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import argparse
import torch
from library.device_utils import init_ipex, clean_memory_on_device
init_ipex()
from library import sdxl_model_util, sdxl_train_util, train_util
import train_network
from library.utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)
class SdxlNetworkTrainer(train_network.NetworkTrainer):
def __init__(self):
super().__init__()
self.vae_scale_factor = sdxl_model_util.VAE_SCALE_FACTOR
self.is_sdxl = True
def assert_extra_args(self, args, train_dataset_group):
super().assert_extra_args(args, train_dataset_group)
sdxl_train_util.verify_sdxl_training_args(args)
if args.cache_text_encoder_outputs:
assert (
train_dataset_group.is_text_encoder_output_cacheable()
), "when caching Text Encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / Text Encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません"
assert (
args.network_train_unet_only or not args.cache_text_encoder_outputs
), "network for Text Encoder cannot be trained with caching Text Encoder outputs / Text Encoderの出力をキャッシュしながらText Encoderのネットワークを学習することはできません"
train_dataset_group.verify_bucket_reso_steps(32)
def load_target_model(self, args, weight_dtype, accelerator):
(
load_stable_diffusion_format,
text_encoder1,
text_encoder2,
vae,
unet,
logit_scale,
ckpt_info,
) = sdxl_train_util.load_target_model(args, accelerator, sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, weight_dtype)
self.load_stable_diffusion_format = load_stable_diffusion_format
self.logit_scale = logit_scale
self.ckpt_info = ckpt_info
return sdxl_model_util.MODEL_VERSION_SDXL_BASE_V1_0, [text_encoder1, text_encoder2], vae, unet
def load_tokenizer(self, args):
tokenizer = sdxl_train_util.load_tokenizers(args)
return tokenizer
def is_text_encoder_outputs_cached(self, args):
return args.cache_text_encoder_outputs
def cache_text_encoder_outputs_if_needed(
self, args, accelerator, unet, vae, tokenizers, text_encoders, dataset: train_util.DatasetGroup, weight_dtype
):
if args.cache_text_encoder_outputs:
if not args.lowram:
# メモリ消費を減らす
logger.info("move vae and unet to cpu to save memory")
org_vae_device = vae.device
org_unet_device = unet.device
vae.to("cpu")
unet.to("cpu")
clean_memory_on_device(accelerator.device)
# When TE is not be trained, it will not be prepared so we need to use explicit autocast
with accelerator.autocast():
dataset.cache_text_encoder_outputs(
tokenizers,
text_encoders,
accelerator.device,
weight_dtype,
args.cache_text_encoder_outputs_to_disk,
accelerator.is_main_process,
)
text_encoders[0].to("cpu", dtype=torch.float32) # Text Encoder doesn't work with fp16 on CPU
text_encoders[1].to("cpu", dtype=torch.float32)
clean_memory_on_device(accelerator.device)
if not args.lowram:
logger.info("move vae and unet back to original device")
vae.to(org_vae_device)
unet.to(org_unet_device)
else:
# Text Encoderから毎回出力を取得するので、GPUに乗せておく
text_encoders[0].to(accelerator.device, dtype=weight_dtype)
text_encoders[1].to(accelerator.device, dtype=weight_dtype)
def get_text_cond(self, args, accelerator, batch, tokenizers, text_encoders, weight_dtype):
if "text_encoder_outputs1_list" not in batch or batch["text_encoder_outputs1_list"] is None:
input_ids1 = batch["input_ids"]
input_ids2 = batch["input_ids2"]
with torch.enable_grad():
# Get the text embedding for conditioning
# TODO support weighted captions
# if args.weighted_captions:
# encoder_hidden_states = get_weighted_text_embeddings(
# tokenizer,
# text_encoder,
# batch["captions"],
# accelerator.device,
# args.max_token_length // 75 if args.max_token_length else 1,
# clip_skip=args.clip_skip,
# )
# else:
input_ids1 = input_ids1.to(accelerator.device)
input_ids2 = input_ids2.to(accelerator.device)
encoder_hidden_states1, encoder_hidden_states2, pool2 = train_util.get_hidden_states_sdxl(
args.max_token_length,
input_ids1,
input_ids2,
tokenizers[0],
tokenizers[1],
text_encoders[0],
text_encoders[1],
None if not args.full_fp16 else weight_dtype,
accelerator=accelerator,
)
else:
encoder_hidden_states1 = batch["text_encoder_outputs1_list"].to(accelerator.device).to(weight_dtype)
encoder_hidden_states2 = batch["text_encoder_outputs2_list"].to(accelerator.device).to(weight_dtype)
pool2 = batch["text_encoder_pool2_list"].to(accelerator.device).to(weight_dtype)
# # verify that the text encoder outputs are correct
# ehs1, ehs2, p2 = train_util.get_hidden_states_sdxl(
# args.max_token_length,
# batch["input_ids"].to(text_encoders[0].device),
# batch["input_ids2"].to(text_encoders[0].device),
# tokenizers[0],
# tokenizers[1],
# text_encoders[0],
# text_encoders[1],
# None if not args.full_fp16 else weight_dtype,
# )
# b_size = encoder_hidden_states1.shape[0]
# assert ((encoder_hidden_states1.to("cpu") - ehs1.to(dtype=weight_dtype)).abs().max() > 1e-2).sum() <= b_size * 2
# assert ((encoder_hidden_states2.to("cpu") - ehs2.to(dtype=weight_dtype)).abs().max() > 1e-2).sum() <= b_size * 2
# assert ((pool2.to("cpu") - p2.to(dtype=weight_dtype)).abs().max() > 1e-2).sum() <= b_size * 2
# logger.info("text encoder outputs verified")
return encoder_hidden_states1, encoder_hidden_states2, pool2
def call_unet(self, args, accelerator, unet, noisy_latents, timesteps, text_conds, batch, weight_dtype):
noisy_latents = noisy_latents.to(weight_dtype) # TODO check why noisy_latents is not weight_dtype
# get size embeddings
orig_size = batch["original_sizes_hw"]
crop_size = batch["crop_top_lefts"]
target_size = batch["target_sizes_hw"]
embs = sdxl_train_util.get_size_embeddings(orig_size, crop_size, target_size, accelerator.device).to(weight_dtype)
# concat embeddings
encoder_hidden_states1, encoder_hidden_states2, pool2 = text_conds
vector_embedding = torch.cat([pool2, embs], dim=1).to(weight_dtype)
text_embedding = torch.cat([encoder_hidden_states1, encoder_hidden_states2], dim=2).to(weight_dtype)
noise_pred = unet(noisy_latents, timesteps, text_embedding, vector_embedding)
return noise_pred
def sample_images(self, accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet):
sdxl_train_util.sample_images(accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet)
def setup_parser() -> argparse.ArgumentParser:
parser = train_network.setup_parser()
sdxl_train_util.add_sdxl_training_arguments(parser)
return parser
if __name__ == "__main__":
parser = setup_parser()
args = parser.parse_args()
train_util.verify_command_line_training_args(args)
args = train_util.read_config_from_file(args, parser)
trainer = SdxlNetworkTrainer()
trainer.train(args)
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