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# coding=utf-8
# Copyright 2023 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 AudioLDM checkpoints."""
import
argparse
import
re
import
torch
from
transformers
import
(
AutoTokenizer
,
ClapTextConfig
,
ClapTextModelWithProjection
,
SpeechT5HifiGan
,
SpeechT5HifiGanConfig
,
)
from
diffusers
import
(
AudioLDMPipeline
,
AutoencoderKL
,
DDIMScheduler
,
DPMSolverMultistepScheduler
,
EulerAncestralDiscreteScheduler
,
EulerDiscreteScheduler
,
HeunDiscreteScheduler
,
LMSDiscreteScheduler
,
PNDMScheduler
,
UNet2DConditionModel
,
)
from
diffusers
.
utils
import
is_omegaconf_available
,
is_safetensors_available
from
diffusers
.
utils
.
import_utils
import
BACKENDS_MAPPING
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments
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
])
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths
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
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths
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
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths
def
renew_attention_paths
(
old_list
):
"""
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
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_attention_paths
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
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint
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
]
else
:
checkpoint
[
new_path
]
=
old_checkpoint
[
path
[
"old"
]]
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear
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_unet_diffusers_config
(
original_config
,
image_size
:
int
):
"""
Creates a UNet config for diffusers based on the config of the original AudioLDM model.
"""
unet_params
=
original_config
.
model
.
params
.
unet_config
.
params
vae_params
=
original_config
.
model
.
params
.
first_stage_config
.
params
.
ddconfig
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
resolution
in
unet_params
.
attention_resolutions
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
resolution
in
unet_params
.
attention_resolutions
else
"UpBlock2D"
up_block_types
.
append
(
block_type
)
resolution
//=
2
vae_scale_factor
=
2
**
(
len
(
vae_params
.
ch_mult
)
-
1
)
cross_attention_dim
=
(
unet_params
.
cross_attention_dim
if
"cross_attention_dim"
in
unet_params
else
block_out_channels
)
class_embed_type
=
"simple_projection"
if
"extra_film_condition_dim"
in
unet_params
else
None
projection_class_embeddings_input_dim
=
(
unet_params
.
extra_film_condition_dim
if
"extra_film_condition_dim"
in
unet_params
else
None
)
class_embeddings_concat
=
unet_params
.
extra_film_use_concat
if
"extra_film_use_concat"
in
unet_params
else
None
config
=
{
"sample_size"
:
image_size
//
vae_scale_factor
,
"in_channels"
:
unet_params
.
in_channels
,
"out_channels"
:
unet_params
.
out_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_res_blocks
,
"cross_attention_dim"
:
cross_attention_dim
,
"class_embed_type"
:
class_embed_type
,
"projection_class_embeddings_input_dim"
:
projection_class_embeddings_input_dim
,
"class_embeddings_concat"
:
class_embeddings_concat
,
}
return
config
# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config
def
create_vae_diffusers_config
(
original_config
,
checkpoint
,
image_size
:
int
):
"""
Creates a VAE config for diffusers based on the config of the original AudioLDM model. Compared to the original
Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE.
"""
vae_params
=
original_config
.
model
.
params
.
first_stage_config
.
params
.
ddconfig
_
=
original_config
.
model
.
params
.
first_stage_config
.
params
.
embed_dim
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
)
scaling_factor
=
checkpoint
[
"scale_factor"
]
if
"scale_by_std"
in
original_config
.
model
.
params
else
0.18215
config
=
{
"sample_size"
:
image_size
,
"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
,
"scaling_factor"
:
float
(
scaling_factor
),
}
return
config
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular
def
create_diffusers_schedular
(
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
# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_unet_checkpoint
def
convert_ldm_unet_checkpoint
(
checkpoint
,
config
,
path
=
None
,
extract_ema
=
False
):
"""
Takes a state dict and a config, and returns a converted checkpoint. Compared to the original Stable Diffusion
conversion, this function additionally converts the learnt film embedding linear layer.
"""
# 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
:
if
key
.
startswith
(
unet_key
):
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"
]
new_checkpoint
[
"class_embedding.weight"
]
=
unet_state_dict
[
"film_emb.weight"
]
new_checkpoint
[
"class_embedding.bias"
]
=
unet_state_dict
[
"film_emb.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"
)
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
]
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
)
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
)
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
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint
def
convert_ldm_vae_checkpoint
(
checkpoint
,
config
):
# extract state dict for VAE
vae_state_dict
=
{}
vae_key
=
"first_stage_model."
keys
=
list
(
checkpoint
.
keys
())
for
key
in
keys
:
if
key
.
startswith
(
vae_key
):
vae_state_dict
[
key
.
replace
(
vae_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
CLAP_KEYS_TO_MODIFY_MAPPING
=
{
"text_branch"
:
"text_model"
,
"attn"
:
"attention.self"
,
"self.proj"
:
"output.dense"
,
"attention.self_mask"
:
"attn_mask"
,
"mlp.fc1"
:
"intermediate.dense"
,
"mlp.fc2"
:
"output.dense"
,
"norm1"
:
"layernorm_before"
,
"norm2"
:
"layernorm_after"
,
"bn0"
:
"batch_norm"
,
}
CLAP_KEYS_TO_IGNORE
=
[
"text_transform"
]
CLAP_EXPECTED_MISSING_KEYS
=
[
"text_model.embeddings.token_type_ids"
]
def
convert_open_clap_checkpoint
(
checkpoint
):
"""
Takes a state dict and returns a converted CLAP checkpoint.
"""
# extract state dict for CLAP text embedding model, discarding the audio component
model_state_dict
=
{}
model_key
=
"cond_stage_model.model.text_"
keys
=
list
(
checkpoint
.
keys
())
for
key
in
keys
:
if
key
.
startswith
(
model_key
):
model_state_dict
[
key
.
replace
(
model_key
,
"text_"
)]
=
checkpoint
.
get
(
key
)
new_checkpoint
=
{}
sequential_layers_pattern
=
r".*sequential.(\d+).*"
text_projection_pattern
=
r".*_projection.(\d+).*"
for
key
,
value
in
model_state_dict
.
items
():
# check if key should be ignored in mapping
if
key
.
split
(
"."
)[
0
]
in
CLAP_KEYS_TO_IGNORE
:
continue
# check if any key needs to be modified
for
key_to_modify
,
new_key
in
CLAP_KEYS_TO_MODIFY_MAPPING
.
items
():
if
key_to_modify
in
key
:
key
=
key
.
replace
(
key_to_modify
,
new_key
)
if
re
.
match
(
sequential_layers_pattern
,
key
):
# replace sequential layers with list
sequential_layer
=
re
.
match
(
sequential_layers_pattern
,
key
).
group
(
1
)
key
=
key
.
replace
(
f"sequential.
{
sequential_layer
}
."
,
f"layers.
{
int
(
sequential_layer
)
//
3
}
.linear."
)
elif
re
.
match
(
text_projection_pattern
,
key
):
projecton_layer
=
int
(
re
.
match
(
text_projection_pattern
,
key
).
group
(
1
))
# Because in CLAP they use `nn.Sequential`...
transformers_projection_layer
=
1
if
projecton_layer
==
0
else
2
key
=
key
.
replace
(
f"_projection.
{
projecton_layer
}
."
,
f"_projection.linear
{
transformers_projection_layer
}
."
)
if
"audio"
and
"qkv"
in
key
:
# split qkv into query key and value
mixed_qkv
=
value
qkv_dim
=
mixed_qkv
.
size
(
0
)
//
3
query_layer
=
mixed_qkv
[:
qkv_dim
]
key_layer
=
mixed_qkv
[
qkv_dim
:
qkv_dim
*
2
]
value_layer
=
mixed_qkv
[
qkv_dim
*
2
:]
new_checkpoint
[
key
.
replace
(
"qkv"
,
"query"
)]
=
query_layer
new_checkpoint
[
key
.
replace
(
"qkv"
,
"key"
)]
=
key_layer
new_checkpoint
[
key
.
replace
(
"qkv"
,
"value"
)]
=
value_layer
else
:
new_checkpoint
[
key
]
=
value
return
new_checkpoint
def
create_transformers_vocoder_config
(
original_config
):
"""
Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model.
"""
vocoder_params
=
original_config
.
model
.
params
.
vocoder_config
.
params
config
=
{
"model_in_dim"
:
vocoder_params
.
num_mels
,
"sampling_rate"
:
vocoder_params
.
sampling_rate
,
"upsample_initial_channel"
:
vocoder_params
.
upsample_initial_channel
,
"upsample_rates"
:
list
(
vocoder_params
.
upsample_rates
),
"upsample_kernel_sizes"
:
list
(
vocoder_params
.
upsample_kernel_sizes
),
"resblock_kernel_sizes"
:
list
(
vocoder_params
.
resblock_kernel_sizes
),
"resblock_dilation_sizes"
: [
list
(
resblock_dilation
)
for
resblock_dilation
in
vocoder_params
.
resblock_dilation_sizes
],
"normalize_before"
:
False
,
}
return
config
def
convert_hifigan_checkpoint
(
checkpoint
,
config
):
"""
Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint.
"""
# extract state dict for vocoder
vocoder_state_dict
=
{}
vocoder_key
=
"first_stage_model.vocoder."
keys
=
list
(
checkpoint
.
keys
())
for
key
in
keys
:
if
key
.
startswith
(
vocoder_key
):
vocoder_state_dict
[
key
.
replace
(
vocoder_key
,
""
)]
=
checkpoint
.
get
(
key
)
# fix upsampler keys, everything else is correct already
for
i
in
range
(
len
(
config
.
upsample_rates
)):
vocoder_state_dict
[
f"upsampler.
{
i
}
.weight"
]
=
vocoder_state_dict
.
pop
(
f"ups.
{
i
}
.weight"
)
vocoder_state_dict
[
f"upsampler.
{
i
}
.bias"
]
=
vocoder_state_dict
.
pop
(
f"ups.
{
i
}
.bias"
)
if
not
config
.
normalize_before
:
# if we don't set normalize_before then these variables are unused, so we set them to their initialised values
vocoder_state_dict
[
"mean"
]
=
torch
.
zeros
(
config
.
model_in_dim
)
vocoder_state_dict
[
"scale"
]
=
torch
.
ones
(
config
.
model_in_dim
)
return
vocoder_state_dict
# Adapted from https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation/blob/84a0384742a22bd80c44e903e241f0623e874f1d/audioldm/utils.py#L72-L73
DEFAULT_CONFIG
=
{
"model"
: {
"params"
: {
"linear_start"
:
0.0015
,
"linear_end"
:
0.0195
,
"timesteps"
:
1000
,
"channels"
:
8
,
"scale_by_std"
:
True
,
"unet_config"
: {
"target"
:
"audioldm.latent_diffusion.openaimodel.UNetModel"
,
"params"
: {
"extra_film_condition_dim"
:
512
,
"extra_film_use_concat"
:
True
,
"in_channels"
:
8
,
"out_channels"
:
8
,
"model_channels"
:
128
,
"attention_resolutions"
: [
8
,
4
,
2
],
"num_res_blocks"
:
2
,
"channel_mult"
: [
1
,
2
,
3
,
5
],
"num_head_channels"
:
32
,
},
},
"first_stage_config"
: {
"target"
:
"audioldm.variational_autoencoder.autoencoder.AutoencoderKL"
,
"params"
: {
"embed_dim"
:
8
,
"ddconfig"
: {
"z_channels"
:
8
,
"resolution"
:
256
,
"in_channels"
:
1
,
"out_ch"
:
1
,
"ch"
:
128
,
"ch_mult"
: [
1
,
2
,
4
],
"num_res_blocks"
:
2
,
},
},
},
"vocoder_config"
: {
"target"
:
"audioldm.first_stage_model.vocoder"
,
"params"
: {
"upsample_rates"
: [
5
,
4
,
2
,
2
,
2
],
"upsample_kernel_sizes"
: [
16
,
16
,
8
,
4
,
4
],
"upsample_initial_channel"
:
1024
,
"resblock_kernel_sizes"
: [
3
,
7
,
11
],
"resblock_dilation_sizes"
: [[
1
,
3
,
5
], [
1
,
3
,
5
], [
1
,
3
,
5
]],
"num_mels"
:
64
,
"sampling_rate"
:
16000
,
},
},
},
},
}
def
load_pipeline_from_original_audioldm_ckpt
(
checkpoint_path
:
str
,
original_config_file
:
str
=
None
,
image_size
:
int
=
512
,
prediction_type
:
str
=
None
,
extract_ema
:
bool
=
False
,
scheduler_type
:
str
=
"ddim"
,
num_in_channels
:
int
=
None
,
device
:
str
=
None
,
from_safetensors
:
bool
=
False
,
)
->
AudioLDMPipeline
:
"""
Load an AudioLDM pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file.
Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the
global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is
recommended that you override the default values and/or supply an `original_config_file` wherever possible.
:param checkpoint_path: Path to `.ckpt` file. :param original_config_file: Path to `.yaml` config file
corresponding to the original architecture.
If `None`, will be automatically instantiated based on default values.
:param image_size: The image size that the model was trained on. Use 512 for original AudioLDM checkpoints. :param
prediction_type: The prediction type that the model was trained on. Use `'epsilon'` for original
AudioLDM checkpoints.
:param num_in_channels: The number of input channels. If `None` number of input channels will be automatically
inferred.
:param scheduler_type: Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler",
"euler-ancestral", "dpm", "ddim"]`.
:param extract_ema: Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract
the EMA weights or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually
yield higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.
:param device: The device to use. Pass `None` to determine automatically. :param from_safetensors: If
`checkpoint_path` is in `safetensors` format, load checkpoint with safetensors
instead of PyTorch.
:return: An AudioLDMPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
"""
if
not
is_omegaconf_available
():
raise
ValueError
(
BACKENDS_MAPPING
[
"omegaconf"
][
1
])
from
omegaconf
import
OmegaConf
if
from_safetensors
:
if
not
is_safetensors_available
():
raise
ValueError
(
BACKENDS_MAPPING
[
"safetensors"
][
1
])
from
safetensors
import
safe_open
checkpoint
=
{}
with
safe_open
(
checkpoint_path
,
framework
=
"pt"
,
device
=
"cpu"
)
as
f
:
for
key
in
f
.
keys
():
checkpoint
[
key
]
=
f
.
get_tensor
(
key
)
else
:
if
device
is
None
:
device
=
"cuda"
if
torch
.
cuda
.
is_available
()
else
"cpu"
checkpoint
=
torch
.
load
(
checkpoint_path
,
map_location
=
device
)
else
:
checkpoint
=
torch
.
load
(
checkpoint_path
,
map_location
=
device
)
if
"state_dict"
in
checkpoint
:
checkpoint
=
checkpoint
[
"state_dict"
]
if
original_config_file
is
None
:
original_config
=
DEFAULT_CONFIG
original_config
=
OmegaConf
.
create
(
original_config
)
else
:
original_config
=
OmegaConf
.
load
(
original_config_file
)
if
num_in_channels
is
not
None
:
original_config
[
"model"
][
"params"
][
"unet_config"
][
"params"
][
"in_channels"
]
=
num_in_channels
if
(
"parameterization"
in
original_config
[
"model"
][
"params"
]
and
original_config
[
"model"
][
"params"
][
"parameterization"
]
==
"v"
):
if
prediction_type
is
None
:
prediction_type
=
"v_prediction"
else
:
if
prediction_type
is
None
:
prediction_type
=
"epsilon"
if
image_size
is
None
:
image_size
=
512
num_train_timesteps
=
original_config
.
model
.
params
.
timesteps
beta_start
=
original_config
.
model
.
params
.
linear_start
beta_end
=
original_config
.
model
.
params
.
linear_end
scheduler
=
DDIMScheduler
(
beta_end
=
beta_end
,
beta_schedule
=
"scaled_linear"
,
beta_start
=
beta_start
,
num_train_timesteps
=
num_train_timesteps
,
steps_offset
=
1
,
clip_sample
=
False
,
set_alpha_to_one
=
False
,
prediction_type
=
prediction_type
,
)
# make sure scheduler works correctly with DDIM
scheduler
.
register_to_config
(
clip_sample
=
False
)
if
scheduler_type
==
"pndm"
:
config
=
dict
(
scheduler
.
config
)
config
[
"skip_prk_steps"
]
=
True
scheduler
=
PNDMScheduler
.
from_config
(
config
)
elif
scheduler_type
==
"lms"
:
scheduler
=
LMSDiscreteScheduler
.
from_config
(
scheduler
.
config
)
elif
scheduler_type
==
"heun"
:
scheduler
=
HeunDiscreteScheduler
.
from_config
(
scheduler
.
config
)
elif
scheduler_type
==
"euler"
:
scheduler
=
EulerDiscreteScheduler
.
from_config
(
scheduler
.
config
)
elif
scheduler_type
==
"euler-ancestral"
:
scheduler
=
EulerAncestralDiscreteScheduler
.
from_config
(
scheduler
.
config
)
elif
scheduler_type
==
"dpm"
:
scheduler
=
DPMSolverMultistepScheduler
.
from_config
(
scheduler
.
config
)
elif
scheduler_type
==
"ddim"
:
scheduler
=
scheduler
else
:
raise
ValueError
(
f"Scheduler of type
{
scheduler_type
}
doesn't exist!"
)
# Convert the UNet2DModel
unet_config
=
create_unet_diffusers_config
(
original_config
,
image_size
=
image_size
)
unet
=
UNet2DConditionModel
(
**
unet_config
)
converted_unet_checkpoint
=
convert_ldm_unet_checkpoint
(
checkpoint
,
unet_config
,
path
=
checkpoint_path
,
extract_ema
=
extract_ema
)
unet
.
load_state_dict
(
converted_unet_checkpoint
)
# Convert the VAE model
vae_config
=
create_vae_diffusers_config
(
original_config
,
checkpoint
=
checkpoint
,
image_size
=
image_size
)
converted_vae_checkpoint
=
convert_ldm_vae_checkpoint
(
checkpoint
,
vae_config
)
vae
=
AutoencoderKL
(
**
vae_config
)
vae
.
load_state_dict
(
converted_vae_checkpoint
)
# Convert the text model
# AudioLDM uses the same configuration and tokenizer as the original CLAP model
config
=
ClapTextConfig
.
from_pretrained
(
"laion/clap-htsat-unfused"
)
tokenizer
=
AutoTokenizer
.
from_pretrained
(
"laion/clap-htsat-unfused"
)
converted_text_model
=
convert_open_clap_checkpoint
(
checkpoint
)
text_model
=
ClapTextModelWithProjection
(
config
)
missing_keys
,
unexpected_keys
=
text_model
.
load_state_dict
(
converted_text_model
,
strict
=
False
)
# we expect not to have token_type_ids in our original state dict so let's ignore them
missing_keys
=
list
(
set
(
missing_keys
)
-
set
(
CLAP_EXPECTED_MISSING_KEYS
))
if
len
(
unexpected_keys
)
>
0
:
raise
ValueError
(
f"Unexpected keys when loading CLAP model:
{
unexpected_keys
}
"
)
if
len
(
missing_keys
)
>
0
:
raise
ValueError
(
f"Missing keys when loading CLAP model:
{
missing_keys
}
"
)
# Convert the vocoder model
vocoder_config
=
create_transformers_vocoder_config
(
original_config
)
vocoder_config
=
SpeechT5HifiGanConfig
(
**
vocoder_config
)
converted_vocoder_checkpoint
=
convert_hifigan_checkpoint
(
checkpoint
,
vocoder_config
)
vocoder
=
SpeechT5HifiGan
(
vocoder_config
)
vocoder
.
load_state_dict
(
converted_vocoder_checkpoint
)
# Instantiate the diffusers pipeline
pipe
=
AudioLDMPipeline
(
vae
=
vae
,
text_encoder
=
text_model
,
tokenizer
=
tokenizer
,
unet
=
unet
,
scheduler
=
scheduler
,
vocoder
=
vocoder
,
)
return
pipe
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
(
"--original_config_file"
,
default
=
None
,
type
=
str
,
help
=
"The YAML config file corresponding to the original architecture."
,
)
parser
.
add_argument
(
"--num_in_channels"
,
default
=
None
,
type
=
int
,
help
=
"The number of input channels. If `None` number of input channels will be automatically inferred."
,
)
parser
.
add_argument
(
"--scheduler_type"
,
default
=
"ddim"
,
type
=
str
,
help
=
"Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']"
,
)
parser
.
add_argument
(
"--image_size"
,
default
=
None
,
type
=
int
,
help
=
(
"The image size that the model was trained on."
),
)
parser
.
add_argument
(
"--prediction_type"
,
default
=
None
,
type
=
str
,
help
=
(
"The prediction type that the model was trained on."
),
)
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
(
"--from_safetensors"
,
action
=
"store_true"
,
help
=
"If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch."
,
)
parser
.
add_argument
(
"--to_safetensors"
,
action
=
"store_true"
,
help
=
"Whether to store pipeline in safetensors format or not."
,
)
parser
.
add_argument
(
"--dump_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the output model."
)
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