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Refactor comfy.ops

Browse Source 
comfy.ops -> comfy.ops.disable_weight_init

This should make it more clear what they actually do.

Some unused code has also been removed.
This commit is contained in:
comfyanonymous 2023-12-11 23:27:13 -05:00
parent b0aab1e4ea
commit 77755ab8db
10 changed files with 94 additions and 170 deletions
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2
comfy/cldm/cldm.py
View File

@ -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__()

2
comfy/clip_vision.py
View File

@ -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):

27
comfy/controlnet.py
View File

@ -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)

13
comfy/ldm/modules/attention.py
View File

@ -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,

39
comfy/ldm/modules/diffusionmodules/model.py
View File

@ -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,

14
comfy/ldm/modules/diffusionmodules/openaimodel.py
View File

@ -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(

41
comfy/ldm/modules/diffusionmodules/util.py
View File

@ -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.

5
comfy/ldm/modules/temporal_ae.py
View File

@ -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

2
comfy/model_base.py
View File

@ -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)

119
comfy/ops.py
View File

@ -1,66 +1,26 @@
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
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}")
def cast_bias_weight(s, input):
bias = None
if s.bias is not None:
bias = s.bias.to(device=input.device, dtype=input.dtype)
weight = s.weight.to(device=input.device, dtype=input.dtype)
return weight, bias
class manual_cast:
class Linear(Linear):
def forward(self, input):
weight, bias = cast_bias_weight(self, input)
return torch.nn.functional.linear(input, weight, bias)
class Conv2d(Conv2d):
def forward(self, input):
weight, bias = cast_bias_weight(self, input)
return self._conv_forward(input, weight, bias)
class Conv3d(Conv3d):
def forward(self, input):
weight, bias = cast_bias_weight(self, input)
return self._conv_forward(input, weight, bias)
class GroupNorm(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):
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)
class LayerNorm(torch.nn.LayerNorm):
def reset_parameters(self):
return None
@classmethod
def conv_nd(s, dims, *args, **kwargs):
@ -71,20 +31,35 @@ class manual_cast:
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)
def cast_bias_weight(s, input):
bias = None
if s.bias is not None:
bias = s.bias.to(device=input.device, dtype=input.dtype)
weight = s.weight.to(device=input.device, dtype=input.dtype)
return weight, bias
torch.nn.Linear = linear_with_dtype
try:
yield
finally:
torch.nn.Linear = old_torch_nn_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(disable_weight_init.Conv2d):
def forward(self, input):
weight, bias = cast_bias_weight(self, input)
return self._conv_forward(input, weight, bias)
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(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(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)