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diffusers/scripts/convert_pixart_alpha_to_diffusers.py at main · idimetrix/diffusers · GitHub
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convert_pixart_alpha_to_diffusers.py
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import
argparse
import
os
import
torch
from
transformers
import
T5EncoderModel
,
T5Tokenizer
from
diffusers
import
AutoencoderKL
,
DPMSolverMultistepScheduler
,
PixArtAlphaPipeline
,
Transformer2DModel
ckpt_id
=
"PixArt-alpha/PixArt-alpha"
# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/scripts/inference.py#L125
interpolation_scale
=
{
256
:
0.5
,
512
:
1
,
1024
:
2
}
def
main
(
args
):
all_state_dict
=
torch
.
load
(
args
.
orig_ckpt_path
,
map_location
=
"cpu"
)
state_dict
=
all_state_dict
.
pop
(
"state_dict"
)
converted_state_dict
=
{}
# Patch embeddings.
converted_state_dict
[
"pos_embed.proj.weight"
]
=
state_dict
.
pop
(
"x_embedder.proj.weight"
)
converted_state_dict
[
"pos_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
[
"adaln_single.emb.timestep_embedder.linear_1.weight"
]
=
state_dict
.
pop
(
"t_embedder.mlp.0.weight"
)
converted_state_dict
[
"adaln_single.emb.timestep_embedder.linear_1.bias"
]
=
state_dict
.
pop
(
"t_embedder.mlp.0.bias"
)
converted_state_dict
[
"adaln_single.emb.timestep_embedder.linear_2.weight"
]
=
state_dict
.
pop
(
"t_embedder.mlp.2.weight"
)
converted_state_dict
[
"adaln_single.emb.timestep_embedder.linear_2.bias"
]
=
state_dict
.
pop
(
"t_embedder.mlp.2.bias"
)
if
args
.
image_size
==
1024
:
# Resolution.
converted_state_dict
[
"adaln_single.emb.resolution_embedder.linear_1.weight"
]
=
state_dict
.
pop
(
"csize_embedder.mlp.0.weight"
)
converted_state_dict
[
"adaln_single.emb.resolution_embedder.linear_1.bias"
]
=
state_dict
.
pop
(
"csize_embedder.mlp.0.bias"
)
converted_state_dict
[
"adaln_single.emb.resolution_embedder.linear_2.weight"
]
=
state_dict
.
pop
(
"csize_embedder.mlp.2.weight"
)
converted_state_dict
[
"adaln_single.emb.resolution_embedder.linear_2.bias"
]
=
state_dict
.
pop
(
"csize_embedder.mlp.2.bias"
)
# Aspect ratio.
converted_state_dict
[
"adaln_single.emb.aspect_ratio_embedder.linear_1.weight"
]
=
state_dict
.
pop
(
"ar_embedder.mlp.0.weight"
)
converted_state_dict
[
"adaln_single.emb.aspect_ratio_embedder.linear_1.bias"
]
=
state_dict
.
pop
(
"ar_embedder.mlp.0.bias"
)
converted_state_dict
[
"adaln_single.emb.aspect_ratio_embedder.linear_2.weight"
]
=
state_dict
.
pop
(
"ar_embedder.mlp.2.weight"
)
converted_state_dict
[
"adaln_single.emb.aspect_ratio_embedder.linear_2.bias"
]
=
state_dict
.
pop
(
"ar_embedder.mlp.2.bias"
)
# Shared norm.
converted_state_dict
[
"adaln_single.linear.weight"
]
=
state_dict
.
pop
(
"t_block.1.weight"
)
converted_state_dict
[
"adaln_single.linear.bias"
]
=
state_dict
.
pop
(
"t_block.1.bias"
)
for
depth
in
range
(
28
):
# Transformer blocks.
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.scale_shift_table"
]
=
state_dict
.
pop
(
f"blocks.
{
depth
}
.scale_shift_table"
)
# Attention is all you need 🤘
# Self attention.
q
,
k
,
v
=
torch
.
chunk
(
state_dict
.
pop
(
f"blocks.
{
depth
}
.attn.qkv.weight"
),
3
,
dim
=
0
)
q_bias
,
k_bias
,
v_bias
=
torch
.
chunk
(
state_dict
.
pop
(
f"blocks.
{
depth
}
.attn.qkv.bias"
),
3
,
dim
=
0
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_q.weight"
]
=
q
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_q.bias"
]
=
q_bias
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_k.weight"
]
=
k
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_k.bias"
]
=
k_bias
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_v.weight"
]
=
v
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_v.bias"
]
=
v_bias
# Projection.
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_out.0.weight"
]
=
state_dict
.
pop
(
f"blocks.
{
depth
}
.attn.proj.weight"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_out.0.bias"
]
=
state_dict
.
pop
(
f"blocks.
{
depth
}
.attn.proj.bias"
)
# Feed-forward.
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.ff.net.0.proj.weight"
]
=
state_dict
.
pop
(
f"blocks.
{
depth
}
.mlp.fc1.weight"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.ff.net.0.proj.bias"
]
=
state_dict
.
pop
(
f"blocks.
{
depth
}
.mlp.fc1.bias"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.ff.net.2.weight"
]
=
state_dict
.
pop
(
f"blocks.
{
depth
}
.mlp.fc2.weight"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.ff.net.2.bias"
]
=
state_dict
.
pop
(
f"blocks.
{
depth
}
.mlp.fc2.bias"
)
# Cross-attention.
q
=
state_dict
.
pop
(
f"blocks.
{
depth
}
.cross_attn.q_linear.weight"
)
q_bias
=
state_dict
.
pop
(
f"blocks.
{
depth
}
.cross_attn.q_linear.bias"
)
k
,
v
=
torch
.
chunk
(
state_dict
.
pop
(
f"blocks.
{
depth
}
.cross_attn.kv_linear.weight"
),
2
,
dim
=
0
)
k_bias
,
v_bias
=
torch
.
chunk
(
state_dict
.
pop
(
f"blocks.
{
depth
}
.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"blocks.
{
depth
}
.cross_attn.proj.weight"
)
converted_state_dict
[
f"transformer_blocks.
{
depth
}
.attn2.to_out.0.bias"
]
=
state_dict
.
pop
(
f"blocks.
{
depth
}
.cross_attn.proj.bias"
)
# Final block.
converted_state_dict
[
"proj_out.weight"
]
=
state_dict
.
pop
(
"final_layer.linear.weight"
)
converted_state_dict
[
"proj_out.bias"
]
=
state_dict
.
pop
(
"final_layer.linear.bias"
)
converted_state_dict
[
"scale_shift_table"
]
=
state_dict
.
pop
(
"final_layer.scale_shift_table"
)
# DiT XL/2
transformer
=
Transformer2DModel
(
sample_size
=
args
.
image_size
//
8
,
num_layers
=
28
,
attention_head_dim
=
72
,
in_channels
=
4
,
out_channels
=
8
,
patch_size
=
2
,
attention_bias
=
True
,
num_attention_heads
=
16
,
cross_attention_dim
=
1152
,
activation_fn
=
"gelu-approximate"
,
num_embeds_ada_norm
=
1000
,
norm_type
=
"ada_norm_single"
,
norm_elementwise_affine
=
False
,
norm_eps
=
1e-6
,
caption_channels
=
4096
,
)
transformer
.
load_state_dict
(
converted_state_dict
,
strict
=
True
)
assert
transformer
.
pos_embed
.
pos_embed
is
not
None
state_dict
.
pop
(
"pos_embed"
)
state_dict
.
pop
(
"y_embedder.y_embedding"
)
assert
len
(
state_dict
)
==
0
,
f"State dict is not empty,
{
state_dict
.
keys
()
}
"
num_model_params
=
sum
(
p
.
numel
()
for
p
in
transformer
.
parameters
())
print
(
f"Total number of transformer parameters:
{
num_model_params
}
"
)
if
args
.
only_transformer
:
transformer
.
save_pretrained
(
os
.
path
.
join
(
args
.
dump_path
,
"transformer"
))
else
:
scheduler
=
DPMSolverMultistepScheduler
()
vae
=
AutoencoderKL
.
from_pretrained
(
ckpt_id
,
subfolder
=
"sd-vae-ft-ema"
)
tokenizer
=
T5Tokenizer
.
from_pretrained
(
ckpt_id
,
subfolder
=
"t5-v1_1-xxl"
)
text_encoder
=
T5EncoderModel
.
from_pretrained
(
ckpt_id
,
subfolder
=
"t5-v1_1-xxl"
)
pipeline
=
PixArtAlphaPipeline
(
tokenizer
=
tokenizer
,
text_encoder
=
text_encoder
,
transformer
=
transformer
,
vae
=
vae
,
scheduler
=
scheduler
)
pipeline
.
save_pretrained
(
args
.
dump_path
)
if
__name__
==
"__main__"
:
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"--orig_ckpt_path"
,
default
=
None
,
type
=
str
,
required
=
False
,
help
=
"Path to the checkpoint to convert."
)
parser
.
add_argument
(
"--image_size"
,
default
=
1024
,
type
=
int
,
choices
=
[
256
,
512
,
1024
],
required
=
False
,
help
=
"Image size of pretrained model, either 512 or 1024."
,
)
parser
.
add_argument
(
"--dump_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the output pipeline."
)
parser
.
add_argument
(
"--only_transformer"
,
default
=
True
,
type
=
bool
,
required
=
True
)
args
=
parser
.
parse_args
()
main
(
args
)
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