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# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Conversion script for the LDM checkpoints. """
import
argparse
import
torch
from
diffusers
import
UNet3DConditionModel
def
assign_to_checkpoint
(
paths
,
checkpoint
,
old_checkpoint
,
attention_paths_to_split
=
None
,
additional_replacements
=
None
,
config
=
None
):
"""
This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
attention layers, and takes into account additional replacements that may arise.
Assigns the weights to the new checkpoint.
"""
assert
isinstance
(
paths
,
list
),
"Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if
attention_paths_to_split
is
not
None
:
for
path
,
path_map
in
attention_paths_to_split
.
items
():
old_tensor
=
old_checkpoint
[
path
]
channels
=
old_tensor
.
shape
[
0
]
//
3
target_shape
=
(
-
1
,
channels
)
if
len
(
old_tensor
.
shape
)
==
3
else
(
-
1
)
num_heads
=
old_tensor
.
shape
[
0
]
//
config
[
"num_head_channels"
]
//
3
old_tensor
=
old_tensor
.
reshape
((
num_heads
,
3
*
channels
//
num_heads
)
+
old_tensor
.
shape
[
1
:])
query
,
key
,
value
=
old_tensor
.
split
(
channels
//
num_heads
,
dim
=
1
)
checkpoint
[
path_map
[
"query"
]]
=
query
.
reshape
(
target_shape
)
checkpoint
[
path_map
[
"key"
]]
=
key
.
reshape
(
target_shape
)
checkpoint
[
path_map
[
"value"
]]
=
value
.
reshape
(
target_shape
)
for
path
in
paths
:
new_path
=
path
[
"new"
]
# These have already been assigned
if
attention_paths_to_split
is
not
None
and
new_path
in
attention_paths_to_split
:
continue
if
additional_replacements
is
not
None
:
for
replacement
in
additional_replacements
:
new_path
=
new_path
.
replace
(
replacement
[
"old"
],
replacement
[
"new"
])
# proj_attn.weight has to be converted from conv 1D to linear
weight
=
old_checkpoint
[
path
[
"old"
]]
names
=
[
"proj_attn.weight"
]
names_2
=
[
"proj_out.weight"
,
"proj_in.weight"
]
if
any
(
k
in
new_path
for
k
in
names
):
checkpoint
[
new_path
]
=
weight
[:, :,
0
]
elif
any
(
k
in
new_path
for
k
in
names_2
)
and
len
(
weight
.
shape
)
>
2
and
".attentions."
not
in
new_path
:
checkpoint
[
new_path
]
=
weight
[:, :,
0
]
else
:
checkpoint
[
new_path
]
=
weight
def
renew_attention_paths
(
old_list
,
n_shave_prefix_segments
=
0
):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping
=
[]
for
old_item
in
old_list
:
new_item
=
old_item
# new_item = new_item.replace('norm.weight', 'group_norm.weight')
# new_item = new_item.replace('norm.bias', 'group_norm.bias')
# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
# new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping
.
append
({
"old"
:
old_item
,
"new"
:
new_item
})
return
mapping
def
shave_segments
(
path
,
n_shave_prefix_segments
=
1
):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if
n_shave_prefix_segments
>=
0
:
return
"."
.
join
(
path
.
split
(
"."
)[
n_shave_prefix_segments
:])
else
:
return
"."
.
join
(
path
.
split
(
"."
)[:
n_shave_prefix_segments
])
def
renew_temp_conv_paths
(
old_list
,
n_shave_prefix_segments
=
0
):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping
=
[]
for
old_item
in
old_list
:
mapping
.
append
({
"old"
:
old_item
,
"new"
:
old_item
})
return
mapping
def
renew_resnet_paths
(
old_list
,
n_shave_prefix_segments
=
0
):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping
=
[]
for
old_item
in
old_list
:
new_item
=
old_item
.
replace
(
"in_layers.0"
,
"norm1"
)
new_item
=
new_item
.
replace
(
"in_layers.2"
,
"conv1"
)
new_item
=
new_item
.
replace
(
"out_layers.0"
,
"norm2"
)
new_item
=
new_item
.
replace
(
"out_layers.3"
,
"conv2"
)
new_item
=
new_item
.
replace
(
"emb_layers.1"
,
"time_emb_proj"
)
new_item
=
new_item
.
replace
(
"skip_connection"
,
"conv_shortcut"
)
new_item
=
shave_segments
(
new_item
,
n_shave_prefix_segments
=
n_shave_prefix_segments
)
if
"temopral_conv"
not
in
old_item
:
mapping
.
append
({
"old"
:
old_item
,
"new"
:
new_item
})
return
mapping
def
convert_ldm_unet_checkpoint
(
checkpoint
,
config
,
path
=
None
,
extract_ema
=
False
):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
# extract state_dict for UNet
unet_state_dict
=
{}
keys
=
list
(
checkpoint
.
keys
())
unet_key
=
"model.diffusion_model."
# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
if
sum
(
k
.
startswith
(
"model_ema"
)
for
k
in
keys
)
>
100
and
extract_ema
:
print
(
f"Checkpoint
{
path
}
has both EMA and non-EMA weights."
)
print
(
"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
)
for
key
in
keys
:
if
key
.
startswith
(
"model.diffusion_model"
):
flat_ema_key
=
"model_ema."
+
""
.
join
(
key
.
split
(
"."
)[
1
:])
unet_state_dict
[
key
.
replace
(
unet_key
,
""
)]
=
checkpoint
.
pop
(
flat_ema_key
)
else
:
if
sum
(
k
.
startswith
(
"model_ema"
)
for
k
in
keys
)
>
100
:
print
(
"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
)
for
key
in
keys
:
unet_state_dict
[
key
.
replace
(
unet_key
,
""
)]
=
checkpoint
.
pop
(
key
)
new_checkpoint
=
{}
new_checkpoint
[
"time_embedding.linear_1.weight"
]
=
unet_state_dict
[
"time_embed.0.weight"
]
new_checkpoint
[
"time_embedding.linear_1.bias"
]
=
unet_state_dict
[
"time_embed.0.bias"
]
new_checkpoint
[
"time_embedding.linear_2.weight"
]
=
unet_state_dict
[
"time_embed.2.weight"
]
new_checkpoint
[
"time_embedding.linear_2.bias"
]
=
unet_state_dict
[
"time_embed.2.bias"
]
if
config
[
"class_embed_type"
]
is
None
:
# No parameters to port
...
elif
config
[
"class_embed_type"
]
==
"timestep"
or
config
[
"class_embed_type"
]
==
"projection"
:
new_checkpoint
[
"class_embedding.linear_1.weight"
]
=
unet_state_dict
[
"label_emb.0.0.weight"
]
new_checkpoint
[
"class_embedding.linear_1.bias"
]
=
unet_state_dict
[
"label_emb.0.0.bias"
]
new_checkpoint
[
"class_embedding.linear_2.weight"
]
=
unet_state_dict
[
"label_emb.0.2.weight"
]
new_checkpoint
[
"class_embedding.linear_2.bias"
]
=
unet_state_dict
[
"label_emb.0.2.bias"
]
else
:
raise
NotImplementedError
(
f"Not implemented `class_embed_type`:
{
config
[
'class_embed_type'
]
}
"
)
new_checkpoint
[
"conv_in.weight"
]
=
unet_state_dict
[
"input_blocks.0.0.weight"
]
new_checkpoint
[
"conv_in.bias"
]
=
unet_state_dict
[
"input_blocks.0.0.bias"
]
first_temp_attention
=
[
v
for
v
in
unet_state_dict
if
v
.
startswith
(
"input_blocks.0.1"
)]
paths
=
renew_attention_paths
(
first_temp_attention
)
meta_path
=
{
"old"
:
"input_blocks.0.1"
,
"new"
:
"transformer_in"
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
new_checkpoint
[
"conv_norm_out.weight"
]
=
unet_state_dict
[
"out.0.weight"
]
new_checkpoint
[
"conv_norm_out.bias"
]
=
unet_state_dict
[
"out.0.bias"
]
new_checkpoint
[
"conv_out.weight"
]
=
unet_state_dict
[
"out.2.weight"
]
new_checkpoint
[
"conv_out.bias"
]
=
unet_state_dict
[
"out.2.bias"
]
# Retrieves the keys for the input blocks only
num_input_blocks
=
len
({
"."
.
join
(
layer
.
split
(
"."
)[:
2
])
for
layer
in
unet_state_dict
if
"input_blocks"
in
layer
})
input_blocks
=
{
layer_id
: [
key
for
key
in
unet_state_dict
if
f"input_blocks.
{
layer_id
}
"
in
key
]
for
layer_id
in
range
(
num_input_blocks
)
}
# Retrieves the keys for the middle blocks only
num_middle_blocks
=
len
({
"."
.
join
(
layer
.
split
(
"."
)[:
2
])
for
layer
in
unet_state_dict
if
"middle_block"
in
layer
})
middle_blocks
=
{
layer_id
: [
key
for
key
in
unet_state_dict
if
f"middle_block.
{
layer_id
}
"
in
key
]
for
layer_id
in
range
(
num_middle_blocks
)
}
# Retrieves the keys for the output blocks only
num_output_blocks
=
len
({
"."
.
join
(
layer
.
split
(
"."
)[:
2
])
for
layer
in
unet_state_dict
if
"output_blocks"
in
layer
})
output_blocks
=
{
layer_id
: [
key
for
key
in
unet_state_dict
if
f"output_blocks.
{
layer_id
}
"
in
key
]
for
layer_id
in
range
(
num_output_blocks
)
}
for
i
in
range
(
1
,
num_input_blocks
):
block_id
=
(
i
-
1
)
//
(
config
[
"layers_per_block"
]
+
1
)
layer_in_block_id
=
(
i
-
1
)
%
(
config
[
"layers_per_block"
]
+
1
)
resnets
=
[
key
for
key
in
input_blocks
[
i
]
if
f"input_blocks.
{
i
}
.0"
in
key
and
f"input_blocks.
{
i
}
.0.op"
not
in
key
]
attentions
=
[
key
for
key
in
input_blocks
[
i
]
if
f"input_blocks.
{
i
}
.1"
in
key
]
temp_attentions
=
[
key
for
key
in
input_blocks
[
i
]
if
f"input_blocks.
{
i
}
.2"
in
key
]
if
f"input_blocks.
{
i
}
.op.weight"
in
unet_state_dict
:
new_checkpoint
[
f"down_blocks.
{
block_id
}
.downsamplers.0.conv.weight"
]
=
unet_state_dict
.
pop
(
f"input_blocks.
{
i
}
.op.weight"
)
new_checkpoint
[
f"down_blocks.
{
block_id
}
.downsamplers.0.conv.bias"
]
=
unet_state_dict
.
pop
(
f"input_blocks.
{
i
}
.op.bias"
)
paths
=
renew_resnet_paths
(
resnets
)
meta_path
=
{
"old"
:
f"input_blocks.
{
i
}
.0"
,
"new"
:
f"down_blocks.
{
block_id
}
.resnets.
{
layer_in_block_id
}
"
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
temporal_convs
=
[
key
for
key
in
resnets
if
"temopral_conv"
in
key
]
paths
=
renew_temp_conv_paths
(
temporal_convs
)
meta_path
=
{
"old"
:
f"input_blocks.
{
i
}
.0.temopral_conv"
,
"new"
:
f"down_blocks.
{
block_id
}
.temp_convs.
{
layer_in_block_id
}
"
,
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
if
len
(
attentions
):
paths
=
renew_attention_paths
(
attentions
)
meta_path
=
{
"old"
:
f"input_blocks.
{
i
}
.1"
,
"new"
:
f"down_blocks.
{
block_id
}
.attentions.
{
layer_in_block_id
}
"
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
if
len
(
temp_attentions
):
paths
=
renew_attention_paths
(
temp_attentions
)
meta_path
=
{
"old"
:
f"input_blocks.
{
i
}
.2"
,
"new"
:
f"down_blocks.
{
block_id
}
.temp_attentions.
{
layer_in_block_id
}
"
,
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
resnet_0
=
middle_blocks
[
0
]
temporal_convs_0
=
[
key
for
key
in
resnet_0
if
"temopral_conv"
in
key
]
attentions
=
middle_blocks
[
1
]
temp_attentions
=
middle_blocks
[
2
]
resnet_1
=
middle_blocks
[
3
]
temporal_convs_1
=
[
key
for
key
in
resnet_1
if
"temopral_conv"
in
key
]
resnet_0_paths
=
renew_resnet_paths
(
resnet_0
)
meta_path
=
{
"old"
:
"middle_block.0"
,
"new"
:
"mid_block.resnets.0"
}
assign_to_checkpoint
(
resnet_0_paths
,
new_checkpoint
,
unet_state_dict
,
config
=
config
,
additional_replacements
=
[
meta_path
]
)
temp_conv_0_paths
=
renew_temp_conv_paths
(
temporal_convs_0
)
meta_path
=
{
"old"
:
"middle_block.0.temopral_conv"
,
"new"
:
"mid_block.temp_convs.0"
}
assign_to_checkpoint
(
temp_conv_0_paths
,
new_checkpoint
,
unet_state_dict
,
config
=
config
,
additional_replacements
=
[
meta_path
]
)
resnet_1_paths
=
renew_resnet_paths
(
resnet_1
)
meta_path
=
{
"old"
:
"middle_block.3"
,
"new"
:
"mid_block.resnets.1"
}
assign_to_checkpoint
(
resnet_1_paths
,
new_checkpoint
,
unet_state_dict
,
config
=
config
,
additional_replacements
=
[
meta_path
]
)
temp_conv_1_paths
=
renew_temp_conv_paths
(
temporal_convs_1
)
meta_path
=
{
"old"
:
"middle_block.3.temopral_conv"
,
"new"
:
"mid_block.temp_convs.1"
}
assign_to_checkpoint
(
temp_conv_1_paths
,
new_checkpoint
,
unet_state_dict
,
config
=
config
,
additional_replacements
=
[
meta_path
]
)
attentions_paths
=
renew_attention_paths
(
attentions
)
meta_path
=
{
"old"
:
"middle_block.1"
,
"new"
:
"mid_block.attentions.0"
}
assign_to_checkpoint
(
attentions_paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
temp_attentions_paths
=
renew_attention_paths
(
temp_attentions
)
meta_path
=
{
"old"
:
"middle_block.2"
,
"new"
:
"mid_block.temp_attentions.0"
}
assign_to_checkpoint
(
temp_attentions_paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
for
i
in
range
(
num_output_blocks
):
block_id
=
i
//
(
config
[
"layers_per_block"
]
+
1
)
layer_in_block_id
=
i
%
(
config
[
"layers_per_block"
]
+
1
)
output_block_layers
=
[
shave_segments
(
name
,
2
)
for
name
in
output_blocks
[
i
]]
output_block_list
=
{}
for
layer
in
output_block_layers
:
layer_id
,
layer_name
=
layer
.
split
(
"."
)[
0
],
shave_segments
(
layer
,
1
)
if
layer_id
in
output_block_list
:
output_block_list
[
layer_id
].
append
(
layer_name
)
else
:
output_block_list
[
layer_id
]
=
[
layer_name
]
if
len
(
output_block_list
)
>
1
:
resnets
=
[
key
for
key
in
output_blocks
[
i
]
if
f"output_blocks.
{
i
}
.0"
in
key
]
attentions
=
[
key
for
key
in
output_blocks
[
i
]
if
f"output_blocks.
{
i
}
.1"
in
key
]
temp_attentions
=
[
key
for
key
in
output_blocks
[
i
]
if
f"output_blocks.
{
i
}
.2"
in
key
]
resnet_0_paths
=
renew_resnet_paths
(
resnets
)
paths
=
renew_resnet_paths
(
resnets
)
meta_path
=
{
"old"
:
f"output_blocks.
{
i
}
.0"
,
"new"
:
f"up_blocks.
{
block_id
}
.resnets.
{
layer_in_block_id
}
"
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
temporal_convs
=
[
key
for
key
in
resnets
if
"temopral_conv"
in
key
]
paths
=
renew_temp_conv_paths
(
temporal_convs
)
meta_path
=
{
"old"
:
f"output_blocks.
{
i
}
.0.temopral_conv"
,
"new"
:
f"up_blocks.
{
block_id
}
.temp_convs.
{
layer_in_block_id
}
"
,
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
output_block_list
=
{
k
:
sorted
(
v
)
for
k
,
v
in
output_block_list
.
items
()}
if
[
"conv.bias"
,
"conv.weight"
]
in
output_block_list
.
values
():
index
=
list
(
output_block_list
.
values
()).
index
([
"conv.bias"
,
"conv.weight"
])
new_checkpoint
[
f"up_blocks.
{
block_id
}
.upsamplers.0.conv.weight"
]
=
unet_state_dict
[
f"output_blocks.
{
i
}
.
{
index
}
.conv.weight"
]
new_checkpoint
[
f"up_blocks.
{
block_id
}
.upsamplers.0.conv.bias"
]
=
unet_state_dict
[
f"output_blocks.
{
i
}
.
{
index
}
.conv.bias"
]
# Clear attentions as they have been attributed above.
if
len
(
attentions
)
==
2
:
attentions
=
[]
if
len
(
attentions
):
paths
=
renew_attention_paths
(
attentions
)
meta_path
=
{
"old"
:
f"output_blocks.
{
i
}
.1"
,
"new"
:
f"up_blocks.
{
block_id
}
.attentions.
{
layer_in_block_id
}
"
,
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
if
len
(
temp_attentions
):
paths
=
renew_attention_paths
(
temp_attentions
)
meta_path
=
{
"old"
:
f"output_blocks.
{
i
}
.2"
,
"new"
:
f"up_blocks.
{
block_id
}
.temp_attentions.
{
layer_in_block_id
}
"
,
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
unet_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
else
:
resnet_0_paths
=
renew_resnet_paths
(
output_block_layers
,
n_shave_prefix_segments
=
1
)
for
path
in
resnet_0_paths
:
old_path
=
"."
.
join
([
"output_blocks"
,
str
(
i
),
path
[
"old"
]])
new_path
=
"."
.
join
([
"up_blocks"
,
str
(
block_id
),
"resnets"
,
str
(
layer_in_block_id
),
path
[
"new"
]])
new_checkpoint
[
new_path
]
=
unet_state_dict
[
old_path
]
temopral_conv_paths
=
[
l
for
l
in
output_block_layers
if
"temopral_conv"
in
l
]
for
path
in
temopral_conv_paths
:
pruned_path
=
path
.
split
(
"temopral_conv."
)[
-
1
]
old_path
=
"."
.
join
([
"output_blocks"
,
str
(
i
),
str
(
block_id
),
"temopral_conv"
,
pruned_path
])
new_path
=
"."
.
join
([
"up_blocks"
,
str
(
block_id
),
"temp_convs"
,
str
(
layer_in_block_id
),
pruned_path
])
new_checkpoint
[
new_path
]
=
unet_state_dict
[
old_path
]
return
new_checkpoint
if
__name__
==
"__main__"
:
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"--checkpoint_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the checkpoint to convert."
)
parser
.
add_argument
(
"--dump_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the output model."
)
args
=
parser
.
parse_args
()
unet_checkpoint
=
torch
.
load
(
args
.
checkpoint_path
,
map_location
=
"cpu"
)
unet
=
UNet3DConditionModel
()
converted_ckpt
=
convert_ldm_unet_checkpoint
(
unet_checkpoint
,
unet
.
config
)
diff_0
=
set
(
unet
.
state_dict
().
keys
())
-
set
(
converted_ckpt
.
keys
())
diff_1
=
set
(
converted_ckpt
.
keys
())
-
set
(
unet
.
state_dict
().
keys
())
assert
len
(
diff_0
)
==
len
(
diff_1
)
==
0
,
"Converted weights don't match"
# load state_dict
unet
.
load_state_dict
(
converted_ckpt
)
unet
.
save_pretrained
(
args
.
dump_path
)
# -- finish converting the unet --
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