loraas / logs /1_log.txt

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	Running 1 job
	{
	"type": "diffusion_trainer",
	"training_folder": "/root/lab/ai-toolkit/output",
	"sqlite_db_path": "/root/lab/ai-toolkit/aitk_db.db",
	"device": "cuda",
	"trigger_word": "l4n4lux",
	"performance_log_every": 10,
	"network": {
	"type": "lora",
	"linear": 64,
	"linear_alpha": 64,
	"conv": 16,
	"conv_alpha": 16,
	"lokr_full_rank": true,
	"lokr_factor": -1,
	"network_kwargs": {
	"ignore_if_contains": []
	}
	},
	"save": {
	"dtype": "bf16",
	"save_every": 250,
	"max_step_saves_to_keep": 4,
	"save_format": "diffusers",
	"push_to_hub": false
	},
	"datasets": [
	{
	"folder_path": "/root/lab/ai-toolkit/datasets/lana",
	"mask_path": null,
	"mask_min_value": 0.1,
	"default_caption": "l4n4lux",
	"caption_ext": "txt",
	"caption_dropout_rate": 0.05,
	"cache_latents_to_disk": false,
	"is_reg": false,
	"network_weight": 1,
	"resolution": [
	512,
	768,
	1024
	],
	"controls": [],
	"shrink_video_to_frames": true,
	"num_frames": 1,
	"flip_x": false,
	"flip_y": false,
	"num_repeats": 1
	}
	],
	"train": {
	"batch_size": 1,
	"bypass_guidance_embedding": false,
	"steps": 5000,
	"gradient_accumulation": 1,
	"train_unet": true,
	"train_text_encoder": false,
	"gradient_checkpointing": true,
	"noise_scheduler": "flowmatch",
	"optimizer": "adamw8bit",
	"timestep_type": "sigmoid",
	"content_or_style": "balanced",
	"optimizer_params": {
	"weight_decay": 0.0001
	},
	"unload_text_encoder": false,
	"cache_text_embeddings": false,
	"lr": 0.0001,
	"ema_config": {
	"use_ema": false,
	"ema_decay": 0.99
	},
	"skip_first_sample": true,
	"force_first_sample": false,
	"disable_sampling": false,
	"dtype": "bf16",
	"diff_output_preservation": true,
	"diff_output_preservation_multiplier": 1,
	"diff_output_preservation_class": "woman",
	"switch_boundary_every": 1,
	"loss_type": "mse"
	},
	"logging": {
	"log_every": 1,
	"use_ui_logger": true
	},
	"model": {
	"name_or_path": "ai-toolkit/Wan2.2-T2V-A14B-Diffusers-bf16",
	"quantize": false,
	"qtype": "qfloat8",
	"quantize_te": false,
	"qtype_te": "qfloat8",
	"arch": "wan22_14b:t2v",
	"low_vram": false,
	"model_kwargs": {
	"train_high_noise": false,
	"train_low_noise": true
	},
	"layer_offloading": false,
	"layer_offloading_text_encoder_percent": 1,
	"layer_offloading_transformer_percent": 1
	},
	"sample": {
	"sampler": "flowmatch",
	"sample_every": 250,
	"width": 1024,
	"height": 1024,
	"samples": [
	{
	"prompt": "l4n4lux, woman with red hair, playing chess at the park, bomb going off in the background"
	}
	],
	"neg": "",
	"seed": 42,
	"walk_seed": true,
	"guidance_scale": 4,
	"sample_steps": 12,
	"num_frames": 1,
	"fps": 1
	}
	}
	Using SQLite database at /root/lab/ai-toolkit/aitk_db.db
	Job ID: "765fbd95-c627-460c-a232-147504194a44"

	#############################################
	# Running job: lana-lora-wan2.2
	#############################################


	Running 1 process
	Loading Wan model
	Loading transformer 1
	Loading checkpoint shards: 0%\| \| 0/3 [00:00<?, ?it/s] Loading checkpoint shards: 0%\| \| 0/3 [00:00<?, ?it/s] Loading checkpoint shards: 33%\|##########################3 \| 1/3 [00:00<00:00, 5.26it/s] Loading checkpoint shards: 33%\|##########################3 \| 1/3 [00:00<00:00, 5.26it/s] Loading checkpoint shards: 67%\|####################################################6 \| 2/3 [00:00<00:00, 4.78it/s] Loading checkpoint shards: 67%\|####################################################6 \| 2/3 [00:00<00:00, 4.78it/s] Loading checkpoint shards: 100%\|###############################################################################\| 3/3 [00:00<00:00, 5.19it/s] Loading checkpoint shards: 100%\|###############################################################################\| 3/3 [00:00<00:00, 5.19it/s] Loading checkpoint shards: 100%\|###############################################################################\| 3/3 [00:00<00:00, 5.11it/s] Loading checkpoint shards: 100%\|###############################################################################\| 3/3 [00:00<00:00, 5.11it/s]

	Loading transformer 2
	Loading checkpoint shards: 0%\| \| 0/3 [00:00<?, ?it/s] Loading checkpoint shards: 0%\| \| 0/3 [00:00<?, ?it/s] Loading checkpoint shards: 33%\|##########################3 \| 1/3 [00:00<00:00, 5.01it/s] Loading checkpoint shards: 33%\|##########################3 \| 1/3 [00:00<00:00, 5.01it/s] Loading checkpoint shards: 67%\|####################################################6 \| 2/3 [00:00<00:00, 5.22it/s] Loading checkpoint shards: 67%\|####################################################6 \| 2/3 [00:00<00:00, 5.22it/s] Loading checkpoint shards: 100%\|###############################################################################\| 3/3 [00:00<00:00, 5.22it/s] Loading checkpoint shards: 100%\|###############################################################################\| 3/3 [00:00<00:00, 5.22it/s] Loading checkpoint shards: 100%\|###############################################################################\| 3/3 [00:00<00:00, 5.19it/s] Loading checkpoint shards: 100%\|###############################################################################\| 3/3 [00:00<00:00, 5.19it/s]

	Creating DualWanTransformer3DModel
	Loading UMT5EncoderModel
	You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers
	You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers
	Using ai-toolkit/umt5_xxl_encoder for UMT5 encoder.
	`torch_dtype` is deprecated! Use `dtype` instead!
	`torch_dtype` is deprecated! Use `dtype` instead!
	Loading checkpoint shards: 0%\| \| 0/3 [00:00<?, ?it/s] Loading checkpoint shards: 0%\| \| 0/3 [00:00<?, ?it/s] Loading checkpoint shards: 67%\|####################################################6 \| 2/3 [00:00<00:00, 9.08it/s] Loading checkpoint shards: 67%\|####################################################6 \| 2/3 [00:00<00:00, 9.08it/s] Loading checkpoint shards: 100%\|###############################################################################\| 3/3 [00:00<00:00, 11.57it/s] Loading checkpoint shards: 100%\|###############################################################################\| 3/3 [00:00<00:00, 11.57it/s]

	Loading VAE
	Making pipe
	Preparing Model
	create LoRA network. base dim (rank): 64, alpha: 64
	neuron dropout: p=None, rank dropout: p=None, module dropout: p=None
	apply LoRA to Conv2d with kernel size (3,3). dim (rank): 16, alpha: 16
	create LoRA for Text Encoder: 0 modules.
	create LoRA for U-Net: 400 modules.
	enable LoRA for U-Net
	#### IMPORTANT RESUMING FROM /root/lab/ai-toolkit/output/lana-lora-wan2.2/lana-lora-wan2.2_000000250_low_noise.safetensors ####
	Loading from /root/lab/ai-toolkit/output/lana-lora-wan2.2/lana-lora-wan2.2_000000250_low_noise.safetensors
	Missing keys: []
	Found step 250 in metadata, starting from there
	Loading optimizer state from /root/lab/ai-toolkit/output/lana-lora-wan2.2/optimizer.pt
	Updating optimizer LR from params
	Dataset: /root/lab/ai-toolkit/datasets/lana
	- Preprocessing image dimensions
	0%\| \| 0/72 [00:00<?, ?it/s] 0%\| \| 0/72 [00:00<?, ?it/s] 100%\|######################################################################################################\| 72/72 [00:00<00:00, 7479.44it/s] 100%\|######################################################################################################\| 72/72 [00:00<00:00, 7479.44it/s]

	- Found 72 images
	Bucket sizes for /root/lab/ai-toolkit/datasets/lana:
	480x512: 72 files
	1 buckets made
	Dataset: /root/lab/ai-toolkit/datasets/lana
	- Preprocessing image dimensions
	0%\| \| 0/72 [00:00<?, ?it/s] 0%\| \| 0/72 [00:00<?, ?it/s] 100%\|#####################################################################################################\| 72/72 [00:00<00:00, 19021.79it/s] 100%\|#####################################################################################################\| 72/72 [00:00<00:00, 19021.79it/s]

	- Found 72 images
	Bucket sizes for /root/lab/ai-toolkit/datasets/lana:
	720x768: 72 files
	1 buckets made
	Dataset: /root/lab/ai-toolkit/datasets/lana
	- Preprocessing image dimensions
	0%\| \| 0/72 [00:00<?, ?it/s] 0%\| \| 0/72 [00:00<?, ?it/s] 100%\|#####################################################################################################\| 72/72 [00:00<00:00, 24011.28it/s] 100%\|#####################################################################################################\| 72/72 [00:00<00:00, 24011.28it/s]

	- Found 72 images
	Bucket sizes for /root/lab/ai-toolkit/datasets/lana:
	960x1024: 72 files
	1 buckets made
	Skipping first sample due to config setting
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[01:13<3:19:23, 2.53s/it, lr: 1.0e-04 loss: 1.146e-01] lana-lora-wan2.2: 6%\|##9 \| 280/5000 [01:14<3:15:49, 2.49s/it, lr: 1.0e-04 loss: 4.839e-02] lana-lora-wan2.2: 6%\|##9 \| 280/5000 [01:14<3:15:49, 2.49s/it, lr: 1.0e-04 loss: 4.839e-02] lana-lora-wan2.2: 6%\|##9 \| 281/5000 [01:16<3:14:59, 2.48s/it, lr: 1.0e-04 loss: 3.127e-02] lana-lora-wan2.2: 6%\|##9 \| 281/5000 [01:16<3:14:59, 2.48s/it, lr: 1.0e-04 loss: 3.127e-02] lana-lora-wan2.2: 6%\|##9 \| 282/5000 [01:20<3:17:50, 2.52s/it, lr: 1.0e-04 loss: 2.933e-02] lana-lora-wan2.2: 6%\|##9 \| 282/5000 [01:20<3:17:50, 2.52s/it, lr: 1.0e-04 loss: 2.933e-02] lana-lora-wan2.2: 6%\|##9 \| 283/5000 [01:22<3:16:59, 2.51s/it, lr: 1.0e-04 loss: 1.442e-01] lana-lora-wan2.2: 6%\|##9 \| 283/5000 [01:22<3:16:59, 2.51s/it, lr: 1.0e-04 loss: 1.442e-01] lana-lora-wan2.2: 6%\|##9 \| 284/5000 [01:24<3:14:38, 2.48s/it, lr: 1.0e-04 loss: 4.585e-02] lana-lora-wan2.2: 6%\|##9 \| 284/5000 [01:24<3:14:38, 2.48s/it, lr: 1.0e-04 loss: 4.585e-02] lana-lora-wan2.2: 6%\|##9 \| 285/5000 [01:25<3:11:40, 2.44s/it, lr: 1.0e-04 loss: 7.121e-02] lana-lora-wan2.2: 6%\|##9 \| 285/5000 [01:25<3:11:40, 2.44s/it, lr: 1.0e-04 loss: 7.121e-02] lana-lora-wan2.2: 6%\|##9 \| 286/5000 [01:29<3:14:16, 2.47s/it, lr: 1.0e-04 loss: 6.658e-02] lana-lora-wan2.2: 6%\|##9 \| 286/5000 [01:29<3:14:16, 2.47s/it, lr: 1.0e-04 loss: 6.658e-02] lana-lora-wan2.2: 6%\|##9 \| 287/5000 [01:31<3:13:35, 2.46s/it, lr: 1.0e-04 loss: 4.912e-02] lana-lora-wan2.2: 6%\|##9 \| 287/5000 [01:31<3:13:35, 2.46s/it, lr: 1.0e-04 loss: 4.912e-02] lana-lora-wan2.2: 6%\|##9 \| 288/5000 [01:34<3:16:01, 2.50s/it, lr: 1.0e-04 loss: 3.703e-02] lana-lora-wan2.2: 6%\|##9 \| 288/5000 [01:34<3:16:01, 2.50s/it, lr: 1.0e-04 loss: 3.703e-02] lana-lora-wan2.2: 6%\|### \| 289/5000 [01:38<3:18:20, 2.53s/it, lr: 1.0e-04 loss: 2.339e-01] lana-lora-wan2.2: 6%\|### \| 289/5000 [01:38<3:18:20, 2.53s/it, lr: 1.0e-04 loss: 2.339e-01] lana-lora-wan2.2: 6%\|### \| 290/5000 [01:40<3:18:09, 2.52s/it, lr: 1.0e-04 loss: 3.657e-02] lana-lora-wan2.2: 6%\|### \| 290/5000 [01:40<3:18:09, 2.52s/it, lr: 1.0e-04 loss: 3.657e-02] lana-lora-wan2.2: 6%\|### \| 291/5000 [01:43<3:17:26, 2.52s/it, lr: 1.0e-04 loss: 3.150e-02] lana-lora-wan2.2: 6%\|### \| 291/5000 [01:43<3:17:26, 2.52s/it, lr: 1.0e-04 loss: 3.150e-02] lana-lora-wan2.2: 6%\|### \| 292/5000 [01:46<3:19:32, 2.54s/it, lr: 1.0e-04 loss: 8.685e-02] lana-lora-wan2.2: 6%\|### \| 292/5000 [01:46<3:19:32, 2.54s/it, lr: 1.0e-04 loss: 8.685e-02] lana-lora-wan2.2: 6%\|### \| 293/5000 [01:50<3:21:31, 2.57s/it, lr: 1.0e-04 loss: 4.755e-02] lana-lora-wan2.2: 6%\|### \| 293/5000 [01:50<3:21:31, 2.57s/it, lr: 1.0e-04 loss: 4.755e-02] lana-lora-wan2.2: 6%\|### \| 294/5000 [01:54<3:23:25, 2.59s/it, lr: 1.0e-04 loss: 2.861e-02] lana-lora-wan2.2: 6%\|### \| 294/5000 [01:54<3:23:25, 2.59s/it, lr: 1.0e-04 loss: 2.861e-02] lana-lora-wan2.2: 6%\|### \| 295/5000 [01:56<3:22:38, 2.58s/it, lr: 1.0e-04 loss: 4.737e-02] lana-lora-wan2.2: 6%\|### \| 295/5000 [01:56<3:22:38, 2.58s/it, lr: 1.0e-04 loss: 4.737e-02] lana-lora-wan2.2: 6%\|### \| 296/5000 [02:00<3:24:50, 2.61s/it, lr: 1.0e-04 loss: 3.219e-02] lana-lora-wan2.2: 6%\|### \| 296/5000 [02:00<3:24:50, 2.61s/it, lr: 1.0e-04 loss: 3.219e-02] lana-lora-wan2.2: 6%\|### \| 297/5000 [02:03<3:26:32, 2.64s/it, lr: 1.0e-04 loss: 1.015e-01] lana-lora-wan2.2: 6%\|### \| 297/5000 [02:03<3:26:32, 2.64s/it, lr: 1.0e-04 loss: 1.015e-01] lana-lora-wan2.2: 6%\|### \| 298/5000 [02:06<3:25:44, 2.63s/it, lr: 1.0e-04 loss: 5.488e-02] lana-lora-wan2.2: 6%\|### \| 298/5000 [02:06<3:25:44, 2.63s/it, lr: 1.0e-04 loss: 5.488e-02] lana-lora-wan2.2: 6%\|###1 \| 299/5000 [02:09<3:27:22, 2.65s/it, lr: 1.0e-04 loss: 3.092e-02] lana-lora-wan2.2: 6%\|###1 \| 299/5000 [02:09<3:27:22, 2.65s/it, lr: 1.0e-04 loss: 3.092e-02] lana-lora-wan2.2: 6%\|###1 \| 300/5000 [02:11<3:26:35, 2.64s/it, lr: 1.0e-04 loss: 9.039e-02] lana-lora-wan2.2: 6%\|###1 \| 300/5000 [02:11<3:26:35, 2.64s/it, lr: 1.0e-04 loss: 9.039e-02] lana-lora-wan2.2: 6%\|###1 \| 301/5000 [02:13<3:24:48, 2.62s/it, lr: 1.0e-04 loss: 4.803e-02] lana-lora-wan2.2: 6%\|###1 \| 301/5000 [02:13<3:24:48, 2.62s/it, lr: 1.0e-04 loss: 4.803e-02] lana-lora-wan2.2: 6%\|###1 \| 302/5000 [02:17<3:26:19, 2.63s/it, lr: 1.0e-04 loss: 3.021e-02] lana-lora-wan2.2: 6%\|###1 \| 302/5000 [02:17<3:26:19, 2.63s/it, lr: 1.0e-04 loss: 3.021e-02] lana-lora-wan2.2: 6%\|###1 \| 303/5000 [02:19<3:25:35, 2.63s/it, lr: 1.0e-04 loss: 4.879e-02] lana-lora-wan2.2: 6%\|###1 \| 303/5000 [02:19<3:25:35, 2.63s/it, lr: 1.0e-04 loss: 4.879e-02] lana-lora-wan2.2: 6%\|###1 \| 304/5000 [02:22<3:27:03, 2.65s/it, lr: 1.0e-04 loss: 1.179e-01] lana-lora-wan2.2: 6%\|###1 \| 304/5000 [02:22<3:27:03, 2.65s/it, lr: 1.0e-04 loss: 1.179e-01] lana-lora-wan2.2: 6%\|###1 \| 305/5000 [02:25<3:26:19, 2.64s/it, lr: 1.0e-04 loss: 2.001e-01] lana-lora-wan2.2: 6%\|###1 \| 305/5000 [02:25<3:26:19, 2.64s/it, lr: 1.0e-04 loss: 2.001e-01] lana-lora-wan2.2: 6%\|###1 \| 306/5000 [02:28<3:27:42, 2.66s/it, lr: 1.0e-04 loss: 4.293e-02] lana-lora-wan2.2: 6%\|###1 \| 306/5000 [02:28<3:27:42, 2.66s/it, lr: 1.0e-04 loss: 4.293e-02] lana-lora-wan2.2: 6%\|###1 \| 307/5000 [02:31<3:27:26, 2.65s/it, lr: 1.0e-04 loss: 4.079e-02] lana-lora-wan2.2: 6%\|###1 \| 307/5000 [02:31<3:27:26, 2.65s/it, lr: 1.0e-04 loss: 4.079e-02] lana-lora-wan2.2: 6%\|###2 \| 308/5000 [02:32<3:25:26, 2.63s/it, lr: 1.0e-04 loss: 9.045e-02] lana-lora-wan2.2: 6%\|###2 \| 308/5000 [02:32<3:25:26, 2.63s/it, lr: 1.0e-04 loss: 9.045e-02] lana-lora-wan2.2: 6%\|###2 \| 309/5000 [02:36<3:26:45, 2.64s/it, lr: 1.0e-04 loss: 4.420e-02] lana-lora-wan2.2: 6%\|###2 \| 309/5000 [02:36<3:26:45, 2.64s/it, lr: 1.0e-04 loss: 4.420e-02] lana-lora-wan2.2: 6%\|###2 \| 310/5000 [02:37<3:24:48, 2.62s/it, lr: 1.0e-04 loss: 3.678e-02] lana-lora-wan2.2: 6%\|###2 \| 310/5000 [02:37<3:24:48, 2.62s/it, lr: 1.0e-04 loss: 3.678e-02] lana-lora-wan2.2: 6%\|###2 \| 311/5000 [02:38<3:22:55, 2.60s/it, lr: 1.0e-04 loss: 2.888e-01] lana-lora-wan2.2: 6%\|###2 \| 311/5000 [02:38<3:22:55, 2.60s/it, lr: 1.0e-04 loss: 2.888e-01] lana-lora-wan2.2: 6%\|###2 \| 312/5000 [02:39<3:21:04, 2.57s/it, lr: 1.0e-04 loss: 1.334e-01] lana-lora-wan2.2: 6%\|###2 \| 312/5000 [02:39<3:21:04, 2.57s/it, lr: 1.0e-04 loss: 1.334e-01] lana-lora-wan2.2: 6%\|###2 \| 313/5000 [02:43<3:22:44, 2.60s/it, lr: 1.0e-04 loss: 3.881e-02] lana-lora-wan2.2: 6%\|###2 \| 313/5000 [02:43<3:22:44, 2.60s/it, lr: 1.0e-04 loss: 3.881e-02] lana-lora-wan2.2: 6%\|###2 \| 314/5000 [02:44<3:20:58, 2.57s/it, lr: 1.0e-04 loss: 1.053e-01] lana-lora-wan2.2: 6%\|###2 \| 314/5000 [02:44<3:20:58, 2.57s/it, lr: 1.0e-04 loss: 1.053e-01] lana-lora-wan2.2: 6%\|###2 \| 315/5000 [02:45<3:19:16, 2.55s/it, lr: 1.0e-04 loss: 2.877e-01] lana-lora-wan2.2: 6%\|###2 \| 315/5000 [02:45<3:19:16, 2.55s/it, lr: 1.0e-04 loss: 2.877e-01] lana-lora-wan2.2: 6%\|###2 \| 316/5000 [02:49<3:20:33, 2.57s/it, lr: 1.0e-04 loss: 4.308e-02] lana-lora-wan2.2: 6%\|###2 \| 316/5000 [02:49<3:20:33, 2.57s/it, lr: 1.0e-04 loss: 4.308e-02] lana-lora-wan2.2: 6%\|###2 \| 317/5000 [02:50<3:18:53, 2.55s/it, lr: 1.0e-04 loss: 1.944e-01] lana-lora-wan2.2: 6%\|###2 \| 317/5000 [02:50<3:18:53, 2.55s/it, lr: 1.0e-04 loss: 1.944e-01] lana-lora-wan2.2: 6%\|###3 \| 318/5000 [02:51<3:17:17, 2.53s/it, lr: 1.0e-04 loss: 3.463e-02] lana-lora-wan2.2: 6%\|###3 \| 318/5000 [02:51<3:17:17, 2.53s/it, lr: 1.0e-04 loss: 3.463e-02] lana-lora-wan2.2: 6%\|###3 \| 319/5000 [02:54<3:17:09, 2.53s/it, lr: 1.0e-04 loss: 1.672e-01] lana-lora-wan2.2: 6%\|###3 \| 319/5000 [02:54<3:17:09, 2.53s/it, lr: 1.0e-04 loss: 1.672e-01] lana-lora-wan2.2: 6%\|###3 \| 320/5000 [02:55<3:15:39, 2.51s/it, lr: 1.0e-04 loss: 4.507e-02] lana-lora-wan2.2: 6%\|###3 \| 320/5000 [02:55<3:15:39, 2.51s/it, lr: 1.0e-04 loss: 4.507e-02] lana-lora-wan2.2: 6%\|###3 \| 321/5000 [02:57<3:15:14, 2.50s/it, lr: 1.0e-04 loss: 3.621e-02] lana-lora-wan2.2: 6%\|###3 \| 321/5000 [02:57<3:15:14, 2.50s/it, lr: 1.0e-04 loss: 3.621e-02] lana-lora-wan2.2: 6%\|###3 \| 322/5000 [03:01<3:16:27, 2.52s/it, lr: 1.0e-04 loss: 3.719e-02] lana-lora-wan2.2: 6%\|###3 \| 322/5000 [03:01<3:16:27, 2.52s/it, lr: 1.0e-04 loss: 3.719e-02] lana-lora-wan2.2: 6%\|###3 \| 323/5000 [03:05<3:17:38, 2.54s/it, lr: 1.0e-04 loss: 3.198e-02] lana-lora-wan2.2: 6%\|###3 \| 323/5000 [03:05<3:17:38, 2.54s/it, lr: 1.0e-04 loss: 3.198e-02] lana-lora-wan2.2: 6%\|###3 \| 324/5000 [03:07<3:17:13, 2.53s/it, lr: 1.0e-04 loss: 7.150e-02] lana-lora-wan2.2: 6%\|###3 \| 324/5000 [03:07<3:17:13, 2.53s/it, lr: 1.0e-04 loss: 7.150e-02] lana-lora-wan2.2: 6%\|###3 \| 325/5000 [03:09<3:17:07, 2.53s/it, lr: 1.0e-04 loss: 2.929e-02] lana-lora-wan2.2: 6%\|###3 \| 325/5000 [03:09<3:17:07, 2.53s/it, lr: 1.0e-04 loss: 2.929e-02] lana-lora-wan2.2: 7%\|###3 \| 326/5000 [03:10<3:15:42, 2.51s/it, lr: 1.0e-04 loss: 8.536e-02] lana-lora-wan2.2: 7%\|###3 \| 326/5000 [03:10<3:15:42, 2.51s/it, lr: 1.0e-04 loss: 8.536e-02] lana-lora-wan2.2: 7%\|###4 \| 327/5000 [03:14<3:16:49, 2.53s/it, lr: 1.0e-04 loss: 2.058e-01] lana-lora-wan2.2: 7%\|###4 \| 327/5000 [03:14<3:16:49, 2.53s/it, lr: 1.0e-04 loss: 2.058e-01] lana-lora-wan2.2: 7%\|###4 \| 328/5000 [03:18<3:17:55, 2.54s/it, lr: 1.0e-04 loss: 3.406e-02] lana-lora-wan2.2: 7%\|###4 \| 328/5000 [03:18<3:17:55, 2.54s/it, lr: 1.0e-04 loss: 3.406e-02] lana-lora-wan2.2: 7%\|###4 \| 329/5000 [03:20<3:17:32, 2.54s/it, lr: 1.0e-04 loss: 7.507e-02] lana-lora-wan2.2: 7%\|###4 \| 329/5000 [03:20<3:17:32, 2.54s/it, lr: 1.0e-04 loss: 7.507e-02] lana-lora-wan2.2: 7%\|###4 \| 330/5000 [03:22<3:17:24, 2.54s/it, lr: 1.0e-04 loss: 6.266e-02] lana-lora-wan2.2: 7%\|###4 \| 330/5000 [03:22<3:17:24, 2.54s/it, lr: 1.0e-04 loss: 6.266e-02] lana-lora-wan2.2: 7%\|###4 \| 331/5000 [03:25<3:17:02, 2.53s/it, lr: 1.0e-04 loss: 1.643e-01] lana-lora-wan2.2: 7%\|###4 \| 331/5000 [03:25<3:17:02, 2.53s/it, lr: 1.0e-04 loss: 1.643e-01] lana-lora-wan2.2: 7%\|###4 \| 332/5000 [03:27<3:16:39, 2.53s/it, lr: 1.0e-04 loss: 4.252e-02] lana-lora-wan2.2: 7%\|###4 \| 332/5000 [03:27<3:16:39, 2.53s/it, lr: 1.0e-04 loss: 4.252e-02] lana-lora-wan2.2: 7%\|###4 \| 333/5000 [03:28<3:15:21, 2.51s/it, lr: 1.0e-04 loss: 3.576e-02] lana-lora-wan2.2: 7%\|###4 \| 333/5000 [03:28<3:15:21, 2.51s/it, lr: 1.0e-04 loss: 3.576e-02] lana-lora-wan2.2: 7%\|###4 \| 334/5000 [03:30<3:15:00, 2.51s/it, lr: 1.0e-04 loss: 1.080e-01] lana-lora-wan2.2: 7%\|###4 \| 334/5000 [03:30<3:15:00, 2.51s/it, lr: 1.0e-04 loss: 1.080e-01] lana-lora-wan2.2: 7%\|###4 \| 335/5000 [03:34<3:16:03, 2.52s/it, lr: 1.0e-04 loss: 5.735e-02] lana-lora-wan2.2: 7%\|###4 \| 335/5000 [03:34<3:16:03, 2.52s/it, lr: 1.0e-04 loss: 5.735e-02] lana-lora-wan2.2: 7%\|###4 \| 336/5000 [03:38<3:17:16, 2.54s/it, lr: 1.0e-04 loss: 7.845e-02] lana-lora-wan2.2: 7%\|###4 \| 336/5000 [03:38<3:17:16, 2.54s/it, lr: 1.0e-04 loss: 7.845e-02] lana-lora-wan2.2: 7%\|###5 \| 337/5000 [03:41<3:18:14, 2.55s/it, lr: 1.0e-04 loss: 4.375e-02] lana-lora-wan2.2: 7%\|###5 \| 337/5000 [03:41<3:18:14, 2.55s/it, lr: 1.0e-04 loss: 4.375e-02] lana-lora-wan2.2: 7%\|###5 \| 338/5000 [03:43<3:16:59, 2.54s/it, lr: 1.0e-04 loss: 5.889e-02] lana-lora-wan2.2: 7%\|###5 \| 338/5000 [03:43<3:16:59, 2.54s/it, lr: 1.0e-04 loss: 5.889e-02] lana-lora-wan2.2: 7%\|###5 \| 339/5000 [03:44<3:15:47, 2.52s/it, lr: 1.0e-04 loss: 7.350e-02] lana-lora-wan2.2: 7%\|###5 \| 339/5000 [03:44<3:15:47, 2.52s/it, lr: 1.0e-04 loss: 7.350e-02] lana-lora-wan2.2: 7%\|###5 \| 340/5000 [03:46<3:15:26, 2.52s/it, lr: 1.0e-04 loss: 2.824e-02] lana-lora-wan2.2: 7%\|###5 \| 340/5000 [03:46<3:15:26, 2.52s/it, lr: 1.0e-04 loss: 2.824e-02] lana-lora-wan2.2: 7%\|###5 \| 341/5000 [03:48<3:15:06, 2.51s/it, lr: 1.0e-04 loss: 8.068e-02] lana-lora-wan2.2: 7%\|###5 \| 341/5000 [03:48<3:15:06, 2.51s/it, lr: 1.0e-04 loss: 8.068e-02] lana-lora-wan2.2: 7%\|###5 \| 342/5000 [03:52<3:16:15, 2.53s/it, lr: 1.0e-04 loss: 2.831e-02] lana-lora-wan2.2: 7%\|###5 \| 342/5000 [03:52<3:16:15, 2.53s/it, lr: 1.0e-04 loss: 2.831e-02] lana-lora-wan2.2: 7%\|###5 \| 343/5000 [03:53<3:15:05, 2.51s/it, lr: 1.0e-04 loss: 2.774e-01] lana-lora-wan2.2: 7%\|###5 \| 343/5000 [03:53<3:15:05, 2.51s/it, lr: 1.0e-04 loss: 2.774e-01] lana-lora-wan2.2: 7%\|###5 \| 344/5000 [03:55<3:14:46, 2.51s/it, lr: 1.0e-04 loss: 3.866e-02] lana-lora-wan2.2: 7%\|###5 \| 344/5000 [03:55<3:14:46, 2.51s/it, lr: 1.0e-04 loss: 3.866e-02] lana-lora-wan2.2: 7%\|###5 \| 345/5000 [03:59<3:15:40, 2.52s/it, lr: 1.0e-04 loss: 3.491e-02] lana-lora-wan2.2: 7%\|###5 \| 345/5000 [03:59<3:15:40, 2.52s/it, lr: 1.0e-04 loss: 3.491e-02] lana-lora-wan2.2: 7%\|###5 \| 346/5000 [04:00<3:14:32, 2.51s/it, lr: 1.0e-04 loss: 8.267e-02] lana-lora-wan2.2: 7%\|###5 \| 346/5000 [04:00<3:14:32, 2.51s/it, lr: 1.0e-04 loss: 8.267e-02] lana-lora-wan2.2: 7%\|###6 \| 347/5000 [04:04<3:15:24, 2.52s/it, lr: 1.0e-04 loss: 2.628e-02] lana-lora-wan2.2: 7%\|###6 \| 347/5000 [04:04<3:15:24, 2.52s/it, lr: 1.0e-04 loss: 2.628e-02] lana-lora-wan2.2: 7%\|###6 \| 348/5000 [04:05<3:14:36, 2.51s/it, lr: 1.0e-04 loss: 5.632e-02] lana-lora-wan2.2: 7%\|###6 \| 348/5000 [04:05<3:14:36, 2.51s/it, lr: 1.0e-04 loss: 5.632e-02] lana-lora-wan2.2: 7%\|###6 \| 349/5000 [04:07<3:13:32, 2.50s/it, lr: 1.0e-04 loss: 3.466e-02] lana-lora-wan2.2: 7%\|###6 \| 349/5000 [04:07<3:13:32, 2.50s/it, lr: 1.0e-04 loss: 3.466e-02] lana-lora-wan2.2: 7%\|###6 \| 350/5000 [04:08<3:12:28, 2.48s/it, lr: 1.0e-04 loss: 1.313e-01] lana-lora-wan2.2: 7%\|###6 \| 350/5000 [04:08<3:12:28, 2.48s/it, lr: 1.0e-04 loss: 1.313e-01] lana-lora-wan2.2: 7%\|###6 \| 351/5000 [04:12<3:13:20, 2.50s/it, lr: 1.0e-04 loss: 3.647e-02] lana-lora-wan2.2: 7%\|###6 \| 351/5000 [04:12<3:13:20, 2.50s/it, lr: 1.0e-04 loss: 3.647e-02] lana-lora-wan2.2: 7%\|###6 \| 352/5000 [04:14<3:13:03, 2.49s/it, lr: 1.0e-04 loss: 2.204e-01] lana-lora-wan2.2: 7%\|###6 \| 352/5000 [04:14<3:13:03, 2.49s/it, lr: 1.0e-04 loss: 2.204e-01] lana-lora-wan2.2: 7%\|###6 \| 353/5000 [04:17<3:13:53, 2.50s/it, lr: 1.0e-04 loss: 1.624e-01] lana-lora-wan2.2: 7%\|###6 \| 353/5000 [04:17<3:13:53, 2.50s/it, lr: 1.0e-04 loss: 1.624e-01] lana-lora-wan2.2: 7%\|###6 \| 354/5000 [04:21<3:14:54, 2.52s/it, lr: 1.0e-04 loss: 4.040e-02] lana-lora-wan2.2: 7%\|###6 \| 354/5000 [04:21<3:14:54, 2.52s/it, lr: 1.0e-04 loss: 4.040e-02] lana-lora-wan2.2: 7%\|###6 \| 355/5000 [04:22<3:13:53, 2.50s/it, lr: 1.0e-04 loss: 4.060e-02] lana-lora-wan2.2: 7%\|###6 \| 355/5000 [04:22<3:13:53, 2.50s/it, lr: 1.0e-04 loss: 4.060e-02] lana-lora-wan2.2: 7%\|###7 \| 356/5000 [04:26<3:14:41, 2.52s/it, lr: 1.0e-04 loss: 3.180e-02] lana-lora-wan2.2: 7%\|###7 \| 356/5000 [04:26<3:14:41, 2.52s/it, lr: 1.0e-04 loss: 3.180e-02] lana-lora-wan2.2: 7%\|###7 \| 357/5000 [04:30<3:15:28, 2.53s/it, lr: 1.0e-04 loss: 3.003e-02] lana-lora-wan2.2: 7%\|###7 \| 357/5000 [04:30<3:15:28, 2.53s/it, lr: 1.0e-04 loss: 3.003e-02] lana-lora-wan2.2: 7%\|###7 \| 358/5000 [04:32<3:15:10, 2.52s/it, lr: 1.0e-04 loss: 1.409e-01] lana-lora-wan2.2: 7%\|###7 \| 358/5000 [04:32<3:15:10, 2.52s/it, lr: 1.0e-04 loss: 1.409e-01] lana-lora-wan2.2: 7%\|###7 \| 359/5000 [04:33<3:14:10, 2.51s/it, lr: 1.0e-04 loss: 6.772e-02] lana-lora-wan2.2: 7%\|###7 \| 359/5000 [04:33<3:14:10, 2.51s/it, lr: 1.0e-04 loss: 6.772e-02] lana-lora-wan2.2: 7%\|###7 \| 360/5000 [04:37<3:15:07, 2.52s/it, lr: 1.0e-04 loss: 5.865e-02] lana-lora-wan2.2: 7%\|###7 \| 360/5000 [04:37<3:15:07, 2.52s/it, lr: 1.0e-04 loss: 5.865e-02] lana-lora-wan2.2: 7%\|###7 \| 361/5000 [04:38<3:14:08, 2.51s/it, lr: 1.0e-04 loss: 3.139e-02] lana-lora-wan2.2: 7%\|###7 \| 361/5000 [04:38<3:14:08, 2.51s/it, lr: 1.0e-04 loss: 3.139e-02] lana-lora-wan2.2: 7%\|###7 \| 362/5000 [04:40<3:13:51, 2.51s/it, lr: 1.0e-04 loss: 1.011e-01] lana-lora-wan2.2: 7%\|###7 \| 362/5000 [04:40<3:13:51, 2.51s/it, lr: 1.0e-04 loss: 1.011e-01] lana-lora-wan2.2: 7%\|###7 \| 363/5000 [04:42<3:12:54, 2.50s/it, lr: 1.0e-04 loss: 8.788e-02] lana-lora-wan2.2: 7%\|###7 \| 363/5000 [04:42<3:12:54, 2.50s/it, lr: 1.0e-04 loss: 8.788e-02] lana-lora-wan2.2: 7%\|###7 \| 364/5000 [04:43<3:11:58, 2.48s/it, lr: 1.0e-04 loss: 4.179e-02] lana-lora-wan2.2: 7%\|###7 \| 364/5000 [04:43<3:11:58, 2.48s/it, lr: 1.0e-04 loss: 4.179e-02] lana-lora-wan2.2: 7%\|###7 \| 365/5000 [04:47<3:12:56, 2.50s/it, lr: 1.0e-04 loss: 3.144e-02] lana-lora-wan2.2: 7%\|###7 \| 365/5000 [04:47<3:12:56, 2.50s/it, lr: 1.0e-04 loss: 3.144e-02] lana-lora-wan2.2: 7%\|###8 \| 366/5000 [04:49<3:12:41, 2.49s/it, lr: 1.0e-04 loss: 6.236e-02] lana-lora-wan2.2: 7%\|###8 \| 366/5000 [04:49<3:12:41, 2.49s/it, lr: 1.0e-04 loss: 6.236e-02] lana-lora-wan2.2: 7%\|###8 \| 367/5000 [04:53<3:13:24, 2.50s/it, lr: 1.0e-04 loss: 6.587e-02] lana-lora-wan2.2: 7%\|###8 \| 367/5000 [04:53<3:13:24, 2.50s/it, lr: 1.0e-04 loss: 6.587e-02] lana-lora-wan2.2: 7%\|###8 \| 368/5000 [04:54<3:12:30, 2.49s/it, lr: 1.0e-04 loss: 7.701e-02] lana-lora-wan2.2: 7%\|###8 \| 368/5000 [04:54<3:12:30, 2.49s/it, lr: 1.0e-04 loss: 7.701e-02] lana-lora-wan2.2: 7%\|###8 \| 369/5000 [04:56<3:12:15, 2.49s/it, lr: 1.0e-04 loss: 8.873e-02] lana-lora-wan2.2: 7%\|###8 \| 369/5000 [04:56<3:12:15, 2.49s/it, lr: 1.0e-04 loss: 8.873e-02] lana-lora-wan2.2: 7%\|###8 \| 370/5000 [05:00<3:12:57, 2.50s/it, lr: 1.0e-04 loss: 6.886e-02] lana-lora-wan2.2: 7%\|###8 \| 370/5000 [05:00<3:12:57, 2.50s/it, lr: 1.0e-04 loss: 6.886e-02] lana-lora-wan2.2: 7%\|###8 \| 371/5000 [05:01<3:12:19, 2.49s/it, lr: 1.0e-04 loss: 1.441e-01] lana-lora-wan2.2: 7%\|###8 \| 371/5000 [05:01<3:12:19, 2.49s/it, lr: 1.0e-04 loss: 1.441e-01] lana-lora-wan2.2: 7%\|###8 \| 372/5000 [05:03<3:12:04, 2.49s/it, lr: 1.0e-04 loss: 3.109e-02] lana-lora-wan2.2: 7%\|###8 \| 372/5000 [05:03<3:12:04, 2.49s/it, lr: 1.0e-04 loss: 3.109e-02] lana-lora-wan2.2: 7%\|###8 \| 373/5000 [05:07<3:12:46, 2.50s/it, lr: 1.0e-04 loss: 3.724e-01] lana-lora-wan2.2: 7%\|###8 \| 373/5000 [05:07<3:12:46, 2.50s/it, lr: 1.0e-04 loss: 3.724e-01] lana-lora-wan2.2: 7%\|###8 \| 374/5000 [05:09<3:12:31, 2.50s/it, lr: 1.0e-04 loss: 1.633e-01] lana-lora-wan2.2: 7%\|###8 \| 374/5000 [05:09<3:12:31, 2.50s/it, lr: 1.0e-04 loss: 1.633e-01] lana-lora-wan2.2: 8%\|###9 \| 375/5000 [05:13<3:13:12, 2.51s/it, lr: 1.0e-04 loss: 7.030e-02] lana-lora-wan2.2: 8%\|###9 \| 375/5000 [05:13<3:13:12, 2.51s/it, lr: 1.0e-04 loss: 7.030e-02] lana-lora-wan2.2: 8%\|###9 \| 376/5000 [05:16<3:13:52, 2.52s/it, lr: 1.0e-04 loss: 7.185e-02] lana-lora-wan2.2: 8%\|###9 \| 376/5000 [05:16<3:13:52, 2.52s/it, lr: 1.0e-04 loss: 7.185e-02] lana-lora-wan2.2: 8%\|###9 \| 377/5000 [05:20<3:14:41, 2.53s/it, lr: 1.0e-04 loss: 2.368e-02] lana-lora-wan2.2: 8%\|###9 \| 377/5000 [05:20<3:14:41, 2.53s/it, lr: 1.0e-04 loss: 2.368e-02] lana-lora-wan2.2: 8%\|###9 \| 378/5000 [05:23<3:14:26, 2.52s/it, lr: 1.0e-04 loss: 2.034e-01] lana-lora-wan2.2: 8%\|###9 \| 378/5000 [05:23<3:14:26, 2.52s/it, lr: 1.0e-04 loss: 2.034e-01] lana-lora-wan2.2: 8%\|###9 \| 379/5000 [05:24<3:13:35, 2.51s/it, lr: 1.0e-04 loss: 1.105e-01] lana-lora-wan2.2: 8%\|###9 \| 379/5000 [05:24<3:13:35, 2.51s/it, lr: 1.0e-04 loss: 1.105e-01] lana-lora-wan2.2: 8%\|###9 \| 380/5000 [05:26<3:13:21, 2.51s/it, lr: 1.0e-04 loss: 6.976e-02] lana-lora-wan2.2: 8%\|###9 \| 380/5000 [05:26<3:13:21, 2.51s/it, lr: 1.0e-04 loss: 6.976e-02] lana-lora-wan2.2: 8%\|###9 \| 381/5000 [05:27<3:12:32, 2.50s/it, lr: 1.0e-04 loss: 6.960e-02] lana-lora-wan2.2: 8%\|###9 \| 381/5000 [05:27<3:12:32, 2.50s/it, lr: 1.0e-04 loss: 6.960e-02] lana-lora-wan2.2: 8%\|###9 \| 382/5000 [05:31<3:13:09, 2.51s/it, lr: 1.0e-04 loss: 2.548e-02] lana-lora-wan2.2: 8%\|###9 \| 382/5000 [05:31<3:13:09, 2.51s/it, lr: 1.0e-04 loss: 2.548e-02] lana-lora-wan2.2: 8%\|###9 \| 383/5000 [05:33<3:13:05, 2.51s/it, lr: 1.0e-04 loss: 3.557e-02] lana-lora-wan2.2: 8%\|###9 \| 383/5000 [05:33<3:13:05, 2.51s/it, lr: 1.0e-04 loss: 3.557e-02] lana-lora-wan2.2: 8%\|###9 \| 384/5000 [05:35<3:12:52, 2.51s/it, lr: 1.0e-04 loss: 1.981e-01] lana-lora-wan2.2: 8%\|###9 \| 384/5000 [05:35<3:12:52, 2.51s/it, lr: 1.0e-04 loss: 1.981e-01] lana-lora-wan2.2: 8%\|#### \| 385/5000 [05:37<3:12:04, 2.50s/it, lr: 1.0e-04 loss: 4.069e-02] lana-lora-wan2.2: 8%\|#### \| 385/5000 [05:37<3:12:04, 2.50s/it, lr: 1.0e-04 loss: 4.069e-02] lana-lora-wan2.2: 8%\|#### \| 386/5000 [05:39<3:11:51, 2.49s/it, lr: 1.0e-04 loss: 4.028e-02] lana-lora-wan2.2: 8%\|#### \| 386/5000 [05:39<3:11:51, 2.49s/it, lr: 1.0e-04 loss: 4.028e-02] lana-lora-wan2.2: 8%\|#### \| 387/5000 [05:41<3:11:38, 2.49s/it, lr: 1.0e-04 loss: 5.965e-02] lana-lora-wan2.2: 8%\|#### \| 387/5000 [05:41<3:11:38, 2.49s/it, lr: 1.0e-04 loss: 5.965e-02] lana-lora-wan2.2: 8%\|#### \| 388/5000 [05:43<3:11:25, 2.49s/it, lr: 1.0e-04 loss: 4.479e-02] lana-lora-wan2.2: 8%\|#### \| 388/5000 [05:43<3:11:25, 2.49s/it, lr: 1.0e-04 loss: 4.479e-02] lana-lora-wan2.2: 8%\|#### \| 389/5000 [05:46<3:11:21, 2.49s/it, lr: 1.0e-04 loss: 6.474e-02] lana-lora-wan2.2: 8%\|#### \| 389/5000 [05:46<3:11:21, 2.49s/it, lr: 1.0e-04 loss: 6.474e-02] lana-lora-wan2.2: 8%\|#### \| 390/5000 [05:47<3:10:36, 2.48s/it, lr: 1.0e-04 loss: 4.118e-02] lana-lora-wan2.2: 8%\|#### \| 390/5000 [05:47<3:10:36, 2.48s/it, lr: 1.0e-04 loss: 4.118e-02] lana-lora-wan2.2: 8%\|#### \| 391/5000 [05:49<3:10:24, 2.48s/it, lr: 1.0e-04 loss: 3.709e-02] lana-lora-wan2.2: 8%\|#### \| 391/5000 [05:49<3:10:24, 2.48s/it, lr: 1.0e-04 loss: 3.709e-02] lana-lora-wan2.2: 8%\|#### \| 392/5000 [05:50<3:09:39, 2.47s/it, lr: 1.0e-04 loss: 5.849e-02] lana-lora-wan2.2: 8%\|#### \| 392/5000 [05:50<3:09:39, 2.47s/it, lr: 1.0e-04 loss: 5.849e-02] lana-lora-wan2.2: 8%\|#### \| 393/5000 [05:52<3:09:27, 2.47s/it, lr: 1.0e-04 loss: 5.374e-02] lana-lora-wan2.2: 8%\|#### \| 393/5000 [05:52<3:09:27, 2.47s/it, lr: 1.0e-04 loss: 5.374e-02] lana-lora-wan2.2: 8%\|#### \| 394/5000 [05:56<3:10:12, 2.48s/it, lr: 1.0e-04 loss: 7.425e-02] lana-lora-wan2.2: 8%\|#### \| 394/5000 [05:56<3:10:12, 2.48s/it, lr: 1.0e-04 loss: 7.425e-02] lana-lora-wan2.2: 8%\|####1 \| 395/5000 [05:58<3:09:59, 2.48s/it, lr: 1.0e-04 loss: 2.637e-01] lana-lora-wan2.2: 8%\|####1 \| 395/5000 [05:58<3:09:59, 2.48s/it, lr: 1.0e-04 loss: 2.637e-01] lana-lora-wan2.2: 8%\|####1 \| 396/5000 [06:00<3:09:16, 2.47s/it, lr: 1.0e-04 loss: 1.242e-01] lana-lora-wan2.2: 8%\|####1 \| 396/5000 [06:00<3:09:16, 2.47s/it, lr: 1.0e-04 loss: 1.242e-01] lana-lora-wan2.2: 8%\|####1 \| 397/5000 [06:01<3:08:33, 2.46s/it, lr: 1.0e-04 loss: 4.880e-02] lana-lora-wan2.2: 8%\|####1 \| 397/5000 [06:01<3:08:33, 2.46s/it, lr: 1.0e-04 loss: 4.880e-02] lana-lora-wan2.2: 8%\|####1 \| 398/5000 [06:02<3:07:51, 2.45s/it, lr: 1.0e-04 loss: 4.204e-02] lana-lora-wan2.2: 8%\|####1 \| 398/5000 [06:02<3:07:51, 2.45s/it, lr: 1.0e-04 loss: 4.204e-02] lana-lora-wan2.2: 8%\|####1 \| 399/5000 [06:03<3:07:10, 2.44s/it, lr: 1.0e-04 loss: 5.576e-02] lana-lora-wan2.2: 8%\|####1 \| 399/5000 [06:03<3:07:10, 2.44s/it, lr: 1.0e-04 loss: 5.576e-02] lana-lora-wan2.2: 8%\|####1 \| 400/5000 [06:07<3:07:54, 2.45s/it, lr: 1.0e-04 loss: 6.434e-02] lana-lora-wan2.2: 8%\|####1 \| 400/5000 [06:07<3:07:54, 2.45s/it, lr: 1.0e-04 loss: 6.434e-02] lana-lora-wan2.2: 8%\|####1 \| 401/5000 [06:09<3:07:43, 2.45s/it, lr: 1.0e-04 loss: 4.582e-02] lana-lora-wan2.2: 8%\|####1 \| 401/5000 [06:09<3:07:43, 2.45s/it, lr: 1.0e-04 loss: 4.582e-02] lana-lora-wan2.2: 8%\|####1 \| 402/5000 [06:13<3:08:18, 2.46s/it, lr: 1.0e-04 loss: 7.673e-02] lana-lora-wan2.2: 8%\|####1 \| 402/5000 [06:13<3:08:18, 2.46s/it, lr: 1.0e-04 loss: 7.673e-02] lana-lora-wan2.2: 8%\|####1 \| 403/5000 [06:14<3:07:37, 2.45s/it, lr: 1.0e-04 loss: 5.556e-02] lana-lora-wan2.2: 8%\|####1 \| 403/5000 [06:14<3:07:37, 2.45s/it, lr: 1.0e-04 loss: 5.556e-02] lana-lora-wan2.2: 8%\|####2 \| 404/5000 [06:16<3:07:27, 2.45s/it, lr: 1.0e-04 loss: 2.685e-01] lana-lora-wan2.2: 8%\|####2 \| 404/5000 [06:16<3:07:27, 2.45s/it, lr: 1.0e-04 loss: 2.685e-01] lana-lora-wan2.2: 8%\|####2 \| 405/5000 [06:18<3:06:47, 2.44s/it, lr: 1.0e-04 loss: 7.116e-02] lana-lora-wan2.2: 8%\|####2 \| 405/5000 [06:18<3:06:47, 2.44s/it, lr: 1.0e-04 loss: 7.116e-02] lana-lora-wan2.2: 8%\|####2 \| 406/5000 [06:20<3:06:45, 2.44s/it, lr: 1.0e-04 loss: 4.470e-02] lana-lora-wan2.2: 8%\|####2 \| 406/5000 [06:20<3:06:45, 2.44s/it, lr: 1.0e-04 loss: 4.470e-02] lana-lora-wan2.2: 8%\|####2 \| 407/5000 [06:21<3:06:06, 2.43s/it, lr: 1.0e-04 loss: 4.527e-02] lana-lora-wan2.2: 8%\|####2 \| 407/5000 [06:21<3:06:06, 2.43s/it, lr: 1.0e-04 loss: 4.527e-02] lana-lora-wan2.2: 8%\|####2 \| 408/5000 [06:23<3:05:57, 2.43s/it, lr: 1.0e-04 loss: 3.018e-01] lana-lora-wan2.2: 8%\|####2 \| 408/5000 [06:23<3:05:57, 2.43s/it, lr: 1.0e-04 loss: 3.018e-01] lana-lora-wan2.2: 8%\|####2 \| 409/5000 [06:26<3:05:48, 2.43s/it, lr: 1.0e-04 loss: 3.725e-02] lana-lora-wan2.2: 8%\|####2 \| 409/5000 [06:26<3:05:48, 2.43s/it, lr: 1.0e-04 loss: 3.725e-02] lana-lora-wan2.2: 8%\|####2 \| 410/5000 [06:29<3:06:21, 2.44s/it, lr: 1.0e-04 loss: 1.382e-01] lana-lora-wan2.2: 8%\|####2 \| 410/5000 [06:29<3:06:21, 2.44s/it, lr: 1.0e-04 loss: 1.382e-01] lana-lora-wan2.2: 8%\|####2 \| 411/5000 [06:33<3:06:54, 2.44s/it, lr: 1.0e-04 loss: 5.161e-02] lana-lora-wan2.2: 8%\|####2 \| 411/5000 [06:33<3:06:54, 2.44s/it, lr: 1.0e-04 loss: 5.161e-02] lana-lora-wan2.2: 8%\|####2 \| 412/5000 [06:34<3:06:24, 2.44s/it, lr: 1.0e-04 loss: 8.173e-02] lana-lora-wan2.2: 8%\|####2 \| 412/5000 [06:34<3:06:24, 2.44s/it, lr: 1.0e-04 loss: 8.173e-02] lana-lora-wan2.2: 8%\|####2 \| 413/5000 [06:36<3:05:47, 2.43s/it, lr: 1.0e-04 loss: 7.660e-02] lana-lora-wan2.2: 8%\|####2 \| 413/5000 [06:36<3:05:47, 2.43s/it, lr: 1.0e-04 loss: 7.660e-02] lana-lora-wan2.2: 8%\|####3 \| 414/5000 [06:37<3:05:10, 2.42s/it, lr: 1.0e-04 loss: 6.957e-02] lana-lora-wan2.2: 8%\|####3 \| 414/5000 [06:37<3:05:10, 2.42s/it, lr: 1.0e-04 loss: 6.957e-02] lana-lora-wan2.2: 8%\|####3 \| 415/5000 [06:40<3:05:42, 2.43s/it, lr: 1.0e-04 loss: 5.552e-02] lana-lora-wan2.2: 8%\|####3 \| 415/5000 [06:40<3:05:42, 2.43s/it, lr: 1.0e-04 loss: 5.552e-02] lana-lora-wan2.2: 8%\|####3 \| 416/5000 [06:44<3:06:13, 2.44s/it, lr: 1.0e-04 loss: 4.332e-02] lana-lora-wan2.2: 8%\|####3 \| 416/5000 [06:44<3:06:13, 2.44s/it, lr: 1.0e-04 loss: 4.332e-02] lana-lora-wan2.2: 8%\|####3 \| 417/5000 [06:48<3:06:44, 2.44s/it, lr: 1.0e-04 loss: 6.664e-02] lana-lora-wan2.2: 8%\|####3 \| 417/5000 [06:48<3:06:44, 2.44s/it, lr: 1.0e-04 loss: 6.664e-02] lana-lora-wan2.2: 8%\|####3 \| 418/5000 [06:49<3:06:17, 2.44s/it, lr: 1.0e-04 loss: 6.365e-02] lana-lora-wan2.2: 8%\|####3 \| 418/5000 [06:49<3:06:17, 2.44s/it, lr: 1.0e-04 loss: 6.365e-02] lana-lora-wan2.2: 8%\|####3 \| 419/5000 [06:50<3:05:40, 2.43s/it, lr: 1.0e-04 loss: 7.167e-02] lana-lora-wan2.2: 8%\|####3 \| 419/5000 [06:50<3:05:40, 2.43s/it, lr: 1.0e-04 loss: 7.167e-02] lana-lora-wan2.2: 8%\|####3 \| 420/5000 [06:52<3:05:04, 2.42s/it, lr: 1.0e-04 loss: 2.360e-01] lana-lora-wan2.2: 8%\|####3 \| 420/5000 [06:52<3:05:04, 2.42s/it, lr: 1.0e-04 loss: 2.360e-01] lana-lora-wan2.2: 8%\|####3 \| 421/5000 [06:55<3:05:35, 2.43s/it, lr: 1.0e-04 loss: 5.811e-02] lana-lora-wan2.2: 8%\|####3 \| 421/5000 [06:55<3:05:35, 2.43s/it, lr: 1.0e-04 loss: 5.811e-02] lana-lora-wan2.2: 8%\|####3 \| 422/5000 [06:59<3:06:05, 2.44s/it, lr: 1.0e-04 loss: 9.248e-02] lana-lora-wan2.2: 8%\|####3 \| 422/5000 [06:59<3:06:05, 2.44s/it, lr: 1.0e-04 loss: 9.248e-02] lana-lora-wan2.2: 8%\|####3 \| 423/5000 [07:01<3:06:04, 2.44s/it, lr: 1.0e-04 loss: 7.027e-02] lana-lora-wan2.2: 8%\|####3 \| 423/5000 [07:01<3:06:04, 2.44s/it, lr: 1.0e-04 loss: 7.027e-02] lana-lora-wan2.2: 8%\|####4 \| 424/5000 [07:04<3:05:54, 2.44s/it, lr: 1.0e-04 loss: 1.388e-01] lana-lora-wan2.2: 8%\|####4 \| 424/5000 [07:04<3:05:54, 2.44s/it, lr: 1.0e-04 loss: 1.388e-01] lana-lora-wan2.2: 8%\|####4 \| 425/5000 [07:06<3:05:45, 2.44s/it, lr: 1.0e-04 loss: 5.904e-02] lana-lora-wan2.2: 8%\|####4 \| 425/5000 [07:06<3:05:45, 2.44s/it, lr: 1.0e-04 loss: 5.904e-02] lana-lora-wan2.2: 9%\|####4 \| 426/5000 [07:07<3:05:10, 2.43s/it, lr: 1.0e-04 loss: 5.342e-02] lana-lora-wan2.2: 9%\|####4 \| 426/5000 [07:07<3:05:10, 2.43s/it, lr: 1.0e-04 loss: 5.342e-02] lana-lora-wan2.2: 9%\|####4 \| 427/5000 [07:08<3:04:36, 2.42s/it, lr: 1.0e-04 loss: 8.439e-02] lana-lora-wan2.2: 9%\|####4 \| 427/5000 [07:08<3:04:36, 2.42s/it, lr: 1.0e-04 loss: 8.439e-02] lana-lora-wan2.2: 9%\|####4 \| 428/5000 [07:09<3:04:02, 2.42s/it, lr: 1.0e-04 loss: 3.080e-02] lana-lora-wan2.2: 9%\|####4 \| 428/5000 [07:09<3:04:02, 2.42s/it, lr: 1.0e-04 loss: 3.080e-02] lana-lora-wan2.2: 9%\|####4 \| 429/5000 [07:11<3:03:38, 2.41s/it, lr: 1.0e-04 loss: 4.971e-02] lana-lora-wan2.2: 9%\|####4 \| 429/5000 [07:11<3:03:38, 2.41s/it, lr: 1.0e-04 loss: 4.971e-02] lana-lora-wan2.2: 9%\|####4 \| 430/5000 [07:15<3:04:07, 2.42s/it, lr: 1.0e-04 loss: 1.978e-02] lana-lora-wan2.2: 9%\|####4 \| 430/5000 [07:15<3:04:07, 2.42s/it, lr: 1.0e-04 loss: 1.978e-02] lana-lora-wan2.2: 9%\|####4 \| 431/5000 [07:18<3:04:36, 2.42s/it, lr: 1.0e-04 loss: 6.386e-02] lana-lora-wan2.2: 9%\|####4 \| 431/5000 [07:18<3:04:36, 2.42s/it, lr: 1.0e-04 loss: 6.386e-02] lana-lora-wan2.2: 9%\|####4 \| 432/5000 [07:20<3:04:27, 2.42s/it, lr: 1.0e-04 loss: 6.934e-02] lana-lora-wan2.2: 9%\|####4 \| 432/5000 [07:20<3:04:27, 2.42s/it, lr: 1.0e-04 loss: 6.934e-02] lana-lora-wan2.2: 9%\|####5 \| 433/5000 [07:24<3:04:56, 2.43s/it, lr: 1.0e-04 loss: 4.762e-02] lana-lora-wan2.2: 9%\|####5 \| 433/5000 [07:24<3:04:56, 2.43s/it, lr: 1.0e-04 loss: 4.762e-02] lana-lora-wan2.2: 9%\|####5 \| 434/5000 [07:26<3:04:47, 2.43s/it, lr: 1.0e-04 loss: 3.164e-02] lana-lora-wan2.2: 9%\|####5 \| 434/5000 [07:26<3:04:47, 2.43s/it, lr: 1.0e-04 loss: 3.164e-02] lana-lora-wan2.2: 9%\|####5 \| 435/5000 [07:28<3:04:22, 2.42s/it, lr: 1.0e-04 loss: 4.287e-02] lana-lora-wan2.2: 9%\|####5 \| 435/5000 [07:28<3:04:22, 2.42s/it, lr: 1.0e-04 loss: 4.287e-02] lana-lora-wan2.2: 9%\|####5 \| 436/5000 [07:30<3:04:14, 2.42s/it, lr: 1.0e-04 loss: 8.674e-02] lana-lora-wan2.2: 9%\|####5 \| 436/5000 [07:30<3:04:14, 2.42s/it, lr: 1.0e-04 loss: 8.674e-02] lana-lora-wan2.2: 9%\|####5 \| 437/5000 [07:34<3:04:42, 2.43s/it, lr: 1.0e-04 loss: 8.480e-02] lana-lora-wan2.2: 9%\|####5 \| 437/5000 [07:34<3:04:42, 2.43s/it, lr: 1.0e-04 loss: 8.480e-02] lana-lora-wan2.2: 9%\|####5 \| 438/5000 [07:35<3:04:09, 2.42s/it, lr: 1.0e-04 loss: 1.399e-01] lana-lora-wan2.2: 9%\|####5 \| 438/5000 [07:35<3:04:09, 2.42s/it, lr: 1.0e-04 loss: 1.399e-01] lana-lora-wan2.2: 9%\|####5 \| 439/5000 [07:39<3:04:38, 2.43s/it, lr: 1.0e-04 loss: 4.081e-02] lana-lora-wan2.2: 9%\|####5 \| 439/5000 [07:39<3:04:38, 2.43s/it, lr: 1.0e-04 loss: 4.081e-02] lana-lora-wan2.2: 9%\|####5 \| 440/5000 [07:42<3:05:11, 2.44s/it, lr: 1.0e-04 loss: 3.716e-02] lana-lora-wan2.2: 9%\|####5 \| 440/5000 [07:42<3:05:11, 2.44s/it, lr: 1.0e-04 loss: 3.716e-02] lana-lora-wan2.2: 9%\|####5 \| 441/5000 [07:45<3:05:03, 2.44s/it, lr: 1.0e-04 loss: 6.229e-02] lana-lora-wan2.2: 9%\|####5 \| 441/5000 [07:45<3:05:03, 2.44s/it, lr: 1.0e-04 loss: 6.229e-02] lana-lora-wan2.2: 9%\|####5 \| 442/5000 [07:46<3:04:31, 2.43s/it, lr: 1.0e-04 loss: 7.461e-02] lana-lora-wan2.2: 9%\|####5 \| 442/5000 [07:46<3:04:31, 2.43s/it, lr: 1.0e-04 loss: 7.461e-02] lana-lora-wan2.2: 9%\|####6 \| 443/5000 [07:48<3:04:22, 2.43s/it, lr: 1.0e-04 loss: 2.647e-01] lana-lora-wan2.2: 9%\|####6 \| 443/5000 [07:48<3:04:22, 2.43s/it, lr: 1.0e-04 loss: 2.647e-01] lana-lora-wan2.2: 9%\|####6 \| 444/5000 [07:49<3:03:51, 2.42s/it, lr: 1.0e-04 loss: 5.557e-02] lana-lora-wan2.2: 9%\|####6 \| 444/5000 [07:49<3:03:51, 2.42s/it, lr: 1.0e-04 loss: 5.557e-02] lana-lora-wan2.2: 9%\|####6 \| 445/5000 [07:50<3:03:20, 2.41s/it, lr: 1.0e-04 loss: 4.595e-02] lana-lora-wan2.2: 9%\|####6 \| 445/5000 [07:50<3:03:20, 2.41s/it, lr: 1.0e-04 loss: 4.595e-02] lana-lora-wan2.2: 9%\|####6 \| 446/5000 [07:52<3:02:58, 2.41s/it, lr: 1.0e-04 loss: 7.474e-02] lana-lora-wan2.2: 9%\|####6 \| 446/5000 [07:52<3:02:58, 2.41s/it, lr: 1.0e-04 loss: 7.474e-02] lana-lora-wan2.2: 9%\|####6 \| 447/5000 [07:56<3:03:25, 2.42s/it, lr: 1.0e-04 loss: 3.131e-02] lana-lora-wan2.2: 9%\|####6 \| 447/5000 [07:56<3:03:25, 2.42s/it, lr: 1.0e-04 loss: 3.131e-02] lana-lora-wan2.2: 9%\|####6 \| 448/5000 [07:58<3:03:16, 2.42s/it, lr: 1.0e-04 loss: 5.225e-02] lana-lora-wan2.2: 9%\|####6 \| 448/5000 [07:58<3:03:16, 2.42s/it, lr: 1.0e-04 loss: 5.225e-02] lana-lora-wan2.2: 9%\|####6 \| 449/5000 [08:00<3:03:09, 2.41s/it, lr: 1.0e-04 loss: 8.512e-02] lana-lora-wan2.2: 9%\|####6 \| 449/5000 [08:00<3:03:09, 2.41s/it, lr: 1.0e-04 loss: 8.512e-02] lana-lora-wan2.2: 9%\|####6 \| 450/5000 [08:04<3:03:34, 2.42s/it, lr: 1.0e-04 loss: 2.894e-02] lana-lora-wan2.2: 9%\|####6 \| 450/5000 [08:04<3:03:34, 2.42s/it, lr: 1.0e-04 loss: 2.894e-02] lana-lora-wan2.2: 9%\|####6 \| 451/5000 [08:07<3:04:00, 2.43s/it, lr: 1.0e-04 loss: 3.507e-02] lana-lora-wan2.2: 9%\|####6 \| 451/5000 [08:07<3:04:00, 2.43s/it, lr: 1.0e-04 loss: 3.507e-02] lana-lora-wan2.2: 9%\|####7 \| 452/5000 [08:11<3:04:32, 2.43s/it, lr: 1.0e-04 loss: 1.299e-01] lana-lora-wan2.2: 9%\|####7 \| 452/5000 [08:11<3:04:32, 2.43s/it, lr: 1.0e-04 loss: 1.299e-01] lana-lora-wan2.2: 9%\|####7 \| 453/5000 [08:13<3:04:24, 2.43s/it, lr: 1.0e-04 loss: 1.339e-01] lana-lora-wan2.2: 9%\|####7 \| 453/5000 [08:13<3:04:24, 2.43s/it, lr: 1.0e-04 loss: 1.339e-01] lana-lora-wan2.2: 9%\|####7 \| 454/5000 [08:16<3:04:16, 2.43s/it, lr: 1.0e-04 loss: 2.896e-02] lana-lora-wan2.2: 9%\|####7 \| 454/5000 [08:16<3:04:16, 2.43s/it, lr: 1.0e-04 loss: 2.896e-02] lana-lora-wan2.2: 9%\|####7 \| 455/5000 [08:18<3:04:08, 2.43s/it, lr: 1.0e-04 loss: 1.604e-01] lana-lora-wan2.2: 9%\|####7 \| 455/5000 [08:18<3:04:08, 2.43s/it, lr: 1.0e-04 loss: 1.604e-01] lana-lora-wan2.2: 9%\|####7 \| 456/5000 [08:20<3:04:00, 2.43s/it, lr: 1.0e-04 loss: 2.987e-02] lana-lora-wan2.2: 9%\|####7 \| 456/5000 [08:20<3:04:00, 2.43s/it, lr: 1.0e-04 loss: 2.987e-02] lana-lora-wan2.2: 9%\|####7 \| 457/5000 [08:21<3:03:31, 2.42s/it, lr: 1.0e-04 loss: 1.869e-01] lana-lora-wan2.2: 9%\|####7 \| 457/5000 [08:21<3:03:31, 2.42s/it, lr: 1.0e-04 loss: 1.869e-01] lana-lora-wan2.2: 9%\|####7 \| 458/5000 [08:25<3:04:02, 2.43s/it, lr: 1.0e-04 loss: 4.052e-02] lana-lora-wan2.2: 9%\|####7 \| 458/5000 [08:25<3:04:02, 2.43s/it, lr: 1.0e-04 loss: 4.052e-02] lana-lora-wan2.2: 9%\|####7 \| 459/5000 [08:29<3:04:27, 2.44s/it, lr: 1.0e-04 loss: 7.517e-02] lana-lora-wan2.2: 9%\|####7 \| 459/5000 [08:29<3:04:27, 2.44s/it, lr: 1.0e-04 loss: 7.517e-02] lana-lora-wan2.2: 9%\|####7 \| 460/5000 [08:31<3:04:19, 2.44s/it, lr: 1.0e-04 loss: 4.033e-02] lana-lora-wan2.2: 9%\|####7 \| 460/5000 [08:31<3:04:19, 2.44s/it, lr: 1.0e-04 loss: 4.033e-02] lana-lora-wan2.2: 9%\|####7 \| 461/5000 [08:35<3:04:43, 2.44s/it, lr: 1.0e-04 loss: 4.517e-02] lana-lora-wan2.2: 9%\|####7 \| 461/5000 [08:35<3:04:43, 2.44s/it, lr: 1.0e-04 loss: 4.517e-02] lana-lora-wan2.2: 9%\|####8 \| 462/5000 [08:36<3:04:13, 2.44s/it, lr: 1.0e-04 loss: 5.253e-02] lana-lora-wan2.2: 9%\|####8 \| 462/5000 [08:36<3:04:13, 2.44s/it, lr: 1.0e-04 loss: 5.253e-02] lana-lora-wan2.2: 9%\|####8 \| 463/5000 [08:37<3:03:44, 2.43s/it, lr: 1.0e-04 loss: 6.561e-02] lana-lora-wan2.2: 9%\|####8 \| 463/5000 [08:37<3:03:44, 2.43s/it, lr: 1.0e-04 loss: 6.561e-02] lana-lora-wan2.2: 9%\|####8 \| 464/5000 [08:40<3:03:43, 2.43s/it, lr: 1.0e-04 loss: 1.045e-01] lana-lora-wan2.2: 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10%\|####9 \| 475/5000 [09:05<3:02:59, 2.43s/it, lr: 1.0e-04 loss: 4.514e-02] lana-lora-wan2.2: 10%\|####9 \| 475/5000 [09:05<3:02:59, 2.43s/it, lr: 1.0e-04 loss: 4.514e-02] lana-lora-wan2.2: 10%\|####9 \| 476/5000 [09:09<3:03:26, 2.43s/it, lr: 1.0e-04 loss: 4.059e-02] lana-lora-wan2.2: 10%\|####9 \| 476/5000 [09:09<3:03:26, 2.43s/it, lr: 1.0e-04 loss: 4.059e-02] lana-lora-wan2.2: 10%\|####9 \| 477/5000 [09:11<3:02:59, 2.43s/it, lr: 1.0e-04 loss: 5.677e-02] lana-lora-wan2.2: 10%\|####9 \| 477/5000 [09:11<3:02:59, 2.43s/it, lr: 1.0e-04 loss: 5.677e-02] lana-lora-wan2.2: 10%\|####9 \| 478/5000 [09:13<3:02:51, 2.43s/it, lr: 1.0e-04 loss: 8.627e-02] lana-lora-wan2.2: 10%\|####9 \| 478/5000 [09:13<3:02:51, 2.43s/it, lr: 1.0e-04 loss: 8.627e-02] lana-lora-wan2.2: 10%\|####9 \| 479/5000 [09:16<3:03:13, 2.43s/it, lr: 1.0e-04 loss: 4.900e-02] lana-lora-wan2.2: 10%\|####9 \| 479/5000 [09:16<3:03:13, 2.43s/it, lr: 1.0e-04 loss: 4.900e-02] lana-lora-wan2.2: 10%\|####9 \| 480/5000 [09:18<3:02:46, 2.43s/it, lr: 1.0e-04 loss: 3.251e-02] lana-lora-wan2.2: 10%\|####9 \| 480/5000 [09:18<3:02:46, 2.43s/it, lr: 1.0e-04 loss: 3.251e-02] lana-lora-wan2.2: 10%\|##### \| 481/5000 [09:21<3:03:08, 2.43s/it, lr: 1.0e-04 loss: 3.556e-02] lana-lora-wan2.2: 10%\|##### \| 481/5000 [09:21<3:03:08, 2.43s/it, lr: 1.0e-04 loss: 3.556e-02] lana-lora-wan2.2: 10%\|##### \| 482/5000 [09:24<3:03:07, 2.43s/it, lr: 1.0e-04 loss: 7.528e-02] lana-lora-wan2.2: 10%\|##### \| 482/5000 [09:24<3:03:07, 2.43s/it, lr: 1.0e-04 loss: 7.528e-02] lana-lora-wan2.2: 10%\|##### \| 483/5000 [09:26<3:03:00, 2.43s/it, lr: 1.0e-04 loss: 4.174e-02] lana-lora-wan2.2: 10%\|##### \| 483/5000 [09:26<3:03:00, 2.43s/it, lr: 1.0e-04 loss: 4.174e-02] lana-lora-wan2.2: 10%\|##### \| 484/5000 [09:30<3:03:22, 2.44s/it, lr: 1.0e-04 loss: 5.012e-02] lana-lora-wan2.2: 10%\|##### \| 484/5000 [09:30<3:03:22, 2.44s/it, lr: 1.0e-04 loss: 5.012e-02] lana-lora-wan2.2: 10%\|##### \| 485/5000 [09:32<3:03:14, 2.44s/it, lr: 1.0e-04 loss: 4.740e-02] 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[09:43<3:02:43, 2.43s/it, lr: 1.0e-04 loss: 5.247e-02] lana-lora-wan2.2: 10%\|#####1 \| 491/5000 [09:45<3:02:36, 2.43s/it, lr: 1.0e-04 loss: 1.929e-01] lana-lora-wan2.2: 10%\|#####1 \| 491/5000 [09:45<3:02:36, 2.43s/it, lr: 1.0e-04 loss: 1.929e-01] lana-lora-wan2.2: 10%\|#####1 \| 492/5000 [09:46<3:02:10, 2.42s/it, lr: 1.0e-04 loss: 5.099e-02] lana-lora-wan2.2: 10%\|#####1 \| 492/5000 [09:46<3:02:10, 2.42s/it, lr: 1.0e-04 loss: 5.099e-02] lana-lora-wan2.2: 10%\|#####1 \| 493/5000 [09:49<3:02:08, 2.42s/it, lr: 1.0e-04 loss: 5.319e-02] lana-lora-wan2.2: 10%\|#####1 \| 493/5000 [09:49<3:02:08, 2.42s/it, lr: 1.0e-04 loss: 5.319e-02] lana-lora-wan2.2: 10%\|#####1 \| 494/5000 [09:50<3:01:43, 2.42s/it, lr: 1.0e-04 loss: 6.880e-02] lana-lora-wan2.2: 10%\|#####1 \| 494/5000 [09:50<3:01:43, 2.42s/it, lr: 1.0e-04 loss: 6.880e-02] lana-lora-wan2.2: 10%\|#####1 \| 495/5000 [09:51<3:01:18, 2.41s/it, lr: 1.0e-04 loss: 8.892e-02] lana-lora-wan2.2: 10%\|#####1 \| 495/5000 [09:51<3:01:18, 2.41s/it, lr: 1.0e-04 loss: 8.892e-02] lana-lora-wan2.2: 10%\|#####1 \| 496/5000 [09:52<3:00:53, 2.41s/it, lr: 1.0e-04 loss: 9.867e-02] lana-lora-wan2.2: 10%\|#####1 \| 496/5000 [09:52<3:00:53, 2.41s/it, lr: 1.0e-04 loss: 9.867e-02] lana-lora-wan2.2: 10%\|#####1 \| 497/5000 [09:56<3:01:13, 2.41s/it, lr: 1.0e-04 loss: 3.728e-02] lana-lora-wan2.2: 10%\|#####1 \| 497/5000 [09:56<3:01:13, 2.41s/it, lr: 1.0e-04 loss: 3.728e-02] lana-lora-wan2.2: 10%\|#####1 \| 498/5000 [09:58<3:01:06, 2.41s/it, lr: 1.0e-04 loss: 3.857e-02] lana-lora-wan2.2: 10%\|#####1 \| 498/5000 [09:58<3:01:06, 2.41s/it, lr: 1.0e-04 loss: 3.857e-02] lana-lora-wan2.2: 10%\|#####1 \| 499/5000 [10:00<3:00:48, 2.41s/it, lr: 1.0e-04 loss: 3.505e-02] lana-lora-wan2.2: 10%\|#####1 \| 499/5000 [10:00<3:00:48, 2.41s/it, lr: 1.0e-04 loss: 3.505e-02]
	Saving at step 500
	Saved checkpoint to /root/lab/ai-toolkit/output/lana-lora-wan2.2/lana-lora-wan2.2_000000500.safetensors
	Saved optimizer to /root/lab/ai-toolkit/output/lana-lora-wan2.2/optimizer.pt


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[10:26<3:00:23, 2.41s/it, lr: 1.0e-04 loss: 5.751e-02] lana-lora-wan2.2: 10%\|#####3 \| 510/5000 [10:26<3:00:23, 2.41s/it, lr: 1.0e-04 loss: 5.751e-02] lana-lora-wan2.2: 10%\|#####3 \| 511/5000 [10:28<3:00:04, 2.41s/it, lr: 1.0e-04 loss: 4.884e-02] lana-lora-wan2.2: 10%\|#####3 \| 511/5000 [10:28<3:00:04, 2.41s/it, lr: 1.0e-04 loss: 4.884e-02] lana-lora-wan2.2: 10%\|#####3 \| 512/5000 [10:30<2:59:58, 2.41s/it, lr: 1.0e-04 loss: 4.020e-02] lana-lora-wan2.2: 10%\|#####3 \| 512/5000 [10:30<2:59:58, 2.41s/it, lr: 1.0e-04 loss: 4.020e-02] lana-lora-wan2.2: 10%\|#####3 \| 513/5000 [10:34<3:00:17, 2.41s/it, lr: 1.0e-04 loss: 5.101e-02] lana-lora-wan2.2: 10%\|#####3 \| 513/5000 [10:34<3:00:17, 2.41s/it, lr: 1.0e-04 loss: 5.101e-02] lana-lora-wan2.2: 10%\|#####3 \| 514/5000 [10:36<3:00:11, 2.41s/it, lr: 1.0e-04 loss: 1.331e-01] lana-lora-wan2.2: 10%\|#####3 \| 514/5000 [10:36<3:00:11, 2.41s/it, lr: 1.0e-04 loss: 1.331e-01] lana-lora-wan2.2: 10%\|#####3 \| 515/5000 [10:39<3:00:30, 2.41s/it, lr: 1.0e-04 loss: 3.165e-02] lana-lora-wan2.2: 10%\|#####3 \| 515/5000 [10:39<3:00:30, 2.41s/it, lr: 1.0e-04 loss: 3.165e-02] lana-lora-wan2.2: 10%\|#####3 \| 516/5000 [10:41<3:00:07, 2.41s/it, lr: 1.0e-04 loss: 7.096e-02] lana-lora-wan2.2: 10%\|#####3 \| 516/5000 [10:41<3:00:07, 2.41s/it, lr: 1.0e-04 loss: 7.096e-02] lana-lora-wan2.2: 10%\|#####3 \| 517/5000 [10:45<3:00:31, 2.42s/it, lr: 1.0e-04 loss: 1.046e-01] lana-lora-wan2.2: 10%\|#####3 \| 517/5000 [10:45<3:00:31, 2.42s/it, lr: 1.0e-04 loss: 1.046e-01] lana-lora-wan2.2: 10%\|#####3 \| 518/5000 [10:48<3:00:49, 2.42s/it, lr: 1.0e-04 loss: 6.118e-02] lana-lora-wan2.2: 10%\|#####3 \| 518/5000 [10:48<3:00:49, 2.42s/it, lr: 1.0e-04 loss: 6.118e-02] lana-lora-wan2.2: 10%\|#####3 \| 519/5000 [10:52<3:01:08, 2.43s/it, lr: 1.0e-04 loss: 5.051e-02] lana-lora-wan2.2: 10%\|#####3 \| 519/5000 [10:52<3:01:08, 2.43s/it, lr: 1.0e-04 loss: 5.051e-02] lana-lora-wan2.2: 10%\|#####4 \| 520/5000 [10:56<3:01:26, 2.43s/it, lr: 1.0e-04 loss: 3.214e-02] 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\| 525/5000 [11:04<3:00:18, 2.42s/it, lr: 1.0e-04 loss: 6.177e-02] lana-lora-wan2.2: 11%\|#####4 \| 526/5000 [11:07<3:00:12, 2.42s/it, lr: 1.0e-04 loss: 1.209e-01] lana-lora-wan2.2: 11%\|#####4 \| 526/5000 [11:07<3:00:12, 2.42s/it, lr: 1.0e-04 loss: 1.209e-01] lana-lora-wan2.2: 11%\|#####4 \| 527/5000 [11:08<2:59:50, 2.41s/it, lr: 1.0e-04 loss: 5.301e-02] lana-lora-wan2.2: 11%\|#####4 \| 527/5000 [11:08<2:59:50, 2.41s/it, lr: 1.0e-04 loss: 5.301e-02] lana-lora-wan2.2: 11%\|#####4 \| 528/5000 [11:11<3:00:08, 2.42s/it, lr: 1.0e-04 loss: 4.693e-02] lana-lora-wan2.2: 11%\|#####4 \| 528/5000 [11:11<3:00:08, 2.42s/it, lr: 1.0e-04 loss: 4.693e-02] lana-lora-wan2.2: 11%\|#####5 \| 529/5000 [11:14<3:00:06, 2.42s/it, lr: 1.0e-04 loss: 3.858e-02] lana-lora-wan2.2: 11%\|#####5 \| 529/5000 [11:14<3:00:06, 2.42s/it, lr: 1.0e-04 loss: 3.858e-02] lana-lora-wan2.2: 11%\|#####5 \| 530/5000 [11:16<3:00:00, 2.42s/it, lr: 1.0e-04 loss: 3.404e-02] lana-lora-wan2.2: 11%\|#####5 \| 530/5000 [11:16<3:00:00, 2.42s/it, lr: 1.0e-04 loss: 3.404e-02] lana-lora-wan2.2: 11%\|#####5 \| 531/5000 [11:20<3:00:17, 2.42s/it, lr: 1.0e-04 loss: 3.821e-01] lana-lora-wan2.2: 11%\|#####5 \| 531/5000 [11:20<3:00:17, 2.42s/it, lr: 1.0e-04 loss: 3.821e-01] lana-lora-wan2.2: 11%\|#####5 \| 532/5000 [11:23<3:00:35, 2.43s/it, lr: 1.0e-04 loss: 3.238e-02] lana-lora-wan2.2: 11%\|#####5 \| 532/5000 [11:23<3:00:35, 2.43s/it, lr: 1.0e-04 loss: 3.238e-02] lana-lora-wan2.2: 11%\|#####5 \| 533/5000 [11:27<3:00:52, 2.43s/it, lr: 1.0e-04 loss: 4.585e-02] lana-lora-wan2.2: 11%\|#####5 \| 533/5000 [11:27<3:00:52, 2.43s/it, lr: 1.0e-04 loss: 4.585e-02] lana-lora-wan2.2: 11%\|#####5 \| 534/5000 [11:29<3:00:49, 2.43s/it, lr: 1.0e-04 loss: 8.655e-02] lana-lora-wan2.2: 11%\|#####5 \| 534/5000 [11:29<3:00:49, 2.43s/it, lr: 1.0e-04 loss: 8.655e-02] lana-lora-wan2.2: 11%\|#####5 \| 535/5000 [11:31<3:00:27, 2.42s/it, lr: 1.0e-04 loss: 4.569e-02] lana-lora-wan2.2: 11%\|#####5 \| 535/5000 [11:31<3:00:27, 2.42s/it, lr: 1.0e-04 loss: 4.569e-02] lana-lora-wan2.2: 11%\|#####5 \| 536/5000 [11:34<3:00:44, 2.43s/it, lr: 1.0e-04 loss: 1.627e-01] lana-lora-wan2.2: 11%\|#####5 \| 536/5000 [11:34<3:00:44, 2.43s/it, lr: 1.0e-04 loss: 1.627e-01] lana-lora-wan2.2: 11%\|#####5 \| 537/5000 [11:38<3:01:00, 2.43s/it, lr: 1.0e-04 loss: 4.601e-02] lana-lora-wan2.2: 11%\|#####5 \| 537/5000 [11:38<3:01:00, 2.43s/it, lr: 1.0e-04 loss: 4.601e-02] lana-lora-wan2.2: 11%\|#####5 \| 538/5000 [11:39<3:00:39, 2.43s/it, lr: 1.0e-04 loss: 9.995e-02] lana-lora-wan2.2: 11%\|#####5 \| 538/5000 [11:39<3:00:39, 2.43s/it, lr: 1.0e-04 loss: 9.995e-02] lana-lora-wan2.2: 11%\|#####6 \| 539/5000 [11:40<3:00:17, 2.42s/it, lr: 1.0e-04 loss: 6.981e-02] lana-lora-wan2.2: 11%\|#####6 \| 539/5000 [11:40<3:00:17, 2.42s/it, lr: 1.0e-04 loss: 6.981e-02] lana-lora-wan2.2: 11%\|#####6 \| 540/5000 [11:43<3:00:15, 2.42s/it, lr: 1.0e-04 loss: 3.378e-02] lana-lora-wan2.2: 11%\|#####6 \| 540/5000 [11:43<3:00:15, 2.42s/it, lr: 1.0e-04 loss: 3.378e-02] lana-lora-wan2.2: 11%\|#####6 \| 541/5000 [11:46<3:00:32, 2.43s/it, lr: 1.0e-04 loss: 1.094e-01] lana-lora-wan2.2: 11%\|#####6 \| 541/5000 [11:46<3:00:32, 2.43s/it, lr: 1.0e-04 loss: 1.094e-01] lana-lora-wan2.2: 11%\|#####6 \| 542/5000 [11:48<3:00:10, 2.42s/it, lr: 1.0e-04 loss: 2.385e-01] lana-lora-wan2.2: 11%\|#####6 \| 542/5000 [11:48<3:00:10, 2.42s/it, lr: 1.0e-04 loss: 2.385e-01] lana-lora-wan2.2: 11%\|#####6 \| 543/5000 [11:49<2:59:49, 2.42s/it, lr: 1.0e-04 loss: 6.513e-02] lana-lora-wan2.2: 11%\|#####6 \| 543/5000 [11:49<2:59:49, 2.42s/it, lr: 1.0e-04 loss: 6.513e-02] lana-lora-wan2.2: 11%\|#####6 \| 544/5000 [11:50<2:59:28, 2.42s/it, lr: 1.0e-04 loss: 6.026e-02] lana-lora-wan2.2: 11%\|#####6 \| 544/5000 [11:50<2:59:28, 2.42s/it, lr: 1.0e-04 loss: 6.026e-02] lana-lora-wan2.2: 11%\|#####6 \| 545/5000 [11:54<2:59:45, 2.42s/it, lr: 1.0e-04 loss: 4.480e-02] lana-lora-wan2.2: 11%\|#####6 \| 545/5000 [11:54<2:59:45, 2.42s/it, lr: 1.0e-04 loss: 4.480e-02] lana-lora-wan2.2: 11%\|#####6 \| 546/5000 [11:56<2:59:43, 2.42s/it, lr: 1.0e-04 loss: 2.574e-02] lana-lora-wan2.2: 11%\|#####6 \| 546/5000 [11:56<2:59:43, 2.42s/it, lr: 1.0e-04 loss: 2.574e-02] lana-lora-wan2.2: 11%\|#####6 \| 547/5000 [12:00<2:59:59, 2.43s/it, lr: 1.0e-04 loss: 3.031e-02] lana-lora-wan2.2: 11%\|#####6 \| 547/5000 [12:00<2:59:59, 2.43s/it, lr: 1.0e-04 loss: 3.031e-02] lana-lora-wan2.2: 11%\|#####6 \| 548/5000 [12:02<2:59:53, 2.42s/it, lr: 1.0e-04 loss: 8.307e-02] lana-lora-wan2.2: 11%\|#####6 \| 548/5000 [12:02<2:59:53, 2.42s/it, lr: 1.0e-04 loss: 8.307e-02] lana-lora-wan2.2: 11%\|#####7 \| 549/5000 [12:04<2:59:47, 2.42s/it, lr: 1.0e-04 loss: 3.670e-02] lana-lora-wan2.2: 11%\|#####7 \| 549/5000 [12:04<2:59:47, 2.42s/it, lr: 1.0e-04 loss: 3.670e-02] lana-lora-wan2.2: 11%\|#####7 \| 550/5000 [12:05<2:59:26, 2.42s/it, lr: 1.0e-04 loss: 9.303e-02] lana-lora-wan2.2: 11%\|#####7 \| 550/5000 [12:05<2:59:26, 2.42s/it, lr: 1.0e-04 loss: 9.303e-02] lana-lora-wan2.2: 11%\|#####7 \| 551/5000 [12:07<2:59:20, 2.42s/it, lr: 1.0e-04 loss: 8.979e-02] lana-lora-wan2.2: 11%\|#####7 \| 551/5000 [12:07<2:59:20, 2.42s/it, lr: 1.0e-04 loss: 8.979e-02] lana-lora-wan2.2: 11%\|#####7 \| 552/5000 [12:11<2:59:40, 2.42s/it, lr: 1.0e-04 loss: 4.392e-02] lana-lora-wan2.2: 11%\|#####7 \| 552/5000 [12:11<2:59:40, 2.42s/it, lr: 1.0e-04 loss: 4.392e-02] lana-lora-wan2.2: 11%\|#####7 \| 553/5000 [12:13<2:59:20, 2.42s/it, lr: 1.0e-04 loss: 5.823e-02] lana-lora-wan2.2: 11%\|#####7 \| 553/5000 [12:13<2:59:20, 2.42s/it, lr: 1.0e-04 loss: 5.823e-02] lana-lora-wan2.2: 11%\|#####7 \| 554/5000 [12:15<2:59:14, 2.42s/it, lr: 1.0e-04 loss: 4.418e-02] lana-lora-wan2.2: 11%\|#####7 \| 554/5000 [12:15<2:59:14, 2.42s/it, lr: 1.0e-04 loss: 4.418e-02] lana-lora-wan2.2: 11%\|#####7 \| 555/5000 [12:16<2:58:53, 2.41s/it, lr: 1.0e-04 loss: 3.368e-02] lana-lora-wan2.2: 11%\|#####7 \| 555/5000 [12:16<2:58:53, 2.41s/it, lr: 1.0e-04 loss: 3.368e-02] lana-lora-wan2.2: 11%\|#####7 \| 556/5000 [12:20<2:59:08, 2.42s/it, lr: 1.0e-04 loss: 3.069e-02] lana-lora-wan2.2: 11%\|#####7 \| 556/5000 [12:20<2:59:08, 2.42s/it, lr: 1.0e-04 loss: 3.069e-02] lana-lora-wan2.2: 11%\|#####7 \| 557/5000 [12:21<2:58:48, 2.41s/it, lr: 1.0e-04 loss: 6.201e-02] lana-lora-wan2.2: 11%\|#####7 \| 557/5000 [12:21<2:58:48, 2.41s/it, lr: 1.0e-04 loss: 6.201e-02] lana-lora-wan2.2: 11%\|#####8 \| 558/5000 [12:23<2:58:47, 2.42s/it, lr: 1.0e-04 loss: 2.812e-02] lana-lora-wan2.2: 11%\|#####8 \| 558/5000 [12:23<2:58:47, 2.42s/it, lr: 1.0e-04 loss: 2.812e-02] lana-lora-wan2.2: 11%\|#####8 \| 559/5000 [12:26<2:58:42, 2.41s/it, lr: 1.0e-04 loss: 3.155e-02] lana-lora-wan2.2: 11%\|#####8 \| 559/5000 [12:26<2:58:42, 2.41s/it, lr: 1.0e-04 loss: 3.155e-02] lana-lora-wan2.2: 11%\|#####8 \| 560/5000 [12:28<2:58:36, 2.41s/it, lr: 1.0e-04 loss: 3.760e-02] lana-lora-wan2.2: 11%\|#####8 \| 560/5000 [12:28<2:58:36, 2.41s/it, lr: 1.0e-04 loss: 3.760e-02] lana-lora-wan2.2: 11%\|#####8 \| 561/5000 [12:30<2:58:30, 2.41s/it, lr: 1.0e-04 loss: 8.156e-02] lana-lora-wan2.2: 11%\|#####8 \| 561/5000 [12:30<2:58:30, 2.41s/it, lr: 1.0e-04 loss: 8.156e-02] lana-lora-wan2.2: 11%\|#####8 \| 562/5000 [12:34<2:58:46, 2.42s/it, lr: 1.0e-04 loss: 2.012e-01] lana-lora-wan2.2: 11%\|#####8 \| 562/5000 [12:34<2:58:46, 2.42s/it, lr: 1.0e-04 loss: 2.012e-01] lana-lora-wan2.2: 11%\|#####8 \| 563/5000 [12:37<2:59:01, 2.42s/it, lr: 1.0e-04 loss: 4.136e-02] lana-lora-wan2.2: 11%\|#####8 \| 563/5000 [12:37<2:59:01, 2.42s/it, lr: 1.0e-04 loss: 4.136e-02] lana-lora-wan2.2: 11%\|#####8 \| 564/5000 [12:39<2:58:46, 2.42s/it, lr: 1.0e-04 loss: 8.402e-02] lana-lora-wan2.2: 11%\|#####8 \| 564/5000 [12:39<2:58:46, 2.42s/it, lr: 1.0e-04 loss: 8.402e-02] lana-lora-wan2.2: 11%\|#####8 \| 565/5000 [12:42<2:59:01, 2.42s/it, lr: 1.0e-04 loss: 6.529e-02] lana-lora-wan2.2: 11%\|#####8 \| 565/5000 [12:42<2:59:01, 2.42s/it, lr: 1.0e-04 loss: 6.529e-02] lana-lora-wan2.2: 11%\|#####8 \| 566/5000 [12:46<2:59:16, 2.43s/it, lr: 1.0e-04 loss: 3.726e-02] lana-lora-wan2.2: 11%\|#####8 \| 566/5000 [12:46<2:59:16, 2.43s/it, lr: 1.0e-04 loss: 3.726e-02] lana-lora-wan2.2: 11%\|#####8 \| 567/5000 [12:47<2:58:56, 2.42s/it, lr: 1.0e-04 loss: 4.052e-02] lana-lora-wan2.2: 11%\|#####8 \| 567/5000 [12:47<2:58:56, 2.42s/it, lr: 1.0e-04 loss: 4.052e-02] lana-lora-wan2.2: 11%\|#####9 \| 568/5000 [12:51<2:59:11, 2.43s/it, lr: 1.0e-04 loss: 3.157e-02] lana-lora-wan2.2: 11%\|#####9 \| 568/5000 [12:51<2:59:11, 2.43s/it, lr: 1.0e-04 loss: 3.157e-02] lana-lora-wan2.2: 11%\|#####9 \| 569/5000 [12:52<2:58:56, 2.42s/it, lr: 1.0e-04 loss: 2.946e-02] lana-lora-wan2.2: 11%\|#####9 \| 569/5000 [12:52<2:58:56, 2.42s/it, lr: 1.0e-04 loss: 2.946e-02] lana-lora-wan2.2: 11%\|#####9 \| 570/5000 [12:54<2:58:36, 2.42s/it, lr: 1.0e-04 loss: 5.688e-02] lana-lora-wan2.2: 11%\|#####9 \| 570/5000 [12:54<2:58:36, 2.42s/it, lr: 1.0e-04 loss: 5.688e-02] lana-lora-wan2.2: 11%\|#####9 \| 571/5000 [12:56<2:58:31, 2.42s/it, lr: 1.0e-04 loss: 8.373e-02] lana-lora-wan2.2: 11%\|#####9 \| 571/5000 [12:56<2:58:31, 2.42s/it, lr: 1.0e-04 loss: 8.373e-02] lana-lora-wan2.2: 11%\|#####9 \| 572/5000 [12:58<2:58:25, 2.42s/it, lr: 1.0e-04 loss: 3.315e-02] lana-lora-wan2.2: 11%\|#####9 \| 572/5000 [12:58<2:58:25, 2.42s/it, lr: 1.0e-04 loss: 3.315e-02] lana-lora-wan2.2: 11%\|#####9 \| 573/5000 [13:00<2:58:19, 2.42s/it, lr: 1.0e-04 loss: 4.396e-02] lana-lora-wan2.2: 11%\|#####9 \| 573/5000 [13:00<2:58:19, 2.42s/it, lr: 1.0e-04 loss: 4.396e-02] lana-lora-wan2.2: 11%\|#####9 \| 574/5000 [13:01<2:58:00, 2.41s/it, lr: 1.0e-04 loss: 3.990e-01] lana-lora-wan2.2: 11%\|#####9 \| 574/5000 [13:01<2:58:00, 2.41s/it, lr: 1.0e-04 loss: 3.990e-01] lana-lora-wan2.2: 12%\|#####9 \| 575/5000 [13:03<2:57:45, 2.41s/it, lr: 1.0e-04 loss: 7.068e-02] lana-lora-wan2.2: 12%\|#####9 \| 575/5000 [13:03<2:57:45, 2.41s/it, lr: 1.0e-04 loss: 7.068e-02] lana-lora-wan2.2: 12%\|#####9 \| 576/5000 [13:05<2:57:39, 2.41s/it, lr: 1.0e-04 loss: 5.484e-02] lana-lora-wan2.2: 12%\|#####9 \| 576/5000 [13:05<2:57:39, 2.41s/it, lr: 1.0e-04 loss: 5.484e-02] lana-lora-wan2.2: 12%\|###### \| 577/5000 [13:06<2:57:20, 2.41s/it, lr: 1.0e-04 loss: 3.860e-02] lana-lora-wan2.2: 12%\|###### \| 577/5000 [13:06<2:57:20, 2.41s/it, lr: 1.0e-04 loss: 3.860e-02] lana-lora-wan2.2: 12%\|###### \| 578/5000 [13:10<2:57:35, 2.41s/it, lr: 1.0e-04 loss: 7.670e-02] lana-lora-wan2.2: 12%\|###### \| 578/5000 [13:10<2:57:35, 2.41s/it, lr: 1.0e-04 loss: 7.670e-02] lana-lora-wan2.2: 12%\|###### \| 579/5000 [13:13<2:57:49, 2.41s/it, lr: 1.0e-04 loss: 3.597e-02] lana-lora-wan2.2: 12%\|###### \| 579/5000 [13:13<2:57:49, 2.41s/it, lr: 1.0e-04 loss: 3.597e-02] lana-lora-wan2.2: 12%\|###### \| 580/5000 [13:15<2:57:30, 2.41s/it, lr: 1.0e-04 loss: 1.064e-01] lana-lora-wan2.2: 12%\|###### \| 580/5000 [13:15<2:57:30, 2.41s/it, lr: 1.0e-04 loss: 1.064e-01] lana-lora-wan2.2: 12%\|###### \| 581/5000 [13:19<2:57:49, 2.41s/it, lr: 1.0e-04 loss: 2.590e-01] lana-lora-wan2.2: 12%\|###### \| 581/5000 [13:19<2:57:49, 2.41s/it, lr: 1.0e-04 loss: 2.590e-01] lana-lora-wan2.2: 12%\|###### \| 582/5000 [13:22<2:58:03, 2.42s/it, lr: 1.0e-04 loss: 2.811e-02] lana-lora-wan2.2: 12%\|###### \| 582/5000 [13:22<2:58:03, 2.42s/it, lr: 1.0e-04 loss: 2.811e-02] lana-lora-wan2.2: 12%\|###### \| 583/5000 [13:24<2:57:57, 2.42s/it, lr: 1.0e-04 loss: 3.837e-02] lana-lora-wan2.2: 12%\|###### \| 583/5000 [13:24<2:57:57, 2.42s/it, lr: 1.0e-04 loss: 3.837e-02] lana-lora-wan2.2: 12%\|###### \| 584/5000 [13:26<2:57:38, 2.41s/it, lr: 1.0e-04 loss: 3.914e-02] lana-lora-wan2.2: 12%\|###### \| 584/5000 [13:26<2:57:38, 2.41s/it, lr: 1.0e-04 loss: 3.914e-02] lana-lora-wan2.2: 12%\|###### \| 585/5000 [13:29<2:57:52, 2.42s/it, lr: 1.0e-04 loss: 8.584e-02] lana-lora-wan2.2: 12%\|###### \| 585/5000 [13:29<2:57:52, 2.42s/it, lr: 1.0e-04 loss: 8.584e-02] lana-lora-wan2.2: 12%\|###### \| 586/5000 [13:33<2:58:06, 2.42s/it, lr: 1.0e-04 loss: 8.148e-02] lana-lora-wan2.2: 12%\|###### \| 586/5000 [13:33<2:58:06, 2.42s/it, lr: 1.0e-04 loss: 8.148e-02] lana-lora-wan2.2: 12%\|######1 \| 587/5000 [13:37<2:58:23, 2.43s/it, lr: 1.0e-04 loss: 2.836e-02] lana-lora-wan2.2: 12%\|######1 \| 587/5000 [13:37<2:58:23, 2.43s/it, lr: 1.0e-04 loss: 2.836e-02] lana-lora-wan2.2: 12%\|######1 \| 588/5000 [13:39<2:58:17, 2.42s/it, lr: 1.0e-04 loss: 4.824e-02] lana-lora-wan2.2: 12%\|######1 \| 588/5000 [13:39<2:58:17, 2.42s/it, lr: 1.0e-04 loss: 4.824e-02] lana-lora-wan2.2: 12%\|######1 \| 589/5000 [13:43<2:58:31, 2.43s/it, lr: 1.0e-04 loss: 4.706e-02] lana-lora-wan2.2: 12%\|######1 \| 589/5000 [13:43<2:58:31, 2.43s/it, lr: 1.0e-04 loss: 4.706e-02] lana-lora-wan2.2: 12%\|######1 \| 590/5000 [13:44<2:58:12, 2.42s/it, lr: 1.0e-04 loss: 1.437e-01] lana-lora-wan2.2: 12%\|######1 \| 590/5000 [13:44<2:58:12, 2.42s/it, lr: 1.0e-04 loss: 1.437e-01] lana-lora-wan2.2: 12%\|######1 \| 591/5000 [13:45<2:57:54, 2.42s/it, lr: 1.0e-04 loss: 9.965e-02] lana-lora-wan2.2: 12%\|######1 \| 591/5000 [13:45<2:57:54, 2.42s/it, lr: 1.0e-04 loss: 9.965e-02] lana-lora-wan2.2: 12%\|######1 \| 592/5000 [13:47<2:57:48, 2.42s/it, lr: 1.0e-04 loss: 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12%\|######2 \| 602/5000 [14:17<2:58:39, 2.44s/it, lr: 1.0e-04 loss: 5.804e-02] lana-lora-wan2.2: 12%\|######2 \| 603/5000 [14:20<2:58:33, 2.44s/it, lr: 1.0e-04 loss: 3.586e-02] lana-lora-wan2.2: 12%\|######2 \| 603/5000 [14:20<2:58:33, 2.44s/it, lr: 1.0e-04 loss: 3.586e-02] lana-lora-wan2.2: 12%\|######2 \| 604/5000 [14:22<2:58:28, 2.44s/it, lr: 1.0e-04 loss: 3.248e-02] lana-lora-wan2.2: 12%\|######2 \| 604/5000 [14:22<2:58:28, 2.44s/it, lr: 1.0e-04 loss: 3.248e-02] lana-lora-wan2.2: 12%\|######2 \| 605/5000 [14:24<2:58:26, 2.44s/it, lr: 1.0e-04 loss: 3.477e-02] lana-lora-wan2.2: 12%\|######2 \| 605/5000 [14:24<2:58:26, 2.44s/it, lr: 1.0e-04 loss: 3.477e-02] lana-lora-wan2.2: 12%\|######3 \| 606/5000 [14:26<2:58:09, 2.43s/it, lr: 1.0e-04 loss: 7.127e-02] lana-lora-wan2.2: 12%\|######3 \| 606/5000 [14:26<2:58:09, 2.43s/it, lr: 1.0e-04 loss: 7.127e-02] lana-lora-wan2.2: 12%\|######3 \| 607/5000 [14:28<2:58:03, 2.43s/it, lr: 1.0e-04 loss: 8.967e-02] lana-lora-wan2.2: 12%\|######3 \| 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[14:39<2:57:39, 2.43s/it, lr: 1.0e-04 loss: 6.340e-02] lana-lora-wan2.2: 12%\|######3 \| 613/5000 [14:40<2:57:21, 2.43s/it, lr: 1.0e-04 loss: 1.235e-01] lana-lora-wan2.2: 12%\|######3 \| 613/5000 [14:40<2:57:21, 2.43s/it, lr: 1.0e-04 loss: 1.235e-01] lana-lora-wan2.2: 12%\|######3 \| 614/5000 [14:42<2:57:16, 2.43s/it, lr: 1.0e-04 loss: 2.910e-02] lana-lora-wan2.2: 12%\|######3 \| 614/5000 [14:42<2:57:16, 2.43s/it, lr: 1.0e-04 loss: 2.910e-02] lana-lora-wan2.2: 12%\|######3 \| 615/5000 [14:44<2:57:10, 2.42s/it, lr: 1.0e-04 loss: 1.303e-01] lana-lora-wan2.2: 12%\|######3 \| 615/5000 [14:44<2:57:10, 2.42s/it, lr: 1.0e-04 loss: 1.303e-01] lana-lora-wan2.2: 12%\|######4 \| 616/5000 [14:47<2:57:08, 2.42s/it, lr: 1.0e-04 loss: 1.168e-01] lana-lora-wan2.2: 12%\|######4 \| 616/5000 [14:47<2:57:08, 2.42s/it, lr: 1.0e-04 loss: 1.168e-01] lana-lora-wan2.2: 12%\|######4 \| 617/5000 [14:49<2:57:03, 2.42s/it, lr: 1.0e-04 loss: 1.005e-01] lana-lora-wan2.2: 12%\|######4 \| 617/5000 [14:49<2:57:03, 2.42s/it, lr: 1.0e-04 loss: 1.005e-01] lana-lora-wan2.2: 12%\|######4 \| 618/5000 [14:51<2:56:58, 2.42s/it, lr: 1.0e-04 loss: 5.748e-02] lana-lora-wan2.2: 12%\|######4 \| 618/5000 [14:51<2:56:58, 2.42s/it, lr: 1.0e-04 loss: 5.748e-02] lana-lora-wan2.2: 12%\|######4 \| 619/5000 [14:52<2:56:40, 2.42s/it, lr: 1.0e-04 loss: 6.118e-02] lana-lora-wan2.2: 12%\|######4 \| 619/5000 [14:52<2:56:40, 2.42s/it, lr: 1.0e-04 loss: 6.118e-02] lana-lora-wan2.2: 12%\|######4 \| 620/5000 [14:56<2:56:53, 2.42s/it, lr: 1.0e-04 loss: 2.963e-02] lana-lora-wan2.2: 12%\|######4 \| 620/5000 [14:56<2:56:53, 2.42s/it, lr: 1.0e-04 loss: 2.963e-02] lana-lora-wan2.2: 12%\|######4 \| 621/5000 [15:00<2:57:05, 2.43s/it, lr: 1.0e-04 loss: 6.031e-02] lana-lora-wan2.2: 12%\|######4 \| 621/5000 [15:00<2:57:05, 2.43s/it, lr: 1.0e-04 loss: 6.031e-02] lana-lora-wan2.2: 12%\|######4 \| 622/5000 [15:04<2:57:21, 2.43s/it, lr: 1.0e-04 loss: 5.458e-02] lana-lora-wan2.2: 12%\|######4 \| 622/5000 [15:04<2:57:21, 2.43s/it, lr: 1.0e-04 loss: 5.458e-02] lana-lora-wan2.2: 12%\|######4 \| 623/5000 [15:07<2:57:33, 2.43s/it, lr: 1.0e-04 loss: 6.449e-02] lana-lora-wan2.2: 12%\|######4 \| 623/5000 [15:07<2:57:33, 2.43s/it, lr: 1.0e-04 loss: 6.449e-02] lana-lora-wan2.2: 12%\|######4 \| 624/5000 [15:09<2:57:16, 2.43s/it, lr: 1.0e-04 loss: 1.030e-01] lana-lora-wan2.2: 12%\|######4 \| 624/5000 [15:09<2:57:16, 2.43s/it, lr: 1.0e-04 loss: 1.030e-01] lana-lora-wan2.2: 12%\|######5 \| 625/5000 [15:10<2:56:58, 2.43s/it, lr: 1.0e-04 loss: 4.795e-02] lana-lora-wan2.2: 12%\|######5 \| 625/5000 [15:10<2:56:58, 2.43s/it, lr: 1.0e-04 loss: 4.795e-02] lana-lora-wan2.2: 13%\|######5 \| 626/5000 [15:12<2:56:53, 2.43s/it, lr: 1.0e-04 loss: 1.173e-01] lana-lora-wan2.2: 13%\|######5 \| 626/5000 [15:12<2:56:53, 2.43s/it, lr: 1.0e-04 loss: 1.173e-01] lana-lora-wan2.2: 13%\|######5 \| 627/5000 [15:13<2:56:36, 2.42s/it, lr: 1.0e-04 loss: 5.019e-02] lana-lora-wan2.2: 13%\|######5 \| 627/5000 [15:13<2:56:36, 2.42s/it, lr: 1.0e-04 loss: 5.019e-02] lana-lora-wan2.2: 13%\|######5 \| 628/5000 [15:15<2:56:23, 2.42s/it, lr: 1.0e-04 loss: 5.859e-02] lana-lora-wan2.2: 13%\|######5 \| 628/5000 [15:15<2:56:23, 2.42s/it, lr: 1.0e-04 loss: 5.859e-02] lana-lora-wan2.2: 13%\|######5 \| 629/5000 [15:16<2:56:06, 2.42s/it, lr: 1.0e-04 loss: 3.357e-02] lana-lora-wan2.2: 13%\|######5 \| 629/5000 [15:16<2:56:06, 2.42s/it, lr: 1.0e-04 loss: 3.357e-02] lana-lora-wan2.2: 13%\|######5 \| 630/5000 [15:17<2:55:49, 2.41s/it, lr: 1.0e-04 loss: 5.274e-02] lana-lora-wan2.2: 13%\|######5 \| 630/5000 [15:17<2:55:49, 2.41s/it, lr: 1.0e-04 loss: 5.274e-02] lana-lora-wan2.2: 13%\|######5 \| 631/5000 [15:21<2:56:01, 2.42s/it, lr: 1.0e-04 loss: 3.840e-02] lana-lora-wan2.2: 13%\|######5 \| 631/5000 [15:21<2:56:01, 2.42s/it, lr: 1.0e-04 loss: 3.840e-02] lana-lora-wan2.2: 13%\|######5 \| 632/5000 [15:23<2:55:56, 2.42s/it, lr: 1.0e-04 loss: 1.877e-01] lana-lora-wan2.2: 13%\|######5 \| 632/5000 [15:23<2:55:56, 2.42s/it, lr: 1.0e-04 loss: 1.877e-01] lana-lora-wan2.2: 13%\|######5 \| 633/5000 [15:24<2:55:40, 2.41s/it, lr: 1.0e-04 loss: 9.385e-02] lana-lora-wan2.2: 13%\|######5 \| 633/5000 [15:24<2:55:40, 2.41s/it, lr: 1.0e-04 loss: 9.385e-02] lana-lora-wan2.2: 13%\|######5 \| 634/5000 [15:26<2:55:38, 2.41s/it, lr: 1.0e-04 loss: 5.114e-02] lana-lora-wan2.2: 13%\|######5 \| 634/5000 [15:26<2:55:38, 2.41s/it, lr: 1.0e-04 loss: 5.114e-02] lana-lora-wan2.2: 13%\|######6 \| 635/5000 [15:28<2:55:22, 2.41s/it, lr: 1.0e-04 loss: 6.129e-02] lana-lora-wan2.2: 13%\|######6 \| 635/5000 [15:28<2:55:22, 2.41s/it, lr: 1.0e-04 loss: 6.129e-02] lana-lora-wan2.2: 13%\|######6 \| 636/5000 [15:30<2:55:17, 2.41s/it, lr: 1.0e-04 loss: 5.616e-02] lana-lora-wan2.2: 13%\|######6 \| 636/5000 [15:30<2:55:17, 2.41s/it, lr: 1.0e-04 loss: 5.616e-02] lana-lora-wan2.2: 13%\|######6 \| 637/5000 [15:33<2:55:29, 2.41s/it, lr: 1.0e-04 loss: 2.832e-02] lana-lora-wan2.2: 13%\|######6 \| 637/5000 [15:33<2:55:29, 2.41s/it, lr: 1.0e-04 loss: 2.832e-02] lana-lora-wan2.2: 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[15:56<2:54:19, 2.40s/it, lr: 1.0e-04 loss: 6.606e-02] lana-lora-wan2.2: 13%\|######7 \| 648/5000 [15:56<2:54:19, 2.40s/it, lr: 1.0e-04 loss: 6.606e-02] lana-lora-wan2.2: 13%\|######7 \| 649/5000 [15:58<2:54:14, 2.40s/it, lr: 1.0e-04 loss: 2.914e-02] lana-lora-wan2.2: 13%\|######7 \| 649/5000 [15:58<2:54:14, 2.40s/it, lr: 1.0e-04 loss: 2.914e-02] lana-lora-wan2.2: 13%\|######7 \| 650/5000 [16:02<2:54:25, 2.41s/it, lr: 1.0e-04 loss: 3.103e-02] lana-lora-wan2.2: 13%\|######7 \| 650/5000 [16:02<2:54:25, 2.41s/it, lr: 1.0e-04 loss: 3.103e-02] lana-lora-wan2.2: 13%\|######7 \| 651/5000 [16:06<2:54:39, 2.41s/it, lr: 1.0e-04 loss: 5.103e-02] lana-lora-wan2.2: 13%\|######7 \| 651/5000 [16:06<2:54:39, 2.41s/it, lr: 1.0e-04 loss: 5.103e-02] lana-lora-wan2.2: 13%\|######7 \| 652/5000 [16:08<2:54:34, 2.41s/it, lr: 1.0e-04 loss: 2.813e-02] lana-lora-wan2.2: 13%\|######7 \| 652/5000 [16:08<2:54:34, 2.41s/it, lr: 1.0e-04 loss: 2.813e-02] lana-lora-wan2.2: 13%\|######7 \| 653/5000 [16:10<2:54:29, 2.41s/it, lr: 1.0e-04 loss: 3.215e-02] lana-lora-wan2.2: 13%\|######7 \| 653/5000 [16:10<2:54:29, 2.41s/it, lr: 1.0e-04 loss: 3.215e-02] lana-lora-wan2.2: 13%\|######8 \| 654/5000 [16:14<2:54:41, 2.41s/it, lr: 1.0e-04 loss: 3.171e-02] lana-lora-wan2.2: 13%\|######8 \| 654/5000 [16:14<2:54:41, 2.41s/it, lr: 1.0e-04 loss: 3.171e-02] lana-lora-wan2.2: 13%\|######8 \| 655/5000 [16:17<2:54:51, 2.41s/it, lr: 1.0e-04 loss: 4.119e-02] lana-lora-wan2.2: 13%\|######8 \| 655/5000 [16:17<2:54:51, 2.41s/it, lr: 1.0e-04 loss: 4.119e-02] lana-lora-wan2.2: 13%\|######8 \| 656/5000 [16:21<2:55:02, 2.42s/it, lr: 1.0e-04 loss: 3.230e-02] lana-lora-wan2.2: 13%\|######8 \| 656/5000 [16:21<2:55:02, 2.42s/it, lr: 1.0e-04 loss: 3.230e-02] lana-lora-wan2.2: 13%\|######8 \| 657/5000 [16:23<2:54:51, 2.42s/it, lr: 1.0e-04 loss: 3.746e-02] lana-lora-wan2.2: 13%\|######8 \| 657/5000 [16:23<2:54:51, 2.42s/it, lr: 1.0e-04 loss: 3.746e-02] lana-lora-wan2.2: 13%\|######8 \| 658/5000 [16:26<2:55:02, 2.42s/it, lr: 1.0e-04 loss: 8.632e-02] lana-lora-wan2.2: 13%\|######8 \| 658/5000 [16:26<2:55:02, 2.42s/it, lr: 1.0e-04 loss: 8.632e-02] lana-lora-wan2.2: 13%\|######8 \| 659/5000 [16:29<2:54:57, 2.42s/it, lr: 1.0e-04 loss: 6.164e-02] lana-lora-wan2.2: 13%\|######8 \| 659/5000 [16:29<2:54:57, 2.42s/it, lr: 1.0e-04 loss: 6.164e-02] lana-lora-wan2.2: 13%\|######8 \| 660/5000 [16:30<2:54:41, 2.42s/it, lr: 1.0e-04 loss: 3.182e-01] lana-lora-wan2.2: 13%\|######8 \| 660/5000 [16:30<2:54:41, 2.42s/it, lr: 1.0e-04 loss: 3.182e-01] lana-lora-wan2.2: 13%\|######8 \| 661/5000 [16:32<2:54:36, 2.41s/it, lr: 1.0e-04 loss: 4.291e-02] lana-lora-wan2.2: 13%\|######8 \| 661/5000 [16:32<2:54:36, 2.41s/it, lr: 1.0e-04 loss: 4.291e-02] lana-lora-wan2.2: 13%\|######8 \| 662/5000 [16:34<2:54:31, 2.41s/it, lr: 1.0e-04 loss: 4.052e-02] lana-lora-wan2.2: 13%\|######8 \| 662/5000 [16:34<2:54:31, 2.41s/it, lr: 1.0e-04 loss: 4.052e-02] lana-lora-wan2.2: 13%\|######8 \| 663/5000 [16:36<2:54:19, 2.41s/it, lr: 1.0e-04 loss: 3.146e-02] lana-lora-wan2.2: 13%\|######8 \| 663/5000 [16:36<2:54:19, 2.41s/it, lr: 1.0e-04 loss: 3.146e-02] lana-lora-wan2.2: 13%\|######9 \| 664/5000 [16:39<2:54:30, 2.41s/it, lr: 1.0e-04 loss: 1.566e-01] lana-lora-wan2.2: 13%\|######9 \| 664/5000 [16:39<2:54:30, 2.41s/it, lr: 1.0e-04 loss: 1.566e-01] lana-lora-wan2.2: 13%\|######9 \| 665/5000 [16:43<2:54:40, 2.42s/it, lr: 1.0e-04 loss: 4.488e-02] lana-lora-wan2.2: 13%\|######9 \| 665/5000 [16:43<2:54:40, 2.42s/it, lr: 1.0e-04 loss: 4.488e-02] lana-lora-wan2.2: 13%\|######9 \| 666/5000 [16:45<2:54:35, 2.42s/it, lr: 1.0e-04 loss: 4.016e-02] lana-lora-wan2.2: 13%\|######9 \| 666/5000 [16:45<2:54:35, 2.42s/it, lr: 1.0e-04 loss: 4.016e-02] lana-lora-wan2.2: 13%\|######9 \| 667/5000 [16:49<2:54:46, 2.42s/it, lr: 1.0e-04 loss: 1.234e-01] lana-lora-wan2.2: 13%\|######9 \| 667/5000 [16:49<2:54:46, 2.42s/it, lr: 1.0e-04 loss: 1.234e-01] lana-lora-wan2.2: 13%\|######9 \| 668/5000 [16:52<2:54:56, 2.42s/it, lr: 1.0e-04 loss: 8.133e-02] lana-lora-wan2.2: 13%\|######9 \| 668/5000 [16:52<2:54:56, 2.42s/it, lr: 1.0e-04 loss: 8.133e-02] lana-lora-wan2.2: 13%\|######9 \| 669/5000 [16:55<2:54:55, 2.42s/it, lr: 1.0e-04 loss: 3.058e-02] lana-lora-wan2.2: 13%\|######9 \| 669/5000 [16:55<2:54:55, 2.42s/it, lr: 1.0e-04 loss: 3.058e-02] lana-lora-wan2.2: 13%\|######9 \| 670/5000 [16:57<2:54:49, 2.42s/it, lr: 1.0e-04 loss: 5.099e-02] lana-lora-wan2.2: 13%\|######9 \| 670/5000 [16:57<2:54:49, 2.42s/it, lr: 1.0e-04 loss: 5.099e-02] lana-lora-wan2.2: 13%\|######9 \| 671/5000 [16:59<2:54:44, 2.42s/it, lr: 1.0e-04 loss: 3.459e-02] lana-lora-wan2.2: 13%\|######9 \| 671/5000 [16:59<2:54:44, 2.42s/it, lr: 1.0e-04 loss: 3.459e-02] lana-lora-wan2.2: 13%\|######9 \| 672/5000 [17:01<2:54:40, 2.42s/it, lr: 1.0e-04 loss: 5.338e-02] lana-lora-wan2.2: 13%\|######9 \| 672/5000 [17:01<2:54:40, 2.42s/it, lr: 1.0e-04 loss: 5.338e-02] lana-lora-wan2.2: 13%\|######9 \| 673/5000 [17:03<2:54:24, 2.42s/it, lr: 1.0e-04 loss: 4.059e-02] lana-lora-wan2.2: 13%\|######9 \| 673/5000 [17:03<2:54:24, 2.42s/it, lr: 1.0e-04 loss: 4.059e-02] lana-lora-wan2.2: 13%\|####### \| 674/5000 [17:04<2:54:09, 2.42s/it, lr: 1.0e-04 loss: 2.841e-02] lana-lora-wan2.2: 13%\|####### \| 674/5000 [17:04<2:54:09, 2.42s/it, lr: 1.0e-04 loss: 2.841e-02] lana-lora-wan2.2: 14%\|####### \| 675/5000 [17:06<2:54:08, 2.42s/it, lr: 1.0e-04 loss: 3.168e-01] lana-lora-wan2.2: 14%\|####### \| 675/5000 [17:06<2:54:08, 2.42s/it, lr: 1.0e-04 loss: 3.168e-01] lana-lora-wan2.2: 14%\|####### \| 676/5000 [17:08<2:54:03, 2.42s/it, lr: 1.0e-04 loss: 2.700e-02] lana-lora-wan2.2: 14%\|####### \| 676/5000 [17:08<2:54:03, 2.42s/it, lr: 1.0e-04 loss: 2.700e-02] lana-lora-wan2.2: 14%\|####### \| 677/5000 [17:12<2:54:13, 2.42s/it, lr: 1.0e-04 loss: 3.474e-02] lana-lora-wan2.2: 14%\|####### \| 677/5000 [17:12<2:54:13, 2.42s/it, lr: 1.0e-04 loss: 3.474e-02] lana-lora-wan2.2: 14%\|####### \| 678/5000 [17:13<2:53:58, 2.42s/it, lr: 1.0e-04 loss: 4.057e-02] lana-lora-wan2.2: 14%\|####### \| 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[17:26<2:53:57, 2.42s/it, lr: 1.0e-04 loss: 7.266e-02] lana-lora-wan2.2: 14%\|#######1 \| 684/5000 [17:30<2:54:07, 2.42s/it, lr: 1.0e-04 loss: 4.664e-02] lana-lora-wan2.2: 14%\|#######1 \| 684/5000 [17:30<2:54:07, 2.42s/it, lr: 1.0e-04 loss: 4.664e-02] lana-lora-wan2.2: 14%\|#######1 \| 685/5000 [17:32<2:54:02, 2.42s/it, lr: 1.0e-04 loss: 5.889e-02] lana-lora-wan2.2: 14%\|#######1 \| 685/5000 [17:32<2:54:02, 2.42s/it, lr: 1.0e-04 loss: 5.889e-02] lana-lora-wan2.2: 14%\|#######1 \| 686/5000 [17:34<2:53:56, 2.42s/it, lr: 1.0e-04 loss: 6.505e-02] lana-lora-wan2.2: 14%\|#######1 \| 686/5000 [17:34<2:53:56, 2.42s/it, lr: 1.0e-04 loss: 6.505e-02] lana-lora-wan2.2: 14%\|#######1 \| 687/5000 [17:38<2:54:05, 2.42s/it, lr: 1.0e-04 loss: 6.803e-02] lana-lora-wan2.2: 14%\|#######1 \| 687/5000 [17:38<2:54:05, 2.42s/it, lr: 1.0e-04 loss: 6.803e-02] lana-lora-wan2.2: 14%\|#######1 \| 688/5000 [17:42<2:54:15, 2.42s/it, lr: 1.0e-04 loss: 5.373e-02] lana-lora-wan2.2: 14%\|#######1 \| 688/5000 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[17:51<2:53:34, 2.42s/it, lr: 1.0e-04 loss: 1.781e-01] lana-lora-wan2.2: 14%\|#######2 \| 694/5000 [17:54<2:53:44, 2.42s/it, lr: 1.0e-04 loss: 2.826e-02] lana-lora-wan2.2: 14%\|#######2 \| 694/5000 [17:54<2:53:44, 2.42s/it, lr: 1.0e-04 loss: 2.826e-02] lana-lora-wan2.2: 14%\|#######2 \| 695/5000 [17:57<2:53:39, 2.42s/it, lr: 1.0e-04 loss: 3.041e-02] lana-lora-wan2.2: 14%\|#######2 \| 695/5000 [17:57<2:53:39, 2.42s/it, lr: 1.0e-04 loss: 3.041e-02] lana-lora-wan2.2: 14%\|#######2 \| 696/5000 [18:00<2:53:48, 2.42s/it, lr: 1.0e-04 loss: 2.613e-02] lana-lora-wan2.2: 14%\|#######2 \| 696/5000 [18:00<2:53:48, 2.42s/it, lr: 1.0e-04 loss: 2.613e-02] lana-lora-wan2.2: 14%\|#######2 \| 697/5000 [18:01<2:53:34, 2.42s/it, lr: 1.0e-04 loss: 7.667e-02] lana-lora-wan2.2: 14%\|#######2 \| 697/5000 [18:01<2:53:34, 2.42s/it, lr: 1.0e-04 loss: 7.667e-02] lana-lora-wan2.2: 14%\|#######2 \| 698/5000 [18:05<2:53:46, 2.42s/it, lr: 1.0e-04 loss: 9.582e-02] lana-lora-wan2.2: 14%\|#######2 \| 698/5000 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[18:18<2:53:41, 2.43s/it, lr: 1.0e-04 loss: 3.106e-02] lana-lora-wan2.2: 14%\|#######3 \| 704/5000 [18:21<2:53:39, 2.43s/it, lr: 1.0e-04 loss: 4.650e-02] lana-lora-wan2.2: 14%\|#######3 \| 704/5000 [18:21<2:53:39, 2.43s/it, lr: 1.0e-04 loss: 4.650e-02] lana-lora-wan2.2: 14%\|#######3 \| 705/5000 [18:23<2:53:34, 2.42s/it, lr: 1.0e-04 loss: 5.075e-02] lana-lora-wan2.2: 14%\|#######3 \| 705/5000 [18:23<2:53:34, 2.42s/it, lr: 1.0e-04 loss: 5.075e-02] lana-lora-wan2.2: 14%\|#######3 \| 706/5000 [18:26<2:53:43, 2.43s/it, lr: 1.0e-04 loss: 5.740e-02] lana-lora-wan2.2: 14%\|#######3 \| 706/5000 [18:26<2:53:43, 2.43s/it, lr: 1.0e-04 loss: 5.740e-02] lana-lora-wan2.2: 14%\|#######3 \| 707/5000 [18:28<2:53:29, 2.42s/it, lr: 1.0e-04 loss: 1.478e-01] lana-lora-wan2.2: 14%\|#######3 \| 707/5000 [18:28<2:53:29, 2.42s/it, lr: 1.0e-04 loss: 1.478e-01] lana-lora-wan2.2: 14%\|#######3 \| 708/5000 [18:31<2:53:38, 2.43s/it, lr: 1.0e-04 loss: 7.397e-02] lana-lora-wan2.2: 14%\|#######3 \| 708/5000 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[18:43<2:53:22, 2.43s/it, lr: 1.0e-04 loss: 4.567e-02] lana-lora-wan2.2: 14%\|#######4 \| 714/5000 [18:47<2:53:31, 2.43s/it, lr: 1.0e-04 loss: 3.723e-02] lana-lora-wan2.2: 14%\|#######4 \| 714/5000 [18:47<2:53:31, 2.43s/it, lr: 1.0e-04 loss: 3.723e-02] lana-lora-wan2.2: 14%\|#######4 \| 715/5000 [18:48<2:53:16, 2.43s/it, lr: 1.0e-04 loss: 4.672e-02] lana-lora-wan2.2: 14%\|#######4 \| 715/5000 [18:48<2:53:16, 2.43s/it, lr: 1.0e-04 loss: 4.672e-02] lana-lora-wan2.2: 14%\|#######4 \| 716/5000 [18:50<2:53:15, 2.43s/it, lr: 1.0e-04 loss: 3.329e-02] lana-lora-wan2.2: 14%\|#######4 \| 716/5000 [18:50<2:53:15, 2.43s/it, lr: 1.0e-04 loss: 3.329e-02] lana-lora-wan2.2: 14%\|#######4 \| 717/5000 [18:54<2:53:24, 2.43s/it, lr: 1.0e-04 loss: 3.995e-02] lana-lora-wan2.2: 14%\|#######4 \| 717/5000 [18:54<2:53:24, 2.43s/it, lr: 1.0e-04 loss: 3.995e-02] lana-lora-wan2.2: 14%\|#######4 \| 718/5000 [18:58<2:53:33, 2.43s/it, lr: 1.0e-04 loss: 3.279e-02] lana-lora-wan2.2: 14%\|#######4 \| 718/5000 [18:58<2:53:33, 2.43s/it, lr: 1.0e-04 loss: 3.279e-02] lana-lora-wan2.2: 14%\|#######4 \| 719/5000 [19:01<2:53:41, 2.43s/it, lr: 1.0e-04 loss: 2.930e-02] lana-lora-wan2.2: 14%\|#######4 \| 719/5000 [19:01<2:53:41, 2.43s/it, lr: 1.0e-04 loss: 2.930e-02] lana-lora-wan2.2: 14%\|#######4 \| 720/5000 [19:02<2:53:27, 2.43s/it, lr: 1.0e-04 loss: 5.570e-02] lana-lora-wan2.2: 14%\|#######4 \| 720/5000 [19:02<2:53:27, 2.43s/it, lr: 1.0e-04 loss: 5.570e-02] lana-lora-wan2.2: 14%\|#######4 \| 721/5000 [19:06<2:53:36, 2.43s/it, lr: 1.0e-04 loss: 2.894e-02] lana-lora-wan2.2: 14%\|#######4 \| 721/5000 [19:06<2:53:36, 2.43s/it, lr: 1.0e-04 loss: 2.894e-02] lana-lora-wan2.2: 14%\|#######5 \| 722/5000 [19:10<2:53:47, 2.44s/it, lr: 1.0e-04 loss: 9.305e-02] lana-lora-wan2.2: 14%\|#######5 \| 722/5000 [19:10<2:53:47, 2.44s/it, lr: 1.0e-04 loss: 9.305e-02] lana-lora-wan2.2: 14%\|#######5 \| 723/5000 [19:11<2:53:32, 2.43s/it, lr: 1.0e-04 loss: 5.426e-02] lana-lora-wan2.2: 14%\|#######5 \| 723/5000 [19:11<2:53:32, 2.43s/it, lr: 1.0e-04 loss: 5.426e-02] lana-lora-wan2.2: 14%\|#######5 \| 724/5000 [19:12<2:53:18, 2.43s/it, lr: 1.0e-04 loss: 5.949e-02] lana-lora-wan2.2: 14%\|#######5 \| 724/5000 [19:12<2:53:18, 2.43s/it, lr: 1.0e-04 loss: 5.949e-02] lana-lora-wan2.2: 14%\|#######5 \| 725/5000 [19:13<2:53:04, 2.43s/it, lr: 1.0e-04 loss: 2.949e-02] lana-lora-wan2.2: 14%\|#######5 \| 725/5000 [19:13<2:53:04, 2.43s/it, lr: 1.0e-04 loss: 2.949e-02] lana-lora-wan2.2: 15%\|#######5 \| 726/5000 [19:16<2:53:00, 2.43s/it, lr: 1.0e-04 loss: 3.630e-02] lana-lora-wan2.2: 15%\|#######5 \| 726/5000 [19:16<2:53:00, 2.43s/it, lr: 1.0e-04 loss: 3.630e-02] lana-lora-wan2.2: 15%\|#######5 \| 727/5000 [19:17<2:52:46, 2.43s/it, lr: 1.0e-04 loss: 7.057e-02] lana-lora-wan2.2: 15%\|#######5 \| 727/5000 [19:17<2:52:46, 2.43s/it, lr: 1.0e-04 loss: 7.057e-02] lana-lora-wan2.2: 15%\|#######5 \| 728/5000 [19:18<2:52:35, 2.42s/it, lr: 1.0e-04 loss: 1.863e-01] lana-lora-wan2.2: 15%\|#######5 \| 728/5000 [19:18<2:52:35, 2.42s/it, lr: 1.0e-04 loss: 1.863e-01] lana-lora-wan2.2: 15%\|#######5 \| 729/5000 [19:22<2:52:44, 2.43s/it, lr: 1.0e-04 loss: 1.762e-01] lana-lora-wan2.2: 15%\|#######5 \| 729/5000 [19:22<2:52:44, 2.43s/it, lr: 1.0e-04 loss: 1.762e-01] lana-lora-wan2.2: 15%\|#######5 \| 730/5000 [19:24<2:52:39, 2.43s/it, lr: 1.0e-04 loss: 5.530e-02] lana-lora-wan2.2: 15%\|#######5 \| 730/5000 [19:24<2:52:39, 2.43s/it, lr: 1.0e-04 loss: 5.530e-02] lana-lora-wan2.2: 15%\|#######6 \| 731/5000 [19:28<2:52:48, 2.43s/it, lr: 1.0e-04 loss: 2.644e-02] lana-lora-wan2.2: 15%\|#######6 \| 731/5000 [19:28<2:52:48, 2.43s/it, lr: 1.0e-04 loss: 2.644e-02] lana-lora-wan2.2: 15%\|#######6 \| 732/5000 [19:30<2:52:43, 2.43s/it, lr: 1.0e-04 loss: 3.198e-02] lana-lora-wan2.2: 15%\|#######6 \| 732/5000 [19:30<2:52:43, 2.43s/it, lr: 1.0e-04 loss: 3.198e-02] lana-lora-wan2.2: 15%\|#######6 \| 733/5000 [19:31<2:52:33, 2.43s/it, lr: 1.0e-04 loss: 8.759e-02] lana-lora-wan2.2: 15%\|#######6 \| 733/5000 [19:31<2:52:33, 2.43s/it, lr: 1.0e-04 loss: 8.759e-02] lana-lora-wan2.2: 15%\|#######6 \| 734/5000 [19:33<2:52:20, 2.42s/it, lr: 1.0e-04 loss: 2.484e-01] lana-lora-wan2.2: 15%\|#######6 \| 734/5000 [19:33<2:52:20, 2.42s/it, lr: 1.0e-04 loss: 2.484e-01] lana-lora-wan2.2: 15%\|#######6 \| 735/5000 [19:34<2:52:06, 2.42s/it, lr: 1.0e-04 loss: 3.820e-02] lana-lora-wan2.2: 15%\|#######6 \| 735/5000 [19:34<2:52:06, 2.42s/it, lr: 1.0e-04 loss: 3.820e-02] lana-lora-wan2.2: 15%\|#######6 \| 736/5000 [19:35<2:51:53, 2.42s/it, lr: 1.0e-04 loss: 5.009e-02] lana-lora-wan2.2: 15%\|#######6 \| 736/5000 [19:35<2:51:53, 2.42s/it, lr: 1.0e-04 loss: 5.009e-02] lana-lora-wan2.2: 15%\|#######6 \| 737/5000 [19:36<2:51:40, 2.42s/it, lr: 1.0e-04 loss: 4.546e-02] lana-lora-wan2.2: 15%\|#######6 \| 737/5000 [19:36<2:51:40, 2.42s/it, lr: 1.0e-04 loss: 4.546e-02] lana-lora-wan2.2: 15%\|#######6 \| 738/5000 [19:37<2:51:26, 2.41s/it, lr: 1.0e-04 loss: 1.030e-01] lana-lora-wan2.2: 15%\|#######6 \| 738/5000 [19:37<2:51:26, 2.41s/it, lr: 1.0e-04 loss: 1.030e-01] lana-lora-wan2.2: 15%\|#######6 \| 739/5000 [19:39<2:51:16, 2.41s/it, lr: 1.0e-04 loss: 3.400e-02] lana-lora-wan2.2: 15%\|#######6 \| 739/5000 [19:39<2:51:16, 2.41s/it, lr: 1.0e-04 loss: 3.400e-02] lana-lora-wan2.2: 15%\|#######6 \| 740/5000 [19:41<2:51:12, 2.41s/it, lr: 1.0e-04 loss: 4.269e-02] lana-lora-wan2.2: 15%\|#######6 \| 740/5000 [19:41<2:51:12, 2.41s/it, lr: 1.0e-04 loss: 4.269e-02] lana-lora-wan2.2: 15%\|#######7 \| 741/5000 [19:43<2:51:07, 2.41s/it, lr: 1.0e-04 loss: 2.407e-01] lana-lora-wan2.2: 15%\|#######7 \| 741/5000 [19:43<2:51:07, 2.41s/it, lr: 1.0e-04 loss: 2.407e-01] lana-lora-wan2.2: 15%\|#######7 \| 742/5000 [19:47<2:51:16, 2.41s/it, lr: 1.0e-04 loss: 2.426e-01] lana-lora-wan2.2: 15%\|#######7 \| 742/5000 [19:47<2:51:16, 2.41s/it, lr: 1.0e-04 loss: 2.426e-01] lana-lora-wan2.2: 15%\|#######7 \| 743/5000 [19:49<2:51:11, 2.41s/it, lr: 1.0e-04 loss: 3.022e-02] lana-lora-wan2.2: 15%\|#######7 \| 743/5000 [19:49<2:51:11, 2.41s/it, lr: 1.0e-04 loss: 3.022e-02] lana-lora-wan2.2: 15%\|#######7 \| 744/5000 [19:53<2:51:20, 2.42s/it, lr: 1.0e-04 loss: 4.533e-02] lana-lora-wan2.2: 15%\|#######7 \| 744/5000 [19:53<2:51:20, 2.42s/it, lr: 1.0e-04 loss: 4.533e-02] lana-lora-wan2.2: 15%\|#######7 \| 745/5000 [19:57<2:51:30, 2.42s/it, lr: 1.0e-04 loss: 2.708e-02] lana-lora-wan2.2: 15%\|#######7 \| 745/5000 [19:57<2:51:30, 2.42s/it, lr: 1.0e-04 loss: 2.708e-02] lana-lora-wan2.2: 15%\|#######7 \| 746/5000 [19:59<2:51:26, 2.42s/it, lr: 1.0e-04 loss: 2.982e-02] lana-lora-wan2.2: 15%\|#######7 \| 746/5000 [19:59<2:51:26, 2.42s/it, lr: 1.0e-04 loss: 2.982e-02] lana-lora-wan2.2: 15%\|#######7 \| 747/5000 [20:01<2:51:21, 2.42s/it, lr: 1.0e-04 loss: 5.532e-02] lana-lora-wan2.2: 15%\|#######7 \| 747/5000 [20:01<2:51:21, 2.42s/it, lr: 1.0e-04 loss: 5.532e-02] lana-lora-wan2.2: 15%\|#######7 \| 748/5000 [20:03<2:51:17, 2.42s/it, lr: 1.0e-04 loss: 4.948e-02] lana-lora-wan2.2: 15%\|#######7 \| 748/5000 [20:03<2:51:17, 2.42s/it, lr: 1.0e-04 loss: 4.948e-02] lana-lora-wan2.2: 15%\|#######7 \| 749/5000 [20:07<2:51:25, 2.42s/it, lr: 1.0e-04 loss: 1.172e-01] lana-lora-wan2.2: 15%\|#######7 \| 749/5000 [20:07<2:51:25, 2.42s/it, lr: 1.0e-04 loss: 1.172e-01]
	Saving at step 750
	Saved checkpoint to /root/lab/ai-toolkit/output/lana-lora-wan2.2/lana-lora-wan2.2_000000750.safetensors
	Saved optimizer to /root/lab/ai-toolkit/output/lana-lora-wan2.2/optimizer.pt


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	Generating Images: 100%\|#######################################################################################\| 1/1 [00:08<00:00, 8.36s/it] Generating Images: 100%\|#######################################################################################\| 1/1 [00:08<00:00, 8.36s/it][A[A

	[A[A lana-lora-wan2.2: 15%\|#######8 \| 750/5000 [20:11<2:51:35, 2.42s/it, lr: 1.0e-04 loss: 9.144e-02] lana-lora-wan2.2: 15%\|#######8 \| 750/5000 [20:11<2:51:35, 2.42s/it, lr: 1.0e-04 loss: 9.144e-02] lana-lora-wan2.2: 15%\|#######8 \| 751/5000 [20:13<2:51:30, 2.42s/it, lr: 1.0e-04 loss: 2.845e-01] lana-lora-wan2.2: 15%\|#######8 \| 751/5000 [20:13<2:51:30, 2.42s/it, lr: 1.0e-04 loss: 2.845e-01] lana-lora-wan2.2: 15%\|#######8 \| 752/5000 [20:15<2:51:26, 2.42s/it, lr: 1.0e-04 loss: 5.125e-02] lana-lora-wan2.2: 15%\|#######8 \| 752/5000 [20:15<2:51:26, 2.42s/it, lr: 1.0e-04 loss: 5.125e-02] lana-lora-wan2.2: 15%\|#######8 \| 753/5000 [20:19<2:51:34, 2.42s/it, lr: 1.0e-04 loss: 3.753e-02] lana-lora-wan2.2: 15%\|#######8 \| 753/5000 [20:19<2:51:34, 2.42s/it, lr: 1.0e-04 loss: 3.753e-02] lana-lora-wan2.2: 15%\|#######8 \| 754/5000 [20:22<2:51:42, 2.43s/it, lr: 1.0e-04 loss: 3.441e-02] lana-lora-wan2.2: 15%\|#######8 \| 754/5000 [20:22<2:51:42, 2.43s/it, lr: 1.0e-04 loss: 3.441e-02] lana-lora-wan2.2: 15%\|#######8 \| 755/5000 [20:25<2:51:39, 2.43s/it, lr: 1.0e-04 loss: 6.364e-02] lana-lora-wan2.2: 15%\|#######8 \| 755/5000 [20:25<2:51:39, 2.43s/it, lr: 1.0e-04 loss: 6.364e-02] lana-lora-wan2.2: 15%\|#######8 \| 756/5000 [20:28<2:51:47, 2.43s/it, lr: 1.0e-04 loss: 2.870e-02] lana-lora-wan2.2: 15%\|#######8 \| 756/5000 [20:28<2:51:47, 2.43s/it, lr: 1.0e-04 loss: 2.870e-02] lana-lora-wan2.2: 15%\|#######8 \| 757/5000 [20:31<2:51:43, 2.43s/it, lr: 1.0e-04 loss: 3.652e-02] lana-lora-wan2.2: 15%\|#######8 \| 757/5000 [20:31<2:51:43, 2.43s/it, lr: 1.0e-04 loss: 3.652e-02] lana-lora-wan2.2: 15%\|#######8 \| 758/5000 [20:33<2:51:38, 2.43s/it, lr: 1.0e-04 loss: 9.965e-02] lana-lora-wan2.2: 15%\|#######8 \| 758/5000 [20:33<2:51:38, 2.43s/it, lr: 1.0e-04 loss: 9.965e-02] lana-lora-wan2.2: 15%\|#######8 \| 759/5000 [20:36<2:51:46, 2.43s/it, lr: 1.0e-04 loss: 6.485e-02] lana-lora-wan2.2: 15%\|#######8 \| 759/5000 [20:36<2:51:46, 2.43s/it, lr: 1.0e-04 loss: 6.485e-02] lana-lora-wan2.2: 15%\|#######9 \| 760/5000 [20:39<2:51:41, 2.43s/it, lr: 1.0e-04 loss: 4.193e-02] lana-lora-wan2.2: 15%\|#######9 \| 760/5000 [20:39<2:51:41, 2.43s/it, lr: 1.0e-04 loss: 4.193e-02] lana-lora-wan2.2: 15%\|#######9 \| 761/5000 [20:41<2:51:40, 2.43s/it, lr: 1.0e-04 loss: 3.155e-02] lana-lora-wan2.2: 15%\|#######9 \| 761/5000 [20:41<2:51:40, 2.43s/it, lr: 1.0e-04 loss: 3.155e-02] lana-lora-wan2.2: 15%\|#######9 \| 762/5000 [20:42<2:51:27, 2.43s/it, lr: 1.0e-04 loss: 2.095e-01] lana-lora-wan2.2: 15%\|#######9 \| 762/5000 [20:42<2:51:27, 2.43s/it, lr: 1.0e-04 loss: 2.095e-01] lana-lora-wan2.2: 15%\|#######9 \| 763/5000 [20:44<2:51:14, 2.42s/it, lr: 1.0e-04 loss: 1.204e-01] lana-lora-wan2.2: 15%\|#######9 \| 763/5000 [20:44<2:51:14, 2.42s/it, lr: 1.0e-04 loss: 1.204e-01] lana-lora-wan2.2: 15%\|#######9 \| 764/5000 [20:45<2:51:01, 2.42s/it, lr: 1.0e-04 loss: 3.199e-02] lana-lora-wan2.2: 15%\|#######9 \| 764/5000 [20:45<2:51:01, 2.42s/it, lr: 1.0e-04 loss: 3.199e-02] lana-lora-wan2.2: 15%\|#######9 \| 765/5000 [20:47<2:50:57, 2.42s/it, lr: 1.0e-04 loss: 3.118e-02] lana-lora-wan2.2: 15%\|#######9 \| 765/5000 [20:47<2:50:57, 2.42s/it, lr: 1.0e-04 loss: 3.118e-02] lana-lora-wan2.2: 15%\|#######9 \| 766/5000 [20:51<2:51:05, 2.42s/it, lr: 1.0e-04 loss: 3.608e-02] lana-lora-wan2.2: 15%\|#######9 \| 766/5000 [20:51<2:51:05, 2.42s/it, lr: 1.0e-04 loss: 3.608e-02] lana-lora-wan2.2: 15%\|#######9 \| 767/5000 [20:53<2:51:03, 2.42s/it, lr: 1.0e-04 loss: 3.167e-02] lana-lora-wan2.2: 15%\|#######9 \| 767/5000 [20:53<2:51:03, 2.42s/it, lr: 1.0e-04 loss: 3.167e-02] lana-lora-wan2.2: 15%\|#######9 \| 768/5000 [20:57<2:51:10, 2.43s/it, lr: 1.0e-04 loss: 3.348e-02] lana-lora-wan2.2: 15%\|#######9 \| 768/5000 [20:57<2:51:10, 2.43s/it, lr: 1.0e-04 loss: 3.348e-02] lana-lora-wan2.2: 15%\|#######9 \| 769/5000 [20:58<2:50:58, 2.42s/it, lr: 1.0e-04 loss: 7.087e-02] lana-lora-wan2.2: 15%\|#######9 \| 769/5000 [20:58<2:50:58, 2.42s/it, lr: 1.0e-04 loss: 7.087e-02] lana-lora-wan2.2: 15%\|######## \| 770/5000 [21:00<2:50:53, 2.42s/it, lr: 1.0e-04 loss: 2.331e-02] lana-lora-wan2.2: 15%\|######## \| 770/5000 [21:00<2:50:53, 2.42s/it, lr: 1.0e-04 loss: 2.331e-02] lana-lora-wan2.2: 15%\|######## \| 771/5000 [21:01<2:50:41, 2.42s/it, lr: 1.0e-04 loss: 4.125e-02] lana-lora-wan2.2: 15%\|######## \| 771/5000 [21:01<2:50:41, 2.42s/it, lr: 1.0e-04 loss: 4.125e-02] lana-lora-wan2.2: 15%\|######## \| 772/5000 [21:03<2:50:36, 2.42s/it, lr: 1.0e-04 loss: 5.731e-02] lana-lora-wan2.2: 15%\|######## \| 772/5000 [21:03<2:50:36, 2.42s/it, lr: 1.0e-04 loss: 5.731e-02] lana-lora-wan2.2: 15%\|######## \| 773/5000 [21:05<2:50:26, 2.42s/it, lr: 1.0e-04 loss: 7.209e-02] lana-lora-wan2.2: 15%\|######## \| 773/5000 [21:05<2:50:26, 2.42s/it, lr: 1.0e-04 loss: 7.209e-02] lana-lora-wan2.2: 15%\|######## \| 774/5000 [21:06<2:50:14, 2.42s/it, lr: 1.0e-04 loss: 2.991e-02] lana-lora-wan2.2: 15%\|######## \| 774/5000 [21:06<2:50:14, 2.42s/it, lr: 1.0e-04 loss: 2.991e-02] lana-lora-wan2.2: 16%\|######## \| 775/5000 [21:08<2:50:09, 2.42s/it, lr: 1.0e-04 loss: 1.382e-01] lana-lora-wan2.2: 16%\|######## \| 775/5000 [21:08<2:50:09, 2.42s/it, lr: 1.0e-04 loss: 1.382e-01] lana-lora-wan2.2: 16%\|######## \| 776/5000 [21:09<2:49:57, 2.41s/it, lr: 1.0e-04 loss: 6.042e-02] lana-lora-wan2.2: 16%\|######## \| 776/5000 [21:09<2:49:57, 2.41s/it, lr: 1.0e-04 loss: 6.042e-02] lana-lora-wan2.2: 16%\|######## \| 777/5000 [21:11<2:49:45, 2.41s/it, lr: 1.0e-04 loss: 9.829e-02] lana-lora-wan2.2: 16%\|######## \| 777/5000 [21:11<2:49:45, 2.41s/it, lr: 1.0e-04 loss: 9.829e-02] lana-lora-wan2.2: 16%\|######## \| 778/5000 [21:14<2:49:52, 2.41s/it, lr: 1.0e-04 loss: 8.893e-02] lana-lora-wan2.2: 16%\|######## \| 778/5000 [21:14<2:49:52, 2.41s/it, lr: 1.0e-04 loss: 8.893e-02] lana-lora-wan2.2: 16%\|########1 \| 779/5000 [21:18<2:50:02, 2.42s/it, lr: 1.0e-04 loss: 1.505e-01] lana-lora-wan2.2: 16%\|########1 \| 779/5000 [21:18<2:50:02, 2.42s/it, lr: 1.0e-04 loss: 1.505e-01] lana-lora-wan2.2: 16%\|########1 \| 780/5000 [21:20<2:49:58, 2.42s/it, lr: 1.0e-04 loss: 3.084e-02] lana-lora-wan2.2: 16%\|########1 \| 780/5000 [21:20<2:49:58, 2.42s/it, lr: 1.0e-04 loss: 3.084e-02] lana-lora-wan2.2: 16%\|########1 \| 781/5000 [21:24<2:50:05, 2.42s/it, lr: 1.0e-04 loss: 2.305e-02] lana-lora-wan2.2: 16%\|########1 \| 781/5000 [21:24<2:50:05, 2.42s/it, lr: 1.0e-04 loss: 2.305e-02] lana-lora-wan2.2: 16%\|########1 \| 782/5000 [21:28<2:50:13, 2.42s/it, lr: 1.0e-04 loss: 9.715e-02] lana-lora-wan2.2: 16%\|########1 \| 782/5000 [21:28<2:50:13, 2.42s/it, lr: 1.0e-04 loss: 9.715e-02] lana-lora-wan2.2: 16%\|########1 \| 783/5000 [21:29<2:50:01, 2.42s/it, lr: 1.0e-04 loss: 3.293e-02] lana-lora-wan2.2: 16%\|########1 \| 783/5000 [21:29<2:50:01, 2.42s/it, lr: 1.0e-04 loss: 3.293e-02] lana-lora-wan2.2: 16%\|########1 \| 784/5000 [21:33<2:50:10, 2.42s/it, lr: 1.0e-04 loss: 1.622e-01] lana-lora-wan2.2: 16%\|########1 \| 784/5000 [21:33<2:50:10, 2.42s/it, lr: 1.0e-04 loss: 1.622e-01] lana-lora-wan2.2: 16%\|########1 \| 785/5000 [21:36<2:50:17, 2.42s/it, lr: 1.0e-04 loss: 2.250e-02] lana-lora-wan2.2: 16%\|########1 \| 785/5000 [21:36<2:50:17, 2.42s/it, lr: 1.0e-04 loss: 2.250e-02] lana-lora-wan2.2: 16%\|########1 \| 786/5000 [21:40<2:50:25, 2.43s/it, lr: 1.0e-04 loss: 6.182e-02] lana-lora-wan2.2: 16%\|########1 \| 786/5000 [21:40<2:50:25, 2.43s/it, lr: 1.0e-04 loss: 6.182e-02] lana-lora-wan2.2: 16%\|########1 \| 787/5000 [21:44<2:50:32, 2.43s/it, lr: 1.0e-04 loss: 3.143e-02] lana-lora-wan2.2: 16%\|########1 \| 787/5000 [21:44<2:50:32, 2.43s/it, lr: 1.0e-04 loss: 3.143e-02] lana-lora-wan2.2: 16%\|########1 \| 788/5000 [21:46<2:50:28, 2.43s/it, lr: 1.0e-04 loss: 6.477e-02] lana-lora-wan2.2: 16%\|########1 \| 788/5000 [21:46<2:50:28, 2.43s/it, lr: 1.0e-04 loss: 6.477e-02] lana-lora-wan2.2: 16%\|########2 \| 789/5000 [21:50<2:50:35, 2.43s/it, lr: 1.0e-04 loss: 9.820e-02] lana-lora-wan2.2: 16%\|########2 \| 789/5000 [21:50<2:50:35, 2.43s/it, lr: 1.0e-04 loss: 9.820e-02] lana-lora-wan2.2: 16%\|########2 \| 790/5000 [21:51<2:50:25, 2.43s/it, lr: 1.0e-04 loss: 6.204e-02] lana-lora-wan2.2: 16%\|########2 \| 790/5000 [21:51<2:50:25, 2.43s/it, lr: 1.0e-04 loss: 6.204e-02] lana-lora-wan2.2: 16%\|########2 \| 791/5000 [21:55<2:50:33, 2.43s/it, lr: 1.0e-04 loss: 4.112e-02] lana-lora-wan2.2: 16%\|########2 \| 791/5000 [21:55<2:50:33, 2.43s/it, lr: 1.0e-04 loss: 4.112e-02] lana-lora-wan2.2: 16%\|########2 \| 792/5000 [21:58<2:50:40, 2.43s/it, lr: 1.0e-04 loss: 4.297e-02] lana-lora-wan2.2: 16%\|########2 \| 792/5000 [21:58<2:50:40, 2.43s/it, lr: 1.0e-04 loss: 4.297e-02] lana-lora-wan2.2: 16%\|########2 \| 793/5000 [22:02<2:50:47, 2.44s/it, lr: 1.0e-04 loss: 2.892e-02] lana-lora-wan2.2: 16%\|########2 \| 793/5000 [22:02<2:50:47, 2.44s/it, lr: 1.0e-04 loss: 2.892e-02] lana-lora-wan2.2: 16%\|########2 \| 794/5000 [22:03<2:50:35, 2.43s/it, lr: 1.0e-04 loss: 3.181e-02] lana-lora-wan2.2: 16%\|########2 \| 794/5000 [22:03<2:50:35, 2.43s/it, lr: 1.0e-04 loss: 3.181e-02] lana-lora-wan2.2: 16%\|########2 \| 795/5000 [22:07<2:50:42, 2.44s/it, lr: 1.0e-04 loss: 7.553e-02] lana-lora-wan2.2: 16%\|########2 \| 795/5000 [22:07<2:50:42, 2.44s/it, lr: 1.0e-04 loss: 7.553e-02] lana-lora-wan2.2: 16%\|########2 \| 796/5000 [22:11<2:50:51, 2.44s/it, lr: 1.0e-04 loss: 3.674e-02] lana-lora-wan2.2: 16%\|########2 \| 796/5000 [22:11<2:50:51, 2.44s/it, lr: 1.0e-04 loss: 3.674e-02] lana-lora-wan2.2: 16%\|########2 \| 797/5000 [22:13<2:50:46, 2.44s/it, lr: 1.0e-04 loss: 6.121e-02] lana-lora-wan2.2: 16%\|########2 \| 797/5000 [22:13<2:50:46, 2.44s/it, lr: 1.0e-04 loss: 6.121e-02] lana-lora-wan2.2: 16%\|########2 \| 798/5000 [22:14<2:50:34, 2.44s/it, lr: 1.0e-04 loss: 5.274e-02] lana-lora-wan2.2: 16%\|########2 \| 798/5000 [22:14<2:50:34, 2.44s/it, lr: 1.0e-04 loss: 5.274e-02] lana-lora-wan2.2: 16%\|########3 \| 799/5000 [22:16<2:50:30, 2.44s/it, lr: 1.0e-04 loss: 5.209e-02] lana-lora-wan2.2: 16%\|########3 \| 799/5000 [22:16<2:50:30, 2.44s/it, lr: 1.0e-04 loss: 5.209e-02] lana-lora-wan2.2: 16%\|########3 \| 800/5000 [22:19<2:50:25, 2.43s/it, lr: 1.0e-04 loss: 1.093e-01] lana-lora-wan2.2: 16%\|########3 \| 800/5000 [22:19<2:50:25, 2.43s/it, lr: 1.0e-04 loss: 1.093e-01] lana-lora-wan2.2: 16%\|########3 \| 801/5000 [22:22<2:50:32, 2.44s/it, lr: 1.0e-04 loss: 8.092e-02] lana-lora-wan2.2: 16%\|########3 \| 801/5000 [22:22<2:50:32, 2.44s/it, lr: 1.0e-04 loss: 8.092e-02] lana-lora-wan2.2: 16%\|########3 \| 802/5000 [22:25<2:50:30, 2.44s/it, lr: 1.0e-04 loss: 3.359e-02] lana-lora-wan2.2: 16%\|########3 \| 802/5000 [22:25<2:50:30, 2.44s/it, lr: 1.0e-04 loss: 3.359e-02] lana-lora-wan2.2: 16%\|########3 \| 803/5000 [22:26<2:50:18, 2.43s/it, lr: 1.0e-04 loss: 5.773e-02] lana-lora-wan2.2: 16%\|########3 \| 803/5000 [22:26<2:50:18, 2.43s/it, lr: 1.0e-04 loss: 5.773e-02] lana-lora-wan2.2: 16%\|########3 \| 804/5000 [22:27<2:50:06, 2.43s/it, lr: 1.0e-04 loss: 2.999e-02] lana-lora-wan2.2: 16%\|########3 \| 804/5000 [22:27<2:50:06, 2.43s/it, lr: 1.0e-04 loss: 2.999e-02] 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2.928e-02] lana-lora-wan2.2: 20%\|##########3 \| 998/5000 [29:59<2:40:27, 2.41s/it, lr: 1.0e-04 loss: 1.387e-01] lana-lora-wan2.2: 20%\|##########3 \| 998/5000 [29:59<2:40:27, 2.41s/it, lr: 1.0e-04 loss: 1.387e-01] lana-lora-wan2.2: 20%\|##########3 \| 999/5000 [30:03<2:40:31, 2.41s/it, lr: 1.0e-04 loss: 2.474e-01] lana-lora-wan2.2: 20%\|##########3 \| 999/5000 [30:03<2:40:31, 2.41s/it, lr: 1.0e-04 loss: 2.474e-01]
	Saving at step 1000
	Saved checkpoint to /root/lab/ai-toolkit/output/lana-lora-wan2.2/lana-lora-wan2.2_000001000.safetensors
	Saved optimizer to /root/lab/ai-toolkit/output/lana-lora-wan2.2/optimizer.pt


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1.0e-04 loss: 1.229e-01] lana-lora-wan2.2: 23%\|###########7 \| 1149/5000 [35:54<2:33:49, 2.40s/it, lr: 1.0e-04 loss: 1.229e-01] lana-lora-wan2.2: 23%\|###########7 \| 1150/5000 [35:56<2:33:46, 2.40s/it, lr: 1.0e-04 loss: 6.377e-02] lana-lora-wan2.2: 23%\|###########7 \| 1150/5000 [35:56<2:33:46, 2.40s/it, lr: 1.0e-04 loss: 6.377e-02] lana-lora-wan2.2: 23%\|###########7 \| 1151/5000 [36:00<2:33:49, 2.40s/it, lr: 1.0e-04 loss: 4.147e-02] lana-lora-wan2.2: 23%\|###########7 \| 1151/5000 [36:00<2:33:49, 2.40s/it, lr: 1.0e-04 loss: 4.147e-02] lana-lora-wan2.2: 23%\|###########7 \| 1152/5000 [36:04<2:33:53, 2.40s/it, lr: 1.0e-04 loss: 4.171e-02] lana-lora-wan2.2: 23%\|###########7 \| 1152/5000 [36:04<2:33:53, 2.40s/it, lr: 1.0e-04 loss: 4.171e-02] lana-lora-wan2.2: 23%\|###########7 \| 1153/5000 [36:06<2:33:50, 2.40s/it, lr: 1.0e-04 loss: 5.023e-02] lana-lora-wan2.2: 23%\|###########7 \| 1153/5000 [36:06<2:33:50, 2.40s/it, lr: 1.0e-04 loss: 5.023e-02] lana-lora-wan2.2: 23%\|###########7 \| 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2.39s/it, lr: 1.0e-04 loss: 5.849e-02] lana-lora-wan2.2: 23%\|###########8 \| 1164/5000 [36:29<2:33:07, 2.40s/it, lr: 1.0e-04 loss: 7.009e-02] lana-lora-wan2.2: 23%\|###########8 \| 1164/5000 [36:29<2:33:07, 2.40s/it, lr: 1.0e-04 loss: 7.009e-02] lana-lora-wan2.2: 23%\|###########8 \| 1165/5000 [36:30<2:32:59, 2.39s/it, lr: 1.0e-04 loss: 1.018e-01] lana-lora-wan2.2: 23%\|###########8 \| 1165/5000 [36:30<2:32:59, 2.39s/it, lr: 1.0e-04 loss: 1.018e-01] lana-lora-wan2.2: 23%\|###########8 \| 1166/5000 [36:32<2:32:56, 2.39s/it, lr: 1.0e-04 loss: 2.784e-01] lana-lora-wan2.2: 23%\|###########8 \| 1166/5000 [36:32<2:32:56, 2.39s/it, lr: 1.0e-04 loss: 2.784e-01] lana-lora-wan2.2: 23%\|###########9 \| 1167/5000 [36:36<2:32:59, 2.39s/it, lr: 1.0e-04 loss: 1.214e-01] lana-lora-wan2.2: 23%\|###########9 \| 1167/5000 [36:36<2:32:59, 2.39s/it, lr: 1.0e-04 loss: 1.214e-01] lana-lora-wan2.2: 23%\|###########9 \| 1168/5000 [36:39<2:33:02, 2.40s/it, lr: 1.0e-04 loss: 4.763e-02] lana-lora-wan2.2: 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1.0e-04 loss: 7.015e-02] lana-lora-wan2.2: 23%\|###########9 \| 1173/5000 [36:49<2:32:40, 2.39s/it, lr: 1.0e-04 loss: 7.015e-02] lana-lora-wan2.2: 23%\|###########9 \| 1174/5000 [36:51<2:32:37, 2.39s/it, lr: 1.0e-04 loss: 4.688e-02] lana-lora-wan2.2: 23%\|###########9 \| 1174/5000 [36:51<2:32:37, 2.39s/it, lr: 1.0e-04 loss: 4.688e-02] lana-lora-wan2.2: 24%\|###########9 \| 1175/5000 [36:53<2:32:34, 2.39s/it, lr: 1.0e-04 loss: 3.804e-02] lana-lora-wan2.2: 24%\|###########9 \| 1175/5000 [36:53<2:32:34, 2.39s/it, lr: 1.0e-04 loss: 3.804e-02] lana-lora-wan2.2: 24%\|###########9 \| 1176/5000 [36:56<2:32:32, 2.39s/it, lr: 1.0e-04 loss: 3.621e-02] lana-lora-wan2.2: 24%\|###########9 \| 1176/5000 [36:56<2:32:32, 2.39s/it, lr: 1.0e-04 loss: 3.621e-02] lana-lora-wan2.2: 24%\|############ \| 1177/5000 [36:59<2:32:35, 2.39s/it, lr: 1.0e-04 loss: 4.706e-02] lana-lora-wan2.2: 24%\|############ \| 1177/5000 [36:59<2:32:35, 2.39s/it, lr: 1.0e-04 loss: 4.706e-02] lana-lora-wan2.2: 24%\|############ \| 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[37:52<2:32:04, 2.40s/it, lr: 1.0e-04 loss: 6.115e-02] lana-lora-wan2.2: 24%\|############2 \| 1197/5000 [37:52<2:32:04, 2.40s/it, lr: 1.0e-04 loss: 6.115e-02] lana-lora-wan2.2: 24%\|############2 \| 1198/5000 [37:53<2:31:57, 2.40s/it, lr: 1.0e-04 loss: 4.632e-02] lana-lora-wan2.2: 24%\|############2 \| 1198/5000 [37:53<2:31:57, 2.40s/it, lr: 1.0e-04 loss: 4.632e-02] lana-lora-wan2.2: 24%\|############2 \| 1199/5000 [37:56<2:31:59, 2.40s/it, lr: 1.0e-04 loss: 6.653e-02] lana-lora-wan2.2: 24%\|############2 \| 1199/5000 [37:56<2:31:59, 2.40s/it, lr: 1.0e-04 loss: 6.653e-02] lana-lora-wan2.2: 24%\|############2 \| 1200/5000 [37:58<2:31:53, 2.40s/it, lr: 1.0e-04 loss: 4.015e-02] lana-lora-wan2.2: 24%\|############2 \| 1200/5000 [37:58<2:31:53, 2.40s/it, lr: 1.0e-04 loss: 4.015e-02] lana-lora-wan2.2: 24%\|############2 \| 1201/5000 [37:59<2:31:46, 2.40s/it, lr: 1.0e-04 loss: 2.646e-01] lana-lora-wan2.2: 24%\|############2 \| 1201/5000 [37:59<2:31:46, 2.40s/it, lr: 1.0e-04 loss: 2.646e-01] 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lana-lora-wan2.2: 25%\|############7 \| 1249/5000 [40:01<2:30:16, 2.40s/it, lr: 1.0e-04 loss: 3.106e-02]
	Saving at step 1250
	Saved checkpoint to /root/lab/ai-toolkit/output/lana-lora-wan2.2/lana-lora-wan2.2_000001250.safetensors
	Saved optimizer to /root/lab/ai-toolkit/output/lana-lora-wan2.2/optimizer.pt


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[40:38<2:29:43, 2.40s/it, lr: 1.0e-04 loss: 6.435e-02] lana-lora-wan2.2: 25%\|############8 \| 1264/5000 [40:38<2:29:43, 2.40s/it, lr: 1.0e-04 loss: 6.435e-02] lana-lora-wan2.2: 25%\|############9 \| 1265/5000 [40:40<2:29:40, 2.40s/it, lr: 1.0e-04 loss: 5.553e-02] lana-lora-wan2.2: 25%\|############9 \| 1265/5000 [40:40<2:29:40, 2.40s/it, lr: 1.0e-04 loss: 5.553e-02] lana-lora-wan2.2: 25%\|############9 \| 1266/5000 [40:42<2:29:37, 2.40s/it, lr: 1.0e-04 loss: 4.753e-02] lana-lora-wan2.2: 25%\|############9 \| 1266/5000 [40:42<2:29:37, 2.40s/it, lr: 1.0e-04 loss: 4.753e-02] lana-lora-wan2.2: 25%\|############9 \| 1267/5000 [40:44<2:29:33, 2.40s/it, lr: 1.0e-04 loss: 1.027e-01] lana-lora-wan2.2: 25%\|############9 \| 1267/5000 [40:44<2:29:33, 2.40s/it, lr: 1.0e-04 loss: 1.027e-01] lana-lora-wan2.2: 25%\|############9 \| 1268/5000 [40:48<2:29:35, 2.41s/it, lr: 1.0e-04 loss: 3.306e-02] lana-lora-wan2.2: 25%\|############9 \| 1268/5000 [40:48<2:29:35, 2.41s/it, lr: 1.0e-04 loss: 3.306e-02] lana-lora-wan2.2: 25%\|############9 \| 1269/5000 [40:50<2:29:32, 2.40s/it, lr: 1.0e-04 loss: 6.755e-02] lana-lora-wan2.2: 25%\|############9 \| 1269/5000 [40:50<2:29:32, 2.40s/it, lr: 1.0e-04 loss: 6.755e-02] lana-lora-wan2.2: 25%\|############9 \| 1270/5000 [40:52<2:29:26, 2.40s/it, lr: 1.0e-04 loss: 8.855e-02] lana-lora-wan2.2: 25%\|############9 \| 1270/5000 [40:52<2:29:26, 2.40s/it, lr: 1.0e-04 loss: 8.855e-02] lana-lora-wan2.2: 25%\|############9 \| 1271/5000 [40:53<2:29:21, 2.40s/it, lr: 1.0e-04 loss: 3.701e-02] lana-lora-wan2.2: 25%\|############9 \| 1271/5000 [40:53<2:29:21, 2.40s/it, lr: 1.0e-04 loss: 3.701e-02] lana-lora-wan2.2: 25%\|############9 \| 1272/5000 [40:57<2:29:23, 2.40s/it, lr: 1.0e-04 loss: 4.634e-02] lana-lora-wan2.2: 25%\|############9 \| 1272/5000 [40:57<2:29:23, 2.40s/it, lr: 1.0e-04 loss: 4.634e-02] lana-lora-wan2.2: 25%\|############9 \| 1273/5000 [40:59<2:29:21, 2.40s/it, lr: 1.0e-04 loss: 2.955e-02] lana-lora-wan2.2: 25%\|############9 \| 1273/5000 [40:59<2:29:21, 2.40s/it, lr: 1.0e-04 loss: 2.955e-02] lana-lora-wan2.2: 25%\|############9 \| 1274/5000 [41:01<2:29:14, 2.40s/it, lr: 1.0e-04 loss: 5.599e-02] lana-lora-wan2.2: 25%\|############9 \| 1274/5000 [41:01<2:29:14, 2.40s/it, lr: 1.0e-04 loss: 5.599e-02]Error running job: element 0 of tensors does not require grad and does not have a grad_fn

	========================================
	Result:
	- 0 completed jobs
	- 1 failure
	========================================
	Traceback (most recent call last):
	Traceback (most recent call last):
	File "/root/lab/ai-toolkit/run.py", line 120, in <module>
	File "/root/lab/ai-toolkit/run.py", line 120, in <module>
	main()main()

	File "/root/lab/ai-toolkit/run.py", line 108, in main
	File "/root/lab/ai-toolkit/run.py", line 108, in main
	raise eraise e

	File "/root/lab/ai-toolkit/run.py", line 96, in main
	File "/root/lab/ai-toolkit/run.py", line 96, in main
	job.run()job.run()

	File "/root/lab/ai-toolkit/jobs/ExtensionJob.py", line 22, in run
	File "/root/lab/ai-toolkit/jobs/ExtensionJob.py", line 22, in run
	process.run()process.run()

	File "/root/lab/ai-toolkit/jobs/process/BaseSDTrainProcess.py", line 2187, in run
	File "/root/lab/ai-toolkit/jobs/process/BaseSDTrainProcess.py", line 2187, in run
	loss_dict = self.hook_train_loop(batch_list)loss_dict = self.hook_train_loop(batch_list)

	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

	File "/root/lab/ai-toolkit/extensions_built_in/sd_trainer/SDTrainer.py", line 2061, in hook_train_loop
	File "/root/lab/ai-toolkit/extensions_built_in/sd_trainer/SDTrainer.py", line 2061, in hook_train_loop
	loss = self.train_single_accumulation(batch)loss = self.train_single_accumulation(batch)

	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

	File "/root/lab/ai-toolkit/extensions_built_in/sd_trainer/SDTrainer.py", line 1989, in train_single_accumulation
	File "/root/lab/ai-toolkit/extensions_built_in/sd_trainer/SDTrainer.py", line 1989, in train_single_accumulation
	self.accelerator.backward(loss)self.accelerator.backward(loss)

	File "/usr/local/lib/python3.11/site-packages/accelerate/accelerator.py", line 2852, in backward
	File "/usr/local/lib/python3.11/site-packages/accelerate/accelerator.py", line 2852, in backward
	loss.backward(kwargs)loss.backward(kwargs)

	File "/usr/local/lib/python3.11/site-packages/torch/_tensor.py", line 648, in backward
	File "/usr/local/lib/python3.11/site-packages/torch/_tensor.py", line 648, in backward
	torch.autograd.backward(torch.autograd.backward(

	File "/usr/local/lib/python3.11/site-packages/torch/autograd/__init__.py", line 353, in backward
	File "/usr/local/lib/python3.11/site-packages/torch/autograd/__init__.py", line 353, in backward
	_engine_run_backward(_engine_run_backward(

	File "/usr/local/lib/python3.11/site-packages/torch/autograd/graph.py", line 824, in _engine_run_backward
	File "/usr/local/lib/python3.11/site-packages/torch/autograd/graph.py", line 824, in _engine_run_backward
	return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward passreturn Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass

	^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

	RuntimeErrorRuntimeError: : element 0 of tensors does not require grad and does not have a grad_fnelement 0 of tensors does not require grad and does not have a grad_fn

	lana-lora-wan2.2: 26%\|############# \| 1275/5000 [41:03<2:29:12, 2.40s/it, lr: 1.0e-04 loss: 5.599e-02] lana-lora-wan2.2: 26%\|############# \| 1275/5000 [41:03<2:29:12, 2.40s/it, lr: 1.0e-04 loss: 5.599e-02]