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diffusers/scripts/convert_models_diffuser_to_diffusers.py at main · fredatgithub/diffusers · GitHub
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convert_models_diffuser_to_diffusers.py
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convert_models_diffuser_to_diffusers.py
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import
json
import
os
import
torch
from
diffusers
import
UNet1DModel
os
.
makedirs
(
"hub/hopper-medium-v2/unet/hor32"
,
exist_ok
=
True
)
os
.
makedirs
(
"hub/hopper-medium-v2/unet/hor128"
,
exist_ok
=
True
)
os
.
makedirs
(
"hub/hopper-medium-v2/value_function"
,
exist_ok
=
True
)
def
unet
(
hor
):
if
hor
==
128
:
down_block_types
=
(
"DownResnetBlock1D"
,
"DownResnetBlock1D"
,
"DownResnetBlock1D"
)
block_out_channels
=
(
32
,
128
,
256
)
up_block_types
=
(
"UpResnetBlock1D"
,
"UpResnetBlock1D"
)
elif
hor
==
32
:
down_block_types
=
(
"DownResnetBlock1D"
,
"DownResnetBlock1D"
,
"DownResnetBlock1D"
,
"DownResnetBlock1D"
)
block_out_channels
=
(
32
,
64
,
128
,
256
)
up_block_types
=
(
"UpResnetBlock1D"
,
"UpResnetBlock1D"
,
"UpResnetBlock1D"
)
model
=
torch
.
load
(
f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor
{
hor
}
.torch"
)
state_dict
=
model
.
state_dict
()
config
=
{
"down_block_types"
:
down_block_types
,
"block_out_channels"
:
block_out_channels
,
"up_block_types"
:
up_block_types
,
"layers_per_block"
:
1
,
"use_timestep_embedding"
:
True
,
"out_block_type"
:
"OutConv1DBlock"
,
"norm_num_groups"
:
8
,
"downsample_each_block"
:
False
,
"in_channels"
:
14
,
"out_channels"
:
14
,
"extra_in_channels"
:
0
,
"time_embedding_type"
:
"positional"
,
"flip_sin_to_cos"
:
False
,
"freq_shift"
:
1
,
"sample_size"
:
65536
,
"mid_block_type"
:
"MidResTemporalBlock1D"
,
"act_fn"
:
"mish"
,
}
hf_value_function
=
UNet1DModel
(
**
config
)
print
(
f"length of state dict:
{
len
(
state_dict
.
keys
())
}
"
)
print
(
f"length of value function dict:
{
len
(
hf_value_function
.
state_dict
().
keys
())
}
"
)
mapping
=
dict
(
zip
(
model
.
state_dict
().
keys
(),
hf_value_function
.
state_dict
().
keys
()))
for
k
,
v
in
mapping
.
items
():
state_dict
[
v
]
=
state_dict
.
pop
(
k
)
hf_value_function
.
load_state_dict
(
state_dict
)
torch
.
save
(
hf_value_function
.
state_dict
(),
f"hub/hopper-medium-v2/unet/hor
{
hor
}
/diffusion_pytorch_model.bin"
)
with
open
(
f"hub/hopper-medium-v2/unet/hor
{
hor
}
/config.json"
,
"w"
)
as
f
:
json
.
dump
(
config
,
f
)
def
value_function
():
config
=
{
"in_channels"
:
14
,
"down_block_types"
: (
"DownResnetBlock1D"
,
"DownResnetBlock1D"
,
"DownResnetBlock1D"
,
"DownResnetBlock1D"
),
"up_block_types"
: (),
"out_block_type"
:
"ValueFunction"
,
"mid_block_type"
:
"ValueFunctionMidBlock1D"
,
"block_out_channels"
: (
32
,
64
,
128
,
256
),
"layers_per_block"
:
1
,
"downsample_each_block"
:
True
,
"sample_size"
:
65536
,
"out_channels"
:
14
,
"extra_in_channels"
:
0
,
"time_embedding_type"
:
"positional"
,
"use_timestep_embedding"
:
True
,
"flip_sin_to_cos"
:
False
,
"freq_shift"
:
1
,
"norm_num_groups"
:
8
,
"act_fn"
:
"mish"
,
}
model
=
torch
.
load
(
"/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"
)
state_dict
=
model
hf_value_function
=
UNet1DModel
(
**
config
)
print
(
f"length of state dict:
{
len
(
state_dict
.
keys
())
}
"
)
print
(
f"length of value function dict:
{
len
(
hf_value_function
.
state_dict
().
keys
())
}
"
)
mapping
=
dict
(
zip
(
state_dict
.
keys
(),
hf_value_function
.
state_dict
().
keys
()))
for
k
,
v
in
mapping
.
items
():
state_dict
[
v
]
=
state_dict
.
pop
(
k
)
hf_value_function
.
load_state_dict
(
state_dict
)
torch
.
save
(
hf_value_function
.
state_dict
(),
"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"
)
with
open
(
"hub/hopper-medium-v2/value_function/config.json"
,
"w"
)
as
f
:
json
.
dump
(
config
,
f
)
if
__name__
==
"__main__"
:
unet
(
32
)
# unet(128)
value_function
()
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