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#!/usr/bin/env python
from
__future__
import
annotations
import
argparse
from
contextlib
import
nullcontext
import
torch
from
accelerate
import
init_empty_weights
from
diffusers
import
(
SanaControlNetModel
,
)
from
diffusers
.
models
.
model_loading_utils
import
load_model_dict_into_meta
from
diffusers
.
utils
.
import_utils
import
is_accelerate_available
CTX
=
init_empty_weights
if
is_accelerate_available
else
nullcontext
def
main
(
args
):
file_path
=
args
.
orig_ckpt_path
all_state_dict
=
torch
.
load
(
file_path
,
weights_only
=
True
)
state_dict
=
all_state_dict
.
pop
(
"state_dict"
)
converted_state_dict
=
{}
# Patch embeddings.
converted_state_dict
[
"patch_embed.proj.weight"
]
=
state_dict
.
pop
(
"x_embedder.proj.weight"
)
converted_state_dict
[
"patch_embed.proj.bias"
]
=
state_dict
.
pop
(
"x_embedder.proj.bias"
)
# Caption projection.
converted_state_dict
[
"caption_projection.linear_1.weight"
]
=
state_dict
.
pop
(
"y_embedder.y_proj.fc1.weight"
)
converted_state_dict
[
"caption_projection.linear_1.bias"
]
=
state_dict
.
pop
(
"y_embedder.y_proj.fc1.bias"
)
converted_state_dict
[
"caption_projection.linear_2.weight"
]
=
state_dict
.
pop
(
"y_embedder.y_proj.fc2.weight"
)
converted_state_dict
[
"caption_projection.linear_2.bias"
]
=
state_dict
.
pop
(
"y_embedder.y_proj.fc2.bias"
)
# AdaLN-single LN
converted_state_dict
[
"time_embed.emb.timestep_embedder.linear_1.weight"
]
=
state_dict
.
pop
(
"t_embedder.mlp.0.weight"
)
converted_state_dict
[
"time_embed.emb.timestep_embedder.linear_1.bias"
]
=
state_dict
.
pop
(
"t_embedder.mlp.0.bias"
)
converted_state_dict
[
"time_embed.emb.timestep_embedder.linear_2.weight"
]
=
state_dict
.
pop
(
"t_embedder.mlp.2.weight"
)
converted_state_dict
[
"time_embed.emb.timestep_embedder.linear_2.bias"
]
=
state_dict
.
pop
(
"t_embedder.mlp.2.bias"
)
# Shared norm.
converted_state_dict
[
"time_embed.linear.weight"
]
=
state_dict
.
pop
(
"t_block.1.weight"
)
converted_state_dict
[
"time_embed.linear.bias"
]
=
state_dict
.
pop
(
"t_block.1.bias"
)
# y norm
converted_state_dict
[
"caption_norm.weight"
]
=
state_dict
.
pop
(
"attention_y_norm.weight"
)
# Positional embedding interpolation scale.
interpolation_scale
=
{
512
:
None
,
1024
:
None
,
2048
:
1.0
,
4096
:
2.0
}
# ControlNet Input Projection.
converted_state_dict
[
"input_block.weight"
]
=
state_dict
.
pop
(
"controlnet.0.before_proj.weight"
)
converted_state_dict
[
"input_block.bias"
]
=
state_dict
.
pop
(
"controlnet.0.before_proj.bias"
)
for
depth
in
range
(
7
):
# Transformer blocks.
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.scale_shift_table"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.scale_shift_table"
)
# Linear Attention is all you need 🤘
# Self attention.
q
,
k
,
v
=
torch
.
chunk
(
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.attn.qkv.weight"
),
3
,
dim
=
0
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_q.weight"
]
=
q
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_k.weight"
]
=
k
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_v.weight"
]
=
v
# Projection.
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_out.0.weight"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.attn.proj.weight"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_out.0.bias"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.attn.proj.bias"
)
# Feed-forward.
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.ff.conv_inverted.weight"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.mlp.inverted_conv.conv.weight"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.ff.conv_inverted.bias"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.mlp.inverted_conv.conv.bias"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.ff.conv_depth.weight"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.mlp.depth_conv.conv.weight"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.ff.conv_depth.bias"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.mlp.depth_conv.conv.bias"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.ff.conv_point.weight"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.mlp.point_conv.conv.weight"
)
# Cross-attention.
q
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.cross_attn.q_linear.weight"
)
q_bias
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.cross_attn.q_linear.bias"
)
k
,
v
=
torch
.
chunk
(
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.cross_attn.kv_linear.weight"
),
2
,
dim
=
0
)
k_bias
,
v_bias
=
torch
.
chunk
(
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.cross_attn.kv_linear.bias"
),
2
,
dim
=
0
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn2.to_q.weight"
]
=
q
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn2.to_q.bias"
]
=
q_bias
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn2.to_k.weight"
]
=
k
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn2.to_k.bias"
]
=
k_bias
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn2.to_v.weight"
]
=
v
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn2.to_v.bias"
]
=
v_bias
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn2.to_out.0.weight"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.cross_attn.proj.weight"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn2.to_out.0.bias"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.copied_block.cross_attn.proj.bias"
)
# ControlNet After Projection
converted_state_dict
[
f"controlnet_blocks.
{
depth
}
.weight"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.after_proj.weight"
)
converted_state_dict
[
f"controlnet_blocks.
{
depth
}
.bias"
]
=
state_dict
.
pop
(
f"controlnet.
{
depth
}
.after_proj.bias"
)
# ControlNet
with
CTX
():
controlnet
=
SanaControlNetModel
(
num_attention_heads
=
model_kwargs
[
args
.
model_type
][
"num_attention_heads"
],
attention_head_dim
=
model_kwargs
[
args
.
model_type
][
"attention_head_dim"
],
num_layers
=
model_kwargs
[
args
.
model_type
][
"num_layers"
],
num_cross_attention_heads
=
model_kwargs
[
args
.
model_type
][
"num_cross_attention_heads"
],
cross_attention_head_dim
=
model_kwargs
[
args
.
model_type
][
"cross_attention_head_dim"
],
cross_attention_dim
=
model_kwargs
[
args
.
model_type
][
"cross_attention_dim"
],
caption_channels
=
2304
,
sample_size
=
args
.
image_size
//
32
,
interpolation_scale
=
interpolation_scale
[
args
.
image_size
],
)
if
is_accelerate_available
():
load_model_dict_into_meta
(
controlnet
,
converted_state_dict
)
else
:
controlnet
.
load_state_dict
(
converted_state_dict
,
strict
=
True
,
assign
=
True
)
num_model_params
=
sum
(
p
.
numel
()
for
p
in
controlnet
.
parameters
())
print
(
f"Total number of controlnet parameters:
{
num_model_params
}
"
)
controlnet
=
controlnet
.
to
(
weight_dtype
)
controlnet
.
load_state_dict
(
converted_state_dict
,
strict
=
True
)
print
(
f"Saving Sana ControlNet in Diffusers format in
{
args
.
dump_path
}
."
)
controlnet
.
save_pretrained
(
args
.
dump_path
)
DTYPE_MAPPING
=
{
"fp32"
:
torch
.
float32
,
"fp16"
:
torch
.
float16
,
"bf16"
:
torch
.
bfloat16
,
}
VARIANT_MAPPING
=
{
"fp32"
:
None
,
"fp16"
:
"fp16"
,
"bf16"
:
"bf16"
,
}
if
__name__
==
"__main__"
:
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"--orig_ckpt_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the checkpoint to convert."
)
parser
.
add_argument
(
"--image_size"
,
default
=
1024
,
type
=
int
,
choices
=
[
512
,
1024
,
2048
,
4096
],
required
=
False
,
help
=
"Image size of pretrained model, 512, 1024, 2048 or 4096."
,
)
parser
.
add_argument
(
"--model_type"
,
default
=
"SanaMS_1600M_P1_ControlNet_D7"
,
type
=
str
,
choices
=
[
"SanaMS_1600M_P1_ControlNet_D7"
,
"SanaMS_600M_P1_ControlNet_D7"
],
)
parser
.
add_argument
(
"--dump_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the output pipeline."
)
parser
.
add_argument
(
"--dtype"
,
default
=
"fp16"
,
type
=
str
,
choices
=
[
"fp32"
,
"fp16"
,
"bf16"
],
help
=
"Weight dtype."
)
args
=
parser
.
parse_args
()
model_kwargs
=
{
"SanaMS_1600M_P1_ControlNet_D7"
: {
"num_attention_heads"
:
70
,
"attention_head_dim"
:
32
,
"num_cross_attention_heads"
:
20
,
"cross_attention_head_dim"
:
112
,
"cross_attention_dim"
:
2240
,
"num_layers"
:
7
,
},
"SanaMS_600M_P1_ControlNet_D7"
: {
"num_attention_heads"
:
36
,
"attention_head_dim"
:
32
,
"num_cross_attention_heads"
:
16
,
"cross_attention_head_dim"
:
72
,
"cross_attention_dim"
:
1152
,
"num_layers"
:
7
,
},
}
device
=
"cuda"
if
torch
.
cuda
.
is_available
()
else
"cpu"
weight_dtype
=
DTYPE_MAPPING
[
args
.
dtype
]
variant
=
VARIANT_MAPPING
[
args
.
dtype
]
main
(
args
)
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