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# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
# *Only* converts the UNet, VAE, and Text Encoder.
# Does not convert optimizer state or any other thing.
import
argparse
import
os
.
path
as
osp
import
re
import
torch
from
safetensors
.
torch
import
load_file
,
save_file
# =================#
# UNet Conversion #
# =================#
unet_conversion_map
=
[
# (stable-diffusion, HF Diffusers)
(
"time_embed.0.weight"
,
"time_embedding.linear_1.weight"
),
(
"time_embed.0.bias"
,
"time_embedding.linear_1.bias"
),
(
"time_embed.2.weight"
,
"time_embedding.linear_2.weight"
),
(
"time_embed.2.bias"
,
"time_embedding.linear_2.bias"
),
(
"input_blocks.0.0.weight"
,
"conv_in.weight"
),
(
"input_blocks.0.0.bias"
,
"conv_in.bias"
),
(
"out.0.weight"
,
"conv_norm_out.weight"
),
(
"out.0.bias"
,
"conv_norm_out.bias"
),
(
"out.2.weight"
,
"conv_out.weight"
),
(
"out.2.bias"
,
"conv_out.bias"
),
# the following are for sdxl
(
"label_emb.0.0.weight"
,
"add_embedding.linear_1.weight"
),
(
"label_emb.0.0.bias"
,
"add_embedding.linear_1.bias"
),
(
"label_emb.0.2.weight"
,
"add_embedding.linear_2.weight"
),
(
"label_emb.0.2.bias"
,
"add_embedding.linear_2.bias"
),
]
unet_conversion_map_resnet
=
[
# (stable-diffusion, HF Diffusers)
(
"in_layers.0"
,
"norm1"
),
(
"in_layers.2"
,
"conv1"
),
(
"out_layers.0"
,
"norm2"
),
(
"out_layers.3"
,
"conv2"
),
(
"emb_layers.1"
,
"time_emb_proj"
),
(
"skip_connection"
,
"conv_shortcut"
),
]
unet_conversion_map_layer
=
[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for
i
in
range
(
3
):
# loop over downblocks/upblocks
for
j
in
range
(
2
):
# loop over resnets/attentions for downblocks
hf_down_res_prefix
=
f"down_blocks.
{
i
}
.resnets.
{
j
}
."
sd_down_res_prefix
=
f"input_blocks.
{
3
*
i
+
j
+
1
}
.0."
unet_conversion_map_layer
.
append
((
sd_down_res_prefix
,
hf_down_res_prefix
))
if
i
>
0
:
hf_down_atn_prefix
=
f"down_blocks.
{
i
}
.attentions.
{
j
}
."
sd_down_atn_prefix
=
f"input_blocks.
{
3
*
i
+
j
+
1
}
.1."
unet_conversion_map_layer
.
append
((
sd_down_atn_prefix
,
hf_down_atn_prefix
))
for
j
in
range
(
4
):
# loop over resnets/attentions for upblocks
hf_up_res_prefix
=
f"up_blocks.
{
i
}
.resnets.
{
j
}
."
sd_up_res_prefix
=
f"output_blocks.
{
3
*
i
+
j
}
.0."
unet_conversion_map_layer
.
append
((
sd_up_res_prefix
,
hf_up_res_prefix
))
if
i
<
2
:
# no attention layers in up_blocks.0
hf_up_atn_prefix
=
f"up_blocks.
{
i
}
.attentions.
{
j
}
."
sd_up_atn_prefix
=
f"output_blocks.
{
3
*
i
+
j
}
.1."
unet_conversion_map_layer
.
append
((
sd_up_atn_prefix
,
hf_up_atn_prefix
))
if
i
<
3
:
# no downsample in down_blocks.3
hf_downsample_prefix
=
f"down_blocks.
{
i
}
.downsamplers.0.conv."
sd_downsample_prefix
=
f"input_blocks.
{
3
*
(
i
+
1
)
}
.0.op."
unet_conversion_map_layer
.
append
((
sd_downsample_prefix
,
hf_downsample_prefix
))
# no upsample in up_blocks.3
hf_upsample_prefix
=
f"up_blocks.
{
i
}
.upsamplers.0."
sd_upsample_prefix
=
f"output_blocks.
{
3
*
i
+
2
}
.
{
1
if
i
==
0
else
2
}
."
unet_conversion_map_layer
.
append
((
sd_upsample_prefix
,
hf_upsample_prefix
))
unet_conversion_map_layer
.
append
((
"output_blocks.2.2.conv."
,
"output_blocks.2.1.conv."
))
hf_mid_atn_prefix
=
"mid_block.attentions.0."
sd_mid_atn_prefix
=
"middle_block.1."
unet_conversion_map_layer
.
append
((
sd_mid_atn_prefix
,
hf_mid_atn_prefix
))
for
j
in
range
(
2
):
hf_mid_res_prefix
=
f"mid_block.resnets.
{
j
}
."
sd_mid_res_prefix
=
f"middle_block.
{
2
*
j
}
."
unet_conversion_map_layer
.
append
((
sd_mid_res_prefix
,
hf_mid_res_prefix
))
def
convert_unet_state_dict
(
unet_state_dict
):
# buyer beware: this is a *brittle* function,
# and correct output requires that all of these pieces interact in
# the exact order in which I have arranged them.
mapping
=
{
k
:
k
for
k
in
unet_state_dict
.
keys
()}
for
sd_name
,
hf_name
in
unet_conversion_map
:
mapping
[
hf_name
]
=
sd_name
for
k
,
v
in
mapping
.
items
():
if
"resnets"
in
k
:
for
sd_part
,
hf_part
in
unet_conversion_map_resnet
:
v
=
v
.
replace
(
hf_part
,
sd_part
)
mapping
[
k
]
=
v
for
k
,
v
in
mapping
.
items
():
for
sd_part
,
hf_part
in
unet_conversion_map_layer
:
v
=
v
.
replace
(
hf_part
,
sd_part
)
mapping
[
k
]
=
v
new_state_dict
=
{
sd_name
:
unet_state_dict
[
hf_name
]
for
hf_name
,
sd_name
in
mapping
.
items
()}
return
new_state_dict
# ================#
# VAE Conversion #
# ================#
vae_conversion_map
=
[
# (stable-diffusion, HF Diffusers)
(
"nin_shortcut"
,
"conv_shortcut"
),
(
"norm_out"
,
"conv_norm_out"
),
(
"mid.attn_1."
,
"mid_block.attentions.0."
),
]
for
i
in
range
(
4
):
# down_blocks have two resnets
for
j
in
range
(
2
):
hf_down_prefix
=
f"encoder.down_blocks.
{
i
}
.resnets.
{
j
}
."
sd_down_prefix
=
f"encoder.down.
{
i
}
.block.
{
j
}
."
vae_conversion_map
.
append
((
sd_down_prefix
,
hf_down_prefix
))
if
i
<
3
:
hf_downsample_prefix
=
f"down_blocks.
{
i
}
.downsamplers.0."
sd_downsample_prefix
=
f"down.
{
i
}
.downsample."
vae_conversion_map
.
append
((
sd_downsample_prefix
,
hf_downsample_prefix
))
hf_upsample_prefix
=
f"up_blocks.
{
i
}
.upsamplers.0."
sd_upsample_prefix
=
f"up.
{
3
-
i
}
.upsample."
vae_conversion_map
.
append
((
sd_upsample_prefix
,
hf_upsample_prefix
))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for
j
in
range
(
3
):
hf_up_prefix
=
f"decoder.up_blocks.
{
i
}
.resnets.
{
j
}
."
sd_up_prefix
=
f"decoder.up.
{
3
-
i
}
.block.
{
j
}
."
vae_conversion_map
.
append
((
sd_up_prefix
,
hf_up_prefix
))
# this part accounts for mid blocks in both the encoder and the decoder
for
i
in
range
(
2
):
hf_mid_res_prefix
=
f"mid_block.resnets.
{
i
}
."
sd_mid_res_prefix
=
f"mid.block_
{
i
+
1
}
."
vae_conversion_map
.
append
((
sd_mid_res_prefix
,
hf_mid_res_prefix
))
vae_conversion_map_attn
=
[
# (stable-diffusion, HF Diffusers)
(
"norm."
,
"group_norm."
),
# the following are for SDXL
(
"q."
,
"to_q."
),
(
"k."
,
"to_k."
),
(
"v."
,
"to_v."
),
(
"proj_out."
,
"to_out.0."
),
]
def
reshape_weight_for_sd
(
w
):
# convert HF linear weights to SD conv2d weights
if
not
w
.
ndim
==
1
:
return
w
.
reshape
(
*
w
.
shape
,
1
,
1
)
else
:
return
w
def
convert_vae_state_dict
(
vae_state_dict
):
mapping
=
{
k
:
k
for
k
in
vae_state_dict
.
keys
()}
for
k
,
v
in
mapping
.
items
():
for
sd_part
,
hf_part
in
vae_conversion_map
:
v
=
v
.
replace
(
hf_part
,
sd_part
)
mapping
[
k
]
=
v
for
k
,
v
in
mapping
.
items
():
if
"attentions"
in
k
:
for
sd_part
,
hf_part
in
vae_conversion_map_attn
:
v
=
v
.
replace
(
hf_part
,
sd_part
)
mapping
[
k
]
=
v
new_state_dict
=
{
v
:
vae_state_dict
[
k
]
for
k
,
v
in
mapping
.
items
()}
weights_to_convert
=
[
"q"
,
"k"
,
"v"
,
"proj_out"
]
for
k
,
v
in
new_state_dict
.
items
():
for
weight_name
in
weights_to_convert
:
if
f"mid.attn_1.
{
weight_name
}
.weight"
in
k
:
print
(
f"Reshaping
{
k
}
for SD format"
)
new_state_dict
[
k
]
=
reshape_weight_for_sd
(
v
)
return
new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
textenc_conversion_lst
=
[
# (stable-diffusion, HF Diffusers)
(
"transformer.resblocks."
,
"text_model.encoder.layers."
),
(
"ln_1"
,
"layer_norm1"
),
(
"ln_2"
,
"layer_norm2"
),
(
".c_fc."
,
".fc1."
),
(
".c_proj."
,
".fc2."
),
(
".attn"
,
".self_attn"
),
(
"ln_final."
,
"text_model.final_layer_norm."
),
(
"token_embedding.weight"
,
"text_model.embeddings.token_embedding.weight"
),
(
"positional_embedding"
,
"text_model.embeddings.position_embedding.weight"
),
]
protected
=
{
re
.
escape
(
x
[
1
]):
x
[
0
]
for
x
in
textenc_conversion_lst
}
textenc_pattern
=
re
.
compile
(
"|"
.
join
(
protected
.
keys
()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
code2idx
=
{
"q"
:
0
,
"k"
:
1
,
"v"
:
2
}
def
convert_openclip_text_enc_state_dict
(
text_enc_dict
):
new_state_dict
=
{}
capture_qkv_weight
=
{}
capture_qkv_bias
=
{}
for
k
,
v
in
text_enc_dict
.
items
():
if
(
k
.
endswith
(
".self_attn.q_proj.weight"
)
or
k
.
endswith
(
".self_attn.k_proj.weight"
)
or
k
.
endswith
(
".self_attn.v_proj.weight"
)
):
k_pre
=
k
[:
-
len
(
".q_proj.weight"
)]
k_code
=
k
[
-
len
(
"q_proj.weight"
)]
if
k_pre
not
in
capture_qkv_weight
:
capture_qkv_weight
[
k_pre
]
=
[
None
,
None
,
None
]
capture_qkv_weight
[
k_pre
][
code2idx
[
k_code
]]
=
v
continue
if
(
k
.
endswith
(
".self_attn.q_proj.bias"
)
or
k
.
endswith
(
".self_attn.k_proj.bias"
)
or
k
.
endswith
(
".self_attn.v_proj.bias"
)
):
k_pre
=
k
[:
-
len
(
".q_proj.bias"
)]
k_code
=
k
[
-
len
(
"q_proj.bias"
)]
if
k_pre
not
in
capture_qkv_bias
:
capture_qkv_bias
[
k_pre
]
=
[
None
,
None
,
None
]
capture_qkv_bias
[
k_pre
][
code2idx
[
k_code
]]
=
v
continue
relabelled_key
=
textenc_pattern
.
sub
(
lambda
m
:
protected
[
re
.
escape
(
m
.
group
(
0
))],
k
)
new_state_dict
[
relabelled_key
]
=
v
for
k_pre
,
tensors
in
capture_qkv_weight
.
items
():
if
None
in
tensors
:
raise
Exception
(
"CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"
)
relabelled_key
=
textenc_pattern
.
sub
(
lambda
m
:
protected
[
re
.
escape
(
m
.
group
(
0
))],
k_pre
)
new_state_dict
[
relabelled_key
+
".in_proj_weight"
]
=
torch
.
cat
(
tensors
)
for
k_pre
,
tensors
in
capture_qkv_bias
.
items
():
if
None
in
tensors
:
raise
Exception
(
"CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"
)
relabelled_key
=
textenc_pattern
.
sub
(
lambda
m
:
protected
[
re
.
escape
(
m
.
group
(
0
))],
k_pre
)
new_state_dict
[
relabelled_key
+
".in_proj_bias"
]
=
torch
.
cat
(
tensors
)
return
new_state_dict
def
convert_openai_text_enc_state_dict
(
text_enc_dict
):
return
text_enc_dict
if
__name__
==
"__main__"
:
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"--model_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the model to convert."
)
parser
.
add_argument
(
"--checkpoint_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the output model."
)
parser
.
add_argument
(
"--half"
,
action
=
"store_true"
,
help
=
"Save weights in half precision."
)
parser
.
add_argument
(
"--use_safetensors"
,
action
=
"store_true"
,
help
=
"Save weights use safetensors, default is ckpt."
)
args
=
parser
.
parse_args
()
assert
args
.
model_path
is
not
None
,
"Must provide a model path!"
assert
args
.
checkpoint_path
is
not
None
,
"Must provide a checkpoint path!"
# Path for safetensors
unet_path
=
osp
.
join
(
args
.
model_path
,
"unet"
,
"diffusion_pytorch_model.safetensors"
)
vae_path
=
osp
.
join
(
args
.
model_path
,
"vae"
,
"diffusion_pytorch_model.safetensors"
)
text_enc_path
=
osp
.
join
(
args
.
model_path
,
"text_encoder"
,
"model.safetensors"
)
text_enc_2_path
=
osp
.
join
(
args
.
model_path
,
"text_encoder_2"
,
"model.safetensors"
)
# Load models from safetensors if it exists, if it doesn't pytorch
if
osp
.
exists
(
unet_path
):
unet_state_dict
=
load_file
(
unet_path
,
device
=
"cpu"
)
else
:
unet_path
=
osp
.
join
(
args
.
model_path
,
"unet"
,
"diffusion_pytorch_model.bin"
)
unet_state_dict
=
torch
.
load
(
unet_path
,
map_location
=
"cpu"
)
if
osp
.
exists
(
vae_path
):
vae_state_dict
=
load_file
(
vae_path
,
device
=
"cpu"
)
else
:
vae_path
=
osp
.
join
(
args
.
model_path
,
"vae"
,
"diffusion_pytorch_model.bin"
)
vae_state_dict
=
torch
.
load
(
vae_path
,
map_location
=
"cpu"
)
if
osp
.
exists
(
text_enc_path
):
text_enc_dict
=
load_file
(
text_enc_path
,
device
=
"cpu"
)
else
:
text_enc_path
=
osp
.
join
(
args
.
model_path
,
"text_encoder"
,
"pytorch_model.bin"
)
text_enc_dict
=
torch
.
load
(
text_enc_path
,
map_location
=
"cpu"
)
if
osp
.
exists
(
text_enc_2_path
):
text_enc_2_dict
=
load_file
(
text_enc_2_path
,
device
=
"cpu"
)
else
:
text_enc_2_path
=
osp
.
join
(
args
.
model_path
,
"text_encoder_2"
,
"pytorch_model.bin"
)
text_enc_2_dict
=
torch
.
load
(
text_enc_2_path
,
map_location
=
"cpu"
)
# Convert the UNet model
unet_state_dict
=
convert_unet_state_dict
(
unet_state_dict
)
unet_state_dict
=
{
"model.diffusion_model."
+
k
:
v
for
k
,
v
in
unet_state_dict
.
items
()}
# Convert the VAE model
vae_state_dict
=
convert_vae_state_dict
(
vae_state_dict
)
vae_state_dict
=
{
"first_stage_model."
+
k
:
v
for
k
,
v
in
vae_state_dict
.
items
()}
# Convert text encoder 1
text_enc_dict
=
convert_openai_text_enc_state_dict
(
text_enc_dict
)
text_enc_dict
=
{
"conditioner.embedders.0.transformer."
+
k
:
v
for
k
,
v
in
text_enc_dict
.
items
()}
# Convert text encoder 2
text_enc_2_dict
=
convert_openclip_text_enc_state_dict
(
text_enc_2_dict
)
text_enc_2_dict
=
{
"conditioner.embedders.1.model."
+
k
:
v
for
k
,
v
in
text_enc_2_dict
.
items
()}
# We call the `.T.contiguous()` to match what's done in
# https://github.com/huggingface/diffusers/blob/84905ca7287876b925b6bf8e9bb92fec21c78764/src/diffusers/loaders/single_file_utils.py#L1085
text_enc_2_dict
[
"conditioner.embedders.1.model.text_projection"
]
=
text_enc_2_dict
.
pop
(
"conditioner.embedders.1.model.text_projection.weight"
).
T
.
contiguous
()
# Put together new checkpoint
state_dict
=
{
**
unet_state_dict
,
**
vae_state_dict
,
**
text_enc_dict
,
**
text_enc_2_dict
}
if
args
.
half
:
state_dict
=
{
k
:
v
.
half
()
for
k
,
v
in
state_dict
.
items
()}
if
args
.
use_safetensors
:
save_file
(
state_dict
,
args
.
checkpoint_path
)
else
:
state_dict
=
{
"state_dict"
:
state_dict
}
torch
.
save
(
state_dict
,
args
.
checkpoint_path
)
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