FazBrowse GitHub Viewer
|
Trending
|
URL:
|
Home
Tools:
[Download Repo ZIP]
[View Raw Code]
[Original HTTPS Page]
diffusers/scripts/convert_ace_step_to_diffusers.py at dev · xlite-dev/diffusers · GitHub
Uh oh!
There was an error while loading.
Please reload this page
.
xlite-dev
/
diffusers
Public
forked from
huggingface/diffusers
Notifications
You must be signed in to change notification settings
Fork
0
Star
0
Code
Pull requests
0
Projects
Security and quality
0
Insights
Additional navigation options
Code
Pull requests
Projects
Security and quality
Insights
Expand file tree
Breadcrumbs
diffusers
/
scripts
/
convert_ace_step_to_diffusers.py
Copy path
More file actions
More file actions
Latest commit
History
History
History
454 lines (410 loc) · 21 KB
Breadcrumbs
diffusers
/
scripts
/
convert_ace_step_to_diffusers.py
Copy path
File metadata and controls
454 lines (410 loc) · 21 KB
Raw
Copy raw file
Download raw file
Open symbols panel
Edit and raw actions
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
# Run this script to convert ACE-Step model weights to a diffusers pipeline.
#
# Usage:
# python scripts/convert_ace_step_to_diffusers.py \
# --checkpoint_dir /path/to/ACE-Step-1.5/checkpoints \
# --dit_config acestep-v15-turbo \
# --output_dir /path/to/output/ACE-Step-v1-5-turbo \
# --dtype bf16
import
argparse
import
json
import
os
import
shutil
import
torch
from
safetensors
.
torch
import
load_file
def
convert_ace_step_weights
(
checkpoint_dir
,
dit_config
,
output_dir
,
dtype_str
=
"bf16"
):
"""
Convert ACE-Step checkpoint weights into a Diffusers-compatible pipeline layout.
The original ACE-Step model stores all weights in a single `model.safetensors` file
under `checkpoints/<dit_config>/`. This script splits the weights into separate
sub-model directories that can be loaded by `AceStepPipeline.from_pretrained()`.
Expected input layout:
checkpoint_dir/
<dit_config>/ # e.g., acestep-v15-turbo
config.json
model.safetensors
silence_latent.pt
vae/
config.json
diffusion_pytorch_model.safetensors
Qwen3-Embedding-0.6B/
config.json
model.safetensors
tokenizer.json
...
Output layout:
output_dir/
model_index.json
transformer/
config.json
diffusion_pytorch_model.safetensors
condition_encoder/
config.json
diffusion_pytorch_model.safetensors
vae/
config.json
diffusion_pytorch_model.safetensors
text_encoder/
config.json
model.safetensors
...
tokenizer/
tokenizer.json
...
"""
# Support `--checkpoint_dir <repo-id>` by snapshot-downloading it first. A
# local path that happens not to exist still raises the clearer FileNotFoundError
# below, so we only fall through to the Hub if the path is missing AND looks like
# a repo id (namespace/name).
if
not
os
.
path
.
exists
(
checkpoint_dir
)
and
"/"
in
checkpoint_dir
and
not
checkpoint_dir
.
startswith
((
"."
,
"~"
,
"/"
)):
try
:
from
huggingface_hub
import
snapshot_download
print
(
f"Downloading `
{
checkpoint_dir
}
` from the Hugging Face Hub ..."
)
checkpoint_dir
=
snapshot_download
(
repo_id
=
checkpoint_dir
)
print
(
f" -> local snapshot at
{
checkpoint_dir
}
"
)
except
ImportError
as
e
:
raise
ImportError
(
"To use a Hugging Face Hub repo id for --checkpoint_dir, install `huggingface_hub`."
)
from
e
# Resolve paths
dit_dir
=
os
.
path
.
join
(
checkpoint_dir
,
dit_config
)
vae_dir
=
os
.
path
.
join
(
checkpoint_dir
,
"vae"
)
text_encoder_dir
=
os
.
path
.
join
(
checkpoint_dir
,
"Qwen3-Embedding-0.6B"
)
# The DiT weights ship either as a single `model.safetensors` (the smaller turbo
# variant) or as sharded safetensors keyed by `model.safetensors.index.json`
# (the 5B XL variant). Resolve both layouts to `dit_weight_files` and load below.
single_model_path
=
os
.
path
.
join
(
dit_dir
,
"model.safetensors"
)
sharded_index_path
=
os
.
path
.
join
(
dit_dir
,
"model.safetensors.index.json"
)
config_path
=
os
.
path
.
join
(
dit_dir
,
"config.json"
)
if
os
.
path
.
exists
(
single_model_path
):
dit_weight_files
=
[
single_model_path
]
elif
os
.
path
.
exists
(
sharded_index_path
):
with
open
(
sharded_index_path
)
as
f
:
shard_index
=
json
.
load
(
f
)
dit_weight_files
=
[
os
.
path
.
join
(
dit_dir
,
s
)
for
s
in
sorted
(
set
(
shard_index
[
"weight_map"
].
values
()))]
for
p
in
dit_weight_files
:
if
not
os
.
path
.
exists
(
p
):
raise
FileNotFoundError
(
f"sharded DiT weight missing:
{
p
}
"
)
else
:
raise
FileNotFoundError
(
f"DiT weights not found at:
{
single_model_path
}
or
{
sharded_index_path
}
. "
"Expected either a single `model.safetensors` or a sharded "
"`model.safetensors.index.json` + per-shard files."
)
for
path
,
name
in
[
(
config_path
,
"config"
),
(
vae_dir
,
"VAE"
),
(
text_encoder_dir
,
"text encoder"
),
]:
if
not
os
.
path
.
exists
(
path
):
raise
FileNotFoundError
(
f"
{
name
}
not found at:
{
path
}
"
)
# Select dtype
dtype_map
=
{
"fp32"
:
torch
.
float32
,
"fp16"
:
torch
.
float16
,
"bf16"
:
torch
.
bfloat16
}
if
dtype_str
not
in
dtype_map
:
raise
ValueError
(
f"Unsupported dtype:
{
dtype_str
}
. Choose from
{
list
(
dtype_map
.
keys
())
}
"
)
target_dtype
=
dtype_map
[
dtype_str
]
# Load original config
with
open
(
config_path
)
as
f
:
original_config
=
json
.
load
(
f
)
print
(
f"Loading DiT weights from
{
len
(
dit_weight_files
)
}
file(s) ..."
)
state_dict
=
{}
for
p
in
dit_weight_files
:
print
(
f" loading
{
os
.
path
.
basename
(
p
)
}
"
)
state_dict
.
update
(
load_file
(
p
))
print
(
f" Total keys:
{
len
(
state_dict
)
}
"
)
# =========================================================================
# 1. Split weights by prefix
# =========================================================================
transformer_sd
=
{}
condition_encoder_sd
=
{}
audio_tokenizer_sd
=
{}
audio_token_detokenizer_sd
=
{}
other_sd
=
{}
# Rename original ACE-Step attention keys to the diffusers `Attention` +
# `AttnProcessor` convention (`to_q`/`to_k`/`to_v`/`to_out.0`/`norm_q`/`norm_k`).
# Applies uniformly to both the DiT (self-attn and cross-attn) and the
# condition-encoder self-attention, since both use `AceStepAttention`.
_ATTN_KEY_RENAMES
=
[
(
".q_proj."
,
".to_q."
),
(
".k_proj."
,
".to_k."
),
(
".v_proj."
,
".to_v."
),
(
".o_proj."
,
".to_out.0."
),
(
".q_norm."
,
".norm_q."
),
(
".k_norm."
,
".norm_k."
),
]
def
_rename_attn_keys
(
key
:
str
)
->
str
:
for
old
,
new
in
_ATTN_KEY_RENAMES
:
key
=
key
.
replace
(
old
,
new
)
return
key
for
key
,
value
in
state_dict
.
items
():
if
key
.
startswith
(
"decoder."
):
# Strip "decoder." prefix for the transformer
new_key
=
key
[
len
(
"decoder."
) :]
# The original model uses nn.Sequential for proj_in/proj_out:
# proj_in = Sequential(Lambda, Conv1d, Lambda)
# proj_out = Sequential(Lambda, ConvTranspose1d, Lambda)
# Only the Conv1d/ConvTranspose1d (index 1) has parameters.
# In diffusers, we use standalone Conv1d/ConvTranspose1d named proj_in_conv/proj_out_conv.
new_key
=
new_key
.
replace
(
"proj_in.1."
,
"proj_in_conv."
)
new_key
=
new_key
.
replace
(
"proj_out.1."
,
"proj_out_conv."
)
new_key
=
_rename_attn_keys
(
new_key
)
transformer_sd
[
new_key
]
=
value
.
to
(
target_dtype
)
elif
key
.
startswith
(
"encoder."
):
# Strip "encoder." prefix for the condition encoder
new_key
=
key
[
len
(
"encoder."
) :]
new_key
=
_rename_attn_keys
(
new_key
)
condition_encoder_sd
[
new_key
]
=
value
.
to
(
target_dtype
)
elif
key
==
"null_condition_emb"
:
# Learned unconditional embedding (used by the base/SFT CFG path).
# Keep it co-located with the condition encoder since that is where the
# pipeline pulls unconditional sequences from.
condition_encoder_sd
[
"null_condition_emb"
]
=
value
.
to
(
target_dtype
)
elif
key
.
startswith
(
"tokenizer."
):
new_key
=
key
[
len
(
"tokenizer."
) :]
new_key
=
_rename_attn_keys
(
new_key
)
audio_tokenizer_sd
[
new_key
]
=
value
.
to
(
target_dtype
)
elif
key
.
startswith
(
"detokenizer."
):
new_key
=
key
[
len
(
"detokenizer."
) :]
new_key
=
_rename_attn_keys
(
new_key
)
audio_token_detokenizer_sd
[
new_key
]
=
value
.
to
(
target_dtype
)
else
:
other_sd
[
key
]
=
value
.
to
(
target_dtype
)
print
(
f" Transformer keys:
{
len
(
transformer_sd
)
}
"
)
print
(
f" Condition encoder keys:
{
len
(
condition_encoder_sd
)
}
"
)
print
(
f" Audio tokenizer keys:
{
len
(
audio_tokenizer_sd
)
}
"
)
print
(
f" Audio token detokenizer keys:
{
len
(
audio_token_detokenizer_sd
)
}
"
)
print
(
f" Other keys:
{
len
(
other_sd
)
}
(
{
list
(
other_sd
.
keys
())[:
5
]
}
...)"
)
# =========================================================================
# 2. Build configs for each sub-model
# =========================================================================
# On the 5B XL turbo the condition encoder is narrower than the DiT
# (`encoder_hidden_size=2048` feeding a `hidden_size=2560` DiT). Non-XL
# turbo / base checkpoints don't set this field, so fall back to
# `hidden_size` — that makes the DiT's `condition_embedder` an identity-width
# Linear as before. Similarly `encoder_intermediate_size` /
# `encoder_num_attention_heads` / `encoder_num_key_value_heads` describe the
# condition encoder on XL only.
encoder_hidden_size
=
original_config
.
get
(
"encoder_hidden_size"
,
original_config
[
"hidden_size"
])
encoder_intermediate_size
=
original_config
.
get
(
"encoder_intermediate_size"
,
original_config
[
"intermediate_size"
])
encoder_num_attention_heads
=
original_config
.
get
(
"encoder_num_attention_heads"
,
original_config
[
"num_attention_heads"
]
)
encoder_num_key_value_heads
=
original_config
.
get
(
"encoder_num_key_value_heads"
,
original_config
[
"num_key_value_heads"
]
)
# Transformer (DiT) config. `is_turbo` / `model_version` propagate the variant so
# the pipeline can pick the right CFG / shift / step-count defaults at inference.
# Note: `max_position_embeddings` is dropped (RoPE computes freqs on-the-fly per call),
# and `use_sliding_window` is implied by the mix of `layer_types`.
transformer_config
=
{
"_class_name"
:
"AceStepTransformer1DModel"
,
"_diffusers_version"
:
"0.33.0.dev0"
,
"hidden_size"
:
original_config
[
"hidden_size"
],
"intermediate_size"
:
original_config
[
"intermediate_size"
],
"num_hidden_layers"
:
original_config
[
"num_hidden_layers"
],
"num_attention_heads"
:
original_config
[
"num_attention_heads"
],
"num_key_value_heads"
:
original_config
[
"num_key_value_heads"
],
"head_dim"
:
original_config
[
"head_dim"
],
"in_channels"
:
original_config
[
"in_channels"
],
"audio_acoustic_hidden_dim"
:
original_config
[
"audio_acoustic_hidden_dim"
],
"patch_size"
:
original_config
[
"patch_size"
],
"rope_theta"
:
original_config
[
"rope_theta"
],
"attention_bias"
:
original_config
[
"attention_bias"
],
"attention_dropout"
:
original_config
[
"attention_dropout"
],
"rms_norm_eps"
:
original_config
[
"rms_norm_eps"
],
"sliding_window"
:
original_config
[
"sliding_window"
],
"layer_types"
:
original_config
[
"layer_types"
],
"encoder_hidden_size"
:
encoder_hidden_size
,
"is_turbo"
:
bool
(
original_config
.
get
(
"is_turbo"
,
False
)),
"model_version"
:
original_config
.
get
(
"model_version"
),
}
# Condition encoder config
condition_encoder_config
=
{
"_class_name"
:
"AceStepConditionEncoder"
,
"_diffusers_version"
:
"0.33.0.dev0"
,
"hidden_size"
:
encoder_hidden_size
,
"intermediate_size"
:
encoder_intermediate_size
,
"text_hidden_dim"
:
original_config
[
"text_hidden_dim"
],
"timbre_hidden_dim"
:
original_config
[
"timbre_hidden_dim"
],
"num_lyric_encoder_hidden_layers"
:
original_config
[
"num_lyric_encoder_hidden_layers"
],
"num_timbre_encoder_hidden_layers"
:
original_config
[
"num_timbre_encoder_hidden_layers"
],
"num_attention_heads"
:
encoder_num_attention_heads
,
"num_key_value_heads"
:
encoder_num_key_value_heads
,
"head_dim"
:
original_config
[
"head_dim"
],
"rope_theta"
:
original_config
[
"rope_theta"
],
"attention_bias"
:
original_config
[
"attention_bias"
],
"attention_dropout"
:
original_config
[
"attention_dropout"
],
"rms_norm_eps"
:
original_config
[
"rms_norm_eps"
],
"sliding_window"
:
original_config
[
"sliding_window"
],
}
audio_tokenizer_config
=
{
"_class_name"
:
"AceStepAudioTokenizer"
,
"_diffusers_version"
:
"0.33.0.dev0"
,
"hidden_size"
:
encoder_hidden_size
,
"intermediate_size"
:
encoder_intermediate_size
,
"audio_acoustic_hidden_dim"
:
original_config
[
"audio_acoustic_hidden_dim"
],
"pool_window_size"
:
original_config
.
get
(
"pool_window_size"
,
5
),
"fsq_dim"
:
original_config
.
get
(
"fsq_dim"
,
encoder_hidden_size
),
"fsq_input_levels"
:
original_config
.
get
(
"fsq_input_levels"
, [
8
,
8
,
8
,
5
,
5
,
5
]),
"fsq_input_num_quantizers"
:
original_config
.
get
(
"fsq_input_num_quantizers"
,
1
),
"num_attention_pooler_hidden_layers"
:
original_config
.
get
(
"num_attention_pooler_hidden_layers"
,
2
),
"num_attention_heads"
:
encoder_num_attention_heads
,
"num_key_value_heads"
:
encoder_num_key_value_heads
,
"head_dim"
:
original_config
[
"head_dim"
],
"rope_theta"
:
original_config
[
"rope_theta"
],
"attention_bias"
:
original_config
[
"attention_bias"
],
"attention_dropout"
:
original_config
[
"attention_dropout"
],
"rms_norm_eps"
:
original_config
[
"rms_norm_eps"
],
"sliding_window"
:
original_config
[
"sliding_window"
],
"layer_types"
:
original_config
[
"layer_types"
][:
original_config
.
get
(
"num_attention_pooler_hidden_layers"
,
2
)],
}
audio_token_detokenizer_config
=
{
"_class_name"
:
"AceStepAudioTokenDetokenizer"
,
"_diffusers_version"
:
"0.33.0.dev0"
,
"hidden_size"
:
encoder_hidden_size
,
"intermediate_size"
:
encoder_intermediate_size
,
"audio_acoustic_hidden_dim"
:
original_config
[
"audio_acoustic_hidden_dim"
],
"pool_window_size"
:
original_config
.
get
(
"pool_window_size"
,
5
),
"num_attention_pooler_hidden_layers"
:
original_config
.
get
(
"num_attention_pooler_hidden_layers"
,
2
),
"num_attention_heads"
:
encoder_num_attention_heads
,
"num_key_value_heads"
:
encoder_num_key_value_heads
,
"head_dim"
:
original_config
[
"head_dim"
],
"rope_theta"
:
original_config
[
"rope_theta"
],
"attention_bias"
:
original_config
[
"attention_bias"
],
"attention_dropout"
:
original_config
[
"attention_dropout"
],
"rms_norm_eps"
:
original_config
[
"rms_norm_eps"
],
"sliding_window"
:
original_config
[
"sliding_window"
],
"layer_types"
:
original_config
[
"layer_types"
][:
original_config
.
get
(
"num_attention_pooler_hidden_layers"
,
2
)],
}
# =========================================================================
# 3. Bake silence_latent into the condition_encoder state dict.
#
# The original loader in
# acestep/core/generation/handler/init_service_loader.py:214 does
# self.silence_latent = torch.load(...).transpose(1, 2)
# converting the stored [B, C=64, T=15000] tensor to [B, T, C=64] before any
# downstream slicing. Do the same transpose here and register it as the
# `silence_latent` buffer on AceStepConditionEncoder — the pipeline slices
# `silence_latent[:, :timbre_fix_frame, :]` to build the "silence" input to the
# timbre encoder when no reference audio is supplied. Passing literal zeros
# produces drone-like audio.
silence_latent_src
=
os
.
path
.
join
(
dit_dir
,
"silence_latent.pt"
)
if
os
.
path
.
exists
(
silence_latent_src
):
silence_raw
=
torch
.
load
(
silence_latent_src
,
weights_only
=
True
,
map_location
=
"cpu"
)
silence_latent
=
silence_raw
.
transpose
(
1
,
2
).
to
(
target_dtype
).
contiguous
()
print
(
f" silence_latent raw shape:
{
tuple
(
silence_raw
.
shape
)
}
-> baked shape:
{
tuple
(
silence_latent
.
shape
)
}
"
)
condition_encoder_sd
[
"silence_latent"
]
=
silence_latent
# =========================================================================
# 4. Build the AceStepPipeline in memory and save via `save_pretrained`.
# Assembling the pipeline directly (rather than hand-writing model_index.json)
# ensures the saved repo stays in sync with the `AceStepPipeline.__init__`
# signature — e.g. a future sub-module added to the pipeline can't silently
# drift out of `model_index.json`.
# =========================================================================
from
transformers
import
AutoModel
,
AutoTokenizer
from
diffusers
import
(
AceStepPipeline
,
AceStepTransformer1DModel
,
AutoencoderOobleck
,
FlowMatchEulerDiscreteScheduler
,
)
from
diffusers
.
pipelines
.
ace_step
import
(
AceStepAudioTokenDetokenizer
,
AceStepAudioTokenizer
,
AceStepConditionEncoder
,
)
# Drop metadata keys — they're re-populated by `save_pretrained` at save time.
transformer_init_kwargs
=
{
k
:
v
for
k
,
v
in
transformer_config
.
items
()
if
not
k
.
startswith
(
"_"
)}
condition_encoder_init_kwargs
=
{
k
:
v
for
k
,
v
in
condition_encoder_config
.
items
()
if
not
k
.
startswith
(
"_"
)}
audio_tokenizer_init_kwargs
=
{
k
:
v
for
k
,
v
in
audio_tokenizer_config
.
items
()
if
not
k
.
startswith
(
"_"
)}
audio_token_detokenizer_init_kwargs
=
{
k
:
v
for
k
,
v
in
audio_token_detokenizer_config
.
items
()
if
not
k
.
startswith
(
"_"
)
}
print
(
"
\n
Constructing transformer ..."
)
transformer
=
AceStepTransformer1DModel
(
**
transformer_init_kwargs
).
to
(
target_dtype
)
transformer
.
load_state_dict
(
transformer_sd
,
strict
=
True
)
print
(
"Constructing condition_encoder ..."
)
condition_encoder
=
AceStepConditionEncoder
(
**
condition_encoder_init_kwargs
).
to
(
target_dtype
)
condition_encoder
.
load_state_dict
(
condition_encoder_sd
,
strict
=
True
)
print
(
"Constructing audio_tokenizer ..."
)
audio_tokenizer
=
AceStepAudioTokenizer
(
**
audio_tokenizer_init_kwargs
).
to
(
target_dtype
)
audio_tokenizer
.
load_state_dict
(
audio_tokenizer_sd
,
strict
=
True
)
print
(
"Constructing audio_token_detokenizer ..."
)
audio_token_detokenizer
=
AceStepAudioTokenDetokenizer
(
**
audio_token_detokenizer_init_kwargs
).
to
(
target_dtype
)
audio_token_detokenizer
.
load_state_dict
(
audio_token_detokenizer_sd
,
strict
=
True
)
print
(
"Loading VAE ..."
)
vae
=
AutoencoderOobleck
.
from_pretrained
(
vae_dir
).
to
(
target_dtype
)
print
(
"Loading text encoder ..."
)
text_encoder
=
AutoModel
.
from_pretrained
(
text_encoder_dir
,
torch_dtype
=
target_dtype
)
print
(
"Loading tokenizer ..."
)
tokenizer
=
AutoTokenizer
.
from_pretrained
(
text_encoder_dir
)
# ACE-Step drives the DiT with t ∈ [0, 1] and computes its own shifted / turbo
# sigma schedule, which it passes to `scheduler.set_timesteps(sigmas=...)` at
# sampling time. So the scheduler needs `num_train_timesteps=1` (so
# `scheduler.timesteps == sigmas`) and `shift=1.0` (so it doesn't re-shift
# already-shifted sigmas). All other defaults are fine.
scheduler
=
FlowMatchEulerDiscreteScheduler
(
num_train_timesteps
=
1
,
shift
=
1.0
)
pipe
=
AceStepPipeline
(
vae
=
vae
,
text_encoder
=
text_encoder
,
tokenizer
=
tokenizer
,
transformer
=
transformer
,
condition_encoder
=
condition_encoder
,
scheduler
=
scheduler
,
audio_tokenizer
=
audio_tokenizer
,
audio_token_detokenizer
=
audio_token_detokenizer
,
)
print
(
f"
\n
Saving pipeline ->
{
output_dir
}
"
)
pipe
.
save_pretrained
(
output_dir
,
safe_serialization
=
True
,
max_shard_size
=
"5GB"
)
# Keep the raw silence_latent.pt at the pipeline root for debugging — not
# required by `from_pretrained`, but makes it easy to re-derive the buffer
# without re-running the full conversion.
if
os
.
path
.
exists
(
silence_latent_src
):
shutil
.
copy2
(
silence_latent_src
,
os
.
path
.
join
(
output_dir
,
"silence_latent.pt"
))
print
(
f" kept raw silence_latent copy at
{
output_dir
}
/silence_latent.pt"
)
# Report any keys that were not saved to registered pipeline modules.
if
other_sd
:
print
(
f"
\n
Note:
{
len
(
other_sd
)
}
keys were dropped:"
)
for
key
in
sorted
(
other_sd
.
keys
())[:
10
]:
print
(
f"
{
key
}
"
)
if
len
(
other_sd
)
>
10
:
print
(
f" ... (
{
len
(
other_sd
)
-
10
}
more)"
)
print
(
f"
\n
Conversion complete! Output saved to:
{
output_dir
}
"
)
print
(
"
\n
To load the pipeline:"
)
print
(
" from diffusers import AceStepPipeline"
)
print
(
f" pipe = AceStepPipeline.from_pretrained('
{
output_dir
}
', torch_dtype=torch.bfloat16)"
)
print
(
" pipe = pipe.to('cuda')"
)
if
__name__
==
"__main__"
:
parser
=
argparse
.
ArgumentParser
(
description
=
"Convert ACE-Step model weights to Diffusers pipeline format"
)
parser
.
add_argument
(
"--checkpoint_dir"
,
type
=
str
,
required
=
True
,
help
=
"Path to the ACE-Step checkpoints directory (containing vae/, Qwen3-Embedding-0.6B/, and dit config dirs)"
,
)
parser
.
add_argument
(
"--dit_config"
,
type
=
str
,
default
=
"acestep-v15-turbo"
,
help
=
"Name of the DiT config directory (default: acestep-v15-turbo)"
,
)
parser
.
add_argument
(
"--output_dir"
,
type
=
str
,
required
=
True
,
help
=
"Path to save the converted Diffusers pipeline"
,
)
parser
.
add_argument
(
"--dtype"
,
type
=
str
,
default
=
"bf16"
,
choices
=
[
"fp32"
,
"fp16"
,
"bf16"
],
help
=
"Data type for saved weights (default: bf16)"
,
)
args
=
parser
.
parse_args
()
convert_ace_step_weights
(
checkpoint_dir
=
args
.
checkpoint_dir
,
dit_config
=
args
.
dit_config
,
output_dir
=
args
.
output_dir
,
dtype_str
=
args
.
dtype
,
)
Back
|
FazBrowse Home
|
New Git URL