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Faster VAE loading.

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comfyanonymous 2023-07-29 16:28:30 -04:00
parent 4b957a0010
commit 95d796fc85
1 changed files with 28 additions and 27 deletions
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55
comfy/ldm/modules/diffusionmodules/model.py
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@ -8,6 +8,7 @@ from typing import Optional, Any
from ..attention import MemoryEfficientCrossAttention
from comfy import model_management
import comfy.ops
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 = torch.nn.Conv2d(in_channels,
self.conv = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=3,
stride=1,
@ -67,7 +68,7 @@ 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 = torch.nn.Conv2d(in_channels,
self.conv = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=3,
stride=2,
@ -95,30 +96,30 @@ class ResnetBlock(nn.Module):
self.swish = torch.nn.SiLU(inplace=True)
self.norm1 = Normalize(in_channels)
self.conv1 = torch.nn.Conv2d(in_channels,
self.conv1 = comfy.ops.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
if temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels,
self.temb_proj = comfy.ops.Linear(temb_channels,
out_channels)
self.norm2 = Normalize(out_channels)
self.dropout = torch.nn.Dropout(dropout, inplace=True)
self.conv2 = torch.nn.Conv2d(out_channels,
self.conv2 = comfy.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 = torch.nn.Conv2d(in_channels,
self.conv_shortcut = comfy.ops.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=1,
padding=1)
else:
self.nin_shortcut = torch.nn.Conv2d(in_channels,
self.nin_shortcut = comfy.ops.Conv2d(in_channels,
out_channels,
kernel_size=1,
stride=1,
@ -188,22 +189,22 @@ class AttnBlock(nn.Module):
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
self.q = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
self.k = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
self.v = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
self.proj_out = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
@ -243,22 +244,22 @@ class MemoryEfficientAttnBlock(nn.Module):
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
self.q = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
self.k = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
self.v = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
self.proj_out = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
@ -302,22 +303,22 @@ class MemoryEfficientAttnBlockPytorch(nn.Module):
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
self.q = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
self.k = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
self.v = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
self.proj_out = comfy.ops.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
@ -399,14 +400,14 @@ class Model(nn.Module):
# timestep embedding
self.temb = nn.Module()
self.temb.dense = nn.ModuleList([
torch.nn.Linear(self.ch,
comfy.ops.Linear(self.ch,
self.temb_ch),
torch.nn.Linear(self.temb_ch,
comfy.ops.Linear(self.temb_ch,
self.temb_ch),
])
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels,
self.conv_in = comfy.ops.Conv2d(in_channels,
self.ch,
kernel_size=3,
stride=1,
@ -475,7 +476,7 @@ class Model(nn.Module):
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
self.conv_out = comfy.ops.Conv2d(block_in,
out_ch,
kernel_size=3,
stride=1,
@ -548,7 +549,7 @@ class Encoder(nn.Module):
self.in_channels = in_channels
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels,
self.conv_in = comfy.ops.Conv2d(in_channels,
self.ch,
kernel_size=3,
stride=1,
@ -593,7 +594,7 @@ class Encoder(nn.Module):
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
self.conv_out = comfy.ops.Conv2d(block_in,
2*z_channels if double_z else z_channels,
kernel_size=3,
stride=1,
@ -653,7 +654,7 @@ class Decoder(nn.Module):
self.z_shape, np.prod(self.z_shape)))
# z to block_in
self.conv_in = torch.nn.Conv2d(z_channels,
self.conv_in = comfy.ops.Conv2d(z_channels,
block_in,
kernel_size=3,
stride=1,
@ -695,7 +696,7 @@ class Decoder(nn.Module):
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv2d(block_in,
self.conv_out = comfy.ops.Conv2d(block_in,
out_ch,
kernel_size=3,
stride=1,