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The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 38 new columns ({'dora_wd', 'unit', 'use_tucker', 'block_alphas', 'train_norm', 'dim_from_weights', 'constrain', 'module_dropout', 'network_alpha', 'training_comment', 'scale_weight_norms', 'stop_text_encoder_training_pct', 'rescaled', 'down_lr_weight', 'train_on_input', 'decompose_both', 'LoRA_type', 'network_dropout', 'LyCORIS_preset', 'conv_dim', 'block_dims', 'network_dim', 'text_encoder_lr', 'use_cp', 'conv_block_alphas', 'block_lr_zero_threshold', 'rank_dropout_scale', 'unet_lr', 'mid_lr_weight', 'conv_block_dims', 'max_grad_norm', 'up_lr_weight', 'use_scalar', 'bypass_mode', 'network_weights', 'factor', 'rank_dropout', 'conv_alpha'}) and 51 missing columns ({'sd3_cache_text_encoder_outputs', 'logit_std', 'save_last_n_epochs', 'sd3_text_encoder_batch_size', 'weighting_scheme', 'learning_rate_te1', 'no_token_padding', 'discrete_flow_shift', 'cpu_offload_checkpointing', 'log_config', 'timestep_sampling', 'flux1_t5xxl', 'skip_cache_check', 'fused_backward_pass', 'fused_optimizer_groups', 'mem_eff_save', 'clip_l', 'lr_scheduler_type', 'save_t5xxl', 'model_prediction_type', 'blocks_to_swap', 'sd3_cache_text_encoder_outputs_to_disk', 'flux1_clip_l', 'flux1_cache_text_encoder_outputs_to_disk', 'flux_fused_backward_pass', 'ae', 'learning_rate_te', 'logit_mean', 'disable_mmap_load_safetensors', 'mode_scale', 'apply_t5_attn_mask', 'flux1_checkbox', 'blockwise_fused_optimizers', 'single_blocks_to_swap', 'split_mode', 'save_clip', 't5xxl_device', 'clip_g', 'save_last_n_epochs_state', 'flux1_cache_text_encoder_outputs', 'save_as_bool', 'stop_text_encoder_training', 'learning_rate_te2', 'double_blocks_to_swap', 't5xxl_dtype', 't5xxl', 't5xxl_max_token_length', 'train_blocks', 'guidance_scale', 'sd3_checkbox', 'lr_warmup_steps'}).

This happened while the json dataset builder was generating data using

hf://datasets/kratosboy507/kratos_configs/senajuo2idol_noobv75_20241209-003658.json (at revision c8e8663bd8a4d74931fd0031d515da7ca06e6f51)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              LoRA_type: string
              LyCORIS_preset: string
              adaptive_noise_scale: int64
              additional_parameters: string
              async_upload: bool
              block_alphas: string
              block_dims: string
              block_lr_zero_threshold: string
              bucket_no_upscale: bool
              bucket_reso_steps: int64
              bypass_mode: bool
              cache_latents: bool
              cache_latents_to_disk: bool
              caption_dropout_every_n_epochs: int64
              caption_dropout_rate: int64
              caption_extension: string
              clip_skip: int64
              color_aug: bool
              constrain: int64
              conv_alpha: int64
              conv_block_alphas: string
              conv_block_dims: string
              conv_dim: int64
              dataset_config: string
              debiased_estimation_loss: bool
              decompose_both: bool
              dim_from_weights: bool
              dora_wd: bool
              down_lr_weight: string
              dynamo_backend: string
              dynamo_mode: string
              dynamo_use_dynamic: bool
              dynamo_use_fullgraph: bool
              enable_bucket: bool
              epoch: int64
              extra_accelerate_launch_args: string
              factor: int64
              flip_aug: bool
              fp8_base: bool
              full_bf16: bool
              full_fp16: bool
              gpu_ids: string
              gradient_accumulation_steps: int64
              gradient_checkpointing: bool
              huber_c: int64
              huber_schedule: string
              huggingface_path_in_repo: string
              huggingface_repo_id: string
              huggingface_repo_type: string
              huggingface_repo_visibility: string
              huggingface_token: string
              ip_noise_gamma: int64
              ip_noise_gamma_random_strength: bool
              keep_tokens: int64
              learning_rate: int64
              log_tracker_config: string
              log_tracker_name: string
              log_with: string
              logging_dir: string
              loss_type: string
              lr_scheduler: string
              lr_scheduler_args: string
              lr_scheduler_num_cycles: int64
              lr_scheduler_power: int64
              lr_warmup: i
              ...
              etwork_dim: int64
              network_dropout: int64
              network_weights: string
              noise_offset: double
              noise_offset_random_strength: bool
              noise_offset_type: string
              num_cpu_threads_per_process: int64
              num_machines: int64
              num_processes: int64
              optimizer: string
              optimizer_args: string
              output_dir: string
              output_name: string
              persistent_data_loader_workers: bool
              pretrained_model_name_or_path: string
              prior_loss_weight: int64
              random_crop: bool
              rank_dropout: int64
              rank_dropout_scale: bool
              reg_data_dir: string
              rescaled: bool
              resume: string
              resume_from_huggingface: string
              sample_every_n_epochs: int64
              sample_every_n_steps: int64
              sample_prompts: string
              sample_sampler: string
              save_every_n_epochs: int64
              save_every_n_steps: int64
              save_last_n_steps: int64
              save_last_n_steps_state: int64
              save_model_as: string
              save_precision: string
              save_state: bool
              save_state_on_train_end: bool
              save_state_to_huggingface: bool
              scale_v_pred_loss_like_noise_pred: bool
              scale_weight_norms: int64
              sdxl: bool
              sdxl_cache_text_encoder_outputs: bool
              sdxl_no_half_vae: bool
              seed: int64
              shuffle_caption: bool
              stop_text_encoder_training_pct: int64
              text_encoder_lr: int64
              train_batch_size: int64
              train_data_dir: string
              train_norm: bool
              train_on_input: bool
              training_comment: string
              unet_lr: int64
              unit: int64
              up_lr_weight: string
              use_cp: bool
              use_scalar: bool
              use_tucker: bool
              v2: bool
              v_parameterization: bool
              v_pred_like_loss: int64
              vae: string
              vae_batch_size: int64
              wandb_api_key: string
              wandb_run_name: string
              weighted_captions: bool
              xformers: string
              to
              {'adaptive_noise_scale': Value(dtype='int64', id=None), 'additional_parameters': Value(dtype='string', id=None), 'ae': Value(dtype='string', id=None), 'apply_t5_attn_mask': Value(dtype='bool', id=None), 'async_upload': Value(dtype='bool', id=None), 'blocks_to_swap': Value(dtype='int64', id=None), 'blockwise_fused_optimizers': Value(dtype='bool', id=None), 'bucket_no_upscale': Value(dtype='bool', id=None), 'bucket_reso_steps': Value(dtype='int64', id=None), 'cache_latents': Value(dtype='bool', id=None), 'cache_latents_to_disk': Value(dtype='bool', id=None), 'caption_dropout_every_n_epochs': Value(dtype='int64', id=None), 'caption_dropout_rate': Value(dtype='int64', id=None), 'caption_extension': Value(dtype='string', id=None), 'clip_g': Value(dtype='string', id=None), 'clip_l': Value(dtype='string', id=None), 'clip_skip': Value(dtype='int64', id=None), 'color_aug': Value(dtype='bool', id=None), 'cpu_offload_checkpointing': Value(dtype='bool', id=None), 'dataset_config': Value(dtype='string', id=None), 'debiased_estimation_loss': Value(dtype='bool', id=None), 'disable_mmap_load_safetensors': Value(dtype='bool', id=None), 'discrete_flow_shift': Value(dtype='float64', id=None), 'double_blocks_to_swap': Value(dtype='int64', id=None), 'dynamo_backend': Value(dtype='string', id=None), 'dynamo_mode': Value(dtype='string', id=None), 'dynamo_use_dynamic': Value(dtype='bool', id=None), 'dynamo_use_fullgraph': Value(dtype='bool', id=None), 'enable_bucket': Value(dtype='bool', id=None), '
              ...
               'sd3_cache_text_encoder_outputs_to_disk': Value(dtype='bool', id=None), 'sd3_checkbox': Value(dtype='bool', id=None), 'sd3_text_encoder_batch_size': Value(dtype='int64', id=None), 'sdxl': Value(dtype='bool', id=None), 'sdxl_cache_text_encoder_outputs': Value(dtype='bool', id=None), 'sdxl_no_half_vae': Value(dtype='bool', id=None), 'seed': Value(dtype='int64', id=None), 'shuffle_caption': Value(dtype='bool', id=None), 'single_blocks_to_swap': Value(dtype='int64', id=None), 'skip_cache_check': Value(dtype='bool', id=None), 'split_mode': Value(dtype='bool', id=None), 'stop_text_encoder_training': Value(dtype='int64', id=None), 't5xxl': Value(dtype='string', id=None), 't5xxl_device': Value(dtype='string', id=None), 't5xxl_dtype': Value(dtype='string', id=None), 't5xxl_max_token_length': Value(dtype='int64', id=None), 'timestep_sampling': Value(dtype='string', id=None), 'train_batch_size': Value(dtype='int64', id=None), 'train_blocks': Value(dtype='string', id=None), 'train_data_dir': Value(dtype='string', id=None), 'v2': Value(dtype='bool', id=None), 'v_parameterization': Value(dtype='bool', id=None), 'v_pred_like_loss': Value(dtype='int64', id=None), 'vae': Value(dtype='string', id=None), 'vae_batch_size': Value(dtype='int64', id=None), 'wandb_api_key': Value(dtype='string', id=None), 'wandb_run_name': Value(dtype='string', id=None), 'weighted_captions': Value(dtype='bool', id=None), 'weighting_scheme': Value(dtype='string', id=None), 'xformers': Value(dtype='string', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1417, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1049, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 38 new columns ({'dora_wd', 'unit', 'use_tucker', 'block_alphas', 'train_norm', 'dim_from_weights', 'constrain', 'module_dropout', 'network_alpha', 'training_comment', 'scale_weight_norms', 'stop_text_encoder_training_pct', 'rescaled', 'down_lr_weight', 'train_on_input', 'decompose_both', 'LoRA_type', 'network_dropout', 'LyCORIS_preset', 'conv_dim', 'block_dims', 'network_dim', 'text_encoder_lr', 'use_cp', 'conv_block_alphas', 'block_lr_zero_threshold', 'rank_dropout_scale', 'unet_lr', 'mid_lr_weight', 'conv_block_dims', 'max_grad_norm', 'up_lr_weight', 'use_scalar', 'bypass_mode', 'network_weights', 'factor', 'rank_dropout', 'conv_alpha'}) and 51 missing columns ({'sd3_cache_text_encoder_outputs', 'logit_std', 'save_last_n_epochs', 'sd3_text_encoder_batch_size', 'weighting_scheme', 'learning_rate_te1', 'no_token_padding', 'discrete_flow_shift', 'cpu_offload_checkpointing', 'log_config', 'timestep_sampling', 'flux1_t5xxl', 'skip_cache_check', 'fused_backward_pass', 'fused_optimizer_groups', 'mem_eff_save', 'clip_l', 'lr_scheduler_type', 'save_t5xxl', 'model_prediction_type', 'blocks_to_swap', 'sd3_cache_text_encoder_outputs_to_disk', 'flux1_clip_l', 'flux1_cache_text_encoder_outputs_to_disk', 'flux_fused_backward_pass', 'ae', 'learning_rate_te', 'logit_mean', 'disable_mmap_load_safetensors', 'mode_scale', 'apply_t5_attn_mask', 'flux1_checkbox', 'blockwise_fused_optimizers', 'single_blocks_to_swap', 'split_mode', 'save_clip', 't5xxl_device', 'clip_g', 'save_last_n_epochs_state', 'flux1_cache_text_encoder_outputs', 'save_as_bool', 'stop_text_encoder_training', 'learning_rate_te2', 'double_blocks_to_swap', 't5xxl_dtype', 't5xxl', 't5xxl_max_token_length', 'train_blocks', 'guidance_scale', 'sd3_checkbox', 'lr_warmup_steps'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/kratosboy507/kratos_configs/senajuo2idol_noobv75_20241209-003658.json (at revision c8e8663bd8a4d74931fd0031d515da7ca06e6f51)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

additional_parameters string	ae string	apply_t5_attn_mask bool	async_upload bool	blocks_to_swap int64	blockwise_fused_optimizers bool	bucket_no_upscale bool	bucket_reso_steps int64	cache_latents bool	cache_latents_to_disk bool	caption_extension string	clip_g string	clip_l string	color_aug bool	cpu_offload_checkpointing bool	debiased_estimation_loss bool	disable_mmap_load_safetensors bool	discrete_flow_shift float64	double_blocks_to_swap int64	dynamo_backend string	dynamo_mode string	dynamo_use_dynamic bool	dynamo_use_fullgraph bool	enable_bucket bool	epoch int64	flip_aug bool	flux1_cache_text_encoder_outputs bool	flux1_cache_text_encoder_outputs_to_disk bool	flux1_checkbox bool	flux1_clip_l string	flux1_t5xxl string	flux_fused_backward_pass bool	fp8_base bool	full_bf16 bool	full_fp16 bool	fused_backward_pass bool	fused_optimizer_groups int64	gpu_ids string	gradient_accumulation_steps int64	gradient_checkpointing bool	guidance_scale int64	huber_c float64	huber_schedule string	ip_noise_gamma_random_strength bool	keep_tokens int64	learning_rate float64	learning_rate_te int64	learning_rate_te1 float64	learning_rate_te2 float64	log_config bool	logging_dir string	logit_mean int64	logit_std int64	loss_type string	lr_scheduler string	lr_scheduler_num_cycles int64	lr_scheduler_power int64	lr_scheduler_type string	lr_warmup int64	lr_warmup_steps int64	masked_loss bool	max_bucket_reso int64	max_resolution string	max_timestep int64	max_token_length int64	mem_eff_attn bool	mem_eff_save bool	min_bucket_reso int64	mixed_precision string	mode_scale float64	model_list string	model_prediction_type string	multi_gpu bool	multires_noise_discount float64	multires_noise_iterations int64	no_token_padding bool	noise_offset float64	noise_offset_random_strength bool	noise_offset_type string	num_cpu_threads_per_process int64	num_machines int64	num_processes int64	optimizer string	optimizer_args string	output_dir string	output_name string	persistent_data_loader_workers bool	pretrained_model_name_or_path string	prior_loss_weight int64	random_crop bool	sample_sampler string	save_as_bool bool	save_clip bool	save_every_n_epochs int64	save_last_n_epochs int64	save_last_n_epochs_state int64	save_model_as string	save_precision string	save_state bool	save_state_on_train_end bool	save_state_to_huggingface bool	save_t5xxl bool	scale_v_pred_loss_like_noise_pred bool	sd3_cache_text_encoder_outputs bool	sd3_cache_text_encoder_outputs_to_disk bool	sd3_checkbox bool	sd3_text_encoder_batch_size int64	sdxl bool	sdxl_cache_text_encoder_outputs bool	sdxl_no_half_vae bool	seed int64	shuffle_caption bool	single_blocks_to_swap int64	skip_cache_check bool	split_mode bool	stop_text_encoder_training int64	t5xxl string	t5xxl_device string	t5xxl_dtype string	t5xxl_max_token_length int64	timestep_sampling string	train_batch_size int64	train_blocks string	train_data_dir string	v2 bool	v_parameterization bool	vae_batch_size int64	weighted_captions bool	weighting_scheme string	xformers string	LoRA_type string	LyCORIS_preset string	block_alphas string	block_dims string	block_lr_zero_threshold string	bypass_mode bool	constrain int64	conv_alpha int64	conv_block_alphas string	conv_block_dims string	conv_dim int64	decompose_both bool	dim_from_weights bool	dora_wd bool	down_lr_weight string	factor int64	max_grad_norm int64	mid_lr_weight string	module_dropout int64	network_alpha int64	network_dim int64	network_dropout int64	network_weights string	rank_dropout int64	rank_dropout_scale bool	rescaled bool	scale_weight_norms int64	stop_text_encoder_training_pct int64	text_encoder_lr int64	train_norm bool	train_on_input bool	training_comment string	unet_lr int64	unit int64	up_lr_weight string	use_cp bool	use_scalar bool	use_tucker bool
	/home/Ubuntu/Downloads/ae.safetensors	false	false	0	false	true	64	true	true	.txt			false	false	false	false	3.1582	0	no	default	false	false	false	200	false	true	true	true	/home/Ubuntu/Downloads/clip_l.safetensors	/home/Ubuntu/Downloads/t5xxl_fp16.safetensors	true	false	true	false	false	0	0	1	true	1	0.1	snr	false	0	0.000004	0	0.00001	0.00001	false		0	1	l2	constant	1	1		0	0	false	2,048	1024,1024	1,000	75	false	true	256	bf16	1.29	custom	raw	false	0.3	0	false	0	false	Original	2	1	1	Adafactor	scale_parameter=False relative_step=False warmup_init=False weight_decay=0.01	/home/Ubuntu/apps/StableSwarmUI/Models/diffusion_models	Quality_1	false	/home/Ubuntu/Downloads/flux1-dev.safetensors	1	false	euler_a	false	false	25	0	0	safetensors	fp16	false	false	false	false	false	false	false	false	1	false	false	false	1	false	0	false	false	0			bf16	512	sigmoid	1	all	/home/Ubuntu/Downloads/training_imgs	false	false	4	false	logit_normal	xformers	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
	/home/Ubuntu/Downloads/ae.safetensors	false	false	0	false	true	64	true	true	.txt			false	false	false	false	3.1582	0	no	default	false	false	false	200	false	true	true	true	/home/Ubuntu/Downloads/clip_l.safetensors	/home/Ubuntu/Downloads/t5xxl_fp16.safetensors	true	false	true	false	false	0	0	1	true	1	0.1	snr	false	0	0.00001	0	0.00001	0.00001	false		0	1	l2	constant	1	1		0	0	false	2,048	1024,1024	1,000	75	false	true	256	bf16	1.29	custom	raw	false	0.3	0	false	0	false	Original	2	1	1	Adafactor	scale_parameter=False relative_step=False warmup_init=False weight_decay=0.01	/home/Ubuntu/apps/StableSwarmUI/Models/diffusion_models	Quality_1	false	/home/Ubuntu/Downloads/flux1-dev.safetensors	1	false	euler_a	false	false	25	0	0	safetensors	fp16	false	false	false	false	false	false	false	false	1	false	false	false	1	false	0	false	false	0			bf16	512	sigmoid	7	all	/home/Ubuntu/Downloads/training_imgs	false	false	4	false	logit_normal	xformers	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
--zero_terminal_snr	null	null	false	null	null	true	32	false	true	.txt	null	null	false	null	false	null	null	null	no	default	false	false	true	12	false	null	null	null	null	null	null	false	false	false	null	null		1	true	null	0	snr	false	3	1	null	null	null	null	D:\lora_model\log	null	null	l2	cosine_with_restarts	1	1	null	5	null	false	2,048	1024,1024	1,000	225	false	null	256	bf16	null	custom	null	false	0.2	6	null	0.12	true	Original	2	1	2	Prodigy	decouple=True weight_decay=0.05 d_coef=1.2 betas=0.9,0.99 use_bias_correction=True safeguard_warmup=True	D:/lora_model/model	senajuo2idol_noobv75	false	E:/stable-diffusion-webui-master/models/Stable-diffusion/noobaiXLNAIXL_vPred075SVersion.safetensors	1	false	dpm_2_a	null	null	1	null	null	safetensors	fp16	false	false	false	null	true	null	null	null	null	true	false	true	4,134	true	null	null	null	null	null	null	null	null	null	8	null	E:\角色相片\學偶\十王\done - 複製	false	true	0	false	null	xformers	Standard	full				false	0	1			1	false	false	false		-1	1		0	8	8	0		0	false	false	0	0	1	false	true		1	1		false	false	false

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