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# 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("\nConstructing 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"\nSaving 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"\nNote: {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"\nConversion complete! Output saved to: {output_dir}")
print("\nTo 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,
)