iframe-proxy | Sunbelt Computer Software

Repository navigation

History

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

#!/usr/bin/env python3

# 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,

)

Sunbelt Computer Software

PL/B Language Development and Support

Navigation Menu

Repository navigation

convert_longcat_audio_dit_to_diffusers.py

convert_longcat_audio_dit_to_diffusers.py

Sunbelt Computer Software

PL/B Language Development and Support

FilesExpand file tree

convert_longcat_audio_dit_to_diffusers.py

Latest commit

History

convert_longcat_audio_dit_to_diffusers.py

File metadata and controls