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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 Versatile Stable Diffusion checkpoints."""
import
argparse
from
argparse
import
Namespace
import
torch
from
transformers
import
(
CLIPImageProcessor
,
CLIPTextModelWithProjection
,
CLIPTokenizer
,
CLIPVisionModelWithProjection
,
)
from
diffusers
import
(
AutoencoderKL
,
DDIMScheduler
,
DPMSolverMultistepScheduler
,
EulerAncestralDiscreteScheduler
,
EulerDiscreteScheduler
,
LMSDiscreteScheduler
,
PNDMScheduler
,
UNet2DConditionModel
,
VersatileDiffusionPipeline
,
)
from
diffusers
.
pipelines
.
versatile_diffusion
.
modeling_text_unet
import
UNetFlatConditionModel
SCHEDULER_CONFIG
=
Namespace
(
**
{
"beta_linear_start"
:
0.00085
,
"beta_linear_end"
:
0.012
,
"timesteps"
:
1000
,
"scale_factor"
:
0.18215
,
}
)
IMAGE_UNET_CONFIG
=
Namespace
(
**
{
"input_channels"
:
4
,
"model_channels"
:
320
,
"output_channels"
:
4
,
"num_noattn_blocks"
: [
2
,
2
,
2
,
2
],
"channel_mult"
: [
1
,
2
,
4
,
4
],
"with_attn"
: [
True
,
True
,
True
,
False
],
"num_heads"
:
8
,
"context_dim"
:
768
,
"use_checkpoint"
:
True
,
}
)
TEXT_UNET_CONFIG
=
Namespace
(
**
{
"input_channels"
:
768
,
"model_channels"
:
320
,
"output_channels"
:
768
,
"num_noattn_blocks"
: [
2
,
2
,
2
,
2
],
"channel_mult"
: [
1
,
2
,
4
,
4
],
"second_dim"
: [
4
,
4
,
4
,
4
],
"with_attn"
: [
True
,
True
,
True
,
False
],
"num_heads"
:
8
,
"context_dim"
:
768
,
"use_checkpoint"
:
True
,
}
)
AUTOENCODER_CONFIG
=
Namespace
(
**
{
"double_z"
:
True
,
"z_channels"
:
4
,
"resolution"
:
256
,
"in_channels"
:
3
,
"out_ch"
:
3
,
"ch"
:
128
,
"ch_mult"
: [
1
,
2
,
4
,
4
],
"num_res_blocks"
:
2
,
"attn_resolutions"
: [],
"dropout"
:
0.0
,
}
)
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_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
)
mapping
.
append
({
"old"
:
old_item
,
"new"
:
new_item
})
return
mapping
def
renew_vae_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
new_item
=
new_item
.
replace
(
"nin_shortcut"
,
"conv_shortcut"
)
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
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
renew_vae_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
(
"q.weight"
,
"query.weight"
)
new_item
=
new_item
.
replace
(
"q.bias"
,
"query.bias"
)
new_item
=
new_item
.
replace
(
"k.weight"
,
"key.weight"
)
new_item
=
new_item
.
replace
(
"k.bias"
,
"key.bias"
)
new_item
=
new_item
.
replace
(
"v.weight"
,
"value.weight"
)
new_item
=
new_item
.
replace
(
"v.bias"
,
"value.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
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
# Global renaming happens here
new_path
=
new_path
.
replace
(
"middle_block.0"
,
"mid_block.resnets.0"
)
new_path
=
new_path
.
replace
(
"middle_block.1"
,
"mid_block.attentions.0"
)
new_path
=
new_path
.
replace
(
"middle_block.2"
,
"mid_block.resnets.1"
)
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
if
"proj_attn.weight"
in
new_path
:
checkpoint
[
new_path
]
=
old_checkpoint
[
path
[
"old"
]][:, :,
0
]
elif
path
[
"old"
]
in
old_checkpoint
:
checkpoint
[
new_path
]
=
old_checkpoint
[
path
[
"old"
]]
def
conv_attn_to_linear
(
checkpoint
):
keys
=
list
(
checkpoint
.
keys
())
attn_keys
=
[
"query.weight"
,
"key.weight"
,
"value.weight"
]
for
key
in
keys
:
if
"."
.
join
(
key
.
split
(
"."
)[
-
2
:])
in
attn_keys
:
if
checkpoint
[
key
].
ndim
>
2
:
checkpoint
[
key
]
=
checkpoint
[
key
][:, :,
0
,
0
]
elif
"proj_attn.weight"
in
key
:
if
checkpoint
[
key
].
ndim
>
2
:
checkpoint
[
key
]
=
checkpoint
[
key
][:, :,
0
]
def
create_image_unet_diffusers_config
(
unet_params
):
"""
Creates a config for the diffusers based on the config of the VD model.
"""
block_out_channels
=
[
unet_params
.
model_channels
*
mult
for
mult
in
unet_params
.
channel_mult
]
down_block_types
=
[]
resolution
=
1
for
i
in
range
(
len
(
block_out_channels
)):
block_type
=
"CrossAttnDownBlock2D"
if
unet_params
.
with_attn
[
i
]
else
"DownBlock2D"
down_block_types
.
append
(
block_type
)
if
i
!=
len
(
block_out_channels
)
-
1
:
resolution
*=
2
up_block_types
=
[]
for
i
in
range
(
len
(
block_out_channels
)):
block_type
=
"CrossAttnUpBlock2D"
if
unet_params
.
with_attn
[
-
i
-
1
]
else
"UpBlock2D"
up_block_types
.
append
(
block_type
)
resolution
//=
2
if
not
all
(
n
==
unet_params
.
num_noattn_blocks
[
0
]
for
n
in
unet_params
.
num_noattn_blocks
):
raise
ValueError
(
"Not all num_res_blocks are equal, which is not supported in this script."
)
config
=
{
"sample_size"
:
None
,
"in_channels"
:
unet_params
.
input_channels
,
"out_channels"
:
unet_params
.
output_channels
,
"down_block_types"
:
tuple
(
down_block_types
),
"up_block_types"
:
tuple
(
up_block_types
),
"block_out_channels"
:
tuple
(
block_out_channels
),
"layers_per_block"
:
unet_params
.
num_noattn_blocks
[
0
],
"cross_attention_dim"
:
unet_params
.
context_dim
,
"attention_head_dim"
:
unet_params
.
num_heads
,
}
return
config
def
create_text_unet_diffusers_config
(
unet_params
):
"""
Creates a config for the diffusers based on the config of the VD model.
"""
block_out_channels
=
[
unet_params
.
model_channels
*
mult
for
mult
in
unet_params
.
channel_mult
]
down_block_types
=
[]
resolution
=
1
for
i
in
range
(
len
(
block_out_channels
)):
block_type
=
"CrossAttnDownBlockFlat"
if
unet_params
.
with_attn
[
i
]
else
"DownBlockFlat"
down_block_types
.
append
(
block_type
)
if
i
!=
len
(
block_out_channels
)
-
1
:
resolution
*=
2
up_block_types
=
[]
for
i
in
range
(
len
(
block_out_channels
)):
block_type
=
"CrossAttnUpBlockFlat"
if
unet_params
.
with_attn
[
-
i
-
1
]
else
"UpBlockFlat"
up_block_types
.
append
(
block_type
)
resolution
//=
2
if
not
all
(
n
==
unet_params
.
num_noattn_blocks
[
0
]
for
n
in
unet_params
.
num_noattn_blocks
):
raise
ValueError
(
"Not all num_res_blocks are equal, which is not supported in this script."
)
config
=
{
"sample_size"
:
None
,
"in_channels"
: (
unet_params
.
input_channels
,
1
,
1
),
"out_channels"
: (
unet_params
.
output_channels
,
1
,
1
),
"down_block_types"
:
tuple
(
down_block_types
),
"up_block_types"
:
tuple
(
up_block_types
),
"block_out_channels"
:
tuple
(
block_out_channels
),
"layers_per_block"
:
unet_params
.
num_noattn_blocks
[
0
],
"cross_attention_dim"
:
unet_params
.
context_dim
,
"attention_head_dim"
:
unet_params
.
num_heads
,
}
return
config
def
create_vae_diffusers_config
(
vae_params
):
"""
Creates a config for the diffusers based on the config of the VD model.
"""
block_out_channels
=
[
vae_params
.
ch
*
mult
for
mult
in
vae_params
.
ch_mult
]
down_block_types
=
[
"DownEncoderBlock2D"
]
*
len
(
block_out_channels
)
up_block_types
=
[
"UpDecoderBlock2D"
]
*
len
(
block_out_channels
)
config
=
{
"sample_size"
:
vae_params
.
resolution
,
"in_channels"
:
vae_params
.
in_channels
,
"out_channels"
:
vae_params
.
out_ch
,
"down_block_types"
:
tuple
(
down_block_types
),
"up_block_types"
:
tuple
(
up_block_types
),
"block_out_channels"
:
tuple
(
block_out_channels
),
"latent_channels"
:
vae_params
.
z_channels
,
"layers_per_block"
:
vae_params
.
num_res_blocks
,
}
return
config
def
create_diffusers_scheduler
(
original_config
):
schedular
=
DDIMScheduler
(
num_train_timesteps
=
original_config
.
model
.
params
.
timesteps
,
beta_start
=
original_config
.
model
.
params
.
linear_start
,
beta_end
=
original_config
.
model
.
params
.
linear_end
,
beta_schedule
=
"scaled_linear"
,
)
return
schedular
def
convert_vd_unet_checkpoint
(
checkpoint
,
config
,
unet_key
,
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
())
# 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
:
print
(
"Checkpoint has both EMA and non-EMA weights."
)
if
extract_ema
:
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
:
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
:
if
key
.
startswith
(
unet_key
):
unet_state_dict
[
key
.
replace
(
unet_key
,
""
)]
=
checkpoint
.
pop
(
key
)
new_checkpoint
=
{}
new_checkpoint
[
"time_embedding.linear_1.weight"
]
=
checkpoint
[
"model.diffusion_model.time_embed.0.weight"
]
new_checkpoint
[
"time_embedding.linear_1.bias"
]
=
checkpoint
[
"model.diffusion_model.time_embed.0.bias"
]
new_checkpoint
[
"time_embedding.linear_2.weight"
]
=
checkpoint
[
"model.diffusion_model.time_embed.2.weight"
]
new_checkpoint
[
"time_embedding.linear_2.bias"
]
=
checkpoint
[
"model.diffusion_model.time_embed.2.bias"
]
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"
]
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
]
if
f"input_blocks.
{
i
}
.0.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
}
.0.op.weight"
)
new_checkpoint
[
f"down_blocks.
{
block_id
}
.downsamplers.0.conv.bias"
]
=
unet_state_dict
.
pop
(
f"input_blocks.
{
i
}
.0.op.bias"
)
elif
f"input_blocks.
{
i
}
.0.weight"
in
unet_state_dict
:
# text_unet uses linear layers in place of downsamplers
shape
=
unet_state_dict
[
f"input_blocks.
{
i
}
.0.weight"
].
shape
if
shape
[
0
]
!=
shape
[
1
]:
continue
new_checkpoint
[
f"down_blocks.
{
block_id
}
.downsamplers.0.weight"
]
=
unet_state_dict
.
pop
(
f"input_blocks.
{
i
}
.0.weight"
)
new_checkpoint
[
f"down_blocks.
{
block_id
}
.downsamplers.0.bias"
]
=
unet_state_dict
.
pop
(
f"input_blocks.
{
i
}
.0.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
)
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
)
resnet_0
=
middle_blocks
[
0
]
attentions
=
middle_blocks
[
1
]
resnet_1
=
middle_blocks
[
2
]
resnet_0_paths
=
renew_resnet_paths
(
resnet_0
)
assign_to_checkpoint
(
resnet_0_paths
,
new_checkpoint
,
unet_state_dict
,
config
=
config
)
resnet_1_paths
=
renew_resnet_paths
(
resnet_1
)
assign_to_checkpoint
(
resnet_1_paths
,
new_checkpoint
,
unet_state_dict
,
config
=
config
)
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
)
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
]
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
)
if
[
"conv.weight"
,
"conv.bias"
]
in
output_block_list
.
values
():
index
=
list
(
output_block_list
.
values
()).
index
([
"conv.weight"
,
"conv.bias"
])
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
=
[]
elif
f"output_blocks.
{
i
}
.1.weight"
in
unet_state_dict
:
# text_unet uses linear layers in place of upsamplers
shape
=
unet_state_dict
[
f"output_blocks.
{
i
}
.1.weight"
].
shape
if
shape
[
0
]
!=
shape
[
1
]:
continue
new_checkpoint
[
f"up_blocks.
{
block_id
}
.upsamplers.0.weight"
]
=
unet_state_dict
.
pop
(
f"output_blocks.
{
i
}
.1.weight"
)
new_checkpoint
[
f"up_blocks.
{
block_id
}
.upsamplers.0.bias"
]
=
unet_state_dict
.
pop
(
f"output_blocks.
{
i
}
.1.bias"
)
# Clear attentions as they have been attributed above.
if
len
(
attentions
)
==
2
:
attentions
=
[]
elif
f"output_blocks.
{
i
}
.2.weight"
in
unet_state_dict
:
# text_unet uses linear layers in place of upsamplers
shape
=
unet_state_dict
[
f"output_blocks.
{
i
}
.2.weight"
].
shape
if
shape
[
0
]
!=
shape
[
1
]:
continue
new_checkpoint
[
f"up_blocks.
{
block_id
}
.upsamplers.0.weight"
]
=
unet_state_dict
.
pop
(
f"output_blocks.
{
i
}
.2.weight"
)
new_checkpoint
[
f"up_blocks.
{
block_id
}
.upsamplers.0.bias"
]
=
unet_state_dict
.
pop
(
f"output_blocks.
{
i
}
.2.bias"
)
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
)
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
]
return
new_checkpoint
def
convert_vd_vae_checkpoint
(
checkpoint
,
config
):
# extract state dict for VAE
vae_state_dict
=
{}
keys
=
list
(
checkpoint
.
keys
())
for
key
in
keys
:
vae_state_dict
[
key
]
=
checkpoint
.
get
(
key
)
new_checkpoint
=
{}
new_checkpoint
[
"encoder.conv_in.weight"
]
=
vae_state_dict
[
"encoder.conv_in.weight"
]
new_checkpoint
[
"encoder.conv_in.bias"
]
=
vae_state_dict
[
"encoder.conv_in.bias"
]
new_checkpoint
[
"encoder.conv_out.weight"
]
=
vae_state_dict
[
"encoder.conv_out.weight"
]
new_checkpoint
[
"encoder.conv_out.bias"
]
=
vae_state_dict
[
"encoder.conv_out.bias"
]
new_checkpoint
[
"encoder.conv_norm_out.weight"
]
=
vae_state_dict
[
"encoder.norm_out.weight"
]
new_checkpoint
[
"encoder.conv_norm_out.bias"
]
=
vae_state_dict
[
"encoder.norm_out.bias"
]
new_checkpoint
[
"decoder.conv_in.weight"
]
=
vae_state_dict
[
"decoder.conv_in.weight"
]
new_checkpoint
[
"decoder.conv_in.bias"
]
=
vae_state_dict
[
"decoder.conv_in.bias"
]
new_checkpoint
[
"decoder.conv_out.weight"
]
=
vae_state_dict
[
"decoder.conv_out.weight"
]
new_checkpoint
[
"decoder.conv_out.bias"
]
=
vae_state_dict
[
"decoder.conv_out.bias"
]
new_checkpoint
[
"decoder.conv_norm_out.weight"
]
=
vae_state_dict
[
"decoder.norm_out.weight"
]
new_checkpoint
[
"decoder.conv_norm_out.bias"
]
=
vae_state_dict
[
"decoder.norm_out.bias"
]
new_checkpoint
[
"quant_conv.weight"
]
=
vae_state_dict
[
"quant_conv.weight"
]
new_checkpoint
[
"quant_conv.bias"
]
=
vae_state_dict
[
"quant_conv.bias"
]
new_checkpoint
[
"post_quant_conv.weight"
]
=
vae_state_dict
[
"post_quant_conv.weight"
]
new_checkpoint
[
"post_quant_conv.bias"
]
=
vae_state_dict
[
"post_quant_conv.bias"
]
# Retrieves the keys for the encoder down blocks only
num_down_blocks
=
len
({
"."
.
join
(
layer
.
split
(
"."
)[:
3
])
for
layer
in
vae_state_dict
if
"encoder.down"
in
layer
})
down_blocks
=
{
layer_id
: [
key
for
key
in
vae_state_dict
if
f"down.
{
layer_id
}
"
in
key
]
for
layer_id
in
range
(
num_down_blocks
)
}
# Retrieves the keys for the decoder up blocks only
num_up_blocks
=
len
({
"."
.
join
(
layer
.
split
(
"."
)[:
3
])
for
layer
in
vae_state_dict
if
"decoder.up"
in
layer
})
up_blocks
=
{
layer_id
: [
key
for
key
in
vae_state_dict
if
f"up.
{
layer_id
}
"
in
key
]
for
layer_id
in
range
(
num_up_blocks
)
}
for
i
in
range
(
num_down_blocks
):
resnets
=
[
key
for
key
in
down_blocks
[
i
]
if
f"down.
{
i
}
"
in
key
and
f"down.
{
i
}
.downsample"
not
in
key
]
if
f"encoder.down.
{
i
}
.downsample.conv.weight"
in
vae_state_dict
:
new_checkpoint
[
f"encoder.down_blocks.
{
i
}
.downsamplers.0.conv.weight"
]
=
vae_state_dict
.
pop
(
f"encoder.down.
{
i
}
.downsample.conv.weight"
)
new_checkpoint
[
f"encoder.down_blocks.
{
i
}
.downsamplers.0.conv.bias"
]
=
vae_state_dict
.
pop
(
f"encoder.down.
{
i
}
.downsample.conv.bias"
)
paths
=
renew_vae_resnet_paths
(
resnets
)
meta_path
=
{
"old"
:
f"down.
{
i
}
.block"
,
"new"
:
f"down_blocks.
{
i
}
.resnets"
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
vae_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
mid_resnets
=
[
key
for
key
in
vae_state_dict
if
"encoder.mid.block"
in
key
]
num_mid_res_blocks
=
2
for
i
in
range
(
1
,
num_mid_res_blocks
+
1
):
resnets
=
[
key
for
key
in
mid_resnets
if
f"encoder.mid.block_
{
i
}
"
in
key
]
paths
=
renew_vae_resnet_paths
(
resnets
)
meta_path
=
{
"old"
:
f"mid.block_
{
i
}
"
,
"new"
:
f"mid_block.resnets.
{
i
-
1
}
"
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
vae_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
mid_attentions
=
[
key
for
key
in
vae_state_dict
if
"encoder.mid.attn"
in
key
]
paths
=
renew_vae_attention_paths
(
mid_attentions
)
meta_path
=
{
"old"
:
"mid.attn_1"
,
"new"
:
"mid_block.attentions.0"
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
vae_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
conv_attn_to_linear
(
new_checkpoint
)
for
i
in
range
(
num_up_blocks
):
block_id
=
num_up_blocks
-
1
-
i
resnets
=
[
key
for
key
in
up_blocks
[
block_id
]
if
f"up.
{
block_id
}
"
in
key
and
f"up.
{
block_id
}
.upsample"
not
in
key
]
if
f"decoder.up.
{
block_id
}
.upsample.conv.weight"
in
vae_state_dict
:
new_checkpoint
[
f"decoder.up_blocks.
{
i
}
.upsamplers.0.conv.weight"
]
=
vae_state_dict
[
f"decoder.up.
{
block_id
}
.upsample.conv.weight"
]
new_checkpoint
[
f"decoder.up_blocks.
{
i
}
.upsamplers.0.conv.bias"
]
=
vae_state_dict
[
f"decoder.up.
{
block_id
}
.upsample.conv.bias"
]
paths
=
renew_vae_resnet_paths
(
resnets
)
meta_path
=
{
"old"
:
f"up.
{
block_id
}
.block"
,
"new"
:
f"up_blocks.
{
i
}
.resnets"
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
vae_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
mid_resnets
=
[
key
for
key
in
vae_state_dict
if
"decoder.mid.block"
in
key
]
num_mid_res_blocks
=
2
for
i
in
range
(
1
,
num_mid_res_blocks
+
1
):
resnets
=
[
key
for
key
in
mid_resnets
if
f"decoder.mid.block_
{
i
}
"
in
key
]
paths
=
renew_vae_resnet_paths
(
resnets
)
meta_path
=
{
"old"
:
f"mid.block_
{
i
}
"
,
"new"
:
f"mid_block.resnets.
{
i
-
1
}
"
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
vae_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
mid_attentions
=
[
key
for
key
in
vae_state_dict
if
"decoder.mid.attn"
in
key
]
paths
=
renew_vae_attention_paths
(
mid_attentions
)
meta_path
=
{
"old"
:
"mid.attn_1"
,
"new"
:
"mid_block.attentions.0"
}
assign_to_checkpoint
(
paths
,
new_checkpoint
,
vae_state_dict
,
additional_replacements
=
[
meta_path
],
config
=
config
)
conv_attn_to_linear
(
new_checkpoint
)
return
new_checkpoint
if
__name__
==
"__main__"
:
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"--unet_checkpoint_path"
,
default
=
None
,
type
=
str
,
required
=
False
,
help
=
"Path to the checkpoint to convert."
)
parser
.
add_argument
(
"--vae_checkpoint_path"
,
default
=
None
,
type
=
str
,
required
=
False
,
help
=
"Path to the checkpoint to convert."
)
parser
.
add_argument
(
"--optimus_checkpoint_path"
,
default
=
None
,
type
=
str
,
required
=
False
,
help
=
"Path to the checkpoint to convert."
)
parser
.
add_argument
(
"--scheduler_type"
,
default
=
"pndm"
,
type
=
str
,
help
=
"Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']"
,
)
parser
.
add_argument
(
"--extract_ema"
,
action
=
"store_true"
,
help
=
(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser
.
add_argument
(
"--dump_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the output model."
)
args
=
parser
.
parse_args
()
scheduler_config
=
SCHEDULER_CONFIG
num_train_timesteps
=
scheduler_config
.
timesteps
beta_start
=
scheduler_config
.
beta_linear_start
beta_end
=
scheduler_config
.
beta_linear_end
if
args
.
scheduler_type
==
"pndm"
:
scheduler
=
PNDMScheduler
(
beta_end
=
beta_end
,
beta_schedule
=
"scaled_linear"
,
beta_start
=
beta_start
,
num_train_timesteps
=
num_train_timesteps
,
skip_prk_steps
=
True
,
steps_offset
=
1
,
)
elif
args
.
scheduler_type
==
"lms"
:
scheduler
=
LMSDiscreteScheduler
(
beta_start
=
beta_start
,
beta_end
=
beta_end
,
beta_schedule
=
"scaled_linear"
)
elif
args
.
scheduler_type
==
"euler"
:
scheduler
=
EulerDiscreteScheduler
(
beta_start
=
beta_start
,
beta_end
=
beta_end
,
beta_schedule
=
"scaled_linear"
)
elif
args
.
scheduler_type
==
"euler-ancestral"
:
scheduler
=
EulerAncestralDiscreteScheduler
(
beta_start
=
beta_start
,
beta_end
=
beta_end
,
beta_schedule
=
"scaled_linear"
)
elif
args
.
scheduler_type
==
"dpm"
:
scheduler
=
DPMSolverMultistepScheduler
(
beta_start
=
beta_start
,
beta_end
=
beta_end
,
beta_schedule
=
"scaled_linear"
)
elif
args
.
scheduler_type
==
"ddim"
:
scheduler
=
DDIMScheduler
(
beta_start
=
beta_start
,
beta_end
=
beta_end
,
beta_schedule
=
"scaled_linear"
,
clip_sample
=
False
,
set_alpha_to_one
=
False
,
steps_offset
=
1
,
)
else
:
raise
ValueError
(
f"Scheduler of type
{
args
.
scheduler_type
}
doesn't exist!"
)
# Convert the UNet2DConditionModel models.
if
args
.
unet_checkpoint_path
is
not
None
:
# image UNet
image_unet_config
=
create_image_unet_diffusers_config
(
IMAGE_UNET_CONFIG
)
checkpoint
=
torch
.
load
(
args
.
unet_checkpoint_path
)
converted_image_unet_checkpoint
=
convert_vd_unet_checkpoint
(
checkpoint
,
image_unet_config
,
unet_key
=
"model.diffusion_model.unet_image."
,
extract_ema
=
args
.
extract_ema
)
image_unet
=
UNet2DConditionModel
(
**
image_unet_config
)
image_unet
.
load_state_dict
(
converted_image_unet_checkpoint
)
# text UNet
text_unet_config
=
create_text_unet_diffusers_config
(
TEXT_UNET_CONFIG
)
converted_text_unet_checkpoint
=
convert_vd_unet_checkpoint
(
checkpoint
,
text_unet_config
,
unet_key
=
"model.diffusion_model.unet_text."
,
extract_ema
=
args
.
extract_ema
)
text_unet
=
UNetFlatConditionModel
(
**
text_unet_config
)
text_unet
.
load_state_dict
(
converted_text_unet_checkpoint
)
# Convert the VAE model.
if
args
.
vae_checkpoint_path
is
not
None
:
vae_config
=
create_vae_diffusers_config
(
AUTOENCODER_CONFIG
)
checkpoint
=
torch
.
load
(
args
.
vae_checkpoint_path
)
converted_vae_checkpoint
=
convert_vd_vae_checkpoint
(
checkpoint
,
vae_config
)
vae
=
AutoencoderKL
(
**
vae_config
)
vae
.
load_state_dict
(
converted_vae_checkpoint
)
tokenizer
=
CLIPTokenizer
.
from_pretrained
(
"openai/clip-vit-large-patch14"
)
image_feature_extractor
=
CLIPImageProcessor
.
from_pretrained
(
"openai/clip-vit-large-patch14"
)
text_encoder
=
CLIPTextModelWithProjection
.
from_pretrained
(
"openai/clip-vit-large-patch14"
)
image_encoder
=
CLIPVisionModelWithProjection
.
from_pretrained
(
"openai/clip-vit-large-patch14"
)
pipe
=
VersatileDiffusionPipeline
(
scheduler
=
scheduler
,
tokenizer
=
tokenizer
,
image_feature_extractor
=
image_feature_extractor
,
text_encoder
=
text_encoder
,
image_encoder
=
image_encoder
,
image_unet
=
image_unet
,
text_unet
=
text_unet
,
vae
=
vae
,
)
pipe
.
save_pretrained
(
args
.
dump_path
)
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