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import
argparse
import
os
import
pathlib
from
typing
import
Any
,
Dict
import
torch
from
accelerate
import
init_empty_weights
from
huggingface_hub
import
hf_hub_download
from
safetensors
.
torch
import
load_file
from
transformers
import
AutoProcessor
,
AutoTokenizer
,
CLIPVisionModelWithProjection
,
UMT5EncoderModel
from
diffusers
import
(
AutoencoderKLWan
,
SkyReelsV2DiffusionForcingPipeline
,
SkyReelsV2ImageToVideoPipeline
,
SkyReelsV2Pipeline
,
SkyReelsV2Transformer3DModel
,
UniPCMultistepScheduler
,
)
TRANSFORMER_KEYS_RENAME_DICT
=
{
"time_embedding.0"
:
"condition_embedder.time_embedder.linear_1"
,
"time_embedding.2"
:
"condition_embedder.time_embedder.linear_2"
,
"text_embedding.0"
:
"condition_embedder.text_embedder.linear_1"
,
"text_embedding.2"
:
"condition_embedder.text_embedder.linear_2"
,
"time_projection.1"
:
"condition_embedder.time_proj"
,
"head.modulation"
:
"scale_shift_table"
,
"head.head"
:
"proj_out"
,
"modulation"
:
"scale_shift_table"
,
"ffn.0"
:
"ffn.net.0.proj"
,
"ffn.2"
:
"ffn.net.2"
,
"fps_projection.0"
:
"fps_projection.net.0.proj"
,
"fps_projection.2"
:
"fps_projection.net.2"
,
# Hack to swap the layer names
# The original model calls the norms in following order: norm1, norm3, norm2
# We convert it to: norm1, norm2, norm3
"norm2"
:
"norm__placeholder"
,
"norm3"
:
"norm2"
,
"norm__placeholder"
:
"norm3"
,
# For the I2V model
"img_emb.proj.0"
:
"condition_embedder.image_embedder.norm1"
,
"img_emb.proj.1"
:
"condition_embedder.image_embedder.ff.net.0.proj"
,
"img_emb.proj.3"
:
"condition_embedder.image_embedder.ff.net.2"
,
"img_emb.proj.4"
:
"condition_embedder.image_embedder.norm2"
,
# for the FLF2V model
"img_emb.emb_pos"
:
"condition_embedder.image_embedder.pos_embed"
,
# Add attention component mappings
"self_attn.q"
:
"attn1.to_q"
,
"self_attn.k"
:
"attn1.to_k"
,
"self_attn.v"
:
"attn1.to_v"
,
"self_attn.o"
:
"attn1.to_out.0"
,
"self_attn.norm_q"
:
"attn1.norm_q"
,
"self_attn.norm_k"
:
"attn1.norm_k"
,
"cross_attn.q"
:
"attn2.to_q"
,
"cross_attn.k"
:
"attn2.to_k"
,
"cross_attn.v"
:
"attn2.to_v"
,
"cross_attn.o"
:
"attn2.to_out.0"
,
"cross_attn.norm_q"
:
"attn2.norm_q"
,
"cross_attn.norm_k"
:
"attn2.norm_k"
,
"attn2.to_k_img"
:
"attn2.add_k_proj"
,
"attn2.to_v_img"
:
"attn2.add_v_proj"
,
"attn2.norm_k_img"
:
"attn2.norm_added_k"
,
}
TRANSFORMER_SPECIAL_KEYS_REMAP
=
{}
def
update_state_dict_
(
state_dict
:
Dict
[
str
,
Any
],
old_key
:
str
,
new_key
:
str
)
->
Dict
[
str
,
Any
]:
state_dict
[
new_key
]
=
state_dict
.
pop
(
old_key
)
def
load_sharded_safetensors
(
dir
:
pathlib
.
Path
):
if
"720P"
in
str
(
dir
):
file_paths
=
list
(
dir
.
glob
(
"diffusion_pytorch_model*.safetensors"
))
else
:
file_paths
=
list
(
dir
.
glob
(
"model*.safetensors"
))
state_dict
=
{}
for
path
in
file_paths
:
state_dict
.
update
(
load_file
(
path
))
return
state_dict
def
get_transformer_config
(
model_type
:
str
)
->
Dict
[
str
,
Any
]:
if
model_type
==
"SkyReels-V2-DF-1.3B-540P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-DF-1.3B-540P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
None
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
8960
,
"freq_dim"
:
256
,
"in_channels"
:
16
,
"num_attention_heads"
:
12
,
"inject_sample_info"
:
True
,
"num_layers"
:
30
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
},
}
elif
model_type
==
"SkyReels-V2-DF-14B-720P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-DF-14B-720P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
None
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
13824
,
"freq_dim"
:
256
,
"in_channels"
:
16
,
"num_attention_heads"
:
40
,
"inject_sample_info"
:
False
,
"num_layers"
:
40
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
},
}
elif
model_type
==
"SkyReels-V2-DF-14B-540P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-DF-14B-540P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
None
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
13824
,
"freq_dim"
:
256
,
"in_channels"
:
16
,
"num_attention_heads"
:
40
,
"inject_sample_info"
:
False
,
"num_layers"
:
40
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
},
}
elif
model_type
==
"SkyReels-V2-T2V-14B-720P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-T2V-14B-720P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
None
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
13824
,
"freq_dim"
:
256
,
"in_channels"
:
16
,
"num_attention_heads"
:
40
,
"inject_sample_info"
:
False
,
"num_layers"
:
40
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
},
}
elif
model_type
==
"SkyReels-V2-T2V-14B-540P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-T2V-14B-540P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
None
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
13824
,
"freq_dim"
:
256
,
"in_channels"
:
16
,
"num_attention_heads"
:
40
,
"inject_sample_info"
:
False
,
"num_layers"
:
40
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
},
}
elif
model_type
==
"SkyReels-V2-I2V-1.3B-540P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-I2V-1.3B-540P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
1536
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
8960
,
"freq_dim"
:
256
,
"in_channels"
:
36
,
"num_attention_heads"
:
12
,
"inject_sample_info"
:
False
,
"num_layers"
:
30
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
"image_dim"
:
1280
,
},
}
elif
model_type
==
"SkyReels-V2-I2V-14B-540P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-I2V-14B-540P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
5120
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
13824
,
"freq_dim"
:
256
,
"in_channels"
:
36
,
"num_attention_heads"
:
40
,
"inject_sample_info"
:
False
,
"num_layers"
:
40
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
"image_dim"
:
1280
,
},
}
elif
model_type
==
"SkyReels-V2-I2V-14B-720P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-I2V-14B-720P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
5120
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
13824
,
"freq_dim"
:
256
,
"in_channels"
:
36
,
"num_attention_heads"
:
40
,
"inject_sample_info"
:
False
,
"num_layers"
:
40
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
"image_dim"
:
1280
,
},
}
elif
model_type
==
"SkyReels-V2-FLF2V-1.3B-540P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-I2V-1.3B-540P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
1536
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
8960
,
"freq_dim"
:
256
,
"in_channels"
:
36
,
"num_attention_heads"
:
12
,
"inject_sample_info"
:
False
,
"num_layers"
:
30
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
"image_dim"
:
1280
,
"pos_embed_seq_len"
:
514
,
},
}
elif
model_type
==
"SkyReels-V2-FLF2V-14B-540P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-I2V-14B-540P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
5120
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
13824
,
"freq_dim"
:
256
,
"in_channels"
:
36
,
"num_attention_heads"
:
40
,
"inject_sample_info"
:
False
,
"num_layers"
:
40
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
"image_dim"
:
1280
,
"pos_embed_seq_len"
:
514
,
},
}
elif
model_type
==
"SkyReels-V2-FLF2V-14B-720P"
:
config
=
{
"model_id"
:
"Skywork/SkyReels-V2-I2V-14B-720P"
,
"diffusers_config"
: {
"added_kv_proj_dim"
:
5120
,
"attention_head_dim"
:
128
,
"cross_attn_norm"
:
True
,
"eps"
:
1e-06
,
"ffn_dim"
:
13824
,
"freq_dim"
:
256
,
"in_channels"
:
36
,
"num_attention_heads"
:
40
,
"inject_sample_info"
:
False
,
"num_layers"
:
40
,
"out_channels"
:
16
,
"patch_size"
: [
1
,
2
,
2
],
"qk_norm"
:
"rms_norm_across_heads"
,
"text_dim"
:
4096
,
"image_dim"
:
1280
,
"pos_embed_seq_len"
:
514
,
},
}
return
config
def
convert_transformer
(
model_type
:
str
):
config
=
get_transformer_config
(
model_type
)
diffusers_config
=
config
[
"diffusers_config"
]
model_id
=
config
[
"model_id"
]
if
"1.3B"
in
model_type
:
original_state_dict
=
load_file
(
hf_hub_download
(
model_id
,
"model.safetensors"
))
else
:
os
.
makedirs
(
model_type
,
exist_ok
=
True
)
model_dir
=
pathlib
.
Path
(
model_type
)
if
"720P"
in
model_type
:
top_shard
=
7
if
"I2V"
in
model_type
else
6
zeros
=
"0"
*
(
4
if
"I2V"
or
"T2V"
in
model_type
else
3
)
model_name
=
"diffusion_pytorch_model"
elif
"540P"
in
model_type
:
top_shard
=
14
if
"I2V"
in
model_type
else
12
model_name
=
"model"
for
i
in
range
(
1
,
top_shard
+
1
):
shard_path
=
f"
{
model_name
}
-
{
i
:05d
}
-of-
{
zeros
}
{
top_shard
}
.safetensors"
hf_hub_download
(
model_id
,
shard_path
,
local_dir
=
model_dir
)
original_state_dict
=
load_sharded_safetensors
(
model_dir
)
with
init_empty_weights
():
transformer
=
SkyReelsV2Transformer3DModel
.
from_config
(
diffusers_config
)
for
key
in
list
(
original_state_dict
.
keys
()):
new_key
=
key
[:]
for
replace_key
,
rename_key
in
TRANSFORMER_KEYS_RENAME_DICT
.
items
():
new_key
=
new_key
.
replace
(
replace_key
,
rename_key
)
update_state_dict_
(
original_state_dict
,
key
,
new_key
)
for
key
in
list
(
original_state_dict
.
keys
()):
for
special_key
,
handler_fn_inplace
in
TRANSFORMER_SPECIAL_KEYS_REMAP
.
items
():
if
special_key
not
in
key
:
continue
handler_fn_inplace
(
key
,
original_state_dict
)
if
"FLF2V"
in
model_type
:
if
(
hasattr
(
transformer
.
condition_embedder
,
"image_embedder"
)
and
hasattr
(
transformer
.
condition_embedder
.
image_embedder
,
"pos_embed"
)
and
transformer
.
condition_embedder
.
image_embedder
.
pos_embed
is
not
None
):
pos_embed_shape
=
transformer
.
condition_embedder
.
image_embedder
.
pos_embed
.
shape
original_state_dict
[
"condition_embedder.image_embedder.pos_embed"
]
=
torch
.
zeros
(
pos_embed_shape
)
transformer
.
load_state_dict
(
original_state_dict
,
strict
=
True
,
assign
=
True
)
return
transformer
def
convert_vae
():
vae_ckpt_path
=
hf_hub_download
(
"Wan-AI/Wan2.1-T2V-14B"
,
"Wan2.1_VAE.pth"
)
old_state_dict
=
torch
.
load
(
vae_ckpt_path
,
weights_only
=
True
)
new_state_dict
=
{}
# Create mappings for specific components
middle_key_mapping
=
{
# Encoder middle block
"encoder.middle.0.residual.0.gamma"
:
"encoder.mid_block.resnets.0.norm1.gamma"
,
"encoder.middle.0.residual.2.bias"
:
"encoder.mid_block.resnets.0.conv1.bias"
,
"encoder.middle.0.residual.2.weight"
:
"encoder.mid_block.resnets.0.conv1.weight"
,
"encoder.middle.0.residual.3.gamma"
:
"encoder.mid_block.resnets.0.norm2.gamma"
,
"encoder.middle.0.residual.6.bias"
:
"encoder.mid_block.resnets.0.conv2.bias"
,
"encoder.middle.0.residual.6.weight"
:
"encoder.mid_block.resnets.0.conv2.weight"
,
"encoder.middle.2.residual.0.gamma"
:
"encoder.mid_block.resnets.1.norm1.gamma"
,
"encoder.middle.2.residual.2.bias"
:
"encoder.mid_block.resnets.1.conv1.bias"
,
"encoder.middle.2.residual.2.weight"
:
"encoder.mid_block.resnets.1.conv1.weight"
,
"encoder.middle.2.residual.3.gamma"
:
"encoder.mid_block.resnets.1.norm2.gamma"
,
"encoder.middle.2.residual.6.bias"
:
"encoder.mid_block.resnets.1.conv2.bias"
,
"encoder.middle.2.residual.6.weight"
:
"encoder.mid_block.resnets.1.conv2.weight"
,
# Decoder middle block
"decoder.middle.0.residual.0.gamma"
:
"decoder.mid_block.resnets.0.norm1.gamma"
,
"decoder.middle.0.residual.2.bias"
:
"decoder.mid_block.resnets.0.conv1.bias"
,
"decoder.middle.0.residual.2.weight"
:
"decoder.mid_block.resnets.0.conv1.weight"
,
"decoder.middle.0.residual.3.gamma"
:
"decoder.mid_block.resnets.0.norm2.gamma"
,
"decoder.middle.0.residual.6.bias"
:
"decoder.mid_block.resnets.0.conv2.bias"
,
"decoder.middle.0.residual.6.weight"
:
"decoder.mid_block.resnets.0.conv2.weight"
,
"decoder.middle.2.residual.0.gamma"
:
"decoder.mid_block.resnets.1.norm1.gamma"
,
"decoder.middle.2.residual.2.bias"
:
"decoder.mid_block.resnets.1.conv1.bias"
,
"decoder.middle.2.residual.2.weight"
:
"decoder.mid_block.resnets.1.conv1.weight"
,
"decoder.middle.2.residual.3.gamma"
:
"decoder.mid_block.resnets.1.norm2.gamma"
,
"decoder.middle.2.residual.6.bias"
:
"decoder.mid_block.resnets.1.conv2.bias"
,
"decoder.middle.2.residual.6.weight"
:
"decoder.mid_block.resnets.1.conv2.weight"
,
}
# Create a mapping for attention blocks
attention_mapping
=
{
# Encoder middle attention
"encoder.middle.1.norm.gamma"
:
"encoder.mid_block.attentions.0.norm.gamma"
,
"encoder.middle.1.to_qkv.weight"
:
"encoder.mid_block.attentions.0.to_qkv.weight"
,
"encoder.middle.1.to_qkv.bias"
:
"encoder.mid_block.attentions.0.to_qkv.bias"
,
"encoder.middle.1.proj.weight"
:
"encoder.mid_block.attentions.0.proj.weight"
,
"encoder.middle.1.proj.bias"
:
"encoder.mid_block.attentions.0.proj.bias"
,
# Decoder middle attention
"decoder.middle.1.norm.gamma"
:
"decoder.mid_block.attentions.0.norm.gamma"
,
"decoder.middle.1.to_qkv.weight"
:
"decoder.mid_block.attentions.0.to_qkv.weight"
,
"decoder.middle.1.to_qkv.bias"
:
"decoder.mid_block.attentions.0.to_qkv.bias"
,
"decoder.middle.1.proj.weight"
:
"decoder.mid_block.attentions.0.proj.weight"
,
"decoder.middle.1.proj.bias"
:
"decoder.mid_block.attentions.0.proj.bias"
,
}
# Create a mapping for the head components
head_mapping
=
{
# Encoder head
"encoder.head.0.gamma"
:
"encoder.norm_out.gamma"
,
"encoder.head.2.bias"
:
"encoder.conv_out.bias"
,
"encoder.head.2.weight"
:
"encoder.conv_out.weight"
,
# Decoder head
"decoder.head.0.gamma"
:
"decoder.norm_out.gamma"
,
"decoder.head.2.bias"
:
"decoder.conv_out.bias"
,
"decoder.head.2.weight"
:
"decoder.conv_out.weight"
,
}
# Create a mapping for the quant components
quant_mapping
=
{
"conv1.weight"
:
"quant_conv.weight"
,
"conv1.bias"
:
"quant_conv.bias"
,
"conv2.weight"
:
"post_quant_conv.weight"
,
"conv2.bias"
:
"post_quant_conv.bias"
,
}
# Process each key in the state dict
for
key
,
value
in
old_state_dict
.
items
():
# Handle middle block keys using the mapping
if
key
in
middle_key_mapping
:
new_key
=
middle_key_mapping
[
key
]
new_state_dict
[
new_key
]
=
value
# Handle attention blocks using the mapping
elif
key
in
attention_mapping
:
new_key
=
attention_mapping
[
key
]
new_state_dict
[
new_key
]
=
value
# Handle head keys using the mapping
elif
key
in
head_mapping
:
new_key
=
head_mapping
[
key
]
new_state_dict
[
new_key
]
=
value
# Handle quant keys using the mapping
elif
key
in
quant_mapping
:
new_key
=
quant_mapping
[
key
]
new_state_dict
[
new_key
]
=
value
# Handle encoder conv1
elif
key
==
"encoder.conv1.weight"
:
new_state_dict
[
"encoder.conv_in.weight"
]
=
value
elif
key
==
"encoder.conv1.bias"
:
new_state_dict
[
"encoder.conv_in.bias"
]
=
value
# Handle decoder conv1
elif
key
==
"decoder.conv1.weight"
:
new_state_dict
[
"decoder.conv_in.weight"
]
=
value
elif
key
==
"decoder.conv1.bias"
:
new_state_dict
[
"decoder.conv_in.bias"
]
=
value
# Handle encoder downsamples
elif
key
.
startswith
(
"encoder.downsamples."
):
# Convert to down_blocks
new_key
=
key
.
replace
(
"encoder.downsamples."
,
"encoder.down_blocks."
)
# Convert residual block naming but keep the original structure
if
".residual.0.gamma"
in
new_key
:
new_key
=
new_key
.
replace
(
".residual.0.gamma"
,
".norm1.gamma"
)
elif
".residual.2.bias"
in
new_key
:
new_key
=
new_key
.
replace
(
".residual.2.bias"
,
".conv1.bias"
)
elif
".residual.2.weight"
in
new_key
:
new_key
=
new_key
.
replace
(
".residual.2.weight"
,
".conv1.weight"
)
elif
".residual.3.gamma"
in
new_key
:
new_key
=
new_key
.
replace
(
".residual.3.gamma"
,
".norm2.gamma"
)
elif
".residual.6.bias"
in
new_key
:
new_key
=
new_key
.
replace
(
".residual.6.bias"
,
".conv2.bias"
)
elif
".residual.6.weight"
in
new_key
:
new_key
=
new_key
.
replace
(
".residual.6.weight"
,
".conv2.weight"
)
elif
".shortcut.bias"
in
new_key
:
new_key
=
new_key
.
replace
(
".shortcut.bias"
,
".conv_shortcut.bias"
)
elif
".shortcut.weight"
in
new_key
:
new_key
=
new_key
.
replace
(
".shortcut.weight"
,
".conv_shortcut.weight"
)
new_state_dict
[
new_key
]
=
value
# Handle decoder upsamples
elif
key
.
startswith
(
"decoder.upsamples."
):
# Convert to up_blocks
parts
=
key
.
split
(
"."
)
block_idx
=
int
(
parts
[
2
])
# Group residual blocks
if
"residual"
in
key
:
if
block_idx
in
[
0
,
1
,
2
]:
new_block_idx
=
0
resnet_idx
=
block_idx
elif
block_idx
in
[
4
,
5
,
6
]:
new_block_idx
=
1
resnet_idx
=
block_idx
-
4
elif
block_idx
in
[
8
,
9
,
10
]:
new_block_idx
=
2
resnet_idx
=
block_idx
-
8
elif
block_idx
in
[
12
,
13
,
14
]:
new_block_idx
=
3
resnet_idx
=
block_idx
-
12
else
:
# Keep as is for other blocks
new_state_dict
[
key
]
=
value
continue
# Convert residual block naming
if
".residual.0.gamma"
in
key
:
new_key
=
f"decoder.up_blocks.
{
new_block_idx
}
.resnets.
{
resnet_idx
}
.norm1.gamma"
elif
".residual.2.bias"
in
key
:
new_key
=
f"decoder.up_blocks.
{
new_block_idx
}
.resnets.
{
resnet_idx
}
.conv1.bias"
elif
".residual.2.weight"
in
key
:
new_key
=
f"decoder.up_blocks.
{
new_block_idx
}
.resnets.
{
resnet_idx
}
.conv1.weight"
elif
".residual.3.gamma"
in
key
:
new_key
=
f"decoder.up_blocks.
{
new_block_idx
}
.resnets.
{
resnet_idx
}
.norm2.gamma"
elif
".residual.6.bias"
in
key
:
new_key
=
f"decoder.up_blocks.
{
new_block_idx
}
.resnets.
{
resnet_idx
}
.conv2.bias"
elif
".residual.6.weight"
in
key
:
new_key
=
f"decoder.up_blocks.
{
new_block_idx
}
.resnets.
{
resnet_idx
}
.conv2.weight"
else
:
new_key
=
key
new_state_dict
[
new_key
]
=
value
# Handle shortcut connections
elif
".shortcut."
in
key
:
if
block_idx
==
4
:
new_key
=
key
.
replace
(
".shortcut."
,
".resnets.0.conv_shortcut."
)
new_key
=
new_key
.
replace
(
"decoder.upsamples.4"
,
"decoder.up_blocks.1"
)
else
:
new_key
=
key
.
replace
(
"decoder.upsamples."
,
"decoder.up_blocks."
)
new_key
=
new_key
.
replace
(
".shortcut."
,
".conv_shortcut."
)
new_state_dict
[
new_key
]
=
value
# Handle upsamplers
elif
".resample."
in
key
or
".time_conv."
in
key
:
if
block_idx
==
3
:
new_key
=
key
.
replace
(
f"decoder.upsamples.
{
block_idx
}
"
,
"decoder.up_blocks.0.upsamplers.0"
)
elif
block_idx
==
7
:
new_key
=
key
.
replace
(
f"decoder.upsamples.
{
block_idx
}
"
,
"decoder.up_blocks.1.upsamplers.0"
)
elif
block_idx
==
11
:
new_key
=
key
.
replace
(
f"decoder.upsamples.
{
block_idx
}
"
,
"decoder.up_blocks.2.upsamplers.0"
)
else
:
new_key
=
key
.
replace
(
"decoder.upsamples."
,
"decoder.up_blocks."
)
new_state_dict
[
new_key
]
=
value
else
:
new_key
=
key
.
replace
(
"decoder.upsamples."
,
"decoder.up_blocks."
)
new_state_dict
[
new_key
]
=
value
else
:
# Keep other keys unchanged
new_state_dict
[
key
]
=
value
with
init_empty_weights
():
vae
=
AutoencoderKLWan
()
vae
.
load_state_dict
(
new_state_dict
,
strict
=
True
,
assign
=
True
)
return
vae
def
get_args
():
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"--model_type"
,
type
=
str
,
default
=
None
)
parser
.
add_argument
(
"--output_path"
,
type
=
str
,
required
=
True
)
parser
.
add_argument
(
"--dtype"
,
default
=
"fp32"
)
return
parser
.
parse_args
()
DTYPE_MAPPING
=
{
"fp32"
:
torch
.
float32
,
"fp16"
:
torch
.
float16
,
"bf16"
:
torch
.
bfloat16
,
}
if
__name__
==
"__main__"
:
args
=
get_args
()
transformer
=
None
dtype
=
DTYPE_MAPPING
[
args
.
dtype
]
transformer
=
convert_transformer
(
args
.
model_type
).
to
(
dtype
=
dtype
)
vae
=
convert_vae
()
text_encoder
=
UMT5EncoderModel
.
from_pretrained
(
"google/umt5-xxl"
)
tokenizer
=
AutoTokenizer
.
from_pretrained
(
"google/umt5-xxl"
)
scheduler
=
UniPCMultistepScheduler
(
prediction_type
=
"flow_prediction"
,
num_train_timesteps
=
1000
,
use_flow_sigmas
=
True
,
)
if
"I2V"
in
args
.
model_type
or
"FLF2V"
in
args
.
model_type
:
image_encoder
=
CLIPVisionModelWithProjection
.
from_pretrained
(
"laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
)
image_processor
=
AutoProcessor
.
from_pretrained
(
"laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
)
pipe
=
SkyReelsV2ImageToVideoPipeline
(
transformer
=
transformer
,
text_encoder
=
text_encoder
,
tokenizer
=
tokenizer
,
vae
=
vae
,
scheduler
=
scheduler
,
image_encoder
=
image_encoder
,
image_processor
=
image_processor
,
)
elif
"T2V"
in
args
.
model_type
:
pipe
=
SkyReelsV2Pipeline
(
transformer
=
transformer
,
text_encoder
=
text_encoder
,
tokenizer
=
tokenizer
,
vae
=
vae
,
scheduler
=
scheduler
,
)
elif
"DF"
in
args
.
model_type
:
pipe
=
SkyReelsV2DiffusionForcingPipeline
(
transformer
=
transformer
,
text_encoder
=
text_encoder
,
tokenizer
=
tokenizer
,
vae
=
vae
,
scheduler
=
scheduler
,
)
pipe
.
save_pretrained
(
args
.
output_path
,
safe_serialization
=
True
,
max_shard_size
=
"5GB"
,
# push_to_hub=True,
# repo_id=f"<place_holder>/{args.model_type}-Diffusers",
)
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