm(torch.nn.LayerNorm):
-    def reset_parameters(self):
-        return None
+    class LayerNorm(torch.nn.LayerNorm):
+        def reset_parameters(self):
+            return None
 
-def conv_nd(dims, *args, **kwargs):
-    if dims == 2:
-        return Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return Conv3d(*args, **kwargs)
-    else:
-        raise ValueError(f"unsupported dimensions: {dims}")
+    @classmethod
+    def conv_nd(s, dims, *args, **kwargs):
+        if dims == 2:
+            return s.Conv2d(*args, **kwargs)
+        elif dims == 3:
+            return s.Conv3d(*args, **kwargs)
+        else:
+            raise ValueError(f"unsupported dimensions: {dims}")
 
 def cast_bias_weight(s, input):
     bias = None
@@ -36,55 +38,28 @@ def cast_bias_weight(s, input):
     weight = s.weight.to(device=input.device, dtype=input.dtype)
     return weight, bias
 
-class manual_cast:
-    class Linear(Linear):
+class manual_cast(disable_weight_init):
+    class Linear(disable_weight_init.Linear):
         def forward(self, input):
             weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.linear(input, weight, bias)
 
-    class Conv2d(Conv2d):
+    class Conv2d(disable_weight_init.Conv2d):
         def forward(self, input):
             weight, bias = cast_bias_weight(self, input)
             return self._conv_forward(input, weight, bias)
 
-    class Conv3d(Conv3d):
+    class Conv3d(disable_weight_init.Conv3d):
         def forward(self, input):
             weight, bias = cast_bias_weight(self, input)
             return self._conv_forward(input, weight, bias)
 
-    class GroupNorm(GroupNorm):
+    class GroupNorm(disable_weight_init.GroupNorm):
         def forward(self, input):
             weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
 
-    class LayerNorm(LayerNorm):
+    class LayerNorm(disable_weight_init.LayerNorm):
         def forward(self, input):
             weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
-
-    @classmethod
-    def conv_nd(s, dims, *args, **kwargs):
-        if dims == 2:
-            return s.Conv2d(*args, **kwargs)
-        elif dims == 3:
-            return s.Conv3d(*args, **kwargs)
-        else:
-            raise ValueError(f"unsupported dimensions: {dims}")
-
-@contextmanager
-def use_comfy_ops(device=None, dtype=None): # Kind of an ugly hack but I can't think of a better way
-    old_torch_nn_linear = torch.nn.Linear
-    force_device = device
-    force_dtype = dtype
-    def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None):
-        if force_device is not None:
-            device = force_device
-        if force_dtype is not None:
-            dtype = force_dtype
-        return Linear(in_features, out_features, bias=bias, device=device, dtype=dtype)
-
-    torch.nn.Linear = linear_with_dtype
-    try:
-        yield
-    finally:
-        torch.nn.Linear = old_torch_nn_linear