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37a6b2a | 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 | #!/usr/bin/env python3
# Copyright 2026 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.
# Usage:
# python scripts/convert_longcat_audio_dit_to_diffusers.py --checkpoint_path /path/to/model --output_path /data/models
# python scripts/convert_longcat_audio_dit_to_diffusers.py --repo_id meituan-longcat/LongCat-AudioDiT-1B --output_path /data/models
# python scripts/convert_longcat_audio_dit_to_diffusers.py --checkpoint_path /path/to/model --output_path /data/models --dtype fp16
import argparse
import json
from pathlib import Path
import torch
from huggingface_hub import snapshot_download
from safetensors.torch import load_file
from transformers import AutoTokenizer, UMT5Config, UMT5EncoderModel
from diffusers import (
FlowMatchEulerDiscreteScheduler,
LongCatAudioDiTPipeline,
LongCatAudioDiTTransformer,
LongCatAudioDiTVae,
)
def find_checkpoint(input_dir: Path):
safetensors_file = input_dir / "model.safetensors"
if safetensors_file.exists():
return input_dir, safetensors_file
index_file = input_dir / "model.safetensors.index.json"
if index_file.exists():
with open(index_file) as f:
index = json.load(f)
weight_map = index.get("weight_map", {})
first_weight = list(weight_map.values())[0]
return input_dir, input_dir / first_weight
for subdir in input_dir.iterdir():
if subdir.is_dir():
safetensors_file = subdir / "model.safetensors"
if safetensors_file.exists():
return subdir, safetensors_file
index_file = subdir / "model.safetensors.index.json"
if index_file.exists():
with open(index_file) as f:
index = json.load(f)
weight_map = index.get("weight_map", {})
first_weight = list(weight_map.values())[0]
return subdir, subdir / first_weight
raise FileNotFoundError(f"No checkpoint found in {input_dir}")
def convert_longcat_audio_dit(
checkpoint_path: str | None = None,
repo_id: str | None = None,
output_path: str = "",
dtype: str = "fp32",
text_encoder_model: str = "google/umt5-xxl",
):
if not checkpoint_path and not repo_id:
raise ValueError("Either --checkpoint_path or --repo_id must be provided")
if checkpoint_path and repo_id:
raise ValueError("Cannot specify both --checkpoint_path and --repo_id")
dtype_map = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}
torch_dtype = dtype_map.get(dtype, torch.float32)
if repo_id:
input_dir = Path(snapshot_download(repo_id, local_files_only=False))
model_name = repo_id.split("/")[-1]
else:
input_dir = Path(checkpoint_path)
if not input_dir.exists():
raise FileNotFoundError(f"Checkpoint path not found: {checkpoint_path}")
model_name = None
model_dir, checkpoint_path = find_checkpoint(input_dir)
if model_name is None:
model_name = model_dir.name
config_path = model_dir / "config.json"
if not config_path.exists():
raise FileNotFoundError(f"config.json not found in {model_dir}")
with open(config_path) as f:
config = json.load(f)
state_dict = load_file(checkpoint_path)
transformer_keys = [k for k in state_dict.keys() if k.startswith("transformer.")]
transformer_state_dict = {key[12:]: state_dict[key] for key in transformer_keys}
vae_keys = [k for k in state_dict.keys() if k.startswith("vae.")]
vae_state_dict = {key[4:]: state_dict[key] for key in vae_keys}
text_encoder_keys = [k for k in state_dict.keys() if k.startswith("text_encoder.")]
text_encoder_state_dict = {key[13:]: state_dict[key] for key in text_encoder_keys}
transformer = LongCatAudioDiTTransformer(
dit_dim=config["dit_dim"],
dit_depth=config["dit_depth"],
dit_heads=config["dit_heads"],
dit_text_dim=config["dit_text_dim"],
latent_dim=config["latent_dim"],
dropout=config.get("dit_dropout", 0.0),
bias=config.get("dit_bias", True),
cross_attn=config.get("dit_cross_attn", True),
adaln_type=config.get("dit_adaln_type", "global"),
adaln_use_text_cond=config.get("dit_adaln_use_text_cond", True),
long_skip=config.get("dit_long_skip", True),
text_conv=config.get("dit_text_conv", True),
qk_norm=config.get("dit_qk_norm", True),
cross_attn_norm=config.get("dit_cross_attn_norm", False),
eps=config.get("dit_eps", 1e-6),
use_latent_condition=config.get("dit_use_latent_condition", True),
ff_mult=config.get("dit_ff_mult", 4),
)
transformer.load_state_dict(transformer_state_dict, strict=True)
transformer = transformer.to(dtype=torch_dtype)
vae_config = dict(config["vae_config"])
vae_config.pop("model_type", None)
vae = LongCatAudioDiTVae(**vae_config)
vae.load_state_dict(vae_state_dict, strict=True)
vae = vae.to(dtype=torch_dtype)
text_encoder_config = UMT5Config.from_dict(config["text_encoder_config"])
text_encoder = UMT5EncoderModel(text_encoder_config)
text_missing, text_unexpected = text_encoder.load_state_dict(text_encoder_state_dict, strict=False)
allowed_missing = {"shared.weight"}
unexpected_missing = set(text_missing) - allowed_missing
if unexpected_missing:
raise RuntimeError(f"Unexpected missing text encoder weights: {sorted(unexpected_missing)}")
if text_unexpected:
raise RuntimeError(f"Unexpected text encoder weights: {sorted(text_unexpected)}")
if "shared.weight" in text_missing:
text_encoder.shared.weight.data.copy_(text_encoder.encoder.embed_tokens.weight.data)
text_encoder = text_encoder.to(dtype=torch_dtype)
tokenizer = AutoTokenizer.from_pretrained(text_encoder_model)
scheduler_config = {"shift": 1.0, "invert_sigmas": True}
scheduler_config.update(config.get("scheduler_config", {}))
scheduler = FlowMatchEulerDiscreteScheduler(**scheduler_config)
pipeline = LongCatAudioDiTPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
scheduler=scheduler,
)
pipeline.sample_rate = config.get("sampling_rate", 24000)
pipeline.vae_scale_factor = config.get("vae_scale_factor", config.get("latent_hop", 2048))
pipeline.max_wav_duration = config.get("max_wav_duration", 30.0)
pipeline.text_norm_feat = config.get("text_norm_feat", True)
pipeline.text_add_embed = config.get("text_add_embed", True)
output_path = Path(output_path) / f"{model_name}-Diffusers"
output_path.mkdir(parents=True, exist_ok=True)
pipeline.save_pretrained(output_path)
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
type=str,
default=None,
help="Path to local model directory",
)
parser.add_argument(
"--repo_id",
type=str,
default=None,
help="HuggingFace repo_id to download model",
)
parser.add_argument("--output_path", type=str, required=True, help="Output directory")
parser.add_argument(
"--dtype",
type=str,
default="fp32",
choices=["fp32", "fp16", "bf16"],
help="Data type for converted weights",
)
parser.add_argument(
"--text_encoder_model",
type=str,
default="google/umt5-xxl",
help="HuggingFace model ID for text encoder tokenizer",
)
return parser.parse_args()
if __name__ == "__main__":
args = get_args()
convert_longcat_audio_dit(
checkpoint_path=args.checkpoint_path,
repo_id=args.repo_id,
output_path=args.output_path,
dtype=args.dtype,
text_encoder_model=args.text_encoder_model,
)
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