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index bec3c06f9d0277c072647ee6bb32c1444c8724ba..68bc9e0a623b6a1af016584f7944c937bbe9674a 100644
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diff --git a/dpdm/eurosat_32_eps10.0trainval-2024-10-24-12-56-31/stdout.txt b/dpdm/eurosat_32_eps10.0trainval-2024-10-24-12-56-31/stdout.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e6578296aa26743fd5e772a0c8ea1227fb051f24
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+++ b/dpdm/eurosat_32_eps10.0trainval-2024-10-24-12-56-31/stdout.txt
@@ -0,0 +1,1605 @@
+INFO - utils.py - 2024-10-24 12:56:35,302 - {'setup': {'method': 'dpsgd-diffusion', 'run_type': 'torchmp', 'n_gpus_per_node': 4, 'n_nodes': 1, 'node_rank': 0, 'master_address': '127.0.0.1', 'master_port': 6025, 'omp_n_threads': 8, 'workdir': 'exp/dpdm/eurosat_32_eps10.0trainval-2024-10-24-12-56-31', 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'root_folder': '.'}, 'public_data': {'name': None}, 'sensitive_data': {'name': 'eurosat', 'num_channels': 3, 'resolution': 32, 'n_classes': 10, 'train_path': 'dataset/eurosat/train_32.zip', 'test_path': 'dataset/eurosat/test_32.zip', 'fid_stats': 'dataset/eurosat/fid_stats_32.npz', 'train_num': 'val'}, 'model': {'ckpt': None, 'denoiser_name': 'edm', 'denoiser_network': 'song', 'ema_rate': 0.999, 'network': {'image_size': 32, 'num_in_channels': 3, 'num_out_channels': 3, 'label_dim': 10, 'attn_resolutions': [16], 'ch_mult': [2, 4]}, 'sampler': {'type': 'ddim', 'stochastic': False, 'num_steps': 50, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0, 'snapshot_batch_size': 80, 'fid_batch_size': 256}, 'sampler_fid': {'type': 'edm', 's_churn': 100, 's_min': 0.05, 's_max': 50, 'num_steps': 250, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0}, 'sampler_acc': {'type': 'edm', 's_churn': 10, 's_min': 0.1, 's_max': 50, 'num_steps': 250, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0, 'labels': 10}, 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'fid_stats': 'dataset/eurosat/fid_stats_32.npz'}, 'pretrain': {'log_dir': 'exp/dpdm/eurosat_32_eps10.0trainval-2024-10-24-12-56-31/pretrain', 'seed': 0, 'batch_size': 64, 'n_epochs': 1, 'log_freq': 100, 'snapshot_freq': 2000, 'snapshot_threshold': 1, 'save_freq': 100000, 'save_threshold': 1, 'fid_freq': 2000, 'fid_samples': 5000, 'fid_threshold': 1, 'label_random': True, 'optim': {'optimizer': 'Adam', 'params': {'lr': 0.0003, 'weight_decay': 0.0}}, 'loss': {'version': 'edm', 'p_mean': -1.2, 'p_std': 1.2, 'n_noise_samples': 1, 'n_classes': 10}}, 'train': {'log_dir': 'exp/dpdm/eurosat_32_eps10.0trainval-2024-10-24-12-56-31/train', 'seed': 0, 'batch_size': 2048, 'n_epochs': 150, 'partly_finetune': False, 'log_freq': 100, 'snapshot_freq': 2000, 'snapshot_threshold': 1, 'save_freq': 100000, 'save_threshold': 1, 'fid_freq': 2000, 'fid_samples': 5000, 'final_fid_samples': 60000, 'fid_threshold': 1, 'gen': False, 'gen_batch_size': 8192, 'optim': {'optimizer': 'Adam', 'params': {'lr': 0.0003, 'weight_decay': 0.0}}, 'loss': {'version': 'edm', 'p_mean': -1.2, 'p_std': 1.2, 'n_noise_samples': 32, 'n_classes': 10}, 'dp': {'sdq': None, 'privacy_history': [[5, 0.1, 75]], 'alpha_num': 0, 'max_grad_norm': 1.0, 'delta': 1e-05, 'epsilon': 10.0, 'max_physical_batch_size': 8192, 'n_splits': 64}}, 'gen': {'data_num': 60000, 'batch_size': 1000, 'log_dir': 'exp/dpdm/eurosat_32_eps10.0trainval-2024-10-24-12-56-31/gen'}, 'eval': {'batch_size': 1000}}
+INFO - dataset_loader.py - 2024-10-24 12:56:35,808 - delta is reset as 4.784738627130138e-06
+INFO - dpsgd_diffusion.py - 2024-10-24 12:56:37,021 - Number of trainable parameters in model: 0
+INFO - dpsgd_diffusion.py - 2024-10-24 12:56:37,021 - Number of total epochs: 150
+INFO - dpsgd_diffusion.py - 2024-10-24 12:56:37,022 - Starting training at step 0
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+INFO - dpsgd_diffusion.py - 2024-10-24 12:58:41,150 - Loss: 0.7667, step: 200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:59:33,247 - Loss: 0.7340, step: 300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:00:27,627 - Loss: 0.7427, step: 400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:01:26,421 - Loss: 0.6948, step: 500
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+INFO - dpsgd_diffusion.py - 2024-10-24 13:03:05,866 - Loss: 0.7436, step: 700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:03:07,937 - Eps-value after 1 epochs: 0.8278
+INFO - dpsgd_diffusion.py - 2024-10-24 13:03:57,446 - Loss: 0.6514, step: 800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:04:45,049 - Loss: 0.6494, step: 900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:05:35,180 - Loss: 0.7007, step: 1000
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+INFO - dpsgd_diffusion.py - 2024-10-24 13:08:09,004 - Loss: 0.5505, step: 1300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:08:59,992 - Loss: 0.5104, step: 1400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:09:03,871 - Eps-value after 2 epochs: 1.0916
+INFO - dpsgd_diffusion.py - 2024-10-24 13:09:55,227 - Loss: 0.5062, step: 1500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:10:46,434 - Loss: 0.4971, step: 1600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:11:36,729 - Loss: 0.4400, step: 1700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:12:27,457 - Loss: 0.3841, step: 1800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:13:17,135 - Loss: 0.4253, step: 1900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:14:07,309 - Loss: 0.4233, step: 2000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:14:07,611 - Saving snapshot checkpoint and sampling single batch at iteration 2000.
+WARNING - image.py - 2024-10-24 13:14:08,311 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 13:14:35,598 - FID at iteration 2000: 266.150087
+INFO - dpsgd_diffusion.py - 2024-10-24 13:15:25,838 - Loss: 0.3838, step: 2100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:15:31,422 - Eps-value after 3 epochs: 1.3018
+INFO - dpsgd_diffusion.py - 2024-10-24 13:15:55,003 - Loss: 0.3859, step: 2200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:16:19,790 - Loss: 0.3638, step: 2300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:16:46,240 - Loss: 0.3702, step: 2400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:17:12,311 - Loss: 0.3091, step: 2500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:17:38,976 - Loss: 0.3323, step: 2600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:18:04,457 - Loss: 0.2781, step: 2700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:18:31,359 - Loss: 0.2657, step: 2800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:18:35,429 - Eps-value after 4 epochs: 1.4836
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+INFO - dpsgd_diffusion.py - 2024-10-24 13:19:24,265 - Loss: 0.2652, step: 3000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:19:50,420 - Loss: 0.2824, step: 3100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:20:15,759 - Loss: 0.3173, step: 3200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:20:42,149 - Loss: 0.2418, step: 3300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:21:08,632 - Loss: 0.2672, step: 3400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:21:35,050 - Loss: 0.2545, step: 3500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:21:39,701 - Eps-value after 5 epochs: 1.6477
+INFO - dpsgd_diffusion.py - 2024-10-24 13:22:01,335 - Loss: 0.2349, step: 3600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:22:26,910 - Loss: 0.2393, step: 3700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:22:53,442 - Loss: 0.2082, step: 3800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:23:20,700 - Loss: 0.2088, step: 3900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:23:47,192 - Loss: 0.2210, step: 4000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:23:47,242 - Saving snapshot checkpoint and sampling single batch at iteration 4000.
+WARNING - image.py - 2024-10-24 13:23:47,729 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 13:24:03,267 - FID at iteration 4000: 220.912688
+INFO - dpsgd_diffusion.py - 2024-10-24 13:24:28,998 - Loss: 0.2411, step: 4100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:24:54,408 - Loss: 0.2651, step: 4200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:25:00,105 - Eps-value after 6 epochs: 1.7955
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+INFO - dpsgd_diffusion.py - 2024-10-24 13:25:46,936 - Loss: 0.2732, step: 4400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:26:12,310 - Loss: 0.2421, step: 4500
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+INFO - dpsgd_diffusion.py - 2024-10-24 13:27:30,096 - Loss: 0.2338, step: 4800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:27:58,132 - Loss: 0.2886, step: 4900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:28:05,753 - Eps-value after 7 epochs: 1.9342
+INFO - dpsgd_diffusion.py - 2024-10-24 13:28:24,585 - Loss: 0.1623, step: 5000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:28:50,852 - Loss: 0.1935, step: 5100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:29:17,563 - Loss: 0.2176, step: 5200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:29:43,804 - Loss: 0.2679, step: 5300
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+INFO - dpsgd_diffusion.py - 2024-10-24 13:31:03,600 - Loss: 0.1662, step: 5600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:31:11,306 - Eps-value after 8 epochs: 2.0644
+INFO - dpsgd_diffusion.py - 2024-10-24 13:31:30,417 - Loss: 0.2490, step: 5700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:31:56,649 - Loss: 0.2281, step: 5800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:32:23,496 - Loss: 0.1905, step: 5900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:32:49,350 - Loss: 0.2154, step: 6000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:32:49,366 - Saving snapshot checkpoint and sampling single batch at iteration 6000.
+WARNING - image.py - 2024-10-24 13:32:49,855 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 13:33:05,342 - FID at iteration 6000: 194.395857
+INFO - dpsgd_diffusion.py - 2024-10-24 13:33:31,760 - Loss: 0.2074, step: 6100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:33:57,371 - Loss: 0.2452, step: 6200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:34:22,976 - Loss: 0.1983, step: 6300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:34:32,114 - Eps-value after 9 epochs: 2.1877
+INFO - dpsgd_diffusion.py - 2024-10-24 13:34:50,010 - Loss: 0.1775, step: 6400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:35:16,274 - Loss: 0.2437, step: 6500
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+INFO - dpsgd_diffusion.py - 2024-10-24 13:36:08,347 - Loss: 0.2101, step: 6700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:36:33,870 - Loss: 0.1987, step: 6800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:36:59,066 - Loss: 0.1800, step: 6900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:37:24,568 - Loss: 0.2086, step: 7000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:37:34,832 - Eps-value after 10 epochs: 2.3052
+INFO - dpsgd_diffusion.py - 2024-10-24 13:37:50,873 - Loss: 0.2201, step: 7100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:38:15,823 - Loss: 0.1715, step: 7200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:38:41,122 - Loss: 0.1657, step: 7300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:39:07,040 - Loss: 0.1232, step: 7400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:39:34,110 - Loss: 0.2695, step: 7500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:40:00,279 - Loss: 0.2419, step: 7600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:40:26,443 - Loss: 0.2113, step: 7700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:40:37,115 - Eps-value after 11 epochs: 2.4178
+INFO - dpsgd_diffusion.py - 2024-10-24 13:40:51,504 - Loss: 0.2180, step: 7800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:41:18,366 - Loss: 0.1712, step: 7900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:41:43,474 - Loss: 0.1980, step: 8000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:41:43,479 - Saving snapshot checkpoint and sampling single batch at iteration 8000.
+WARNING - image.py - 2024-10-24 13:41:43,968 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 13:41:59,454 - FID at iteration 8000: 192.123321
+INFO - dpsgd_diffusion.py - 2024-10-24 13:42:26,105 - Loss: 0.2284, step: 8100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:42:52,083 - Loss: 0.1595, step: 8200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:43:18,285 - Loss: 0.1376, step: 8300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:43:44,143 - Loss: 0.2073, step: 8400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:43:56,504 - Eps-value after 12 epochs: 2.5261
+INFO - dpsgd_diffusion.py - 2024-10-24 13:44:10,056 - Loss: 0.1691, step: 8500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:44:35,391 - Loss: 0.1793, step: 8600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:45:00,140 - Loss: 0.1707, step: 8700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:45:25,971 - Loss: 0.1313, step: 8800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:45:52,011 - Loss: 0.1904, step: 8900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:46:17,945 - Loss: 0.1762, step: 9000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:46:44,792 - Loss: 0.1873, step: 9100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:46:58,742 - Eps-value after 13 epochs: 2.6304
+INFO - dpsgd_diffusion.py - 2024-10-24 13:47:11,674 - Loss: 0.1356, step: 9200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:47:39,321 - Loss: 0.1847, step: 9300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:48:05,054 - Loss: 0.1534, step: 9400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:48:30,947 - Loss: 0.1844, step: 9500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:48:56,712 - Loss: 0.2153, step: 9600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:49:22,690 - Loss: 0.1945, step: 9700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:49:48,580 - Loss: 0.1908, step: 9800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:50:03,451 - Eps-value after 14 epochs: 2.7315
+INFO - dpsgd_diffusion.py - 2024-10-24 13:50:15,041 - Loss: 0.1547, step: 9900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:50:40,195 - Loss: 0.1191, step: 10000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:50:40,213 - Saving snapshot checkpoint and sampling single batch at iteration 10000.
+WARNING - image.py - 2024-10-24 13:50:40,717 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 13:50:56,307 - FID at iteration 10000: 196.074296
+INFO - dpsgd_diffusion.py - 2024-10-24 13:51:22,465 - Loss: 0.1982, step: 10100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:51:48,320 - Loss: 0.1706, step: 10200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:52:13,678 - Loss: 0.1724, step: 10300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:52:39,932 - Loss: 0.1728, step: 10400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:53:06,628 - Loss: 0.1105, step: 10500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:53:21,582 - Eps-value after 15 epochs: 2.8295
+INFO - dpsgd_diffusion.py - 2024-10-24 13:53:33,217 - Loss: 0.1581, step: 10600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:53:58,975 - Loss: 0.1720, step: 10700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:54:25,405 - Loss: 0.1344, step: 10800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:54:52,223 - Loss: 0.1203, step: 10900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:55:17,052 - Loss: 0.1438, step: 11000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:55:43,551 - Loss: 0.1537, step: 11100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:56:09,936 - Loss: 0.1640, step: 11200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:56:26,769 - Eps-value after 16 epochs: 2.9248
+INFO - dpsgd_diffusion.py - 2024-10-24 13:56:37,140 - Loss: 0.1618, step: 11300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:57:02,808 - Loss: 0.1609, step: 11400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:57:29,409 - Loss: 0.1570, step: 11500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:57:55,751 - Loss: 0.1626, step: 11600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:58:22,768 - Loss: 0.1559, step: 11700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:58:48,107 - Loss: 0.2149, step: 11800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:59:13,792 - Loss: 0.1228, step: 11900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:59:30,949 - Eps-value after 17 epochs: 3.0175
+INFO - dpsgd_diffusion.py - 2024-10-24 13:59:39,547 - Loss: 0.2009, step: 12000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:59:39,554 - Saving snapshot checkpoint and sampling single batch at iteration 12000.
+WARNING - image.py - 2024-10-24 13:59:40,065 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 13:59:55,483 - FID at iteration 12000: 197.204586
+INFO - dpsgd_diffusion.py - 2024-10-24 14:00:20,955 - Loss: 0.1201, step: 12100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:00:47,188 - Loss: 0.1025, step: 12200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:01:13,379 - Loss: 0.2092, step: 12300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:01:40,186 - Loss: 0.1480, step: 12400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:02:06,421 - Loss: 0.0967, step: 12500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:02:31,340 - Loss: 0.1094, step: 12600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:02:49,591 - Eps-value after 18 epochs: 3.1081
+INFO - dpsgd_diffusion.py - 2024-10-24 14:02:57,107 - Loss: 0.1282, step: 12700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:03:21,811 - Loss: 0.1484, step: 12800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:03:48,523 - Loss: 0.1548, step: 12900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:04:14,312 - Loss: 0.1149, step: 13000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:04:39,599 - Loss: 0.1872, step: 13100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:05:05,755 - Loss: 0.1314, step: 13200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:05:31,389 - Loss: 0.1576, step: 13300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:05:51,033 - Eps-value after 19 epochs: 3.1964
+INFO - dpsgd_diffusion.py - 2024-10-24 14:05:57,531 - Loss: 0.1740, step: 13400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:06:23,890 - Loss: 0.1431, step: 13500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:06:48,835 - Loss: 0.1857, step: 13600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:07:14,566 - Loss: 0.1741, step: 13700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:07:41,261 - Loss: 0.1703, step: 13800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:08:06,890 - Loss: 0.1781, step: 13900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:08:32,263 - Loss: 0.1664, step: 14000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:08:32,266 - Saving snapshot checkpoint and sampling single batch at iteration 14000.
+WARNING - image.py - 2024-10-24 14:08:32,772 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 14:08:48,275 - FID at iteration 14000: 198.862773
+INFO - dpsgd_diffusion.py - 2024-10-24 14:09:09,072 - Eps-value after 20 epochs: 3.2829
+INFO - dpsgd_diffusion.py - 2024-10-24 14:09:14,323 - Loss: 0.1423, step: 14100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:09:41,418 - Loss: 0.1383, step: 14200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:10:08,613 - Loss: 0.1360, step: 14300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:10:34,028 - Loss: 0.1831, step: 14400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:11:01,809 - Loss: 0.1155, step: 14500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:11:27,577 - Loss: 0.1403, step: 14600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:11:54,725 - Loss: 0.1369, step: 14700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:12:17,590 - Eps-value after 21 epochs: 3.3677
+INFO - dpsgd_diffusion.py - 2024-10-24 14:12:21,972 - Loss: 0.1770, step: 14800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:12:47,886 - Loss: 0.1546, step: 14900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:13:13,936 - Loss: 0.1602, step: 15000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:13:40,668 - Loss: 0.1604, step: 15100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:14:06,689 - Loss: 0.1581, step: 15200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:14:32,690 - Loss: 0.1539, step: 15300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:14:58,656 - Loss: 0.1362, step: 15400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:15:22,080 - Eps-value after 22 epochs: 3.4506
+INFO - dpsgd_diffusion.py - 2024-10-24 14:15:25,250 - Loss: 0.1920, step: 15500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:15:51,574 - Loss: 0.1070, step: 15600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:16:17,292 - Loss: 0.1299, step: 15700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:16:42,665 - Loss: 0.1404, step: 15800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:17:08,031 - Loss: 0.1065, step: 15900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:17:32,680 - Loss: 0.1515, step: 16000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:17:32,684 - Saving snapshot checkpoint and sampling single batch at iteration 16000.
+WARNING - image.py - 2024-10-24 14:17:33,210 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 14:17:48,605 - FID at iteration 16000: 195.976260
+INFO - dpsgd_diffusion.py - 2024-10-24 14:18:14,851 - Loss: 0.1387, step: 16100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:18:38,641 - Eps-value after 23 epochs: 3.5321
+INFO - dpsgd_diffusion.py - 2024-10-24 14:18:40,794 - Loss: 0.1341, step: 16200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:19:07,386 - Loss: 0.1498, step: 16300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:19:34,501 - Loss: 0.1696, step: 16400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:20:00,017 - Loss: 0.1539, step: 16500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:20:26,633 - Loss: 0.1616, step: 16600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:20:52,516 - Loss: 0.1447, step: 16700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:21:19,469 - Loss: 0.1797, step: 16800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:21:45,182 - Eps-value after 24 epochs: 3.6120
+INFO - dpsgd_diffusion.py - 2024-10-24 14:21:46,257 - Loss: 0.1373, step: 16900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:22:12,960 - Loss: 0.1428, step: 17000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:22:40,401 - Loss: 0.1871, step: 17100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:23:06,799 - Loss: 0.1556, step: 17200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:23:33,191 - Loss: 0.1710, step: 17300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:23:59,075 - Loss: 0.1073, step: 17400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:24:24,733 - Loss: 0.1358, step: 17500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:24:49,875 - Loss: 0.1217, step: 17600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:24:49,890 - Eps-value after 25 epochs: 3.6907
+INFO - dpsgd_diffusion.py - 2024-10-24 14:25:16,249 - Loss: 0.0980, step: 17700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:25:41,247 - Loss: 0.1839, step: 17800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:26:07,925 - Loss: 0.1424, step: 17900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:26:32,819 - Loss: 0.1647, step: 18000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:26:32,835 - Saving snapshot checkpoint and sampling single batch at iteration 18000.
+WARNING - image.py - 2024-10-24 14:26:33,347 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 14:26:48,936 - FID at iteration 18000: 196.076727
+INFO - dpsgd_diffusion.py - 2024-10-24 14:27:15,555 - Loss: 0.1944, step: 18100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:27:42,082 - Loss: 0.1536, step: 18200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:28:08,837 - Loss: 0.1600, step: 18300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:28:09,901 - Eps-value after 26 epochs: 3.7681
+INFO - dpsgd_diffusion.py - 2024-10-24 14:28:34,909 - Loss: 0.1963, step: 18400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:28:59,764 - Loss: 0.1250, step: 18500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:29:25,722 - Loss: 0.1269, step: 18600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:29:52,079 - Loss: 0.1851, step: 18700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:30:17,858 - Loss: 0.1114, step: 18800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:30:44,319 - Loss: 0.1566, step: 18900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:31:10,984 - Loss: 0.1643, step: 19000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:31:13,030 - Eps-value after 27 epochs: 3.8441
+INFO - dpsgd_diffusion.py - 2024-10-24 14:31:37,355 - Loss: 0.1323, step: 19100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:32:03,022 - Loss: 0.1189, step: 19200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:32:29,458 - Loss: 0.0876, step: 19300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:32:55,509 - Loss: 0.1230, step: 19400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:33:22,085 - Loss: 0.1294, step: 19500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:33:47,801 - Loss: 0.1676, step: 19600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:34:14,347 - Loss: 0.1439, step: 19700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:34:17,490 - Eps-value after 28 epochs: 3.9190
+INFO - dpsgd_diffusion.py - 2024-10-24 14:34:40,194 - Loss: 0.1969, step: 19800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:35:06,225 - Loss: 0.1577, step: 19900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:35:31,805 - Loss: 0.1460, step: 20000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:35:31,842 - Saving snapshot checkpoint and sampling single batch at iteration 20000.
+WARNING - image.py - 2024-10-24 14:35:32,358 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 14:35:47,834 - FID at iteration 20000: 193.356779
+INFO - dpsgd_diffusion.py - 2024-10-24 14:36:13,387 - Loss: 0.1523, step: 20100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:36:39,801 - Loss: 0.1452, step: 20200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:37:04,196 - Loss: 0.1988, step: 20300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:37:30,902 - Loss: 0.1866, step: 20400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:37:34,722 - Eps-value after 29 epochs: 3.9928
+INFO - dpsgd_diffusion.py - 2024-10-24 14:37:56,730 - Loss: 0.1366, step: 20500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:38:23,521 - Loss: 0.1325, step: 20600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:38:49,421 - Loss: 0.1384, step: 20700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:39:15,348 - Loss: 0.1536, step: 20800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:39:41,329 - Loss: 0.1803, step: 20900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:40:06,316 - Loss: 0.0948, step: 21000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:40:32,528 - Loss: 0.1067, step: 21100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:40:37,491 - Eps-value after 30 epochs: 4.0657
+INFO - dpsgd_diffusion.py - 2024-10-24 14:40:59,533 - Loss: 0.0944, step: 21200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:41:25,324 - Loss: 0.1335, step: 21300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:41:50,566 - Loss: 0.1489, step: 21400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:42:16,285 - Loss: 0.1136, step: 21500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:42:42,672 - Loss: 0.0987, step: 21600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:43:09,409 - Loss: 0.1222, step: 21700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:43:35,231 - Loss: 0.1507, step: 21800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:43:41,494 - Eps-value after 31 epochs: 4.1376
+INFO - dpsgd_diffusion.py - 2024-10-24 14:44:00,578 - Loss: 0.1238, step: 21900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:44:26,382 - Loss: 0.1131, step: 22000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:44:26,421 - Saving snapshot checkpoint and sampling single batch at iteration 22000.
+WARNING - image.py - 2024-10-24 14:44:26,950 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 14:44:42,451 - FID at iteration 22000: 191.417865
+INFO - dpsgd_diffusion.py - 2024-10-24 14:45:08,873 - Loss: 0.1485, step: 22100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:45:34,877 - Loss: 0.1115, step: 22200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:46:00,621 - Loss: 0.1220, step: 22300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:46:26,595 - Loss: 0.1848, step: 22400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:46:52,202 - Loss: 0.1269, step: 22500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:46:58,799 - Eps-value after 32 epochs: 4.2083
+INFO - dpsgd_diffusion.py - 2024-10-24 14:47:17,518 - Loss: 0.1639, step: 22600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:47:43,582 - Loss: 0.0862, step: 22700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:48:09,961 - Loss: 0.1565, step: 22800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:48:36,181 - Loss: 0.1554, step: 22900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:49:02,914 - Loss: 0.1575, step: 23000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:49:29,824 - Loss: 0.1559, step: 23100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:49:55,894 - Loss: 0.1221, step: 23200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:50:03,631 - Eps-value after 33 epochs: 4.2784
+INFO - dpsgd_diffusion.py - 2024-10-24 14:50:22,070 - Loss: 0.1337, step: 23300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:50:48,034 - Loss: 0.1255, step: 23400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:51:14,863 - Loss: 0.1524, step: 23500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:51:40,394 - Loss: 0.1310, step: 23600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:52:05,827 - Loss: 0.1555, step: 23700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:52:31,393 - Loss: 0.1449, step: 23800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:52:57,934 - Loss: 0.1118, step: 23900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:53:07,420 - Eps-value after 34 epochs: 4.3475
+INFO - dpsgd_diffusion.py - 2024-10-24 14:53:24,311 - Loss: 0.1032, step: 24000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:53:24,315 - Saving snapshot checkpoint and sampling single batch at iteration 24000.
+WARNING - image.py - 2024-10-24 14:53:24,851 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 14:53:40,367 - FID at iteration 24000: 189.174934
+INFO - dpsgd_diffusion.py - 2024-10-24 14:54:06,179 - Loss: 0.1038, step: 24100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:54:30,650 - Loss: 0.1314, step: 24200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:54:56,976 - Loss: 0.1310, step: 24300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:55:22,294 - Loss: 0.1621, step: 24400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:55:48,379 - Loss: 0.1492, step: 24500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:56:14,633 - Loss: 0.1413, step: 24600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:56:25,492 - Eps-value after 35 epochs: 4.4157
+INFO - dpsgd_diffusion.py - 2024-10-24 14:56:41,631 - Loss: 0.1745, step: 24700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:57:07,209 - Loss: 0.1353, step: 24800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:57:32,372 - Loss: 0.1158, step: 24900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:57:58,631 - Loss: 0.1464, step: 25000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:58:24,187 - Loss: 0.1393, step: 25100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:58:50,311 - Loss: 0.1200, step: 25200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:59:15,855 - Loss: 0.1473, step: 25300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:59:26,202 - Eps-value after 36 epochs: 4.4835
+INFO - dpsgd_diffusion.py - 2024-10-24 14:59:40,216 - Loss: 0.1011, step: 25400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:00:06,619 - Loss: 0.1551, step: 25500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:00:32,276 - Loss: 0.1519, step: 25600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:00:58,071 - Loss: 0.1285, step: 25700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:01:24,015 - Loss: 0.1132, step: 25800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:01:49,660 - Loss: 0.0982, step: 25900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:02:14,626 - Loss: 0.1145, step: 26000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:02:14,641 - Saving snapshot checkpoint and sampling single batch at iteration 26000.
+WARNING - image.py - 2024-10-24 15:02:15,129 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 15:02:30,700 - FID at iteration 26000: 186.974627
+INFO - dpsgd_diffusion.py - 2024-10-24 15:02:43,560 - Eps-value after 37 epochs: 4.5500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:02:56,801 - Loss: 0.1727, step: 26100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:03:22,527 - Loss: 0.1183, step: 26200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:03:47,644 - Loss: 0.1892, step: 26300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:04:13,654 - Loss: 0.1499, step: 26400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:04:39,396 - Loss: 0.0969, step: 26500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:05:05,209 - Loss: 0.1119, step: 26600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:05:30,808 - Loss: 0.1072, step: 26700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:05:44,156 - Eps-value after 38 epochs: 4.6163
+INFO - dpsgd_diffusion.py - 2024-10-24 15:05:57,116 - Loss: 0.1350, step: 26800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:06:22,868 - Loss: 0.1237, step: 26900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:06:48,694 - Loss: 0.0910, step: 27000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:07:14,817 - Loss: 0.1510, step: 27100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:07:40,431 - Loss: 0.1130, step: 27200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:08:06,550 - Loss: 0.1493, step: 27300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:08:32,510 - Loss: 0.1627, step: 27400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:08:47,501 - Eps-value after 39 epochs: 4.6814
+INFO - dpsgd_diffusion.py - 2024-10-24 15:08:59,396 - Loss: 0.1310, step: 27500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:09:25,485 - Loss: 0.0985, step: 27600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:09:52,348 - Loss: 0.1338, step: 27700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:10:18,036 - Loss: 0.1166, step: 27800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:10:43,942 - Loss: 0.1569, step: 27900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:11:10,397 - Loss: 0.0813, step: 28000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:11:10,448 - Saving snapshot checkpoint and sampling single batch at iteration 28000.
+WARNING - image.py - 2024-10-24 15:11:10,989 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 15:11:26,563 - FID at iteration 28000: 185.782042
+INFO - dpsgd_diffusion.py - 2024-10-24 15:11:51,716 - Loss: 0.1460, step: 28100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:12:07,293 - Eps-value after 40 epochs: 4.7464
+INFO - dpsgd_diffusion.py - 2024-10-24 15:12:17,901 - Loss: 0.1061, step: 28200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:12:42,993 - Loss: 0.1894, step: 28300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:13:09,253 - Loss: 0.1461, step: 28400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:13:35,677 - Loss: 0.1478, step: 28500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:14:00,965 - Loss: 0.1658, step: 28600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:14:27,364 - Loss: 0.1071, step: 28700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:14:52,168 - Loss: 0.1717, step: 28800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:15:09,070 - Eps-value after 41 epochs: 4.8101
+INFO - dpsgd_diffusion.py - 2024-10-24 15:15:18,792 - Loss: 0.1422, step: 28900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:15:45,609 - Loss: 0.1277, step: 29000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:16:12,517 - Loss: 0.0895, step: 29100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:16:38,197 - Loss: 0.1455, step: 29200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:17:04,477 - Loss: 0.1530, step: 29300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:17:30,171 - Loss: 0.1178, step: 29400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:17:56,494 - Loss: 0.1307, step: 29500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:18:13,482 - Eps-value after 42 epochs: 4.8739
+INFO - dpsgd_diffusion.py - 2024-10-24 15:18:22,123 - Loss: 0.1603, step: 29600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:18:47,452 - Loss: 0.1249, step: 29700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:19:13,814 - Loss: 0.1426, step: 29800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:19:40,208 - Loss: 0.1837, step: 29900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:20:07,455 - Loss: 0.1573, step: 30000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:20:07,505 - Saving snapshot checkpoint and sampling single batch at iteration 30000.
+WARNING - image.py - 2024-10-24 15:20:08,027 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 15:20:23,619 - FID at iteration 30000: 185.736478
+INFO - dpsgd_diffusion.py - 2024-10-24 15:20:49,632 - Loss: 0.1451, step: 30100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:21:15,629 - Loss: 0.1307, step: 30200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:21:34,723 - Eps-value after 43 epochs: 4.9364
+INFO - dpsgd_diffusion.py - 2024-10-24 15:21:42,167 - Loss: 0.1410, step: 30300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:22:08,285 - Loss: 0.1139, step: 30400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:22:35,040 - Loss: 0.1529, step: 30500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:23:00,604 - Loss: 0.1614, step: 30600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:23:26,548 - Loss: 0.1103, step: 30700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:23:53,403 - Loss: 0.1308, step: 30800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:24:19,889 - Loss: 0.1315, step: 30900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:24:39,725 - Eps-value after 44 epochs: 4.9988
+INFO - dpsgd_diffusion.py - 2024-10-24 15:24:46,178 - Loss: 0.1159, step: 31000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:25:11,647 - Loss: 0.1504, step: 31100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:25:37,647 - Loss: 0.1782, step: 31200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:26:03,383 - Loss: 0.1285, step: 31300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:26:30,599 - Loss: 0.1135, step: 31400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:26:56,189 - Loss: 0.1789, step: 31500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:27:21,342 - Loss: 0.1284, step: 31600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:27:41,207 - Eps-value after 45 epochs: 5.0603
+INFO - dpsgd_diffusion.py - 2024-10-24 15:27:46,610 - Loss: 0.0979, step: 31700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:28:13,203 - Loss: 0.1028, step: 31800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:28:39,414 - Loss: 0.1161, step: 31900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:29:04,805 - Loss: 0.1590, step: 32000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:29:04,810 - Saving snapshot checkpoint and sampling single batch at iteration 32000.
+WARNING - image.py - 2024-10-24 15:29:05,329 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 15:29:20,823 - FID at iteration 32000: 181.458855
+INFO - dpsgd_diffusion.py - 2024-10-24 15:29:47,470 - Loss: 0.0914, step: 32100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:30:13,885 - Loss: 0.1768, step: 32200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:30:38,837 - Loss: 0.1089, step: 32300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:30:59,966 - Eps-value after 46 epochs: 5.1214
+INFO - dpsgd_diffusion.py - 2024-10-24 15:31:04,263 - Loss: 0.1010, step: 32400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:31:30,413 - Loss: 0.1637, step: 32500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:31:56,298 - Loss: 0.1705, step: 32600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:32:21,568 - Loss: 0.0999, step: 32700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:32:47,870 - Loss: 0.1169, step: 32800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:33:13,915 - Loss: 0.1717, step: 32900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:33:39,817 - Loss: 0.1153, step: 33000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:34:02,306 - Eps-value after 47 epochs: 5.1823
+INFO - dpsgd_diffusion.py - 2024-10-24 15:34:05,551 - Loss: 0.1005, step: 33100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:34:31,517 - Loss: 0.1149, step: 33200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:34:57,208 - Loss: 0.0918, step: 33300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:35:22,626 - Loss: 0.0994, step: 33400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:35:48,514 - Loss: 0.1346, step: 33500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:36:15,112 - Loss: 0.1093, step: 33600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:36:42,986 - Loss: 0.1299, step: 33700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:37:06,131 - Eps-value after 48 epochs: 5.2421
+INFO - dpsgd_diffusion.py - 2024-10-24 15:37:08,294 - Loss: 0.1523, step: 33800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:37:34,744 - Loss: 0.1144, step: 33900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:38:00,593 - Loss: 0.0992, step: 34000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:38:00,645 - Saving snapshot checkpoint and sampling single batch at iteration 34000.
+WARNING - image.py - 2024-10-24 15:38:01,263 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 15:38:16,854 - FID at iteration 34000: 180.363880
+INFO - dpsgd_diffusion.py - 2024-10-24 15:38:43,011 - Loss: 0.1443, step: 34100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:39:08,494 - Loss: 0.1154, step: 34200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:39:33,746 - Loss: 0.0846, step: 34300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:40:00,671 - Loss: 0.1261, step: 34400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:40:25,573 - Eps-value after 49 epochs: 5.3019
+INFO - dpsgd_diffusion.py - 2024-10-24 15:40:26,646 - Loss: 0.1201, step: 34500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:40:53,224 - Loss: 0.1499, step: 34600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:41:19,654 - Loss: 0.1524, step: 34700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:41:46,116 - Loss: 0.1614, step: 34800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:42:12,854 - Loss: 0.0934, step: 34900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:42:39,073 - Loss: 0.1265, step: 35000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:43:04,190 - Loss: 0.1434, step: 35100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:43:31,051 - Loss: 0.0878, step: 35200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:43:31,065 - Eps-value after 50 epochs: 5.3610
+INFO - dpsgd_diffusion.py - 2024-10-24 15:43:57,328 - Loss: 0.1711, step: 35300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:44:22,282 - Loss: 0.1579, step: 35400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:44:48,541 - Loss: 0.1462, step: 35500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:45:14,972 - Loss: 0.0920, step: 35600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:45:40,740 - Loss: 0.1449, step: 35700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:06,416 - Loss: 0.1389, step: 35800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:30,599 - Loss: 0.1266, step: 35900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:31,605 - Eps-value after 51 epochs: 5.4194
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:56,537 - Loss: 0.1213, step: 36000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:56,588 - Saving snapshot checkpoint and sampling single batch at iteration 36000.
+WARNING - image.py - 2024-10-24 15:46:57,077 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 15:47:12,654 - FID at iteration 36000: 178.568675
+INFO - dpsgd_diffusion.py - 2024-10-24 15:47:39,610 - Loss: 0.1275, step: 36100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:48:05,616 - Loss: 0.1379, step: 36200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:48:32,019 - Loss: 0.1380, step: 36300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:48:58,704 - Loss: 0.1253, step: 36400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:49:25,026 - Loss: 0.1670, step: 36500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:49:50,452 - Loss: 0.1192, step: 36600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:49:52,363 - Eps-value after 52 epochs: 5.4779
+INFO - dpsgd_diffusion.py - 2024-10-24 15:50:17,409 - Loss: 0.1637, step: 36700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:50:43,192 - Loss: 0.1364, step: 36800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:51:08,960 - Loss: 0.1297, step: 36900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:51:34,303 - Loss: 0.0796, step: 37000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:52:00,448 - Loss: 0.1118, step: 37100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:52:25,721 - Loss: 0.1169, step: 37200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:52:52,268 - Loss: 0.1179, step: 37300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:52:55,112 - Eps-value after 53 epochs: 5.5356
+INFO - dpsgd_diffusion.py - 2024-10-24 15:53:18,039 - Loss: 0.1555, step: 37400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:53:44,525 - Loss: 0.1365, step: 37500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:54:10,912 - Loss: 0.1006, step: 37600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:54:36,475 - Loss: 0.1258, step: 37700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:55:02,687 - Loss: 0.1189, step: 37800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:55:28,730 - Loss: 0.1341, step: 37900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:55:54,933 - Loss: 0.1256, step: 38000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:55:54,984 - Saving snapshot checkpoint and sampling single batch at iteration 38000.
+WARNING - image.py - 2024-10-24 15:55:55,477 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 15:56:10,998 - FID at iteration 38000: 178.995517
+INFO - dpsgd_diffusion.py - 2024-10-24 15:56:15,414 - Eps-value after 54 epochs: 5.5927
+INFO - dpsgd_diffusion.py - 2024-10-24 15:56:37,880 - Loss: 0.1628, step: 38100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:57:03,291 - Loss: 0.1264, step: 38200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:57:28,853 - Loss: 0.1278, step: 38300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:57:54,616 - Loss: 0.1464, step: 38400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:58:21,636 - Loss: 0.1280, step: 38500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:58:46,787 - Loss: 0.1286, step: 38600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:59:12,192 - Loss: 0.1368, step: 38700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:59:16,891 - Eps-value after 55 epochs: 5.6499
+INFO - dpsgd_diffusion.py - 2024-10-24 15:59:37,534 - Loss: 0.1402, step: 38800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:00:04,273 - Loss: 0.0946, step: 38900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:00:30,457 - Loss: 0.0839, step: 39000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:00:56,144 - Loss: 0.0940, step: 39100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:01:22,010 - Loss: 0.1945, step: 39200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:01:48,451 - Loss: 0.1537, step: 39300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:02:15,517 - Loss: 0.1028, step: 39400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:02:21,445 - Eps-value after 56 epochs: 5.7064
+INFO - dpsgd_diffusion.py - 2024-10-24 16:02:42,063 - Loss: 0.1253, step: 39500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:03:09,188 - Loss: 0.1475, step: 39600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:03:36,382 - Loss: 0.1074, step: 39700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:04:02,570 - Loss: 0.0937, step: 39800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:04:29,542 - Loss: 0.1415, step: 39900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:04:55,720 - Loss: 0.1649, step: 40000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:04:55,725 - Saving snapshot checkpoint and sampling single batch at iteration 40000.
+WARNING - image.py - 2024-10-24 16:04:56,232 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 16:05:11,721 - FID at iteration 40000: 177.382928
+INFO - dpsgd_diffusion.py - 2024-10-24 16:05:38,114 - Loss: 0.1459, step: 40100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:05:45,412 - Eps-value after 57 epochs: 5.7623
+INFO - dpsgd_diffusion.py - 2024-10-24 16:06:04,522 - Loss: 0.1113, step: 40200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:06:30,570 - Loss: 0.1028, step: 40300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:06:56,046 - Loss: 0.1206, step: 40400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:07:23,440 - Loss: 0.0977, step: 40500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:07:48,423 - Loss: 0.0827, step: 40600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:08:13,773 - Loss: 0.1042, step: 40700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:08:39,735 - Loss: 0.0757, step: 40800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:08:47,944 - Eps-value after 58 epochs: 5.8182
+INFO - dpsgd_diffusion.py - 2024-10-24 16:09:04,866 - Loss: 0.1302, step: 40900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:09:30,277 - Loss: 0.1026, step: 41000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:09:55,495 - Loss: 0.1542, step: 41100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:10:22,211 - Loss: 0.1078, step: 41200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:10:47,829 - Loss: 0.1025, step: 41300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:11:13,398 - Loss: 0.1410, step: 41400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:11:39,535 - Loss: 0.1460, step: 41500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:11:48,682 - Eps-value after 59 epochs: 5.8739
+INFO - dpsgd_diffusion.py - 2024-10-24 16:12:05,567 - Loss: 0.0993, step: 41600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:12:32,646 - Loss: 0.1048, step: 41700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:12:58,292 - Loss: 0.1973, step: 41800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:13:24,086 - Loss: 0.0937, step: 41900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:13:51,180 - Loss: 0.1424, step: 42000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:13:51,188 - Saving snapshot checkpoint and sampling single batch at iteration 42000.
+WARNING - image.py - 2024-10-24 16:13:51,707 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 16:14:07,242 - FID at iteration 42000: 176.516807
+INFO - dpsgd_diffusion.py - 2024-10-24 16:14:33,113 - Loss: 0.1687, step: 42100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:14:59,684 - Loss: 0.0933, step: 42200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:15:10,058 - Eps-value after 60 epochs: 5.9285
+INFO - dpsgd_diffusion.py - 2024-10-24 16:15:24,881 - Loss: 0.1347, step: 42300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:15:51,560 - Loss: 0.1547, step: 42400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:16:17,265 - Loss: 0.1713, step: 42500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:16:43,395 - Loss: 0.1187, step: 42600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:17:09,446 - Loss: 0.1131, step: 42700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:17:34,870 - Loss: 0.1186, step: 42800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:18:00,821 - Loss: 0.1013, step: 42900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:18:11,430 - Eps-value after 61 epochs: 5.9831
+INFO - dpsgd_diffusion.py - 2024-10-24 16:18:26,944 - Loss: 0.0978, step: 43000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:18:53,373 - Loss: 0.1190, step: 43100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:19:20,148 - Loss: 0.1186, step: 43200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:19:46,567 - Loss: 0.0844, step: 43300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:20:13,357 - Loss: 0.0950, step: 43400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:20:39,386 - Loss: 0.1226, step: 43500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:21:04,188 - Loss: 0.1253, step: 43600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:21:17,279 - Eps-value after 62 epochs: 6.0377
+INFO - dpsgd_diffusion.py - 2024-10-24 16:21:31,123 - Loss: 0.1125, step: 43700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:21:57,523 - Loss: 0.1695, step: 43800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:22:23,773 - Loss: 0.0808, step: 43900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:22:50,267 - Loss: 0.1367, step: 44000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:22:50,319 - Saving snapshot checkpoint and sampling single batch at iteration 44000.
+WARNING - image.py - 2024-10-24 16:22:50,910 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 16:23:06,704 - FID at iteration 44000: 173.910018
+INFO - dpsgd_diffusion.py - 2024-10-24 16:23:33,067 - Loss: 0.1130, step: 44100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:23:58,991 - Loss: 0.1838, step: 44200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:24:25,581 - Loss: 0.1877, step: 44300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:24:39,779 - Eps-value after 63 epochs: 6.0918
+INFO - dpsgd_diffusion.py - 2024-10-24 16:24:52,085 - Loss: 0.1301, step: 44400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:25:19,179 - Loss: 0.1075, step: 44500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:25:46,101 - Loss: 0.1629, step: 44600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:26:12,911 - Loss: 0.1813, step: 44700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:26:38,837 - Loss: 0.1329, step: 44800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:27:05,726 - Loss: 0.0888, step: 44900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:27:31,938 - Loss: 0.1097, step: 45000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:27:46,298 - Eps-value after 64 epochs: 6.1451
+INFO - dpsgd_diffusion.py - 2024-10-24 16:27:58,239 - Loss: 0.0801, step: 45100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:28:24,448 - Loss: 0.1121, step: 45200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:28:50,603 - Loss: 0.1199, step: 45300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:29:17,488 - Loss: 0.1652, step: 45400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:29:43,496 - Loss: 0.1549, step: 45500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:30:10,556 - Loss: 0.0963, step: 45600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:30:36,127 - Loss: 0.1253, step: 45700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:30:52,547 - Eps-value after 65 epochs: 6.1984
+INFO - dpsgd_diffusion.py - 2024-10-24 16:31:03,337 - Loss: 0.1156, step: 45800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:31:28,933 - Loss: 0.1177, step: 45900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:31:55,575 - Loss: 0.1282, step: 46000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:31:55,622 - Saving snapshot checkpoint and sampling single batch at iteration 46000.
+WARNING - image.py - 2024-10-24 16:31:56,108 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 16:32:11,551 - FID at iteration 46000: 172.700200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:32:37,817 - Loss: 0.1359, step: 46100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:33:03,941 - Loss: 0.1205, step: 46200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:33:31,080 - Loss: 0.0767, step: 46300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:33:56,492 - Loss: 0.1012, step: 46400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:34:12,874 - Eps-value after 66 epochs: 6.2517
+INFO - dpsgd_diffusion.py - 2024-10-24 16:34:22,480 - Loss: 0.1346, step: 46500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:34:47,211 - Loss: 0.0980, step: 46600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:35:13,989 - Loss: 0.1263, step: 46700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:35:39,404 - Loss: 0.1694, step: 46800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:36:05,044 - Loss: 0.1160, step: 46900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:36:30,808 - Loss: 0.2164, step: 47000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:36:56,585 - Loss: 0.1660, step: 47100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:37:14,226 - Eps-value after 67 epochs: 6.3045
+INFO - dpsgd_diffusion.py - 2024-10-24 16:37:22,659 - Loss: 0.1449, step: 47200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:37:49,344 - Loss: 0.1072, step: 47300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:38:15,079 - Loss: 0.1384, step: 47400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:38:42,064 - Loss: 0.1585, step: 47500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:39:08,517 - Loss: 0.0932, step: 47600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:39:35,334 - Loss: 0.0580, step: 47700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:40:01,015 - Loss: 0.1342, step: 47800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:40:18,707 - Eps-value after 68 epochs: 6.3565
+INFO - dpsgd_diffusion.py - 2024-10-24 16:40:26,265 - Loss: 0.0964, step: 47900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:40:51,672 - Loss: 0.1519, step: 48000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:40:51,676 - Saving snapshot checkpoint and sampling single batch at iteration 48000.
+WARNING - image.py - 2024-10-24 16:40:52,156 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 16:41:07,585 - FID at iteration 48000: 171.985666
+INFO - dpsgd_diffusion.py - 2024-10-24 16:41:33,919 - Loss: 0.1174, step: 48100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:42:00,638 - Loss: 0.1746, step: 48200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:42:27,218 - Loss: 0.0923, step: 48300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:42:52,820 - Loss: 0.1078, step: 48400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:43:19,177 - Loss: 0.0860, step: 48500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:43:39,478 - Eps-value after 69 epochs: 6.4085
+INFO - dpsgd_diffusion.py - 2024-10-24 16:43:45,930 - Loss: 0.1409, step: 48600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:44:11,936 - Loss: 0.1014, step: 48700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:44:38,947 - Loss: 0.1209, step: 48800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:45:04,159 - Loss: 0.1496, step: 48900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:45:30,637 - Loss: 0.1063, step: 49000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:45:56,717 - Loss: 0.1632, step: 49100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:46:22,956 - Loss: 0.1027, step: 49200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:46:43,019 - Eps-value after 70 epochs: 6.4606
+INFO - dpsgd_diffusion.py - 2024-10-24 16:46:48,468 - Loss: 0.0956, step: 49300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:47:15,414 - Loss: 0.1498, step: 49400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:47:41,090 - Loss: 0.0898, step: 49500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:48:06,151 - Loss: 0.1136, step: 49600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:48:32,155 - Loss: 0.1371, step: 49700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:48:57,779 - Loss: 0.1761, step: 49800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:49:23,437 - Loss: 0.1192, step: 49900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:49:44,833 - Eps-value after 71 epochs: 6.5126
+INFO - dpsgd_diffusion.py - 2024-10-24 16:49:49,145 - Loss: 0.0867, step: 50000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:49:49,159 - Saving snapshot checkpoint and sampling single batch at iteration 50000.
+WARNING - image.py - 2024-10-24 16:49:49,656 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 16:50:05,193 - FID at iteration 50000: 170.214472
+INFO - dpsgd_diffusion.py - 2024-10-24 16:50:30,160 - Loss: 0.1219, step: 50100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:50:56,583 - Loss: 0.1173, step: 50200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:51:22,803 - Loss: 0.1527, step: 50300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:51:48,906 - Loss: 0.1900, step: 50400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:52:14,700 - Loss: 0.0949, step: 50500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:52:41,695 - Loss: 0.1303, step: 50600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:53:05,076 - Eps-value after 72 epochs: 6.5634
+INFO - dpsgd_diffusion.py - 2024-10-24 16:53:08,301 - Loss: 0.1028, step: 50700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:53:33,521 - Loss: 0.1209, step: 50800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:54:00,541 - Loss: 0.1494, step: 50900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:54:26,349 - Loss: 0.1169, step: 51000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:54:53,209 - Loss: 0.1012, step: 51100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:55:19,481 - Loss: 0.0879, step: 51200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:55:45,343 - Loss: 0.1393, step: 51300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:56:09,136 - Eps-value after 73 epochs: 6.6141
+INFO - dpsgd_diffusion.py - 2024-10-24 16:56:11,282 - Loss: 0.1587, step: 51400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:56:37,541 - Loss: 0.1026, step: 51500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:57:03,169 - Loss: 0.1606, step: 51600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:57:29,301 - Loss: 0.1265, step: 51700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:57:55,046 - Loss: 0.1059, step: 51800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:58:21,659 - Loss: 0.0690, step: 51900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:58:47,807 - Loss: 0.1041, step: 52000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:58:47,842 - Saving snapshot checkpoint and sampling single batch at iteration 52000.
+WARNING - image.py - 2024-10-24 16:58:48,355 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 16:59:03,727 - FID at iteration 52000: 168.868095
+INFO - dpsgd_diffusion.py - 2024-10-24 16:59:29,252 - Eps-value after 74 epochs: 6.6649
+INFO - dpsgd_diffusion.py - 2024-10-24 16:59:30,417 - Loss: 0.1012, step: 52100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:59:57,111 - Loss: 0.1176, step: 52200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:00:22,795 - Loss: 0.2093, step: 52300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:00:48,915 - Loss: 0.1401, step: 52400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:01:14,273 - Loss: 0.1100, step: 52500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:01:41,891 - Loss: 0.0956, step: 52600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:02:08,650 - Loss: 0.1720, step: 52700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:02:33,851 - Loss: 0.1269, step: 52800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:02:33,865 - Eps-value after 75 epochs: 6.7156
+INFO - dpsgd_diffusion.py - 2024-10-24 17:02:59,645 - Loss: 0.1477, step: 52900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:03:26,278 - Loss: 0.1917, step: 53000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:03:51,889 - Loss: 0.1420, step: 53100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:04:17,836 - Loss: 0.1254, step: 53200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:04:43,687 - Loss: 0.1404, step: 53300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:05:10,446 - Loss: 0.1557, step: 53400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:05:35,723 - Loss: 0.1304, step: 53500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:05:36,696 - Eps-value after 76 epochs: 6.7662
+INFO - dpsgd_diffusion.py - 2024-10-24 17:06:01,344 - Loss: 0.1440, step: 53600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:06:27,146 - Loss: 0.1311, step: 53700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:06:53,518 - Loss: 0.2113, step: 53800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:07:20,263 - Loss: 0.1184, step: 53900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:07:46,279 - Loss: 0.1333, step: 54000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:07:46,281 - Saving snapshot checkpoint and sampling single batch at iteration 54000.
+WARNING - image.py - 2024-10-24 17:07:46,778 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 17:08:02,228 - FID at iteration 54000: 167.692893
+INFO - dpsgd_diffusion.py - 2024-10-24 17:08:28,369 - Loss: 0.1387, step: 54100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:08:54,338 - Loss: 0.1347, step: 54200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:08:56,243 - Eps-value after 77 epochs: 6.8156
+INFO - dpsgd_diffusion.py - 2024-10-24 17:09:20,385 - Loss: 0.1065, step: 54300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:09:45,280 - Loss: 0.0917, step: 54400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:10:12,392 - Loss: 0.1450, step: 54500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:10:38,423 - Loss: 0.1781, step: 54600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:11:04,600 - Loss: 0.0926, step: 54700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:11:30,756 - Loss: 0.1058, step: 54800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:11:56,385 - Loss: 0.1318, step: 54900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:11:59,156 - Eps-value after 78 epochs: 6.8651
+INFO - dpsgd_diffusion.py - 2024-10-24 17:12:21,295 - Loss: 0.1490, step: 55000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:12:47,246 - Loss: 0.1394, step: 55100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:13:13,177 - Loss: 0.1599, step: 55200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:13:38,696 - Loss: 0.1279, step: 55300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:14:04,870 - Loss: 0.1200, step: 55400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:14:31,297 - Loss: 0.1078, step: 55500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:14:57,692 - Loss: 0.1179, step: 55600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:15:01,464 - Eps-value after 79 epochs: 6.9146
+INFO - dpsgd_diffusion.py - 2024-10-24 17:15:23,490 - Loss: 0.1364, step: 55700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:15:50,002 - Loss: 0.0986, step: 55800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:16:16,465 - Loss: 0.1114, step: 55900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:16:42,010 - Loss: 0.1283, step: 56000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:16:42,014 - Saving snapshot checkpoint and sampling single batch at iteration 56000.
+WARNING - image.py - 2024-10-24 17:16:42,628 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 17:16:58,027 - FID at iteration 56000: 165.064041
+INFO - dpsgd_diffusion.py - 2024-10-24 17:17:24,543 - Loss: 0.1269, step: 56100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:17:49,443 - Loss: 0.1338, step: 56200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:18:16,420 - Loss: 0.1003, step: 56300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:18:21,962 - Eps-value after 80 epochs: 6.9641
+INFO - dpsgd_diffusion.py - 2024-10-24 17:18:42,763 - Loss: 0.1155, step: 56400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:19:08,827 - Loss: 0.1573, step: 56500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:19:34,239 - Loss: 0.1413, step: 56600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:20:00,138 - Loss: 0.0893, step: 56700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:20:25,345 - Loss: 0.1069, step: 56800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:20:51,866 - Loss: 0.1257, step: 56900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:21:18,801 - Loss: 0.1101, step: 57000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:21:24,570 - Eps-value after 81 epochs: 7.0136
+INFO - dpsgd_diffusion.py - 2024-10-24 17:21:44,803 - Loss: 0.1631, step: 57100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:22:11,790 - Loss: 0.1240, step: 57200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:22:38,362 - Loss: 0.1598, step: 57300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:23:04,202 - Loss: 0.1339, step: 57400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:23:30,242 - Loss: 0.1212, step: 57500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:23:56,302 - Loss: 0.0874, step: 57600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:24:22,859 - Loss: 0.1196, step: 57700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:24:29,732 - Eps-value after 82 epochs: 7.0620
+INFO - dpsgd_diffusion.py - 2024-10-24 17:24:47,449 - Loss: 0.1312, step: 57800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:25:13,536 - Loss: 0.1296, step: 57900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:25:38,949 - Loss: 0.1405, step: 58000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:25:38,999 - Saving snapshot checkpoint and sampling single batch at iteration 58000.
+WARNING - image.py - 2024-10-24 17:25:39,501 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 17:25:55,021 - FID at iteration 58000: 165.825792
+INFO - dpsgd_diffusion.py - 2024-10-24 17:26:21,723 - Loss: 0.1245, step: 58100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:26:49,167 - Loss: 0.1148, step: 58200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:27:14,952 - Loss: 0.1314, step: 58300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:27:39,878 - Loss: 0.1811, step: 58400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:27:47,872 - Eps-value after 83 epochs: 7.1102
+INFO - dpsgd_diffusion.py - 2024-10-24 17:28:04,758 - Loss: 0.1227, step: 58500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:28:30,765 - Loss: 0.0995, step: 58600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:28:56,353 - Loss: 0.1904, step: 58700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:29:21,985 - Loss: 0.1151, step: 58800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:29:48,187 - Loss: 0.1419, step: 58900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:30:13,118 - Loss: 0.1258, step: 59000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:30:38,355 - Loss: 0.1640, step: 59100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:30:46,906 - Eps-value after 84 epochs: 7.1584
+INFO - dpsgd_diffusion.py - 2024-10-24 17:31:03,816 - Loss: 0.1361, step: 59200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:31:30,453 - Loss: 0.1142, step: 59300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:31:56,475 - Loss: 0.1092, step: 59400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:32:22,921 - Loss: 0.0801, step: 59500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:32:48,709 - Loss: 0.1143, step: 59600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:33:13,984 - Loss: 0.1550, step: 59700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:33:39,279 - Loss: 0.1089, step: 59800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:33:49,194 - Eps-value after 85 epochs: 7.2066
+INFO - dpsgd_diffusion.py - 2024-10-24 17:34:04,356 - Loss: 0.1860, step: 59900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:34:29,849 - Loss: 0.0789, step: 60000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:34:29,851 - Saving snapshot checkpoint and sampling single batch at iteration 60000.
+WARNING - image.py - 2024-10-24 17:34:30,345 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 17:34:45,681 - FID at iteration 60000: 164.159931
+INFO - dpsgd_diffusion.py - 2024-10-24 17:35:11,445 - Loss: 0.1309, step: 60100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:35:38,270 - Loss: 0.1099, step: 60200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:36:04,482 - Loss: 0.1273, step: 60300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:36:30,287 - Loss: 0.0960, step: 60400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:36:55,865 - Loss: 0.0978, step: 60500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:37:07,335 - Eps-value after 86 epochs: 7.2549
+INFO - dpsgd_diffusion.py - 2024-10-24 17:37:22,481 - Loss: 0.0995, step: 60600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:37:48,389 - Loss: 0.0739, step: 60700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:38:14,245 - Loss: 0.1316, step: 60800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:38:40,161 - Loss: 0.1500, step: 60900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:39:06,084 - Loss: 0.1074, step: 61000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:39:32,429 - Loss: 0.1060, step: 61100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:39:58,801 - Loss: 0.1349, step: 61200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:40:10,337 - Eps-value after 87 epochs: 7.3031
+INFO - dpsgd_diffusion.py - 2024-10-24 17:40:23,993 - Loss: 0.1071, step: 61300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:40:49,525 - Loss: 0.1735, step: 61400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:41:14,584 - Loss: 0.1085, step: 61500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:41:40,971 - Loss: 0.0691, step: 61600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:42:06,976 - Loss: 0.1098, step: 61700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:42:32,356 - Loss: 0.1074, step: 61800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:42:58,851 - Loss: 0.1183, step: 61900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:43:13,340 - Eps-value after 88 epochs: 7.3500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:43:26,155 - Loss: 0.0816, step: 62000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:43:26,157 - Saving snapshot checkpoint and sampling single batch at iteration 62000.
+WARNING - image.py - 2024-10-24 17:43:26,650 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 17:43:41,990 - FID at iteration 62000: 164.978133
+INFO - dpsgd_diffusion.py - 2024-10-24 17:44:08,479 - Loss: 0.0942, step: 62100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:44:35,097 - Loss: 0.1176, step: 62200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:45:02,619 - Loss: 0.1318, step: 62300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:45:28,664 - Loss: 0.1088, step: 62400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:45:54,504 - Loss: 0.1049, step: 62500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:46:19,993 - Loss: 0.1143, step: 62600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:46:34,072 - Eps-value after 89 epochs: 7.3970
+INFO - dpsgd_diffusion.py - 2024-10-24 17:46:45,938 - Loss: 0.1542, step: 62700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:47:11,260 - Loss: 0.0989, step: 62800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:47:37,898 - Loss: 0.1228, step: 62900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:48:03,618 - Loss: 0.1354, step: 63000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:48:28,571 - Loss: 0.1596, step: 63100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:48:54,338 - Loss: 0.1213, step: 63200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:49:20,766 - Loss: 0.1187, step: 63300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:49:36,158 - Eps-value after 90 epochs: 7.4440
+INFO - dpsgd_diffusion.py - 2024-10-24 17:49:46,968 - Loss: 0.1298, step: 63400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:50:12,761 - Loss: 0.1001, step: 63500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:50:38,632 - Loss: 0.0997, step: 63600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:51:04,753 - Loss: 0.1278, step: 63700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:51:30,659 - Loss: 0.1218, step: 63800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:51:55,879 - Loss: 0.1271, step: 63900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:52:21,740 - Loss: 0.1533, step: 64000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:52:21,744 - Saving snapshot checkpoint and sampling single batch at iteration 64000.
+WARNING - image.py - 2024-10-24 17:52:22,239 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 17:52:37,537 - FID at iteration 64000: 162.495674
+INFO - dpsgd_diffusion.py - 2024-10-24 17:52:55,127 - Eps-value after 91 epochs: 7.4909
+INFO - dpsgd_diffusion.py - 2024-10-24 17:53:04,501 - Loss: 0.1420, step: 64100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:53:29,944 - Loss: 0.1485, step: 64200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:53:56,911 - Loss: 0.1104, step: 64300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:54:22,973 - Loss: 0.0963, step: 64400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:54:49,705 - Loss: 0.1088, step: 64500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:55:16,389 - Loss: 0.1704, step: 64600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:55:42,498 - Loss: 0.1164, step: 64700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:56:01,268 - Eps-value after 92 epochs: 7.5379
+INFO - dpsgd_diffusion.py - 2024-10-24 17:56:09,888 - Loss: 0.1298, step: 64800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:56:36,102 - Loss: 0.0908, step: 64900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:57:02,164 - Loss: 0.1703, step: 65000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:57:27,129 - Loss: 0.1198, step: 65100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:57:52,616 - Loss: 0.1996, step: 65200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:58:18,174 - Loss: 0.1018, step: 65300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:58:44,939 - Loss: 0.1596, step: 65400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:59:04,274 - Eps-value after 93 epochs: 7.5849
+INFO - dpsgd_diffusion.py - 2024-10-24 17:59:11,818 - Loss: 0.1246, step: 65500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:59:38,494 - Loss: 0.1283, step: 65600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:00:04,541 - Loss: 0.1512, step: 65700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:00:29,969 - Loss: 0.1166, step: 65800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:00:55,739 - Loss: 0.1524, step: 65900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:01:21,978 - Loss: 0.1407, step: 66000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:01:21,993 - Saving snapshot checkpoint and sampling single batch at iteration 66000.
+WARNING - image.py - 2024-10-24 18:01:22,496 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 18:01:38,029 - FID at iteration 66000: 161.952902
+INFO - dpsgd_diffusion.py - 2024-10-24 18:02:03,346 - Loss: 0.1100, step: 66100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:02:23,279 - Eps-value after 94 epochs: 7.6311
+INFO - dpsgd_diffusion.py - 2024-10-24 18:02:29,626 - Loss: 0.1220, step: 66200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:02:55,583 - Loss: 0.1103, step: 66300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:03:21,314 - Loss: 0.1077, step: 66400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:03:48,534 - Loss: 0.1254, step: 66500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:04:14,138 - Loss: 0.0865, step: 66600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:04:41,102 - Loss: 0.1768, step: 66700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:05:07,812 - Loss: 0.1399, step: 66800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:05:27,978 - Eps-value after 95 epochs: 7.6768
+INFO - dpsgd_diffusion.py - 2024-10-24 18:05:33,354 - Loss: 0.1081, step: 66900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:05:58,223 - Loss: 0.1281, step: 67000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:06:24,250 - Loss: 0.1124, step: 67100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:06:49,803 - Loss: 0.1471, step: 67200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:07:15,752 - Loss: 0.0976, step: 67300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:07:41,526 - Loss: 0.1321, step: 67400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:08:06,563 - Loss: 0.1430, step: 67500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:08:27,041 - Eps-value after 96 epochs: 7.7225
+INFO - dpsgd_diffusion.py - 2024-10-24 18:08:31,351 - Loss: 0.1050, step: 67600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:08:57,283 - Loss: 0.0909, step: 67700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:09:23,545 - Loss: 0.1368, step: 67800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:09:49,939 - Loss: 0.0970, step: 67900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:10:16,139 - Loss: 0.1393, step: 68000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:10:16,141 - Saving snapshot checkpoint and sampling single batch at iteration 68000.
+WARNING - image.py - 2024-10-24 18:10:16,644 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 18:10:31,964 - FID at iteration 68000: 160.332441
+INFO - dpsgd_diffusion.py - 2024-10-24 18:10:58,176 - Loss: 0.1092, step: 68100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:11:24,219 - Loss: 0.1057, step: 68200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:11:45,979 - Eps-value after 97 epochs: 7.7683
+INFO - dpsgd_diffusion.py - 2024-10-24 18:11:49,213 - Loss: 0.1092, step: 68300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:12:14,277 - Loss: 0.1708, step: 68400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:12:40,337 - Loss: 0.1458, step: 68500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:13:06,861 - Loss: 0.1189, step: 68600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:13:32,782 - Loss: 0.1236, step: 68700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:13:59,045 - Loss: 0.1100, step: 68800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:14:25,521 - Loss: 0.1307, step: 68900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:14:48,960 - Eps-value after 98 epochs: 7.8140
+INFO - dpsgd_diffusion.py - 2024-10-24 18:14:51,107 - Loss: 0.1229, step: 69000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:15:17,152 - Loss: 0.1620, step: 69100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:15:42,921 - Loss: 0.1351, step: 69200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:16:09,314 - Loss: 0.0820, step: 69300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:16:35,210 - Loss: 0.1157, step: 69400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:17:01,205 - Loss: 0.1434, step: 69500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:17:27,264 - Loss: 0.1626, step: 69600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:17:52,054 - Eps-value after 99 epochs: 7.8597
+INFO - dpsgd_diffusion.py - 2024-10-24 18:17:53,117 - Loss: 0.1319, step: 69700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:18:19,062 - Loss: 0.1909, step: 69800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:18:44,688 - Loss: 0.1939, step: 69900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:19:11,272 - Loss: 0.1138, step: 70000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:19:11,308 - Saving snapshot checkpoint and sampling single batch at iteration 70000.
+WARNING - image.py - 2024-10-24 18:19:11,814 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 18:19:27,141 - FID at iteration 70000: 158.920321
+INFO - dpsgd_diffusion.py - 2024-10-24 18:19:53,070 - Loss: 0.1031, step: 70100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:20:18,247 - Loss: 0.1524, step: 70200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:20:43,846 - Loss: 0.1120, step: 70300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:21:09,789 - Loss: 0.1098, step: 70400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:21:09,803 - Eps-value after 100 epochs: 7.9054
+INFO - dpsgd_diffusion.py - 2024-10-24 18:21:35,976 - Loss: 0.1118, step: 70500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:22:00,523 - Loss: 0.0931, step: 70600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:22:27,725 - Loss: 0.1641, step: 70700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:22:55,057 - Loss: 0.1282, step: 70800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:23:21,022 - Loss: 0.1009, step: 70900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:23:47,327 - Loss: 0.1018, step: 71000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:24:14,060 - Loss: 0.1518, step: 71100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:24:15,123 - Eps-value after 101 epochs: 7.9506
+INFO - dpsgd_diffusion.py - 2024-10-24 18:24:40,920 - Loss: 0.1370, step: 71200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:25:05,194 - Loss: 0.1604, step: 71300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:25:31,346 - Loss: 0.1272, step: 71400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:25:57,067 - Loss: 0.1325, step: 71500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:26:22,774 - Loss: 0.0986, step: 71600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:26:49,825 - Loss: 0.0959, step: 71700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:27:15,190 - Loss: 0.1331, step: 71800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:27:17,080 - Eps-value after 102 epochs: 7.9951
+INFO - dpsgd_diffusion.py - 2024-10-24 18:27:41,319 - Loss: 0.1335, step: 71900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:28:08,078 - Loss: 0.1446, step: 72000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:28:08,083 - Saving snapshot checkpoint and sampling single batch at iteration 72000.
+WARNING - image.py - 2024-10-24 18:28:08,584 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 18:28:23,929 - FID at iteration 72000: 159.503606
+INFO - dpsgd_diffusion.py - 2024-10-24 18:28:49,482 - Loss: 0.1268, step: 72100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:29:14,718 - Loss: 0.1213, step: 72200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:29:40,857 - Loss: 0.1231, step: 72300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:30:07,467 - Loss: 0.1326, step: 72400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:30:33,304 - Loss: 0.1149, step: 72500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:30:36,445 - Eps-value after 103 epochs: 8.0395
+INFO - dpsgd_diffusion.py - 2024-10-24 18:30:59,474 - Loss: 0.1277, step: 72600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:31:26,407 - Loss: 0.1410, step: 72700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:31:52,903 - Loss: 0.1284, step: 72800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:32:18,042 - Loss: 0.1253, step: 72900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:32:43,950 - Loss: 0.1051, step: 73000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:33:10,052 - Loss: 0.1304, step: 73100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:33:35,532 - Loss: 0.1137, step: 73200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:33:39,216 - Eps-value after 104 epochs: 8.0840
+INFO - dpsgd_diffusion.py - 2024-10-24 18:34:00,679 - Loss: 0.1909, step: 73300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:34:26,239 - Loss: 0.1361, step: 73400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:34:51,301 - Loss: 0.1084, step: 73500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:35:16,547 - Loss: 0.0799, step: 73600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:35:42,727 - Loss: 0.1391, step: 73700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:36:09,121 - Loss: 0.1453, step: 73800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:36:35,886 - Loss: 0.1011, step: 73900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:36:41,262 - Eps-value after 105 epochs: 8.1285
+INFO - dpsgd_diffusion.py - 2024-10-24 18:37:01,781 - Loss: 0.1769, step: 74000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:37:01,826 - Saving snapshot checkpoint and sampling single batch at iteration 74000.
+WARNING - image.py - 2024-10-24 18:37:02,339 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 18:37:17,724 - FID at iteration 74000: 159.595089
+INFO - dpsgd_diffusion.py - 2024-10-24 18:37:43,929 - Loss: 0.1041, step: 74100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:38:09,554 - Loss: 0.1194, step: 74200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:38:35,773 - Loss: 0.1284, step: 74300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:39:01,533 - Loss: 0.1141, step: 74400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:39:27,182 - Loss: 0.0961, step: 74500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:39:53,896 - Loss: 0.1184, step: 74600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:40:00,282 - Eps-value after 106 epochs: 8.1730
+INFO - dpsgd_diffusion.py - 2024-10-24 18:40:19,964 - Loss: 0.1177, step: 74700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:40:45,388 - Loss: 0.1014, step: 74800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:41:11,467 - Loss: 0.0939, step: 74900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:41:38,360 - Loss: 0.0744, step: 75000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:42:04,458 - Loss: 0.0931, step: 75100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:42:30,070 - Loss: 0.1317, step: 75200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:42:56,309 - Loss: 0.1014, step: 75300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:43:03,972 - Eps-value after 107 epochs: 8.2174
+INFO - dpsgd_diffusion.py - 2024-10-24 18:43:22,724 - Loss: 0.1223, step: 75400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:43:48,968 - Loss: 0.1271, step: 75500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:44:14,602 - Loss: 0.0841, step: 75600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:44:41,422 - Loss: 0.1204, step: 75700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:45:07,645 - Loss: 0.1214, step: 75800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:45:33,327 - Loss: 0.1397, step: 75900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:45:59,801 - Loss: 0.1264, step: 76000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:45:59,846 - Saving snapshot checkpoint and sampling single batch at iteration 76000.
+WARNING - image.py - 2024-10-24 18:46:00,338 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 18:46:15,737 - FID at iteration 76000: 158.372211
+INFO - dpsgd_diffusion.py - 2024-10-24 18:46:23,263 - Eps-value after 108 epochs: 8.2619
+INFO - dpsgd_diffusion.py - 2024-10-24 18:46:40,209 - Loss: 0.0908, step: 76100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:47:07,082 - Loss: 0.1062, step: 76200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:47:33,958 - Loss: 0.1175, step: 76300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:48:00,992 - Loss: 0.1302, step: 76400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:48:26,950 - Loss: 0.1427, step: 76500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:48:54,073 - Loss: 0.1517, step: 76600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:49:20,622 - Loss: 0.1267, step: 76700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:49:29,245 - Eps-value after 109 epochs: 8.3058
+INFO - dpsgd_diffusion.py - 2024-10-24 18:49:45,596 - Loss: 0.1581, step: 76800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:50:11,398 - Loss: 0.1217, step: 76900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:50:37,310 - Loss: 0.1385, step: 77000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:51:03,758 - Loss: 0.0775, step: 77100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:51:29,196 - Loss: 0.1221, step: 77200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:51:53,909 - Loss: 0.1299, step: 77300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:52:19,900 - Loss: 0.1259, step: 77400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:52:29,668 - Eps-value after 110 epochs: 8.3490
+INFO - dpsgd_diffusion.py - 2024-10-24 18:52:45,791 - Loss: 0.1017, step: 77500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:53:11,772 - Loss: 0.1187, step: 77600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:53:38,732 - Loss: 0.1048, step: 77700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:54:04,894 - Loss: 0.1120, step: 77800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:54:29,850 - Loss: 0.1170, step: 77900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:54:56,443 - Loss: 0.1184, step: 78000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:54:56,456 - Saving snapshot checkpoint and sampling single batch at iteration 78000.
+WARNING - image.py - 2024-10-24 18:54:56,960 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 18:55:12,492 - FID at iteration 78000: 157.527604
+INFO - dpsgd_diffusion.py - 2024-10-24 18:55:37,198 - Loss: 0.1289, step: 78100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:55:48,012 - Eps-value after 111 epochs: 8.3922
+INFO - dpsgd_diffusion.py - 2024-10-24 18:56:02,614 - Loss: 0.0866, step: 78200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:56:28,221 - Loss: 0.1282, step: 78300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:56:54,492 - Loss: 0.1641, step: 78400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:57:21,335 - Loss: 0.1208, step: 78500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:57:47,672 - Loss: 0.1189, step: 78600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:58:14,330 - Loss: 0.0857, step: 78700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:58:40,192 - Loss: 0.1011, step: 78800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:58:52,227 - Eps-value after 112 epochs: 8.4355
+INFO - dpsgd_diffusion.py - 2024-10-24 18:59:06,360 - Loss: 0.1230, step: 78900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:59:32,450 - Loss: 0.1308, step: 79000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:59:58,707 - Loss: 0.1341, step: 79100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:00:25,098 - Loss: 0.1279, step: 79200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:00:50,232 - Loss: 0.1118, step: 79300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:01:17,208 - Loss: 0.1542, step: 79400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:01:42,643 - Loss: 0.1698, step: 79500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:01:55,549 - Eps-value after 113 epochs: 8.4787
+INFO - dpsgd_diffusion.py - 2024-10-24 19:02:08,377 - Loss: 0.1145, step: 79600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:02:34,486 - Loss: 0.0750, step: 79700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:02:59,705 - Loss: 0.1136, step: 79800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:03:25,712 - Loss: 0.1321, step: 79900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:03:51,786 - Loss: 0.0966, step: 80000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:03:51,791 - Saving snapshot checkpoint and sampling single batch at iteration 80000.
+WARNING - image.py - 2024-10-24 19:03:52,287 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 19:04:07,717 - FID at iteration 80000: 156.572413
+INFO - dpsgd_diffusion.py - 2024-10-24 19:04:33,271 - Loss: 0.1232, step: 80100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:04:59,215 - Loss: 0.1235, step: 80200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:05:14,755 - Eps-value after 114 epochs: 8.5219
+INFO - dpsgd_diffusion.py - 2024-10-24 19:05:26,625 - Loss: 0.1237, step: 80300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:05:52,265 - Loss: 0.1176, step: 80400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:06:17,935 - Loss: 0.1549, step: 80500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:06:44,172 - Loss: 0.0935, step: 80600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:07:09,269 - Loss: 0.1224, step: 80700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:07:36,316 - Loss: 0.0808, step: 80800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:08:01,636 - Loss: 0.0795, step: 80900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:08:16,868 - Eps-value after 115 epochs: 8.5652
+INFO - dpsgd_diffusion.py - 2024-10-24 19:08:27,267 - Loss: 0.1630, step: 81000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:08:53,180 - Loss: 0.1829, step: 81100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:09:19,172 - Loss: 0.1503, step: 81200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:09:45,866 - Loss: 0.1374, step: 81300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:10:12,145 - Loss: 0.1056, step: 81400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:10:40,019 - Loss: 0.1261, step: 81500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:11:04,984 - Loss: 0.1344, step: 81600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:11:21,636 - Eps-value after 116 epochs: 8.6084
+INFO - dpsgd_diffusion.py - 2024-10-24 19:11:31,324 - Loss: 0.1264, step: 81700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:11:56,319 - Loss: 0.1549, step: 81800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:12:23,409 - Loss: 0.0842, step: 81900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:12:49,282 - Loss: 0.1797, step: 82000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:12:49,332 - Saving snapshot checkpoint and sampling single batch at iteration 82000.
+WARNING - image.py - 2024-10-24 19:12:49,817 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 19:13:05,275 - FID at iteration 82000: 156.147662
+INFO - dpsgd_diffusion.py - 2024-10-24 19:13:29,625 - Loss: 0.0929, step: 82100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:13:54,998 - Loss: 0.0942, step: 82200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:14:20,741 - Loss: 0.1430, step: 82300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:14:38,290 - Eps-value after 117 epochs: 8.6517
+INFO - dpsgd_diffusion.py - 2024-10-24 19:14:46,908 - Loss: 0.1276, step: 82400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:15:12,828 - Loss: 0.0820, step: 82500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:15:39,667 - Loss: 0.1145, step: 82600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:16:05,558 - Loss: 0.1765, step: 82700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:16:32,090 - Loss: 0.1426, step: 82800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:16:57,482 - Loss: 0.0841, step: 82900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:17:23,183 - Loss: 0.1047, step: 83000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:17:41,742 - Eps-value after 118 epochs: 8.6941
+INFO - dpsgd_diffusion.py - 2024-10-24 19:17:49,359 - Loss: 0.1410, step: 83100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:18:16,293 - Loss: 0.1193, step: 83200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:18:41,384 - Loss: 0.1092, step: 83300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:19:06,854 - Loss: 0.1131, step: 83400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:19:33,025 - Loss: 0.1194, step: 83500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:19:58,655 - Loss: 0.0626, step: 83600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:20:25,051 - Loss: 0.1185, step: 83700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:20:44,280 - Eps-value after 119 epochs: 8.7361
+INFO - dpsgd_diffusion.py - 2024-10-24 19:20:50,698 - Loss: 0.1472, step: 83800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:21:17,362 - Loss: 0.0988, step: 83900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:21:43,171 - Loss: 0.1237, step: 84000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:21:43,184 - Saving snapshot checkpoint and sampling single batch at iteration 84000.
+WARNING - image.py - 2024-10-24 19:21:43,673 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 19:21:59,165 - FID at iteration 84000: 155.076771
+INFO - dpsgd_diffusion.py - 2024-10-24 19:22:25,299 - Loss: 0.1610, step: 84100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:22:52,462 - Loss: 0.1278, step: 84200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:23:18,902 - Loss: 0.1341, step: 84300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:23:44,950 - Loss: 0.0945, step: 84400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:24:06,514 - Eps-value after 120 epochs: 8.7781
+INFO - dpsgd_diffusion.py - 2024-10-24 19:24:11,664 - Loss: 0.0939, step: 84500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:24:37,534 - Loss: 0.1061, step: 84600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:25:03,996 - Loss: 0.1529, step: 84700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:25:30,326 - Loss: 0.1217, step: 84800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:25:55,699 - Loss: 0.1252, step: 84900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:26:21,780 - Loss: 0.1088, step: 85000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:26:48,006 - Loss: 0.1193, step: 85100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:27:09,292 - Eps-value after 121 epochs: 8.8201
+INFO - dpsgd_diffusion.py - 2024-10-24 19:27:13,559 - Loss: 0.1107, step: 85200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:27:39,822 - Loss: 0.1453, step: 85300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:28:05,549 - Loss: 0.1013, step: 85400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:28:31,245 - Loss: 0.0905, step: 85500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:28:57,120 - Loss: 0.1514, step: 85600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:29:23,684 - Loss: 0.0869, step: 85700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:29:50,066 - Loss: 0.1565, step: 85800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:30:12,686 - Eps-value after 122 epochs: 8.8621
+INFO - dpsgd_diffusion.py - 2024-10-24 19:30:15,875 - Loss: 0.1718, step: 85900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:30:41,442 - Loss: 0.1504, step: 86000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:30:41,455 - Saving snapshot checkpoint and sampling single batch at iteration 86000.
+WARNING - image.py - 2024-10-24 19:30:41,940 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 19:30:57,344 - FID at iteration 86000: 155.241948
+INFO - dpsgd_diffusion.py - 2024-10-24 19:31:22,624 - Loss: 0.1171, step: 86100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:31:48,119 - Loss: 0.0726, step: 86200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:32:13,374 - Loss: 0.0973, step: 86300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:32:38,731 - Loss: 0.1070, step: 86400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:33:05,991 - Loss: 0.1686, step: 86500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:33:30,072 - Eps-value after 123 epochs: 8.9041
+INFO - dpsgd_diffusion.py - 2024-10-24 19:33:32,178 - Loss: 0.1165, step: 86600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:33:58,417 - Loss: 0.1067, step: 86700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:34:23,670 - Loss: 0.1086, step: 86800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:34:49,219 - Loss: 0.1727, step: 86900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:35:15,027 - Loss: 0.1649, step: 87000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:35:40,418 - Loss: 0.1090, step: 87100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:36:05,599 - Loss: 0.1329, step: 87200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:36:30,720 - Eps-value after 124 epochs: 8.9461
+INFO - dpsgd_diffusion.py - 2024-10-24 19:36:31,788 - Loss: 0.0920, step: 87300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:36:57,483 - Loss: 0.0974, step: 87400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:37:23,403 - Loss: 0.1946, step: 87500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:37:49,407 - Loss: 0.1366, step: 87600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:38:14,448 - Loss: 0.1079, step: 87700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:38:40,300 - Loss: 0.1657, step: 87800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:39:06,576 - Loss: 0.0979, step: 87900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:39:33,671 - Loss: 0.1189, step: 88000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:39:33,685 - Eps-value after 125 epochs: 8.9881
+INFO - dpsgd_diffusion.py - 2024-10-24 19:39:33,687 - Saving snapshot checkpoint and sampling single batch at iteration 88000.
+WARNING - image.py - 2024-10-24 19:39:34,168 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 19:39:49,663 - FID at iteration 88000: 153.849312
+INFO - dpsgd_diffusion.py - 2024-10-24 19:40:15,998 - Loss: 0.1113, step: 88100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:40:41,896 - Loss: 0.1286, step: 88200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:41:08,384 - Loss: 0.1537, step: 88300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:41:34,453 - Loss: 0.1151, step: 88400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:42:00,777 - Loss: 0.1345, step: 88500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:42:26,589 - Loss: 0.1387, step: 88600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:42:53,662 - Loss: 0.1085, step: 88700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:42:54,725 - Eps-value after 126 epochs: 9.0301
+INFO - dpsgd_diffusion.py - 2024-10-24 19:43:20,113 - Loss: 0.1593, step: 88800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:43:46,345 - Loss: 0.0985, step: 88900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:44:13,317 - Loss: 0.0884, step: 89000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:44:40,588 - Loss: 0.1754, step: 89100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:45:06,845 - Loss: 0.1101, step: 89200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:45:32,382 - Loss: 0.1229, step: 89300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:45:58,362 - Loss: 0.0958, step: 89400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:46:00,295 - Eps-value after 127 epochs: 9.0722
+INFO - dpsgd_diffusion.py - 2024-10-24 19:46:24,486 - Loss: 0.1431, step: 89500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:46:49,392 - Loss: 0.0935, step: 89600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:47:15,559 - Loss: 0.1407, step: 89700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:47:41,232 - Loss: 0.1048, step: 89800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:48:07,784 - Loss: 0.1003, step: 89900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:48:34,555 - Loss: 0.1399, step: 90000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:48:34,569 - Saving snapshot checkpoint and sampling single batch at iteration 90000.
+WARNING - image.py - 2024-10-24 19:48:35,053 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 19:48:50,499 - FID at iteration 90000: 154.718266
+INFO - dpsgd_diffusion.py - 2024-10-24 19:49:17,363 - Loss: 0.0882, step: 90100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:49:20,331 - Eps-value after 128 epochs: 9.1133
+INFO - dpsgd_diffusion.py - 2024-10-24 19:49:42,316 - Loss: 0.1174, step: 90200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:50:08,399 - Loss: 0.1098, step: 90300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:50:34,860 - Loss: 0.1413, step: 90400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:51:01,201 - Loss: 0.0642, step: 90500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:51:27,484 - Loss: 0.1000, step: 90600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:51:52,878 - Loss: 0.1439, step: 90700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:52:19,174 - Loss: 0.1303, step: 90800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:52:23,461 - Eps-value after 129 epochs: 9.1540
+INFO - dpsgd_diffusion.py - 2024-10-24 19:52:46,098 - Loss: 0.1052, step: 90900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:53:12,514 - Loss: 0.0982, step: 91000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:53:38,355 - Loss: 0.1575, step: 91100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:54:03,056 - Loss: 0.1050, step: 91200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:54:28,523 - Loss: 0.1583, step: 91300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:54:54,171 - Loss: 0.1026, step: 91400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:55:19,919 - Loss: 0.1155, step: 91500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:55:24,754 - Eps-value after 130 epochs: 9.1948
+INFO - dpsgd_diffusion.py - 2024-10-24 19:55:46,013 - Loss: 0.1538, step: 91600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:56:10,768 - Loss: 0.0949, step: 91700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:56:37,020 - Loss: 0.0956, step: 91800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:57:02,513 - Loss: 0.0989, step: 91900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:57:27,952 - Loss: 0.1118, step: 92000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:57:27,996 - Saving snapshot checkpoint and sampling single batch at iteration 92000.
+WARNING - image.py - 2024-10-24 19:57:28,482 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 19:57:43,847 - FID at iteration 92000: 153.544975
+INFO - dpsgd_diffusion.py - 2024-10-24 19:58:09,549 - Loss: 0.0820, step: 92100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:58:35,078 - Loss: 0.1032, step: 92200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:58:40,914 - Eps-value after 131 epochs: 9.2356
+INFO - dpsgd_diffusion.py - 2024-10-24 19:59:00,960 - Loss: 0.1429, step: 92300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:59:26,692 - Loss: 0.1436, step: 92400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:59:52,424 - Loss: 0.1297, step: 92500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:00:18,140 - Loss: 0.0709, step: 92600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:00:44,777 - Loss: 0.0952, step: 92700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:01:10,665 - Loss: 0.1159, step: 92800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:01:37,234 - Loss: 0.0782, step: 92900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:01:43,720 - Eps-value after 132 epochs: 9.2764
+INFO - dpsgd_diffusion.py - 2024-10-24 20:02:02,744 - Loss: 0.1315, step: 93000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:02:28,857 - Loss: 0.1368, step: 93100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:02:54,743 - Loss: 0.1542, step: 93200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:03:20,559 - Loss: 0.1269, step: 93300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:03:46,332 - Loss: 0.1314, step: 93400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:04:12,206 - Loss: 0.1318, step: 93500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:04:38,044 - Loss: 0.1457, step: 93600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:04:45,999 - Eps-value after 133 epochs: 9.3172
+INFO - dpsgd_diffusion.py - 2024-10-24 20:05:03,207 - Loss: 0.0736, step: 93700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:05:28,465 - Loss: 0.1512, step: 93800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:05:53,476 - Loss: 0.1703, step: 93900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:06:19,202 - Loss: 0.0957, step: 94000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:06:19,247 - Saving snapshot checkpoint and sampling single batch at iteration 94000.
+WARNING - image.py - 2024-10-24 20:06:19,732 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 20:06:35,035 - FID at iteration 94000: 153.390955
+INFO - dpsgd_diffusion.py - 2024-10-24 20:07:00,693 - Loss: 0.1393, step: 94100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:07:26,637 - Loss: 0.1234, step: 94200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:07:52,412 - Loss: 0.1460, step: 94300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:08:02,078 - Eps-value after 134 epochs: 9.3579
+INFO - dpsgd_diffusion.py - 2024-10-24 20:08:19,431 - Loss: 0.1301, step: 94400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:08:45,149 - Loss: 0.1504, step: 94500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:09:09,909 - Loss: 0.1040, step: 94600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:09:36,430 - Loss: 0.1331, step: 94700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:10:01,391 - Loss: 0.1558, step: 94800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:10:27,430 - Loss: 0.1450, step: 94900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:10:54,521 - Loss: 0.1106, step: 95000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:11:05,162 - Eps-value after 135 epochs: 9.3987
+INFO - dpsgd_diffusion.py - 2024-10-24 20:11:21,748 - Loss: 0.1395, step: 95100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:11:48,153 - Loss: 0.1680, step: 95200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:12:14,360 - Loss: 0.1018, step: 95300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:12:40,758 - Loss: 0.0976, step: 95400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:13:06,982 - Loss: 0.0948, step: 95500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:13:33,732 - Loss: 0.0869, step: 95600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:13:59,609 - Loss: 0.0977, step: 95700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:14:11,241 - Eps-value after 136 epochs: 9.4395
+INFO - dpsgd_diffusion.py - 2024-10-24 20:14:25,728 - Loss: 0.1157, step: 95800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:14:51,925 - Loss: 0.1030, step: 95900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:15:17,792 - Loss: 0.2090, step: 96000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:15:17,797 - Saving snapshot checkpoint and sampling single batch at iteration 96000.
+WARNING - image.py - 2024-10-24 20:15:18,281 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 20:15:33,596 - FID at iteration 96000: 152.756155
+INFO - dpsgd_diffusion.py - 2024-10-24 20:16:00,067 - Loss: 0.1218, step: 96100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:16:26,255 - Loss: 0.1660, step: 96200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:16:51,845 - Loss: 0.0829, step: 96300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:17:18,121 - Loss: 0.1135, step: 96400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:17:30,116 - Eps-value after 137 epochs: 9.4803
+INFO - dpsgd_diffusion.py - 2024-10-24 20:17:43,782 - Loss: 0.1430, step: 96500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:18:09,646 - Loss: 0.1094, step: 96600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:18:34,789 - Loss: 0.1303, step: 96700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:19:00,604 - Loss: 0.1876, step: 96800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:19:27,263 - Loss: 0.0878, step: 96900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:19:53,962 - Loss: 0.1042, step: 97000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:20:19,722 - Loss: 0.1474, step: 97100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:20:33,064 - Eps-value after 138 epochs: 9.5211
+INFO - dpsgd_diffusion.py - 2024-10-24 20:20:46,082 - Loss: 0.1195, step: 97200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:21:12,111 - Loss: 0.1478, step: 97300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:21:38,820 - Loss: 0.1677, step: 97400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:22:05,555 - Loss: 0.1086, step: 97500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:22:31,199 - Loss: 0.1123, step: 97600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:22:57,277 - Loss: 0.1117, step: 97700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:23:24,109 - Loss: 0.1203, step: 97800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:23:38,653 - Eps-value after 139 epochs: 9.5612
+INFO - dpsgd_diffusion.py - 2024-10-24 20:23:50,518 - Loss: 0.0967, step: 97900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:24:18,022 - Loss: 0.1366, step: 98000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:24:18,031 - Saving snapshot checkpoint and sampling single batch at iteration 98000.
+WARNING - image.py - 2024-10-24 20:24:18,543 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 20:24:33,858 - FID at iteration 98000: 152.735970
+INFO - dpsgd_diffusion.py - 2024-10-24 20:24:59,496 - Loss: 0.1116, step: 98100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:25:25,410 - Loss: 0.1018, step: 98200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:25:51,218 - Loss: 0.0719, step: 98300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:26:17,096 - Loss: 0.1427, step: 98400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:26:43,532 - Loss: 0.1400, step: 98500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:26:59,798 - Eps-value after 140 epochs: 9.6007
+INFO - dpsgd_diffusion.py - 2024-10-24 20:27:10,606 - Loss: 0.1243, step: 98600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:27:36,873 - Loss: 0.1489, step: 98700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:28:02,816 - Loss: 0.0966, step: 98800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:28:29,289 - Loss: 0.0810, step: 98900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:28:54,447 - Loss: 0.0579, step: 99000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:29:20,549 - Loss: 0.1473, step: 99100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:29:46,427 - Loss: 0.0856, step: 99200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:30:03,069 - Eps-value after 141 epochs: 9.6403
+INFO - dpsgd_diffusion.py - 2024-10-24 20:30:12,742 - Loss: 0.1125, step: 99300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:30:38,526 - Loss: 0.1174, step: 99400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:31:04,630 - Loss: 0.1283, step: 99500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:31:30,884 - Loss: 0.0945, step: 99600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:31:56,131 - Loss: 0.0822, step: 99700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:32:22,341 - Loss: 0.1052, step: 99800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:32:48,722 - Loss: 0.1024, step: 99900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:33:06,511 - Eps-value after 142 epochs: 9.6798
+INFO - dpsgd_diffusion.py - 2024-10-24 20:33:15,484 - Loss: 0.1198, step: 100000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:33:15,533 - Saving snapshot checkpoint and sampling single batch at iteration 100000.
+WARNING - image.py - 2024-10-24 20:33:16,046 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 20:33:31,505 - FID at iteration 100000: 151.771362
+INFO - dpsgd_diffusion.py - 2024-10-24 20:33:32,316 - Saving  checkpoint at iteration 100000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:33:58,799 - Loss: 0.0812, step: 100100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:34:25,292 - Loss: 0.1250, step: 100200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:34:50,264 - Loss: 0.1614, step: 100300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:35:16,303 - Loss: 0.1134, step: 100400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:35:41,781 - Loss: 0.1582, step: 100500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:36:06,661 - Loss: 0.0836, step: 100600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:36:25,311 - Eps-value after 143 epochs: 9.7194
+INFO - dpsgd_diffusion.py - 2024-10-24 20:36:32,885 - Loss: 0.1007, step: 100700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:36:59,250 - Loss: 0.1285, step: 100800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:37:25,477 - Loss: 0.1005, step: 100900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:37:51,198 - Loss: 0.1185, step: 101000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:38:16,942 - Loss: 0.1233, step: 101100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:38:42,124 - Loss: 0.1227, step: 101200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:39:08,639 - Loss: 0.1565, step: 101300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:39:27,549 - Eps-value after 144 epochs: 9.7589
+INFO - dpsgd_diffusion.py - 2024-10-24 20:39:34,012 - Loss: 0.1065, step: 101400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:39:59,785 - Loss: 0.1468, step: 101500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:40:26,399 - Loss: 0.1128, step: 101600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:40:51,948 - Loss: 0.1356, step: 101700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:41:18,492 - Loss: 0.1342, step: 101800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:41:44,232 - Loss: 0.1519, step: 101900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:42:09,992 - Loss: 0.0995, step: 102000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:42:10,005 - Saving snapshot checkpoint and sampling single batch at iteration 102000.
+WARNING - image.py - 2024-10-24 20:42:10,484 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 20:42:25,799 - FID at iteration 102000: 152.187870
+INFO - dpsgd_diffusion.py - 2024-10-24 20:42:47,076 - Eps-value after 145 epochs: 9.7985
+INFO - dpsgd_diffusion.py - 2024-10-24 20:42:52,711 - Loss: 0.1283, step: 102100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:43:18,856 - Loss: 0.1475, step: 102200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:43:44,194 - Loss: 0.1031, step: 102300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:44:10,668 - Loss: 0.1300, step: 102400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:44:37,361 - Loss: 0.1326, step: 102500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:45:03,966 - Loss: 0.1487, step: 102600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:45:31,434 - Loss: 0.0783, step: 102700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:45:53,333 - Eps-value after 146 epochs: 9.8381
+INFO - dpsgd_diffusion.py - 2024-10-24 20:45:57,649 - Loss: 0.1194, step: 102800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:46:24,342 - Loss: 0.1160, step: 102900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:46:50,973 - Loss: 0.1529, step: 103000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:47:16,404 - Loss: 0.1053, step: 103100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:47:42,806 - Loss: 0.1077, step: 103200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:48:08,792 - Loss: 0.1314, step: 103300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:48:36,001 - Loss: 0.1237, step: 103400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:48:58,980 - Eps-value after 147 epochs: 9.8776
+INFO - dpsgd_diffusion.py - 2024-10-24 20:49:02,192 - Loss: 0.1050, step: 103500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:49:27,673 - Loss: 0.0958, step: 103600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:49:53,506 - Loss: 0.1444, step: 103700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:50:19,137 - Loss: 0.1205, step: 103800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:50:46,077 - Loss: 0.1196, step: 103900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:51:13,062 - Loss: 0.1184, step: 104000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:51:13,067 - Saving snapshot checkpoint and sampling single batch at iteration 104000.
+WARNING - image.py - 2024-10-24 20:51:13,580 - Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+INFO - dpsgd_diffusion.py - 2024-10-24 20:51:28,947 - FID at iteration 104000: 151.751985
+INFO - dpsgd_diffusion.py - 2024-10-24 20:51:55,587 - Loss: 0.1108, step: 104100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:52:19,317 - Eps-value after 148 epochs: 9.9172
+INFO - dpsgd_diffusion.py - 2024-10-24 20:52:21,471 - Loss: 0.1097, step: 104200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:52:47,499 - Loss: 0.0939, step: 104300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:53:13,412 - Loss: 0.0935, step: 104400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:53:39,015 - Loss: 0.1421, step: 104500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:54:05,564 - Loss: 0.1210, step: 104600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:54:31,042 - Loss: 0.1050, step: 104700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:54:56,646 - Loss: 0.1045, step: 104800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:55:21,426 - Eps-value after 149 epochs: 9.9567
+INFO - dpsgd_diffusion.py - 2024-10-24 20:55:22,589 - Loss: 0.1053, step: 104900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:55:49,518 - Loss: 0.1656, step: 105000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:56:15,459 - Loss: 0.0754, step: 105100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:56:41,835 - Loss: 0.1885, step: 105200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:57:08,881 - Loss: 0.1372, step: 105300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:57:34,392 - Loss: 0.1370, step: 105400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:58:00,456 - Loss: 0.1092, step: 105500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:58:26,679 - Loss: 0.1250, step: 105600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:58:26,694 - Eps-value after 150 epochs: 9.9963
+INFO - dpsgd_diffusion.py - 2024-10-24 20:58:27,358 - Saving final checkpoint.
+INFO - dpsgd_diffusion.py - 2024-10-24 20:58:27,361 - start to generate 60000 samples
+INFO - dpsgd_diffusion.py - 2024-10-24 21:23:09,342 - Generation Finished!
+INFO - dataset_loader.py - 2024-10-24 22:49:41,036 - delta is reset as 4.784738627130138e-06
+INFO - dataset_loader.py - 2024-10-24 22:50:38,278 - delta is reset as 4.784738627130138e-06
+INFO - evaluator.py - 2024-10-24 22:51:09,104 - Epoch: 0 Train acc: 52.62909090909091 Val acc: 51.800000000000004 Test acc52.175000000000004; Train loss: 0.00459802634824406 Val loss: 0.001895334303379059
+INFO - evaluator.py - 2024-10-24 22:51:33,608 - Epoch: 1 Train acc: 80.25818181818181 Val acc: 54.449999999999996 Test acc53.349999999999994; Train loss: 0.001955782922289588 Val loss: 0.002315470337867737
+INFO - evaluator.py - 2024-10-24 22:51:58,516 - Epoch: 2 Train acc: 88.29272727272728 Val acc: 68.30000000000001 Test acc67.7; Train loss: 0.001212140411951325 Val loss: 0.0015394399762153625
+INFO - evaluator.py - 2024-10-24 22:52:23,145 - Epoch: 3 Train acc: 90.97454545454545 Val acc: 64.14999999999999 Test acc62.4; Train loss: 0.0009345969351855191 Val loss: 0.0020058746337890624
+INFO - evaluator.py - 2024-10-24 22:52:47,555 - Epoch: 4 Train acc: 92.71818181818182 Val acc: 51.74999999999999 Test acc52.925; Train loss: 0.0007577096176418391 Val loss: 0.0033070638179779053
+INFO - evaluator.py - 2024-10-24 22:53:12,058 - Epoch: 5 Train acc: 94.24181818181819 Val acc: 57.35 Test acc55.85; Train loss: 0.0006092410737817938 Val loss: 0.0033007450103759764
+INFO - evaluator.py - 2024-10-24 22:53:36,381 - Epoch: 6 Train acc: 95.47272727272727 Val acc: 45.300000000000004 Test acc43.9; Train loss: 0.0004939061130312356 Val loss: 0.004624915361404419
+INFO - evaluator.py - 2024-10-24 22:54:01,025 - Epoch: 7 Train acc: 96.34727272727272 Val acc: 51.949999999999996 Test acc49.6; Train loss: 0.00040593057494949213 Val loss: 0.003773866653442383
+INFO - evaluator.py - 2024-10-24 22:54:25,034 - Epoch: 8 Train acc: 97.71272727272728 Val acc: 61.5 Test acc61.324999999999996; Train loss: 0.00025172679346393455 Val loss: 0.002503492474555969
+INFO - evaluator.py - 2024-10-24 22:54:49,432 - Epoch: 9 Train acc: 98.45454545454545 Val acc: 48.25 Test acc48.075; Train loss: 0.0001744429766962474 Val loss: 0.0048469557762145996
+INFO - evaluator.py - 2024-10-24 22:55:13,801 - Epoch: 10 Train acc: 98.57090909090908 Val acc: 55.2 Test acc54.725; Train loss: 0.00015872892928733067 Val loss: 0.0037689046859741213
+INFO - evaluator.py - 2024-10-24 22:55:38,063 - Epoch: 11 Train acc: 98.80727272727273 Val acc: 52.1 Test acc51.37500000000001; Train loss: 0.00012970780437304214 Val loss: 0.0044431591033935545
+INFO - evaluator.py - 2024-10-24 22:56:02,682 - Epoch: 12 Train acc: 98.99454545454546 Val acc: 51.05 Test acc50.9; Train loss: 0.00011740445760925385 Val loss: 0.004799304723739624
+INFO - evaluator.py - 2024-10-24 22:56:27,124 - Epoch: 13 Train acc: 99.33454545454545 Val acc: 53.15 Test acc53.7; Train loss: 7.616966039640829e-05 Val loss: 0.004435483694076538
+INFO - evaluator.py - 2024-10-24 22:56:51,454 - Epoch: 14 Train acc: 99.28 Val acc: 56.599999999999994 Test acc54.25; Train loss: 7.968995672523636e-05 Val loss: 0.004886510848999023
+INFO - evaluator.py - 2024-10-24 22:57:15,734 - Epoch: 15 Train acc: 99.24909090909091 Val acc: 38.25 Test acc37.574999999999996; Train loss: 8.16481162920933e-05 Val loss: 0.008571805477142334
+INFO - evaluator.py - 2024-10-24 22:57:40,331 - Epoch: 16 Train acc: 99.00727272727273 Val acc: 49.85 Test acc49.55; Train loss: 0.00011600895509420132 Val loss: 0.005283456802368164
+INFO - evaluator.py - 2024-10-24 22:58:04,849 - Epoch: 17 Train acc: 99.58727272727272 Val acc: 57.15 Test acc55.45; Train loss: 4.713061477946626e-05 Val loss: 0.004439867496490478
+INFO - evaluator.py - 2024-10-24 22:58:29,235 - Epoch: 18 Train acc: 99.52545454545455 Val acc: 45.1 Test acc43.974999999999994; Train loss: 5.4494589691008016e-05 Val loss: 0.007676331043243409
+INFO - evaluator.py - 2024-10-24 22:58:53,221 - Epoch: 19 Train acc: 99.46363636363637 Val acc: 39.1 Test acc38.574999999999996; Train loss: 5.683849297717891e-05 Val loss: 0.010028017044067384
+INFO - evaluator.py - 2024-10-24 22:59:17,616 - Epoch: 20 Train acc: 99.87636363636364 Val acc: 45.15 Test acc44.3; Train loss: 1.6319362114617518e-05 Val loss: 0.00798742651939392
+INFO - evaluator.py - 2024-10-24 22:59:41,870 - Epoch: 21 Train acc: 99.95636363636363 Val acc: 57.85 Test acc57.675; Train loss: 6.312759846861644e-06 Val loss: 0.00479584002494812
+INFO - evaluator.py - 2024-10-24 23:00:06,801 - Epoch: 22 Train acc: 99.98181818181818 Val acc: 64.8 Test acc62.949999999999996; Train loss: 3.63375636595513e-06 Val loss: 0.00423354184627533
+INFO - evaluator.py - 2024-10-24 23:00:30,972 - Epoch: 23 Train acc: 99.98181818181818 Val acc: 67.9 Test acc66.3; Train loss: 3.177259838478428e-06 Val loss: 0.004008064508438111
+INFO - evaluator.py - 2024-10-24 23:00:55,553 - Epoch: 24 Train acc: 99.99636363636364 Val acc: 69.19999999999999 Test acc67.825; Train loss: 2.1495432958049193e-06 Val loss: 0.004449884414672852
+INFO - evaluator.py - 2024-10-24 23:01:19,856 - Epoch: 25 Train acc: 99.98727272727272 Val acc: 68.65 Test acc67.425; Train loss: 2.0609301171134574e-06 Val loss: 0.005450949668884277
+INFO - evaluator.py - 2024-10-24 23:01:44,404 - Epoch: 26 Train acc: 99.9890909090909 Val acc: 70.25 Test acc69.3; Train loss: 1.9767250624268357e-06 Val loss: 0.00563336181640625
+INFO - evaluator.py - 2024-10-24 23:02:08,455 - Epoch: 27 Train acc: 99.98181818181818 Val acc: 69.75 Test acc69.0; Train loss: 2.626789915790472e-06 Val loss: 0.00426655912399292
+INFO - evaluator.py - 2024-10-24 23:02:32,981 - Epoch: 28 Train acc: 99.97636363636364 Val acc: 71.6 Test acc69.575; Train loss: 3.6696184358747403e-06 Val loss: 0.003748357057571411
+INFO - evaluator.py - 2024-10-24 23:02:57,486 - Epoch: 29 Train acc: 99.95272727272727 Val acc: 62.8 Test acc61.375; Train loss: 5.344681606551272e-06 Val loss: 0.005278959989547729
+INFO - evaluator.py - 2024-10-24 23:03:21,758 - Epoch: 30 Train acc: 99.94545454545455 Val acc: 69.45 Test acc67.77499999999999; Train loss: 6.610012404582283e-06 Val loss: 0.00487679648399353
+INFO - evaluator.py - 2024-10-24 23:03:46,110 - Epoch: 31 Train acc: 99.98727272727272 Val acc: 70.19999999999999 Test acc69.55; Train loss: 2.0570539922532177e-06 Val loss: 0.0046778938770294185
+INFO - evaluator.py - 2024-10-24 23:04:11,118 - Epoch: 32 Train acc: 99.71636363636364 Val acc: 23.799999999999997 Test acc24.625; Train loss: 4.641181366944445e-05 Val loss: 0.020109850883483885
+INFO - evaluator.py - 2024-10-24 23:04:36,479 - Epoch: 33 Train acc: 99.74909090909091 Val acc: 59.199999999999996 Test acc55.85; Train loss: 2.8923304222883997e-05 Val loss: 0.006345075368881225
+INFO - evaluator.py - 2024-10-24 23:05:01,187 - Epoch: 34 Train acc: 99.9509090909091 Val acc: 64.4 Test acc62.975; Train loss: 8.42228055123335e-06 Val loss: 0.0044340333938598635
+INFO - evaluator.py - 2024-10-24 23:05:26,047 - Epoch: 35 Train acc: 99.96363636363637 Val acc: 66.9 Test acc66.2; Train loss: 4.483538669195365e-06 Val loss: 0.0037785327434539796
+INFO - evaluator.py - 2024-10-24 23:05:50,955 - Epoch: 36 Train acc: 99.97636363636364 Val acc: 65.3 Test acc65.10000000000001; Train loss: 4.178788588515917e-06 Val loss: 0.004434606075286865
+INFO - evaluator.py - 2024-10-24 23:06:16,108 - Epoch: 37 Train acc: 99.95454545454545 Val acc: 55.75 Test acc54.425000000000004; Train loss: 5.233517927759253e-06 Val loss: 0.007836352586746215
+INFO - evaluator.py - 2024-10-24 23:06:40,740 - Epoch: 38 Train acc: 99.96727272727273 Val acc: 61.25000000000001 Test acc60.099999999999994; Train loss: 4.259373761967502e-06 Val loss: 0.0059009168148040775
+INFO - evaluator.py - 2024-10-24 23:07:05,877 - Epoch: 39 Train acc: 99.92 Val acc: 60.25 Test acc59.35; Train loss: 8.187161287060008e-06 Val loss: 0.005716907501220703
+INFO - evaluator.py - 2024-10-24 23:07:30,294 - Epoch: 40 Train acc: 99.98 Val acc: 62.45 Test acc61.199999999999996; Train loss: 2.997166705939559e-06 Val loss: 0.005018232107162476
+INFO - evaluator.py - 2024-10-24 23:07:54,884 - Epoch: 41 Train acc: 99.99272727272728 Val acc: 65.55 Test acc64.75; Train loss: 1.327240529347116e-06 Val loss: 0.004414258480072021
+INFO - evaluator.py - 2024-10-24 23:08:19,206 - Epoch: 42 Train acc: 99.99818181818182 Val acc: 67.7 Test acc67.10000000000001; Train loss: 9.29639635912529e-07 Val loss: 0.003922232151031494
+INFO - evaluator.py - 2024-10-24 23:08:43,943 - Epoch: 43 Train acc: 99.99818181818182 Val acc: 70.05 Test acc68.575; Train loss: 1.0649162141238446e-06 Val loss: 0.003747068405151367
+INFO - evaluator.py - 2024-10-24 23:09:08,462 - Epoch: 44 Train acc: 100.0 Val acc: 70.95 Test acc69.69999999999999; Train loss: 6.353483251090934e-07 Val loss: 0.003639364242553711
+INFO - evaluator.py - 2024-10-24 23:09:33,966 - Epoch: 45 Train acc: 100.0 Val acc: 70.95 Test acc69.72500000000001; Train loss: 5.147883332028869e-07 Val loss: 0.003524302840232849
+INFO - evaluator.py - 2024-10-24 23:09:58,496 - Epoch: 46 Train acc: 99.99636363636364 Val acc: 72.1 Test acc71.0; Train loss: 7.919674747261689e-07 Val loss: 0.0033815619945526125
+INFO - evaluator.py - 2024-10-24 23:10:23,347 - Epoch: 47 Train acc: 100.0 Val acc: 70.05 Test acc69.425; Train loss: 6.501257183903032e-07 Val loss: 0.004025580883026123
+INFO - evaluator.py - 2024-10-24 23:10:47,788 - Epoch: 48 Train acc: 100.0 Val acc: 69.8 Test acc68.875; Train loss: 3.2696530646283213e-07 Val loss: 0.004051419019699097
+INFO - evaluator.py - 2024-10-24 23:11:12,517 - Epoch: 49 Train acc: 99.99636363636364 Val acc: 70.1 Test acc69.45; Train loss: 7.448237561034561e-07 Val loss: 0.0041076080799102785
+INFO - evaluator.py - 2024-10-24 23:11:12,531 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnet is 72.1 and 71.0
+INFO - evaluator.py - 2024-10-24 23:11:12,531 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnet is 72.1 and 71.0
+INFO - evaluator.py - 2024-10-24 23:11:12,531 - The best acc test dataset from resnet is 71.0
+INFO - evaluator.py - 2024-10-24 23:11:43,537 - Epoch: 0 Train acc: 61.57818181818182 Val acc: 55.2 Test acc55.2; Train loss: 0.0036573020268570295 Val loss: 0.0016448172330856324
+INFO - evaluator.py - 2024-10-24 23:12:14,164 - Epoch: 1 Train acc: 82.50363636363637 Val acc: 63.55 Test acc62.1; Train loss: 0.0017879909786311062 Val loss: 0.00152641761302948
+INFO - evaluator.py - 2024-10-24 23:12:44,730 - Epoch: 2 Train acc: 88.42909090909092 Val acc: 59.599999999999994 Test acc59.325; Train loss: 0.0011832708209753037 Val loss: 0.002017769753932953
+INFO - evaluator.py - 2024-10-24 23:13:15,359 - Epoch: 3 Train acc: 90.69272727272727 Val acc: 59.550000000000004 Test acc59.150000000000006; Train loss: 0.0009559547483921051 Val loss: 0.0022871843576431276
+INFO - evaluator.py - 2024-10-24 23:13:45,506 - Epoch: 4 Train acc: 92.43272727272728 Val acc: 61.75000000000001 Test acc61.35; Train loss: 0.0007998157681389288 Val loss: 0.0022320865392684935
+INFO - evaluator.py - 2024-10-24 23:14:15,934 - Epoch: 5 Train acc: 94.46545454545455 Val acc: 68.45 Test acc68.22500000000001; Train loss: 0.0005939700829034501 Val loss: 0.0016383219957351685
+INFO - evaluator.py - 2024-10-24 23:14:46,507 - Epoch: 6 Train acc: 95.91272727272727 Val acc: 71.39999999999999 Test acc70.525; Train loss: 0.00044769041565331547 Val loss: 0.0016360289454460143
+INFO - evaluator.py - 2024-10-24 23:15:16,583 - Epoch: 7 Train acc: 96.67999999999999 Val acc: 73.95 Test acc72.89999999999999; Train loss: 0.00037981528768485243 Val loss: 0.0015030399560928345
+INFO - evaluator.py - 2024-10-24 23:15:47,036 - Epoch: 8 Train acc: 97.05272727272727 Val acc: 66.8 Test acc67.025; Train loss: 0.0003242235124788501 Val loss: 0.002095808744430542
+INFO - evaluator.py - 2024-10-24 23:16:17,764 - Epoch: 9 Train acc: 97.16727272727272 Val acc: 67.2 Test acc67.025; Train loss: 0.0003116489423608238 Val loss: 0.0020401179790496826
+INFO - evaluator.py - 2024-10-24 23:16:47,793 - Epoch: 10 Train acc: 96.70363636363636 Val acc: 62.3 Test acc62.949999999999996; Train loss: 0.00037432277632707897 Val loss: 0.0022966527938842775
+INFO - evaluator.py - 2024-10-24 23:17:18,080 - Epoch: 11 Train acc: 98.02 Val acc: 67.85 Test acc68.525; Train loss: 0.0002143556313419884 Val loss: 0.002365014672279358
+INFO - evaluator.py - 2024-10-24 23:17:48,372 - Epoch: 12 Train acc: 98.33090909090909 Val acc: 62.7 Test acc64.67500000000001; Train loss: 0.00018706797227602113 Val loss: 0.0028835949897766115
+INFO - evaluator.py - 2024-10-24 23:18:18,441 - Epoch: 13 Train acc: 98.61090909090909 Val acc: 56.3 Test acc57.225; Train loss: 0.00015486068898303942 Val loss: 0.004023892879486084
+INFO - evaluator.py - 2024-10-24 23:18:49,193 - Epoch: 14 Train acc: 98.43454545454546 Val acc: 43.65 Test acc44.025; Train loss: 0.00017633556802333755 Val loss: 0.007195839643478394
+INFO - evaluator.py - 2024-10-24 23:19:19,451 - Epoch: 15 Train acc: 98.26 Val acc: 39.2 Test acc41.25; Train loss: 0.0001917688992551782 Val loss: 0.005598679780960083
+INFO - evaluator.py - 2024-10-24 23:19:49,607 - Epoch: 16 Train acc: 98.92 Val acc: 63.9 Test acc64.325; Train loss: 0.00012090251914818179 Val loss: 0.002518591523170471
+INFO - evaluator.py - 2024-10-24 23:20:20,061 - Epoch: 17 Train acc: 98.93272727272728 Val acc: 60.650000000000006 Test acc61.12499999999999; Train loss: 0.00011808309816670689 Val loss: 0.0031483683586120605
+INFO - evaluator.py - 2024-10-24 23:20:50,425 - Epoch: 18 Train acc: 99.21636363636364 Val acc: 62.2 Test acc62.724999999999994; Train loss: 9.165899843316187e-05 Val loss: 0.0031980221271514894
+INFO - evaluator.py - 2024-10-24 23:21:21,082 - Epoch: 19 Train acc: 99.2909090909091 Val acc: 70.05 Test acc71.125; Train loss: 7.909742741824382e-05 Val loss: 0.002352153301239014
+INFO - evaluator.py - 2024-10-24 23:21:51,263 - Epoch: 20 Train acc: 99.74 Val acc: 73.35000000000001 Test acc73.75; Train loss: 3.194841333474456e-05 Val loss: 0.0021931300163269044
+INFO - evaluator.py - 2024-10-24 23:22:21,565 - Epoch: 21 Train acc: 99.83999999999999 Val acc: 71.95 Test acc71.675; Train loss: 2.1966275370108304e-05 Val loss: 0.002588621139526367
+INFO - evaluator.py - 2024-10-24 23:22:51,822 - Epoch: 22 Train acc: 99.86545454545454 Val acc: 71.15 Test acc70.75; Train loss: 1.6752940672449766e-05 Val loss: 0.0027280601263046265
+INFO - evaluator.py - 2024-10-24 23:23:22,026 - Epoch: 23 Train acc: 99.86545454545454 Val acc: 70.55 Test acc71.325; Train loss: 1.625341120207767e-05 Val loss: 0.002787893295288086
+INFO - evaluator.py - 2024-10-24 23:23:52,525 - Epoch: 24 Train acc: 99.86545454545454 Val acc: 69.05 Test acc69.125; Train loss: 1.661831462141973e-05 Val loss: 0.0031182248592376708
+INFO - evaluator.py - 2024-10-24 23:24:22,595 - Epoch: 25 Train acc: 99.84727272727272 Val acc: 67.60000000000001 Test acc67.2; Train loss: 1.8576775240944698e-05 Val loss: 0.0034999403953552247
+INFO - evaluator.py - 2024-10-24 23:24:53,137 - Epoch: 26 Train acc: 99.88909090909091 Val acc: 67.30000000000001 Test acc67.225; Train loss: 1.3294085903775836e-05 Val loss: 0.0035871838331222533
+INFO - evaluator.py - 2024-10-24 23:25:23,310 - Epoch: 27 Train acc: 99.85636363636362 Val acc: 67.10000000000001 Test acc68.15; Train loss: 1.7351531712549993e-05 Val loss: 0.003544648289680481
+INFO - evaluator.py - 2024-10-24 23:25:53,587 - Epoch: 28 Train acc: 99.85636363636362 Val acc: 67.60000000000001 Test acc66.825; Train loss: 1.5948639258699998e-05 Val loss: 0.003509529113769531
+INFO - evaluator.py - 2024-10-24 23:26:23,805 - Epoch: 29 Train acc: 99.87818181818182 Val acc: 73.2 Test acc72.3; Train loss: 1.576831853228875e-05 Val loss: 0.0029616342782974245
+INFO - evaluator.py - 2024-10-24 23:26:54,288 - Epoch: 30 Train acc: 99.89454545454547 Val acc: 70.05 Test acc69.27499999999999; Train loss: 1.3166842539530162e-05 Val loss: 0.0031562288999557493
+INFO - evaluator.py - 2024-10-24 23:27:24,737 - Epoch: 31 Train acc: 99.8490909090909 Val acc: 67.35 Test acc67.05; Train loss: 1.6121305150657215e-05 Val loss: 0.003796804308891296
+INFO - evaluator.py - 2024-10-24 23:27:54,939 - Epoch: 32 Train acc: 99.87636363636364 Val acc: 69.95 Test acc70.075; Train loss: 1.548511181886583e-05 Val loss: 0.003374535322189331
+INFO - evaluator.py - 2024-10-24 23:28:25,745 - Epoch: 33 Train acc: 99.83999999999999 Val acc: 69.65 Test acc69.35; Train loss: 1.494185608779927e-05 Val loss: 0.0037068828344345092
+INFO - evaluator.py - 2024-10-24 23:28:55,745 - Epoch: 34 Train acc: 99.88 Val acc: 65.5 Test acc65.3; Train loss: 1.528590144999643e-05 Val loss: 0.004693195343017578
+INFO - evaluator.py - 2024-10-24 23:29:25,992 - Epoch: 35 Train acc: 99.91272727272728 Val acc: 69.15 Test acc69.95; Train loss: 1.0388480974043804e-05 Val loss: 0.003959752440452576
+INFO - evaluator.py - 2024-10-24 23:29:56,315 - Epoch: 36 Train acc: 99.88909090909091 Val acc: 71.55 Test acc71.89999999999999; Train loss: 1.4398434688204856e-05 Val loss: 0.0034954993724822998
+INFO - evaluator.py - 2024-10-24 23:30:26,667 - Epoch: 37 Train acc: 99.85454545454544 Val acc: 72.05 Test acc72.3; Train loss: 1.648455986446193e-05 Val loss: 0.0034181938171386718
+INFO - evaluator.py - 2024-10-24 23:30:57,356 - Epoch: 38 Train acc: 99.81818181818181 Val acc: 71.1 Test acc70.89999999999999; Train loss: 2.0458191475799222e-05 Val loss: 0.0034592366218566895
+INFO - evaluator.py - 2024-10-24 23:31:27,834 - Epoch: 39 Train acc: 99.92727272727274 Val acc: 72.1 Test acc71.575; Train loss: 9.295520517083456e-06 Val loss: 0.003197150707244873
+INFO - evaluator.py - 2024-10-24 23:31:57,893 - Epoch: 40 Train acc: 99.96545454545455 Val acc: 72.95 Test acc72.2; Train loss: 5.044640745671296e-06 Val loss: 0.0031341391801834105
+INFO - evaluator.py - 2024-10-24 23:32:28,131 - Epoch: 41 Train acc: 99.98181818181818 Val acc: 72.2 Test acc72.1; Train loss: 3.551631612473019e-06 Val loss: 0.003319173812866211
+INFO - evaluator.py - 2024-10-24 23:32:58,413 - Epoch: 42 Train acc: 99.97272727272727 Val acc: 72.6 Test acc72.075; Train loss: 3.434622156485354e-06 Val loss: 0.003398456335067749
+INFO - evaluator.py - 2024-10-24 23:33:28,612 - Epoch: 43 Train acc: 99.96363636363637 Val acc: 69.95 Test acc69.25; Train loss: 4.567462635796718e-06 Val loss: 0.003873103976249695
+INFO - evaluator.py - 2024-10-24 23:33:58,908 - Epoch: 44 Train acc: 99.97090909090909 Val acc: 70.1 Test acc70.1; Train loss: 3.929452223994965e-06 Val loss: 0.003957748651504517
+INFO - evaluator.py - 2024-10-24 23:34:29,256 - Epoch: 45 Train acc: 99.98181818181818 Val acc: 70.0 Test acc70.0; Train loss: 3.3226555704541998e-06 Val loss: 0.003966548085212708
+INFO - evaluator.py - 2024-10-24 23:34:59,388 - Epoch: 46 Train acc: 99.96727272727273 Val acc: 69.45 Test acc69.5; Train loss: 3.8621788428909814e-06 Val loss: 0.004078387022018432
+INFO - evaluator.py - 2024-10-24 23:35:29,591 - Epoch: 47 Train acc: 99.97636363636364 Val acc: 66.10000000000001 Test acc65.625; Train loss: 3.0747620481633932e-06 Val loss: 0.004922346115112304
+INFO - evaluator.py - 2024-10-24 23:35:59,699 - Epoch: 48 Train acc: 99.98363636363636 Val acc: 67.25 Test acc67.4; Train loss: 2.583426593297521e-06 Val loss: 0.004467953205108643
+INFO - evaluator.py - 2024-10-24 23:36:30,229 - Epoch: 49 Train acc: 99.98727272727272 Val acc: 68.2 Test acc68.575; Train loss: 2.0632798044268633e-06 Val loss: 0.004334133386611938
+INFO - evaluator.py - 2024-10-24 23:36:30,245 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from wrn is 73.95 and 72.89999999999999
+INFO - evaluator.py - 2024-10-24 23:36:30,246 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from wrn is 73.95 and 72.89999999999999
+INFO - evaluator.py - 2024-10-24 23:36:30,246 - The best acc test dataset from wrn is 73.75
+INFO - evaluator.py - 2024-10-24 23:38:29,597 - Epoch: 0 Train acc: 70.18909090909091 Val acc: 63.2 Test acc61.5; Train loss: 0.0034132947130636735 Val loss: 0.008771986007690429
+INFO - evaluator.py - 2024-10-24 23:40:27,967 - Epoch: 1 Train acc: 83.13636363636364 Val acc: 24.5 Test acc23.775; Train loss: 0.0017567890855399045 Val loss: 0.033399398803710935
+INFO - evaluator.py - 2024-10-24 23:42:26,774 - Epoch: 2 Train acc: 87.13454545454546 Val acc: 60.150000000000006 Test acc60.175; Train loss: 0.0013084654220125891 Val loss: 0.004343567967414856
+INFO - evaluator.py - 2024-10-24 23:44:25,085 - Epoch: 3 Train acc: 90.45272727272727 Val acc: 48.199999999999996 Test acc49.0; Train loss: 0.0009928921458396045 Val loss: 0.005575345754623413
+INFO - evaluator.py - 2024-10-24 23:46:23,453 - Epoch: 4 Train acc: 94.84 Val acc: 69.89999999999999 Test acc69.925; Train loss: 0.0005663763346997174 Val loss: 0.004749590158462524
+INFO - evaluator.py - 2024-10-24 23:48:21,882 - Epoch: 5 Train acc: 95.97272727272727 Val acc: 64.8 Test acc65.425; Train loss: 0.00044349661330607805 Val loss: 0.0025824271440505983
+INFO - evaluator.py - 2024-10-24 23:50:20,336 - Epoch: 6 Train acc: 96.85090909090908 Val acc: 71.39999999999999 Test acc70.3; Train loss: 0.00035176979411732066 Val loss: 0.006405244827270508
+INFO - evaluator.py - 2024-10-24 23:52:18,654 - Epoch: 7 Train acc: 90.09454545454545 Val acc: 17.849999999999998 Test acc17.150000000000002; Train loss: 0.001294684584100138 Val loss: 0.005126301050186157
+INFO - evaluator.py - 2024-10-24 23:54:16,939 - Epoch: 8 Train acc: 93.82909090909091 Val acc: 61.85000000000001 Test acc61.3; Train loss: 0.0006647765991362658 Val loss: 0.0018032006025314332
+INFO - evaluator.py - 2024-10-24 23:56:15,426 - Epoch: 9 Train acc: 96.73818181818183 Val acc: 65.2 Test acc65.675; Train loss: 0.00036169368238611653 Val loss: 0.0023017044067382814
+INFO - evaluator.py - 2024-10-24 23:58:14,037 - Epoch: 10 Train acc: 97.62545454545455 Val acc: 67.5 Test acc67.35; Train loss: 0.00027009534266862 Val loss: 0.0028670748472213745
+INFO - evaluator.py - 2024-10-25 00:00:12,364 - Epoch: 11 Train acc: 97.87272727272727 Val acc: 72.25 Test acc71.05; Train loss: 0.00023816498321565716 Val loss: 0.00240938138961792
+INFO - evaluator.py - 2024-10-25 00:02:10,594 - Epoch: 12 Train acc: 97.47818181818182 Val acc: 32.9 Test acc32.15; Train loss: 0.00028410455428741195 Val loss: 0.00920572853088379
+INFO - evaluator.py - 2024-10-25 00:04:09,013 - Epoch: 13 Train acc: 97.45454545454545 Val acc: 45.2 Test acc46.5; Train loss: 0.00027564335048876025 Val loss: 0.008594076633453369
+INFO - evaluator.py - 2024-10-25 00:06:07,412 - Epoch: 14 Train acc: 98.42727272727274 Val acc: 56.2 Test acc57.35; Train loss: 0.00017201823790303686 Val loss: 0.005341665744781494
+INFO - evaluator.py - 2024-10-25 00:08:05,725 - Epoch: 15 Train acc: 98.71272727272728 Val acc: 62.35000000000001 Test acc63.275000000000006; Train loss: 0.00014415985266254708 Val loss: 0.003789194107055664
+INFO - evaluator.py - 2024-10-25 00:10:04,331 - Epoch: 16 Train acc: 98.95272727272727 Val acc: 66.05 Test acc66.4; Train loss: 0.00011403267197310925 Val loss: 0.003515527367591858
+INFO - evaluator.py - 2024-10-25 00:12:02,579 - Epoch: 17 Train acc: 99.06363636363636 Val acc: 70.45 Test acc70.475; Train loss: 0.00010103871323252944 Val loss: 0.0028190208673477173
+INFO - evaluator.py - 2024-10-25 00:14:00,821 - Epoch: 18 Train acc: 98.99272727272728 Val acc: 71.2 Test acc71.275; Train loss: 0.00011649215035563843 Val loss: 0.002747001051902771
+INFO - evaluator.py - 2024-10-25 00:15:59,110 - Epoch: 19 Train acc: 99.28 Val acc: 68.7 Test acc69.325; Train loss: 8.209642254408788e-05 Val loss: 0.0028703317642211914
+INFO - evaluator.py - 2024-10-25 00:17:57,405 - Epoch: 20 Train acc: 99.86 Val acc: 68.05 Test acc68.89999999999999; Train loss: 1.9749354616082697e-05 Val loss: 0.0031885946989059447
+INFO - evaluator.py - 2024-10-25 00:19:55,760 - Epoch: 21 Train acc: 99.94 Val acc: 69.75 Test acc69.975; Train loss: 1.1533009722056291e-05 Val loss: 0.00361420214176178
+INFO - evaluator.py - 2024-10-25 00:21:54,033 - Epoch: 22 Train acc: 99.94909090909091 Val acc: 68.75 Test acc69.35; Train loss: 9.845990913824856e-06 Val loss: 0.004213708877563477
+INFO - evaluator.py - 2024-10-25 00:23:52,561 - Epoch: 23 Train acc: 99.95818181818181 Val acc: 72.0 Test acc71.89999999999999; Train loss: 7.301343258983583e-06 Val loss: 0.004240903615951538
+INFO - evaluator.py - 2024-10-25 00:25:50,801 - Epoch: 24 Train acc: 99.94181818181819 Val acc: 71.65 Test acc71.65; Train loss: 7.957806916039607e-06 Val loss: 0.004229672908782959
+INFO - evaluator.py - 2024-10-25 00:27:49,235 - Epoch: 25 Train acc: 99.97818181818182 Val acc: 71.65 Test acc71.025; Train loss: 6.108989440029042e-06 Val loss: 0.004519701480865479
+INFO - evaluator.py - 2024-10-25 00:29:47,684 - Epoch: 26 Train acc: 99.99090909090908 Val acc: 71.7 Test acc71.575; Train loss: 3.543103088172343e-06 Val loss: 0.004524744749069214
+INFO - evaluator.py - 2024-10-25 00:31:45,933 - Epoch: 27 Train acc: 99.98727272727272 Val acc: 73.15 Test acc72.2; Train loss: 3.613163019194458e-06 Val loss: 0.004124531388282776
+INFO - evaluator.py - 2024-10-25 00:33:44,263 - Epoch: 28 Train acc: 99.94909090909091 Val acc: 70.65 Test acc70.125; Train loss: 7.449635953922883e-06 Val loss: 0.004101044535636902
+INFO - evaluator.py - 2024-10-25 00:35:42,726 - Epoch: 29 Train acc: 99.84181818181818 Val acc: 72.35000000000001 Test acc71.8; Train loss: 1.6885072726687545e-05 Val loss: 0.004118200540542603
+INFO - evaluator.py - 2024-10-25 00:37:41,067 - Epoch: 30 Train acc: 99.97090909090909 Val acc: 68.95 Test acc68.925; Train loss: 5.073618715422609e-06 Val loss: 0.005415146112442017
+INFO - evaluator.py - 2024-10-25 00:39:39,500 - Epoch: 31 Train acc: 99.88181818181818 Val acc: 71.6 Test acc71.22500000000001; Train loss: 1.3198710313670084e-05 Val loss: 0.003660891890525818
+INFO - evaluator.py - 2024-10-25 00:41:37,935 - Epoch: 32 Train acc: 99.90909090909092 Val acc: 72.6 Test acc72.2; Train loss: 1.1054300546939803e-05 Val loss: 0.0035997662544250486
+INFO - evaluator.py - 2024-10-25 00:43:36,220 - Epoch: 33 Train acc: 99.90545454545455 Val acc: 73.45 Test acc71.475; Train loss: 1.12038983311901e-05 Val loss: 0.0036493484973907473
+INFO - evaluator.py - 2024-10-25 00:45:34,462 - Epoch: 34 Train acc: 99.91272727272728 Val acc: 71.8 Test acc70.375; Train loss: 1.1110017184579523e-05 Val loss: 0.004378213882446289
+INFO - evaluator.py - 2024-10-25 00:47:32,792 - Epoch: 35 Train acc: 99.94545454545455 Val acc: 73.85000000000001 Test acc72.2; Train loss: 6.752435186816993e-06 Val loss: 0.004388522148132324
+INFO - evaluator.py - 2024-10-25 00:49:31,111 - Epoch: 36 Train acc: 99.88909090909091 Val acc: 71.45 Test acc70.42500000000001; Train loss: 1.3973889051439156e-05 Val loss: 0.004485782623291016
+INFO - evaluator.py - 2024-10-25 00:51:29,614 - Epoch: 37 Train acc: 99.84727272727272 Val acc: 72.6 Test acc71.85000000000001; Train loss: 1.7222252379889887e-05 Val loss: 0.003891424059867859
+INFO - evaluator.py - 2024-10-25 00:53:27,925 - Epoch: 38 Train acc: 99.93636363636364 Val acc: 70.35 Test acc69.475; Train loss: 7.653198392869274e-06 Val loss: 0.005344523668289185
+INFO - evaluator.py - 2024-10-25 00:55:26,230 - Epoch: 39 Train acc: 99.94363636363637 Val acc: 69.89999999999999 Test acc69.425; Train loss: 8.297323248155987e-06 Val loss: 0.004658754348754883
+INFO - evaluator.py - 2024-10-25 00:57:24,537 - Epoch: 40 Train acc: 99.97272727272727 Val acc: 73.5 Test acc73.02499999999999; Train loss: 3.3003695770193274e-06 Val loss: 0.0042316668033599856
+INFO - evaluator.py - 2024-10-25 00:59:22,859 - Epoch: 41 Train acc: 99.99818181818182 Val acc: 73.05 Test acc71.6; Train loss: 1.1775782022761466e-06 Val loss: 0.004646750926971435
+INFO - evaluator.py - 2024-10-25 01:01:21,113 - Epoch: 42 Train acc: 99.99454545454546 Val acc: 73.0 Test acc72.3; Train loss: 1.0908869890045025e-06 Val loss: 0.004601541757583618
+INFO - evaluator.py - 2024-10-25 01:03:19,455 - Epoch: 43 Train acc: 100.0 Val acc: 72.95 Test acc72.35000000000001; Train loss: 7.401025242705311e-07 Val loss: 0.004489787101745605
+INFO - evaluator.py - 2024-10-25 01:05:17,717 - Epoch: 44 Train acc: 99.99272727272728 Val acc: 72.3 Test acc70.85000000000001; Train loss: 1.3966939378488776e-06 Val loss: 0.004767521142959595
+INFO - evaluator.py - 2024-10-25 01:07:16,123 - Epoch: 45 Train acc: 100.0 Val acc: 72.39999999999999 Test acc71.575; Train loss: 5.111674722601575e-07 Val loss: 0.004543119430541992
+INFO - evaluator.py - 2024-10-25 01:09:14,441 - Epoch: 46 Train acc: 99.99454545454546 Val acc: 73.05 Test acc71.275; Train loss: 8.038768954561832e-07 Val loss: 0.004788972377777099
+INFO - evaluator.py - 2024-10-25 01:11:12,797 - Epoch: 47 Train acc: 99.99818181818182 Val acc: 72.89999999999999 Test acc71.95; Train loss: 5.764217911002395e-07 Val loss: 0.00476385498046875
+INFO - evaluator.py - 2024-10-25 01:13:11,064 - Epoch: 48 Train acc: 100.0 Val acc: 73.05 Test acc71.975; Train loss: 3.977563882389487e-07 Val loss: 0.004525252819061279
+INFO - evaluator.py - 2024-10-25 01:15:09,174 - Epoch: 49 Train acc: 100.0 Val acc: 73.15 Test acc71.85000000000001; Train loss: 5.253839633289979e-07 Val loss: 0.004571496486663818
+INFO - evaluator.py - 2024-10-25 01:15:09,179 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnext is 73.85000000000001 and 72.2
+INFO - evaluator.py - 2024-10-25 01:15:09,179 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnext is 73.85000000000001 and 72.2
+INFO - evaluator.py - 2024-10-25 01:15:09,179 - The best acc test dataset from resnext is 73.02499999999999
+INFO - evaluator.py - 2024-10-25 01:15:09,179 - The best acc of accuracy (using synthetic images as the validation set) of synthetic images from resnet, wrn, and resnext are [71.0, 72.89999999999999, 72.2].
+INFO - evaluator.py - 2024-10-25 01:15:09,179 - The average and std of accuracy of synthetic images are 72.03 and 0.78
+INFO - dataset_loader.py - 2024-10-28 19:02:36,227 - delta is reset as 4.784738627130138e-06
+INFO - evaluator.py - 2024-10-28 19:04:13,137 - Epoch: 0 Train acc: 54.22 Val acc: 52.55 Test acc53.0; Train loss: 0.004496214628219605 Val loss: 0.0019025841951370239
+INFO - evaluator.py - 2024-10-28 19:04:39,587 - Epoch: 1 Train acc: 80.92363636363636 Val acc: 58.35 Test acc57.95; Train loss: 0.0019223004503683611 Val loss: 0.0021931112408638
+INFO - evaluator.py - 2024-10-28 19:05:31,154 - Epoch: 2 Train acc: 87.58181818181818 Val acc: 65.7 Test acc65.4; Train loss: 0.0012890176350420172 Val loss: 0.0016296623349189758
+INFO - evaluator.py - 2024-10-28 19:06:30,816 - Epoch: 3 Train acc: 90.77272727272727 Val acc: 56.3 Test acc57.15; Train loss: 0.0009493101225657896 Val loss: 0.002528361797332764
+INFO - evaluator.py - 2024-10-28 19:07:27,446 - Epoch: 4 Train acc: 92.76727272727273 Val acc: 68.7 Test acc67.825; Train loss: 0.0007631585255265236 Val loss: 0.0016453205943107606
+INFO - evaluator.py - 2024-10-28 19:08:23,634 - Epoch: 5 Train acc: 94.29636363636364 Val acc: 62.050000000000004 Test acc62.150000000000006; Train loss: 0.0005929255117746916 Val loss: 0.002195235848426819
+INFO - evaluator.py - 2024-10-28 19:09:25,880 - Epoch: 6 Train acc: 95.93090909090908 Val acc: 66.35 Test acc65.525; Train loss: 0.00044019992676648225 Val loss: 0.002089178204536438
+INFO - evaluator.py - 2024-10-28 19:10:17,225 - Epoch: 7 Train acc: 96.93272727272728 Val acc: 57.49999999999999 Test acc56.425000000000004; Train loss: 0.00033595377196642487 Val loss: 0.003218263030052185
+INFO - evaluator.py - 2024-10-28 19:11:13,829 - Epoch: 8 Train acc: 97.41636363636363 Val acc: 39.5 Test acc38.7; Train loss: 0.00027992312569509855 Val loss: 0.006479241609573364
+INFO - evaluator.py - 2024-10-28 19:12:15,318 - Epoch: 9 Train acc: 98.01818181818182 Val acc: 43.75 Test acc43.3; Train loss: 0.0002246371470222419 Val loss: 0.00718038010597229
+INFO - evaluator.py - 2024-10-28 19:13:10,727 - Epoch: 10 Train acc: 98.41818181818182 Val acc: 59.150000000000006 Test acc58.375; Train loss: 0.00017900215824219313 Val loss: 0.003131550192832947
+INFO - evaluator.py - 2024-10-28 19:14:11,910 - Epoch: 11 Train acc: 98.8 Val acc: 53.0 Test acc52.87500000000001; Train loss: 0.0001380771663547917 Val loss: 0.005785536766052246
+INFO - evaluator.py - 2024-10-28 19:15:07,629 - Epoch: 12 Train acc: 98.86181818181818 Val acc: 53.849999999999994 Test acc52.800000000000004; Train loss: 0.00012355751204727724 Val loss: 0.005075566530227661
+INFO - evaluator.py - 2024-10-28 19:16:07,692 - Epoch: 13 Train acc: 98.81818181818181 Val acc: 34.35 Test acc33.625; Train loss: 0.00014267195519059896 Val loss: 0.013265875816345215
+INFO - evaluator.py - 2024-10-28 19:17:07,617 - Epoch: 14 Train acc: 99.04727272727273 Val acc: 41.699999999999996 Test acc41.85; Train loss: 0.00010351212686139413 Val loss: 0.0062788381576538085
+INFO - evaluator.py - 2024-10-28 19:18:01,029 - Epoch: 15 Train acc: 99.38181818181818 Val acc: 44.25 Test acc43.6; Train loss: 7.05402458340607e-05 Val loss: 0.006109714508056641
+INFO - evaluator.py - 2024-10-28 19:18:58,406 - Epoch: 16 Train acc: 99.24545454545455 Val acc: 41.3 Test acc40.075; Train loss: 8.70264353399927e-05 Val loss: 0.00857599401473999
+INFO - evaluator.py - 2024-10-28 19:19:59,592 - Epoch: 17 Train acc: 99.46181818181819 Val acc: 48.1 Test acc47.55; Train loss: 6.339853039561686e-05 Val loss: 0.0069325056076049805
+INFO - evaluator.py - 2024-10-28 19:20:52,098 - Epoch: 18 Train acc: 99.40727272727273 Val acc: 44.45 Test acc43.525000000000006; Train loss: 6.74703901019794e-05 Val loss: 0.006911242723464966
+INFO - evaluator.py - 2024-10-28 19:21:52,431 - Epoch: 19 Train acc: 99.42909090909092 Val acc: 45.6 Test acc44.6; Train loss: 6.618499178333547e-05 Val loss: 0.005566668272018432
+INFO - dataset_loader.py - 2024-10-28 19:23:03,509 - delta is reset as 4.784738627130138e-06
+INFO - evaluator.py - 2024-10-28 22:07:00,927 - The FID of synthetic images is 168.7356543854038
+INFO - evaluator.py - 2024-10-28 22:07:00,932 - The Inception Score of synthetic images is 1.6657798290252686
+INFO - evaluator.py - 2024-10-28 22:07:00,932 - The Precision and Recall of synthetic images is 0.6199531555175781 and 0.009565217420458794
+INFO - evaluator.py - 2024-10-28 22:07:00,932 - The FLD of synthetic images is 20.005881786346436
+INFO - evaluator.py - 2024-10-28 22:07:00,932 - The ImageReward of synthetic images is -1.5740510936975478
+INFO - dataset_loader.py - 2024-10-28 22:46:39,941 - delta is reset as 4.784738627130138e-06
+INFO - dataset_loader.py - 2024-10-29 11:06:17,775 - delta is reset as 4.329102935418938e-06
+INFO - evaluator.py - 2024-10-29 11:39:57,591 - The FID of synthetic images is 168.59217462930627
+INFO - evaluator.py - 2024-10-29 11:39:57,596 - The Inception Score of synthetic images is 1.6652277708053589
+INFO - evaluator.py - 2024-10-29 11:39:57,596 - The Precision and Recall of synthetic images is 0.6194375157356262 and 0.00952173862606287
+INFO - evaluator.py - 2024-10-29 11:39:57,596 - The FLD of synthetic images is 20.778346061706543
+INFO - evaluator.py - 2024-10-29 11:39:57,596 - The ImageReward of synthetic images is -1.5740511079076678
+INFO - dataset_loader.py - 2024-10-29 11:39:58,114 - delta is reset as 1.8484667129285888e-06
+INFO - evaluator.py - 2024-10-29 12:12:19,140 - The FID of synthetic images is 231.37436795903784
+INFO - evaluator.py - 2024-10-29 12:12:19,145 - The Inception Score of synthetic images is 1.7306236028671265
+INFO - evaluator.py - 2024-10-29 12:12:19,145 - The Precision and Recall of synthetic images is 0.7602222561836243 and 0.00043999997433274984
+INFO - evaluator.py - 2024-10-29 12:12:19,145 - The FLD of synthetic images is 23.48649501800537
+INFO - evaluator.py - 2024-10-29 12:12:19,146 - The ImageReward of synthetic images is -2.2615407764571054
+INFO - dataset_loader.py - 2024-10-29 12:12:19,367 - delta is reset as 4.329102935418938e-06
+INFO - evaluator.py - 2024-10-29 12:45:16,662 - The FID of synthetic images is 237.36948091544997
+INFO - evaluator.py - 2024-10-29 12:45:16,666 - The Inception Score of synthetic images is 1.2807329893112183
+INFO - evaluator.py - 2024-10-29 12:45:16,667 - The Precision and Recall of synthetic images is 0.5577656626701355 and 4.347825961303897e-05
+INFO - evaluator.py - 2024-10-29 12:45:16,667 - The FLD of synthetic images is 30.49933910369873
+INFO - evaluator.py - 2024-10-29 12:45:16,667 - The ImageReward of synthetic images is -1.8509714604625478
+INFO - dataset_loader.py - 2024-10-29 12:45:17,305 - delta is reset as 1.5148623360286113e-06
+INFO - evaluator.py - 2024-10-29 13:18:11,048 - The FID of synthetic images is 53.50594213505724
+INFO - evaluator.py - 2024-10-29 13:18:11,054 - The Inception Score of synthetic images is 3.4386539459228516
+INFO - evaluator.py - 2024-10-29 13:18:11,055 - The Precision and Recall of synthetic images is 0.26606249809265137 and 0.12056666612625122
+INFO - evaluator.py - 2024-10-29 13:18:11,055 - The FLD of synthetic images is 20.395588874816895
+INFO - evaluator.py - 2024-10-29 13:18:11,055 - The ImageReward of synthetic images is -1.8617446345779531
+INFO - dataset_loader.py - 2024-10-29 13:18:11,648 - delta is reset as 1.5148623360286113e-06
+INFO - evaluator.py - 2024-10-29 13:50:35,681 - The FID of synthetic images is 4.446889068230405
+INFO - evaluator.py - 2024-10-29 13:50:35,832 - The Inception Score of synthetic images is 2.0761497020721436
+INFO - evaluator.py - 2024-10-29 13:50:35,832 - The Precision and Recall of synthetic images is 0.6322698593139648 and 0.7390333414077759
+INFO - evaluator.py - 2024-10-29 13:50:35,833 - The FLD of synthetic images is 3.283095359802246
+INFO - evaluator.py - 2024-10-29 13:50:35,833 - The ImageReward of synthetic images is -2.0057078354216755
+INFO - dataset_loader.py - 2024-10-29 13:50:37,371 - delta is reset as 5.11965868690912e-07
+INFO - evaluator.py - 2024-10-29 14:31:49,267 - The FID of synthetic images is 28.848900967099837
+INFO - evaluator.py - 2024-10-29 14:31:49,353 - The Inception Score of synthetic images is 2.238304853439331
+INFO - evaluator.py - 2024-10-29 14:31:49,353 - The Precision and Recall of synthetic images is 0.6088594198226929 and 0.1520366072654724
+INFO - evaluator.py - 2024-10-29 14:31:49,354 - The FLD of synthetic images is nan
+INFO - evaluator.py - 2024-10-29 14:31:49,354 - The ImageReward of synthetic images is -1.3833920579410202
+INFO - dataset_loader.py - 2024-10-29 14:31:49,986 - delta is reset as 1.8484667129285888e-06
+INFO - dataset_loader.py - 2024-10-30 01:13:43,912 - delta is reset as 4.784738627130138e-06
+INFO - evaluator.py - 2024-10-30 01:29:45,411 - The FID of synthetic images is 168.63809636892853
+INFO - evaluator.py - 2024-10-30 01:29:45,412 - The Inception Score of synthetic images is 1.6657798290252686
+INFO - evaluator.py - 2024-10-30 01:29:45,412 - The Precision and Recall of synthetic images is 0.6199531555175781 and 0.009565217420458794
+INFO - evaluator.py - 2024-10-30 01:29:45,412 - The FLD of synthetic images is 20.328164100646973
+INFO - evaluator.py - 2024-10-30 01:29:45,412 - The ImageReward of synthetic images is -1.5740511857806705
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