File size: 3,718 Bytes
157a4c0 | 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 | import argparse
import torch
from musubi_tuner.dataset import config_utils
from musubi_tuner.dataset.config_utils import BlueprintGenerator, ConfigSanitizer
from musubi_tuner.dataset.image_video_dataset import ItemInfo, save_text_encoder_output_cache_flux_2
from musubi_tuner.flux_2 import flux2_utils
import musubi_tuner.cache_text_encoder_outputs as cache_text_encoder_outputs
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
def encode_and_save_batch(text_embedder: torch.nn.Module, batch: list[ItemInfo], device: torch.device, arch_full: str):
prompts = [item.caption for item in batch]
autocast_dtype = torch.bfloat16 if text_embedder.dtype.itemsize == 1 else text_embedder.dtype # use bfloat16 for fp8 models
with torch.autocast(device_type=device.type, dtype=autocast_dtype), torch.no_grad():
ctx_vec = text_embedder(prompts)
ctx_vec = ctx_vec.cpu() # [1, 512, 15360]
# save prompt cache
for item, _ctx_vec in zip(batch, ctx_vec):
save_text_encoder_output_cache_flux_2(item, _ctx_vec, arch_full=arch_full)
def main():
parser = cache_text_encoder_outputs.setup_parser_common()
parser = flux_2_setup_parser(parser)
args = parser.parse_args()
model_version_info = flux2_utils.FLUX2_MODEL_INFO[args.model_version]
device = args.device if args.device is not None else "cuda" if torch.cuda.is_available() else "cpu"
device = torch.device(device)
# Load dataset config
blueprint_generator = BlueprintGenerator(ConfigSanitizer())
logger.info(f"Load dataset config from {args.dataset_config}")
user_config = config_utils.load_user_config(args.dataset_config)
blueprint = blueprint_generator.generate(user_config, args, architecture=model_version_info.architecture)
train_dataset_group = config_utils.generate_dataset_group_by_blueprint(blueprint.dataset_group)
datasets = train_dataset_group.datasets
# prepare cache files and paths: all_cache_files_for_dataset = exisiting cache files, all_cache_paths_for_dataset = all cache paths in the dataset
all_cache_files_for_dataset, all_cache_paths_for_dataset = cache_text_encoder_outputs.prepare_cache_files_and_paths(datasets)
# Load Mistral 3 or Qwen-3 text encoder
m3_dtype = torch.float8_e4m3fn if args.fp8_text_encoder else torch.bfloat16
text_embedder = flux2_utils.load_text_embedder(
model_version_info, args.text_encoder, dtype=m3_dtype, device=device, disable_mmap=True
)
# Encode with Mistral 3 or Qwen-3 text encoder
logger.info("Encoding with text encoder")
def encode_for_text_encoder(batch: list[ItemInfo]):
nonlocal text_embedder
encode_and_save_batch(text_embedder, batch, device, model_version_info.architecture_full)
cache_text_encoder_outputs.process_text_encoder_batches(
args.num_workers,
args.skip_existing,
args.batch_size,
datasets,
all_cache_files_for_dataset,
all_cache_paths_for_dataset,
encode_for_text_encoder,
)
del text_embedder
# remove cache files not in dataset
cache_text_encoder_outputs.post_process_cache_files(
datasets, all_cache_files_for_dataset, all_cache_paths_for_dataset, args.keep_cache
)
def flux_2_setup_parser(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
parser.add_argument("--text_encoder", type=str, default=None, required=True, help="text encoder (mistral 3) checkpoint path")
parser.add_argument("--fp8_text_encoder", action="store_true", help="use fp8 for Text Encoder model")
flux2_utils.add_model_version_args(parser)
return parser
if __name__ == "__main__":
main()
|