diff --git "a/logs/0_log.txt" "b/logs/0_log.txt"
new file mode 100644--- /dev/null
+++ "b/logs/0_log.txt"
@@ -0,0 +1,403 @@
+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"
+            },
+            {
+                "prompt": "l4n4lux, woman holding a coffee cup, in a beanie, sitting at a cafe"
+            },
+            {
+                "prompt": "l4n4lux, woman playing the guitar, on stage, singing a song, laser lights, punk rocker"
+            }
+        ],
+        "neg": "",
+        "seed": 42,
+        "walk_seed": true,
+        "guidance_scale": 4,
+        "sample_steps": 25,
+        "num_frames": 41,
+        "fps": 16
+    }
+}
+Changing sample extention to animated webp
+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
+
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+
+
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+
+
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transformer/diffusion_pytorch_model-0000(…):  99%|#####################################################4| 9.86G/9.97G [00:44<00:01, 89.2MB/s]
transformer/diffusion_pytorch_model-0000(…):  99%|#####################################################6| 9.90G/9.97G [00:45<00:00, 82.8MB/s]
transformer/diffusion_pytorch_model-0000(…):  99%|#####################################################6| 9.90G/9.97G [00:45<00:00, 82.8MB/s]
transformer/diffusion_pytorch_model-0000(…): 100%|######################################################| 9.97G/9.97G [00:45<00:00, 93.1MB/s]
transformer/diffusion_pytorch_model-0000(…): 100%|######################################################| 9.97G/9.97G [00:45<00:00, 93.1MB/s]
transformer/diffusion_pytorch_model-0000(…): 100%|#######################################################| 9.97G/9.97G [00:45<00:00, 218MB/s]
transformer/diffusion_pytorch_model-0000(…): 100%|#######################################################| 9.97G/9.97G [00:45<00:00, 218MB/s]
+
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transformer/diffusion_pytorch_model-0000(…):   0%|                                                               | 0.00/9.89G [00:00<?, ?B/s]
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transformer/diffusion_pytorch_model-0000(…):   0%|                                                    | 2.33M/9.89G [00:01<1:50:45, 1.49MB/s]
transformer/diffusion_pytorch_model-0000(…):   0%|                                                    | 2.33M/9.89G [00:01<1:50:45, 1.49MB/s]
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transformer/diffusion_pytorch_model-0000(…):   0%|                                                    | 2.75M/9.89G [00:01<1:43:17, 1.60MB/s]
transformer/diffusion_pytorch_model-0000(…):   0%|1                                                     | 24.1M/9.89G [00:02<08:23, 19.6MB/s]
transformer/diffusion_pytorch_model-0000(…):   0%|1                                                     | 24.1M/9.89G [00:02<08:23, 19.6MB/s]
transformer/diffusion_pytorch_model-0000(…):   0%|2                                                     | 38.2M/9.89G [00:02<06:47, 24.2MB/s]
transformer/diffusion_pytorch_model-0000(…):   0%|2                                                     | 38.2M/9.89G [00:02<06:47, 24.2MB/s]
transformer/diffusion_pytorch_model-0000(…):   1%|3                                                     | 66.5M/9.89G [00:03<05:09, 31.7MB/s]
transformer/diffusion_pytorch_model-0000(…):   1%|3                                                     | 66.5M/9.89G [00:03<05:09, 31.7MB/s]
transformer/diffusion_pytorch_model-0000(…):   1%|3                                                     | 72.8M/9.89G [00:03<05:47, 28.2MB/s]
transformer/diffusion_pytorch_model-0000(…):   1%|3                                                     | 72.8M/9.89G [00:03<05:47, 28.2MB/s]
transformer/diffusion_pytorch_model-0000(…):   1%|4                                                     | 89.5M/9.89G [00:03<04:17, 38.1MB/s]
transformer/diffusion_pytorch_model-0000(…):   1%|4                                                     | 89.5M/9.89G [00:03<04:17, 38.1MB/s]
transformer/diffusion_pytorch_model-0000(…):   1%|6                                                      | 117M/9.89G [00:03<02:39, 61.2MB/s]
transformer/diffusion_pytorch_model-0000(…):   1%|6                                                      | 117M/9.89G [00:03<02:39, 61.2MB/s]
transformer/diffusion_pytorch_model-0000(…):   2%|8                                                      | 155M/9.89G [00:04<02:09, 75.1MB/s]
transformer/diffusion_pytorch_model-0000(…):   2%|8                                                      | 155M/9.89G [00:04<02:09, 75.1MB/s]
transformer/diffusion_pytorch_model-0000(…):   2%|#2                                                     | 225M/9.89G [00:04<01:51, 86.5MB/s]
transformer/diffusion_pytorch_model-0000(…):   2%|#2                                                     | 225M/9.89G [00:04<01:51, 86.5MB/s]
transformer/diffusion_pytorch_model-0000(…):   4%|##                                                      | 360M/9.89G [00:05<01:00, 156MB/s]
transformer/diffusion_pytorch_model-0000(…):   4%|##                                                      | 360M/9.89G [00:05<01:00, 156MB/s]
transformer/diffusion_pytorch_model-0000(…):   5%|##5                                                     | 458M/9.89G [00:05<00:42, 224MB/s]
transformer/diffusion_pytorch_model-0000(…):   5%|##5                                                     | 458M/9.89G [00:05<00:42, 224MB/s]
transformer/diffusion_pytorch_model-0000(…):   5%|##8                                                     | 504M/9.89G [00:05<00:44, 211MB/s]
transformer/diffusion_pytorch_model-0000(…):   5%|##8                                                     | 504M/9.89G [00:05<00:44, 211MB/s]
transformer/diffusion_pytorch_model-0000(…):   6%|###1                                                    | 549M/9.89G [00:06<00:58, 160MB/s]
transformer/diffusion_pytorch_model-0000(…):   6%|###1                                                    | 549M/9.89G [00:06<00:58, 160MB/s]
transformer/diffusion_pytorch_model-0000(…):   6%|###3                                                    | 584M/9.89G [00:06<00:55, 167MB/s]
transformer/diffusion_pytorch_model-0000(…):   6%|###3                                                    | 584M/9.89G [00:06<00:55, 167MB/s]
transformer/diffusion_pytorch_model-0000(…):   7%|###6                                                    | 647M/9.89G [00:06<00:41, 220MB/s]
transformer/diffusion_pytorch_model-0000(…):   7%|###6                                                    | 647M/9.89G [00:06<00:41, 220MB/s]
transformer/diffusion_pytorch_model-0000(…):   7%|###8                                                    | 685M/9.89G [00:06<00:41, 223MB/s]
transformer/diffusion_pytorch_model-0000(…):   7%|###8                                                    | 685M/9.89G [00:06<00:41, 223MB/s]
transformer/diffusion_pytorch_model-0000(…):   8%|####2                                                   | 746M/9.89G [00:07<00:44, 207MB/s]
transformer/diffusion_pytorch_model-0000(…):   8%|####2                                                   | 746M/9.89G [00:07<00:44, 207MB/s]
transformer/diffusion_pytorch_model-0000(…):   9%|#####                                                   | 895M/9.89G [00:07<00:24, 367MB/s]
transformer/diffusion_pytorch_model-0000(…):   9%|#####                                                   | 895M/9.89G [00:07<00:24, 367MB/s]
transformer/diffusion_pytorch_model-0000(…):  10%|#####4                                                  | 962M/9.89G [00:07<00:21, 414MB/s]
transformer/diffusion_pytorch_model-0000(…):  10%|#####4                                                  | 962M/9.89G [00:07<00:21, 414MB/s]
transformer/diffusion_pytorch_model-0000(…):  10%|#####7                                                 | 1.03G/9.89G [00:07<00:20, 425MB/s]
transformer/diffusion_pytorch_model-0000(…):  10%|#####7                                                 | 1.03G/9.89G [00:07<00:20, 425MB/s]
transformer/diffusion_pytorch_model-0000(…):  11%|######2                                                | 1.13G/9.89G [00:08<00:33, 265MB/s]
transformer/diffusion_pytorch_model-0000(…):  11%|######2                                                | 1.13G/9.89G [00:08<00:33, 265MB/s]
transformer/diffusion_pytorch_model-0000(…):  12%|######7                                                | 1.22G/9.89G [00:08<00:25, 336MB/s]
transformer/diffusion_pytorch_model-0000(…):  12%|######7                                                | 1.22G/9.89G [00:08<00:25, 336MB/s]
transformer/diffusion_pytorch_model-0000(…):  13%|#######2                                               | 1.30G/9.89G [00:08<00:27, 308MB/s]
transformer/diffusion_pytorch_model-0000(…):  13%|#######2                                               | 1.30G/9.89G [00:08<00:27, 308MB/s]
transformer/diffusion_pytorch_model-0000(…):  14%|#######5                                               | 1.36G/9.89G [00:08<00:29, 290MB/s]
transformer/diffusion_pytorch_model-0000(…):  14%|#######5                                               | 1.36G/9.89G [00:08<00:29, 290MB/s]
transformer/diffusion_pytorch_model-0000(…):  14%|#######8                                               | 1.41G/9.89G [00:08<00:27, 304MB/s]
transformer/diffusion_pytorch_model-0000(…):  14%|#######8                                               | 1.41G/9.89G [00:08<00:27, 304MB/s]
transformer/diffusion_pytorch_model-0000(…):  15%|########3                                              | 1.50G/9.89G [00:08<00:22, 381MB/s]
transformer/diffusion_pytorch_model-0000(…):  15%|########3                                              | 1.50G/9.89G [00:08<00:22, 381MB/s]
transformer/diffusion_pytorch_model-0000(…):  16%|########5                                              | 1.55G/9.89G [00:09<00:20, 403MB/s]
transformer/diffusion_pytorch_model-0000(…):  16%|########5                                              | 1.55G/9.89G [00:09<00:20, 403MB/s]
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transformer/diffusion_pytorch_model-0000(…):  16%|########8                                              | 1.60G/9.89G [00:09<00:19, 420MB/s]
transformer/diffusion_pytorch_model-0000(…):  17%|#########2                                             | 1.66G/9.89G [00:09<00:22, 366MB/s]
transformer/diffusion_pytorch_model-0000(…):  17%|#########2                                             | 1.66G/9.89G [00:09<00:22, 366MB/s]
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transformer/diffusion_pytorch_model-0000(…):  18%|##########                                             | 1.81G/9.89G [00:10<00:32, 253MB/s]
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transformer/diffusion_pytorch_model-0000(…):  22%|###########8                                           | 2.13G/9.89G [00:10<00:18, 420MB/s]
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transformer/diffusion_pytorch_model-0000(…):  29%|################1                                      | 2.91G/9.89G [00:12<00:18, 382MB/s]
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+
+
transformer/diffusion_pytorch_model-0000(…):   0%|                                                               | 0.00/8.72G [00:00<?, ?B/s]
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transformer/diffusion_pytorch_model-0000(…):   0%|                                                    | 2.07M/8.72G [00:01<1:21:00, 1.79MB/s]
transformer/diffusion_pytorch_model-0000(…):   0%|                                                    | 2.07M/8.72G [00:01<1:21:00, 1.79MB/s]
transformer/diffusion_pytorch_model-0000(…):   0%|1                                                     | 25.6M/8.72G [00:01<05:54, 24.5MB/s]
transformer/diffusion_pytorch_model-0000(…):   0%|1                                                     | 25.6M/8.72G [00:01<05:54, 24.5MB/s]
transformer/diffusion_pytorch_model-0000(…):   1%|3                                                     | 48.8M/8.72G [00:01<03:21, 43.0MB/s]
transformer/diffusion_pytorch_model-0000(…):   1%|3                                                     | 48.8M/8.72G [00:01<03:21, 43.0MB/s]
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transformer/diffusion_pytorch_model-0000(…):   1%|5                                                     | 86.4M/8.72G [00:01<02:17, 62.7MB/s]
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transformer/diffusion_pytorch_model-0000(…):   1%|6                                                      | 104M/8.72G [00:02<02:09, 66.4MB/s]
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transformer/diffusion_pytorch_model-0000(…):   1%|7                                                      | 113M/8.72G [00:02<02:21, 60.8MB/s]
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transformer/diffusion_pytorch_model-0000(…):   1%|8                                                      | 129M/8.72G [00:02<02:30, 56.9MB/s]
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transformer/diffusion_pytorch_model-0000(…):   2%|8                                                      | 140M/8.72G [00:02<02:24, 59.3MB/s]
transformer/diffusion_pytorch_model-0000(…):   2%|#                                                      | 171M/8.72G [00:03<01:47, 79.6MB/s]
transformer/diffusion_pytorch_model-0000(…):   2%|#                                                      | 171M/8.72G [00:03<01:47, 79.6MB/s]
transformer/diffusion_pytorch_model-0000(…):   2%|#2                                                     | 190M/8.72G [00:03<01:31, 93.4MB/s]
transformer/diffusion_pytorch_model-0000(…):   2%|#2                                                     | 190M/8.72G [00:03<01:31, 93.4MB/s]
transformer/diffusion_pytorch_model-0000(…):   2%|#3                                                     | 216M/8.72G [00:03<01:31, 92.8MB/s]
transformer/diffusion_pytorch_model-0000(…):   2%|#3                                                     | 216M/8.72G [00:03<01:31, 92.8MB/s]
transformer/diffusion_pytorch_model-0000(…):   3%|#5                                                     | 242M/8.72G [00:04<02:36, 54.2MB/s]
transformer/diffusion_pytorch_model-0000(…):   3%|#5                                                     | 242M/8.72G [00:04<02:36, 54.2MB/s]
transformer/diffusion_pytorch_model-0000(…):   4%|##3                                                     | 362M/8.72G [00:04<01:04, 129MB/s]
transformer/diffusion_pytorch_model-0000(…):   4%|##3                                                     | 362M/8.72G [00:04<01:04, 129MB/s]
transformer/diffusion_pytorch_model-0000(…):   4%|##4                                                     | 383M/8.72G [00:04<01:01, 136MB/s]
transformer/diffusion_pytorch_model-0000(…):   4%|##4                                                     | 383M/8.72G [00:04<01:01, 136MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  27%|##############7                                        | 2.67G/9.97G [00:18<00:37, 196MB/s]
transformer_2/diffusion_pytorch_model-00(…):  28%|###############2                                       | 2.76G/9.97G [00:18<00:32, 221MB/s]
transformer_2/diffusion_pytorch_model-00(…):  28%|###############2                                       | 2.76G/9.97G [00:18<00:32, 221MB/s]
transformer_2/diffusion_pytorch_model-00(…):  28%|###############5                                       | 2.83G/9.97G [00:18<00:27, 256MB/s]
transformer_2/diffusion_pytorch_model-00(…):  28%|###############5                                       | 2.83G/9.97G [00:18<00:27, 256MB/s]
transformer_2/diffusion_pytorch_model-00(…):  29%|###############8                                       | 2.87G/9.97G [00:19<00:25, 275MB/s]
transformer_2/diffusion_pytorch_model-00(…):  29%|###############8                                       | 2.87G/9.97G [00:19<00:25, 275MB/s]
transformer_2/diffusion_pytorch_model-00(…):  29%|################1                                      | 2.94G/9.97G [00:19<00:21, 331MB/s]
transformer_2/diffusion_pytorch_model-00(…):  29%|################1                                      | 2.94G/9.97G [00:19<00:21, 331MB/s]
transformer_2/diffusion_pytorch_model-00(…):  30%|################5                                      | 3.00G/9.97G [00:19<00:25, 272MB/s]
transformer_2/diffusion_pytorch_model-00(…):  30%|################5                                      | 3.00G/9.97G [00:19<00:25, 272MB/s]
transformer_2/diffusion_pytorch_model-00(…):  31%|#################1                                     | 3.11G/9.97G [00:19<00:24, 278MB/s]
transformer_2/diffusion_pytorch_model-00(…):  31%|#################1                                     | 3.11G/9.97G [00:19<00:24, 278MB/s]
transformer_2/diffusion_pytorch_model-00(…):  32%|#################4                                     | 3.16G/9.97G [00:20<00:24, 280MB/s]
transformer_2/diffusion_pytorch_model-00(…):  32%|#################4                                     | 3.16G/9.97G [00:20<00:24, 280MB/s]
transformer_2/diffusion_pytorch_model-00(…):  32%|#################6                                     | 3.20G/9.97G [00:20<00:29, 233MB/s]
transformer_2/diffusion_pytorch_model-00(…):  32%|#################6                                     | 3.20G/9.97G [00:20<00:29, 233MB/s]
transformer_2/diffusion_pytorch_model-00(…):  33%|##################1                                    | 3.30G/9.97G [00:20<00:20, 333MB/s]
transformer_2/diffusion_pytorch_model-00(…):  33%|##################1                                    | 3.30G/9.97G [00:20<00:20, 333MB/s]
transformer_2/diffusion_pytorch_model-00(…):  34%|##################9                                    | 3.43G/9.97G [00:20<00:14, 454MB/s]
transformer_2/diffusion_pytorch_model-00(…):  34%|##################9                                    | 3.43G/9.97G [00:20<00:14, 454MB/s]
transformer_2/diffusion_pytorch_model-00(…):  35%|###################2                                   | 3.49G/9.97G [00:20<00:19, 326MB/s]
transformer_2/diffusion_pytorch_model-00(…):  35%|###################2                                   | 3.49G/9.97G [00:20<00:19, 326MB/s]
transformer_2/diffusion_pytorch_model-00(…):  36%|###################9                                   | 3.61G/9.97G [00:21<00:16, 387MB/s]
transformer_2/diffusion_pytorch_model-00(…):  36%|###################9                                   | 3.61G/9.97G [00:21<00:16, 387MB/s]
transformer_2/diffusion_pytorch_model-00(…):  37%|####################2                                  | 3.66G/9.97G [00:21<00:23, 266MB/s]
transformer_2/diffusion_pytorch_model-00(…):  37%|####################2                                  | 3.66G/9.97G [00:21<00:23, 266MB/s]
transformer_2/diffusion_pytorch_model-00(…):  38%|####################6                                  | 3.75G/9.97G [00:21<00:21, 287MB/s]
transformer_2/diffusion_pytorch_model-00(…):  38%|####################6                                  | 3.75G/9.97G [00:21<00:21, 287MB/s]
transformer_2/diffusion_pytorch_model-00(…):  38%|####################9                                  | 3.80G/9.97G [00:22<00:22, 276MB/s]
transformer_2/diffusion_pytorch_model-00(…):  38%|####################9                                  | 3.80G/9.97G [00:22<00:22, 276MB/s]
transformer_2/diffusion_pytorch_model-00(…):  39%|#####################1                                 | 3.84G/9.97G [00:22<00:22, 276MB/s]
transformer_2/diffusion_pytorch_model-00(…):  39%|#####################1                                 | 3.84G/9.97G [00:22<00:22, 276MB/s]
transformer_2/diffusion_pytorch_model-00(…):  39%|#####################4                                 | 3.89G/9.97G [00:22<00:20, 298MB/s]
transformer_2/diffusion_pytorch_model-00(…):  39%|#####################4                                 | 3.89G/9.97G [00:22<00:20, 298MB/s]
transformer_2/diffusion_pytorch_model-00(…):  39%|#####################7                                 | 3.94G/9.97G [00:22<00:26, 224MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  41%|######################6                                | 4.10G/9.97G [00:22<00:16, 348MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  44%|########################2                              | 4.39G/9.97G [00:23<00:17, 319MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  45%|########################6                              | 4.47G/9.97G [00:24<00:21, 259MB/s]
transformer_2/diffusion_pytorch_model-00(…):  45%|########################6                              | 4.47G/9.97G [00:24<00:21, 259MB/s]
transformer_2/diffusion_pytorch_model-00(…):  46%|#########################1                             | 4.55G/9.97G [00:24<00:22, 237MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  47%|#########################9                             | 4.71G/9.97G [00:24<00:15, 349MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  48%|##########################3                            | 4.77G/9.97G [00:25<00:14, 357MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  50%|###########################6                           | 5.02G/9.97G [00:26<00:18, 271MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  59%|################################3                      | 5.86G/9.97G [00:28<00:13, 308MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  88%|###############################################3      | 8.73G/9.97G [00:42<00:17, 71.3MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  89%|###############################################7      | 8.82G/9.97G [00:43<00:15, 74.3MB/s]
transformer_2/diffusion_pytorch_model-00(…):  89%|###############################################7      | 8.82G/9.97G [00:43<00:15, 74.3MB/s]
transformer_2/diffusion_pytorch_model-00(…):  89%|###############################################9      | 8.86G/9.97G [00:44<00:13, 81.0MB/s]
transformer_2/diffusion_pytorch_model-00(…):  89%|###############################################9      | 8.86G/9.97G [00:44<00:13, 81.0MB/s]
transformer_2/diffusion_pytorch_model-00(…):  89%|################################################2     | 8.90G/9.97G [00:45<00:16, 66.3MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  92%|##################################################5    | 9.15G/9.97G [00:47<00:06, 128MB/s]
transformer_2/diffusion_pytorch_model-00(…):  92%|##################################################5    | 9.15G/9.97G [00:47<00:06, 128MB/s]
transformer_2/diffusion_pytorch_model-00(…):  92%|##################################################8    | 9.21G/9.97G [00:47<00:05, 143MB/s]
transformer_2/diffusion_pytorch_model-00(…):  92%|##################################################8    | 9.21G/9.97G [00:47<00:05, 143MB/s]
transformer_2/diffusion_pytorch_model-00(…):  93%|###################################################1   | 9.28G/9.97G [00:48<00:05, 122MB/s]
transformer_2/diffusion_pytorch_model-00(…):  93%|###################################################1   | 9.28G/9.97G [00:48<00:05, 122MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  95%|####################################################   | 9.43G/9.97G [00:48<00:03, 167MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  98%|###################################################### | 9.79G/9.97G [00:50<00:00, 256MB/s]
transformer_2/diffusion_pytorch_model-00(…):  99%|#####################################################3| 9.84G/9.97G [00:51<00:01, 97.0MB/s]
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transformer_2/diffusion_pytorch_model-00(…): 100%|#######################################################| 9.97G/9.97G [00:58<00:00, 171MB/s]
+
+
transformer_2/diffusion_pytorch_model-00(…):   0%|                                                               | 0.00/9.89G [00:00<?, ?B/s]
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transformer_2/diffusion_pytorch_model-00(…):   0%|                                                   | 30.3k/9.89G [00:01<94:36:11, 29.0kB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|1                                                     | 21.4M/9.89G [00:01<07:24, 22.2MB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|1                                                     | 21.4M/9.89G [00:01<07:24, 22.2MB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|2                                                     | 41.5M/9.89G [00:01<05:18, 30.9MB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|2                                                     | 41.5M/9.89G [00:01<05:18, 30.9MB/s]
transformer_2/diffusion_pytorch_model-00(…):   1%|3                                                     | 64.2M/9.89G [00:02<03:43, 44.0MB/s]
transformer_2/diffusion_pytorch_model-00(…):   1%|3                                                     | 64.2M/9.89G [00:02<03:43, 44.0MB/s]
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transformer_2/diffusion_pytorch_model-00(…):   1%|4                                                     | 81.0M/9.89G [00:02<03:03, 53.6MB/s]
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transformer_2/diffusion_pytorch_model-00(…):   1%|5                                                      | 104M/9.89G [00:02<03:14, 50.3MB/s]
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transformer_2/diffusion_pytorch_model-00(…):   4%|#9                                                      | 349M/9.89G [00:04<00:53, 177MB/s]
transformer_2/diffusion_pytorch_model-00(…):   4%|#9                                                      | 349M/9.89G [00:04<00:53, 177MB/s]
transformer_2/diffusion_pytorch_model-00(…):   4%|##4                                                     | 435M/9.89G [00:04<00:43, 218MB/s]
transformer_2/diffusion_pytorch_model-00(…):   4%|##4                                                     | 435M/9.89G [00:04<00:43, 218MB/s]
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transformer_2/diffusion_pytorch_model-00(…):   5%|##6                                                     | 471M/9.89G [00:04<00:42, 224MB/s]
transformer_2/diffusion_pytorch_model-00(…):   5%|##9                                                     | 519M/9.89G [00:04<00:53, 174MB/s]
transformer_2/diffusion_pytorch_model-00(…):   5%|##9                                                     | 519M/9.89G [00:04<00:53, 174MB/s]
transformer_2/diffusion_pytorch_model-00(…):   7%|###7                                                    | 671M/9.89G [00:05<00:28, 321MB/s]
transformer_2/diffusion_pytorch_model-00(…):   7%|###7                                                    | 671M/9.89G [00:05<00:28, 321MB/s]
transformer_2/diffusion_pytorch_model-00(…):   8%|####2                                                   | 747M/9.89G [00:05<00:24, 367MB/s]
transformer_2/diffusion_pytorch_model-00(…):   8%|####2                                                   | 747M/9.89G [00:05<00:24, 367MB/s]
transformer_2/diffusion_pytorch_model-00(…):   9%|####9                                                   | 877M/9.89G [00:05<00:17, 515MB/s]
transformer_2/diffusion_pytorch_model-00(…):   9%|####9                                                   | 877M/9.89G [00:05<00:17, 515MB/s]
transformer_2/diffusion_pytorch_model-00(…):  10%|#####5                                                  | 979M/9.89G [00:05<00:22, 399MB/s]
transformer_2/diffusion_pytorch_model-00(…):  10%|#####5                                                  | 979M/9.89G [00:05<00:22, 399MB/s]
transformer_2/diffusion_pytorch_model-00(…):  11%|######                                                 | 1.09G/9.89G [00:05<00:21, 402MB/s]
transformer_2/diffusion_pytorch_model-00(…):  11%|######                                                 | 1.09G/9.89G [00:05<00:21, 402MB/s]
transformer_2/diffusion_pytorch_model-00(…):  13%|#######1                                               | 1.29G/9.89G [00:06<00:14, 582MB/s]
transformer_2/diffusion_pytorch_model-00(…):  13%|#######1                                               | 1.29G/9.89G [00:06<00:14, 582MB/s]
transformer_2/diffusion_pytorch_model-00(…):  14%|#######8                                               | 1.42G/9.89G [00:06<00:13, 616MB/s]
transformer_2/diffusion_pytorch_model-00(…):  14%|#######8                                               | 1.42G/9.89G [00:06<00:13, 616MB/s]
transformer_2/diffusion_pytorch_model-00(…):  16%|########7                                              | 1.58G/9.89G [00:06<00:11, 737MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  95%|####################################################1  | 9.38G/9.89G [00:38<00:04, 122MB/s]
transformer_2/diffusion_pytorch_model-00(…):  95%|####################################################1  | 9.38G/9.89G [00:38<00:04, 122MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  97%|#####################################################3 | 9.59G/9.89G [00:40<00:02, 106MB/s]
transformer_2/diffusion_pytorch_model-00(…):  98%|#####################################################6 | 9.65G/9.89G [00:41<00:02, 103MB/s]
transformer_2/diffusion_pytorch_model-00(…):  98%|#####################################################6 | 9.65G/9.89G [00:41<00:02, 103MB/s]
transformer_2/diffusion_pytorch_model-00(…):  98%|#####################################################9 | 9.70G/9.89G [00:41<00:01, 101MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  98%|#####################################################1| 9.73G/9.89G [00:42<00:01, 85.4MB/s]
transformer_2/diffusion_pytorch_model-00(…):  98%|#####################################################1| 9.73G/9.89G [00:42<00:01, 85.4MB/s]
transformer_2/diffusion_pytorch_model-00(…):  99%|######################################################4| 9.78G/9.89G [00:42<00:00, 113MB/s]
transformer_2/diffusion_pytorch_model-00(…):  99%|######################################################4| 9.78G/9.89G [00:42<00:00, 113MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  99%|#####################################################6| 9.83G/9.89G [00:43<00:00, 74.6MB/s]
transformer_2/diffusion_pytorch_model-00(…): 100%|######################################################| 9.89G/9.89G [00:44<00:00, 58.8MB/s]
transformer_2/diffusion_pytorch_model-00(…): 100%|######################################################| 9.89G/9.89G [00:44<00:00, 58.8MB/s]
transformer_2/diffusion_pytorch_model-00(…): 100%|#######################################################| 9.89G/9.89G [00:44<00:00, 220MB/s]
transformer_2/diffusion_pytorch_model-00(…): 100%|#######################################################| 9.89G/9.89G [00:44<00:00, 220MB/s]
+
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transformer_2/diffusion_pytorch_model-00(…):   0%|                                                               | 0.00/8.72G [00:00<?, ?B/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|                                                               | 0.00/8.72G [00:00<?, ?B/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|                                                    | 2.53M/8.72G [00:01<1:08:30, 2.12MB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|                                                    | 2.53M/8.72G [00:01<1:08:30, 2.12MB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|                                                      | 8.51M/8.72G [00:01<19:22, 7.49MB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|                                                      | 8.51M/8.72G [00:01<19:22, 7.49MB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|1                                                     | 24.9M/8.72G [00:01<06:26, 22.5MB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|1                                                     | 24.9M/8.72G [00:01<06:26, 22.5MB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|2                                                     | 39.1M/8.72G [00:01<03:51, 37.5MB/s]
transformer_2/diffusion_pytorch_model-00(…):   0%|2                                                     | 39.1M/8.72G [00:01<03:51, 37.5MB/s]
transformer_2/diffusion_pytorch_model-00(…):   1%|3                                                     | 56.3M/8.72G [00:02<03:48, 37.9MB/s]
transformer_2/diffusion_pytorch_model-00(…):   1%|3                                                     | 56.3M/8.72G [00:02<03:48, 37.9MB/s]
transformer_2/diffusion_pytorch_model-00(…):   1%|5                                                     | 88.1M/8.72G [00:02<02:20, 61.2MB/s]
transformer_2/diffusion_pytorch_model-00(…):   1%|5                                                     | 88.1M/8.72G [00:02<02:20, 61.2MB/s]
transformer_2/diffusion_pytorch_model-00(…):   2%|8                                                      | 132M/8.72G [00:02<01:26, 98.8MB/s]
transformer_2/diffusion_pytorch_model-00(…):   2%|8                                                      | 132M/8.72G [00:02<01:26, 98.8MB/s]
transformer_2/diffusion_pytorch_model-00(…):   2%|#3                                                      | 213M/8.72G [00:02<00:49, 172MB/s]
transformer_2/diffusion_pytorch_model-00(…):   2%|#3                                                      | 213M/8.72G [00:02<00:49, 172MB/s]
transformer_2/diffusion_pytorch_model-00(…):   3%|#5                                                      | 238M/8.72G [00:03<00:52, 162MB/s]
transformer_2/diffusion_pytorch_model-00(…):   3%|#5                                                      | 238M/8.72G [00:03<00:52, 162MB/s]
transformer_2/diffusion_pytorch_model-00(…):   4%|##4                                                     | 380M/8.72G [00:03<00:53, 155MB/s]
transformer_2/diffusion_pytorch_model-00(…):   4%|##4                                                     | 380M/8.72G [00:03<00:53, 155MB/s]
transformer_2/diffusion_pytorch_model-00(…):   8%|####6                                                   | 725M/8.72G [00:04<00:20, 385MB/s]
transformer_2/diffusion_pytorch_model-00(…):   8%|####6                                                   | 725M/8.72G [00:04<00:20, 385MB/s]
transformer_2/diffusion_pytorch_model-00(…):   9%|####9                                                   | 775M/8.72G [00:04<00:26, 301MB/s]
transformer_2/diffusion_pytorch_model-00(…):   9%|####9                                                   | 775M/8.72G [00:04<00:26, 301MB/s]
transformer_2/diffusion_pytorch_model-00(…):  10%|#####5                                                  | 865M/8.72G [00:04<00:26, 298MB/s]
transformer_2/diffusion_pytorch_model-00(…):  10%|#####5                                                  | 865M/8.72G [00:04<00:26, 298MB/s]
transformer_2/diffusion_pytorch_model-00(…):  11%|######                                                  | 949M/8.72G [00:05<00:24, 313MB/s]
transformer_2/diffusion_pytorch_model-00(…):  11%|######                                                  | 949M/8.72G [00:05<00:24, 313MB/s]
transformer_2/diffusion_pytorch_model-00(…):  12%|######4                                                | 1.02G/8.72G [00:05<00:22, 345MB/s]
transformer_2/diffusion_pytorch_model-00(…):  12%|######4                                                | 1.02G/8.72G [00:05<00:22, 345MB/s]
transformer_2/diffusion_pytorch_model-00(…):  13%|######8                                                | 1.09G/8.72G [00:05<00:28, 268MB/s]
transformer_2/diffusion_pytorch_model-00(…):  13%|######8                                                | 1.09G/8.72G [00:05<00:28, 268MB/s]
transformer_2/diffusion_pytorch_model-00(…):  13%|#######2                                               | 1.15G/8.72G [00:05<00:24, 304MB/s]
transformer_2/diffusion_pytorch_model-00(…):  13%|#######2                                               | 1.15G/8.72G [00:05<00:24, 304MB/s]
transformer_2/diffusion_pytorch_model-00(…):  14%|#######5                                               | 1.19G/8.72G [00:06<00:31, 241MB/s]
transformer_2/diffusion_pytorch_model-00(…):  14%|#######5                                               | 1.19G/8.72G [00:06<00:31, 241MB/s]
transformer_2/diffusion_pytorch_model-00(…):  14%|#######9                                               | 1.26G/8.72G [00:06<00:27, 271MB/s]
transformer_2/diffusion_pytorch_model-00(…):  14%|#######9                                               | 1.26G/8.72G [00:06<00:27, 271MB/s]
transformer_2/diffusion_pytorch_model-00(…):  15%|########2                                              | 1.31G/8.72G [00:06<00:26, 278MB/s]
transformer_2/diffusion_pytorch_model-00(…):  15%|########2                                              | 1.31G/8.72G [00:06<00:26, 278MB/s]
transformer_2/diffusion_pytorch_model-00(…):  17%|#########1                                             | 1.45G/8.72G [00:06<00:16, 435MB/s]
transformer_2/diffusion_pytorch_model-00(…):  17%|#########1                                             | 1.45G/8.72G [00:06<00:16, 435MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  17%|#########4                                             | 1.50G/8.72G [00:06<00:17, 423MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  18%|#########9                                             | 1.57G/8.72G [00:07<00:18, 383MB/s]
transformer_2/diffusion_pytorch_model-00(…):  19%|##########3                                            | 1.65G/8.72G [00:07<00:15, 442MB/s]
transformer_2/diffusion_pytorch_model-00(…):  19%|##########3                                            | 1.65G/8.72G [00:07<00:15, 442MB/s]
transformer_2/diffusion_pytorch_model-00(…):  20%|##########8                                            | 1.71G/8.72G [00:07<00:16, 432MB/s]
transformer_2/diffusion_pytorch_model-00(…):  20%|##########8                                            | 1.71G/8.72G [00:07<00:16, 432MB/s]
transformer_2/diffusion_pytorch_model-00(…):  21%|###########3                                           | 1.80G/8.72G [00:07<00:22, 303MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  22%|############1                                          | 1.92G/8.72G [00:08<00:24, 277MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  25%|#############6                                         | 2.16G/8.72G [00:08<00:18, 358MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  27%|##############6                                        | 2.33G/8.72G [00:09<00:11, 537MB/s]
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transformer_2/diffusion_pytorch_model-00(…):  30%|################2                                      | 2.57G/8.72G [00:10<00:35, 175MB/s]
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+
+
Loading checkpoint shards:   0%|                                                                                       | 0/3 [00:00<?, ?it/s]
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+
+Creating DualWanTransformer3DModel
+Loading UMT5EncoderModel
+
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+
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tokenizer/spiece.model:   0%|                                                                                    | 0.00/4.55M [00:00<?, ?B/s]
tokenizer/spiece.model:   3%|##2                                                                          | 130k/4.55M [00:00<00:11, 381kB/s]
tokenizer/spiece.model:   3%|##2                                                                          | 130k/4.55M [00:00<00:11, 381kB/s]
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+
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+
+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!
+
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+
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text_encoder/model-00001-of-00003.safete(…):   3%|#5                                                     | 139M/4.94G [00:05<02:29, 32.0MB/s]
text_encoder/model-00001-of-00003.safete(…):   3%|#5                                                     | 139M/4.94G [00:05<02:29, 32.0MB/s]
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text_encoder/model-00001-of-00003.safete(…):  11%|######1                                                 | 541M/4.94G [00:07<00:27, 159MB/s]
text_encoder/model-00001-of-00003.safete(…):  11%|######1                                                 | 541M/4.94G [00:07<00:27, 159MB/s]
text_encoder/model-00001-of-00003.safete(…):  12%|######9                                                 | 611M/4.94G [00:08<00:25, 170MB/s]
text_encoder/model-00001-of-00003.safete(…):  12%|######9                                                 | 611M/4.94G [00:08<00:25, 170MB/s]
text_encoder/model-00001-of-00003.safete(…):  15%|########4                                               | 743M/4.94G [00:08<00:22, 190MB/s]
text_encoder/model-00001-of-00003.safete(…):  15%|########4                                               | 743M/4.94G [00:08<00:22, 190MB/s]
text_encoder/model-00001-of-00003.safete(…):  16%|#########1                                              | 810M/4.94G [00:08<00:18, 219MB/s]
text_encoder/model-00001-of-00003.safete(…):  16%|#########1                                              | 810M/4.94G [00:08<00:18, 219MB/s]
text_encoder/model-00001-of-00003.safete(…):  18%|#########9                                              | 877M/4.94G [00:09<00:25, 162MB/s]
text_encoder/model-00001-of-00003.safete(…):  18%|#########9                                              | 877M/4.94G [00:09<00:25, 162MB/s]
text_encoder/model-00001-of-00003.safete(…):  19%|##########7                                             | 945M/4.94G [00:09<00:20, 192MB/s]
text_encoder/model-00001-of-00003.safete(…):  19%|##########7                                             | 945M/4.94G [00:09<00:20, 192MB/s]
text_encoder/model-00001-of-00003.safete(…):  22%|############                                           | 1.08G/4.94G [00:09<00:13, 279MB/s]
text_encoder/model-00001-of-00003.safete(…):  22%|############                                           | 1.08G/4.94G [00:09<00:13, 279MB/s]
text_encoder/model-00001-of-00003.safete(…):  23%|############7                                          | 1.15G/4.94G [00:10<00:15, 245MB/s]
text_encoder/model-00001-of-00003.safete(…):  23%|############7                                          | 1.15G/4.94G [00:10<00:15, 245MB/s]
text_encoder/model-00001-of-00003.safete(…):  25%|#############5                                         | 1.21G/4.94G [00:10<00:14, 266MB/s]
text_encoder/model-00001-of-00003.safete(…):  25%|#############5                                         | 1.21G/4.94G [00:10<00:14, 266MB/s]
text_encoder/model-00001-of-00003.safete(…):  27%|###############                                        | 1.35G/4.94G [00:10<00:12, 286MB/s]
text_encoder/model-00001-of-00003.safete(…):  27%|###############                                        | 1.35G/4.94G [00:10<00:12, 286MB/s]
text_encoder/model-00001-of-00003.safete(…):  30%|################5                                      | 1.48G/4.94G [00:11<00:12, 273MB/s]
text_encoder/model-00001-of-00003.safete(…):  30%|################5                                      | 1.48G/4.94G [00:11<00:12, 273MB/s]
text_encoder/model-00001-of-00003.safete(…):  33%|#################9                                     | 1.62G/4.94G [00:11<00:09, 347MB/s]
text_encoder/model-00001-of-00003.safete(…):  33%|#################9                                     | 1.62G/4.94G [00:11<00:09, 347MB/s]
text_encoder/model-00001-of-00003.safete(…):  34%|##################7                                    | 1.68G/4.94G [00:11<00:09, 336MB/s]
text_encoder/model-00001-of-00003.safete(…):  34%|##################7                                    | 1.68G/4.94G [00:11<00:09, 336MB/s]
text_encoder/model-00001-of-00003.safete(…):  35%|###################4                                   | 1.75G/4.94G [00:12<00:08, 364MB/s]
text_encoder/model-00001-of-00003.safete(…):  35%|###################4                                   | 1.75G/4.94G [00:12<00:08, 364MB/s]
text_encoder/model-00001-of-00003.safete(…):  37%|####################2                                  | 1.82G/4.94G [00:13<00:21, 144MB/s]
text_encoder/model-00001-of-00003.safete(…):  37%|####################2                                  | 1.82G/4.94G [00:13<00:21, 144MB/s]
text_encoder/model-00001-of-00003.safete(…):  38%|####################9                                  | 1.88G/4.94G [00:13<00:19, 158MB/s]
text_encoder/model-00001-of-00003.safete(…):  38%|####################9                                  | 1.88G/4.94G [00:13<00:19, 158MB/s]
text_encoder/model-00001-of-00003.safete(…):  40%|#####################7                                 | 1.95G/4.94G [00:13<00:15, 191MB/s]
text_encoder/model-00001-of-00003.safete(…):  40%|#####################7                                 | 1.95G/4.94G [00:13<00:15, 191MB/s]
text_encoder/model-00001-of-00003.safete(…):  41%|######################4                                | 2.02G/4.94G [00:13<00:13, 223MB/s]
text_encoder/model-00001-of-00003.safete(…):  41%|######################4                                | 2.02G/4.94G [00:13<00:13, 223MB/s]
text_encoder/model-00001-of-00003.safete(…):  42%|#######################2                               | 2.08G/4.94G [00:14<00:11, 238MB/s]
text_encoder/model-00001-of-00003.safete(…):  42%|#######################2                               | 2.08G/4.94G [00:14<00:11, 238MB/s]
text_encoder/model-00001-of-00003.safete(…):  44%|#######################9                               | 2.15G/4.94G [00:14<00:10, 275MB/s]
text_encoder/model-00001-of-00003.safete(…):  44%|#######################9                               | 2.15G/4.94G [00:14<00:10, 275MB/s]
text_encoder/model-00001-of-00003.safete(…):  45%|########################7                              | 2.22G/4.94G [00:14<00:13, 200MB/s]
text_encoder/model-00001-of-00003.safete(…):  45%|########################7                              | 2.22G/4.94G [00:14<00:13, 200MB/s]
text_encoder/model-00001-of-00003.safete(…):  48%|##########################2                            | 2.35G/4.94G [00:15<00:10, 237MB/s]
text_encoder/model-00001-of-00003.safete(…):  48%|##########################2                            | 2.35G/4.94G [00:15<00:10, 237MB/s]
text_encoder/model-00001-of-00003.safete(…):  49%|##########################9                            | 2.42G/4.94G [00:15<00:12, 205MB/s]
text_encoder/model-00001-of-00003.safete(…):  49%|##########################9                            | 2.42G/4.94G [00:15<00:12, 205MB/s]
text_encoder/model-00001-of-00003.safete(…):  52%|############################4                          | 2.55G/4.94G [00:16<00:10, 236MB/s]
text_encoder/model-00001-of-00003.safete(…):  52%|############################4                          | 2.55G/4.94G [00:16<00:10, 236MB/s]
text_encoder/model-00001-of-00003.safete(…):  53%|#############################1                         | 2.62G/4.94G [00:16<00:09, 253MB/s]
text_encoder/model-00001-of-00003.safete(…):  53%|#############################1                         | 2.62G/4.94G [00:16<00:09, 253MB/s]
text_encoder/model-00001-of-00003.safete(…):  56%|##############################6                        | 2.75G/4.94G [00:17<00:10, 204MB/s]
text_encoder/model-00001-of-00003.safete(…):  56%|##############################6                        | 2.75G/4.94G [00:17<00:10, 204MB/s]
text_encoder/model-00001-of-00003.safete(…):  57%|###############################4                       | 2.82G/4.94G [00:18<00:13, 152MB/s]
text_encoder/model-00001-of-00003.safete(…):  57%|###############################4                       | 2.82G/4.94G [00:18<00:13, 152MB/s]
text_encoder/model-00001-of-00003.safete(…):  59%|################################6                      | 2.93G/4.94G [00:18<00:09, 207MB/s]
text_encoder/model-00001-of-00003.safete(…):  59%|################################6                      | 2.93G/4.94G [00:18<00:09, 207MB/s]
text_encoder/model-00001-of-00003.safete(…):  61%|#################################3                     | 2.99G/4.94G [00:18<00:08, 232MB/s]
text_encoder/model-00001-of-00003.safete(…):  61%|#################################3                     | 2.99G/4.94G [00:18<00:08, 232MB/s]
text_encoder/model-00001-of-00003.safete(…):  62%|##################################1                    | 3.06G/4.94G [00:18<00:07, 254MB/s]
text_encoder/model-00001-of-00003.safete(…):  62%|##################################1                    | 3.06G/4.94G [00:18<00:07, 254MB/s]
text_encoder/model-00001-of-00003.safete(…):  63%|##################################8                    | 3.13G/4.94G [00:18<00:06, 260MB/s]
text_encoder/model-00001-of-00003.safete(…):  63%|##################################8                    | 3.13G/4.94G [00:18<00:06, 260MB/s]
text_encoder/model-00001-of-00003.safete(…):  65%|###################################6                   | 3.20G/4.94G [00:19<00:06, 276MB/s]
text_encoder/model-00001-of-00003.safete(…):  65%|###################################6                   | 3.20G/4.94G [00:19<00:06, 276MB/s]
text_encoder/model-00001-of-00003.safete(…):  66%|####################################3                  | 3.26G/4.94G [00:19<00:05, 320MB/s]
text_encoder/model-00001-of-00003.safete(…):  66%|####################################3                  | 3.26G/4.94G [00:19<00:05, 320MB/s]
text_encoder/model-00001-of-00003.safete(…):  69%|#####################################8                 | 3.40G/4.94G [00:19<00:03, 423MB/s]
text_encoder/model-00001-of-00003.safete(…):  69%|#####################################8                 | 3.40G/4.94G [00:19<00:03, 423MB/s]
text_encoder/model-00001-of-00003.safete(…):  72%|#######################################3               | 3.53G/4.94G [00:19<00:03, 421MB/s]
text_encoder/model-00001-of-00003.safete(…):  72%|#######################################3               | 3.53G/4.94G [00:19<00:03, 421MB/s]
text_encoder/model-00001-of-00003.safete(…):  73%|########################################               | 3.60G/4.94G [00:19<00:03, 431MB/s]
text_encoder/model-00001-of-00003.safete(…):  73%|########################################               | 3.60G/4.94G [00:19<00:03, 431MB/s]
text_encoder/model-00001-of-00003.safete(…):  74%|########################################8              | 3.66G/4.94G [00:20<00:03, 326MB/s]
text_encoder/model-00001-of-00003.safete(…):  74%|########################################8              | 3.66G/4.94G [00:20<00:03, 326MB/s]
text_encoder/model-00001-of-00003.safete(…):  76%|#########################################5             | 3.73G/4.94G [00:20<00:04, 279MB/s]
text_encoder/model-00001-of-00003.safete(…):  76%|#########################################5             | 3.73G/4.94G [00:20<00:04, 279MB/s]
text_encoder/model-00001-of-00003.safete(…):  77%|##########################################2            | 3.80G/4.94G [00:21<00:06, 185MB/s]
text_encoder/model-00001-of-00003.safete(…):  77%|##########################################2            | 3.80G/4.94G [00:21<00:06, 185MB/s]
text_encoder/model-00001-of-00003.safete(…):  80%|###########################################7           | 3.93G/4.94G [00:21<00:04, 251MB/s]
text_encoder/model-00001-of-00003.safete(…):  80%|###########################################7           | 3.93G/4.94G [00:21<00:04, 251MB/s]
text_encoder/model-00001-of-00003.safete(…):  81%|############################################5          | 4.00G/4.94G [00:21<00:03, 284MB/s]
text_encoder/model-00001-of-00003.safete(…):  81%|############################################5          | 4.00G/4.94G [00:21<00:03, 284MB/s]
text_encoder/model-00001-of-00003.safete(…):  84%|##############################################         | 4.13G/4.94G [00:22<00:03, 254MB/s]
text_encoder/model-00001-of-00003.safete(…):  84%|##############################################         | 4.13G/4.94G [00:22<00:03, 254MB/s]
text_encoder/model-00001-of-00003.safete(…):  86%|###############################################5       | 4.27G/4.94G [00:22<00:01, 358MB/s]
text_encoder/model-00001-of-00003.safete(…):  86%|###############################################5       | 4.27G/4.94G [00:22<00:01, 358MB/s]
text_encoder/model-00001-of-00003.safete(…):  88%|################################################2      | 4.33G/4.94G [00:22<00:02, 293MB/s]
text_encoder/model-00001-of-00003.safete(…):  88%|################################################2      | 4.33G/4.94G [00:22<00:02, 293MB/s]
text_encoder/model-00001-of-00003.safete(…):  90%|#################################################7     | 4.47G/4.94G [00:22<00:01, 403MB/s]
text_encoder/model-00001-of-00003.safete(…):  90%|#################################################7     | 4.47G/4.94G [00:22<00:01, 403MB/s]
text_encoder/model-00001-of-00003.safete(…):  93%|###################################################2   | 4.60G/4.94G [00:23<00:00, 489MB/s]
text_encoder/model-00001-of-00003.safete(…):  93%|###################################################2   | 4.60G/4.94G [00:23<00:00, 489MB/s]
text_encoder/model-00001-of-00003.safete(…):  97%|#####################################################5 | 4.80G/4.94G [00:23<00:00, 681MB/s]
text_encoder/model-00001-of-00003.safete(…):  97%|#####################################################5 | 4.80G/4.94G [00:23<00:00, 681MB/s]
text_encoder/model-00001-of-00003.safete(…): 100%|#######################################################| 4.94G/4.94G [00:24<00:00, 309MB/s]
text_encoder/model-00001-of-00003.safete(…): 100%|#######################################################| 4.94G/4.94G [00:24<00:00, 309MB/s]
text_encoder/model-00001-of-00003.safete(…): 100%|#######################################################| 4.94G/4.94G [00:24<00:00, 203MB/s]
text_encoder/model-00001-of-00003.safete(…): 100%|#######################################################| 4.94G/4.94G [00:24<00:00, 203MB/s]
+
+
text_encoder/model-00002-of-00003.safete(…):   0%|                                                               | 0.00/4.98G [00:00<?, ?B/s]
text_encoder/model-00002-of-00003.safete(…):   0%|                                                               | 0.00/4.98G [00:00<?, ?B/s]
text_encoder/model-00002-of-00003.safete(…):   0%|                                                      | 710k/4.98G [00:00<1:23:53, 990kB/s]
text_encoder/model-00002-of-00003.safete(…):   0%|                                                      | 710k/4.98G [00:00<1:23:53, 990kB/s]
text_encoder/model-00002-of-00003.safete(…):   0%|                                                      | 2.12M/4.98G [00:01<52:20, 1.59MB/s]
text_encoder/model-00002-of-00003.safete(…):   0%|                                                      | 2.12M/4.98G [00:01<52:20, 1.59MB/s]
text_encoder/model-00002-of-00003.safete(…):   0%|                                                      | 4.31M/4.98G [00:01<28:23, 2.92MB/s]
text_encoder/model-00002-of-00003.safete(…):   0%|                                                      | 4.31M/4.98G [00:01<28:23, 2.92MB/s]
text_encoder/model-00002-of-00003.safete(…):   1%|7                                                     | 71.3M/4.98G [00:05<04:56, 16.5MB/s]
text_encoder/model-00002-of-00003.safete(…):   1%|7                                                     | 71.3M/4.98G [00:05<04:56, 16.5MB/s]
text_encoder/model-00002-of-00003.safete(…):   3%|#5                                                     | 138M/4.98G [00:05<02:17, 35.3MB/s]
text_encoder/model-00002-of-00003.safete(…):   3%|#5                                                     | 138M/4.98G [00:05<02:17, 35.3MB/s]
text_encoder/model-00002-of-00003.safete(…):   8%|####5                                                   | 407M/4.98G [00:05<00:31, 145MB/s]
text_encoder/model-00002-of-00003.safete(…):   8%|####5                                                   | 407M/4.98G [00:05<00:31, 145MB/s]
text_encoder/model-00002-of-00003.safete(…):  11%|######                                                  | 541M/4.98G [00:06<00:24, 179MB/s]
text_encoder/model-00002-of-00003.safete(…):  11%|######                                                  | 541M/4.98G [00:06<00:24, 179MB/s]
text_encoder/model-00002-of-00003.safete(…):  14%|#######5                                                | 675M/4.98G [00:06<00:19, 222MB/s]
text_encoder/model-00002-of-00003.safete(…):  14%|#######5                                                | 675M/4.98G [00:06<00:19, 222MB/s]
text_encoder/model-00002-of-00003.safete(…):  15%|########3                                               | 742M/4.98G [00:06<00:16, 252MB/s]
text_encoder/model-00002-of-00003.safete(…):  15%|########3                                               | 742M/4.98G [00:06<00:16, 252MB/s]
text_encoder/model-00002-of-00003.safete(…):  16%|#########                                               | 809M/4.98G [00:06<00:16, 250MB/s]
text_encoder/model-00002-of-00003.safete(…):  16%|#########                                               | 809M/4.98G [00:06<00:16, 250MB/s]
text_encoder/model-00002-of-00003.safete(…):  18%|#########8                                              | 879M/4.98G [00:06<00:15, 258MB/s]
text_encoder/model-00002-of-00003.safete(…):  18%|#########8                                              | 879M/4.98G [00:06<00:15, 258MB/s]
text_encoder/model-00002-of-00003.safete(…):  19%|##########6                                             | 946M/4.98G [00:07<00:13, 289MB/s]
text_encoder/model-00002-of-00003.safete(…):  19%|##########6                                             | 946M/4.98G [00:07<00:13, 289MB/s]
text_encoder/model-00002-of-00003.safete(…):  20%|###########1                                           | 1.01G/4.98G [00:07<00:20, 195MB/s]
text_encoder/model-00002-of-00003.safete(…):  20%|###########1                                           | 1.01G/4.98G [00:07<00:20, 195MB/s]
text_encoder/model-00002-of-00003.safete(…):  23%|############6                                          | 1.15G/4.98G [00:07<00:12, 304MB/s]
text_encoder/model-00002-of-00003.safete(…):  23%|############6                                          | 1.15G/4.98G [00:07<00:12, 304MB/s]
text_encoder/model-00002-of-00003.safete(…):  26%|##############1                                        | 1.28G/4.98G [00:08<00:10, 351MB/s]
text_encoder/model-00002-of-00003.safete(…):  26%|##############1                                        | 1.28G/4.98G [00:08<00:10, 351MB/s]
text_encoder/model-00002-of-00003.safete(…):  27%|##############8                                        | 1.35G/4.98G [00:08<00:10, 357MB/s]
text_encoder/model-00002-of-00003.safete(…):  27%|##############8                                        | 1.35G/4.98G [00:08<00:10, 357MB/s]
text_encoder/model-00002-of-00003.safete(…):  28%|###############6                                       | 1.42G/4.98G [00:08<00:12, 297MB/s]
text_encoder/model-00002-of-00003.safete(…):  28%|###############6                                       | 1.42G/4.98G [00:08<00:12, 297MB/s]
text_encoder/model-00002-of-00003.safete(…):  30%|################3                                      | 1.48G/4.98G [00:09<00:13, 263MB/s]
text_encoder/model-00002-of-00003.safete(…):  30%|################3                                      | 1.48G/4.98G [00:09<00:13, 263MB/s]
text_encoder/model-00002-of-00003.safete(…):  35%|###################3                                   | 1.75G/4.98G [00:09<00:05, 555MB/s]
text_encoder/model-00002-of-00003.safete(…):  35%|###################3                                   | 1.75G/4.98G [00:09<00:05, 555MB/s]
text_encoder/model-00002-of-00003.safete(…):  38%|####################7                                  | 1.88G/4.98G [00:09<00:05, 604MB/s]
text_encoder/model-00002-of-00003.safete(…):  38%|####################7                                  | 1.88G/4.98G [00:09<00:05, 604MB/s]
text_encoder/model-00002-of-00003.safete(…):  40%|######################2                                | 2.02G/4.98G [00:09<00:05, 539MB/s]
text_encoder/model-00002-of-00003.safete(…):  40%|######################2                                | 2.02G/4.98G [00:09<00:05, 539MB/s]
text_encoder/model-00002-of-00003.safete(…):  43%|#######################7                               | 2.15G/4.98G [00:10<00:06, 413MB/s]
text_encoder/model-00002-of-00003.safete(…):  43%|#######################7                               | 2.15G/4.98G [00:10<00:06, 413MB/s]
text_encoder/model-00002-of-00003.safete(…):  45%|########################7                              | 2.24G/4.98G [00:10<00:06, 423MB/s]
text_encoder/model-00002-of-00003.safete(…):  45%|########################7                              | 2.24G/4.98G [00:10<00:06, 423MB/s]
text_encoder/model-00002-of-00003.safete(…):  46%|#########################4                             | 2.31G/4.98G [00:10<00:06, 437MB/s]
text_encoder/model-00002-of-00003.safete(…):  46%|#########################4                             | 2.31G/4.98G [00:10<00:06, 437MB/s]
text_encoder/model-00002-of-00003.safete(…):  49%|##########################9                            | 2.44G/4.98G [00:10<00:05, 502MB/s]
text_encoder/model-00002-of-00003.safete(…):  49%|##########################9                            | 2.44G/4.98G [00:10<00:05, 502MB/s]
text_encoder/model-00002-of-00003.safete(…):  52%|############################4                          | 2.57G/4.98G [00:10<00:04, 527MB/s]
text_encoder/model-00002-of-00003.safete(…):  52%|############################4                          | 2.57G/4.98G [00:10<00:04, 527MB/s]
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+
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text_encoder/model-00003-of-00003.safete(…):   2%|#3                                                    | 35.1M/1.44G [00:03<02:18, 10.2MB/s]
text_encoder/model-00003-of-00003.safete(…):   2%|#3                                                    | 35.1M/1.44G [00:03<02:18, 10.2MB/s]
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text_encoder/model-00003-of-00003.safete(…):  12%|######4                                                | 169M/1.44G [00:04<00:22, 57.6MB/s]
text_encoder/model-00003-of-00003.safete(…):  12%|######4                                                | 169M/1.44G [00:04<00:22, 57.6MB/s]
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+
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+
+Loading VAE
+
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+
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diffusion_pytorch_model.safetensors:   1%|8                                                              | 3.24M/254M [00:01<00:59, 4.22MB/s]
diffusion_pytorch_model.safetensors:   4%|##6                                                            | 10.6M/254M [00:01<00:15, 15.3MB/s]
diffusion_pytorch_model.safetensors:   4%|##6                                                            | 10.6M/254M [00:01<00:15, 15.3MB/s]
diffusion_pytorch_model.safetensors:   6%|###8                                                           | 15.4M/254M [00:01<00:11, 20.7MB/s]
diffusion_pytorch_model.safetensors:   6%|###8                                                           | 15.4M/254M [00:01<00:11, 20.7MB/s]
diffusion_pytorch_model.safetensors:  15%|#########5                                                     | 38.4M/254M [00:01<00:03, 57.0MB/s]
diffusion_pytorch_model.safetensors:  15%|#########5                                                     | 38.4M/254M [00:01<00:03, 57.0MB/s]
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+
+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
+Dataset: /root/lab/ai-toolkit/datasets/lana
+  -  Preprocessing image dimensions
+
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  1%|#4                                                                                                       | 1/72 [00:00<00:16,  4.42it/s]
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  3%|##9                                                                                                      | 2/72 [00:00<00:15,  4.64it/s]
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+
+  -  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
+
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100%|#####################################################################################################| 72/72 [00:00<00:00, 16520.23it/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
+
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100%|#####################################################################################################| 72/72 [00:00<00:00, 19415.58it/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
+
lana-lora-wan2.2:   0%|                                                                                             | 0/5000 [00:00<?, ?it/s]
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lana-lora-wan2.2:   0%|                                                     | 1/5000 [00:13<19:20:54, 13.93s/it, lr: 1.0e-04 loss: 6.737e-02]
lana-lora-wan2.2:   0%|                                                     | 2/5000 [00:17<12:12:14,  8.79s/it, lr: 1.0e-04 loss: 6.782e-02]
lana-lora-wan2.2:   0%|                                                     | 2/5000 [00:17<12:12:14,  8.79s/it, lr: 1.0e-04 loss: 6.782e-02]
lana-lora-wan2.2:   0%|                                                      | 3/5000 [00:18<8:40:43,  6.25s/it, lr: 1.0e-04 loss: 9.284e-02]
lana-lora-wan2.2:   0%|                                                      | 3/5000 [00:18<8:40:43,  6.25s/it, lr: 1.0e-04 loss: 9.284e-02]
lana-lora-wan2.2:   0%|                                                      | 4/5000 [00:20<7:16:20,  5.24s/it, lr: 1.0e-04 loss: 5.123e-02]
lana-lora-wan2.2:   0%|                                                      | 4/5000 [00:20<7:16:20,  5.24s/it, lr: 1.0e-04 loss: 5.123e-02]
lana-lora-wan2.2:   0%|                                                      | 5/5000 [00:23<6:29:14,  4.68s/it, lr: 1.0e-04 loss: 2.434e-01]
lana-lora-wan2.2:   0%|                                                      | 5/5000 [00:23<6:29:14,  4.68s/it, lr: 1.0e-04 loss: 2.434e-01]
lana-lora-wan2.2:   0%|                                                      | 6/5000 [00:25<5:54:10,  4.26s/it, lr: 1.0e-04 loss: 4.051e-01]
lana-lora-wan2.2:   0%|                                                      | 6/5000 [00:25<5:54:10,  4.26s/it, lr: 1.0e-04 loss: 4.051e-01]
lana-lora-wan2.2:   0%|                                                      | 7/5000 [00:29<5:46:50,  4.17s/it, lr: 1.0e-04 loss: 5.544e-02]
lana-lora-wan2.2:   0%|                                                      | 7/5000 [00:29<5:46:50,  4.17s/it, lr: 1.0e-04 loss: 5.544e-02]
lana-lora-wan2.2:   0%|                                                      | 8/5000 [00:31<5:25:49,  3.92s/it, lr: 1.0e-04 loss: 5.706e-02]
lana-lora-wan2.2:   0%|                                                      | 8/5000 [00:31<5:25:49,  3.92s/it, lr: 1.0e-04 loss: 5.706e-02]
lana-lora-wan2.2:   0%|                                                      | 9/5000 [00:34<5:23:13,  3.89s/it, lr: 1.0e-04 loss: 3.845e-02]
lana-lora-wan2.2:   0%|                                                      | 9/5000 [00:34<5:23:13,  3.89s/it, lr: 1.0e-04 loss: 3.845e-02]
lana-lora-wan2.2:   0%|1                                                    | 10/5000 [00:38<5:21:13,  3.86s/it, lr: 1.0e-04 loss: 3.584e-02]
lana-lora-wan2.2:   0%|1                                                    | 10/5000 [00:38<5:21:13,  3.86s/it, lr: 1.0e-04 loss: 3.584e-02]
lana-lora-wan2.2:   0%|1                                                    | 11/5000 [00:42<5:21:17,  3.86s/it, lr: 1.0e-04 loss: 7.291e-02]
lana-lora-wan2.2:   0%|1                                                    | 11/5000 [00:42<5:21:17,  3.86s/it, lr: 1.0e-04 loss: 7.291e-02]
lana-lora-wan2.2:   0%|1                                                    | 12/5000 [00:44<5:09:23,  3.72s/it, lr: 1.0e-04 loss: 4.011e-02]
lana-lora-wan2.2:   0%|1                                                    | 12/5000 [00:44<5:09:23,  3.72s/it, lr: 1.0e-04 loss: 4.011e-02]
lana-lora-wan2.2:   0%|1                                                    | 13/5000 [00:48<5:08:50,  3.72s/it, lr: 1.0e-04 loss: 5.898e-02]
lana-lora-wan2.2:   0%|1                                                    | 13/5000 [00:48<5:08:50,  3.72s/it, lr: 1.0e-04 loss: 5.898e-02]
lana-lora-wan2.2:   0%|1                                                    | 14/5000 [00:49<4:53:39,  3.53s/it, lr: 1.0e-04 loss: 1.467e-01]
lana-lora-wan2.2:   0%|1                                                    | 14/5000 [00:49<4:53:39,  3.53s/it, lr: 1.0e-04 loss: 1.467e-01]
lana-lora-wan2.2:   0%|1                                                    | 15/5000 [00:53<4:54:16,  3.54s/it, lr: 1.0e-04 loss: 5.659e-02]
lana-lora-wan2.2:   0%|1                                                    | 15/5000 [00:53<4:54:16,  3.54s/it, lr: 1.0e-04 loss: 5.659e-02]
lana-lora-wan2.2:   0%|1                                                    | 16/5000 [00:56<4:54:43,  3.55s/it, lr: 1.0e-04 loss: 4.569e-02]
lana-lora-wan2.2:   0%|1                                                    | 16/5000 [00:56<4:54:43,  3.55s/it, lr: 1.0e-04 loss: 4.569e-02]
lana-lora-wan2.2:   0%|1                                                    | 17/5000 [00:58<4:44:30,  3.43s/it, lr: 1.0e-04 loss: 9.113e-02]
lana-lora-wan2.2:   0%|1                                                    | 17/5000 [00:58<4:44:30,  3.43s/it, lr: 1.0e-04 loss: 9.113e-02]
lana-lora-wan2.2:   0%|1                                                    | 18/5000 [00:59<4:34:02,  3.30s/it, lr: 1.0e-04 loss: 6.089e-02]
lana-lora-wan2.2:   0%|1                                                    | 18/5000 [00:59<4:34:02,  3.30s/it, lr: 1.0e-04 loss: 6.089e-02]
lana-lora-wan2.2:   0%|2                                                    | 19/5000 [01:03<4:35:27,  3.32s/it, lr: 1.0e-04 loss: 4.118e-02]
lana-lora-wan2.2:   0%|2                                                    | 19/5000 [01:03<4:35:27,  3.32s/it, lr: 1.0e-04 loss: 4.118e-02]
lana-lora-wan2.2:   0%|2                                                    | 20/5000 [01:04<4:26:27,  3.21s/it, lr: 1.0e-04 loss: 9.467e-02]
lana-lora-wan2.2:   0%|2                                                    | 20/5000 [01:04<4:26:27,  3.21s/it, lr: 1.0e-04 loss: 9.467e-02]
lana-lora-wan2.2:   0%|2                                                    | 21/5000 [01:07<4:28:04,  3.23s/it, lr: 1.0e-04 loss: 4.535e-02]
lana-lora-wan2.2:   0%|2                                                    | 21/5000 [01:07<4:28:04,  3.23s/it, lr: 1.0e-04 loss: 4.535e-02]
lana-lora-wan2.2:   0%|2                                                    | 22/5000 [01:10<4:24:54,  3.19s/it, lr: 1.0e-04 loss: 3.978e-02]
lana-lora-wan2.2:   0%|2                                                    | 22/5000 [01:10<4:24:54,  3.19s/it, lr: 1.0e-04 loss: 3.978e-02]
lana-lora-wan2.2:   0%|2                                                    | 23/5000 [01:11<4:17:31,  3.10s/it, lr: 1.0e-04 loss: 4.907e-02]
lana-lora-wan2.2:   0%|2                                                    | 23/5000 [01:11<4:17:31,  3.10s/it, lr: 1.0e-04 loss: 4.907e-02]
lana-lora-wan2.2:   0%|2                                                    | 24/5000 [01:13<4:14:11,  3.07s/it, lr: 1.0e-04 loss: 4.585e-02]
lana-lora-wan2.2:   0%|2                                                    | 24/5000 [01:13<4:14:11,  3.07s/it, lr: 1.0e-04 loss: 4.585e-02]
lana-lora-wan2.2:   0%|2                                                    | 25/5000 [01:17<4:16:08,  3.09s/it, lr: 1.0e-04 loss: 5.057e-02]
lana-lora-wan2.2:   0%|2                                                    | 25/5000 [01:17<4:16:08,  3.09s/it, lr: 1.0e-04 loss: 5.057e-02]
lana-lora-wan2.2:   1%|2                                                    | 26/5000 [01:18<4:09:59,  3.02s/it, lr: 1.0e-04 loss: 2.710e-01]
lana-lora-wan2.2:   1%|2                                                    | 26/5000 [01:18<4:09:59,  3.02s/it, lr: 1.0e-04 loss: 2.710e-01]
lana-lora-wan2.2:   1%|2                                                    | 27/5000 [01:19<4:04:16,  2.95s/it, lr: 1.0e-04 loss: 1.722e-01]
lana-lora-wan2.2:   1%|2                                                    | 27/5000 [01:19<4:04:16,  2.95s/it, lr: 1.0e-04 loss: 1.722e-01]
lana-lora-wan2.2:   1%|2                                                    | 28/5000 [01:22<4:02:41,  2.93s/it, lr: 1.0e-04 loss: 5.108e-02]
lana-lora-wan2.2:   1%|2                                                    | 28/5000 [01:22<4:02:41,  2.93s/it, lr: 1.0e-04 loss: 5.108e-02]
lana-lora-wan2.2:   1%|3                                                    | 29/5000 [01:24<4:00:26,  2.90s/it, lr: 1.0e-04 loss: 3.685e-02]
lana-lora-wan2.2:   1%|3                                                    | 29/5000 [01:24<4:00:26,  2.90s/it, lr: 1.0e-04 loss: 3.685e-02]
lana-lora-wan2.2:   1%|3                                                    | 30/5000 [01:25<3:55:35,  2.84s/it, lr: 1.0e-04 loss: 4.120e-02]
lana-lora-wan2.2:   1%|3                                                    | 30/5000 [01:25<3:55:35,  2.84s/it, lr: 1.0e-04 loss: 4.120e-02]
lana-lora-wan2.2:   1%|3                                                    | 31/5000 [01:28<3:57:42,  2.87s/it, lr: 1.0e-04 loss: 3.414e-02]
lana-lora-wan2.2:   1%|3                                                    | 31/5000 [01:28<3:57:42,  2.87s/it, lr: 1.0e-04 loss: 3.414e-02]
lana-lora-wan2.2:   1%|3                                                    | 32/5000 [01:30<3:53:14,  2.82s/it, lr: 1.0e-04 loss: 8.446e-02]
lana-lora-wan2.2:   1%|3                                                    | 32/5000 [01:30<3:53:14,  2.82s/it, lr: 1.0e-04 loss: 8.446e-02]
lana-lora-wan2.2:   1%|3                                                    | 33/5000 [01:33<3:55:17,  2.84s/it, lr: 1.0e-04 loss: 4.584e-02]
lana-lora-wan2.2:   1%|3                                                    | 33/5000 [01:33<3:55:17,  2.84s/it, lr: 1.0e-04 loss: 4.584e-02]
lana-lora-wan2.2:   1%|3                                                    | 34/5000 [01:36<3:54:11,  2.83s/it, lr: 1.0e-04 loss: 9.477e-02]
lana-lora-wan2.2:   1%|3                                                    | 34/5000 [01:36<3:54:11,  2.83s/it, lr: 1.0e-04 loss: 9.477e-02]
lana-lora-wan2.2:   1%|3                                                    | 35/5000 [01:38<3:52:34,  2.81s/it, lr: 1.0e-04 loss: 4.197e-02]
lana-lora-wan2.2:   1%|3                                                    | 35/5000 [01:38<3:52:34,  2.81s/it, lr: 1.0e-04 loss: 4.197e-02]
lana-lora-wan2.2:   1%|3                                                    | 36/5000 [01:40<3:51:02,  2.79s/it, lr: 1.0e-04 loss: 5.821e-02]
lana-lora-wan2.2:   1%|3                                                    | 36/5000 [01:40<3:51:02,  2.79s/it, lr: 1.0e-04 loss: 5.821e-02]
lana-lora-wan2.2:   1%|3                                                    | 37/5000 [01:44<3:52:56,  2.82s/it, lr: 1.0e-04 loss: 3.028e-02]
lana-lora-wan2.2:   1%|3                                                    | 37/5000 [01:44<3:52:56,  2.82s/it, lr: 1.0e-04 loss: 3.028e-02]
lana-lora-wan2.2:   1%|4                                                    | 38/5000 [01:46<3:51:28,  2.80s/it, lr: 1.0e-04 loss: 4.343e-02]
lana-lora-wan2.2:   1%|4                                                    | 38/5000 [01:46<3:51:28,  2.80s/it, lr: 1.0e-04 loss: 4.343e-02]
lana-lora-wan2.2:   1%|4                                                    | 39/5000 [01:49<3:53:12,  2.82s/it, lr: 1.0e-04 loss: 4.025e-02]
lana-lora-wan2.2:   1%|4                                                    | 39/5000 [01:49<3:53:12,  2.82s/it, lr: 1.0e-04 loss: 4.025e-02]
lana-lora-wan2.2:   1%|4                                                    | 40/5000 [01:53<3:55:18,  2.85s/it, lr: 1.0e-04 loss: 6.604e-02]
lana-lora-wan2.2:   1%|4                                                    | 40/5000 [01:53<3:55:18,  2.85s/it, lr: 1.0e-04 loss: 6.604e-02]
lana-lora-wan2.2:   1%|4                                                    | 41/5000 [01:56<3:53:53,  2.83s/it, lr: 1.0e-04 loss: 9.956e-02]
lana-lora-wan2.2:   1%|4                                                    | 41/5000 [01:56<3:53:53,  2.83s/it, lr: 1.0e-04 loss: 9.956e-02]
lana-lora-wan2.2:   1%|4                                                    | 42/5000 [01:57<3:50:33,  2.79s/it, lr: 1.0e-04 loss: 8.752e-02]
lana-lora-wan2.2:   1%|4                                                    | 42/5000 [01:57<3:50:33,  2.79s/it, lr: 1.0e-04 loss: 8.752e-02]
lana-lora-wan2.2:   1%|4                                                    | 43/5000 [02:00<3:52:08,  2.81s/it, lr: 1.0e-04 loss: 5.554e-02]
lana-lora-wan2.2:   1%|4                                                    | 43/5000 [02:00<3:52:08,  2.81s/it, lr: 1.0e-04 loss: 5.554e-02]
lana-lora-wan2.2:   1%|4                                                    | 44/5000 [02:01<3:49:01,  2.77s/it, lr: 1.0e-04 loss: 7.589e-02]
lana-lora-wan2.2:   1%|4                                                    | 44/5000 [02:01<3:49:01,  2.77s/it, lr: 1.0e-04 loss: 7.589e-02]
lana-lora-wan2.2:   1%|4                                                    | 45/5000 [02:05<3:51:01,  2.80s/it, lr: 1.0e-04 loss: 4.606e-02]
lana-lora-wan2.2:   1%|4                                                    | 45/5000 [02:05<3:51:01,  2.80s/it, lr: 1.0e-04 loss: 4.606e-02]
lana-lora-wan2.2:   1%|4                                                    | 46/5000 [02:09<3:52:30,  2.82s/it, lr: 1.0e-04 loss: 9.219e-02]
lana-lora-wan2.2:   1%|4                                                    | 46/5000 [02:09<3:52:30,  2.82s/it, lr: 1.0e-04 loss: 9.219e-02]
lana-lora-wan2.2:   1%|4                                                    | 47/5000 [02:11<3:51:18,  2.80s/it, lr: 1.0e-04 loss: 3.360e-02]
lana-lora-wan2.2:   1%|4                                                    | 47/5000 [02:11<3:51:18,  2.80s/it, lr: 1.0e-04 loss: 3.360e-02]
lana-lora-wan2.2:   1%|5                                                    | 48/5000 [02:15<3:52:43,  2.82s/it, lr: 1.0e-04 loss: 5.971e-02]
lana-lora-wan2.2:   1%|5                                                    | 48/5000 [02:15<3:52:43,  2.82s/it, lr: 1.0e-04 loss: 5.971e-02]
lana-lora-wan2.2:   1%|5                                                    | 49/5000 [02:18<3:54:04,  2.84s/it, lr: 1.0e-04 loss: 3.558e-02]
lana-lora-wan2.2:   1%|5                                                    | 49/5000 [02:18<3:54:04,  2.84s/it, lr: 1.0e-04 loss: 3.558e-02]
lana-lora-wan2.2:   1%|5                                                    | 50/5000 [02:20<3:51:15,  2.80s/it, lr: 1.0e-04 loss: 1.797e-01]
lana-lora-wan2.2:   1%|5                                                    | 50/5000 [02:20<3:51:15,  2.80s/it, lr: 1.0e-04 loss: 1.797e-01]
lana-lora-wan2.2:   1%|5                                                    | 51/5000 [02:21<3:49:01,  2.78s/it, lr: 1.0e-04 loss: 4.968e-02]
lana-lora-wan2.2:   1%|5                                                    | 51/5000 [02:21<3:49:01,  2.78s/it, lr: 1.0e-04 loss: 4.968e-02]
lana-lora-wan2.2:   1%|5                                                    | 52/5000 [02:22<3:46:26,  2.75s/it, lr: 1.0e-04 loss: 9.020e-02]
lana-lora-wan2.2:   1%|5                                                    | 52/5000 [02:22<3:46:26,  2.75s/it, lr: 1.0e-04 loss: 9.020e-02]
lana-lora-wan2.2:   1%|5                                                    | 53/5000 [02:24<3:45:28,  2.73s/it, lr: 1.0e-04 loss: 4.965e-02]
lana-lora-wan2.2:   1%|5                                                    | 53/5000 [02:24<3:45:28,  2.73s/it, lr: 1.0e-04 loss: 4.965e-02]
lana-lora-wan2.2:   1%|5                                                    | 54/5000 [02:27<3:44:32,  2.72s/it, lr: 1.0e-04 loss: 5.662e-02]
lana-lora-wan2.2:   1%|5                                                    | 54/5000 [02:27<3:44:32,  2.72s/it, lr: 1.0e-04 loss: 5.662e-02]
lana-lora-wan2.2:   1%|5                                                    | 55/5000 [02:30<3:45:54,  2.74s/it, lr: 1.0e-04 loss: 1.266e-01]
lana-lora-wan2.2:   1%|5                                                    | 55/5000 [02:30<3:45:54,  2.74s/it, lr: 1.0e-04 loss: 1.266e-01]
lana-lora-wan2.2:   1%|5                                                    | 56/5000 [02:32<3:45:00,  2.73s/it, lr: 1.0e-04 loss: 6.381e-02]
lana-lora-wan2.2:   1%|5                                                    | 56/5000 [02:32<3:45:00,  2.73s/it, lr: 1.0e-04 loss: 6.381e-02]
lana-lora-wan2.2:   1%|6                                                    | 57/5000 [02:34<3:43:08,  2.71s/it, lr: 1.0e-04 loss: 1.827e-01]
lana-lora-wan2.2:   1%|6                                                    | 57/5000 [02:34<3:43:08,  2.71s/it, lr: 1.0e-04 loss: 1.827e-01]
lana-lora-wan2.2:   1%|6                                                    | 58/5000 [02:36<3:42:19,  2.70s/it, lr: 1.0e-04 loss: 8.195e-02]
lana-lora-wan2.2:   1%|6                                                    | 58/5000 [02:36<3:42:19,  2.70s/it, lr: 1.0e-04 loss: 8.195e-02]
lana-lora-wan2.2:   1%|6                                                    | 59/5000 [02:40<3:43:36,  2.72s/it, lr: 1.0e-04 loss: 1.062e-01]
lana-lora-wan2.2:   1%|6                                                    | 59/5000 [02:40<3:43:36,  2.72s/it, lr: 1.0e-04 loss: 1.062e-01]
lana-lora-wan2.2:   1%|6                                                    | 60/5000 [02:42<3:42:47,  2.71s/it, lr: 1.0e-04 loss: 7.848e-02]
lana-lora-wan2.2:   1%|6                                                    | 60/5000 [02:42<3:42:47,  2.71s/it, lr: 1.0e-04 loss: 7.848e-02]
lana-lora-wan2.2:   1%|6                                                    | 61/5000 [02:46<3:44:01,  2.72s/it, lr: 1.0e-04 loss: 3.160e-02]
lana-lora-wan2.2:   1%|6                                                    | 61/5000 [02:46<3:44:01,  2.72s/it, lr: 1.0e-04 loss: 3.160e-02]
lana-lora-wan2.2:   1%|6                                                    | 62/5000 [02:48<3:43:14,  2.71s/it, lr: 1.0e-04 loss: 4.666e-02]
lana-lora-wan2.2:   1%|6                                                    | 62/5000 [02:48<3:43:14,  2.71s/it, lr: 1.0e-04 loss: 4.666e-02]
lana-lora-wan2.2:   1%|6                                                    | 63/5000 [02:49<3:41:32,  2.69s/it, lr: 1.0e-04 loss: 5.525e-02]
lana-lora-wan2.2:   1%|6                                                    | 63/5000 [02:49<3:41:32,  2.69s/it, lr: 1.0e-04 loss: 5.525e-02]
lana-lora-wan2.2:   1%|6                                                    | 64/5000 [02:53<3:42:44,  2.71s/it, lr: 1.0e-04 loss: 4.264e-02]
lana-lora-wan2.2:   1%|6                                                    | 64/5000 [02:53<3:42:44,  2.71s/it, lr: 1.0e-04 loss: 4.264e-02]
lana-lora-wan2.2:   1%|6                                                    | 65/5000 [02:55<3:41:59,  2.70s/it, lr: 1.0e-04 loss: 4.394e-02]
lana-lora-wan2.2:   1%|6                                                    | 65/5000 [02:55<3:41:59,  2.70s/it, lr: 1.0e-04 loss: 4.394e-02]
lana-lora-wan2.2:   1%|6                                                    | 66/5000 [02:57<3:41:16,  2.69s/it, lr: 1.0e-04 loss: 7.670e-02]
lana-lora-wan2.2:   1%|6                                                    | 66/5000 [02:57<3:41:16,  2.69s/it, lr: 1.0e-04 loss: 7.670e-02]
lana-lora-wan2.2:   1%|7                                                    | 67/5000 [02:59<3:40:34,  2.68s/it, lr: 1.0e-04 loss: 1.846e-01]
lana-lora-wan2.2:   1%|7                                                    | 67/5000 [02:59<3:40:34,  2.68s/it, lr: 1.0e-04 loss: 1.846e-01]
lana-lora-wan2.2:   1%|7                                                    | 68/5000 [03:00<3:38:41,  2.66s/it, lr: 1.0e-04 loss: 1.253e-01]
lana-lora-wan2.2:   1%|7                                                    | 68/5000 [03:00<3:38:41,  2.66s/it, lr: 1.0e-04 loss: 1.253e-01]
lana-lora-wan2.2:   1%|7                                                    | 69/5000 [03:03<3:38:19,  2.66s/it, lr: 1.0e-04 loss: 3.718e-02]
lana-lora-wan2.2:   1%|7                                                    | 69/5000 [03:03<3:38:19,  2.66s/it, lr: 1.0e-04 loss: 3.718e-02]
lana-lora-wan2.2:   1%|7                                                    | 70/5000 [03:06<3:39:26,  2.67s/it, lr: 1.0e-04 loss: 9.532e-02]
lana-lora-wan2.2:   1%|7                                                    | 70/5000 [03:06<3:39:26,  2.67s/it, lr: 1.0e-04 loss: 9.532e-02]
lana-lora-wan2.2:   1%|7                                                    | 71/5000 [03:09<3:38:48,  2.66s/it, lr: 1.0e-04 loss: 2.471e-01]
lana-lora-wan2.2:   1%|7                                                    | 71/5000 [03:09<3:38:48,  2.66s/it, lr: 1.0e-04 loss: 2.471e-01]
lana-lora-wan2.2:   1%|7                                                    | 72/5000 [03:11<3:38:10,  2.66s/it, lr: 1.0e-04 loss: 6.056e-02]
lana-lora-wan2.2:   1%|7                                                    | 72/5000 [03:11<3:38:10,  2.66s/it, lr: 1.0e-04 loss: 6.056e-02]
lana-lora-wan2.2:   1%|7                                                    | 73/5000 [03:14<3:39:14,  2.67s/it, lr: 1.0e-04 loss: 3.961e-02]
lana-lora-wan2.2:   1%|7                                                    | 73/5000 [03:14<3:39:14,  2.67s/it, lr: 1.0e-04 loss: 3.961e-02]
lana-lora-wan2.2:   1%|7                                                    | 74/5000 [03:16<3:37:51,  2.65s/it, lr: 1.0e-04 loss: 1.285e-01]
lana-lora-wan2.2:   1%|7                                                    | 74/5000 [03:16<3:37:51,  2.65s/it, lr: 1.0e-04 loss: 1.285e-01]
lana-lora-wan2.2:   2%|7                                                    | 75/5000 [03:18<3:37:16,  2.65s/it, lr: 1.0e-04 loss: 4.073e-02]
lana-lora-wan2.2:   2%|7                                                    | 75/5000 [03:18<3:37:16,  2.65s/it, lr: 1.0e-04 loss: 4.073e-02]
lana-lora-wan2.2:   2%|8                                                    | 76/5000 [03:19<3:35:38,  2.63s/it, lr: 1.0e-04 loss: 5.688e-02]
lana-lora-wan2.2:   2%|8                                                    | 76/5000 [03:19<3:35:38,  2.63s/it, lr: 1.0e-04 loss: 5.688e-02]
lana-lora-wan2.2:   2%|8                                                    | 77/5000 [03:23<3:36:40,  2.64s/it, lr: 1.0e-04 loss: 2.359e-02]
lana-lora-wan2.2:   2%|8                                                    | 77/5000 [03:23<3:36:40,  2.64s/it, lr: 1.0e-04 loss: 2.359e-02]
lana-lora-wan2.2:   2%|8                                                    | 78/5000 [03:26<3:37:41,  2.65s/it, lr: 1.0e-04 loss: 6.121e-02]
lana-lora-wan2.2:   2%|8                                                    | 78/5000 [03:26<3:37:41,  2.65s/it, lr: 1.0e-04 loss: 6.121e-02]
lana-lora-wan2.2:   2%|8                                                    | 79/5000 [03:28<3:36:06,  2.63s/it, lr: 1.0e-04 loss: 6.473e-02]
lana-lora-wan2.2:   2%|8                                                    | 79/5000 [03:28<3:36:06,  2.63s/it, lr: 1.0e-04 loss: 6.473e-02]
lana-lora-wan2.2:   2%|8                                                    | 80/5000 [03:30<3:35:50,  2.63s/it, lr: 1.0e-04 loss: 3.186e-02]
lana-lora-wan2.2:   2%|8                                                    | 80/5000 [03:30<3:35:50,  2.63s/it, lr: 1.0e-04 loss: 3.186e-02]
lana-lora-wan2.2:   2%|8                                                    | 81/5000 [03:34<3:36:51,  2.65s/it, lr: 1.0e-04 loss: 1.905e-01]
lana-lora-wan2.2:   2%|8                                                    | 81/5000 [03:34<3:36:51,  2.65s/it, lr: 1.0e-04 loss: 1.905e-01]
lana-lora-wan2.2:   2%|8                                                    | 82/5000 [03:36<3:36:19,  2.64s/it, lr: 1.0e-04 loss: 4.428e-01]
lana-lora-wan2.2:   2%|8                                                    | 82/5000 [03:36<3:36:19,  2.64s/it, lr: 1.0e-04 loss: 4.428e-01]
lana-lora-wan2.2:   2%|8                                                    | 83/5000 [03:37<3:34:48,  2.62s/it, lr: 1.0e-04 loss: 5.234e-02]
lana-lora-wan2.2:   2%|8                                                    | 83/5000 [03:37<3:34:48,  2.62s/it, lr: 1.0e-04 loss: 5.234e-02]
lana-lora-wan2.2:   2%|8                                                    | 84/5000 [03:38<3:33:21,  2.60s/it, lr: 1.0e-04 loss: 4.373e-02]
lana-lora-wan2.2:   2%|8                                                    | 84/5000 [03:38<3:33:21,  2.60s/it, lr: 1.0e-04 loss: 4.373e-02]
lana-lora-wan2.2:   2%|9                                                    | 85/5000 [03:40<3:32:53,  2.60s/it, lr: 1.0e-04 loss: 3.448e-02]
lana-lora-wan2.2:   2%|9                                                    | 85/5000 [03:40<3:32:53,  2.60s/it, lr: 1.0e-04 loss: 3.448e-02]
lana-lora-wan2.2:   2%|9                                                    | 86/5000 [03:42<3:31:44,  2.59s/it, lr: 1.0e-04 loss: 3.837e-02]
lana-lora-wan2.2:   2%|9                                                    | 86/5000 [03:42<3:31:44,  2.59s/it, lr: 1.0e-04 loss: 3.837e-02]
lana-lora-wan2.2:   2%|9                                                    | 87/5000 [03:44<3:31:18,  2.58s/it, lr: 1.0e-04 loss: 3.924e-02]
lana-lora-wan2.2:   2%|9                                                    | 87/5000 [03:44<3:31:18,  2.58s/it, lr: 1.0e-04 loss: 3.924e-02]
lana-lora-wan2.2:   2%|9                                                    | 88/5000 [03:46<3:30:52,  2.58s/it, lr: 1.0e-04 loss: 7.401e-02]
lana-lora-wan2.2:   2%|9                                                    | 88/5000 [03:46<3:30:52,  2.58s/it, lr: 1.0e-04 loss: 7.401e-02]
lana-lora-wan2.2:   2%|9                                                    | 89/5000 [03:47<3:29:33,  2.56s/it, lr: 1.0e-04 loss: 5.976e-02]
lana-lora-wan2.2:   2%|9                                                    | 89/5000 [03:47<3:29:33,  2.56s/it, lr: 1.0e-04 loss: 5.976e-02]
lana-lora-wan2.2:   2%|9                                                    | 90/5000 [03:50<3:29:09,  2.56s/it, lr: 1.0e-04 loss: 2.016e-01]
lana-lora-wan2.2:   2%|9                                                    | 90/5000 [03:50<3:29:09,  2.56s/it, lr: 1.0e-04 loss: 2.016e-01]
lana-lora-wan2.2:   2%|9                                                    | 91/5000 [03:51<3:27:51,  2.54s/it, lr: 1.0e-04 loss: 1.434e-01]
lana-lora-wan2.2:   2%|9                                                    | 91/5000 [03:51<3:27:51,  2.54s/it, lr: 1.0e-04 loss: 1.434e-01]
lana-lora-wan2.2:   2%|9                                                    | 92/5000 [03:52<3:26:52,  2.53s/it, lr: 1.0e-04 loss: 7.277e-02]
lana-lora-wan2.2:   2%|9                                                    | 92/5000 [03:52<3:26:52,  2.53s/it, lr: 1.0e-04 loss: 7.277e-02]
lana-lora-wan2.2:   2%|9                                                    | 93/5000 [03:53<3:25:38,  2.51s/it, lr: 1.0e-04 loss: 5.428e-02]
lana-lora-wan2.2:   2%|9                                                    | 93/5000 [03:53<3:25:38,  2.51s/it, lr: 1.0e-04 loss: 5.428e-02]
lana-lora-wan2.2:   2%|9                                                    | 94/5000 [03:55<3:24:27,  2.50s/it, lr: 1.0e-04 loss: 8.043e-02]
lana-lora-wan2.2:   2%|9                                                    | 94/5000 [03:55<3:24:27,  2.50s/it, lr: 1.0e-04 loss: 8.043e-02]
lana-lora-wan2.2:   2%|#                                                    | 95/5000 [03:57<3:24:07,  2.50s/it, lr: 1.0e-04 loss: 5.013e-02]
lana-lora-wan2.2:   2%|#                                                    | 95/5000 [03:57<3:24:07,  2.50s/it, lr: 1.0e-04 loss: 5.013e-02]
lana-lora-wan2.2:   2%|#                                                    | 96/5000 [03:59<3:23:48,  2.49s/it, lr: 1.0e-04 loss: 8.160e-02]
lana-lora-wan2.2:   2%|#                                                    | 96/5000 [03:59<3:23:48,  2.49s/it, lr: 1.0e-04 loss: 8.160e-02]
lana-lora-wan2.2:   2%|#                                                    | 97/5000 [04:00<3:22:38,  2.48s/it, lr: 1.0e-04 loss: 4.044e-02]
lana-lora-wan2.2:   2%|#                                                    | 97/5000 [04:00<3:22:38,  2.48s/it, lr: 1.0e-04 loss: 4.044e-02]
lana-lora-wan2.2:   2%|#                                                    | 98/5000 [04:04<3:23:45,  2.49s/it, lr: 1.0e-04 loss: 4.929e-02]
lana-lora-wan2.2:   2%|#                                                    | 98/5000 [04:04<3:23:45,  2.49s/it, lr: 1.0e-04 loss: 4.929e-02]
lana-lora-wan2.2:   2%|#                                                    | 99/5000 [04:05<3:22:36,  2.48s/it, lr: 1.0e-04 loss: 4.797e-02]
lana-lora-wan2.2:   2%|#                                                    | 99/5000 [04:05<3:22:36,  2.48s/it, lr: 1.0e-04 loss: 4.797e-02]
lana-lora-wan2.2:   2%|#                                                   | 100/5000 [04:06<3:21:29,  2.47s/it, lr: 1.0e-04 loss: 5.794e-02]
lana-lora-wan2.2:   2%|#                                                   | 100/5000 [04:06<3:21:29,  2.47s/it, lr: 1.0e-04 loss: 5.794e-02]
lana-lora-wan2.2:   2%|#                                                   | 101/5000 [04:08<3:21:11,  2.46s/it, lr: 1.0e-04 loss: 4.013e-02]
lana-lora-wan2.2:   2%|#                                                   | 101/5000 [04:08<3:21:11,  2.46s/it, lr: 1.0e-04 loss: 4.013e-02]
lana-lora-wan2.2:   2%|#                                                   | 102/5000 [04:10<3:20:06,  2.45s/it, lr: 1.0e-04 loss: 4.267e-02]
lana-lora-wan2.2:   2%|#                                                   | 102/5000 [04:10<3:20:06,  2.45s/it, lr: 1.0e-04 loss: 4.267e-02]
lana-lora-wan2.2:   2%|#                                                   | 103/5000 [04:13<3:21:12,  2.47s/it, lr: 1.0e-04 loss: 4.815e-02]
lana-lora-wan2.2:   2%|#                                                   | 103/5000 [04:13<3:21:12,  2.47s/it, lr: 1.0e-04 loss: 4.815e-02]
lana-lora-wan2.2:   2%|#                                                   | 104/5000 [04:16<3:20:55,  2.46s/it, lr: 1.0e-04 loss: 2.062e-01]
lana-lora-wan2.2:   2%|#                                                   | 104/5000 [04:16<3:20:55,  2.46s/it, lr: 1.0e-04 loss: 2.062e-01]
lana-lora-wan2.2:   2%|#                                                   | 105/5000 [04:19<3:21:48,  2.47s/it, lr: 1.0e-04 loss: 5.323e-02]
lana-lora-wan2.2:   2%|#                                                   | 105/5000 [04:19<3:21:48,  2.47s/it, lr: 1.0e-04 loss: 5.323e-02]
lana-lora-wan2.2:   2%|#1                                                  | 106/5000 [04:20<3:20:45,  2.46s/it, lr: 1.0e-04 loss: 3.130e-02]
lana-lora-wan2.2:   2%|#1                                                  | 106/5000 [04:20<3:20:45,  2.46s/it, lr: 1.0e-04 loss: 3.130e-02]
lana-lora-wan2.2:   2%|#1                                                  | 107/5000 [04:22<3:19:43,  2.45s/it, lr: 1.0e-04 loss: 1.152e-01]
lana-lora-wan2.2:   2%|#1                                                  | 107/5000 [04:22<3:19:43,  2.45s/it, lr: 1.0e-04 loss: 1.152e-01]
lana-lora-wan2.2:   2%|#1                                                  | 108/5000 [04:23<3:18:43,  2.44s/it, lr: 1.0e-04 loss: 2.180e-01]
lana-lora-wan2.2:   2%|#1                                                  | 108/5000 [04:23<3:18:43,  2.44s/it, lr: 1.0e-04 loss: 2.180e-01]
lana-lora-wan2.2:   2%|#1                                                  | 109/5000 [04:27<3:19:45,  2.45s/it, lr: 1.0e-04 loss: 5.044e-02]
lana-lora-wan2.2:   2%|#1                                                  | 109/5000 [04:27<3:19:45,  2.45s/it, lr: 1.0e-04 loss: 5.044e-02]
lana-lora-wan2.2:   2%|#1                                                  | 110/5000 [04:29<3:19:30,  2.45s/it, lr: 1.0e-04 loss: 3.096e-02]
lana-lora-wan2.2:   2%|#1                                                  | 110/5000 [04:29<3:19:30,  2.45s/it, lr: 1.0e-04 loss: 3.096e-02]
lana-lora-wan2.2:   2%|#1                                                  | 111/5000 [04:32<3:20:20,  2.46s/it, lr: 1.0e-04 loss: 4.001e-02]
lana-lora-wan2.2:   2%|#1                                                  | 111/5000 [04:32<3:20:20,  2.46s/it, lr: 1.0e-04 loss: 4.001e-02]
lana-lora-wan2.2:   2%|#1                                                  | 112/5000 [04:36<3:21:09,  2.47s/it, lr: 1.0e-04 loss: 5.836e-02]
lana-lora-wan2.2:   2%|#1                                                  | 112/5000 [04:36<3:21:09,  2.47s/it, lr: 1.0e-04 loss: 5.836e-02]
lana-lora-wan2.2:   2%|#1                                                  | 113/5000 [04:38<3:20:54,  2.47s/it, lr: 1.0e-04 loss: 3.940e-02]
lana-lora-wan2.2:   2%|#1                                                  | 113/5000 [04:38<3:20:54,  2.47s/it, lr: 1.0e-04 loss: 3.940e-02]
lana-lora-wan2.2:   2%|#1                                                  | 114/5000 [04:42<3:21:43,  2.48s/it, lr: 1.0e-04 loss: 8.150e-02]
lana-lora-wan2.2:   2%|#1                                                  | 114/5000 [04:42<3:21:43,  2.48s/it, lr: 1.0e-04 loss: 8.150e-02]
lana-lora-wan2.2:   2%|#1                                                  | 115/5000 [04:44<3:21:38,  2.48s/it, lr: 1.0e-04 loss: 2.020e-01]
lana-lora-wan2.2:   2%|#1                                                  | 115/5000 [04:44<3:21:38,  2.48s/it, lr: 1.0e-04 loss: 2.020e-01]
lana-lora-wan2.2:   2%|#2                                                  | 116/5000 [04:45<3:20:41,  2.47s/it, lr: 1.0e-04 loss: 1.601e-01]
lana-lora-wan2.2:   2%|#2                                                  | 116/5000 [04:45<3:20:41,  2.47s/it, lr: 1.0e-04 loss: 1.601e-01]
lana-lora-wan2.2:   2%|#2                                                  | 117/5000 [04:49<3:21:28,  2.48s/it, lr: 1.0e-04 loss: 3.078e-02]
lana-lora-wan2.2:   2%|#2                                                  | 117/5000 [04:49<3:21:28,  2.48s/it, lr: 1.0e-04 loss: 3.078e-02]
lana-lora-wan2.2:   2%|#2                                                  | 118/5000 [04:50<3:20:32,  2.46s/it, lr: 1.0e-04 loss: 5.165e-02]
lana-lora-wan2.2:   2%|#2                                                  | 118/5000 [04:50<3:20:32,  2.46s/it, lr: 1.0e-04 loss: 5.165e-02]
lana-lora-wan2.2:   2%|#2                                                  | 119/5000 [04:54<3:21:18,  2.47s/it, lr: 1.0e-04 loss: 3.669e-02]
lana-lora-wan2.2:   2%|#2                                                  | 119/5000 [04:54<3:21:18,  2.47s/it, lr: 1.0e-04 loss: 3.669e-02]
lana-lora-wan2.2:   2%|#2                                                  | 120/5000 [04:58<3:22:04,  2.48s/it, lr: 1.0e-04 loss: 1.825e-01]
lana-lora-wan2.2:   2%|#2                                                  | 120/5000 [04:58<3:22:04,  2.48s/it, lr: 1.0e-04 loss: 1.825e-01]
lana-lora-wan2.2:   2%|#2                                                  | 121/5000 [05:00<3:22:02,  2.48s/it, lr: 1.0e-04 loss: 7.231e-02]
lana-lora-wan2.2:   2%|#2                                                  | 121/5000 [05:00<3:22:02,  2.48s/it, lr: 1.0e-04 loss: 7.231e-02]
lana-lora-wan2.2:   2%|#2                                                  | 122/5000 [05:01<3:21:07,  2.47s/it, lr: 1.0e-04 loss: 1.075e-01]
lana-lora-wan2.2:   2%|#2                                                  | 122/5000 [05:01<3:21:07,  2.47s/it, lr: 1.0e-04 loss: 1.075e-01]
lana-lora-wan2.2:   2%|#2                                                  | 123/5000 [05:02<3:20:13,  2.46s/it, lr: 1.0e-04 loss: 1.134e-01]
lana-lora-wan2.2:   2%|#2                                                  | 123/5000 [05:02<3:20:13,  2.46s/it, lr: 1.0e-04 loss: 1.134e-01]
lana-lora-wan2.2:   2%|#2                                                  | 124/5000 [05:04<3:19:20,  2.45s/it, lr: 1.0e-04 loss: 3.949e-02]
lana-lora-wan2.2:   2%|#2                                                  | 124/5000 [05:04<3:19:20,  2.45s/it, lr: 1.0e-04 loss: 3.949e-02]
lana-lora-wan2.2:   2%|#3                                                  | 125/5000 [05:06<3:19:07,  2.45s/it, lr: 1.0e-04 loss: 4.284e-02]
lana-lora-wan2.2:   2%|#3                                                  | 125/5000 [05:06<3:19:07,  2.45s/it, lr: 1.0e-04 loss: 4.284e-02]
lana-lora-wan2.2:   3%|#3                                                  | 126/5000 [05:10<3:19:51,  2.46s/it, lr: 1.0e-04 loss: 8.966e-02]
lana-lora-wan2.2:   3%|#3                                                  | 126/5000 [05:10<3:19:51,  2.46s/it, lr: 1.0e-04 loss: 8.966e-02]
lana-lora-wan2.2:   3%|#3                                                  | 127/5000 [05:11<3:19:14,  2.45s/it, lr: 1.0e-04 loss: 9.119e-02]
lana-lora-wan2.2:   3%|#3                                                  | 127/5000 [05:11<3:19:14,  2.45s/it, lr: 1.0e-04 loss: 9.119e-02]
lana-lora-wan2.2:   3%|#3                                                  | 128/5000 [05:13<3:19:01,  2.45s/it, lr: 1.0e-04 loss: 2.634e-01]
lana-lora-wan2.2:   3%|#3                                                  | 128/5000 [05:13<3:19:01,  2.45s/it, lr: 1.0e-04 loss: 2.634e-01]
lana-lora-wan2.2:   3%|#3                                                  | 129/5000 [05:15<3:18:48,  2.45s/it, lr: 1.0e-04 loss: 3.725e-01]
lana-lora-wan2.2:   3%|#3                                                  | 129/5000 [05:15<3:18:48,  2.45s/it, lr: 1.0e-04 loss: 3.725e-01]
lana-lora-wan2.2:   3%|#3                                                  | 130/5000 [05:17<3:17:58,  2.44s/it, lr: 1.0e-04 loss: 2.161e-01]
lana-lora-wan2.2:   3%|#3                                                  | 130/5000 [05:17<3:17:58,  2.44s/it, lr: 1.0e-04 loss: 2.161e-01]
lana-lora-wan2.2:   3%|#3                                                  | 131/5000 [05:19<3:17:45,  2.44s/it, lr: 1.0e-04 loss: 3.813e-02]
lana-lora-wan2.2:   3%|#3                                                  | 131/5000 [05:19<3:17:45,  2.44s/it, lr: 1.0e-04 loss: 3.813e-02]
lana-lora-wan2.2:   3%|#3                                                  | 132/5000 [05:21<3:17:32,  2.43s/it, lr: 1.0e-04 loss: 6.363e-02]
lana-lora-wan2.2:   3%|#3                                                  | 132/5000 [05:21<3:17:32,  2.43s/it, lr: 1.0e-04 loss: 6.363e-02]
lana-lora-wan2.2:   3%|#3                                                  | 133/5000 [05:22<3:16:54,  2.43s/it, lr: 1.0e-04 loss: 1.232e-01]
lana-lora-wan2.2:   3%|#3                                                  | 133/5000 [05:22<3:16:54,  2.43s/it, lr: 1.0e-04 loss: 1.232e-01]
lana-lora-wan2.2:   3%|#3                                                  | 134/5000 [05:24<3:16:07,  2.42s/it, lr: 1.0e-04 loss: 4.836e-02]
lana-lora-wan2.2:   3%|#3                                                  | 134/5000 [05:24<3:16:07,  2.42s/it, lr: 1.0e-04 loss: 4.836e-02]
lana-lora-wan2.2:   3%|#4                                                  | 135/5000 [05:27<3:16:49,  2.43s/it, lr: 1.0e-04 loss: 3.959e-02]
lana-lora-wan2.2:   3%|#4                                                  | 135/5000 [05:27<3:16:49,  2.43s/it, lr: 1.0e-04 loss: 3.959e-02]
lana-lora-wan2.2:   3%|#4                                                  | 136/5000 [05:31<3:17:30,  2.44s/it, lr: 1.0e-04 loss: 3.936e-02]
lana-lora-wan2.2:   3%|#4                                                  | 136/5000 [05:31<3:17:30,  2.44s/it, lr: 1.0e-04 loss: 3.936e-02]
lana-lora-wan2.2:   3%|#4                                                  | 137/5000 [05:32<3:16:43,  2.43s/it, lr: 1.0e-04 loss: 7.784e-02]
lana-lora-wan2.2:   3%|#4                                                  | 137/5000 [05:32<3:16:43,  2.43s/it, lr: 1.0e-04 loss: 7.784e-02]
lana-lora-wan2.2:   3%|#4                                                  | 138/5000 [05:36<3:17:35,  2.44s/it, lr: 1.0e-04 loss: 2.993e-02]
lana-lora-wan2.2:   3%|#4                                                  | 138/5000 [05:36<3:17:35,  2.44s/it, lr: 1.0e-04 loss: 2.993e-02]
lana-lora-wan2.2:   3%|#4                                                  | 139/5000 [05:40<3:18:16,  2.45s/it, lr: 1.0e-04 loss: 9.283e-02]
lana-lora-wan2.2:   3%|#4                                                  | 139/5000 [05:40<3:18:16,  2.45s/it, lr: 1.0e-04 loss: 9.283e-02]
lana-lora-wan2.2:   3%|#4                                                  | 140/5000 [05:42<3:18:03,  2.45s/it, lr: 1.0e-04 loss: 2.143e-01]
lana-lora-wan2.2:   3%|#4                                                  | 140/5000 [05:42<3:18:03,  2.45s/it, lr: 1.0e-04 loss: 2.143e-01]
lana-lora-wan2.2:   3%|#4                                                  | 141/5000 [05:45<3:18:43,  2.45s/it, lr: 1.0e-04 loss: 3.902e-02]
lana-lora-wan2.2:   3%|#4                                                  | 141/5000 [05:45<3:18:43,  2.45s/it, lr: 1.0e-04 loss: 3.902e-02]
lana-lora-wan2.2:   3%|#4                                                  | 142/5000 [05:49<3:19:21,  2.46s/it, lr: 1.0e-04 loss: 5.785e-02]
lana-lora-wan2.2:   3%|#4                                                  | 142/5000 [05:49<3:19:21,  2.46s/it, lr: 1.0e-04 loss: 5.785e-02]
lana-lora-wan2.2:   3%|#4                                                  | 143/5000 [05:50<3:18:35,  2.45s/it, lr: 1.0e-04 loss: 1.170e-01]
lana-lora-wan2.2:   3%|#4                                                  | 143/5000 [05:50<3:18:35,  2.45s/it, lr: 1.0e-04 loss: 1.170e-01]
lana-lora-wan2.2:   3%|#4                                                  | 144/5000 [05:54<3:19:21,  2.46s/it, lr: 1.0e-04 loss: 1.232e-01]
lana-lora-wan2.2:   3%|#4                                                  | 144/5000 [05:54<3:19:21,  2.46s/it, lr: 1.0e-04 loss: 1.232e-01]
lana-lora-wan2.2:   3%|#5                                                  | 145/5000 [05:55<3:18:36,  2.45s/it, lr: 1.0e-04 loss: 4.715e-02]
lana-lora-wan2.2:   3%|#5                                                  | 145/5000 [05:55<3:18:36,  2.45s/it, lr: 1.0e-04 loss: 4.715e-02]
lana-lora-wan2.2:   3%|#5                                                  | 146/5000 [05:57<3:17:51,  2.45s/it, lr: 1.0e-04 loss: 4.521e-02]
lana-lora-wan2.2:   3%|#5                                                  | 146/5000 [05:57<3:17:51,  2.45s/it, lr: 1.0e-04 loss: 4.521e-02]
lana-lora-wan2.2:   3%|#5                                                  | 147/5000 [05:59<3:17:40,  2.44s/it, lr: 1.0e-04 loss: 1.041e-01]
lana-lora-wan2.2:   3%|#5                                                  | 147/5000 [05:59<3:17:40,  2.44s/it, lr: 1.0e-04 loss: 1.041e-01]
lana-lora-wan2.2:   3%|#5                                                  | 148/5000 [06:00<3:16:55,  2.44s/it, lr: 1.0e-04 loss: 4.929e-02]
lana-lora-wan2.2:   3%|#5                                                  | 148/5000 [06:00<3:16:55,  2.44s/it, lr: 1.0e-04 loss: 4.929e-02]
lana-lora-wan2.2:   3%|#5                                                  | 149/5000 [06:01<3:16:12,  2.43s/it, lr: 1.0e-04 loss: 4.028e-02]
lana-lora-wan2.2:   3%|#5                                                  | 149/5000 [06:01<3:16:12,  2.43s/it, lr: 1.0e-04 loss: 4.028e-02]
lana-lora-wan2.2:   3%|#5                                                  | 150/5000 [06:03<3:15:40,  2.42s/it, lr: 1.0e-04 loss: 4.624e-02]
lana-lora-wan2.2:   3%|#5                                                  | 150/5000 [06:03<3:15:40,  2.42s/it, lr: 1.0e-04 loss: 4.624e-02]
lana-lora-wan2.2:   3%|#5                                                  | 151/5000 [06:04<3:14:57,  2.41s/it, lr: 1.0e-04 loss: 4.166e-02]
lana-lora-wan2.2:   3%|#5                                                  | 151/5000 [06:04<3:14:57,  2.41s/it, lr: 1.0e-04 loss: 4.166e-02]
lana-lora-wan2.2:   3%|#5                                                  | 152/5000 [06:07<3:15:35,  2.42s/it, lr: 1.0e-04 loss: 5.387e-02]
lana-lora-wan2.2:   3%|#5                                                  | 152/5000 [06:07<3:15:35,  2.42s/it, lr: 1.0e-04 loss: 5.387e-02]
lana-lora-wan2.2:   3%|#5                                                  | 153/5000 [06:11<3:16:11,  2.43s/it, lr: 1.0e-04 loss: 8.119e-02]
lana-lora-wan2.2:   3%|#5                                                  | 153/5000 [06:11<3:16:11,  2.43s/it, lr: 1.0e-04 loss: 8.119e-02]
lana-lora-wan2.2:   3%|#6                                                  | 154/5000 [06:15<3:16:48,  2.44s/it, lr: 1.0e-04 loss: 6.161e-01]
lana-lora-wan2.2:   3%|#6                                                  | 154/5000 [06:15<3:16:48,  2.44s/it, lr: 1.0e-04 loss: 6.161e-01]
lana-lora-wan2.2:   3%|#6                                                  | 155/5000 [06:17<3:16:36,  2.43s/it, lr: 1.0e-04 loss: 3.620e-02]
lana-lora-wan2.2:   3%|#6                                                  | 155/5000 [06:17<3:16:36,  2.43s/it, lr: 1.0e-04 loss: 3.620e-02]
lana-lora-wan2.2:   3%|#6                                                  | 156/5000 [06:18<3:16:04,  2.43s/it, lr: 1.0e-04 loss: 3.563e-02]
lana-lora-wan2.2:   3%|#6                                                  | 156/5000 [06:18<3:16:04,  2.43s/it, lr: 1.0e-04 loss: 3.563e-02]
lana-lora-wan2.2:   3%|#6                                                  | 157/5000 [06:22<3:16:40,  2.44s/it, lr: 1.0e-04 loss: 5.108e-02]
lana-lora-wan2.2:   3%|#6                                                  | 157/5000 [06:22<3:16:40,  2.44s/it, lr: 1.0e-04 loss: 5.108e-02]
lana-lora-wan2.2:   3%|#6                                                  | 158/5000 [06:24<3:16:29,  2.43s/it, lr: 1.0e-04 loss: 3.215e-02]
lana-lora-wan2.2:   3%|#6                                                  | 158/5000 [06:24<3:16:29,  2.43s/it, lr: 1.0e-04 loss: 3.215e-02]
lana-lora-wan2.2:   3%|#6                                                  | 159/5000 [06:26<3:16:18,  2.43s/it, lr: 1.0e-04 loss: 5.110e-02]
lana-lora-wan2.2:   3%|#6                                                  | 159/5000 [06:26<3:16:18,  2.43s/it, lr: 1.0e-04 loss: 5.110e-02]
lana-lora-wan2.2:   3%|#6                                                  | 160/5000 [06:30<3:16:54,  2.44s/it, lr: 1.0e-04 loss: 5.688e-02]
lana-lora-wan2.2:   3%|#6                                                  | 160/5000 [06:30<3:16:54,  2.44s/it, lr: 1.0e-04 loss: 5.688e-02]
lana-lora-wan2.2:   3%|#6                                                  | 161/5000 [06:32<3:16:43,  2.44s/it, lr: 1.0e-04 loss: 2.522e-01]
lana-lora-wan2.2:   3%|#6                                                  | 161/5000 [06:32<3:16:43,  2.44s/it, lr: 1.0e-04 loss: 2.522e-01]
lana-lora-wan2.2:   3%|#6                                                  | 162/5000 [06:35<3:16:43,  2.44s/it, lr: 1.0e-04 loss: 4.319e-02]
lana-lora-wan2.2:   3%|#6                                                  | 162/5000 [06:35<3:16:43,  2.44s/it, lr: 1.0e-04 loss: 4.319e-02]
lana-lora-wan2.2:   3%|#6                                                  | 163/5000 [06:36<3:16:03,  2.43s/it, lr: 1.0e-04 loss: 6.155e-02]
lana-lora-wan2.2:   3%|#6                                                  | 163/5000 [06:36<3:16:03,  2.43s/it, lr: 1.0e-04 loss: 6.155e-02]
lana-lora-wan2.2:   3%|#7                                                  | 164/5000 [06:37<3:15:23,  2.42s/it, lr: 1.0e-04 loss: 5.135e-02]
lana-lora-wan2.2:   3%|#7                                                  | 164/5000 [06:37<3:15:23,  2.42s/it, lr: 1.0e-04 loss: 5.135e-02]
lana-lora-wan2.2:   3%|#7                                                  | 165/5000 [06:39<3:15:13,  2.42s/it, lr: 1.0e-04 loss: 4.405e-02]
lana-lora-wan2.2:   3%|#7                                                  | 165/5000 [06:39<3:15:13,  2.42s/it, lr: 1.0e-04 loss: 4.405e-02]
lana-lora-wan2.2:   3%|#7                                                  | 166/5000 [06:43<3:15:47,  2.43s/it, lr: 1.0e-04 loss: 1.089e-01]
lana-lora-wan2.2:   3%|#7                                                  | 166/5000 [06:43<3:15:47,  2.43s/it, lr: 1.0e-04 loss: 1.089e-01]
lana-lora-wan2.2:   3%|#7                                                  | 167/5000 [06:44<3:15:17,  2.42s/it, lr: 1.0e-04 loss: 7.429e-02]
lana-lora-wan2.2:   3%|#7                                                  | 167/5000 [06:44<3:15:17,  2.42s/it, lr: 1.0e-04 loss: 7.429e-02]
lana-lora-wan2.2:   3%|#7                                                  | 168/5000 [06:48<3:15:50,  2.43s/it, lr: 1.0e-04 loss: 3.179e-02]
lana-lora-wan2.2:   3%|#7                                                  | 168/5000 [06:48<3:15:50,  2.43s/it, lr: 1.0e-04 loss: 3.179e-02]
lana-lora-wan2.2:   3%|#7                                                  | 169/5000 [06:50<3:15:40,  2.43s/it, lr: 1.0e-04 loss: 2.924e-02]
lana-lora-wan2.2:   3%|#7                                                  | 169/5000 [06:50<3:15:40,  2.43s/it, lr: 1.0e-04 loss: 2.924e-02]
lana-lora-wan2.2:   3%|#7                                                  | 170/5000 [06:52<3:15:31,  2.43s/it, lr: 1.0e-04 loss: 4.869e-02]
lana-lora-wan2.2:   3%|#7                                                  | 170/5000 [06:52<3:15:31,  2.43s/it, lr: 1.0e-04 loss: 4.869e-02]
lana-lora-wan2.2:   3%|#7                                                  | 171/5000 [06:56<3:16:03,  2.44s/it, lr: 1.0e-04 loss: 4.359e-02]
lana-lora-wan2.2:   3%|#7                                                  | 171/5000 [06:56<3:16:03,  2.44s/it, lr: 1.0e-04 loss: 4.359e-02]
lana-lora-wan2.2:   3%|#7                                                  | 172/5000 [06:57<3:15:25,  2.43s/it, lr: 1.0e-04 loss: 2.881e-01]
lana-lora-wan2.2:   3%|#7                                                  | 172/5000 [06:57<3:15:25,  2.43s/it, lr: 1.0e-04 loss: 2.881e-01]
lana-lora-wan2.2:   3%|#7                                                  | 173/5000 [07:01<3:16:06,  2.44s/it, lr: 1.0e-04 loss: 3.833e-02]
lana-lora-wan2.2:   3%|#7                                                  | 173/5000 [07:01<3:16:06,  2.44s/it, lr: 1.0e-04 loss: 3.833e-02]
lana-lora-wan2.2:   3%|#8                                                  | 174/5000 [07:03<3:15:56,  2.44s/it, lr: 1.0e-04 loss: 2.876e-02]
lana-lora-wan2.2:   3%|#8                                                  | 174/5000 [07:03<3:15:56,  2.44s/it, lr: 1.0e-04 loss: 2.876e-02]
lana-lora-wan2.2:   4%|#8                                                  | 175/5000 [07:07<3:16:27,  2.44s/it, lr: 1.0e-04 loss: 5.419e-02]
lana-lora-wan2.2:   4%|#8                                                  | 175/5000 [07:07<3:16:27,  2.44s/it, lr: 1.0e-04 loss: 5.419e-02]
lana-lora-wan2.2:   4%|#8                                                  | 176/5000 [07:08<3:15:51,  2.44s/it, lr: 1.0e-04 loss: 4.162e-02]
lana-lora-wan2.2:   4%|#8                                                  | 176/5000 [07:08<3:15:51,  2.44s/it, lr: 1.0e-04 loss: 4.162e-02]
lana-lora-wan2.2:   4%|#8                                                  | 177/5000 [07:10<3:15:41,  2.43s/it, lr: 1.0e-04 loss: 1.779e-01]
lana-lora-wan2.2:   4%|#8                                                  | 177/5000 [07:10<3:15:41,  2.43s/it, lr: 1.0e-04 loss: 1.779e-01]
lana-lora-wan2.2:   4%|#8                                                  | 178/5000 [07:13<3:15:31,  2.43s/it, lr: 1.0e-04 loss: 1.455e-01]
lana-lora-wan2.2:   4%|#8                                                  | 178/5000 [07:13<3:15:31,  2.43s/it, lr: 1.0e-04 loss: 1.455e-01]
lana-lora-wan2.2:   4%|#8                                                  | 179/5000 [07:17<3:16:10,  2.44s/it, lr: 1.0e-04 loss: 3.050e-01]
lana-lora-wan2.2:   4%|#8                                                  | 179/5000 [07:17<3:16:10,  2.44s/it, lr: 1.0e-04 loss: 3.050e-01]
lana-lora-wan2.2:   4%|#8                                                  | 180/5000 [07:18<3:15:34,  2.43s/it, lr: 1.0e-04 loss: 8.536e-02]
lana-lora-wan2.2:   4%|#8                                                  | 180/5000 [07:18<3:15:34,  2.43s/it, lr: 1.0e-04 loss: 8.536e-02]
lana-lora-wan2.2:   4%|#8                                                  | 181/5000 [07:21<3:16:04,  2.44s/it, lr: 1.0e-04 loss: 3.469e-02]
lana-lora-wan2.2:   4%|#8                                                  | 181/5000 [07:21<3:16:04,  2.44s/it, lr: 1.0e-04 loss: 3.469e-02]
lana-lora-wan2.2:   4%|#8                                                  | 182/5000 [07:25<3:16:34,  2.45s/it, lr: 1.0e-04 loss: 3.293e-02]
lana-lora-wan2.2:   4%|#8                                                  | 182/5000 [07:25<3:16:34,  2.45s/it, lr: 1.0e-04 loss: 3.293e-02]
lana-lora-wan2.2:   4%|#9                                                  | 183/5000 [07:26<3:15:59,  2.44s/it, lr: 1.0e-04 loss: 7.248e-02]
lana-lora-wan2.2:   4%|#9                                                  | 183/5000 [07:26<3:15:59,  2.44s/it, lr: 1.0e-04 loss: 7.248e-02]
lana-lora-wan2.2:   4%|#9                                                  | 184/5000 [07:27<3:15:24,  2.43s/it, lr: 1.0e-04 loss: 3.506e-02]
lana-lora-wan2.2:   4%|#9                                                  | 184/5000 [07:27<3:15:24,  2.43s/it, lr: 1.0e-04 loss: 3.506e-02]
lana-lora-wan2.2:   4%|#9                                                  | 185/5000 [07:30<3:15:21,  2.43s/it, lr: 1.0e-04 loss: 5.901e-02]
lana-lora-wan2.2:   4%|#9                                                  | 185/5000 [07:30<3:15:21,  2.43s/it, lr: 1.0e-04 loss: 5.901e-02]
lana-lora-wan2.2:   4%|#9                                                  | 186/5000 [07:31<3:14:47,  2.43s/it, lr: 1.0e-04 loss: 1.573e-01]
lana-lora-wan2.2:   4%|#9                                                  | 186/5000 [07:31<3:14:47,  2.43s/it, lr: 1.0e-04 loss: 1.573e-01]
lana-lora-wan2.2:   4%|#9                                                  | 187/5000 [07:33<3:14:38,  2.43s/it, lr: 1.0e-04 loss: 7.842e-02]
lana-lora-wan2.2:   4%|#9                                                  | 187/5000 [07:33<3:14:38,  2.43s/it, lr: 1.0e-04 loss: 7.842e-02]
lana-lora-wan2.2:   4%|#9                                                  | 188/5000 [07:37<3:15:06,  2.43s/it, lr: 1.0e-04 loss: 2.937e-02]
lana-lora-wan2.2:   4%|#9                                                  | 188/5000 [07:37<3:15:06,  2.43s/it, lr: 1.0e-04 loss: 2.937e-02]
lana-lora-wan2.2:   4%|#9                                                  | 189/5000 [07:39<3:14:57,  2.43s/it, lr: 1.0e-04 loss: 8.551e-02]
lana-lora-wan2.2:   4%|#9                                                  | 189/5000 [07:39<3:14:57,  2.43s/it, lr: 1.0e-04 loss: 8.551e-02]
lana-lora-wan2.2:   4%|#9                                                  | 190/5000 [07:43<3:15:31,  2.44s/it, lr: 1.0e-04 loss: 6.691e-02]
lana-lora-wan2.2:   4%|#9                                                  | 190/5000 [07:43<3:15:31,  2.44s/it, lr: 1.0e-04 loss: 6.691e-02]
lana-lora-wan2.2:   4%|#9                                                  | 191/5000 [07:47<3:15:59,  2.45s/it, lr: 1.0e-04 loss: 1.324e-01]
lana-lora-wan2.2:   4%|#9                                                  | 191/5000 [07:47<3:15:59,  2.45s/it, lr: 1.0e-04 loss: 1.324e-01]
lana-lora-wan2.2:   4%|#9                                                  | 192/5000 [07:49<3:15:50,  2.44s/it, lr: 1.0e-04 loss: 4.142e-02]
lana-lora-wan2.2:   4%|#9                                                  | 192/5000 [07:49<3:15:50,  2.44s/it, lr: 1.0e-04 loss: 4.142e-02]
lana-lora-wan2.2:   4%|##                                                  | 193/5000 [07:50<3:15:15,  2.44s/it, lr: 1.0e-04 loss: 1.665e-01]
lana-lora-wan2.2:   4%|##                                                  | 193/5000 [07:50<3:15:15,  2.44s/it, lr: 1.0e-04 loss: 1.665e-01]
lana-lora-wan2.2:   4%|##                                                  | 194/5000 [07:54<3:15:43,  2.44s/it, lr: 1.0e-04 loss: 6.330e-02]
lana-lora-wan2.2:   4%|##                                                  | 194/5000 [07:54<3:15:43,  2.44s/it, lr: 1.0e-04 loss: 6.330e-02]
lana-lora-wan2.2:   4%|##                                                  | 195/5000 [07:55<3:15:10,  2.44s/it, lr: 1.0e-04 loss: 1.021e-01]
lana-lora-wan2.2:   4%|##                                                  | 195/5000 [07:55<3:15:10,  2.44s/it, lr: 1.0e-04 loss: 1.021e-01]
lana-lora-wan2.2:   4%|##                                                  | 196/5000 [07:59<3:15:43,  2.44s/it, lr: 1.0e-04 loss: 6.801e-02]
lana-lora-wan2.2:   4%|##                                                  | 196/5000 [07:59<3:15:43,  2.44s/it, lr: 1.0e-04 loss: 6.801e-02]
lana-lora-wan2.2:   4%|##                                                  | 197/5000 [08:01<3:15:33,  2.44s/it, lr: 1.0e-04 loss: 3.186e-02]
lana-lora-wan2.2:   4%|##                                                  | 197/5000 [08:01<3:15:33,  2.44s/it, lr: 1.0e-04 loss: 3.186e-02]
lana-lora-wan2.2:   4%|##                                                  | 198/5000 [08:03<3:15:24,  2.44s/it, lr: 1.0e-04 loss: 3.026e-02]
lana-lora-wan2.2:   4%|##                                                  | 198/5000 [08:03<3:15:24,  2.44s/it, lr: 1.0e-04 loss: 3.026e-02]
lana-lora-wan2.2:   4%|##                                                  | 199/5000 [08:04<3:14:51,  2.44s/it, lr: 1.0e-04 loss: 4.903e-02]
lana-lora-wan2.2:   4%|##                                                  | 199/5000 [08:04<3:14:51,  2.44s/it, lr: 1.0e-04 loss: 4.903e-02]
lana-lora-wan2.2:   4%|##                                                  | 200/5000 [08:06<3:14:42,  2.43s/it, lr: 1.0e-04 loss: 3.639e-02]
lana-lora-wan2.2:   4%|##                                                  | 200/5000 [08:06<3:14:42,  2.43s/it, lr: 1.0e-04 loss: 3.639e-02]
lana-lora-wan2.2:   4%|##                                                  | 201/5000 [08:10<3:15:08,  2.44s/it, lr: 1.0e-04 loss: 4.466e-02]
lana-lora-wan2.2:   4%|##                                                  | 201/5000 [08:10<3:15:08,  2.44s/it, lr: 1.0e-04 loss: 4.466e-02]
lana-lora-wan2.2:   4%|##1                                                 | 202/5000 [08:14<3:15:41,  2.45s/it, lr: 1.0e-04 loss: 3.593e-02]
lana-lora-wan2.2:   4%|##1                                                 | 202/5000 [08:14<3:15:41,  2.45s/it, lr: 1.0e-04 loss: 3.593e-02]
lana-lora-wan2.2:   4%|##1                                                 | 203/5000 [08:15<3:15:08,  2.44s/it, lr: 1.0e-04 loss: 4.012e-02]
lana-lora-wan2.2:   4%|##1                                                 | 203/5000 [08:15<3:15:08,  2.44s/it, lr: 1.0e-04 loss: 4.012e-02]
lana-lora-wan2.2:   4%|##1                                                 | 204/5000 [08:17<3:14:59,  2.44s/it, lr: 1.0e-04 loss: 1.033e-01]
lana-lora-wan2.2:   4%|##1                                                 | 204/5000 [08:17<3:14:59,  2.44s/it, lr: 1.0e-04 loss: 1.033e-01]
lana-lora-wan2.2:   4%|##1                                                 | 205/5000 [08:19<3:14:50,  2.44s/it, lr: 1.0e-04 loss: 3.743e-02]
lana-lora-wan2.2:   4%|##1                                                 | 205/5000 [08:19<3:14:50,  2.44s/it, lr: 1.0e-04 loss: 3.743e-02]
lana-lora-wan2.2:   4%|##1                                                 | 206/5000 [08:21<3:14:42,  2.44s/it, lr: 1.0e-04 loss: 6.320e-02]
lana-lora-wan2.2:   4%|##1                                                 | 206/5000 [08:21<3:14:42,  2.44s/it, lr: 1.0e-04 loss: 6.320e-02]
lana-lora-wan2.2:   4%|##1                                                 | 207/5000 [08:25<3:15:08,  2.44s/it, lr: 1.0e-04 loss: 1.235e-01]
lana-lora-wan2.2:   4%|##1                                                 | 207/5000 [08:25<3:15:08,  2.44s/it, lr: 1.0e-04 loss: 1.235e-01]
lana-lora-wan2.2:   4%|##1                                                 | 208/5000 [08:27<3:14:45,  2.44s/it, lr: 1.0e-04 loss: 4.845e-02]
lana-lora-wan2.2:   4%|##1                                                 | 208/5000 [08:27<3:14:45,  2.44s/it, lr: 1.0e-04 loss: 4.845e-02]
lana-lora-wan2.2:   4%|##1                                                 | 209/5000 [08:28<3:14:14,  2.43s/it, lr: 1.0e-04 loss: 5.580e-02]
lana-lora-wan2.2:   4%|##1                                                 | 209/5000 [08:28<3:14:14,  2.43s/it, lr: 1.0e-04 loss: 5.580e-02]
lana-lora-wan2.2:   4%|##1                                                 | 210/5000 [08:29<3:13:43,  2.43s/it, lr: 1.0e-04 loss: 1.313e-01]
lana-lora-wan2.2:   4%|##1                                                 | 210/5000 [08:29<3:13:43,  2.43s/it, lr: 1.0e-04 loss: 1.313e-01]
lana-lora-wan2.2:   4%|##1                                                 | 211/5000 [08:31<3:13:35,  2.43s/it, lr: 1.0e-04 loss: 4.477e-02]
lana-lora-wan2.2:   4%|##1                                                 | 211/5000 [08:31<3:13:35,  2.43s/it, lr: 1.0e-04 loss: 4.477e-02]
lana-lora-wan2.2:   4%|##2                                                 | 212/5000 [08:33<3:13:27,  2.42s/it, lr: 1.0e-04 loss: 5.940e-02]
lana-lora-wan2.2:   4%|##2                                                 | 212/5000 [08:33<3:13:27,  2.42s/it, lr: 1.0e-04 loss: 5.940e-02]
lana-lora-wan2.2:   4%|##2                                                 | 213/5000 [08:35<3:12:56,  2.42s/it, lr: 1.0e-04 loss: 1.324e-01]
lana-lora-wan2.2:   4%|##2                                                 | 213/5000 [08:35<3:12:56,  2.42s/it, lr: 1.0e-04 loss: 1.324e-01]
lana-lora-wan2.2:   4%|##2                                                 | 214/5000 [08:39<3:13:27,  2.43s/it, lr: 1.0e-04 loss: 4.316e-02]
lana-lora-wan2.2:   4%|##2                                                 | 214/5000 [08:39<3:13:27,  2.43s/it, lr: 1.0e-04 loss: 4.316e-02]
lana-lora-wan2.2:   4%|##2                                                 | 215/5000 [08:42<3:13:41,  2.43s/it, lr: 1.0e-04 loss: 4.852e-02]
lana-lora-wan2.2:   4%|##2                                                 | 215/5000 [08:42<3:13:41,  2.43s/it, lr: 1.0e-04 loss: 4.852e-02]
lana-lora-wan2.2:   4%|##2                                                 | 216/5000 [08:44<3:13:33,  2.43s/it, lr: 1.0e-04 loss: 1.045e-01]
lana-lora-wan2.2:   4%|##2                                                 | 216/5000 [08:44<3:13:33,  2.43s/it, lr: 1.0e-04 loss: 1.045e-01]
lana-lora-wan2.2:   4%|##2                                                 | 217/5000 [08:45<3:13:03,  2.42s/it, lr: 1.0e-04 loss: 3.830e-02]
lana-lora-wan2.2:   4%|##2                                                 | 217/5000 [08:45<3:13:03,  2.42s/it, lr: 1.0e-04 loss: 3.830e-02]
lana-lora-wan2.2:   4%|##2                                                 | 218/5000 [08:47<3:12:55,  2.42s/it, lr: 1.0e-04 loss: 4.114e-02]
lana-lora-wan2.2:   4%|##2                                                 | 218/5000 [08:47<3:12:55,  2.42s/it, lr: 1.0e-04 loss: 4.114e-02]
lana-lora-wan2.2:   4%|##2                                                 | 219/5000 [08:48<3:12:25,  2.41s/it, lr: 1.0e-04 loss: 3.734e-02]
lana-lora-wan2.2:   4%|##2                                                 | 219/5000 [08:48<3:12:25,  2.41s/it, lr: 1.0e-04 loss: 3.734e-02]
lana-lora-wan2.2:   4%|##2                                                 | 220/5000 [08:50<3:12:11,  2.41s/it, lr: 1.0e-04 loss: 1.136e-01]
lana-lora-wan2.2:   4%|##2                                                 | 220/5000 [08:50<3:12:11,  2.41s/it, lr: 1.0e-04 loss: 1.136e-01]
lana-lora-wan2.2:   4%|##2                                                 | 221/5000 [08:54<3:12:36,  2.42s/it, lr: 1.0e-04 loss: 9.777e-02]
lana-lora-wan2.2:   4%|##2                                                 | 221/5000 [08:54<3:12:36,  2.42s/it, lr: 1.0e-04 loss: 9.777e-02]
lana-lora-wan2.2:   4%|##3                                                 | 222/5000 [08:55<3:12:07,  2.41s/it, lr: 1.0e-04 loss: 1.316e-01]
lana-lora-wan2.2:   4%|##3                                                 | 222/5000 [08:55<3:12:07,  2.41s/it, lr: 1.0e-04 loss: 1.316e-01]
lana-lora-wan2.2:   4%|##3                                                 | 223/5000 [08:57<3:11:59,  2.41s/it, lr: 1.0e-04 loss: 4.016e-02]
lana-lora-wan2.2:   4%|##3                                                 | 223/5000 [08:57<3:11:59,  2.41s/it, lr: 1.0e-04 loss: 4.016e-02]
lana-lora-wan2.2:   4%|##3                                                 | 224/5000 [08:59<3:11:51,  2.41s/it, lr: 1.0e-04 loss: 1.045e-01]
lana-lora-wan2.2:   4%|##3                                                 | 224/5000 [08:59<3:11:51,  2.41s/it, lr: 1.0e-04 loss: 1.045e-01]
lana-lora-wan2.2:   4%|##3                                                 | 225/5000 [09:01<3:11:29,  2.41s/it, lr: 1.0e-04 loss: 4.798e-02]
lana-lora-wan2.2:   4%|##3                                                 | 225/5000 [09:01<3:11:29,  2.41s/it, lr: 1.0e-04 loss: 4.798e-02]
lana-lora-wan2.2:   5%|##3                                                 | 226/5000 [09:02<3:11:01,  2.40s/it, lr: 1.0e-04 loss: 5.715e-02]
lana-lora-wan2.2:   5%|##3                                                 | 226/5000 [09:02<3:11:01,  2.40s/it, lr: 1.0e-04 loss: 5.715e-02]
lana-lora-wan2.2:   5%|##3                                                 | 227/5000 [09:03<3:10:33,  2.40s/it, lr: 1.0e-04 loss: 3.947e-02]
lana-lora-wan2.2:   5%|##3                                                 | 227/5000 [09:03<3:10:33,  2.40s/it, lr: 1.0e-04 loss: 3.947e-02]
lana-lora-wan2.2:   5%|##3                                                 | 228/5000 [09:05<3:10:25,  2.39s/it, lr: 1.0e-04 loss: 5.477e-02]
lana-lora-wan2.2:   5%|##3                                                 | 228/5000 [09:05<3:10:25,  2.39s/it, lr: 1.0e-04 loss: 5.477e-02]
lana-lora-wan2.2:   5%|##3                                                 | 229/5000 [09:07<3:09:58,  2.39s/it, lr: 1.0e-04 loss: 6.492e-02]
lana-lora-wan2.2:   5%|##3                                                 | 229/5000 [09:07<3:09:58,  2.39s/it, lr: 1.0e-04 loss: 6.492e-02]
lana-lora-wan2.2:   5%|##3                                                 | 230/5000 [09:09<3:09:51,  2.39s/it, lr: 1.0e-04 loss: 1.060e-01]
lana-lora-wan2.2:   5%|##3                                                 | 230/5000 [09:09<3:09:51,  2.39s/it, lr: 1.0e-04 loss: 1.060e-01]
lana-lora-wan2.2:   5%|##4                                                 | 231/5000 [09:13<3:10:19,  2.39s/it, lr: 1.0e-04 loss: 1.002e-01]
lana-lora-wan2.2:   5%|##4                                                 | 231/5000 [09:13<3:10:19,  2.39s/it, lr: 1.0e-04 loss: 1.002e-01]
lana-lora-wan2.2:   5%|##4                                                 | 232/5000 [09:15<3:10:12,  2.39s/it, lr: 1.0e-04 loss: 1.298e-01]
lana-lora-wan2.2:   5%|##4                                                 | 232/5000 [09:15<3:10:12,  2.39s/it, lr: 1.0e-04 loss: 1.298e-01]
lana-lora-wan2.2:   5%|##4                                                 | 233/5000 [09:16<3:09:45,  2.39s/it, lr: 1.0e-04 loss: 6.857e-02]
lana-lora-wan2.2:   5%|##4                                                 | 233/5000 [09:16<3:09:45,  2.39s/it, lr: 1.0e-04 loss: 6.857e-02]
lana-lora-wan2.2:   5%|##4                                                 | 234/5000 [09:20<3:10:09,  2.39s/it, lr: 1.0e-04 loss: 6.032e-02]
lana-lora-wan2.2:   5%|##4                                                 | 234/5000 [09:20<3:10:09,  2.39s/it, lr: 1.0e-04 loss: 6.032e-02]
lana-lora-wan2.2:   5%|##4                                                 | 235/5000 [09:21<3:09:42,  2.39s/it, lr: 1.0e-04 loss: 8.852e-02]
lana-lora-wan2.2:   5%|##4                                                 | 235/5000 [09:21<3:09:42,  2.39s/it, lr: 1.0e-04 loss: 8.852e-02]
lana-lora-wan2.2:   5%|##4                                                 | 236/5000 [09:22<3:09:15,  2.38s/it, lr: 1.0e-04 loss: 1.226e-01]
lana-lora-wan2.2:   5%|##4                                                 | 236/5000 [09:22<3:09:15,  2.38s/it, lr: 1.0e-04 loss: 1.226e-01]
lana-lora-wan2.2:   5%|##4                                                 | 237/5000 [09:26<3:09:43,  2.39s/it, lr: 1.0e-04 loss: 4.186e-02]
lana-lora-wan2.2:   5%|##4                                                 | 237/5000 [09:26<3:09:43,  2.39s/it, lr: 1.0e-04 loss: 4.186e-02]
lana-lora-wan2.2:   5%|##4                                                 | 238/5000 [09:27<3:09:16,  2.38s/it, lr: 1.0e-04 loss: 4.156e-01]
lana-lora-wan2.2:   5%|##4                                                 | 238/5000 [09:27<3:09:16,  2.38s/it, lr: 1.0e-04 loss: 4.156e-01]
lana-lora-wan2.2:   5%|##4                                                 | 239/5000 [09:31<3:09:39,  2.39s/it, lr: 1.0e-04 loss: 1.224e-01]
lana-lora-wan2.2:   5%|##4                                                 | 239/5000 [09:31<3:09:39,  2.39s/it, lr: 1.0e-04 loss: 1.224e-01]
lana-lora-wan2.2:   5%|##4                                                 | 240/5000 [09:32<3:09:12,  2.39s/it, lr: 1.0e-04 loss: 7.789e-02]
lana-lora-wan2.2:   5%|##4                                                 | 240/5000 [09:32<3:09:12,  2.39s/it, lr: 1.0e-04 loss: 7.789e-02]
lana-lora-wan2.2:   5%|##5                                                 | 241/5000 [09:34<3:09:06,  2.38s/it, lr: 1.0e-04 loss: 3.558e-02]
lana-lora-wan2.2:   5%|##5                                                 | 241/5000 [09:34<3:09:06,  2.38s/it, lr: 1.0e-04 loss: 3.558e-02]
lana-lora-wan2.2:   5%|##5                                                 | 242/5000 [09:35<3:08:40,  2.38s/it, lr: 1.0e-04 loss: 4.561e-02]
lana-lora-wan2.2:   5%|##5                                                 | 242/5000 [09:35<3:08:40,  2.38s/it, lr: 1.0e-04 loss: 4.561e-02]
lana-lora-wan2.2:   5%|##5                                                 | 243/5000 [09:39<3:09:07,  2.39s/it, lr: 1.0e-04 loss: 4.593e-02]
lana-lora-wan2.2:   5%|##5                                                 | 243/5000 [09:39<3:09:07,  2.39s/it, lr: 1.0e-04 loss: 4.593e-02]
lana-lora-wan2.2:   5%|##5                                                 | 244/5000 [09:41<3:09:00,  2.38s/it, lr: 1.0e-04 loss: 6.828e-02]
lana-lora-wan2.2:   5%|##5                                                 | 244/5000 [09:41<3:09:00,  2.38s/it, lr: 1.0e-04 loss: 6.828e-02]
lana-lora-wan2.2:   5%|##5                                                 | 245/5000 [09:43<3:08:54,  2.38s/it, lr: 1.0e-04 loss: 1.055e-01]
lana-lora-wan2.2:   5%|##5                                                 | 245/5000 [09:43<3:08:54,  2.38s/it, lr: 1.0e-04 loss: 1.055e-01]
lana-lora-wan2.2:   5%|##5                                                 | 246/5000 [09:45<3:08:28,  2.38s/it, lr: 1.0e-04 loss: 6.014e-02]
lana-lora-wan2.2:   5%|##5                                                 | 246/5000 [09:45<3:08:28,  2.38s/it, lr: 1.0e-04 loss: 6.014e-02]
lana-lora-wan2.2:   5%|##5                                                 | 247/5000 [09:48<3:08:51,  2.38s/it, lr: 1.0e-04 loss: 4.216e-02]
lana-lora-wan2.2:   5%|##5                                                 | 247/5000 [09:48<3:08:51,  2.38s/it, lr: 1.0e-04 loss: 4.216e-02]
lana-lora-wan2.2:   5%|##5                                                 | 248/5000 [09:51<3:08:44,  2.38s/it, lr: 1.0e-04 loss: 3.221e-02]
lana-lora-wan2.2:   5%|##5                                                 | 248/5000 [09:51<3:08:44,  2.38s/it, lr: 1.0e-04 loss: 3.221e-02]
lana-lora-wan2.2:   5%|##5                                                 | 249/5000 [09:53<3:08:45,  2.38s/it, lr: 1.0e-04 loss: 8.259e-02]
lana-lora-wan2.2:   5%|##5                                                 | 249/5000 [09:53<3:08:45,  2.38s/it, lr: 1.0e-04 loss: 8.259e-02]
+Saving at step 250
+Saved checkpoint to /root/lab/ai-toolkit/output/lana-lora-wan2.2/lana-lora-wan2.2_000000250.safetensors
+Saved optimizer to /root/lab/ai-toolkit/output/lana-lora-wan2.2/optimizer.pt
+
+
+
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+
+
+
+
                                                                                                                                             
                                                                                                                                             Error running job: Job stopped
+
+========================================
+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 2282, in run
+  File "/root/lab/ai-toolkit/jobs/process/BaseSDTrainProcess.py", line 2282, in run
+        self.sample(self.step_num)self.sample(self.step_num)
+
+  File "/root/lab/ai-toolkit/extensions_built_in/sd_trainer/DiffusionTrainer.py", line 306, in sample
+  File "/root/lab/ai-toolkit/extensions_built_in/sd_trainer/DiffusionTrainer.py", line 306, in sample
+        super().sample(step, is_first)super().sample(step, is_first)
+
+  File "/root/lab/ai-toolkit/jobs/process/BaseSDTrainProcess.py", line 368, in sample
+  File "/root/lab/ai-toolkit/jobs/process/BaseSDTrainProcess.py", line 368, in sample
+        self.sd.generate_images(gen_img_config_list, sampler=sample_config.sampler)self.sd.generate_images(gen_img_config_list, sampler=sample_config.sampler)
+
+  File "/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+  File "/usr/local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
+        return func(*args, **kwargs)return func(*args, **kwargs)
+
+                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+  File "/root/lab/ai-toolkit/toolkit/models/base_model.py", line 665, in generate_images
+  File "/root/lab/ai-toolkit/toolkit/models/base_model.py", line 665, in generate_images
+        self._after_sample_image(i, len(image_configs))self._after_sample_image(i, len(image_configs))
+
+  File "/root/lab/ai-toolkit/toolkit/models/base_model.py", line 348, in _after_sample_image
+  File "/root/lab/ai-toolkit/toolkit/models/base_model.py", line 348, in _after_sample_image
+        hook(img_num, total_imgs)hook(img_num, total_imgs)
+
+  File "/root/lab/ai-toolkit/extensions_built_in/sd_trainer/DiffusionTrainer.py", line 298, in sample_step_hook
+  File "/root/lab/ai-toolkit/extensions_built_in/sd_trainer/DiffusionTrainer.py", line 298, in sample_step_hook
+        self.maybe_stop()self.maybe_stop()
+
+  File "/root/lab/ai-toolkit/extensions_built_in/sd_trainer/DiffusionTrainer.py", line 147, in maybe_stop
+  File "/root/lab/ai-toolkit/extensions_built_in/sd_trainer/DiffusionTrainer.py", line 147, in maybe_stop
+        raise Exception("Job stopped")raise Exception("Job stopped")
+
+ExceptionException: : Job stoppedJob stopped
+
+
lana-lora-wan2.2:   5%|##5                                                 | 249/5000 [16:23<5:12:39,  3.95s/it, lr: 1.0e-04 loss: 8.259e-02]
lana-lora-wan2.2:   5%|##5                                                 | 249/5000 [16:23<5:12:39,  3.95s/it, lr: 1.0e-04 loss: 8.259e-02]
+