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"""
This script modified from
https://github.com/huggingface/diffusers/blob/bc691231360a4cbc7d19a58742ebb8ed0f05e027/scripts/convert_original_stable_diffusion_to_diffusers.py
Convert original Zero1to3 checkpoint to diffusers checkpoint.
# run the convert script
$ python convert_zero123_to_diffusers.py
\
--checkpoint_path /path/zero123/105000.ckpt
\
--dump_path ./zero1to3
\
--original_config_file /path/zero123/configs/sd-objaverse-finetune-c_concat-256.yaml
```
"""
import
argparse
import
torch
import
yaml
from
accelerate
import
init_empty_weights
from
accelerate
.
utils
import
set_module_tensor_to_device
from
pipeline_zero1to3
import
CCProjection
,
Zero1to3StableDiffusionPipeline
from
transformers
import
(
CLIPImageProcessor
,
CLIPVisionModelWithProjection
,
)
from
diffusers
.
models
import
(
AutoencoderKL
,
UNet2DConditionModel
,
)
from
diffusers
.
schedulers
import
DDIMScheduler
from
diffusers
.
utils
import
logging
logger
=
logging
.
get_logger
(
__name__
)
def
create_unet_diffusers_config
(
original_config
,
image_size
:
int
,
controlnet
=
False
):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
if
controlnet
:
unet_params
=
original_config
[
"model"
][
"params"
][
"control_stage_config"
][
"params"
]
else
:
if
(
"unet_config"
in
original_config
[
"model"
][
"params"
]
and
original_config
[
"model"
][
"params"
][
"unet_config"
]
is
not
None
):
unet_params
=
original_config
[
"model"
][
"params"
][
"unet_config"
][
"params"
]
else
:
unet_params
=
original_config
[
"model"
][
"params"
][
"network_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
if
unet_params
[
"transformer_depth"
]
is
not
None
:
transformer_layers_per_block
=
(
unet_params
[
"transformer_depth"
]
if
isinstance
(
unet_params
[
"transformer_depth"
],
int
)
else
list
(
unet_params
[
"transformer_depth"
])
)
else
:
transformer_layers_per_block
=
1
vae_scale_factor
=
2
**
(
len
(
vae_params
[
"ch_mult"
])
-
1
)
head_dim
=
unet_params
[
"num_heads"
]
if
"num_heads"
in
unet_params
else
None
use_linear_projection
=
(
unet_params
[
"use_linear_in_transformer"
]
if
"use_linear_in_transformer"
in
unet_params
else
False
)
if
use_linear_projection
:
# stable diffusion 2-base-512 and 2-768
if
head_dim
is
None
:
head_dim_mult
=
unet_params
[
"model_channels"
]
//
unet_params
[
"num_head_channels"
]
head_dim
=
[
head_dim_mult
*
c
for
c
in
list
(
unet_params
[
"channel_mult"
])]
class_embed_type
=
None
addition_embed_type
=
None
addition_time_embed_dim
=
None
projection_class_embeddings_input_dim
=
None
context_dim
=
None
if
unet_params
[
"context_dim"
]
is
not
None
:
context_dim
=
(
unet_params
[
"context_dim"
]
if
isinstance
(
unet_params
[
"context_dim"
],
int
)
else
unet_params
[
"context_dim"
][
0
]
)
if
"num_classes"
in
unet_params
:
if
unet_params
[
"num_classes"
]
==
"sequential"
:
if
context_dim
in
[
2048
,
1280
]:
# SDXL
addition_embed_type
=
"text_time"
addition_time_embed_dim
=
256
else
:
class_embed_type
=
"projection"
assert
"adm_in_channels"
in
unet_params
projection_class_embeddings_input_dim
=
unet_params
[
"adm_in_channels"
]
else
:
raise
NotImplementedError
(
f"Unknown conditional unet num_classes config:
{
unet_params
[
'num_classes'
]
}
"
)
config
=
{
"sample_size"
:
image_size
//
vae_scale_factor
,
"in_channels"
:
unet_params
[
"in_channels"
],
"down_block_types"
:
tuple
(
down_block_types
),
"block_out_channels"
:
tuple
(
block_out_channels
),
"layers_per_block"
:
unet_params
[
"num_res_blocks"
],
"cross_attention_dim"
:
context_dim
,
"attention_head_dim"
:
head_dim
,
"use_linear_projection"
:
use_linear_projection
,
"class_embed_type"
:
class_embed_type
,
"addition_embed_type"
:
addition_embed_type
,
"addition_time_embed_dim"
:
addition_time_embed_dim
,
"projection_class_embeddings_input_dim"
:
projection_class_embeddings_input_dim
,
"transformer_layers_per_block"
:
transformer_layers_per_block
,
}
if
controlnet
:
config
[
"conditioning_channels"
]
=
unet_params
[
"hint_channels"
]
else
:
config
[
"out_channels"
]
=
unet_params
[
"out_channels"
]
config
[
"up_block_types"
]
=
tuple
(
up_block_types
)
return
config
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
is_attn_weight
=
"proj_attn.weight"
in
new_path
or
(
"attentions"
in
new_path
and
"to_"
in
new_path
)
shape
=
old_checkpoint
[
path
[
"old"
]].
shape
if
is_attn_weight
and
len
(
shape
)
==
3
:
checkpoint
[
new_path
]
=
old_checkpoint
[
path
[
"old"
]][:, :,
0
]
elif
is_attn_weight
and
len
(
shape
)
==
4
:
checkpoint
[
new_path
]
=
old_checkpoint
[
path
[
"old"
]][:, :,
0
,
0
]
else
:
checkpoint
[
new_path
]
=
old_checkpoint
[
path
[
"old"
]]
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_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
convert_ldm_unet_checkpoint
(
checkpoint
,
config
,
path
=
None
,
extract_ema
=
False
,
controlnet
=
False
,
skip_extract_state_dict
=
False
):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
if
skip_extract_state_dict
:
unet_state_dict
=
checkpoint
else
:
# extract state_dict for UNet
unet_state_dict
=
{}
keys
=
list
(
checkpoint
.
keys
())
if
controlnet
:
unet_key
=
"control_model."
else
:
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
:
logger
.
warning
(
f"Checkpoint
{
path
}
has both EMA and non-EMA weights."
)
logger
.
warning
(
"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
[
flat_ema_key
]
else
:
if
sum
(
k
.
startswith
(
"model_ema"
)
for
k
in
keys
)
>
100
:
logger
.
warning
(
"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
[
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'
]
}
"
)
if
config
[
"addition_embed_type"
]
==
"text_time"
:
new_checkpoint
[
"add_embedding.linear_1.weight"
]
=
unet_state_dict
[
"label_emb.0.0.weight"
]
new_checkpoint
[
"add_embedding.linear_1.bias"
]
=
unet_state_dict
[
"label_emb.0.0.bias"
]
new_checkpoint
[
"add_embedding.linear_2.weight"
]
=
unet_state_dict
[
"label_emb.0.2.weight"
]
new_checkpoint
[
"add_embedding.linear_2.bias"
]
=
unet_state_dict
[
"label_emb.0.2.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"
]
if
not
controlnet
:
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
]
if
controlnet
:
# conditioning embedding
orig_index
=
0
new_checkpoint
[
"controlnet_cond_embedding.conv_in.weight"
]
=
unet_state_dict
.
pop
(
f"input_hint_block.
{
orig_index
}
.weight"
)
new_checkpoint
[
"controlnet_cond_embedding.conv_in.bias"
]
=
unet_state_dict
.
pop
(
f"input_hint_block.
{
orig_index
}
.bias"
)
orig_index
+=
2
diffusers_index
=
0
while
diffusers_index
<
6
:
new_checkpoint
[
f"controlnet_cond_embedding.blocks.
{
diffusers_index
}
.weight"
]
=
unet_state_dict
.
pop
(
f"input_hint_block.
{
orig_index
}
.weight"
)
new_checkpoint
[
f"controlnet_cond_embedding.blocks.
{
diffusers_index
}
.bias"
]
=
unet_state_dict
.
pop
(
f"input_hint_block.
{
orig_index
}
.bias"
)
diffusers_index
+=
1
orig_index
+=
2
new_checkpoint
[
"controlnet_cond_embedding.conv_out.weight"
]
=
unet_state_dict
.
pop
(
f"input_hint_block.
{
orig_index
}
.weight"
)
new_checkpoint
[
"controlnet_cond_embedding.conv_out.bias"
]
=
unet_state_dict
.
pop
(
f"input_hint_block.
{
orig_index
}
.bias"
)
# down blocks
for
i
in
range
(
num_input_blocks
):
new_checkpoint
[
f"controlnet_down_blocks.
{
i
}
.weight"
]
=
unet_state_dict
.
pop
(
f"zero_convs.
{
i
}
.0.weight"
)
new_checkpoint
[
f"controlnet_down_blocks.
{
i
}
.bias"
]
=
unet_state_dict
.
pop
(
f"zero_convs.
{
i
}
.0.bias"
)
# mid block
new_checkpoint
[
"controlnet_mid_block.weight"
]
=
unet_state_dict
.
pop
(
"middle_block_out.0.weight"
)
new_checkpoint
[
"controlnet_mid_block.bias"
]
=
unet_state_dict
.
pop
(
"middle_block_out.0.bias"
)
return
new_checkpoint
def
create_vae_diffusers_config
(
original_config
,
image_size
:
int
):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
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
)
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"
],
}
return
config
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
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_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"
,
"to_q.weight"
)
new_item
=
new_item
.
replace
(
"q.bias"
,
"to_q.bias"
)
new_item
=
new_item
.
replace
(
"k.weight"
,
"to_k.weight"
)
new_item
=
new_item
.
replace
(
"k.bias"
,
"to_k.bias"
)
new_item
=
new_item
.
replace
(
"v.weight"
,
"to_v.weight"
)
new_item
=
new_item
.
replace
(
"v.bias"
,
"to_v.bias"
)
new_item
=
new_item
.
replace
(
"proj_out.weight"
,
"to_out.0.weight"
)
new_item
=
new_item
.
replace
(
"proj_out.bias"
,
"to_out.0.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
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
convert_from_original_zero123_ckpt
(
checkpoint_path
,
original_config_file
,
extract_ema
,
device
):
ckpt
=
torch
.
load
(
checkpoint_path
,
map_location
=
device
)
ckpt
[
"global_step"
]
checkpoint
=
ckpt
[
"state_dict"
]
del
ckpt
torch
.
cuda
.
empty_cache
()
original_config
=
yaml
.
safe_load
(
original_config_file
)
original_config
[
"model"
][
"params"
][
"cond_stage_config"
][
"target"
].
split
(
"."
)[
-
1
]
num_in_channels
=
8
original_config
[
"model"
][
"params"
][
"unet_config"
][
"params"
][
"in_channels"
]
=
num_in_channels
prediction_type
=
"epsilon"
image_size
=
256
num_train_timesteps
=
getattr
(
original_config
[
"model"
][
"params"
],
"timesteps"
,
None
)
or
1000
beta_start
=
getattr
(
original_config
[
"model"
][
"params"
],
"linear_start"
,
None
)
or
0.02
beta_end
=
getattr
(
original_config
[
"model"
][
"params"
],
"linear_end"
,
None
)
or
0.085
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
,
)
scheduler
.
register_to_config
(
clip_sample
=
False
)
# Convert the UNet2DConditionModel model.
upcast_attention
=
None
unet_config
=
create_unet_diffusers_config
(
original_config
,
image_size
=
image_size
)
unet_config
[
"upcast_attention"
]
=
upcast_attention
with
init_empty_weights
():
unet
=
UNet2DConditionModel
(
**
unet_config
)
converted_unet_checkpoint
=
convert_ldm_unet_checkpoint
(
checkpoint
,
unet_config
,
path
=
None
,
extract_ema
=
extract_ema
)
for
param_name
,
param
in
converted_unet_checkpoint
.
items
():
set_module_tensor_to_device
(
unet
,
param_name
,
"cpu"
,
value
=
param
)
# Convert the VAE model.
vae_config
=
create_vae_diffusers_config
(
original_config
,
image_size
=
image_size
)
converted_vae_checkpoint
=
convert_ldm_vae_checkpoint
(
checkpoint
,
vae_config
)
if
(
"model"
in
original_config
and
"params"
in
original_config
[
"model"
]
and
"scale_factor"
in
original_config
[
"model"
][
"params"
]
):
vae_scaling_factor
=
original_config
[
"model"
][
"params"
][
"scale_factor"
]
else
:
vae_scaling_factor
=
0.18215
# default SD scaling factor
vae_config
[
"scaling_factor"
]
=
vae_scaling_factor
with
init_empty_weights
():
vae
=
AutoencoderKL
(
**
vae_config
)
for
param_name
,
param
in
converted_vae_checkpoint
.
items
():
set_module_tensor_to_device
(
vae
,
param_name
,
"cpu"
,
value
=
param
)
feature_extractor
=
CLIPImageProcessor
.
from_pretrained
(
"lambdalabs/sd-image-variations-diffusers"
,
subfolder
=
"feature_extractor"
)
image_encoder
=
CLIPVisionModelWithProjection
.
from_pretrained
(
"lambdalabs/sd-image-variations-diffusers"
,
subfolder
=
"image_encoder"
)
cc_projection
=
CCProjection
()
cc_projection
.
load_state_dict
(
{
"projection.weight"
:
checkpoint
[
"cc_projection.weight"
].
cpu
(),
"projection.bias"
:
checkpoint
[
"cc_projection.bias"
].
cpu
(),
}
)
pipe
=
Zero1to3StableDiffusionPipeline
(
vae
,
image_encoder
,
unet
,
scheduler
,
None
,
feature_extractor
,
cc_projection
,
requires_safety_checker
=
False
)
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
(
"--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
(
"--to_safetensors"
,
action
=
"store_true"
,
help
=
"Whether to store pipeline in safetensors format or not."
,
)
parser
.
add_argument
(
"--half"
,
action
=
"store_true"
,
help
=
"Save weights in half precision."
)
parser
.
add_argument
(
"--dump_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the output model."
)
parser
.
add_argument
(
"--device"
,
type
=
str
,
help
=
"Device to use (e.g. cpu, cuda:0, cuda:1, etc.)"
)
args
=
parser
.
parse_args
()
pipe
=
convert_from_original_zero123_ckpt
(
checkpoint_path
=
args
.
checkpoint_path
,
original_config_file
=
args
.
original_config_file
,
extract_ema
=
args
.
extract_ema
,
device
=
args
.
device
,
)
if
args
.
half
:
pipe
.
to
(
dtype
=
torch
.
float16
)
pipe
.
save_pretrained
(
args
.
dump_path
,
safe_serialization
=
args
.
to_safetensors
)
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