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diffusers/scripts/convert_hunyuandit_controlnet_to_diffusers.py at main · rasmi/diffusers · GitHub
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import
argparse
import
torch
from
diffusers
import
HunyuanDiT2DControlNetModel
def
main
(
args
):
state_dict
=
torch
.
load
(
args
.
pt_checkpoint_path
,
map_location
=
"cpu"
)
if
args
.
load_key
!=
"none"
:
try
:
state_dict
=
state_dict
[
args
.
load_key
]
except
KeyError
:
raise
KeyError
(
f"
{
args
.
load_key
}
not found in the checkpoint."
"Please load from the following keys:{state_dict.keys()}"
)
device
=
"cuda"
model_config
=
HunyuanDiT2DControlNetModel
.
load_config
(
"Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers"
,
subfolder
=
"transformer"
)
model_config
[
"use_style_cond_and_image_meta_size"
]
=
(
args
.
use_style_cond_and_image_meta_size
)
### version <= v1.1: True; version >= v1.2: False
print
(
model_config
)
for
key
in
state_dict
:
print
(
"local:"
,
key
)
model
=
HunyuanDiT2DControlNetModel
.
from_config
(
model_config
).
to
(
device
)
for
key
in
model
.
state_dict
():
print
(
"diffusers:"
,
key
)
num_layers
=
19
for
i
in
range
(
num_layers
):
# attn1
# Wkqv -> to_q, to_k, to_v
q
,
k
,
v
=
torch
.
chunk
(
state_dict
[
f"blocks.
{
i
}
.attn1.Wqkv.weight"
],
3
,
dim
=
0
)
q_bias
,
k_bias
,
v_bias
=
torch
.
chunk
(
state_dict
[
f"blocks.
{
i
}
.attn1.Wqkv.bias"
],
3
,
dim
=
0
)
state_dict
[
f"blocks.
{
i
}
.attn1.to_q.weight"
]
=
q
state_dict
[
f"blocks.
{
i
}
.attn1.to_q.bias"
]
=
q_bias
state_dict
[
f"blocks.
{
i
}
.attn1.to_k.weight"
]
=
k
state_dict
[
f"blocks.
{
i
}
.attn1.to_k.bias"
]
=
k_bias
state_dict
[
f"blocks.
{
i
}
.attn1.to_v.weight"
]
=
v
state_dict
[
f"blocks.
{
i
}
.attn1.to_v.bias"
]
=
v_bias
state_dict
.
pop
(
f"blocks.
{
i
}
.attn1.Wqkv.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn1.Wqkv.bias"
)
# q_norm, k_norm -> norm_q, norm_k
state_dict
[
f"blocks.
{
i
}
.attn1.norm_q.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.attn1.q_norm.weight"
]
state_dict
[
f"blocks.
{
i
}
.attn1.norm_q.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.attn1.q_norm.bias"
]
state_dict
[
f"blocks.
{
i
}
.attn1.norm_k.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.attn1.k_norm.weight"
]
state_dict
[
f"blocks.
{
i
}
.attn1.norm_k.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.attn1.k_norm.bias"
]
state_dict
.
pop
(
f"blocks.
{
i
}
.attn1.q_norm.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn1.q_norm.bias"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn1.k_norm.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn1.k_norm.bias"
)
# out_proj -> to_out
state_dict
[
f"blocks.
{
i
}
.attn1.to_out.0.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.attn1.out_proj.weight"
]
state_dict
[
f"blocks.
{
i
}
.attn1.to_out.0.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.attn1.out_proj.bias"
]
state_dict
.
pop
(
f"blocks.
{
i
}
.attn1.out_proj.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn1.out_proj.bias"
)
# attn2
# kq_proj -> to_k, to_v
k
,
v
=
torch
.
chunk
(
state_dict
[
f"blocks.
{
i
}
.attn2.kv_proj.weight"
],
2
,
dim
=
0
)
k_bias
,
v_bias
=
torch
.
chunk
(
state_dict
[
f"blocks.
{
i
}
.attn2.kv_proj.bias"
],
2
,
dim
=
0
)
state_dict
[
f"blocks.
{
i
}
.attn2.to_k.weight"
]
=
k
state_dict
[
f"blocks.
{
i
}
.attn2.to_k.bias"
]
=
k_bias
state_dict
[
f"blocks.
{
i
}
.attn2.to_v.weight"
]
=
v
state_dict
[
f"blocks.
{
i
}
.attn2.to_v.bias"
]
=
v_bias
state_dict
.
pop
(
f"blocks.
{
i
}
.attn2.kv_proj.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn2.kv_proj.bias"
)
# q_proj -> to_q
state_dict
[
f"blocks.
{
i
}
.attn2.to_q.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.attn2.q_proj.weight"
]
state_dict
[
f"blocks.
{
i
}
.attn2.to_q.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.attn2.q_proj.bias"
]
state_dict
.
pop
(
f"blocks.
{
i
}
.attn2.q_proj.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn2.q_proj.bias"
)
# q_norm, k_norm -> norm_q, norm_k
state_dict
[
f"blocks.
{
i
}
.attn2.norm_q.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.attn2.q_norm.weight"
]
state_dict
[
f"blocks.
{
i
}
.attn2.norm_q.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.attn2.q_norm.bias"
]
state_dict
[
f"blocks.
{
i
}
.attn2.norm_k.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.attn2.k_norm.weight"
]
state_dict
[
f"blocks.
{
i
}
.attn2.norm_k.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.attn2.k_norm.bias"
]
state_dict
.
pop
(
f"blocks.
{
i
}
.attn2.q_norm.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn2.q_norm.bias"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn2.k_norm.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn2.k_norm.bias"
)
# out_proj -> to_out
state_dict
[
f"blocks.
{
i
}
.attn2.to_out.0.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.attn2.out_proj.weight"
]
state_dict
[
f"blocks.
{
i
}
.attn2.to_out.0.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.attn2.out_proj.bias"
]
state_dict
.
pop
(
f"blocks.
{
i
}
.attn2.out_proj.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.attn2.out_proj.bias"
)
# switch norm 2 and norm 3
norm2_weight
=
state_dict
[
f"blocks.
{
i
}
.norm2.weight"
]
norm2_bias
=
state_dict
[
f"blocks.
{
i
}
.norm2.bias"
]
state_dict
[
f"blocks.
{
i
}
.norm2.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.norm3.weight"
]
state_dict
[
f"blocks.
{
i
}
.norm2.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.norm3.bias"
]
state_dict
[
f"blocks.
{
i
}
.norm3.weight"
]
=
norm2_weight
state_dict
[
f"blocks.
{
i
}
.norm3.bias"
]
=
norm2_bias
# norm1 -> norm1.norm
# default_modulation.1 -> norm1.linear
state_dict
[
f"blocks.
{
i
}
.norm1.norm.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.norm1.weight"
]
state_dict
[
f"blocks.
{
i
}
.norm1.norm.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.norm1.bias"
]
state_dict
[
f"blocks.
{
i
}
.norm1.linear.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.default_modulation.1.weight"
]
state_dict
[
f"blocks.
{
i
}
.norm1.linear.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.default_modulation.1.bias"
]
state_dict
.
pop
(
f"blocks.
{
i
}
.norm1.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.norm1.bias"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.default_modulation.1.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.default_modulation.1.bias"
)
# mlp.fc1 -> ff.net.0, mlp.fc2 -> ff.net.2
state_dict
[
f"blocks.
{
i
}
.ff.net.0.proj.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.mlp.fc1.weight"
]
state_dict
[
f"blocks.
{
i
}
.ff.net.0.proj.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.mlp.fc1.bias"
]
state_dict
[
f"blocks.
{
i
}
.ff.net.2.weight"
]
=
state_dict
[
f"blocks.
{
i
}
.mlp.fc2.weight"
]
state_dict
[
f"blocks.
{
i
}
.ff.net.2.bias"
]
=
state_dict
[
f"blocks.
{
i
}
.mlp.fc2.bias"
]
state_dict
.
pop
(
f"blocks.
{
i
}
.mlp.fc1.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.mlp.fc1.bias"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.mlp.fc2.weight"
)
state_dict
.
pop
(
f"blocks.
{
i
}
.mlp.fc2.bias"
)
# after_proj_list -> controlnet_blocks
state_dict
[
f"controlnet_blocks.
{
i
}
.weight"
]
=
state_dict
[
f"after_proj_list.
{
i
}
.weight"
]
state_dict
[
f"controlnet_blocks.
{
i
}
.bias"
]
=
state_dict
[
f"after_proj_list.
{
i
}
.bias"
]
state_dict
.
pop
(
f"after_proj_list.
{
i
}
.weight"
)
state_dict
.
pop
(
f"after_proj_list.
{
i
}
.bias"
)
# before_proj -> input_block
state_dict
[
"input_block.weight"
]
=
state_dict
[
"before_proj.weight"
]
state_dict
[
"input_block.bias"
]
=
state_dict
[
"before_proj.bias"
]
state_dict
.
pop
(
"before_proj.weight"
)
state_dict
.
pop
(
"before_proj.bias"
)
# pooler -> time_extra_emb
state_dict
[
"time_extra_emb.pooler.positional_embedding"
]
=
state_dict
[
"pooler.positional_embedding"
]
state_dict
[
"time_extra_emb.pooler.k_proj.weight"
]
=
state_dict
[
"pooler.k_proj.weight"
]
state_dict
[
"time_extra_emb.pooler.k_proj.bias"
]
=
state_dict
[
"pooler.k_proj.bias"
]
state_dict
[
"time_extra_emb.pooler.q_proj.weight"
]
=
state_dict
[
"pooler.q_proj.weight"
]
state_dict
[
"time_extra_emb.pooler.q_proj.bias"
]
=
state_dict
[
"pooler.q_proj.bias"
]
state_dict
[
"time_extra_emb.pooler.v_proj.weight"
]
=
state_dict
[
"pooler.v_proj.weight"
]
state_dict
[
"time_extra_emb.pooler.v_proj.bias"
]
=
state_dict
[
"pooler.v_proj.bias"
]
state_dict
[
"time_extra_emb.pooler.c_proj.weight"
]
=
state_dict
[
"pooler.c_proj.weight"
]
state_dict
[
"time_extra_emb.pooler.c_proj.bias"
]
=
state_dict
[
"pooler.c_proj.bias"
]
state_dict
.
pop
(
"pooler.k_proj.weight"
)
state_dict
.
pop
(
"pooler.k_proj.bias"
)
state_dict
.
pop
(
"pooler.q_proj.weight"
)
state_dict
.
pop
(
"pooler.q_proj.bias"
)
state_dict
.
pop
(
"pooler.v_proj.weight"
)
state_dict
.
pop
(
"pooler.v_proj.bias"
)
state_dict
.
pop
(
"pooler.c_proj.weight"
)
state_dict
.
pop
(
"pooler.c_proj.bias"
)
state_dict
.
pop
(
"pooler.positional_embedding"
)
# t_embedder -> time_embedding (`TimestepEmbedding`)
state_dict
[
"time_extra_emb.timestep_embedder.linear_1.bias"
]
=
state_dict
[
"t_embedder.mlp.0.bias"
]
state_dict
[
"time_extra_emb.timestep_embedder.linear_1.weight"
]
=
state_dict
[
"t_embedder.mlp.0.weight"
]
state_dict
[
"time_extra_emb.timestep_embedder.linear_2.bias"
]
=
state_dict
[
"t_embedder.mlp.2.bias"
]
state_dict
[
"time_extra_emb.timestep_embedder.linear_2.weight"
]
=
state_dict
[
"t_embedder.mlp.2.weight"
]
state_dict
.
pop
(
"t_embedder.mlp.0.bias"
)
state_dict
.
pop
(
"t_embedder.mlp.0.weight"
)
state_dict
.
pop
(
"t_embedder.mlp.2.bias"
)
state_dict
.
pop
(
"t_embedder.mlp.2.weight"
)
# x_embedder -> pos_embd (`PatchEmbed`)
state_dict
[
"pos_embed.proj.weight"
]
=
state_dict
[
"x_embedder.proj.weight"
]
state_dict
[
"pos_embed.proj.bias"
]
=
state_dict
[
"x_embedder.proj.bias"
]
state_dict
.
pop
(
"x_embedder.proj.weight"
)
state_dict
.
pop
(
"x_embedder.proj.bias"
)
# mlp_t5 -> text_embedder
state_dict
[
"text_embedder.linear_1.bias"
]
=
state_dict
[
"mlp_t5.0.bias"
]
state_dict
[
"text_embedder.linear_1.weight"
]
=
state_dict
[
"mlp_t5.0.weight"
]
state_dict
[
"text_embedder.linear_2.bias"
]
=
state_dict
[
"mlp_t5.2.bias"
]
state_dict
[
"text_embedder.linear_2.weight"
]
=
state_dict
[
"mlp_t5.2.weight"
]
state_dict
.
pop
(
"mlp_t5.0.bias"
)
state_dict
.
pop
(
"mlp_t5.0.weight"
)
state_dict
.
pop
(
"mlp_t5.2.bias"
)
state_dict
.
pop
(
"mlp_t5.2.weight"
)
# extra_embedder -> extra_embedder
state_dict
[
"time_extra_emb.extra_embedder.linear_1.bias"
]
=
state_dict
[
"extra_embedder.0.bias"
]
state_dict
[
"time_extra_emb.extra_embedder.linear_1.weight"
]
=
state_dict
[
"extra_embedder.0.weight"
]
state_dict
[
"time_extra_emb.extra_embedder.linear_2.bias"
]
=
state_dict
[
"extra_embedder.2.bias"
]
state_dict
[
"time_extra_emb.extra_embedder.linear_2.weight"
]
=
state_dict
[
"extra_embedder.2.weight"
]
state_dict
.
pop
(
"extra_embedder.0.bias"
)
state_dict
.
pop
(
"extra_embedder.0.weight"
)
state_dict
.
pop
(
"extra_embedder.2.bias"
)
state_dict
.
pop
(
"extra_embedder.2.weight"
)
# style_embedder
if
model_config
[
"use_style_cond_and_image_meta_size"
]:
print
(
state_dict
[
"style_embedder.weight"
])
print
(
state_dict
[
"style_embedder.weight"
].
shape
)
state_dict
[
"time_extra_emb.style_embedder.weight"
]
=
state_dict
[
"style_embedder.weight"
][
0
:
1
]
state_dict
.
pop
(
"style_embedder.weight"
)
model
.
load_state_dict
(
state_dict
)
if
args
.
save
:
model
.
save_pretrained
(
args
.
output_checkpoint_path
)
if
__name__
==
"__main__"
:
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"--save"
,
default
=
True
,
type
=
bool
,
required
=
False
,
help
=
"Whether to save the converted pipeline or not."
)
parser
.
add_argument
(
"--pt_checkpoint_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the .pt pretrained model."
)
parser
.
add_argument
(
"--output_checkpoint_path"
,
default
=
None
,
type
=
str
,
required
=
False
,
help
=
"Path to the output converted diffusers pipeline."
,
)
parser
.
add_argument
(
"--load_key"
,
default
=
"none"
,
type
=
str
,
required
=
False
,
help
=
"The key to load from the pretrained .pt file"
)
parser
.
add_argument
(
"--use_style_cond_and_image_meta_size"
,
type
=
bool
,
default
=
False
,
help
=
"version <= v1.1: True; version >= v1.2: False"
,
)
args
=
parser
.
parse_args
()
main
(
args
)
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