{{ message }}
Repository navigation
Expand file tree
/
Copy pathconvert_stable_audio_3_to_diffusers.py
More file actions
854 lines (718 loc) · 39.7 KB
/
Copy pathconvert_stable_audio_3_to_diffusers.py
File metadata and controls
854 lines (718 loc) · 39.7 KB
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
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
#!/usr/bin/env python3
# Copyright 2025 Stability AI and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Convert a Stable Audio 3 checkpoint (stable-audio-tools format) to HuggingFace
Diffusers components.
The script handles the complete SA3-Medium architecture conversion:
┌──────────────────────────────────────────────────────────┐
│ Source (stabilityai/stable-audio-3-medium checkpoint) │
│ pretransform.model.* → VAE (AutoencoderSAME) │
│ conditioner.conditioners.seconds_total.* │
│ → DurationEmbedder │
│ conditioner.conditioners.prompt.model.* │
│ → TextEncoder (T5Gemma) │
│ model.* → DiT (StableAudio3DiTModel) │
└──────────────────────────────────────────────────────────┘
Key conversion details:
• WNConv1d: weight_g / weight_v kept as-is (both sides use weight_norm).
• Differential self-attention QKV reorder:
ref [q | k | v | q2 | k2] → diffusers [q1 | q2 | k1 | k2 | v]
• SAME norm renames: pre_norm→norm_attn, ff_norm→norm_ff,
cross_attend_norm→norm2, pre_norm(DiT)→norm1.
• SAME FF renames: ff.ff.0.proj.* → ff.proj_in.* | ff.ff.2.* → ff.proj_out.*
• Bottleneck renames: scaling_factor → scale.
• DiT weights live under the "model.model." prefix; the full architecture
(AdaLN to_scale_shift_gate + global_cond_embedder, memory_tokens,
rotary_pos_emb, per-block to_local_embed for inpainting) is converted.
Usage:
python scripts/convert_stable_audio_3_to_diffusers.py \\
--checkpoint_path stabilityai/stable-audio-3-medium \\
--model_config_path /path/to/model_config.json \\
--output_dir /path/to/output \\
[--text_encoder_repo google/t5gemma-b-b-ul2] \\
[--dtype bfloat16]
"""
import argparse
import json
import math
import sys
from pathlib import Path
from typing import Optional
import torch
from safetensors.torch import load_file
# Ensure UTF-8 stdout/stderr for Unicode output
if hasattr(sys.stdout, "reconfigure"):
sys.stdout.reconfigure(encoding="utf-8")
sys.stderr.reconfigure(encoding="utf-8")
# ──────────────────────────────────────────────────────────────────────────────
# Tensor-level helpers
# ──────────────────────────────────────────────────────────────────────────────
def _reorder_differential_qkv(weight: torch.Tensor) -> torch.Tensor:
"""Reorder fused self-attention QKV weight from reference layout to diffusers layout.
Reference layout (stable-audio-tools):
to_qkv output chunks: [q | k | v | q2 | k2]
Diffusers layout (StableAudio3SelfAttention):
to_qkv output chunks: [q1 | q2 | k1 | k2 | v]
"""
D = weight.shape[0] // 5
q, k, v, q2, k2 = weight.split(D, dim=0)
return torch.cat([q, q2, k, k2, v], dim=0)
# ──────────────────────────────────────────────────────────────────────────────
# Key-transformation helpers
# ──────────────────────────────────────────────────────────────────────────────
# Inside a TransformerResamplingBlock's per-layer transformer blocks:
# ref key → our key
_TRB_BLOCK_RENAMES = [
("pre_norm.", "norm_attn."),
("self_attn.to_qkv.", "attn.to_qkv."), # fused; QKV reorder applied separately
("self_attn.to_out.", "attn.to_out."),
("self_attn.q_norm.", "attn.q_norm."),
("self_attn.k_norm.", "attn.k_norm."),
("ff_norm.", "norm_ff."),
("ff.ff.0.proj.", "ff.proj_in."), # GLU → proj_in
("ff.ff.2.", "ff.proj_out."), # linear_out → proj_out
]
# Inside a DiT block (StableAudio3DiTBlock). The diffusers block keeps the reference norm and
# attention names, so most entries are identity; only the SwiGLU feed-forward is renamed.
# ref key → our key
_DIT_BLOCK_RENAMES = [
("ff.ff.0.proj.", "ff.proj_in."),
("ff.ff.2.", "ff.proj_out."),
]
def _apply_renames(local_key: str, renames) -> str:
for old, new in renames:
if old in local_key:
local_key = local_key.replace(old, new, 1)
break
return local_key
def _is_differential_qkv_key(key: str) -> bool:
"""Return True if this key is a fused QKV weight that needs reordering."""
return "self_attn.to_qkv.weight" in key or "attn.to_qkv.weight" in key
# ──────────────────────────────────────────────────────────────────────────────
# VAE converter
# ──────────────────────────────────────────────────────────────────────────────
def _infer_trb_depth(ref_sd: dict, base: str) -> int:
"""Count how many TRB blocks exist under `base` (e.g. 'encoder.layers')."""
depth = 0
while f"{base}.{depth}.new_tokens" in ref_sd:
depth += 1
return depth
def _convert_trb_block(
ref_sd: dict,
ref_prefix: str, # e.g. "encoder.layers.0."
our_prefix: str, # e.g. "encoder.blocks.0."
differential: bool,
out: dict,
skipped: list,
):
"""Copy all keys inside one TransformerResamplingBlock."""
for key, val in ref_sd.items():
if not key.startswith(ref_prefix):
continue
local = key[len(ref_prefix) :] # strip leading prefix
# --- mapping (WNConv1d): weight_g, weight_v, bias copied as-is ---
if local.startswith("mapping."):
out[our_prefix + local] = val
continue
# --- new_tokens ---
if local == "new_tokens":
out[our_prefix + local] = val
continue
# --- transformers.{j}.* ---
if local.startswith("transformers."):
new_local = _apply_renames(local, _TRB_BLOCK_RENAMES)
new_key = our_prefix + new_local
if differential and _is_differential_qkv_key(new_key):
val = _reorder_differential_qkv(val)
out[new_key] = val
continue
skipped.append(key)
def convert_vae(ref_sd: dict, differential: bool = True) -> dict:
"""Build diffusers AutoencoderSAME state dict from reference checkpoint."""
out = {}
skipped = []
ae_prefix = "pretransform.model."
enc_base = ae_prefix + "encoder.layers"
dec_base = ae_prefix + "decoder.layers"
enc_depth = _infer_trb_depth(ref_sd, enc_base)
if enc_depth == 0:
print("WARNING: could not detect encoder TRB depth; defaulting to 1.")
enc_depth = 1
# ---- ENCODER TRBs ----
# ref: encoder.layers.{i} (i = 0..enc_depth-1) → our: encoder.blocks.{i}
for i in range(enc_depth):
_convert_trb_block(
ref_sd,
f"{enc_base}.{i}.",
f"encoder.blocks.{i}.",
differential,
out,
skipped,
)
# ---- ENCODER projection (Linear after all TRBs) ----
# Comes after enc_depth TRBs and one Transpose → index enc_depth+1
# But Transpose has no params, so the Linear shows as layers.{enc_depth+1}
# (enc_depth TRBs, then 2 Transposes and 1 Linear: layers[enc_depth] = Transpose,
# layers[enc_depth+1] = Linear)
enc_linear_idx = enc_depth + 1
enc_linear_prefix = f"{enc_base}.{enc_linear_idx}."
for key, val in ref_sd.items():
if key.startswith(enc_linear_prefix):
suffix = key[len(enc_linear_prefix) :]
out[f"encoder.proj.{suffix}"] = val
# ---- DECODER projection (Linear before all TRBs) ----
# Decoder layers: [Transpose, Linear, Transpose, TRB_0, TRB_1, ...]
# Linear is always at index 1
dec_linear_prefix = f"{dec_base}.1."
for key, val in ref_sd.items():
if key.startswith(dec_linear_prefix):
suffix = key[len(dec_linear_prefix) :]
out[f"decoder.proj.{suffix}"] = val
# ---- DECODER TRBs ----
# Decoder TRBs start at index 3 in reference.
# Reference builds them in reverse order (channel_dims[depth]→[depth-1], …).
# Our SAMEDecoder.blocks also reverses: blocks[0] = largest stride, same order.
for i in range(enc_depth): # same depth as encoder
ref_idx = 3 + i
_convert_trb_block(
ref_sd,
f"{dec_base}.{ref_idx}.",
f"decoder.blocks.{i}.",
differential,
out,
skipped,
)
# ---- BOTTLENECK ----
bn_prefix = ae_prefix + "bottleneck."
bottleneck_renames = {
"scaling_factor": "scale",
"bias": "bias",
"running_std": "running_std",
}
for ref_suffix, our_suffix in bottleneck_renames.items():
full_key = bn_prefix + ref_suffix
if full_key in ref_sd:
out[f"bottleneck.{our_suffix}"] = ref_sd[full_key]
if skipped:
print(f" VAE: skipped {len(skipped)} keys (pretransform.* patcher, noise_scaling_factor, ...)")
return out
# ──────────────────────────────────────────────────────────────────────────────
# Duration embedder converter
# ──────────────────────────────────────────────────────────────────────────────
def convert_duration_embedder(ref_sd: dict, min_freq: float = 0.5, max_freq: float = 10000.0) -> dict:
"""Build diffusers StableAudio3DurationEmbedder state dict."""
out = {}
base = "conditioner.conditioners.seconds_total.embedder.embedding."
# index 0 = ExpoFourierFeatures (no learnable params)
# index 1 = nn.Linear
for suffix in ("weight", "bias"):
src = f"{base}1.{suffix}"
if src in ref_sd:
out[f"linear.{suffix}"] = ref_sd[src]
else:
print(f" DurationEmbedder: key not found: {src}")
# Compute the freqs buffer (matches StableAudio3DurationEmbedder.__init__)
if "linear.weight" in out:
fourier_dim = out["linear.weight"].shape[1]
half = fourier_dim // 2
ramp = torch.linspace(0.0, 1.0, half)
log_min = math.log(min_freq)
log_max = math.log(max_freq)
freqs = torch.exp(ramp * (log_max - log_min) + log_min)
out["freqs"] = freqs
return out
# ──────────────────────────────────────────────────────────────────────────────
# DiT converter
# ──────────────────────────────────────────────────────────────────────────────
def convert_dit(ref_sd: dict, differential: bool = True) -> dict:
"""Build diffusers StableAudio3DiTModel state dict."""
out = {}
# The DiT weights live under the "model.model." prefix in the checkpoint.
p = "model.model."
# Top-level keys (outside the inner transformer).
top_renames = {
f"{p}to_timestep_embed.0.weight": "to_timestep_embed.0.weight",
f"{p}to_timestep_embed.0.bias": "to_timestep_embed.0.bias",
f"{p}to_timestep_embed.2.weight": "to_timestep_embed.2.weight",
f"{p}to_timestep_embed.2.bias": "to_timestep_embed.2.bias",
f"{p}to_cond_embed.0.weight": "to_cond_embed.0.weight",
f"{p}to_cond_embed.2.weight": "to_cond_embed.2.weight",
f"{p}to_global_embed.0.weight": "to_global_embed.0.weight",
f"{p}to_global_embed.2.weight": "to_global_embed.2.weight",
f"{p}preprocess_conv.weight": "preprocess_conv.weight",
f"{p}postprocess_conv.weight": "postprocess_conv.weight",
# Inner transformer module-level keys.
f"{p}transformer.project_in.weight": "proj_in.weight",
f"{p}transformer.project_out.weight": "proj_out.weight",
f"{p}transformer.memory_tokens": "memory_tokens",
f"{p}transformer.rotary_pos_emb.inv_freq": "rotary_pos_emb.inv_freq",
f"{p}transformer.global_cond_embedder.0.weight": "global_cond_embedder.0.weight",
f"{p}transformer.global_cond_embedder.0.bias": "global_cond_embedder.0.bias",
f"{p}transformer.global_cond_embedder.2.weight": "global_cond_embedder.2.weight",
f"{p}transformer.global_cond_embedder.2.bias": "global_cond_embedder.2.bias",
}
for ref_key, our_key in top_renames.items():
if ref_key in ref_sd:
out[our_key] = ref_sd[ref_key]
else:
print(f" DiT: top-level key not found: {ref_key}")
# Per-block keys: model.model.transformer.layers.{i}.*
block_prefix = f"{p}transformer.layers."
block_indices = set()
for k in ref_sd:
if k.startswith(block_prefix):
idx_str = k[len(block_prefix) :].split(".")[0]
if idx_str.isdigit():
block_indices.add(int(idx_str))
for i in sorted(block_indices):
ref_blk = f"{block_prefix}{i}."
for key, val in ref_sd.items():
if not key.startswith(ref_blk):
continue
local = key[len(ref_blk) :]
new_local = _apply_renames(local, _DIT_BLOCK_RENAMES)
new_key = f"transformer_blocks.{i}.{new_local}"
# Self-attention fused QKV needs reordering ([q|k|v|q2|k2] → [q1|q2|k1|k2|v]).
# Differential cross-attention to_q ([q|q2]) and to_kv ([k|k2|v]) already match the
# diffusers layout, so no cross-attention reorder is required.
if differential and _is_differential_qkv_key(new_key):
val = _reorder_differential_qkv(val)
out[new_key] = val
# The learned text-padding embedding lives on the conditioner in the reference checkpoint,
# but the diffusers DiT owns it: it replaces padded cross-attention positions with this vector
# and then attends to the full context (the reference disables the cross-attention mask).
pad_key = "conditioner.conditioners.prompt.padding_embedding"
if pad_key in ref_sd:
out["prompt_padding_embedding"] = ref_sd[pad_key]
else:
print(f" DiT: padding embedding not found: {pad_key}")
return out
# ──────────────────────────────────────────────────────────────────────────────
# Text encoder extractor
# ──────────────────────────────────────────────────────────────────────────────
def extract_text_encoder(ref_sd: dict) -> dict:
"""Strip the conditioner prefix and return the T5Gemma model state dict."""
prefix = "conditioner.conditioners.prompt.model."
te_sd = {}
for key, val in ref_sd.items():
if key.startswith(prefix):
te_sd[key[len(prefix) :]] = val
return te_sd
# ──────────────────────────────────────────────────────────────────────────────
# Config inference from checkpoint
# ──────────────────────────────────────────────────────────────────────────────
def _infer_vae_config(ref_sd: dict, model_config: Optional[dict] = None) -> dict:
"""
Infer AutoencoderSAME config from checkpoint tensor shapes.
Some hyper-parameters (the TRB stride, the sliding-window width and the sinusoidal-FFN layer counts) are NOT
recoverable from the weights alone — with ``variable_stride`` the encoder/decoder ``new_tokens`` collapse to a
single shared token, so the stride is invisible. When ``model_config`` (the parsed ``model_config.json``) is
provided, those values are read directly from it; otherwise production SAME-L/S defaults are used.
"""
# Bottleneck scale shape: (1, latent_dim, 1)
latent_dim = ref_sd.get(
"pretransform.model.bottleneck.scaling_factor",
ref_sd.get("pretransform.model.bottleneck.scale", torch.zeros(1, 256, 1)),
).shape[1]
# Encoder linear weight: (latent_dim, channel_dims[-1])
# We detect enc_depth to find the linear key
enc_base = "pretransform.model.encoder.layers"
enc_depth = _infer_trb_depth(ref_sd, enc_base)
if enc_depth == 0:
enc_depth = 1
enc_linear_key = f"{enc_base}.{enc_depth + 1}.weight"
if enc_linear_key in ref_sd:
enc_final_ch = ref_sd[enc_linear_key].shape[1] # (latent_dim, enc_final_ch)
else:
enc_final_ch = 768 # SAME-S default
# TRB 0 new_tokens shape (encoder): (1, 1, out_channels)
trb0_nt = ref_sd.get(f"{enc_base}.0.new_tokens")
enc_out_ch = trb0_nt.shape[2] if trb0_nt is not None else enc_final_ch
# TRB 0 mapping.weight_v shape (encoder): (out_ch, in_ch, kernel)
mapping_wv = ref_sd.get(f"{enc_base}.0.mapping.weight_v")
if mapping_wv is not None:
patched_in = mapping_wv.shape[1] # = audio_channels * patch_size
else:
patched_in = 512 # default: 2ch * 256 patch
# Infer transformer depth per TRB:
trb_trans_depth = sum(
1
for k in ref_sd
if k.startswith(f"{enc_base}.0.transformers.")
and k.endswith(".new_tokens") is False
and ".pre_norm.alpha" in k
)
if trb_trans_depth == 0:
trb_trans_depth = 6 # default
# Dim heads inferred from q_norm shape: (dim_heads,)
q_norm_key = f"{enc_base}.0.transformers.0.self_attn.q_norm.gamma"
dim_heads = ref_sd[q_norm_key].shape[0] if q_norm_key in ref_sd else 64
# channels base: choose so that enc_out_ch = channels * c_mults[0]
# We use c_mults = [6] for both SAME-S/L
c_mults = [6]
enc_channels_base = enc_out_ch // c_mults[0]
# ── Weight-invisible hyper-parameters ────────────────────────────────────
# These come from model_config.json when available (see docstring).
audio_channels = 2
sliding_window = 1
encoder_sinusoidal_blocks = [0] * enc_depth
decoder_sinusoidal_blocks = [0] * enc_depth
if model_config is not None:
ae_cfg = model_config["model"]["pretransform"]["config"]
enc_cfg = ae_cfg["encoder"]["config"]
dec_cfg = ae_cfg["decoder"]["config"]
strides = list(enc_cfg["strides"])
transformer_depths = list(enc_cfg["transformer_depths"])
c_mults = list(enc_cfg["c_mults"])
enc_channels_base = enc_cfg["channels"]
latent_dim = enc_cfg.get("latent_dim", latent_dim)
dim_heads = enc_cfg.get("dim_heads", dim_heads)
audio_channels = ae_cfg.get("io_channels", audio_channels)
downsampling_ratio = ae_cfg["downsampling_ratio"]
patch_size = downsampling_ratio // int(math.prod(strides))
# sliding_window in the reference is a per-side list like [1, 1]; take the (symmetric) half-width.
sw = enc_cfg.get("sliding_window") or [sliding_window]
sliding_window = sw[0]
encoder_sinusoidal_blocks = list(enc_cfg.get("sinusoidal_blocks", encoder_sinusoidal_blocks))
decoder_sinusoidal_blocks = list(dec_cfg.get("sinusoidal_blocks", decoder_sinusoidal_blocks))
else:
# Stride is NOT recoverable from weights under variable_stride; assume the production value of 16.
strides = [16] * enc_depth
transformer_depths = [trb_trans_depth] * enc_depth
patch_size = patched_in // audio_channels
return {
"audio_channels": audio_channels,
"patch_size": patch_size,
"encoder_channels": enc_channels_base,
"encoder_c_mults": c_mults,
"encoder_strides": strides,
"encoder_transformer_depths": transformer_depths,
"latent_dim": latent_dim,
"use_differential_attention": True,
"dim_heads": dim_heads,
"ff_mult": 3,
"sliding_window": sliding_window,
"encoder_sinusoidal_blocks": encoder_sinusoidal_blocks,
"decoder_sinusoidal_blocks": decoder_sinusoidal_blocks,
"sampling_rate": 44100,
}
def _infer_dit_config(ref_sd: dict) -> dict:
"""Infer StableAudio3DiTModel config from checkpoint tensor shapes."""
p = "model.model."
# embed_dim from to_timestep_embed.0.weight shape (embed_dim, features_dim)
ts_w = ref_sd.get(f"{p}to_timestep_embed.0.weight")
embed_dim = ts_w.shape[0] if ts_w is not None else 1536
timestep_features_dim = ts_w.shape[1] if ts_w is not None else 256
# depth: count transformer blocks (RMSNorm → pre_norm.gamma)
depth = 0
while f"{p}transformer.layers.{depth}.pre_norm.gamma" in ref_sd:
depth += 1
if depth == 0:
depth = 24
# num_heads: from self_attn.q_norm.gamma shape (dim_heads,) and embed_dim
q_norm_key = f"{p}transformer.layers.0.self_attn.q_norm.gamma"
dim_heads = ref_sd[q_norm_key].shape[0] if q_norm_key in ref_sd else 64
num_heads = embed_dim // dim_heads
# cond_token_dim / global_cond_dim from the projection in-weights (embed_dim, *)
cond_w = ref_sd.get(f"{p}to_cond_embed.0.weight")
cond_token_dim = cond_w.shape[1] if cond_w is not None else 768
glob_w = ref_sd.get(f"{p}to_global_embed.0.weight")
global_cond_dim = glob_w.shape[1] if glob_w is not None else 768
# io_channels from preprocess_conv.weight (io_ch, io_ch, 1)
pc_w = ref_sd.get(f"{p}preprocess_conv.weight")
io_channels = pc_w.shape[0] if pc_w is not None else 256
# ff_mult from ff.ff.0.proj.weight (inner*2, embed_dim)
ff_w = ref_sd.get(f"{p}transformer.layers.0.ff.ff.0.proj.weight")
ff_mult = (ff_w.shape[0] // (2 * embed_dim)) if ff_w is not None else 4
# local_add_cond_dim from to_local_embed.0.weight (embed_dim, local_add_cond_dim)
loc_w = ref_sd.get(f"{p}transformer.layers.0.to_local_embed.0.weight")
local_add_cond_dim = loc_w.shape[1] if loc_w is not None else 257
# num_memory_tokens from memory_tokens (num_memory_tokens, embed_dim)
mem = ref_sd.get(f"{p}transformer.memory_tokens")
num_memory_tokens = mem.shape[0] if mem is not None else 64
# differential: self_attn.to_qkv rows = embed_dim*5 (differential) vs *3 (standard)
qkv_w = ref_sd.get(f"{p}transformer.layers.0.self_attn.to_qkv.weight")
use_differential = qkv_w is not None and qkv_w.shape[0] == embed_dim * 5
return {
"io_channels": io_channels,
"patch_size": 1,
"embed_dim": embed_dim,
"depth": depth,
"num_heads": num_heads,
"cond_token_dim": cond_token_dim,
"global_cond_dim": global_cond_dim,
"local_add_cond_dim": local_add_cond_dim,
"timestep_features_dim": timestep_features_dim,
"ff_mult": ff_mult,
"num_memory_tokens": num_memory_tokens,
"use_differential_attention": use_differential,
}
def _infer_duration_embedder_config(ref_sd: dict) -> dict:
"""Infer StableAudio3DurationEmbedder config from checkpoint shapes."""
w = ref_sd.get("conditioner.conditioners.seconds_total.embedder.embedding.1.weight")
if w is not None:
output_dim = w.shape[0]
fourier_dim = w.shape[1]
else:
output_dim, fourier_dim = 768, 256
return {
"output_dim": output_dim,
"fourier_dim": fourier_dim,
"min_val": 0.0,
"max_val": 384.0,
"min_freq": 0.5,
"max_freq": 10000.0,
}
# ──────────────────────────────────────────────────────────────────────────────
# Main conversion
# ──────────────────────────────────────────────────────────────────────────────
def convert(args):
# ── Load checkpoint ──────────────────────────────────────────────────────
checkpoint_path = args.checkpoint_path
hub_model_config_path = None
if not Path(checkpoint_path).exists():
# Try to download from HF Hub
try:
from huggingface_hub import hf_hub_download
print(f"Downloading checkpoint from HF Hub: {checkpoint_path}")
repo_id = checkpoint_path
checkpoint_path = hf_hub_download(
repo_id=repo_id,
filename="model.safetensors",
)
# Also grab model_config.json so we can pick the right scheduler.
try:
hub_model_config_path = hf_hub_download(repo_id=repo_id, filename="model_config.json")
except Exception:
hub_model_config_path = None
except Exception as exc:
print(f"Could not download checkpoint: {exc}")
sys.exit(1)
print(f"Loading checkpoint: {checkpoint_path}")
ref_sd = load_file(checkpoint_path, device="cpu")
print(f" Loaded {len(ref_sd)} tensors.")
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
dtype = getattr(torch, args.dtype)
import diffusers
from diffusers import (
AutoencoderSAME,
FlowMatchEulerDiscreteScheduler,
StableAudio3DiTModel,
StableAudio3DurationEmbedder,
StableAudio3Pipeline,
)
# ── Parse model_config.json if provided ─────────────────────────────────
model_config = None
model_config_path = args.model_config_path or hub_model_config_path
if model_config_path and Path(model_config_path).exists():
with open(model_config_path) as f:
model_config = json.load(f)
print(f"Loaded model config: {model_config_path}")
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# 1. VAE
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
print("\n-- Converting VAE --------------------------------------------------")
vae_cfg = _infer_vae_config(ref_sd, model_config)
print(f" Inferred VAE config: {vae_cfg}")
vae_sd = convert_vae(ref_sd, differential=vae_cfg["use_differential_attention"])
vae = AutoencoderSAME(**vae_cfg)
missing, unexpected = vae.load_state_dict(vae_sd, strict=False)
if missing:
print(f" VAE missing keys: {missing[:10]}")
if unexpected:
print(f" VAE unexpected keys: {unexpected[:10]}")
print(f" VAE: loaded {len(vae_sd)} keys, {len(missing)} missing, {len(unexpected)} unexpected")
vae = vae.to(dtype)
vae.save_pretrained(output_dir / "vae")
print(f" Saved -> {output_dir / 'vae'}")
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# 2. Duration embedder
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
print("\n-- Converting DurationEmbedder -------------------------------------")
dur_cfg = _infer_duration_embedder_config(ref_sd)
print(f" Inferred config: {dur_cfg}")
dur_sd = convert_duration_embedder(ref_sd)
dur_emb = StableAudio3DurationEmbedder(**dur_cfg)
dur_missing, dur_unexpected = dur_emb.load_state_dict(dur_sd, strict=False)
if dur_missing:
print(f" DurationEmbedder missing keys: {dur_missing}")
dur_emb = dur_emb.to(dtype)
dur_emb.save_pretrained(output_dir / "duration_embedder")
print(f" Saved -> {output_dir / 'duration_embedder'}")
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# 3. Text encoder + tokenizer
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
print("\n-- Converting TextEncoder ------------------------------------------")
te_sd = extract_text_encoder(ref_sd)
text_encoder_repo = args.text_encoder_repo
# Concrete class names for model_index.json. diffusers cannot load the
# abstract "AutoTokenizer"/"AutoModel" entries, so we record the resolved
# concrete classes (e.g. "GemmaTokenizerFast", "T5GemmaEncoderModel").
tokenizer_cls_name = "AutoTokenizer"
text_encoder_cls_name = "T5GemmaEncoderModel"
try:
from transformers import AutoConfig, AutoTokenizer, T5GemmaEncoderModel
print(f" Loading T5Gemma from: {text_encoder_repo}")
tokenizer = AutoTokenizer.from_pretrained(text_encoder_repo)
tokenizer_cls_name = type(tokenizer).__name__
te_config = AutoConfig.from_pretrained(text_encoder_repo)
te_config.is_encoder_decoder = False
text_encoder = T5GemmaEncoderModel.from_pretrained(text_encoder_repo, config=te_config)
if te_sd:
print(f" Applying {len(te_sd)} weights extracted from SA3 checkpoint ...")
te_missing, te_unexpected = text_encoder.load_state_dict(te_sd, strict=False)
if te_missing:
print(f" TE missing: {te_missing[:5]} ...")
if te_unexpected:
print(f" TE unexpected: {te_unexpected[:5]} ...")
else:
print(
" No text-encoder weights found in SA3 checkpoint (expected if frozen). Using base T5Gemma weights."
)
text_encoder = text_encoder.to(dtype)
text_encoder_cls_name = type(text_encoder).__name__
text_encoder.save_pretrained(output_dir / "text_encoder")
tokenizer.save_pretrained(output_dir / "tokenizer")
print(f" Saved -> {output_dir / 'text_encoder'}, {output_dir / 'tokenizer'}")
except ImportError:
print(" WARNING: transformers not installed; skipping text_encoder & tokenizer.")
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# 4. DiT
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
print("\n-- Converting DiT --------------------------------------------------")
dit_cfg = _infer_dit_config(ref_sd)
print(f" Inferred DiT config: {dit_cfg}")
dit_sd = convert_dit(ref_sd, differential=dit_cfg["use_differential_attention"])
transformer = StableAudio3DiTModel(**dit_cfg)
dit_missing, dit_unexpected = transformer.load_state_dict(dit_sd, strict=False)
if dit_missing:
print(f" DiT missing keys ({len(dit_missing)}): {dit_missing[:5]} ...")
if dit_unexpected:
print(f" DiT unexpected keys ({len(dit_unexpected)}): {dit_unexpected[:5]} ...")
print(f" DiT: loaded {len(dit_sd)} keys ({len(dit_missing)} missing, {len(dit_unexpected)} unexpected)")
transformer = transformer.to(dtype)
transformer.save_pretrained(output_dir / "transformer")
print(f" Saved -> {output_dir / 'transformer'}")
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# 5. Scheduler
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
print("\n-- Saving scheduler ------------------------------------------------")
# The base model is a (non-distilled) rectified_flow model that samples with deterministic
# Euler over many steps; the distilled model (rf_denoiser) uses the 8-step ping-pong sampler.
diffusion_objective = None
if model_config is not None:
diffusion_objective = (
model_config.get("model", {}).get("diffusion", {}).get("diffusion_objective")
or model_config.get("model", {}).get("diffusion_objective")
or model_config.get("diffusion_objective")
)
if diffusion_objective == "rf_denoiser":
# Distilled model: stochastic ping-pong sampling, 8 steps by default.
scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1, shift=1.0, stochastic_sampling=True)
else:
# Default (and explicit "rectified_flow"): base model -> deterministic Euler sampler, ~100 steps.
if diffusion_objective is None:
print(" diffusion_objective not found in model_config; defaulting to Euler (base model).")
scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1, shift=1.0, stochastic_sampling=False)
scheduler_cls_name = "FlowMatchEulerDiscreteScheduler"
scheduler.save_pretrained(output_dir / "scheduler")
print(f" Saved {scheduler_cls_name} -> {output_dir / 'scheduler'}")
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# 6. model_index.json
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
print("\n-- Writing model_index.json -----------------------------------------")
model_index = {
"_class_name": "StableAudio3Pipeline",
"_diffusers_version": diffusers.__version__,
"vae": ["diffusers", "AutoencoderSAME"],
"text_encoder": ["transformers", text_encoder_cls_name],
"tokenizer": ["transformers", tokenizer_cls_name],
"duration_embedder": ["diffusers", "StableAudio3DurationEmbedder"],
"transformer": ["diffusers", "StableAudio3DiTModel"],
"scheduler": ["diffusers", scheduler_cls_name],
}
with open(output_dir / "model_index.json", "w") as f:
json.dump(model_index, f, indent=2)
print(f" Saved -> {output_dir / 'model_index.json'}")
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# 7. Round-trip sanity check
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
if args.skip_sanity_check:
print("\n-- Skipping round-trip sanity check (--skip_sanity_check) ---------")
else:
print("\n-- Round-trip sanity check ------------------------------------------")
try:
pipeline = StableAudio3Pipeline.from_pretrained(str(output_dir))
print(" [DONE] Pipeline loaded successfully.")
# Quick VAE encode/decode check
dummy = torch.zeros(1, 2, 44100)
with torch.no_grad():
lat = pipeline.vae.encode(dummy).latents
rec = pipeline.vae.decode(lat).sample
print(f" [DONE] VAE encode->decode: input {dummy.shape} -> latent {lat.shape} -> output {rec.shape}")
# Quick duration embedder check
with torch.no_grad():
d = pipeline.duration_embedder(torch.tensor([10.0]))
print(f" [DONE] DurationEmbedder output shape: {d.shape}")
except Exception as exc:
print(f" WARNING: sanity check failed: {exc}")
print(f"\n[DONE] Conversion complete. Output at: {output_dir}")
print("\nThe full DiT architecture is converted (AdaLN, memory tokens, RoPE, inpaint).")
# ──────────────────────────────────────────────────────────────────────────────
# CLI
# ──────────────────────────────────────────────────────────────────────────────
def parse_args():
parser = argparse.ArgumentParser(description="Convert Stable Audio 3 checkpoint to HuggingFace Diffusers.")
parser.add_argument(
"--checkpoint_path",
type=str,
required=True,
help="Path to model.safetensors, OR HF repo id (e.g. stabilityai/stable-audio-3-medium).",
)
parser.add_argument(
"--model_config_path",
type=str,
default=None,
help="Optional path to model_config.json from the SA3 repo.",
)
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Directory where the converted pipeline will be saved.",
)
parser.add_argument(
"--text_encoder_repo",
type=str,
default="google/t5gemma-b-b-ul2",
help="HF model id or local path for the T5Gemma text encoder.",
)
parser.add_argument(
"--dtype",
type=str,
default="float32",
choices=["float32", "float16", "bfloat16"],
help="Output dtype for saved weights.",
)
parser.add_argument(
"--skip_sanity_check",
action="store_true",
help="Skip the round-trip load/forward-pass sanity check.",
)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
convert(args)
You can’t perform that action at this time.