File size: 5,316 Bytes
1eb9457 |
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 |
import argparse
import os
import regex
import torch
from library.device_utils import init_ipex
init_ipex()
from library import sdxl_model_util, sdxl_train_util, train_util
import train_textual_inversion
class SdxlTextualInversionTrainer(train_textual_inversion.TextualInversionTrainer):
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, supportTextEncoderCaching=False)
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 get_text_cond(self, args, accelerator, batch, tokenizers, text_encoders, weight_dtype):
input_ids1 = batch["input_ids"]
input_ids2 = batch["input_ids2"]
with torch.enable_grad():
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,
)
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, prompt_replacement):
sdxl_train_util.sample_images(
accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet, prompt_replacement
)
def save_weights(self, file, updated_embs, save_dtype, metadata):
state_dict = {"clip_l": updated_embs[0], "clip_g": updated_embs[1]}
if save_dtype is not None:
for key in list(state_dict.keys()):
v = state_dict[key]
v = v.detach().clone().to("cpu").to(save_dtype)
state_dict[key] = v
if os.path.splitext(file)[1] == ".safetensors":
from safetensors.torch import save_file
save_file(state_dict, file, metadata)
else:
torch.save(state_dict, file)
def load_weights(self, file):
if os.path.splitext(file)[1] == ".safetensors":
from safetensors.torch import load_file
data = load_file(file)
else:
data = torch.load(file, map_location="cpu")
emb_l = data.get("clip_l", None) # ViT-L text encoder 1
emb_g = data.get("clip_g", None) # BiG-G text encoder 2
assert (
emb_l is not None or emb_g is not None
), f"weight file does not contains weights for text encoder 1 or 2 / 重みファイルにテキストエンコーダー1または2の重みが含まれていません: {file}"
return [emb_l, emb_g]
def setup_parser() -> argparse.ArgumentParser:
parser = train_textual_inversion.setup_parser()
# don't add sdxl_train_util.add_sdxl_training_arguments(parser): because it only adds text encoder caching
# 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 = SdxlTextualInversionTrainer()
trainer.train(args)
|