SDXL Lora training failing even with square images dataset/captions: RuntimeError: Error(s) in loading state_dict for UNet2DConditionModel: Missing key(s) in state_dict:

I installed kohya-ss with ease but have had a hard time creating the dataset to get training start on sdxl model. I'm triying to use the dreamturbo model as well as base sdxl model for this with no luck. I have 58 1024x0124 images and the captions are just tags given by tagger in kohya-ss. I've tried the beginner as well as advanced lora training as well. I am on windows 10 executed from powershell with admin privileges. I've tried adam8 optimizer first with no luck so it is lion here I can change that back though. My dataset is in data/train/3_zkz/(images.jpg/png).

Here are my config details:
pretrained_model_name_or_path = "C:/Users/socie/Documents/newui/new_ComfyUI_windows_portable_nvidia_cu121_or_cpu/ComfyUI_windows_portable/ComfyUI/models/checkpoints/sd_xl_base_1.0.safetensors"
train_data_dir = "C:/Users/socie/Downloads/data/train"
resolution = "1024,1024"
enable_bucket = true
min_bucket_reso = 256
max_bucket_reso = 1024
output_name = "aki"
output_dir = "./output"
save_model_as = "safetensors"
save_every_n_epochs = 2
max_train_epochs = 10
train_batch_size = 1
network_train_unet_only = false
network_train_text_encoder_only = false
learning_rate = 0.0001
unet_lr = 0.0001
text_encoder_lr = 0.00001
lr_scheduler = "cosine_with_restarts"
optimizer_type = "Lion"
lr_scheduler_num_cycles = 1
network_module = "networks.lora"
network_dim = 8
network_alpha = 32
logging_dir = "./logs"
caption_extension = ".txt"
shuffle_caption = true
keep_tokens = 0
max_token_length = 255
seed = 1337
prior_loss_weight = 1
clip_skip = 2
mixed_precision = "fp16"
save_precision = "fp16"
xformers = true
cache_latents = true
persistent_data_loader_workers = true
lr_warmup_steps = 0
no_half_vae = true

Here is the error I got:

16:55:53-891443 INFO Training started with config file / 训练开始，使用配置文件:
C:\users\socie\Downloads\lora-scripts\config\autosave\20240610-165553.toml
16:55:53-894442 INFO Task 12c7214c-d92d-46a4-85f0-1c18877aa2c9 created
2024-06-10 16:56:01 INFO Loading settings from train_util.py:3744
C:\users\socie\Downloads\lora-scripts\config\autosave\20240610-165553.to
ml...
INFO C:\users\socie\Downloads\lora-scripts\config\autosave\20240610-165553 train_util.py:3763
2024-06-10 16:56:01 INFO prepare tokenizer train_util.py:4227
INFO update token length: 255 train_util.py:4244
INFO Using DreamBooth method. train_network.py:172
INFO prepare images. train_util.py:1572
INFO found directory C:\Users\socie\Downloads\data\train\3_zkz contains 58 train_util.py:1519
image files
INFO 174 train images with repeating. train_util.py:1613
INFO 0 reg images. train_util.py:1616
WARNING no regularization images / 正則化画像が見つかりませんでした train_util.py:1621
INFO [Dataset 0] config_util.py:565
batch_size: 1
resolution: (1024, 1024)
enable_bucket: True
network_multiplier: 1.0
min_bucket_reso: 256
max_bucket_reso: 1024
bucket_reso_steps: 64
bucket_no_upscale: False

[Subset 0 of Dataset 0]
image_dir: "C:\Users\socie\Downloads\data\train\3_zkz"
image_count: 58
num_repeats: 3
shuffle_caption: True
keep_tokens: 0
keep_tokens_separator:
secondary_separator: None
enable_wildcard: False
caption_dropout_rate: 0.0
caption_dropout_every_n_epoches: 0
caption_tag_dropout_rate: 0.0
caption_prefix: None
caption_suffix: None
color_aug: False
flip_aug: False
face_crop_aug_range: None
random_crop: False
token_warmup_min: 1,
token_warmup_step: 0,
is_reg: False
class_tokens: zkz
caption_extension: .txt

INFO [Dataset 0] config_util.py:571
INFO loading image sizes. train_util.py:853
100%|████████████████████████████████████████████████████████████████████████████████| 58/58 [00:00<00:00, 9664.29it/s]
INFO make buckets train_util.py:859
INFO number of images (including repeats) / train_util.py:905
各bucketの画像枚数（繰り返し回数を含む）
INFO bucket 0: resolution (1024, 1024), count: 174 train_util.py:910
INFO mean ar error (without repeats): 0.0 train_util.py:915
INFO preparing accelerator train_network.py:225
accelerator device: cuda
INFO loading model for process 0/1 train_util.py:4385
INFO load StableDiffusion checkpoint: train_util.py:4341
C:/Users/socie/Documents/newui/new_ComfyUI_windows_portable_nvidia_cu121
_or_cpu/ComfyUI_windows_portable/ComfyUI/models/checkpoints/sd_xl_base_1
.0.safetensors
INFO UNet2DConditionModel: 64, 8, 768, False, False original_unet.py:1387
Traceback (most recent call last):
File "C:\users\socie\Downloads\lora-scripts\sd-scripts\train_network.py", line 1117, in
trainer.train(args)
File "C:\users\socie\Downloads\lora-scripts\sd-scripts\train_network.py", line 234, in train
model_version, text_encoder, vae, unet = self.load_target_model(args, weight_dtype, accelerator)
File "C:\users\socie\Downloads\lora-scripts\sd-scripts\train_network.py", line 101, in load_target_model
text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator)
File "C:\users\socie\Downloads\lora-scripts\sd-scripts\library\train_util.py", line 4387, in load_target_model
text_encoder, vae, unet, load_stable_diffusion_format = _load_target_model(
File "C:\users\socie\Downloads\lora-scripts\sd-scripts\library\train_util.py", line 4342, in _load_target_model
text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(
File "C:\users\socie\Downloads\lora-scripts\sd-scripts\library\model_util.py", line 1008, in load_models_from_stable_diffusion_checkpoint
info = unet.load_state_dict(converted_unet_checkpoint)
File "C:\users\socie\Downloads\lora-scripts\venv\lib\site-packages\torch\nn\modules\module.py", line 2153, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for UNet2DConditionModel:
Missing key(s) in state_dict: "down_blocks.0.attentions.0.norm.weight", "down_blocks.0.attentions.0.norm.bias", "down_blocks.0.attentions.0.proj_in.weight", "down_blocks.0.attentions.0.proj_in.bias", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_q.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_k.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_v.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_out.0.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_out.0.bias", "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.0.proj.weight", "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.0.proj.bias", "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.2.weight", "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.2.bias", "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_q.weight", "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_k.weight", 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"up_blocks.3.attentions.2.transformer_blocks.0.ff.net.0.proj.bias", "up_blocks.3.attentions.2.transformer_blocks.0.ff.net.2.weight", "up_blocks.3.attentions.2.transformer_blocks.0.ff.net.2.bias", "up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_q.weight", "up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_k.weight", "up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_v.weight", "up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0.weight", "up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0.bias", "up_blocks.3.attentions.2.transformer_blocks.0.norm1.weight", "up_blocks.3.attentions.2.transformer_blocks.0.norm1.bias", "up_blocks.3.attentions.2.transformer_blocks.0.norm2.weight", "up_blocks.3.attentions.2.transformer_blocks.0.norm2.bias", "up_blocks.3.attentions.2.transformer_blocks.0.norm3.weight", "up_blocks.3.attentions.2.transformer_blocks.0.norm3.bias", "up_blocks.3.attentions.2.proj_out.weight", "up_blocks.3.attentions.2.proj_out.bias", "up_blocks.3.resnets.0.norm1.weight", "up_blocks.3.resnets.0.norm1.bias", "up_blocks.3.resnets.0.conv1.weight", "up_blocks.3.resnets.0.conv1.bias", "up_blocks.3.resnets.0.time_emb_proj.weight", "up_blocks.3.resnets.0.time_emb_proj.bias", "up_blocks.3.resnets.0.norm2.weight", "up_blocks.3.resnets.0.norm2.bias", "up_blocks.3.resnets.0.conv2.weight", "up_blocks.3.resnets.0.conv2.bias", "up_blocks.3.resnets.0.conv_shortcut.weight", "up_blocks.3.resnets.0.conv_shortcut.bias", "up_blocks.3.resnets.1.norm1.weight", "up_blocks.3.resnets.1.norm1.bias", "up_blocks.3.resnets.1.conv1.weight", "up_blocks.3.resnets.1.conv1.bias", "up_blocks.3.resnets.1.time_emb_proj.weight", "up_blocks.3.resnets.1.time_emb_proj.bias", "up_blocks.3.resnets.1.norm2.weight", "up_blocks.3.resnets.1.norm2.bias", "up_blocks.3.resnets.1.conv2.weight", "up_blocks.3.resnets.1.conv2.bias", "up_blocks.3.resnets.1.conv_shortcut.weight", "up_blocks.3.resnets.1.conv_shortcut.bias", "up_blocks.3.resnets.2.norm1.weight", "up_blocks.3.resnets.2.norm1.bias", "up_blocks.3.resnets.2.conv1.weight", "up_blocks.3.resnets.2.conv1.bias", "up_blocks.3.resnets.2.time_emb_proj.weight", "up_blocks.3.resnets.2.time_emb_proj.bias", "up_blocks.3.resnets.2.norm2.weight", "up_blocks.3.resnets.2.norm2.bias", "up_blocks.3.resnets.2.conv2.weight", "up_blocks.3.resnets.2.conv2.bias", "up_blocks.3.resnets.2.conv_shortcut.weight", "up_blocks.3.resnets.2.conv_shortcut.bias".
Unexpected key(s) in state_dict: "down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_k.weight", "down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_out.0.bias", "down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_out.0.weight", "down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_q.weight", "down_blocks.1.attentions.0.transformer_blocks.1.attn1.to_v.weight", "down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_k.weight", "down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_out.0.bias", "down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_out.0.weight", "down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_q.weight", "down_blocks.1.attentions.0.transformer_blocks.1.attn2.to_v.weight", "down_blocks.1.attentions.0.transformer_blocks.1.ff.net.0.proj.bias", "down_blocks.1.attentions.0.transformer_blocks.1.ff.net.0.proj.weight", "down_blocks.1.attentions.0.transformer_blocks.1.ff.net.2.bias", "down_blocks.1.attentions.0.transformer_blocks.1.ff.net.2.weight", "down_blocks.1.attentions.0.transformer_blocks.1.norm1.bias", "down_blocks.1.attentions.0.transformer_blocks.1.norm1.weight", "down_blocks.1.attentions.0.transformer_blocks.1.norm2.bias", "down_blocks.1.attentions.0.transformer_blocks.1.norm2.weight
16:56:06-165346 ERROR Training failed / 训练失败

Start by reading library/model_util.py around load_models_from_stable_diffusion_checkpoint and the callers in library/train_util.py and train_network.py. Reproduce the failure with the supplied SDXL checkpoint and configuration, then determine what checkpoint or loading compatibility is required for training to proceed without the reported missing UNet keys.

Written by the indexing model from the issue text.