按之前正常的训练配置甚至降低了梯度累加步数，连续两个训练MemoryError

[testworksizan]

训练中途（该阶段电脑完全闲置）报错
Average key norm=0.776, Keys Scaled=211, avr_loss=0.0816]
saving checkpoint: ./output\0405end-000003.safetensors
MemoryError
thread '' panicked at C:\Users\runneradmin.cargo\registry\src\index.crates.io-6f17d22bba15001f\pyo3-0.20.2\src\err\mod.rs:788:5:
Python API call failed
note: run with RUST_BACKTRACE=1 environment variable to display a backtrace
Traceback (most recent call last):
File "C:\Users\songz\Documents\app\lora-scripts-v1.8.5\sd-scripts\sdxl_train_network.py", line 184, in
trainer.train(args)
File "C:\Users\songz\Documents\app\lora-scripts-v1.8.5\sd-scripts\train_network.py", line 916, in train
save_model(ckpt_name, accelerator.unwrap_model(network), global_step, epoch + 1)
File "C:\Users\songz\Documents\app\lora-scripts-v1.8.5\sd-scripts\train_network.py", line 732, in save_model
unwrapped_nw.save_weights(ckpt_file, save_dtype, metadata_to_save)
File "C:\Users\songz\Documents\app\lora-scripts-v1.8.5\sd-scripts\networks\lora.py", line 1116, in save_weights
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
File "C:\Users\songz\Documents\app\lora-scripts-v1.8.5\sd-scripts\library\train_util.py", line 2430, in precalculate_safetensors_hashes
bytes = safetensors.torch.save(tensors, metadata)
File "C:\Users\songz\Documents\app\lora-scripts-v1.8.5\python\lib\site-packages\safetensors\torch.py", line 245, in save
serialized = serialize(_flatten(tensors), metadata=metadata)
pyo3_runtime.PanicException: Python API call failed

model_train_type = "sdxl-lora"
pretrained_model_name_or_path = "C:/Users/songz/Documents/app/lora-scripts-v1.8.5/sd-models/animagineXLV31_v31.safetensors"
vae = "C:/Users/songz/Documents/app/lora-scripts-v1.8.5/sd-models/sdxl_vae.safetensors"
v2 = false
train_data_dir = "C:/Users/songz/Documents/app/lora-scripts-v1.8.5/train/ai0406/train"
reg_data_dir = "C:/Users/songz/Documents/app/lora-scripts-v1.8.5/train/ai0406/reg"
prior_loss_weight = 1
resolution = "1024,1024"
enable_bucket = true
min_bucket_reso = 128
max_bucket_reso = 2048
bucket_reso_steps = 32
output_name = "0405end"
output_dir = "./output"
save_model_as = "safetensors"
save_precision = "bf16"
save_every_n_epochs = 1
max_train_epochs = 12
train_batch_size = 2
gradient_checkpointing = true
gradient_accumulation_steps = 3
network_train_unet_only = false
network_train_text_encoder_only = false
learning_rate = 0.0001
unet_lr = 0.0000377
text_encoder_lr = 0.00000753
lr_scheduler = "cosine"
lr_warmup_steps = 0
optimizer_type = "Lion8bit"
min_snr_gamma = 5
network_module = "networks.lora"
network_dim = 128
network_alpha = 64
scale_weight_norms = 1
sample_prompts = "1girl, solo,, --n lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry, --w 1024 --h 1024 --l 7 --s 28 --d 1337"
sample_sampler = "euler_a"
sample_every_n_epochs = 1
log_with = "tensorboard"
logging_dir = "./logs"
caption_extension = ".txt"
shuffle_caption = true
keep_tokens = 0
max_token_length = 255
seed = 1337
mixed_precision = "bf16"
full_bf16 = true
no_half_vae = true
xformers = true
lowram = false
cache_latents = true
cache_latents_to_disk = true
persistent_data_loader_workers = true