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Patrick von Platen
2022-06-29 17:30:48 +00:00
parent 358531be9d
commit 26ce60c46d
2 changed files with 49 additions and 48 deletions
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14
src/diffusers/models/resnet.py
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@@ -330,8 +330,8 @@ class ResBlock(TimestepBlock):
result = self.skip_connection(x) + h
# TODO(Patrick) Use for glide at later stage
# result = self.forward_2(x, emb)
# TODO(Patrick) Use for glide at later stage
# result = self.forward_2(x, emb)
return result
@@ -439,9 +439,9 @@ class ResnetBlock(nn.Module):
self.res_conv = torch.nn.Identity()
elif self.overwrite_for_ldm:
dims = 2
# eps = 1e-5
# non_linearity = "silu"
# overwrite_for_ldm
# eps = 1e-5
# non_linearity = "silu"
# overwrite_for_ldm
channels = in_channels
emb_channels = temb_channels
use_scale_shift_norm = False
@@ -466,8 +466,8 @@ class ResnetBlock(nn.Module):
)
if self.out_channels == in_channels:
self.skip_connection = nn.Identity()
# elif use_conv:
# self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
# elif use_conv:
# self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)

83
src/diffusers/models/unet_ldm.py
View File

@@ -10,9 +10,10 @@ from ..configuration_utils import ConfigMixin
from ..modeling_utils import ModelMixin
from .attention import AttentionBlock
from .embeddings import get_timestep_embedding
from .resnet import Downsample, TimestepBlock, Upsample
from .resnet import ResnetBlock
#from .resnet import ResBlock
from .resnet import Downsample, ResnetBlock, TimestepBlock, Upsample
# from .resnet import ResBlock
def exists(val):
@@ -561,14 +562,14 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResnetBlock(
in_channels=ch,
out_channels=mult * model_channels,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
overwrite_for_ldm=True,
ResnetBlock(
in_channels=ch,
out_channels=mult * model_channels,
dropout=dropout,
temb_channels=time_embed_dim,
eps=1e-5,
non_linearity="silu",
overwrite_for_ldm=True,
)
]
ch = mult * model_channels
@@ -601,16 +602,16 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
# ResBlock(
# ch,
# time_embed_dim,
# dropout,
# out_channels=out_ch,
# dims=dims,
# use_checkpoint=use_checkpoint,
# use_scale_shift_norm=use_scale_shift_norm,
# down=True,
# )
# ResBlock(
# ch,
# time_embed_dim,
# dropout,
# out_channels=out_ch,
# dims=dims,
# use_checkpoint=use_checkpoint,
# use_scale_shift_norm=use_scale_shift_norm,
# down=True,
# )
None
if resblock_updown
else Downsample(
@@ -703,16 +704,16 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
if level and i == num_res_blocks:
out_ch = ch
layers.append(
# ResBlock(
# ch,
# time_embed_dim,
# dropout,
# out_channels=out_ch,
# dims=dims,
# use_checkpoint=use_checkpoint,
# use_scale_shift_norm=use_scale_shift_norm,
# up=True,
# )
# ResBlock(
# ch,
# time_embed_dim,
# dropout,
# out_channels=out_ch,
# dims=dims,
# use_checkpoint=use_checkpoint,
# use_scale_shift_norm=use_scale_shift_norm,
# up=True,
# )
None
if resblock_updown
else Upsample(ch, use_conv=conv_resample, dims=dims, out_channels=out_ch)
@@ -876,16 +877,16 @@ class EncoderUNetModel(nn.Module):
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
# ResBlock(
# ch,
# time_embed_dim,
# dropout,
# out_channels=out_ch,
# dims=dims,
# use_checkpoint=use_checkpoint,
# use_scale_shift_norm=use_scale_shift_norm,
# down=True,
# )
# ResBlock(
# ch,
# time_embed_dim,
# dropout,
# out_channels=out_ch,
# dims=dims,
# use_checkpoint=use_checkpoint,
# use_scale_shift_norm=use_scale_shift_norm,
# down=True,
# )
None
if resblock_updown
else Downsample(