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import
argparse
import
time
from
pathlib
import
Path
from
typing
import
Any
,
Dict
,
Literal
import
torch
from
diffusers
import
AsymmetricAutoencoderKL
ASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG
=
{
"in_channels"
:
3
,
"out_channels"
:
3
,
"down_block_types"
: [
"DownEncoderBlock2D"
,
"DownEncoderBlock2D"
,
"DownEncoderBlock2D"
,
"DownEncoderBlock2D"
,
],
"down_block_out_channels"
: [
128
,
256
,
512
,
512
],
"layers_per_down_block"
:
2
,
"up_block_types"
: [
"UpDecoderBlock2D"
,
"UpDecoderBlock2D"
,
"UpDecoderBlock2D"
,
"UpDecoderBlock2D"
,
],
"up_block_out_channels"
: [
192
,
384
,
768
,
768
],
"layers_per_up_block"
:
3
,
"act_fn"
:
"silu"
,
"latent_channels"
:
4
,
"norm_num_groups"
:
32
,
"sample_size"
:
256
,
"scaling_factor"
:
0.18215
,
}
ASYMMETRIC_AUTOENCODER_KL_x_2_CONFIG
=
{
"in_channels"
:
3
,
"out_channels"
:
3
,
"down_block_types"
: [
"DownEncoderBlock2D"
,
"DownEncoderBlock2D"
,
"DownEncoderBlock2D"
,
"DownEncoderBlock2D"
,
],
"down_block_out_channels"
: [
128
,
256
,
512
,
512
],
"layers_per_down_block"
:
2
,
"up_block_types"
: [
"UpDecoderBlock2D"
,
"UpDecoderBlock2D"
,
"UpDecoderBlock2D"
,
"UpDecoderBlock2D"
,
],
"up_block_out_channels"
: [
256
,
512
,
1024
,
1024
],
"layers_per_up_block"
:
5
,
"act_fn"
:
"silu"
,
"latent_channels"
:
4
,
"norm_num_groups"
:
32
,
"sample_size"
:
256
,
"scaling_factor"
:
0.18215
,
}
def
convert_asymmetric_autoencoder_kl_state_dict
(
original_state_dict
:
Dict
[
str
,
Any
])
->
Dict
[
str
,
Any
]:
converted_state_dict
=
{}
for
k
,
v
in
original_state_dict
.
items
():
if
k
.
startswith
(
"encoder."
):
converted_state_dict
[
k
.
replace
(
"encoder.down."
,
"encoder.down_blocks."
)
.
replace
(
"encoder.mid."
,
"encoder.mid_block."
)
.
replace
(
"encoder.norm_out."
,
"encoder.conv_norm_out."
)
.
replace
(
".downsample."
,
".downsamplers.0."
)
.
replace
(
".nin_shortcut."
,
".conv_shortcut."
)
.
replace
(
".block."
,
".resnets."
)
.
replace
(
".block_1."
,
".resnets.0."
)
.
replace
(
".block_2."
,
".resnets.1."
)
.
replace
(
".attn_1.k."
,
".attentions.0.to_k."
)
.
replace
(
".attn_1.q."
,
".attentions.0.to_q."
)
.
replace
(
".attn_1.v."
,
".attentions.0.to_v."
)
.
replace
(
".attn_1.proj_out."
,
".attentions.0.to_out.0."
)
.
replace
(
".attn_1.norm."
,
".attentions.0.group_norm."
)
]
=
v
elif
k
.
startswith
(
"decoder."
)
and
"up_layers"
not
in
k
:
converted_state_dict
[
k
.
replace
(
"decoder.encoder."
,
"decoder.condition_encoder."
)
.
replace
(
".norm_out."
,
".conv_norm_out."
)
.
replace
(
".up.0."
,
".up_blocks.3."
)
.
replace
(
".up.1."
,
".up_blocks.2."
)
.
replace
(
".up.2."
,
".up_blocks.1."
)
.
replace
(
".up.3."
,
".up_blocks.0."
)
.
replace
(
".block."
,
".resnets."
)
.
replace
(
"mid"
,
"mid_block"
)
.
replace
(
".0.upsample."
,
".0.upsamplers.0."
)
.
replace
(
".1.upsample."
,
".1.upsamplers.0."
)
.
replace
(
".2.upsample."
,
".2.upsamplers.0."
)
.
replace
(
".nin_shortcut."
,
".conv_shortcut."
)
.
replace
(
".block_1."
,
".resnets.0."
)
.
replace
(
".block_2."
,
".resnets.1."
)
.
replace
(
".attn_1.k."
,
".attentions.0.to_k."
)
.
replace
(
".attn_1.q."
,
".attentions.0.to_q."
)
.
replace
(
".attn_1.v."
,
".attentions.0.to_v."
)
.
replace
(
".attn_1.proj_out."
,
".attentions.0.to_out.0."
)
.
replace
(
".attn_1.norm."
,
".attentions.0.group_norm."
)
]
=
v
elif
k
.
startswith
(
"quant_conv."
):
converted_state_dict
[
k
]
=
v
elif
k
.
startswith
(
"post_quant_conv."
):
converted_state_dict
[
k
]
=
v
else
:
print
(
f" skipping key `
{
k
}
`"
)
# fix weights shape
for
k
,
v
in
converted_state_dict
.
items
():
if
(
(
k
.
startswith
(
"encoder.mid_block.attentions.0"
)
or
k
.
startswith
(
"decoder.mid_block.attentions.0"
))
and
k
.
endswith
(
"weight"
)
and
(
"to_q"
in
k
or
"to_k"
in
k
or
"to_v"
in
k
or
"to_out"
in
k
)
):
converted_state_dict
[
k
]
=
converted_state_dict
[
k
][:, :,
0
,
0
]
return
converted_state_dict
def
get_asymmetric_autoencoder_kl_from_original_checkpoint
(
scale
:
Literal
[
"1.5"
,
"2"
],
original_checkpoint_path
:
str
,
map_location
:
torch
.
device
)
->
AsymmetricAutoencoderKL
:
print
(
"Loading original state_dict"
)
original_state_dict
=
torch
.
load
(
original_checkpoint_path
,
map_location
=
map_location
)
original_state_dict
=
original_state_dict
[
"state_dict"
]
print
(
"Converting state_dict"
)
converted_state_dict
=
convert_asymmetric_autoencoder_kl_state_dict
(
original_state_dict
)
kwargs
=
ASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG
if
scale
==
"1.5"
else
ASYMMETRIC_AUTOENCODER_KL_x_2_CONFIG
print
(
"Initializing AsymmetricAutoencoderKL model"
)
asymmetric_autoencoder_kl
=
AsymmetricAutoencoderKL
(
**
kwargs
)
print
(
"Loading weight from converted state_dict"
)
asymmetric_autoencoder_kl
.
load_state_dict
(
converted_state_dict
)
asymmetric_autoencoder_kl
.
eval
()
print
(
"AsymmetricAutoencoderKL successfully initialized"
)
return
asymmetric_autoencoder_kl
if
__name__
==
"__main__"
:
start
=
time
.
time
()
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"--scale"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Asymmetric VQGAN scale: `1.5` or `2`"
,
)
parser
.
add_argument
(
"--original_checkpoint_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the original Asymmetric VQGAN checkpoint"
,
)
parser
.
add_argument
(
"--output_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to save pretrained AsymmetricAutoencoderKL model"
,
)
parser
.
add_argument
(
"--map_location"
,
default
=
"cpu"
,
type
=
str
,
required
=
False
,
help
=
"The device passed to `map_location` when loading the checkpoint"
,
)
args
=
parser
.
parse_args
()
assert
args
.
scale
in
[
"1.5"
,
"2"
],
f"
{
args
.
scale
}
should be `1.5` of `2`"
assert
Path
(
args
.
original_checkpoint_path
).
is_file
()
asymmetric_autoencoder_kl
=
get_asymmetric_autoencoder_kl_from_original_checkpoint
(
scale
=
args
.
scale
,
original_checkpoint_path
=
args
.
original_checkpoint_path
,
map_location
=
torch
.
device
(
args
.
map_location
),
)
print
(
"Saving pretrained AsymmetricAutoencoderKL"
)
asymmetric_autoencoder_kl
.
save_pretrained
(
args
.
output_path
)
print
(
f"Done in
{
time
.
time
()
-
start
:.2f
}
seconds"
)
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