File size: 4,878 Bytes
0707b22 | 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 | import argparse
import torch
from transformers import CLIPTextModel, T5EncoderModel, CLIPTokenizer, T5Tokenizer
from musubi_tuner.dataset import config_utils
from musubi_tuner.dataset.config_utils import BlueprintGenerator, ConfigSanitizer
from musubi_tuner.dataset.image_video_dataset import (
ARCHITECTURE_FLUX_KONTEXT,
ItemInfo,
save_text_encoder_output_cache_flux_kontext,
)
from musubi_tuner.flux import flux_models
from musubi_tuner.flux import flux_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(
tokenizer1: T5Tokenizer,
text_encoder1: T5EncoderModel,
tokenizer2: CLIPTokenizer,
text_encoder2: CLIPTextModel,
batch: list[ItemInfo],
device: torch.device,
):
prompts = [item.caption for item in batch]
# print(prompts)
# encode prompt
t5_tokens = tokenizer1(
prompts,
max_length=flux_models.T5XXL_MAX_LENGTH,
padding="max_length",
return_length=False,
return_overflowing_tokens=False,
truncation=True,
return_tensors="pt",
)["input_ids"]
l_tokens = tokenizer2(prompts, max_length=77, padding="max_length", truncation=True, return_tensors="pt")["input_ids"]
with torch.autocast(device_type=device.type, dtype=text_encoder1.dtype), torch.no_grad():
t5_vec = text_encoder1(input_ids=t5_tokens.to(text_encoder1.device), attention_mask=None, output_hidden_states=False)[
"last_hidden_state"
]
assert torch.isnan(t5_vec).any() == False, "T5 vector contains NaN values"
t5_vec = t5_vec.cpu()
with torch.autocast(device_type=device.type, dtype=text_encoder2.dtype), torch.no_grad():
clip_l_pooler = text_encoder2(l_tokens.to(text_encoder2.device))["pooler_output"]
clip_l_pooler = clip_l_pooler.cpu()
# save prompt cache
for item, t5_vec, clip_ctx in zip(batch, t5_vec, clip_l_pooler):
save_text_encoder_output_cache_flux_kontext(item, t5_vec, clip_ctx)
def main():
parser = cache_text_encoder_outputs.setup_parser_common()
parser = flux_kontext_setup_parser(parser)
args = parser.parse_args()
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=ARCHITECTURE_FLUX_KONTEXT)
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 T5 and CLIP text encoders
t5_dtype = torch.float8e4m3fn if args.fp8_t5 else torch.bfloat16
tokenizer1, text_encoder1 = flux_utils.load_t5xxl(args.text_encoder1, dtype=t5_dtype, device=device, disable_mmap=True)
tokenizer2, text_encoder2 = flux_utils.load_clip_l(args.text_encoder2, dtype=torch.bfloat16, device=device, disable_mmap=True)
# Encode with T5 and CLIP text encoders
logger.info("Encoding with T5 and CLIP text encoders")
def encode_for_text_encoder(batch: list[ItemInfo]):
nonlocal tokenizer1, text_encoder1, tokenizer2, text_encoder2
encode_and_save_batch(tokenizer1, text_encoder1, tokenizer2, text_encoder2, batch, device)
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_encoder1
del text_encoder2
# 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_kontext_setup_parser(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
parser.add_argument("--text_encoder1", type=str, default=None, required=True, help="text encoder (T5XXL) checkpoint path")
parser.add_argument("--text_encoder2", type=str, default=None, required=True, help="text encoder 2 (CLIP-L) checkpoint path")
parser.add_argument("--fp8_t5", action="store_true", help="use fp8 for Text Encoder model")
return parser
if __name__ == "__main__":
main()
|