RuntimeError: Error(s) in loading state_dict for UNet2DConditionModel:

I need some help!

`Train Tools: get_project_version_list: outputs\train_tools\projects\mmj_ori_1.0\versions\v1 Train Tools: ui_refresh_project: ['mmj_ori_1.0'] Train Tools: on_ui_change_project_click: mmj_ori_1.0 Train Tools: get_project_version_list: outputs\train_tools\projects\mmj_ori_1.0\versions\v1 Train Tools: on_ui_change_project_version_click: mmj_ori_1.0 v1 Train Tools: on_train_begin_click {'base_model': 'G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\models\Stable-diffusion\7th_anime_v3_A.safetensors', 'img_folder': 'G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\outputs\train_tools\projects\mmj_ori_1.0\versions\v1\dataset\processed', 'output_folder': 'G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\outputs\train_tools\projects\mmj_ori_1.0\versions\v1\trains\7th_anime_v3_A-bs-1-ep-20-op-Lion-lr-0_0001-net-128-ap-64', 'save_json_folder': None, 'save_json_name': None, 'load_json_path': None, 'reg_img_folder': None, 'sample_prompts': None, 'change_output_name': 'mmj_ori_1.0-v1', 'json_load_skip_list': None, 'training_comment': None, 'save_json_only': False, 'tag_occurrence_txt_file': True, 'sort_tag_occurrence_alphabetically': False, 'optimizer_type': 'Lion', 'optimizer_args': {'weight_decay': '0.1', 'betas': '0.9,0.99'}, 'scheduler': 'cosine', 'cosine_restarts': 1, 'scheduler_power': 1, 'learning_rate': 0.0001, 'unet_lr': None, 'text_encoder_lr': None, 'warmup_lr_ratio': None, 'unet_only': False, 'net_dim': 128, 'alpha': 64, 'train_resolution': 512, 'height_resolution': None, 'batch_size': 1, 'clip_skip': 2, 'test_seed': 23, 'mixed_precision': 'fp16', 'save_precision': 'fp16', 'lyco': False, 'network_args': None, 'num_epochs': 20, 'save_every_n_epochs': 2, 'save_n_epoch_ratio': None, 'save_last_n_epochs': None, 'max_steps': None, 'sample_sampler': None, 'sample_every_n_steps': None, 'sample_every_n_epochs': None, 'buckets': True, 'min_bucket_resolution': 320, 'max_bucket_resolution': 960, 'bucket_reso_steps': None, 'bucket_no_upscale': False, 'shuffle_captions': False, 'keep_tokens': None, 'xformers': True, 'cache_latents': True, 'flip_aug': False, 'v2': True, 'v_parameterization': False, 'gradient_checkpointing': False, 'gradient_acc_steps': None, 'noise_offset': None, 'mem_eff_attn': False, 'lora_model_for_resume': None, 'save_state': False, 'resume': None, 'text_only': False, 'vae': None, 'log_dir': None, 'log_prefix': None, 'tokenizer_cache_dir': None, 'dataset_config': None, 'lowram': False, 'no_meta': False, 'color_aug': False, 'random_crop': False, 'use_8bit_adam': False, 'use_lion': False, 'caption_dropout_rate': None, 'caption_dropout_every_n_epochs': None, 'caption_tag_dropout_rate': None, 'prior_loss_weight': 1, 'max_grad_norm': 1, 'save_as': 'safetensors', 'caption_extension': '.txt', 'max_clip_token_length': 150, 'save_last_n_epochs_state': None, 'num_workers': 8, 'persistent_workers': True, 'face_crop_aug_range': None, 'network_module': 'networks.lora', 'locon_dim': None, 'locon_alpha': None, 'locon': False, 'use_sdxl': False, 'no_half_vae': False, 'cache_text_encoder_outputs': False, 'cache_text_encoder_outputs_to_disk': False, 'ext_sd_script_args': ''} G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\outputs\train_tools\projects\mmj_ori_1.0\versions\v1\dataset\processed 36_mmj_ori_1.0 Created a txt file named mmj_ori_1.0-v1.txt in the output folder Train Tools: train begin Namespace(console_log_level=None, console_log_file=None, console_log_simple=False, v2=True, v_parameterization=False, pretrained_model_name_or_path='G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\models\Stable-diffusion\7th_anime_v3_A.safetensors', tokenizer_cache_dir=None, train_data_dir='G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\outputs\train_tools\projects\mmj_ori_1.0\versions\v1\dataset\processed', shuffle_caption=False, caption_separator=',', caption_extension='.txt', caption_extention=None, keep_tokens=0, keep_tokens_separator='', caption_prefix=None, caption_suffix=None, color_aug=False, flip_aug=False, face_crop_aug_range=None, random_crop=False, debug_dataset=False, resolution='512', cache_latents=True, vae_batch_size=1, cache_latents_to_disk=False, enable_bucket=True, min_bucket_reso=320, max_bucket_reso=960, bucket_reso_steps=64, bucket_no_upscale=False, token_warmup_min=1, token_warmup_step=0, dataset_class=None, caption_dropout_rate=0.0, caption_dropout_every_n_epochs=0, caption_tag_dropout_rate=0.0, reg_data_dir=None, in_json=None, dataset_repeats=1, output_dir='G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\outputs\train_tools\projects\mmj_ori_1.0\versions\v1\trains\7th_anime_v3_A-bs-1-ep-20-op-Lion-lr-0_0001-net-128-ap-64', output_name='mmj_ori_1.0-v1', huggingface_repo_id=None, huggingface_repo_type=None, huggingface_path_in_repo=None, huggingface_token=None, huggingface_repo_visibility=None, save_state_to_huggingface=False, resume_from_huggingface=False, async_upload=False, save_precision='fp16', save_every_n_epochs=2, save_every_n_steps=None, save_n_epoch_ratio=None, save_last_n_epochs=None, save_last_n_epochs_state=None, save_last_n_steps=None, save_last_n_steps_state=None, save_state=False, resume=None, train_batch_size=1, max_token_length=150, mem_eff_attn=False, torch_compile=False, dynamo_backend='inductor', xformers=True, sdpa=False, vae=None, max_train_steps=1600, max_train_epochs=20, max_data_loader_n_workers=8, persistent_data_loader_workers=True, seed=23, gradient_checkpointing=False, gradient_accumulation_steps=1, mixed_precision='fp16', full_fp16=False, full_bf16=False, fp8_base=False, ddp_timeout=None, ddp_gradient_as_bucket_view=False, ddp_static_graph=False, clip_skip=2, logging_dir=None, log_with=None, log_prefix=None, log_tracker_name=None, wandb_run_name=None, log_tracker_config=None, wandb_api_key=None, noise_offset=None, multires_noise_iterations=None, ip_noise_gamma=None, multires_noise_discount=0.3, adaptive_noise_scale=None, zero_terminal_snr=False, min_timestep=None, max_timestep=None, lowram=False, highvram=False, sample_every_n_steps=None, sample_at_first=False, sample_every_n_epochs=None, sample_prompts=None, sample_sampler='ddim', config_file=None, output_config=False, metadata_title=None, metadata_author=None, metadata_description=None, metadata_license=None, metadata_tags=None, prior_loss_weight=1.0, optimizer_type='Lion', use_8bit_adam=False, use_lion_optimizer=False, learning_rate=0.0001, max_grad_norm=1.0, optimizer_args=['weight_decay=0.1', 'betas=0.9,0.99'], lr_scheduler_type='', lr_scheduler_args=None, lr_scheduler='cosine', lr_warmup_steps=0, lr_scheduler_num_cycles=1, lr_scheduler_power=1.0, dataset_config=None, min_snr_gamma=None, scale_v_pred_loss_like_noise_pred=False, v_pred_like_loss=None, debiased_estimation_loss=False, weighted_captions=False, no_metadata=False, save_model_as='safetensors', unet_lr=None, text_encoder_lr=None, network_weights=None, network_module='networks.lora', network_dim=128, network_alpha=64.0, network_dropout=None, network_args=None, network_train_unet_only=False, network_train_text_encoder_only=False, training_comment=None, dim_from_weights=False, scale_weight_norms=None, base_weights=None, base_weights_multiplier=None, no_half_vae=False, cache_text_encoder_outputs=False, cache_text_encoder_outputs_to_disk=False) WARNING:library.train_util:v2 with clip_skip will be unexpected / v2でclip_skipを使用することは想定されていません rich is not installed, using basic logging prepare tokenizer update token length: 150 Using DreamBooth method. prepare images. found directory G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\outputs\train_tools\projects\mmj_ori_1.0\versions\v1\dataset\processed\36_mmj_ori_1.0 contains 28 image files 1008 train images with repeating. 0 reg images. no regularization images / 正則化画像が見つかりませんでした [Dataset 0] batch_size: 1 resolution: (512, 512) enable_bucket: True network_multiplier: 1.0 min_bucket_reso: 320 max_bucket_reso: 960 bucket_reso_steps: 64 bucket_no_upscale: False

[Subset 0 of Dataset 0] image_dir: "G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\outputs\train_tools\projects\mmj_ori_1.0\versions\v1\dataset\processed\36_mmj_ori_1.0" image_count: 28 num_repeats: 36 shuffle_caption: False keep_tokens: 0 keep_tokens_separator: 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: mmj_ori_1.0 caption_extension: .txt

[Dataset 0] loading image sizes. make buckets number of images (including repeats) / 各bucketの画像枚数（繰り返し回数を含む） bucket 0: resolution (512, 512), count: 1008 mean ar error (without repeats): 0.0 preparing accelerator accelerator device: cuda loading model for process 0/1 load StableDiffusion checkpoint: G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\models\Stable-diffusion\7th_anime_v3_A.safetensors UNet2DConditionModel: 64, [5, 10, 20, 20], 1024, False, False Train Tools: train.train error Error(s) in loading state_dict for UNet2DConditionModel: size mismatch for down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). Traceback (most recent call last): File "G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\extensions\sd-webui-train-tools\liasece_sd_webui_train_tools\train_ui.py", line 133, in on_train_begin_click train.train(cfg) File "G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\extensions\sd-webui-train-tools\liasece_sd_webui_train_tools\train.py", line 44, in train trainer.train(args) File "G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\extensions\sd-webui-train-tools\liasece_sd_webui_train_tools\sd_scripts\train_network.py", line 235, in train model_version, text_encoder, vae, unet = self.load_target_model(args, weight_dtype, accelerator) File "G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\extensions\sd-webui-train-tools\liasece_sd_webui_train_tools\sd_scripts\train_network.py", line 103, in load_target_model textencoder, vae, unet, = train_util.load_target_model(args, weight_dtype, accelerator) File "G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\extensions\sd-webui-train-tools\liasece_sd_webui_train_tools\sd_scripts\library\train_util.py", line 4113, in load_target_model text_encoder, vae, unet, load_stable_diffusion_format = _load_target_model( File "G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\extensions\sd-webui-train-tools\liasece_sd_webui_train_tools\sd_scripts\library\train_util.py", line 4067, in _load_target_model text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint( File "G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\extensions\sd-webui-train-tools\liasece_sd_webui_train_tools\sd_scripts\library\model_util.py", line 1008, in load_models_from_stable_diffusion_checkpoint info = unet.load_state_dict(converted_unet_checkpoint) File "G:\AI Program\sd-webui-aki\sd-webui-aki\sd-webui-aki-v4\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 2041, 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: size mismatch for down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.1.attentions.2.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.2.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.2.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.2.attentions.2.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([640, 768]) from checkpoint, the shape in current model is torch.Size([640, 1024]). size mismatch for up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for up_blocks.3.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 768]) from checkpoint, the shape in current model is torch.Size([320, 1024]). size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]). size mismatch for mid_block.attentions.0.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([1280, 768]) from checkpoint, the shape in current model is torch.Size([1280, 1024]).

My images are all in 512*512, all parameters are default

liasece / sd-webui-train-tools

RuntimeError: Error(s) in loading state_dict for UNet2DConditionModel: #57