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import
argparse
import
os
import
torch
from
torchvision
.
datasets
.
utils
import
download_url
from
diffusers
import
AutoencoderKL
,
DDIMScheduler
,
DiTPipeline
,
Transformer2DModel
pretrained_models
=
{
512
:
"DiT-XL-2-512x512.pt"
,
256
:
"DiT-XL-2-256x256.pt"
}
def
download_model
(
model_name
):
"""
Downloads a pre-trained DiT model from the web.
"""
local_path
=
f"pretrained_models/
{
model_name
}
"
if
not
os
.
path
.
isfile
(
local_path
):
os
.
makedirs
(
"pretrained_models"
,
exist_ok
=
True
)
web_path
=
f"https://dl.fbaipublicfiles.com/DiT/models/
{
model_name
}
"
download_url
(
web_path
,
"pretrained_models"
)
model
=
torch
.
load
(
local_path
,
map_location
=
lambda
storage
,
loc
:
storage
)
return
model
def
main
(
args
):
state_dict
=
download_model
(
pretrained_models
[
args
.
image_size
])
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"
)
for
depth
in
range
(
28
):
state_dict
[
f"transformer_blocks.
{
depth
}
.norm1.emb.timestep_embedder.linear_1.weight"
]
=
state_dict
[
"t_embedder.mlp.0.weight"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.norm1.emb.timestep_embedder.linear_1.bias"
]
=
state_dict
[
"t_embedder.mlp.0.bias"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.norm1.emb.timestep_embedder.linear_2.weight"
]
=
state_dict
[
"t_embedder.mlp.2.weight"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.norm1.emb.timestep_embedder.linear_2.bias"
]
=
state_dict
[
"t_embedder.mlp.2.bias"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.norm1.emb.class_embedder.embedding_table.weight"
]
=
state_dict
[
"y_embedder.embedding_table.weight"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.norm1.linear.weight"
]
=
state_dict
[
f"blocks.
{
depth
}
.adaLN_modulation.1.weight"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.norm1.linear.bias"
]
=
state_dict
[
f"blocks.
{
depth
}
.adaLN_modulation.1.bias"
]
q
,
k
,
v
=
torch
.
chunk
(
state_dict
[
f"blocks.
{
depth
}
.attn.qkv.weight"
],
3
,
dim
=
0
)
q_bias
,
k_bias
,
v_bias
=
torch
.
chunk
(
state_dict
[
f"blocks.
{
depth
}
.attn.qkv.bias"
],
3
,
dim
=
0
)
state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_q.weight"
]
=
q
state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_q.bias"
]
=
q_bias
state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_k.weight"
]
=
k
state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_k.bias"
]
=
k_bias
state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_v.weight"
]
=
v
state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_v.bias"
]
=
v_bias
state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_out.0.weight"
]
=
state_dict
[
f"blocks.
{
depth
}
.attn.proj.weight"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.attn1.to_out.0.bias"
]
=
state_dict
[
f"blocks.
{
depth
}
.attn.proj.bias"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.ff.net.0.proj.weight"
]
=
state_dict
[
f"blocks.
{
depth
}
.mlp.fc1.weight"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.ff.net.0.proj.bias"
]
=
state_dict
[
f"blocks.
{
depth
}
.mlp.fc1.bias"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.ff.net.2.weight"
]
=
state_dict
[
f"blocks.
{
depth
}
.mlp.fc2.weight"
]
state_dict
[
f"transformer_blocks.
{
depth
}
.ff.net.2.bias"
]
=
state_dict
[
f"blocks.
{
depth
}
.mlp.fc2.bias"
]
state_dict
.
pop
(
f"blocks.
{
depth
}
.attn.qkv.weight"
)
state_dict
.
pop
(
f"blocks.
{
depth
}
.attn.qkv.bias"
)
state_dict
.
pop
(
f"blocks.
{
depth
}
.attn.proj.weight"
)
state_dict
.
pop
(
f"blocks.
{
depth
}
.attn.proj.bias"
)
state_dict
.
pop
(
f"blocks.
{
depth
}
.mlp.fc1.weight"
)
state_dict
.
pop
(
f"blocks.
{
depth
}
.mlp.fc1.bias"
)
state_dict
.
pop
(
f"blocks.
{
depth
}
.mlp.fc2.weight"
)
state_dict
.
pop
(
f"blocks.
{
depth
}
.mlp.fc2.bias"
)
state_dict
.
pop
(
f"blocks.
{
depth
}
.adaLN_modulation.1.weight"
)
state_dict
.
pop
(
f"blocks.
{
depth
}
.adaLN_modulation.1.bias"
)
state_dict
.
pop
(
"t_embedder.mlp.0.weight"
)
state_dict
.
pop
(
"t_embedder.mlp.0.bias"
)
state_dict
.
pop
(
"t_embedder.mlp.2.weight"
)
state_dict
.
pop
(
"t_embedder.mlp.2.bias"
)
state_dict
.
pop
(
"y_embedder.embedding_table.weight"
)
state_dict
[
"proj_out_1.weight"
]
=
state_dict
[
"final_layer.adaLN_modulation.1.weight"
]
state_dict
[
"proj_out_1.bias"
]
=
state_dict
[
"final_layer.adaLN_modulation.1.bias"
]
state_dict
[
"proj_out_2.weight"
]
=
state_dict
[
"final_layer.linear.weight"
]
state_dict
[
"proj_out_2.bias"
]
=
state_dict
[
"final_layer.linear.bias"
]
state_dict
.
pop
(
"final_layer.linear.weight"
)
state_dict
.
pop
(
"final_layer.linear.bias"
)
state_dict
.
pop
(
"final_layer.adaLN_modulation.1.weight"
)
state_dict
.
pop
(
"final_layer.adaLN_modulation.1.bias"
)
# 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
,
activation_fn
=
"gelu-approximate"
,
num_embeds_ada_norm
=
1000
,
norm_type
=
"ada_norm_zero"
,
norm_elementwise_affine
=
False
,
)
transformer
.
load_state_dict
(
state_dict
,
strict
=
True
)
scheduler
=
DDIMScheduler
(
num_train_timesteps
=
1000
,
beta_schedule
=
"linear"
,
prediction_type
=
"epsilon"
,
clip_sample
=
False
,
)
vae
=
AutoencoderKL
.
from_pretrained
(
args
.
vae_model
)
pipeline
=
DiTPipeline
(
transformer
=
transformer
,
vae
=
vae
,
scheduler
=
scheduler
)
if
args
.
save
:
pipeline
.
save_pretrained
(
args
.
checkpoint_path
)
if
__name__
==
"__main__"
:
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"--image_size"
,
default
=
256
,
type
=
int
,
required
=
False
,
help
=
"Image size of pretrained model, either 256 or 512."
,
)
parser
.
add_argument
(
"--vae_model"
,
default
=
"stabilityai/sd-vae-ft-ema"
,
type
=
str
,
required
=
False
,
help
=
"Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema."
,
)
parser
.
add_argument
(
"--save"
,
default
=
True
,
type
=
bool
,
required
=
False
,
help
=
"Whether to save the converted pipeline or not."
)
parser
.
add_argument
(
"--checkpoint_path"
,
default
=
None
,
type
=
str
,
required
=
True
,
help
=
"Path to the output pipeline."
)
args
=
parser
.
parse_args
()
main
(
args
)
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