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+++ b/dpdm/celeba_male_32_eps10.0trainval-2024-10-24-00-28-59/stdout.txt
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+INFO - utils.py - 2024-10-24 00:29:06,346 - {'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/celeba_male_32_eps10.0trainval-2024-10-24-00-28-59', 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'root_folder': '.'}, 'public_data': {'name': None, 'num_channels': 3, 'resolution': 32, 'n_classes': 1000, 'train_path': 'dataset/imagenet/imagenet_32', 'selective': {'ratio': 1.0}}, 'sensitive_data': {'name': 'celeba_male_32', 'num_channels': 3, 'resolution': 32, 'n_classes': 2, 'train_path': 'dataset/celeba/train_32_Male.zip', 'test_path': 'dataset/celeba/test_32_Male.zip', 'fid_stats': 'dataset/celeba/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': 2, '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': 'ddim', 'stochastic': False, 'num_steps': 250, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0}, 'sampler_acc': {'type': 'ddim', 'stochastic': False, 'num_steps': 250, 'tmin': 0.002, 'tmax': 80.0, 'rho': 7.0, 'guid_scale': 0.0}, 'local_rank': 0, 'global_rank': 0, 'global_size': 4, 'fid_stats': 'dataset/celeba/fid_stats_32.npz'}, 'pretrain': {'log_dir': 'exp/dpdm/celeba_male_32_eps10.0trainval-2024-10-24-00-28-59/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': 2}}, 'train': {'log_dir': 'exp/dpdm/celeba_male_32_eps10.0trainval-2024-10-24-00-28-59/train', 'seed': 0, 'batch_size': 4096, 'n_epochs': 100, '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': 2}, 'dp': {'sdq': None, 'max_grad_norm': 1.0, 'delta': 1e-06, 'epsilon': 10.0, 'max_physical_batch_size': 8192, 'n_splits': 64}}, 'gen': {'data_num': 60000, 'batch_size': 1000, 'log_dir': 'exp/dpdm/celeba_male_32_eps10.0trainval-2024-10-24-00-28-59/gen'}, 'eval': {'batch_size': 1000}}
+INFO - dataset_loader.py - 2024-10-24 00:29:19,978 - delta is reset as 5.11965868690912e-07
+INFO - dpsgd_diffusion.py - 2024-10-24 00:29:23,255 - Number of trainable parameters in model: 0
+INFO - dpsgd_diffusion.py - 2024-10-24 00:29:23,255 - Number of total epochs: 100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:29:23,255 - Starting training at step 0
+INFO - dpsgd_diffusion.py - 2024-10-24 00:30:44,302 - Loss: 1.0652, step: 100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:31:46,877 - Loss: 0.9251, step: 200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:32:44,143 - Loss: 0.8938, step: 300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:33:40,391 - Loss: 0.8598, step: 400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:34:36,566 - Loss: 0.8406, step: 500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:35:34,061 - Loss: 0.8531, step: 600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:36:31,981 - Loss: 0.8322, step: 700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:37:26,536 - Loss: 0.8121, step: 800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:38:25,947 - Loss: 0.8344, step: 900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:39:21,125 - Loss: 0.8089, step: 1000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:40:15,865 - Loss: 0.8196, step: 1100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:41:13,037 - Loss: 0.8350, step: 1200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:42:08,162 - Loss: 0.7998, step: 1300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:43:04,212 - Loss: 0.7823, step: 1400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:43:59,139 - Loss: 0.7543, step: 1500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:44:54,781 - Loss: 0.7391, step: 1600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:45:50,074 - Loss: 0.7738, step: 1700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:46:44,190 - Loss: 0.6911, step: 1800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:47:40,331 - Loss: 0.6704, step: 1900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:48:36,309 - Loss: 0.6757, step: 2000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:48:36,322 - Saving snapshot checkpoint and sampling single batch at iteration 2000.
+WARNING - image.py - 2024-10-24 00:48:38,092 - 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 00:48:59,857 - FID at iteration 2000: 362.060993
+INFO - dpsgd_diffusion.py - 2024-10-24 00:49:59,333 - Loss: 0.6706, step: 2100
+INFO - dpsgd_diffusion.py - 2024-10-24 00:50:54,773 - Loss: 0.6268, step: 2200
+INFO - dpsgd_diffusion.py - 2024-10-24 00:51:49,908 - Loss: 0.6639, step: 2300
+INFO - dpsgd_diffusion.py - 2024-10-24 00:52:46,017 - Loss: 0.5960, step: 2400
+INFO - dpsgd_diffusion.py - 2024-10-24 00:53:42,406 - Loss: 0.5952, step: 2500
+INFO - dpsgd_diffusion.py - 2024-10-24 00:54:16,413 - Eps-value after 1 epochs: 1.5946
+INFO - dpsgd_diffusion.py - 2024-10-24 00:54:38,211 - Loss: 0.5637, step: 2600
+INFO - dpsgd_diffusion.py - 2024-10-24 00:55:33,867 - Loss: 0.5993, step: 2700
+INFO - dpsgd_diffusion.py - 2024-10-24 00:56:28,890 - Loss: 0.5371, step: 2800
+INFO - dpsgd_diffusion.py - 2024-10-24 00:57:24,065 - Loss: 0.5461, step: 2900
+INFO - dpsgd_diffusion.py - 2024-10-24 00:58:20,204 - Loss: 0.5531, step: 3000
+INFO - dpsgd_diffusion.py - 2024-10-24 00:59:15,379 - Loss: 0.5142, step: 3100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:00:11,300 - Loss: 0.4860, step: 3200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:01:07,294 - Loss: 0.4958, step: 3300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:02:02,369 - Loss: 0.4720, step: 3400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:02:58,865 - Loss: 0.4815, step: 3500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:03:55,477 - Loss: 0.4490, step: 3600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:04:52,675 - Loss: 0.4407, step: 3700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:05:48,976 - Loss: 0.4347, step: 3800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:06:43,480 - Loss: 0.4606, step: 3900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:07:39,951 - Loss: 0.4074, step: 4000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:07:39,965 - Saving snapshot checkpoint and sampling single batch at iteration 4000.
+WARNING - image.py - 2024-10-24 01:07:40,556 - 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 01:07:59,485 - FID at iteration 4000: 397.878915
+INFO - dpsgd_diffusion.py - 2024-10-24 01:08:55,106 - Loss: 0.4251, step: 4100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:09:50,143 - Loss: 0.4243, step: 4200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:10:47,121 - Loss: 0.3991, step: 4300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:11:43,265 - Loss: 0.4288, step: 4400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:12:38,675 - Loss: 0.4037, step: 4500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:13:35,573 - Loss: 0.3801, step: 4600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:14:31,311 - Loss: 0.4020, step: 4700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:15:27,021 - Loss: 0.3885, step: 4800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:16:23,873 - Loss: 0.3755, step: 4900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:17:20,367 - Loss: 0.4021, step: 5000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:18:17,741 - Loss: 0.3880, step: 5100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:18:28,652 - Eps-value after 2 epochs: 1.8230
+INFO - dpsgd_diffusion.py - 2024-10-24 01:19:13,494 - Loss: 0.3793, step: 5200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:20:08,373 - Loss: 0.3513, step: 5300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:21:04,614 - Loss: 0.3806, step: 5400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:21:59,378 - Loss: 0.3645, step: 5500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:22:54,560 - Loss: 0.3609, step: 5600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:23:51,031 - Loss: 0.3550, step: 5700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:24:46,470 - Loss: 0.3301, step: 5800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:25:42,456 - Loss: 0.3478, step: 5900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:26:37,036 - Loss: 0.3435, step: 6000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:26:37,049 - Saving snapshot checkpoint and sampling single batch at iteration 6000.
+WARNING - image.py - 2024-10-24 01:26:37,636 - 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 01:26:56,851 - FID at iteration 6000: 370.352945
+INFO - dpsgd_diffusion.py - 2024-10-24 01:27:51,672 - Loss: 0.3573, step: 6100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:28:46,570 - Loss: 0.3437, step: 6200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:29:42,343 - Loss: 0.3525, step: 6300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:30:37,444 - Loss: 0.3433, step: 6400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:31:33,286 - Loss: 0.3500, step: 6500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:32:29,130 - Loss: 0.3420, step: 6600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:33:26,206 - Loss: 0.3279, step: 6700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:34:22,179 - Loss: 0.3303, step: 6800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:35:17,296 - Loss: 0.3417, step: 6900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:36:12,198 - Loss: 0.3404, step: 7000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:37:06,862 - Loss: 0.2868, step: 7100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:38:05,376 - Loss: 0.3473, step: 7200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:39:02,394 - Loss: 0.3158, step: 7300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:39:59,106 - Loss: 0.3059, step: 7400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:40:54,069 - Loss: 0.3024, step: 7500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:41:47,894 - Loss: 0.3251, step: 7600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:42:36,512 - Eps-value after 3 epochs: 2.0232
+INFO - dpsgd_diffusion.py - 2024-10-24 01:42:47,838 - Loss: 0.3084, step: 7700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:43:42,261 - Loss: 0.3100, step: 7800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:44:37,970 - Loss: 0.2981, step: 7900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:45:33,833 - Loss: 0.3191, step: 8000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:45:33,850 - Saving snapshot checkpoint and sampling single batch at iteration 8000.
+WARNING - image.py - 2024-10-24 01:45:34,438 - 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 01:45:52,829 - FID at iteration 8000: 335.930391
+INFO - dpsgd_diffusion.py - 2024-10-24 01:46:49,629 - Loss: 0.3448, step: 8100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:47:43,911 - Loss: 0.3214, step: 8200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:48:40,872 - Loss: 0.3033, step: 8300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:49:35,939 - Loss: 0.3043, step: 8400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:50:31,775 - Loss: 0.3360, step: 8500
+INFO - dpsgd_diffusion.py - 2024-10-24 01:51:27,133 - Loss: 0.3046, step: 8600
+INFO - dpsgd_diffusion.py - 2024-10-24 01:52:21,334 - Loss: 0.3107, step: 8700
+INFO - dpsgd_diffusion.py - 2024-10-24 01:53:17,260 - Loss: 0.3174, step: 8800
+INFO - dpsgd_diffusion.py - 2024-10-24 01:54:13,552 - Loss: 0.3228, step: 8900
+INFO - dpsgd_diffusion.py - 2024-10-24 01:55:09,747 - Loss: 0.3090, step: 9000
+INFO - dpsgd_diffusion.py - 2024-10-24 01:56:05,244 - Loss: 0.2845, step: 9100
+INFO - dpsgd_diffusion.py - 2024-10-24 01:57:00,927 - Loss: 0.2972, step: 9200
+INFO - dpsgd_diffusion.py - 2024-10-24 01:57:57,754 - Loss: 0.2836, step: 9300
+INFO - dpsgd_diffusion.py - 2024-10-24 01:58:52,169 - Loss: 0.2978, step: 9400
+INFO - dpsgd_diffusion.py - 2024-10-24 01:59:47,629 - Loss: 0.2946, step: 9500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:00:42,510 - Loss: 0.3153, step: 9600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:01:39,180 - Loss: 0.3135, step: 9700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:02:35,358 - Loss: 0.2949, step: 9800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:03:29,750 - Loss: 0.2913, step: 9900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:04:26,329 - Loss: 0.3184, step: 10000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:04:26,407 - Saving snapshot checkpoint and sampling single batch at iteration 10000.
+WARNING - image.py - 2024-10-24 02:04:26,995 - 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 02:04:45,587 - FID at iteration 10000: 310.757770
+INFO - dpsgd_diffusion.py - 2024-10-24 02:05:41,138 - Loss: 0.2965, step: 10100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:06:37,370 - Loss: 0.2788, step: 10200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:06:59,760 - Eps-value after 4 epochs: 2.2071
+INFO - dpsgd_diffusion.py - 2024-10-24 02:07:33,157 - Loss: 0.3326, step: 10300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:08:28,079 - Loss: 0.2918, step: 10400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:09:23,352 - Loss: 0.2926, step: 10500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:10:17,716 - Loss: 0.2827, step: 10600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:11:12,342 - Loss: 0.2938, step: 10700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:12:07,986 - Loss: 0.2937, step: 10800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:13:04,395 - Loss: 0.2920, step: 10900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:13:58,892 - Loss: 0.2905, step: 11000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:14:54,313 - Loss: 0.2808, step: 11100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:15:48,181 - Loss: 0.2715, step: 11200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:16:42,396 - Loss: 0.3048, step: 11300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:17:37,436 - Loss: 0.2951, step: 11400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:18:35,433 - Loss: 0.2800, step: 11500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:19:31,307 - Loss: 0.2764, step: 11600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:20:27,166 - Loss: 0.3091, step: 11700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:21:22,800 - Loss: 0.2668, step: 11800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:22:17,971 - Loss: 0.2763, step: 11900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:23:12,196 - Loss: 0.2707, step: 12000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:23:12,244 - Saving snapshot checkpoint and sampling single batch at iteration 12000.
+WARNING - image.py - 2024-10-24 02:23:12,839 - 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 02:23:31,343 - FID at iteration 12000: 289.343845
+INFO - dpsgd_diffusion.py - 2024-10-24 02:24:26,456 - Loss: 0.2866, step: 12100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:25:21,684 - Loss: 0.2656, step: 12200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:26:16,369 - Loss: 0.2856, step: 12300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:27:11,461 - Loss: 0.2731, step: 12400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:28:05,573 - Loss: 0.2627, step: 12500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:29:01,045 - Loss: 0.2948, step: 12600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:29:57,100 - Loss: 0.2703, step: 12700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:30:52,893 - Loss: 0.2893, step: 12800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:30:52,963 - Eps-value after 5 epochs: 2.3798
+INFO - dpsgd_diffusion.py - 2024-10-24 02:31:48,104 - Loss: 0.3055, step: 12900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:32:43,868 - Loss: 0.2920, step: 13000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:33:40,348 - Loss: 0.2907, step: 13100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:34:35,576 - Loss: 0.2616, step: 13200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:35:31,705 - Loss: 0.2906, step: 13300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:36:27,684 - Loss: 0.2610, step: 13400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:37:22,636 - Loss: 0.2695, step: 13500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:38:18,628 - Loss: 0.2800, step: 13600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:39:14,033 - Loss: 0.2605, step: 13700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:40:10,317 - Loss: 0.2654, step: 13800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:41:07,121 - Loss: 0.2799, step: 13900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:42:04,288 - Loss: 0.2852, step: 14000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:42:04,332 - Saving snapshot checkpoint and sampling single batch at iteration 14000.
+WARNING - image.py - 2024-10-24 02:42:04,921 - 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 02:42:23,503 - FID at iteration 14000: 268.308691
+INFO - dpsgd_diffusion.py - 2024-10-24 02:43:19,935 - Loss: 0.2943, step: 14100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:44:14,929 - Loss: 0.2554, step: 14200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:45:10,828 - Loss: 0.2716, step: 14300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:46:05,423 - Loss: 0.2808, step: 14400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:46:59,363 - Loss: 0.2703, step: 14500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:47:54,879 - Loss: 0.2600, step: 14600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:48:49,748 - Loss: 0.2515, step: 14700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:49:45,347 - Loss: 0.2503, step: 14800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:50:40,987 - Loss: 0.2688, step: 14900
+INFO - dpsgd_diffusion.py - 2024-10-24 02:51:37,402 - Loss: 0.2691, step: 15000
+INFO - dpsgd_diffusion.py - 2024-10-24 02:52:33,292 - Loss: 0.2935, step: 15100
+INFO - dpsgd_diffusion.py - 2024-10-24 02:53:28,472 - Loss: 0.2730, step: 15200
+INFO - dpsgd_diffusion.py - 2024-10-24 02:54:24,051 - Loss: 0.2688, step: 15300
+INFO - dpsgd_diffusion.py - 2024-10-24 02:54:57,340 - Eps-value after 6 epochs: 2.5435
+INFO - dpsgd_diffusion.py - 2024-10-24 02:55:19,055 - Loss: 0.2643, step: 15400
+INFO - dpsgd_diffusion.py - 2024-10-24 02:56:14,098 - Loss: 0.2483, step: 15500
+INFO - dpsgd_diffusion.py - 2024-10-24 02:57:08,823 - Loss: 0.2964, step: 15600
+INFO - dpsgd_diffusion.py - 2024-10-24 02:58:02,997 - Loss: 0.2690, step: 15700
+INFO - dpsgd_diffusion.py - 2024-10-24 02:58:57,370 - Loss: 0.2674, step: 15800
+INFO - dpsgd_diffusion.py - 2024-10-24 02:59:53,790 - Loss: 0.2754, step: 15900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:00:48,380 - Loss: 0.2687, step: 16000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:00:48,399 - Saving snapshot checkpoint and sampling single batch at iteration 16000.
+WARNING - image.py - 2024-10-24 03:00:48,988 - 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 03:01:08,237 - FID at iteration 16000: 247.412754
+INFO - dpsgd_diffusion.py - 2024-10-24 03:02:05,157 - Loss: 0.2671, step: 16100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:03:01,621 - Loss: 0.2494, step: 16200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:03:57,069 - Loss: 0.2670, step: 16300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:04:53,054 - Loss: 0.2577, step: 16400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:05:50,447 - Loss: 0.2926, step: 16500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:06:45,929 - Loss: 0.2426, step: 16600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:07:40,910 - Loss: 0.2805, step: 16700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:08:40,854 - Loss: 0.2613, step: 16800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:09:36,123 - Loss: 0.2455, step: 16900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:10:32,038 - Loss: 0.2656, step: 17000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:11:28,345 - Loss: 0.2538, step: 17100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:12:23,548 - Loss: 0.2452, step: 17200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:13:19,306 - Loss: 0.2897, step: 17300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:14:15,023 - Loss: 0.2660, step: 17400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:15:11,269 - Loss: 0.2483, step: 17500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:16:06,873 - Loss: 0.2624, step: 17600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:17:02,407 - Loss: 0.2584, step: 17700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:17:57,051 - Loss: 0.2439, step: 17800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:18:53,142 - Loss: 0.2765, step: 17900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:19:04,067 - Eps-value after 7 epochs: 2.6996
+INFO - dpsgd_diffusion.py - 2024-10-24 03:19:48,393 - Loss: 0.2531, step: 18000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:19:48,405 - Saving snapshot checkpoint and sampling single batch at iteration 18000.
+WARNING - image.py - 2024-10-24 03:19:48,995 - 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 03:20:08,054 - FID at iteration 18000: 225.778642
+INFO - dpsgd_diffusion.py - 2024-10-24 03:21:03,992 - Loss: 0.2780, step: 18100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:21:58,715 - Loss: 0.2520, step: 18200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:22:54,437 - Loss: 0.2598, step: 18300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:23:51,056 - Loss: 0.2521, step: 18400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:24:47,314 - Loss: 0.2418, step: 18500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:25:42,991 - Loss: 0.2604, step: 18600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:26:36,859 - Loss: 0.2642, step: 18700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:27:33,037 - Loss: 0.2607, step: 18800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:28:28,849 - Loss: 0.2616, step: 18900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:29:26,094 - Loss: 0.2361, step: 19000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:30:21,816 - Loss: 0.2622, step: 19100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:31:16,962 - Loss: 0.2571, step: 19200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:32:12,619 - Loss: 0.2334, step: 19300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:33:09,135 - Loss: 0.2520, step: 19400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:34:04,492 - Loss: 0.2556, step: 19500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:35:01,076 - Loss: 0.2455, step: 19600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:35:57,562 - Loss: 0.2581, step: 19700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:36:51,732 - Loss: 0.2586, step: 19800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:37:46,511 - Loss: 0.2623, step: 19900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:38:42,068 - Loss: 0.2721, step: 20000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:38:42,145 - Saving snapshot checkpoint and sampling single batch at iteration 20000.
+WARNING - image.py - 2024-10-24 03:38:42,737 - 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 03:39:01,755 - FID at iteration 20000: 203.527494
+INFO - dpsgd_diffusion.py - 2024-10-24 03:39:56,824 - Loss: 0.2524, step: 20100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:40:53,859 - Loss: 0.2297, step: 20200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:41:48,059 - Loss: 0.2367, step: 20300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:42:44,615 - Loss: 0.2628, step: 20400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:43:29,265 - Eps-value after 8 epochs: 2.8492
+INFO - dpsgd_diffusion.py - 2024-10-24 03:43:40,755 - Loss: 0.2697, step: 20500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:44:37,195 - Loss: 0.2300, step: 20600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:45:33,566 - Loss: 0.2549, step: 20700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:46:29,505 - Loss: 0.2356, step: 20800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:47:25,314 - Loss: 0.2344, step: 20900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:48:21,830 - Loss: 0.2428, step: 21000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:49:18,290 - Loss: 0.2324, step: 21100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:50:13,606 - Loss: 0.2582, step: 21200
+INFO - dpsgd_diffusion.py - 2024-10-24 03:51:07,848 - Loss: 0.2394, step: 21300
+INFO - dpsgd_diffusion.py - 2024-10-24 03:52:03,079 - Loss: 0.2478, step: 21400
+INFO - dpsgd_diffusion.py - 2024-10-24 03:52:58,679 - Loss: 0.2362, step: 21500
+INFO - dpsgd_diffusion.py - 2024-10-24 03:53:54,674 - Loss: 0.2414, step: 21600
+INFO - dpsgd_diffusion.py - 2024-10-24 03:54:49,695 - Loss: 0.2365, step: 21700
+INFO - dpsgd_diffusion.py - 2024-10-24 03:55:46,141 - Loss: 0.2594, step: 21800
+INFO - dpsgd_diffusion.py - 2024-10-24 03:56:42,952 - Loss: 0.2476, step: 21900
+INFO - dpsgd_diffusion.py - 2024-10-24 03:57:39,141 - Loss: 0.2648, step: 22000
+INFO - dpsgd_diffusion.py - 2024-10-24 03:57:39,156 - Saving snapshot checkpoint and sampling single batch at iteration 22000.
+WARNING - image.py - 2024-10-24 03:57:39,743 - 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 03:57:58,093 - FID at iteration 22000: 180.745073
+INFO - dpsgd_diffusion.py - 2024-10-24 03:58:54,964 - Loss: 0.2475, step: 22100
+INFO - dpsgd_diffusion.py - 2024-10-24 03:59:49,063 - Loss: 0.2726, step: 22200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:00:44,412 - Loss: 0.2629, step: 22300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:01:38,448 - Loss: 0.2433, step: 22400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:02:35,247 - Loss: 0.2406, step: 22500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:03:30,437 - Loss: 0.2351, step: 22600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:04:25,709 - Loss: 0.2413, step: 22700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:05:19,943 - Loss: 0.2464, step: 22800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:06:15,693 - Loss: 0.2654, step: 22900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:07:11,051 - Loss: 0.2307, step: 23000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:07:33,083 - Eps-value after 9 epochs: 2.9925
+INFO - dpsgd_diffusion.py - 2024-10-24 04:08:06,441 - Loss: 0.2699, step: 23100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:09:01,643 - Loss: 0.2359, step: 23200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:09:56,901 - Loss: 0.2307, step: 23300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:10:51,626 - Loss: 0.2353, step: 23400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:11:47,547 - Loss: 0.2428, step: 23500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:12:44,341 - Loss: 0.2542, step: 23600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:13:39,505 - Loss: 0.2231, step: 23700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:14:33,142 - Loss: 0.2608, step: 23800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:15:28,408 - Loss: 0.2561, step: 23900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:16:24,285 - Loss: 0.2293, step: 24000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:16:24,304 - Saving snapshot checkpoint and sampling single batch at iteration 24000.
+WARNING - image.py - 2024-10-24 04:16:24,894 - 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 04:16:43,950 - FID at iteration 24000: 160.694153
+INFO - dpsgd_diffusion.py - 2024-10-24 04:17:41,437 - Loss: 0.2644, step: 24100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:18:35,575 - Loss: 0.2742, step: 24200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:19:31,984 - Loss: 0.2534, step: 24300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:20:28,150 - Loss: 0.2271, step: 24400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:21:24,264 - Loss: 0.2464, step: 24500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:22:19,823 - Loss: 0.2560, step: 24600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:23:15,047 - Loss: 0.2463, step: 24700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:24:09,436 - Loss: 0.2524, step: 24800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:25:04,084 - Loss: 0.2506, step: 24900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:25:59,617 - Loss: 0.2193, step: 25000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:26:53,862 - Loss: 0.2480, step: 25100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:27:49,558 - Loss: 0.2323, step: 25200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:28:45,704 - Loss: 0.2453, step: 25300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:29:40,591 - Loss: 0.2474, step: 25400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:30:35,327 - Loss: 0.2321, step: 25500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:31:29,456 - Loss: 0.2332, step: 25600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:31:29,500 - Eps-value after 10 epochs: 3.1307
+INFO - dpsgd_diffusion.py - 2024-10-24 04:32:27,405 - Loss: 0.2364, step: 25700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:33:22,422 - Loss: 0.2176, step: 25800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:34:18,502 - Loss: 0.2366, step: 25900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:35:14,638 - Loss: 0.2520, step: 26000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:35:14,651 - Saving snapshot checkpoint and sampling single batch at iteration 26000.
+WARNING - image.py - 2024-10-24 04:35:15,241 - 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 04:35:34,415 - FID at iteration 26000: 144.196951
+INFO - dpsgd_diffusion.py - 2024-10-24 04:36:29,357 - Loss: 0.2519, step: 26100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:37:25,971 - Loss: 0.2300, step: 26200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:38:22,254 - Loss: 0.2243, step: 26300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:39:17,900 - Loss: 0.2393, step: 26400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:40:11,974 - Loss: 0.2286, step: 26500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:41:08,373 - Loss: 0.2551, step: 26600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:42:04,701 - Loss: 0.2560, step: 26700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:42:59,847 - Loss: 0.2509, step: 26800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:43:55,203 - Loss: 0.2537, step: 26900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:44:50,487 - Loss: 0.2292, step: 27000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:45:48,408 - Loss: 0.2121, step: 27100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:46:43,705 - Loss: 0.2454, step: 27200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:47:38,697 - Loss: 0.2389, step: 27300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:48:34,086 - Loss: 0.2249, step: 27400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:49:29,810 - Loss: 0.2230, step: 27500
+INFO - dpsgd_diffusion.py - 2024-10-24 04:50:25,435 - Loss: 0.2258, step: 27600
+INFO - dpsgd_diffusion.py - 2024-10-24 04:51:20,366 - Loss: 0.2310, step: 27700
+INFO - dpsgd_diffusion.py - 2024-10-24 04:52:17,441 - Loss: 0.2657, step: 27800
+INFO - dpsgd_diffusion.py - 2024-10-24 04:53:12,279 - Loss: 0.2279, step: 27900
+INFO - dpsgd_diffusion.py - 2024-10-24 04:54:08,843 - Loss: 0.2472, step: 28000
+INFO - dpsgd_diffusion.py - 2024-10-24 04:54:08,856 - Saving snapshot checkpoint and sampling single batch at iteration 28000.
+WARNING - image.py - 2024-10-24 04:54:09,445 - 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 04:54:27,940 - FID at iteration 28000: 128.132019
+INFO - dpsgd_diffusion.py - 2024-10-24 04:55:24,209 - Loss: 0.2387, step: 28100
+INFO - dpsgd_diffusion.py - 2024-10-24 04:55:57,482 - Eps-value after 11 epochs: 3.2644
+INFO - dpsgd_diffusion.py - 2024-10-24 04:56:20,412 - Loss: 0.2248, step: 28200
+INFO - dpsgd_diffusion.py - 2024-10-24 04:57:16,131 - Loss: 0.2446, step: 28300
+INFO - dpsgd_diffusion.py - 2024-10-24 04:58:10,582 - Loss: 0.2339, step: 28400
+INFO - dpsgd_diffusion.py - 2024-10-24 04:59:05,643 - Loss: 0.2241, step: 28500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:00:01,593 - Loss: 0.2408, step: 28600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:00:58,723 - Loss: 0.2314, step: 28700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:01:55,422 - Loss: 0.2528, step: 28800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:02:49,920 - Loss: 0.2409, step: 28900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:03:45,467 - Loss: 0.2399, step: 29000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:04:40,756 - Loss: 0.2466, step: 29100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:05:35,748 - Loss: 0.2480, step: 29200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:06:30,471 - Loss: 0.2636, step: 29300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:07:26,093 - Loss: 0.2435, step: 29400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:08:21,656 - Loss: 0.2399, step: 29500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:09:18,058 - Loss: 0.2258, step: 29600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:10:13,090 - Loss: 0.2438, step: 29700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:11:07,201 - Loss: 0.2167, step: 29800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:12:01,791 - Loss: 0.2356, step: 29900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:12:56,620 - Loss: 0.2427, step: 30000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:12:56,663 - Saving snapshot checkpoint and sampling single batch at iteration 30000.
+WARNING - image.py - 2024-10-24 05:12:57,339 - 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 05:13:15,921 - FID at iteration 30000: 116.380647
+INFO - dpsgd_diffusion.py - 2024-10-24 05:14:13,522 - Loss: 0.2337, step: 30100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:15:08,395 - Loss: 0.2427, step: 30200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:16:02,467 - Loss: 0.2282, step: 30300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:16:56,324 - Loss: 0.2102, step: 30400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:17:51,076 - Loss: 0.2437, step: 30500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:18:46,169 - Loss: 0.2387, step: 30600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:19:41,787 - Loss: 0.2338, step: 30700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:19:52,697 - Eps-value after 12 epochs: 3.3933
+INFO - dpsgd_diffusion.py - 2024-10-24 05:20:40,476 - Loss: 0.2417, step: 30800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:21:37,648 - Loss: 0.2472, step: 30900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:22:32,993 - Loss: 0.2214, step: 31000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:23:28,215 - Loss: 0.2556, step: 31100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:24:23,119 - Loss: 0.2543, step: 31200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:25:17,784 - Loss: 0.2248, step: 31300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:26:14,267 - Loss: 0.2238, step: 31400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:27:10,896 - Loss: 0.2451, step: 31500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:28:05,784 - Loss: 0.2497, step: 31600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:29:01,245 - Loss: 0.2239, step: 31700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:29:55,688 - Loss: 0.2280, step: 31800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:30:50,352 - Loss: 0.2378, step: 31900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:31:45,054 - Loss: 0.2137, step: 32000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:31:45,073 - Saving snapshot checkpoint and sampling single batch at iteration 32000.
+WARNING - image.py - 2024-10-24 05:31:45,774 - 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 05:32:04,438 - FID at iteration 32000: 105.914540
+INFO - dpsgd_diffusion.py - 2024-10-24 05:33:00,230 - Loss: 0.2421, step: 32100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:33:56,772 - Loss: 0.2163, step: 32200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:34:52,189 - Loss: 0.2345, step: 32300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:35:46,761 - Loss: 0.2173, step: 32400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:36:41,563 - Loss: 0.2501, step: 32500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:37:36,800 - Loss: 0.2404, step: 32600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:38:32,782 - Loss: 0.2473, step: 32700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:39:27,434 - Loss: 0.2397, step: 32800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:40:22,121 - Loss: 0.2413, step: 32900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:41:18,180 - Loss: 0.2357, step: 33000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:42:13,407 - Loss: 0.2266, step: 33100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:43:07,419 - Loss: 0.2402, step: 33200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:43:51,151 - Eps-value after 13 epochs: 3.5186
+INFO - dpsgd_diffusion.py - 2024-10-24 05:44:01,976 - Loss: 0.2459, step: 33300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:44:56,525 - Loss: 0.2361, step: 33400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:45:50,721 - Loss: 0.2421, step: 33500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:46:45,818 - Loss: 0.2451, step: 33600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:47:40,749 - Loss: 0.2088, step: 33700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:48:36,386 - Loss: 0.2402, step: 33800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:49:32,782 - Loss: 0.2271, step: 33900
+INFO - dpsgd_diffusion.py - 2024-10-24 05:50:27,960 - Loss: 0.2300, step: 34000
+INFO - dpsgd_diffusion.py - 2024-10-24 05:50:28,006 - Saving snapshot checkpoint and sampling single batch at iteration 34000.
+WARNING - image.py - 2024-10-24 05:50:28,595 - 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 05:50:46,541 - FID at iteration 34000: 97.362108
+INFO - dpsgd_diffusion.py - 2024-10-24 05:51:43,713 - Loss: 0.2312, step: 34100
+INFO - dpsgd_diffusion.py - 2024-10-24 05:52:39,614 - Loss: 0.2147, step: 34200
+INFO - dpsgd_diffusion.py - 2024-10-24 05:53:36,565 - Loss: 0.2305, step: 34300
+INFO - dpsgd_diffusion.py - 2024-10-24 05:54:31,254 - Loss: 0.2064, step: 34400
+INFO - dpsgd_diffusion.py - 2024-10-24 05:55:26,391 - Loss: 0.2211, step: 34500
+INFO - dpsgd_diffusion.py - 2024-10-24 05:56:20,595 - Loss: 0.2321, step: 34600
+INFO - dpsgd_diffusion.py - 2024-10-24 05:57:15,500 - Loss: 0.2252, step: 34700
+INFO - dpsgd_diffusion.py - 2024-10-24 05:58:10,733 - Loss: 0.2182, step: 34800
+INFO - dpsgd_diffusion.py - 2024-10-24 05:59:06,470 - Loss: 0.2418, step: 34900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:00:03,137 - Loss: 0.2148, step: 35000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:01:00,693 - Loss: 0.2327, step: 35100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:01:55,834 - Loss: 0.2348, step: 35200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:02:52,709 - Loss: 0.2300, step: 35300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:03:48,898 - Loss: 0.2288, step: 35400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:04:43,614 - Loss: 0.2313, step: 35500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:05:39,491 - Loss: 0.2251, step: 35600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:06:34,563 - Loss: 0.2099, step: 35700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:07:30,366 - Loss: 0.2190, step: 35800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:07:52,035 - Eps-value after 14 epochs: 3.6402
+INFO - dpsgd_diffusion.py - 2024-10-24 06:08:25,647 - Loss: 0.2424, step: 35900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:09:20,753 - Loss: 0.2296, step: 36000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:09:20,765 - Saving snapshot checkpoint and sampling single batch at iteration 36000.
+WARNING - image.py - 2024-10-24 06:09:21,356 - 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 06:09:39,818 - FID at iteration 36000: 90.292806
+INFO - dpsgd_diffusion.py - 2024-10-24 06:10:35,693 - Loss: 0.2193, step: 36100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:11:32,761 - Loss: 0.2146, step: 36200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:12:28,326 - Loss: 0.2126, step: 36300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:13:24,852 - Loss: 0.2159, step: 36400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:14:21,710 - Loss: 0.2285, step: 36500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:15:19,098 - Loss: 0.2264, step: 36600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:16:13,817 - Loss: 0.1967, step: 36700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:17:09,097 - Loss: 0.2471, step: 36800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:18:05,579 - Loss: 0.2223, step: 36900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:19:01,207 - Loss: 0.2319, step: 37000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:19:57,521 - Loss: 0.2516, step: 37100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:20:55,078 - Loss: 0.2098, step: 37200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:21:50,990 - Loss: 0.2170, step: 37300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:22:47,052 - Loss: 0.2235, step: 37400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:23:42,356 - Loss: 0.2416, step: 37500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:24:37,812 - Loss: 0.2206, step: 37600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:25:34,472 - Loss: 0.2157, step: 37700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:26:30,608 - Loss: 0.2220, step: 37800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:27:27,918 - Loss: 0.2259, step: 37900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:28:22,743 - Loss: 0.2172, step: 38000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:28:22,785 - Saving snapshot checkpoint and sampling single batch at iteration 38000.
+WARNING - image.py - 2024-10-24 06:28:23,374 - 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 06:28:41,862 - FID at iteration 38000: 86.313620
+INFO - dpsgd_diffusion.py - 2024-10-24 06:29:38,474 - Loss: 0.2213, step: 38100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:30:33,310 - Loss: 0.2460, step: 38200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:31:30,121 - Loss: 0.2614, step: 38300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:32:25,260 - Loss: 0.2166, step: 38400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:32:25,304 - Eps-value after 15 epochs: 3.7585
+INFO - dpsgd_diffusion.py - 2024-10-24 06:33:19,274 - Loss: 0.2225, step: 38500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:34:13,455 - Loss: 0.2248, step: 38600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:35:08,358 - Loss: 0.2220, step: 38700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:36:03,553 - Loss: 0.2300, step: 38800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:36:59,391 - Loss: 0.2156, step: 38900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:37:53,617 - Loss: 0.2096, step: 39000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:38:48,239 - Loss: 0.2233, step: 39100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:39:43,280 - Loss: 0.2453, step: 39200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:40:38,675 - Loss: 0.2328, step: 39300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:41:34,136 - Loss: 0.2178, step: 39400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:42:28,872 - Loss: 0.2220, step: 39500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:43:23,634 - Loss: 0.2277, step: 39600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:44:19,589 - Loss: 0.2192, step: 39700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:45:14,535 - Loss: 0.2333, step: 39800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:46:08,604 - Loss: 0.2120, step: 39900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:47:05,625 - Loss: 0.2065, step: 40000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:47:05,644 - Saving snapshot checkpoint and sampling single batch at iteration 40000.
+WARNING - image.py - 2024-10-24 06:47:06,235 - 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 06:47:24,243 - FID at iteration 40000: 80.465032
+INFO - dpsgd_diffusion.py - 2024-10-24 06:48:21,125 - Loss: 0.2385, step: 40100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:49:16,445 - Loss: 0.2360, step: 40200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:50:13,731 - Loss: 0.2272, step: 40300
+INFO - dpsgd_diffusion.py - 2024-10-24 06:51:10,766 - Loss: 0.2108, step: 40400
+INFO - dpsgd_diffusion.py - 2024-10-24 06:52:07,345 - Loss: 0.2085, step: 40500
+INFO - dpsgd_diffusion.py - 2024-10-24 06:53:04,918 - Loss: 0.2229, step: 40600
+INFO - dpsgd_diffusion.py - 2024-10-24 06:53:58,532 - Loss: 0.2355, step: 40700
+INFO - dpsgd_diffusion.py - 2024-10-24 06:54:55,822 - Loss: 0.1970, step: 40800
+INFO - dpsgd_diffusion.py - 2024-10-24 06:55:51,160 - Loss: 0.2301, step: 40900
+INFO - dpsgd_diffusion.py - 2024-10-24 06:56:24,622 - Eps-value after 16 epochs: 3.8739
+INFO - dpsgd_diffusion.py - 2024-10-24 06:56:45,783 - Loss: 0.2315, step: 41000
+INFO - dpsgd_diffusion.py - 2024-10-24 06:57:40,500 - Loss: 0.2330, step: 41100
+INFO - dpsgd_diffusion.py - 2024-10-24 06:58:34,509 - Loss: 0.2181, step: 41200
+INFO - dpsgd_diffusion.py - 2024-10-24 06:59:29,595 - Loss: 0.2336, step: 41300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:00:24,767 - Loss: 0.2126, step: 41400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:01:21,313 - Loss: 0.2211, step: 41500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:02:15,964 - Loss: 0.2117, step: 41600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:03:13,503 - Loss: 0.2317, step: 41700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:04:09,170 - Loss: 0.2264, step: 41800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:05:05,625 - Loss: 0.2190, step: 41900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:06:00,712 - Loss: 0.2515, step: 42000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:06:00,724 - Saving snapshot checkpoint and sampling single batch at iteration 42000.
+WARNING - image.py - 2024-10-24 07:06:01,315 - 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 07:06:19,790 - FID at iteration 42000: 77.414350
+INFO - dpsgd_diffusion.py - 2024-10-24 07:07:14,261 - Loss: 0.2168, step: 42100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:08:10,858 - Loss: 0.2126, step: 42200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:09:05,677 - Loss: 0.2442, step: 42300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:10:01,542 - Loss: 0.2066, step: 42400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:10:55,399 - Loss: 0.2017, step: 42500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:11:52,851 - Loss: 0.2250, step: 42600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:12:47,647 - Loss: 0.2216, step: 42700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:13:43,322 - Loss: 0.2095, step: 42800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:14:39,929 - Loss: 0.2085, step: 42900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:15:35,237 - Loss: 0.2682, step: 43000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:16:30,047 - Loss: 0.2217, step: 43100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:17:25,404 - Loss: 0.2008, step: 43200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:18:20,405 - Loss: 0.2290, step: 43300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:19:16,404 - Loss: 0.2075, step: 43400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:20:11,122 - Loss: 0.2170, step: 43500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:20:21,552 - Eps-value after 17 epochs: 3.9865
+INFO - dpsgd_diffusion.py - 2024-10-24 07:21:05,578 - Loss: 0.2070, step: 43600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:22:01,846 - Loss: 0.2238, step: 43700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:22:57,777 - Loss: 0.2225, step: 43800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:23:51,676 - Loss: 0.2608, step: 43900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:24:48,235 - Loss: 0.2288, step: 44000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:24:48,247 - Saving snapshot checkpoint and sampling single batch at iteration 44000.
+WARNING - image.py - 2024-10-24 07:24:48,838 - 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 07:25:07,318 - FID at iteration 44000: 73.750778
+INFO - dpsgd_diffusion.py - 2024-10-24 07:26:01,218 - Loss: 0.2223, step: 44100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:26:57,546 - Loss: 0.2309, step: 44200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:27:51,897 - Loss: 0.2283, step: 44300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:28:46,844 - Loss: 0.2550, step: 44400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:29:42,180 - Loss: 0.2196, step: 44500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:30:39,116 - Loss: 0.2008, step: 44600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:31:34,833 - Loss: 0.2246, step: 44700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:32:31,190 - Loss: 0.2355, step: 44800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:33:25,228 - Loss: 0.2203, step: 44900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:34:20,411 - Loss: 0.2345, step: 45000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:35:18,246 - Loss: 0.1948, step: 45100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:36:13,186 - Loss: 0.2192, step: 45200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:37:08,830 - Loss: 0.2085, step: 45300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:38:03,523 - Loss: 0.2030, step: 45400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:38:58,656 - Loss: 0.2295, step: 45500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:39:54,644 - Loss: 0.2293, step: 45600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:40:48,817 - Loss: 0.2145, step: 45700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:41:44,040 - Loss: 0.1999, step: 45800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:42:39,766 - Loss: 0.2597, step: 45900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:43:35,651 - Loss: 0.2319, step: 46000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:43:35,664 - Saving snapshot checkpoint and sampling single batch at iteration 46000.
+WARNING - image.py - 2024-10-24 07:43:36,253 - 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 07:43:55,252 - FID at iteration 46000: 70.203288
+INFO - dpsgd_diffusion.py - 2024-10-24 07:44:39,330 - Eps-value after 18 epochs: 4.0966
+INFO - dpsgd_diffusion.py - 2024-10-24 07:44:50,827 - Loss: 0.2279, step: 46100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:45:46,689 - Loss: 0.2264, step: 46200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:46:43,912 - Loss: 0.2239, step: 46300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:47:38,742 - Loss: 0.2389, step: 46400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:48:33,830 - Loss: 0.2360, step: 46500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:49:30,566 - Loss: 0.2275, step: 46600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:50:24,741 - Loss: 0.2100, step: 46700
+INFO - dpsgd_diffusion.py - 2024-10-24 07:51:19,839 - Loss: 0.2144, step: 46800
+INFO - dpsgd_diffusion.py - 2024-10-24 07:52:14,831 - Loss: 0.2177, step: 46900
+INFO - dpsgd_diffusion.py - 2024-10-24 07:53:11,314 - Loss: 0.2322, step: 47000
+INFO - dpsgd_diffusion.py - 2024-10-24 07:54:06,644 - Loss: 0.2116, step: 47100
+INFO - dpsgd_diffusion.py - 2024-10-24 07:55:03,497 - Loss: 0.2463, step: 47200
+INFO - dpsgd_diffusion.py - 2024-10-24 07:55:57,863 - Loss: 0.2163, step: 47300
+INFO - dpsgd_diffusion.py - 2024-10-24 07:56:54,023 - Loss: 0.2438, step: 47400
+INFO - dpsgd_diffusion.py - 2024-10-24 07:57:50,063 - Loss: 0.2406, step: 47500
+INFO - dpsgd_diffusion.py - 2024-10-24 07:58:45,303 - Loss: 0.2212, step: 47600
+INFO - dpsgd_diffusion.py - 2024-10-24 07:59:40,005 - Loss: 0.2122, step: 47700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:00:35,189 - Loss: 0.2135, step: 47800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:01:30,116 - Loss: 0.2424, step: 47900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:02:27,237 - Loss: 0.2195, step: 48000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:02:27,258 - Saving snapshot checkpoint and sampling single batch at iteration 48000.
+WARNING - image.py - 2024-10-24 08:02:27,849 - 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 08:02:46,832 - FID at iteration 48000: 67.138149
+INFO - dpsgd_diffusion.py - 2024-10-24 08:03:42,889 - Loss: 0.2206, step: 48100
+INFO - dpsgd_diffusion.py - 2024-10-24 08:04:39,896 - Loss: 0.2128, step: 48200
+INFO - dpsgd_diffusion.py - 2024-10-24 08:05:35,999 - Loss: 0.2186, step: 48300
+INFO - dpsgd_diffusion.py - 2024-10-24 08:06:31,903 - Loss: 0.2299, step: 48400
+INFO - dpsgd_diffusion.py - 2024-10-24 08:07:27,585 - Loss: 0.2276, step: 48500
+INFO - dpsgd_diffusion.py - 2024-10-24 08:08:22,070 - Loss: 0.2130, step: 48600
+INFO - dpsgd_diffusion.py - 2024-10-24 08:08:43,467 - Eps-value after 19 epochs: 4.2042
+INFO - dpsgd_diffusion.py - 2024-10-24 08:09:18,260 - Loss: 0.2108, step: 48700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:10:14,382 - Loss: 0.2392, step: 48800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:11:10,785 - Loss: 0.2004, step: 48900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:12:06,264 - Loss: 0.2237, step: 49000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:13:01,030 - Loss: 0.2242, step: 49100
+INFO - dpsgd_diffusion.py - 2024-10-24 08:13:55,096 - Loss: 0.2219, step: 49200
+INFO - dpsgd_diffusion.py - 2024-10-24 08:14:51,179 - Loss: 0.2221, step: 49300
+INFO - dpsgd_diffusion.py - 2024-10-24 08:15:47,512 - Loss: 0.2167, step: 49400
+INFO - dpsgd_diffusion.py - 2024-10-24 08:16:41,959 - Loss: 0.2053, step: 49500
+INFO - dpsgd_diffusion.py - 2024-10-24 08:17:37,094 - Loss: 0.2170, step: 49600
+INFO - dpsgd_diffusion.py - 2024-10-24 08:18:32,253 - Loss: 0.2213, step: 49700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:19:27,328 - Loss: 0.2339, step: 49800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:20:22,536 - Loss: 0.2117, step: 49900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:21:17,597 - Loss: 0.2283, step: 50000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:21:17,645 - Saving snapshot checkpoint and sampling single batch at iteration 50000.
+WARNING - image.py - 2024-10-24 08:21:18,238 - 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 08:21:37,393 - FID at iteration 50000: 65.082333
+INFO - dpsgd_diffusion.py - 2024-10-24 08:22:31,753 - Loss: 0.2148, step: 50100
+INFO - dpsgd_diffusion.py - 2024-10-24 08:23:27,928 - Loss: 0.2011, step: 50200
+INFO - dpsgd_diffusion.py - 2024-10-24 08:24:24,151 - Loss: 0.2241, step: 50300
+INFO - dpsgd_diffusion.py - 2024-10-24 08:25:20,130 - Loss: 0.2112, step: 50400
+INFO - dpsgd_diffusion.py - 2024-10-24 08:26:14,685 - Loss: 0.2170, step: 50500
+INFO - dpsgd_diffusion.py - 2024-10-24 08:27:10,752 - Loss: 0.2152, step: 50600
+INFO - dpsgd_diffusion.py - 2024-10-24 08:28:05,841 - Loss: 0.2198, step: 50700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:29:00,977 - Loss: 0.2181, step: 50800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:29:54,814 - Loss: 0.2136, step: 50900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:30:48,925 - Loss: 0.2033, step: 51000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:31:43,230 - Loss: 0.2291, step: 51100
+INFO - dpsgd_diffusion.py - 2024-10-24 08:32:38,134 - Loss: 0.2198, step: 51200
+INFO - dpsgd_diffusion.py - 2024-10-24 08:32:38,175 - Eps-value after 20 epochs: 4.3096
+INFO - dpsgd_diffusion.py - 2024-10-24 08:33:32,779 - Loss: 0.2179, step: 51300
+INFO - dpsgd_diffusion.py - 2024-10-24 08:34:27,583 - Loss: 0.2303, step: 51400
+INFO - dpsgd_diffusion.py - 2024-10-24 08:35:23,491 - Loss: 0.2395, step: 51500
+INFO - dpsgd_diffusion.py - 2024-10-24 08:36:18,768 - Loss: 0.2159, step: 51600
+INFO - dpsgd_diffusion.py - 2024-10-24 08:37:13,324 - Loss: 0.2334, step: 51700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:38:09,956 - Loss: 0.2029, step: 51800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:39:04,880 - Loss: 0.2282, step: 51900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:40:01,220 - Loss: 0.2057, step: 52000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:40:01,234 - Saving snapshot checkpoint and sampling single batch at iteration 52000.
+WARNING - image.py - 2024-10-24 08:40:01,823 - 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 08:40:20,881 - FID at iteration 52000: 63.783730
+INFO - dpsgd_diffusion.py - 2024-10-24 08:41:16,943 - Loss: 0.2053, step: 52100
+INFO - dpsgd_diffusion.py - 2024-10-24 08:42:11,391 - Loss: 0.2511, step: 52200
+INFO - dpsgd_diffusion.py - 2024-10-24 08:43:05,999 - Loss: 0.2161, step: 52300
+INFO - dpsgd_diffusion.py - 2024-10-24 08:44:00,303 - Loss: 0.2092, step: 52400
+INFO - dpsgd_diffusion.py - 2024-10-24 08:44:55,527 - Loss: 0.2051, step: 52500
+INFO - dpsgd_diffusion.py - 2024-10-24 08:45:54,635 - Loss: 0.2051, step: 52600
+INFO - dpsgd_diffusion.py - 2024-10-24 08:46:50,296 - Loss: 0.2342, step: 52700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:47:45,301 - Loss: 0.2441, step: 52800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:48:41,460 - Loss: 0.2154, step: 52900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:49:37,269 - Loss: 0.2026, step: 53000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:50:33,264 - Loss: 0.2265, step: 53100
+INFO - dpsgd_diffusion.py - 2024-10-24 08:51:27,425 - Loss: 0.2232, step: 53200
+INFO - dpsgd_diffusion.py - 2024-10-24 08:52:21,869 - Loss: 0.2333, step: 53300
+INFO - dpsgd_diffusion.py - 2024-10-24 08:53:17,719 - Loss: 0.2175, step: 53400
+INFO - dpsgd_diffusion.py - 2024-10-24 08:54:12,965 - Loss: 0.2055, step: 53500
+INFO - dpsgd_diffusion.py - 2024-10-24 08:55:10,215 - Loss: 0.1987, step: 53600
+INFO - dpsgd_diffusion.py - 2024-10-24 08:56:06,452 - Loss: 0.2090, step: 53700
+INFO - dpsgd_diffusion.py - 2024-10-24 08:56:39,747 - Eps-value after 21 epochs: 4.4129
+INFO - dpsgd_diffusion.py - 2024-10-24 08:57:03,385 - Loss: 0.2107, step: 53800
+INFO - dpsgd_diffusion.py - 2024-10-24 08:58:00,147 - Loss: 0.2340, step: 53900
+INFO - dpsgd_diffusion.py - 2024-10-24 08:58:56,198 - Loss: 0.2095, step: 54000
+INFO - dpsgd_diffusion.py - 2024-10-24 08:58:56,212 - Saving snapshot checkpoint and sampling single batch at iteration 54000.
+WARNING - image.py - 2024-10-24 08:58:56,802 - 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 08:59:16,342 - FID at iteration 54000: 62.202019
+INFO - dpsgd_diffusion.py - 2024-10-24 09:00:12,037 - Loss: 0.2089, step: 54100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:01:07,538 - Loss: 0.2462, step: 54200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:02:04,099 - Loss: 0.2031, step: 54300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:02:59,870 - Loss: 0.2180, step: 54400
+INFO - dpsgd_diffusion.py - 2024-10-24 09:03:54,933 - Loss: 0.2404, step: 54500
+INFO - dpsgd_diffusion.py - 2024-10-24 09:04:50,394 - Loss: 0.2110, step: 54600
+INFO - dpsgd_diffusion.py - 2024-10-24 09:05:45,304 - Loss: 0.2059, step: 54700
+INFO - dpsgd_diffusion.py - 2024-10-24 09:06:42,603 - Loss: 0.2180, step: 54800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:07:39,115 - Loss: 0.2196, step: 54900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:08:35,644 - Loss: 0.2371, step: 55000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:09:30,904 - Loss: 0.2117, step: 55100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:10:26,508 - Loss: 0.2298, step: 55200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:11:19,972 - Loss: 0.2322, step: 55300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:12:16,297 - Loss: 0.2189, step: 55400
+INFO - dpsgd_diffusion.py - 2024-10-24 09:13:12,070 - Loss: 0.2046, step: 55500
+INFO - dpsgd_diffusion.py - 2024-10-24 09:14:07,391 - Loss: 0.2070, step: 55600
+INFO - dpsgd_diffusion.py - 2024-10-24 09:15:04,364 - Loss: 0.2400, step: 55700
+INFO - dpsgd_diffusion.py - 2024-10-24 09:16:00,861 - Loss: 0.2034, step: 55800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:16:55,996 - Loss: 0.2174, step: 55900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:17:51,630 - Loss: 0.1903, step: 56000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:17:51,649 - Saving snapshot checkpoint and sampling single batch at iteration 56000.
+WARNING - image.py - 2024-10-24 09:17:52,240 - 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 09:18:10,753 - FID at iteration 56000: 59.766537
+INFO - dpsgd_diffusion.py - 2024-10-24 09:19:06,528 - Loss: 0.2172, step: 56100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:20:01,694 - Loss: 0.2028, step: 56200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:20:57,539 - Loss: 0.2094, step: 56300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:21:08,426 - Eps-value after 22 epochs: 4.5143
+INFO - dpsgd_diffusion.py - 2024-10-24 09:21:52,122 - Loss: 0.1956, step: 56400
+INFO - dpsgd_diffusion.py - 2024-10-24 09:22:47,617 - Loss: 0.2291, step: 56500
+INFO - dpsgd_diffusion.py - 2024-10-24 09:23:43,224 - Loss: 0.2162, step: 56600
+INFO - dpsgd_diffusion.py - 2024-10-24 09:24:40,547 - Loss: 0.2150, step: 56700
+INFO - dpsgd_diffusion.py - 2024-10-24 09:25:35,730 - Loss: 0.2023, step: 56800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:26:30,578 - Loss: 0.2167, step: 56900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:27:26,278 - Loss: 0.2360, step: 57000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:28:20,167 - Loss: 0.2022, step: 57100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:29:16,213 - Loss: 0.1948, step: 57200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:30:11,600 - Loss: 0.2290, step: 57300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:31:06,581 - Loss: 0.2171, step: 57400
+INFO - dpsgd_diffusion.py - 2024-10-24 09:32:01,356 - Loss: 0.2211, step: 57500
+INFO - dpsgd_diffusion.py - 2024-10-24 09:32:55,752 - Loss: 0.2123, step: 57600
+INFO - dpsgd_diffusion.py - 2024-10-24 09:33:49,895 - Loss: 0.2134, step: 57700
+INFO - dpsgd_diffusion.py - 2024-10-24 09:34:45,060 - Loss: 0.2070, step: 57800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:35:39,757 - Loss: 0.2144, step: 57900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:36:35,347 - Loss: 0.2163, step: 58000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:36:35,393 - Saving snapshot checkpoint and sampling single batch at iteration 58000.
+WARNING - image.py - 2024-10-24 09:36:36,198 - 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 09:36:54,833 - FID at iteration 58000: 58.334432
+INFO - dpsgd_diffusion.py - 2024-10-24 09:37:49,814 - Loss: 0.2136, step: 58100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:38:44,155 - Loss: 0.1915, step: 58200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:39:39,261 - Loss: 0.2109, step: 58300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:40:35,095 - Loss: 0.2155, step: 58400
+INFO - dpsgd_diffusion.py - 2024-10-24 09:41:30,957 - Loss: 0.2287, step: 58500
+INFO - dpsgd_diffusion.py - 2024-10-24 09:42:27,660 - Loss: 0.2283, step: 58600
+INFO - dpsgd_diffusion.py - 2024-10-24 09:43:24,088 - Loss: 0.2156, step: 58700
+INFO - dpsgd_diffusion.py - 2024-10-24 09:44:18,365 - Loss: 0.2216, step: 58800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:45:02,400 - Eps-value after 23 epochs: 4.6140
+INFO - dpsgd_diffusion.py - 2024-10-24 09:45:13,893 - Loss: 0.2240, step: 58900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:46:08,903 - Loss: 0.2027, step: 59000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:47:06,684 - Loss: 0.2481, step: 59100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:48:03,110 - Loss: 0.2039, step: 59200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:49:00,386 - Loss: 0.2100, step: 59300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:49:56,043 - Loss: 0.2118, step: 59400
+INFO - dpsgd_diffusion.py - 2024-10-24 09:50:51,023 - Loss: 0.1987, step: 59500
+INFO - dpsgd_diffusion.py - 2024-10-24 09:51:46,358 - Loss: 0.2150, step: 59600
+INFO - dpsgd_diffusion.py - 2024-10-24 09:52:41,720 - Loss: 0.1959, step: 59700
+INFO - dpsgd_diffusion.py - 2024-10-24 09:53:37,534 - Loss: 0.2179, step: 59800
+INFO - dpsgd_diffusion.py - 2024-10-24 09:54:34,382 - Loss: 0.2135, step: 59900
+INFO - dpsgd_diffusion.py - 2024-10-24 09:55:29,499 - Loss: 0.2037, step: 60000
+INFO - dpsgd_diffusion.py - 2024-10-24 09:55:29,512 - Saving snapshot checkpoint and sampling single batch at iteration 60000.
+WARNING - image.py - 2024-10-24 09:55:30,256 - 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 09:55:48,839 - FID at iteration 60000: 57.293067
+INFO - dpsgd_diffusion.py - 2024-10-24 09:56:44,716 - Loss: 0.2122, step: 60100
+INFO - dpsgd_diffusion.py - 2024-10-24 09:57:39,488 - Loss: 0.2312, step: 60200
+INFO - dpsgd_diffusion.py - 2024-10-24 09:58:35,379 - Loss: 0.2060, step: 60300
+INFO - dpsgd_diffusion.py - 2024-10-24 09:59:30,058 - Loss: 0.2104, step: 60400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:00:26,123 - Loss: 0.2316, step: 60500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:01:20,579 - Loss: 0.1941, step: 60600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:02:15,097 - Loss: 0.2293, step: 60700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:03:10,698 - Loss: 0.2222, step: 60800
+INFO - dpsgd_diffusion.py - 2024-10-24 10:04:07,009 - Loss: 0.2290, step: 60900
+INFO - dpsgd_diffusion.py - 2024-10-24 10:05:03,199 - Loss: 0.2336, step: 61000
+INFO - dpsgd_diffusion.py - 2024-10-24 10:06:00,164 - Loss: 0.2220, step: 61100
+INFO - dpsgd_diffusion.py - 2024-10-24 10:06:55,567 - Loss: 0.2004, step: 61200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:07:50,696 - Loss: 0.2006, step: 61300
+INFO - dpsgd_diffusion.py - 2024-10-24 10:08:49,752 - Loss: 0.2041, step: 61400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:09:11,880 - Eps-value after 24 epochs: 4.7121
+INFO - dpsgd_diffusion.py - 2024-10-24 10:09:45,436 - Loss: 0.2243, step: 61500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:10:41,641 - Loss: 0.2155, step: 61600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:11:36,864 - Loss: 0.2024, step: 61700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:12:32,771 - Loss: 0.2152, step: 61800
+INFO - dpsgd_diffusion.py - 2024-10-24 10:13:28,063 - Loss: 0.2195, step: 61900
+INFO - dpsgd_diffusion.py - 2024-10-24 10:14:25,121 - Loss: 0.2101, step: 62000
+INFO - dpsgd_diffusion.py - 2024-10-24 10:14:25,170 - Saving snapshot checkpoint and sampling single batch at iteration 62000.
+WARNING - image.py - 2024-10-24 10:14:25,801 - 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 10:14:45,391 - FID at iteration 62000: 56.030635
+INFO - dpsgd_diffusion.py - 2024-10-24 10:15:42,629 - Loss: 0.2320, step: 62100
+INFO - dpsgd_diffusion.py - 2024-10-24 10:16:37,188 - Loss: 0.2277, step: 62200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:17:32,513 - Loss: 0.2262, step: 62300
+INFO - dpsgd_diffusion.py - 2024-10-24 10:18:27,408 - Loss: 0.2120, step: 62400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:19:23,390 - Loss: 0.2241, step: 62500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:20:19,219 - Loss: 0.2179, step: 62600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:21:12,788 - Loss: 0.1964, step: 62700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:22:08,218 - Loss: 0.2193, step: 62800
+INFO - dpsgd_diffusion.py - 2024-10-24 10:23:04,662 - Loss: 0.2052, step: 62900
+INFO - dpsgd_diffusion.py - 2024-10-24 10:24:00,151 - Loss: 0.2291, step: 63000
+INFO - dpsgd_diffusion.py - 2024-10-24 10:24:56,597 - Loss: 0.2144, step: 63100
+INFO - dpsgd_diffusion.py - 2024-10-24 10:25:52,728 - Loss: 0.2227, step: 63200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:26:48,939 - Loss: 0.1993, step: 63300
+INFO - dpsgd_diffusion.py - 2024-10-24 10:27:45,333 - Loss: 0.2114, step: 63400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:28:39,585 - Loss: 0.2276, step: 63500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:29:34,056 - Loss: 0.2221, step: 63600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:30:29,158 - Loss: 0.2021, step: 63700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:31:24,335 - Loss: 0.2035, step: 63800
+INFO - dpsgd_diffusion.py - 2024-10-24 10:32:19,987 - Loss: 0.2455, step: 63900
+INFO - dpsgd_diffusion.py - 2024-10-24 10:33:14,376 - Loss: 0.2027, step: 64000
+INFO - dpsgd_diffusion.py - 2024-10-24 10:33:14,418 - Eps-value after 25 epochs: 4.8081
+INFO - dpsgd_diffusion.py - 2024-10-24 10:33:14,432 - Saving snapshot checkpoint and sampling single batch at iteration 64000.
+WARNING - image.py - 2024-10-24 10:33:15,025 - 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 10:33:33,547 - FID at iteration 64000: 54.540821
+INFO - dpsgd_diffusion.py - 2024-10-24 10:34:30,020 - Loss: 0.1892, step: 64100
+INFO - dpsgd_diffusion.py - 2024-10-24 10:35:26,056 - Loss: 0.1941, step: 64200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:36:21,374 - Loss: 0.1994, step: 64300
+INFO - dpsgd_diffusion.py - 2024-10-24 10:37:16,555 - Loss: 0.2057, step: 64400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:38:10,837 - Loss: 0.2072, step: 64500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:39:05,768 - Loss: 0.2188, step: 64600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:40:00,596 - Loss: 0.2322, step: 64700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:40:55,544 - Loss: 0.1999, step: 64800
+INFO - dpsgd_diffusion.py - 2024-10-24 10:41:50,495 - Loss: 0.2276, step: 64900
+INFO - dpsgd_diffusion.py - 2024-10-24 10:42:44,870 - Loss: 0.2017, step: 65000
+INFO - dpsgd_diffusion.py - 2024-10-24 10:43:40,536 - Loss: 0.2083, step: 65100
+INFO - dpsgd_diffusion.py - 2024-10-24 10:44:37,195 - Loss: 0.2322, step: 65200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:45:33,537 - Loss: 0.2095, step: 65300
+INFO - dpsgd_diffusion.py - 2024-10-24 10:46:30,678 - Loss: 0.2063, step: 65400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:47:27,325 - Loss: 0.2185, step: 65500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:48:23,002 - Loss: 0.2106, step: 65600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:49:17,672 - Loss: 0.2086, step: 65700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:50:11,861 - Loss: 0.2341, step: 65800
+INFO - dpsgd_diffusion.py - 2024-10-24 10:51:06,919 - Loss: 0.2116, step: 65900
+INFO - dpsgd_diffusion.py - 2024-10-24 10:52:01,573 - Loss: 0.2138, step: 66000
+INFO - dpsgd_diffusion.py - 2024-10-24 10:52:01,587 - Saving snapshot checkpoint and sampling single batch at iteration 66000.
+WARNING - image.py - 2024-10-24 10:52:02,181 - 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 10:52:20,606 - FID at iteration 66000: 53.484656
+INFO - dpsgd_diffusion.py - 2024-10-24 10:53:18,600 - Loss: 0.1990, step: 66100
+INFO - dpsgd_diffusion.py - 2024-10-24 10:54:13,923 - Loss: 0.2103, step: 66200
+INFO - dpsgd_diffusion.py - 2024-10-24 10:55:08,878 - Loss: 0.2181, step: 66300
+INFO - dpsgd_diffusion.py - 2024-10-24 10:56:04,798 - Loss: 0.2090, step: 66400
+INFO - dpsgd_diffusion.py - 2024-10-24 10:57:00,675 - Loss: 0.2044, step: 66500
+INFO - dpsgd_diffusion.py - 2024-10-24 10:57:36,130 - Eps-value after 26 epochs: 4.9028
+INFO - dpsgd_diffusion.py - 2024-10-24 10:57:57,731 - Loss: 0.2143, step: 66600
+INFO - dpsgd_diffusion.py - 2024-10-24 10:58:52,420 - Loss: 0.2429, step: 66700
+INFO - dpsgd_diffusion.py - 2024-10-24 10:59:48,379 - Loss: 0.1971, step: 66800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:00:44,852 - Loss: 0.2013, step: 66900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:01:40,952 - Loss: 0.2017, step: 67000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:02:37,648 - Loss: 0.2059, step: 67100
+INFO - dpsgd_diffusion.py - 2024-10-24 11:03:32,373 - Loss: 0.2042, step: 67200
+INFO - dpsgd_diffusion.py - 2024-10-24 11:04:28,428 - Loss: 0.1921, step: 67300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:05:24,708 - Loss: 0.2230, step: 67400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:06:21,201 - Loss: 0.2196, step: 67500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:07:15,376 - Loss: 0.2030, step: 67600
+INFO - dpsgd_diffusion.py - 2024-10-24 11:08:10,072 - Loss: 0.1874, step: 67700
+INFO - dpsgd_diffusion.py - 2024-10-24 11:09:06,401 - Loss: 0.2267, step: 67800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:10:02,954 - Loss: 0.2081, step: 67900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:10:59,544 - Loss: 0.2052, step: 68000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:10:59,587 - Saving snapshot checkpoint and sampling single batch at iteration 68000.
+WARNING - image.py - 2024-10-24 11:11:00,177 - 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 11:11:18,888 - FID at iteration 68000: 52.650965
+INFO - dpsgd_diffusion.py - 2024-10-24 11:12:15,579 - Loss: 0.2333, step: 68100
+INFO - dpsgd_diffusion.py - 2024-10-24 11:13:11,331 - Loss: 0.2189, step: 68200
+INFO - dpsgd_diffusion.py - 2024-10-24 11:14:09,245 - Loss: 0.2306, step: 68300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:15:04,211 - Loss: 0.2144, step: 68400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:15:59,308 - Loss: 0.1993, step: 68500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:16:55,286 - Loss: 0.1973, step: 68600
+INFO - dpsgd_diffusion.py - 2024-10-24 11:17:50,667 - Loss: 0.1990, step: 68700
+INFO - dpsgd_diffusion.py - 2024-10-24 11:18:45,936 - Loss: 0.2169, step: 68800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:19:42,158 - Loss: 0.2134, step: 68900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:20:38,346 - Loss: 0.2232, step: 69000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:21:33,096 - Loss: 0.2188, step: 69100
+INFO - dpsgd_diffusion.py - 2024-10-24 11:21:43,947 - Eps-value after 27 epochs: 4.9963
+INFO - dpsgd_diffusion.py - 2024-10-24 11:22:27,551 - Loss: 0.2115, step: 69200
+INFO - dpsgd_diffusion.py - 2024-10-24 11:23:21,680 - Loss: 0.2007, step: 69300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:24:16,288 - Loss: 0.2339, step: 69400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:25:12,072 - Loss: 0.2116, step: 69500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:26:09,339 - Loss: 0.2121, step: 69600
+INFO - dpsgd_diffusion.py - 2024-10-24 11:27:04,791 - Loss: 0.2017, step: 69700
+INFO - dpsgd_diffusion.py - 2024-10-24 11:28:01,093 - Loss: 0.2150, step: 69800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:28:54,974 - Loss: 0.1851, step: 69900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:29:48,933 - Loss: 0.1976, step: 70000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:29:48,946 - Saving snapshot checkpoint and sampling single batch at iteration 70000.
+WARNING - image.py - 2024-10-24 11:29:49,535 - 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 11:30:07,876 - FID at iteration 70000: 51.836475
+INFO - dpsgd_diffusion.py - 2024-10-24 11:31:03,647 - Loss: 0.2110, step: 70100
+INFO - dpsgd_diffusion.py - 2024-10-24 11:31:58,618 - Loss: 0.2026, step: 70200
+INFO - dpsgd_diffusion.py - 2024-10-24 11:32:55,956 - Loss: 0.2090, step: 70300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:33:52,316 - Loss: 0.2153, step: 70400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:34:46,591 - Loss: 0.2150, step: 70500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:35:40,812 - Loss: 0.1998, step: 70600
+INFO - dpsgd_diffusion.py - 2024-10-24 11:36:36,123 - Loss: 0.1968, step: 70700
+INFO - dpsgd_diffusion.py - 2024-10-24 11:37:31,825 - Loss: 0.2212, step: 70800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:38:25,492 - Loss: 0.2218, step: 70900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:39:20,527 - Loss: 0.2085, step: 71000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:40:16,020 - Loss: 0.2120, step: 71100
+INFO - dpsgd_diffusion.py - 2024-10-24 11:41:11,438 - Loss: 0.2250, step: 71200
+INFO - dpsgd_diffusion.py - 2024-10-24 11:42:07,135 - Loss: 0.2123, step: 71300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:43:02,897 - Loss: 0.2032, step: 71400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:43:57,711 - Loss: 0.2223, step: 71500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:44:52,115 - Loss: 0.2046, step: 71600
+INFO - dpsgd_diffusion.py - 2024-10-24 11:45:36,050 - Eps-value after 28 epochs: 5.0881
+INFO - dpsgd_diffusion.py - 2024-10-24 11:45:47,491 - Loss: 0.2136, step: 71700
+INFO - dpsgd_diffusion.py - 2024-10-24 11:46:43,562 - Loss: 0.2072, step: 71800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:47:39,178 - Loss: 0.2005, step: 71900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:48:34,834 - Loss: 0.2053, step: 72000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:48:34,853 - Saving snapshot checkpoint and sampling single batch at iteration 72000.
+WARNING - image.py - 2024-10-24 11:48:35,443 - 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 11:48:54,300 - FID at iteration 72000: 49.987487
+INFO - dpsgd_diffusion.py - 2024-10-24 11:49:50,543 - Loss: 0.2110, step: 72100
+INFO - dpsgd_diffusion.py - 2024-10-24 11:50:48,131 - Loss: 0.2324, step: 72200
+INFO - dpsgd_diffusion.py - 2024-10-24 11:51:45,222 - Loss: 0.2302, step: 72300
+INFO - dpsgd_diffusion.py - 2024-10-24 11:52:40,889 - Loss: 0.1969, step: 72400
+INFO - dpsgd_diffusion.py - 2024-10-24 11:53:35,472 - Loss: 0.2232, step: 72500
+INFO - dpsgd_diffusion.py - 2024-10-24 11:54:30,756 - Loss: 0.1989, step: 72600
+INFO - dpsgd_diffusion.py - 2024-10-24 11:55:27,858 - Loss: 0.2045, step: 72700
+INFO - dpsgd_diffusion.py - 2024-10-24 11:56:24,867 - Loss: 0.2231, step: 72800
+INFO - dpsgd_diffusion.py - 2024-10-24 11:57:19,755 - Loss: 0.2239, step: 72900
+INFO - dpsgd_diffusion.py - 2024-10-24 11:58:16,254 - Loss: 0.1941, step: 73000
+INFO - dpsgd_diffusion.py - 2024-10-24 11:59:12,600 - Loss: 0.2211, step: 73100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:00:07,173 - Loss: 0.1964, step: 73200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:01:01,929 - Loss: 0.2200, step: 73300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:01:57,145 - Loss: 0.2120, step: 73400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:02:51,949 - Loss: 0.2139, step: 73500
+INFO - dpsgd_diffusion.py - 2024-10-24 12:03:48,864 - Loss: 0.2244, step: 73600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:04:43,944 - Loss: 0.2283, step: 73700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:05:39,068 - Loss: 0.2134, step: 73800
+INFO - dpsgd_diffusion.py - 2024-10-24 12:06:34,349 - Loss: 0.2153, step: 73900
+INFO - dpsgd_diffusion.py - 2024-10-24 12:07:29,667 - Loss: 0.2170, step: 74000
+INFO - dpsgd_diffusion.py - 2024-10-24 12:07:29,680 - Saving snapshot checkpoint and sampling single batch at iteration 74000.
+WARNING - image.py - 2024-10-24 12:07:30,329 - 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 12:07:49,258 - FID at iteration 74000: 50.002813
+INFO - dpsgd_diffusion.py - 2024-10-24 12:08:44,035 - Loss: 0.2082, step: 74100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:09:39,598 - Loss: 0.2269, step: 74200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:10:01,264 - Eps-value after 29 epochs: 5.1787
+INFO - dpsgd_diffusion.py - 2024-10-24 12:10:34,914 - Loss: 0.1985, step: 74300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:11:30,675 - Loss: 0.2081, step: 74400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:12:25,172 - Loss: 0.2124, step: 74500
+INFO - dpsgd_diffusion.py - 2024-10-24 12:13:21,487 - Loss: 0.2097, step: 74600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:14:16,443 - Loss: 0.2117, step: 74700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:15:12,007 - Loss: 0.2089, step: 74800
+INFO - dpsgd_diffusion.py - 2024-10-24 12:16:06,947 - Loss: 0.1870, step: 74900
+INFO - dpsgd_diffusion.py - 2024-10-24 12:17:02,171 - Loss: 0.1991, step: 75000
+INFO - dpsgd_diffusion.py - 2024-10-24 12:17:58,211 - Loss: 0.2002, step: 75100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:18:54,159 - Loss: 0.2003, step: 75200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:19:51,319 - Loss: 0.2348, step: 75300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:20:47,404 - Loss: 0.2168, step: 75400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:21:40,495 - Loss: 0.2087, step: 75500
+INFO - dpsgd_diffusion.py - 2024-10-24 12:22:36,885 - Loss: 0.2223, step: 75600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:23:32,999 - Loss: 0.2325, step: 75700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:24:28,631 - Loss: 0.2099, step: 75800
+INFO - dpsgd_diffusion.py - 2024-10-24 12:25:23,607 - Loss: 0.2002, step: 75900
+INFO - dpsgd_diffusion.py - 2024-10-24 12:26:19,466 - Loss: 0.2240, step: 76000
+INFO - dpsgd_diffusion.py - 2024-10-24 12:26:19,480 - Saving snapshot checkpoint and sampling single batch at iteration 76000.
+WARNING - image.py - 2024-10-24 12:26:20,207 - 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 12:26:39,130 - FID at iteration 76000: 49.344754
+INFO - dpsgd_diffusion.py - 2024-10-24 12:27:35,533 - Loss: 0.2191, step: 76100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:28:30,735 - Loss: 0.2221, step: 76200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:29:25,392 - Loss: 0.1939, step: 76300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:30:21,069 - Loss: 0.2367, step: 76400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:31:17,097 - Loss: 0.1977, step: 76500
+INFO - dpsgd_diffusion.py - 2024-10-24 12:32:11,378 - Loss: 0.2048, step: 76600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:33:06,378 - Loss: 0.2168, step: 76700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:34:00,793 - Loss: 0.1946, step: 76800
+INFO - dpsgd_diffusion.py - 2024-10-24 12:34:00,834 - Eps-value after 30 epochs: 5.2684
+INFO - dpsgd_diffusion.py - 2024-10-24 12:34:57,741 - Loss: 0.2009, step: 76900
+INFO - dpsgd_diffusion.py - 2024-10-24 12:35:52,326 - Loss: 0.2188, step: 77000
+INFO - dpsgd_diffusion.py - 2024-10-24 12:36:49,113 - Loss: 0.2249, step: 77100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:37:45,110 - Loss: 0.2281, step: 77200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:38:40,964 - Loss: 0.2187, step: 77300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:39:34,851 - Loss: 0.2000, step: 77400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:40:29,376 - Loss: 0.2158, step: 77500
+INFO - dpsgd_diffusion.py - 2024-10-24 12:41:25,324 - Loss: 0.1956, step: 77600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:42:20,980 - Loss: 0.1984, step: 77700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:43:14,741 - Loss: 0.2089, step: 77800
+INFO - dpsgd_diffusion.py - 2024-10-24 12:44:09,102 - Loss: 0.2031, step: 77900
+INFO - dpsgd_diffusion.py - 2024-10-24 12:45:03,502 - Loss: 0.1997, step: 78000
+INFO - dpsgd_diffusion.py - 2024-10-24 12:45:03,514 - Saving snapshot checkpoint and sampling single batch at iteration 78000.
+WARNING - image.py - 2024-10-24 12:45:04,137 - 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 12:45:22,864 - FID at iteration 78000: 48.725140
+INFO - dpsgd_diffusion.py - 2024-10-24 12:46:18,277 - Loss: 0.2050, step: 78100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:47:13,315 - Loss: 0.2031, step: 78200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:48:10,129 - Loss: 0.2114, step: 78300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:49:03,560 - Loss: 0.2199, step: 78400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:49:59,245 - Loss: 0.1951, step: 78500
+INFO - dpsgd_diffusion.py - 2024-10-24 12:50:55,066 - Loss: 0.2103, step: 78600
+INFO - dpsgd_diffusion.py - 2024-10-24 12:51:50,005 - Loss: 0.2012, step: 78700
+INFO - dpsgd_diffusion.py - 2024-10-24 12:52:46,426 - Loss: 0.2019, step: 78800
+INFO - dpsgd_diffusion.py - 2024-10-24 12:53:42,227 - Loss: 0.2081, step: 78900
+INFO - dpsgd_diffusion.py - 2024-10-24 12:54:38,091 - Loss: 0.2219, step: 79000
+INFO - dpsgd_diffusion.py - 2024-10-24 12:55:32,635 - Loss: 0.2108, step: 79100
+INFO - dpsgd_diffusion.py - 2024-10-24 12:56:28,617 - Loss: 0.1909, step: 79200
+INFO - dpsgd_diffusion.py - 2024-10-24 12:57:23,737 - Loss: 0.2193, step: 79300
+INFO - dpsgd_diffusion.py - 2024-10-24 12:57:59,215 - Eps-value after 31 epochs: 5.3562
+INFO - dpsgd_diffusion.py - 2024-10-24 12:58:22,230 - Loss: 0.2063, step: 79400
+INFO - dpsgd_diffusion.py - 2024-10-24 12:59:18,339 - Loss: 0.2023, step: 79500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:00:15,696 - Loss: 0.2143, step: 79600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:01:11,113 - Loss: 0.1967, step: 79700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:02:07,496 - Loss: 0.2083, step: 79800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:03:03,679 - Loss: 0.2017, step: 79900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:03:57,650 - Loss: 0.2098, step: 80000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:03:57,669 - Saving snapshot checkpoint and sampling single batch at iteration 80000.
+WARNING - image.py - 2024-10-24 13:03:58,262 - 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 13:04:16,848 - FID at iteration 80000: 47.613302
+INFO - dpsgd_diffusion.py - 2024-10-24 13:05:13,134 - Loss: 0.2055, step: 80100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:06:08,911 - Loss: 0.1980, step: 80200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:07:04,646 - Loss: 0.2001, step: 80300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:08:00,625 - Loss: 0.2149, step: 80400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:08:57,689 - Loss: 0.2121, step: 80500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:09:55,004 - Loss: 0.2076, step: 80600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:10:49,736 - Loss: 0.2152, step: 80700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:11:44,482 - Loss: 0.1812, step: 80800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:12:41,598 - Loss: 0.2134, step: 80900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:13:36,601 - Loss: 0.1905, step: 81000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:14:30,983 - Loss: 0.1996, step: 81100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:15:27,315 - Loss: 0.2082, step: 81200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:16:22,599 - Loss: 0.1842, step: 81300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:17:17,325 - Loss: 0.2055, step: 81400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:18:13,172 - Loss: 0.2039, step: 81500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:19:07,014 - Loss: 0.1990, step: 81600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:20:02,608 - Loss: 0.2129, step: 81700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:20:57,553 - Loss: 0.2146, step: 81800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:21:53,553 - Loss: 0.2149, step: 81900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:22:04,449 - Eps-value after 32 epochs: 5.4437
+INFO - dpsgd_diffusion.py - 2024-10-24 13:22:49,176 - Loss: 0.1931, step: 82000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:22:49,221 - Saving snapshot checkpoint and sampling single batch at iteration 82000.
+WARNING - image.py - 2024-10-24 13:22:49,813 - 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 13:23:08,473 - FID at iteration 82000: 46.681020
+INFO - dpsgd_diffusion.py - 2024-10-24 13:24:04,047 - Loss: 0.2131, step: 82100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:24:59,518 - Loss: 0.2124, step: 82200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:25:55,599 - Loss: 0.2121, step: 82300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:26:50,620 - Loss: 0.1983, step: 82400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:27:46,490 - Loss: 0.2138, step: 82500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:28:42,645 - Loss: 0.2003, step: 82600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:29:37,932 - Loss: 0.2224, step: 82700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:30:31,752 - Loss: 0.2066, step: 82800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:31:27,157 - Loss: 0.2132, step: 82900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:32:21,200 - Loss: 0.2030, step: 83000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:33:18,210 - Loss: 0.2010, step: 83100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:34:12,983 - Loss: 0.2104, step: 83200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:35:10,174 - Loss: 0.2341, step: 83300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:36:04,585 - Loss: 0.2161, step: 83400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:36:59,002 - Loss: 0.1964, step: 83500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:37:54,893 - Loss: 0.1920, step: 83600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:38:50,494 - Loss: 0.2046, step: 83700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:39:46,531 - Loss: 0.2155, step: 83800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:40:43,031 - Loss: 0.2139, step: 83900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:41:38,612 - Loss: 0.2250, step: 84000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:41:38,624 - Saving snapshot checkpoint and sampling single batch at iteration 84000.
+WARNING - image.py - 2024-10-24 13:41:39,216 - 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 13:41:58,556 - FID at iteration 84000: 45.823946
+INFO - dpsgd_diffusion.py - 2024-10-24 13:42:55,167 - Loss: 0.2242, step: 84100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:43:50,487 - Loss: 0.2194, step: 84200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:44:45,968 - Loss: 0.2022, step: 84300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:45:41,472 - Loss: 0.2000, step: 84400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:46:28,147 - Eps-value after 33 epochs: 5.5293
+INFO - dpsgd_diffusion.py - 2024-10-24 13:46:38,929 - Loss: 0.1839, step: 84500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:47:33,049 - Loss: 0.2112, step: 84600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:48:30,397 - Loss: 0.2418, step: 84700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:49:25,336 - Loss: 0.1920, step: 84800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:50:22,044 - Loss: 0.1994, step: 84900
+INFO - dpsgd_diffusion.py - 2024-10-24 13:51:18,051 - Loss: 0.2042, step: 85000
+INFO - dpsgd_diffusion.py - 2024-10-24 13:52:13,242 - Loss: 0.2006, step: 85100
+INFO - dpsgd_diffusion.py - 2024-10-24 13:53:08,807 - Loss: 0.2005, step: 85200
+INFO - dpsgd_diffusion.py - 2024-10-24 13:54:04,362 - Loss: 0.2061, step: 85300
+INFO - dpsgd_diffusion.py - 2024-10-24 13:54:58,994 - Loss: 0.1879, step: 85400
+INFO - dpsgd_diffusion.py - 2024-10-24 13:55:54,072 - Loss: 0.1947, step: 85500
+INFO - dpsgd_diffusion.py - 2024-10-24 13:56:51,044 - Loss: 0.1858, step: 85600
+INFO - dpsgd_diffusion.py - 2024-10-24 13:57:46,334 - Loss: 0.2109, step: 85700
+INFO - dpsgd_diffusion.py - 2024-10-24 13:58:43,131 - Loss: 0.2086, step: 85800
+INFO - dpsgd_diffusion.py - 2024-10-24 13:59:38,472 - Loss: 0.2140, step: 85900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:00:34,364 - Loss: 0.2217, step: 86000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:00:34,377 - Saving snapshot checkpoint and sampling single batch at iteration 86000.
+WARNING - image.py - 2024-10-24 14:00:34,967 - 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 14:00:53,434 - FID at iteration 86000: 45.990705
+INFO - dpsgd_diffusion.py - 2024-10-24 14:01:48,731 - Loss: 0.2038, step: 86100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:02:43,988 - Loss: 0.1928, step: 86200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:03:40,696 - Loss: 0.2030, step: 86300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:04:35,969 - Loss: 0.2193, step: 86400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:05:30,902 - Loss: 0.2226, step: 86500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:06:26,439 - Loss: 0.2123, step: 86600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:07:22,433 - Loss: 0.1980, step: 86700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:08:17,740 - Loss: 0.1867, step: 86800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:09:12,109 - Loss: 0.1989, step: 86900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:10:08,949 - Loss: 0.1975, step: 87000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:10:31,090 - Eps-value after 34 epochs: 5.6146
+INFO - dpsgd_diffusion.py - 2024-10-24 14:11:03,356 - Loss: 0.1946, step: 87100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:11:57,711 - Loss: 0.2149, step: 87200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:12:54,669 - Loss: 0.2201, step: 87300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:13:50,868 - Loss: 0.2048, step: 87400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:14:45,451 - Loss: 0.2156, step: 87500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:15:39,757 - Loss: 0.2185, step: 87600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:16:34,364 - Loss: 0.1857, step: 87700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:17:28,802 - Loss: 0.2158, step: 87800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:18:24,634 - Loss: 0.2065, step: 87900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:19:19,519 - Loss: 0.2309, step: 88000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:19:19,537 - Saving snapshot checkpoint and sampling single batch at iteration 88000.
+WARNING - image.py - 2024-10-24 14:19:20,129 - 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 14:19:38,664 - FID at iteration 88000: 44.737342
+INFO - dpsgd_diffusion.py - 2024-10-24 14:20:35,359 - Loss: 0.1941, step: 88100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:21:31,173 - Loss: 0.1965, step: 88200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:22:27,066 - Loss: 0.2000, step: 88300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:23:22,057 - Loss: 0.2122, step: 88400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:24:18,649 - Loss: 0.2137, step: 88500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:25:13,646 - Loss: 0.1985, step: 88600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:26:10,691 - Loss: 0.1919, step: 88700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:27:06,061 - Loss: 0.2065, step: 88800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:28:01,310 - Loss: 0.2075, step: 88900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:28:57,135 - Loss: 0.1961, step: 89000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:29:52,570 - Loss: 0.1919, step: 89100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:30:48,610 - Loss: 0.2126, step: 89200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:31:43,771 - Loss: 0.2337, step: 89300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:32:38,922 - Loss: 0.2055, step: 89400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:33:32,992 - Loss: 0.2290, step: 89500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:34:29,091 - Loss: 0.2094, step: 89600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:34:29,135 - Eps-value after 35 epochs: 5.6982
+INFO - dpsgd_diffusion.py - 2024-10-24 14:35:25,638 - Loss: 0.2165, step: 89700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:36:21,232 - Loss: 0.2262, step: 89800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:37:15,858 - Loss: 0.1887, step: 89900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:38:10,317 - Loss: 0.1989, step: 90000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:38:10,330 - Saving snapshot checkpoint and sampling single batch at iteration 90000.
+WARNING - image.py - 2024-10-24 14:38:10,921 - 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 14:38:30,007 - FID at iteration 90000: 44.844457
+INFO - dpsgd_diffusion.py - 2024-10-24 14:39:26,562 - Loss: 0.1868, step: 90100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:40:22,572 - Loss: 0.1944, step: 90200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:41:18,585 - Loss: 0.1955, step: 90300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:42:14,125 - Loss: 0.2239, step: 90400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:43:09,634 - Loss: 0.2255, step: 90500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:44:03,970 - Loss: 0.1960, step: 90600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:44:58,763 - Loss: 0.2013, step: 90700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:45:53,316 - Loss: 0.1949, step: 90800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:46:48,136 - Loss: 0.2092, step: 90900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:47:44,090 - Loss: 0.1846, step: 91000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:48:38,243 - Loss: 0.2044, step: 91100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:49:32,752 - Loss: 0.2095, step: 91200
+INFO - dpsgd_diffusion.py - 2024-10-24 14:50:27,778 - Loss: 0.2149, step: 91300
+INFO - dpsgd_diffusion.py - 2024-10-24 14:51:23,147 - Loss: 0.2114, step: 91400
+INFO - dpsgd_diffusion.py - 2024-10-24 14:52:18,733 - Loss: 0.1814, step: 91500
+INFO - dpsgd_diffusion.py - 2024-10-24 14:53:13,967 - Loss: 0.1935, step: 91600
+INFO - dpsgd_diffusion.py - 2024-10-24 14:54:09,514 - Loss: 0.1847, step: 91700
+INFO - dpsgd_diffusion.py - 2024-10-24 14:55:05,105 - Loss: 0.2169, step: 91800
+INFO - dpsgd_diffusion.py - 2024-10-24 14:55:59,079 - Loss: 0.1984, step: 91900
+INFO - dpsgd_diffusion.py - 2024-10-24 14:56:55,897 - Loss: 0.2292, step: 92000
+INFO - dpsgd_diffusion.py - 2024-10-24 14:56:55,944 - Saving snapshot checkpoint and sampling single batch at iteration 92000.
+WARNING - image.py - 2024-10-24 14:56:56,535 - 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 14:57:15,110 - FID at iteration 92000: 44.089258
+INFO - dpsgd_diffusion.py - 2024-10-24 14:58:11,304 - Loss: 0.2175, step: 92100
+INFO - dpsgd_diffusion.py - 2024-10-24 14:58:43,705 - Eps-value after 36 epochs: 5.7817
+INFO - dpsgd_diffusion.py - 2024-10-24 14:59:06,590 - Loss: 0.2039, step: 92200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:00:02,320 - Loss: 0.2028, step: 92300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:00:56,665 - Loss: 0.2160, step: 92400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:01:51,936 - Loss: 0.1906, step: 92500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:02:48,881 - Loss: 0.1971, step: 92600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:03:44,911 - Loss: 0.1964, step: 92700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:04:41,257 - Loss: 0.1959, step: 92800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:05:36,740 - Loss: 0.2111, step: 92900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:06:32,069 - Loss: 0.2049, step: 93000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:07:28,540 - Loss: 0.2014, step: 93100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:08:23,427 - Loss: 0.2050, step: 93200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:09:18,072 - Loss: 0.2037, step: 93300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:10:14,195 - Loss: 0.1959, step: 93400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:11:09,372 - Loss: 0.1962, step: 93500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:12:05,194 - Loss: 0.2118, step: 93600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:13:00,517 - Loss: 0.1876, step: 93700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:13:56,540 - Loss: 0.2187, step: 93800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:14:51,985 - Loss: 0.2281, step: 93900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:15:47,196 - Loss: 0.2185, step: 94000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:15:47,207 - Saving snapshot checkpoint and sampling single batch at iteration 94000.
+WARNING - image.py - 2024-10-24 15:15:47,798 - 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 15:16:06,376 - FID at iteration 94000: 44.182691
+INFO - dpsgd_diffusion.py - 2024-10-24 15:17:00,903 - Loss: 0.2104, step: 94100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:17:55,838 - Loss: 0.2026, step: 94200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:18:51,228 - Loss: 0.2036, step: 94300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:19:50,297 - Loss: 0.2096, step: 94400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:20:45,942 - Loss: 0.2049, step: 94500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:21:41,370 - Loss: 0.2055, step: 94600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:22:37,012 - Loss: 0.1866, step: 94700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:22:47,888 - Eps-value after 37 epochs: 5.8634
+INFO - dpsgd_diffusion.py - 2024-10-24 15:23:32,787 - Loss: 0.2040, step: 94800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:24:30,327 - Loss: 0.2053, step: 94900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:25:27,207 - Loss: 0.2037, step: 95000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:26:22,073 - Loss: 0.2055, step: 95100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:27:17,270 - Loss: 0.2077, step: 95200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:28:11,132 - Loss: 0.1962, step: 95300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:29:08,101 - Loss: 0.2079, step: 95400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:30:03,469 - Loss: 0.2112, step: 95500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:30:58,663 - Loss: 0.2129, step: 95600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:31:54,983 - Loss: 0.1958, step: 95700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:32:48,451 - Loss: 0.1882, step: 95800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:33:43,358 - Loss: 0.2248, step: 95900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:34:38,980 - Loss: 0.2095, step: 96000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:34:39,003 - Saving snapshot checkpoint and sampling single batch at iteration 96000.
+WARNING - image.py - 2024-10-24 15:34:39,593 - 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 15:34:58,106 - FID at iteration 96000: 42.717722
+INFO - dpsgd_diffusion.py - 2024-10-24 15:35:53,800 - Loss: 0.1921, step: 96100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:36:48,906 - Loss: 0.2259, step: 96200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:37:43,468 - Loss: 0.2020, step: 96300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:38:38,658 - Loss: 0.2230, step: 96400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:39:34,786 - Loss: 0.2142, step: 96500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:40:29,601 - Loss: 0.1889, step: 96600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:41:24,277 - Loss: 0.2019, step: 96700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:42:19,295 - Loss: 0.1884, step: 96800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:43:13,631 - Loss: 0.2020, step: 96900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:44:09,116 - Loss: 0.2023, step: 97000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:45:05,919 - Loss: 0.1960, step: 97100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:01,282 - Loss: 0.2006, step: 97200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:45,480 - Eps-value after 38 epochs: 5.9450
+INFO - dpsgd_diffusion.py - 2024-10-24 15:46:56,948 - Loss: 0.2035, step: 97300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:47:51,000 - Loss: 0.1879, step: 97400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:48:46,964 - Loss: 0.1941, step: 97500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:49:42,359 - Loss: 0.2112, step: 97600
+INFO - dpsgd_diffusion.py - 2024-10-24 15:50:38,730 - Loss: 0.2038, step: 97700
+INFO - dpsgd_diffusion.py - 2024-10-24 15:51:34,589 - Loss: 0.2132, step: 97800
+INFO - dpsgd_diffusion.py - 2024-10-24 15:52:29,771 - Loss: 0.1961, step: 97900
+INFO - dpsgd_diffusion.py - 2024-10-24 15:53:25,210 - Loss: 0.2100, step: 98000
+INFO - dpsgd_diffusion.py - 2024-10-24 15:53:25,257 - Saving snapshot checkpoint and sampling single batch at iteration 98000.
+WARNING - image.py - 2024-10-24 15:53:25,848 - 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 15:53:44,434 - FID at iteration 98000: 42.654354
+INFO - dpsgd_diffusion.py - 2024-10-24 15:54:39,763 - Loss: 0.1963, step: 98100
+INFO - dpsgd_diffusion.py - 2024-10-24 15:55:35,808 - Loss: 0.2000, step: 98200
+INFO - dpsgd_diffusion.py - 2024-10-24 15:56:31,035 - Loss: 0.1889, step: 98300
+INFO - dpsgd_diffusion.py - 2024-10-24 15:57:27,113 - Loss: 0.1978, step: 98400
+INFO - dpsgd_diffusion.py - 2024-10-24 15:58:23,292 - Loss: 0.2029, step: 98500
+INFO - dpsgd_diffusion.py - 2024-10-24 15:59:17,376 - Loss: 0.2268, step: 98600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:00:12,000 - Loss: 0.1833, step: 98700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:01:09,043 - Loss: 0.2043, step: 98800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:02:04,707 - Loss: 0.1945, step: 98900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:02:59,150 - Loss: 0.2007, step: 99000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:03:55,765 - Loss: 0.2052, step: 99100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:04:50,638 - Loss: 0.2069, step: 99200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:05:45,420 - Loss: 0.1993, step: 99300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:06:39,642 - Loss: 0.2063, step: 99400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:07:36,416 - Loss: 0.1901, step: 99500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:08:33,185 - Loss: 0.2197, step: 99600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:09:28,613 - Loss: 0.2240, step: 99700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:10:22,978 - Loss: 0.1987, step: 99800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:10:44,109 - Eps-value after 39 epochs: 6.0250
+INFO - dpsgd_diffusion.py - 2024-10-24 16:11:17,044 - Loss: 0.1899, step: 99900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:12:14,159 - Loss: 0.1993, step: 100000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:12:14,236 - Saving snapshot checkpoint and sampling single batch at iteration 100000.
+WARNING - image.py - 2024-10-24 16:12:14,834 - 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 16:12:33,379 - FID at iteration 100000: 42.176656
+INFO - dpsgd_diffusion.py - 2024-10-24 16:12:33,676 - Saving  checkpoint at iteration 100000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:13:29,383 - Loss: 0.2068, step: 100100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:14:25,463 - Loss: 0.1998, step: 100200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:15:21,035 - Loss: 0.2295, step: 100300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:16:15,997 - Loss: 0.2099, step: 100400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:17:11,617 - Loss: 0.2131, step: 100500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:18:07,431 - Loss: 0.1892, step: 100600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:19:02,005 - Loss: 0.2305, step: 100700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:19:57,204 - Loss: 0.2192, step: 100800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:20:54,303 - Loss: 0.2050, step: 100900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:21:50,511 - Loss: 0.1892, step: 101000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:22:46,270 - Loss: 0.1922, step: 101100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:23:41,594 - Loss: 0.2153, step: 101200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:24:35,883 - Loss: 0.2063, step: 101300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:25:32,413 - Loss: 0.1890, step: 101400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:26:26,598 - Loss: 0.1933, step: 101500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:27:22,407 - Loss: 0.1975, step: 101600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:28:16,847 - Loss: 0.1994, step: 101700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:29:11,748 - Loss: 0.2098, step: 101800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:30:07,216 - Loss: 0.2122, step: 101900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:31:02,848 - Loss: 0.2207, step: 102000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:31:02,892 - Saving snapshot checkpoint and sampling single batch at iteration 102000.
+WARNING - image.py - 2024-10-24 16:31:03,481 - 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 16:31:22,053 - FID at iteration 102000: 41.432163
+INFO - dpsgd_diffusion.py - 2024-10-24 16:32:18,198 - Loss: 0.1851, step: 102100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:33:14,426 - Loss: 0.2009, step: 102200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:34:10,855 - Loss: 0.1937, step: 102300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:35:06,137 - Loss: 0.1957, step: 102400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:35:06,178 - Eps-value after 40 epochs: 6.1047
+INFO - dpsgd_diffusion.py - 2024-10-24 16:36:01,739 - Loss: 0.1957, step: 102500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:36:57,372 - Loss: 0.2100, step: 102600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:37:53,057 - Loss: 0.1880, step: 102700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:38:48,902 - Loss: 0.2072, step: 102800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:39:44,875 - Loss: 0.2109, step: 102900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:40:40,318 - Loss: 0.1951, step: 103000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:41:36,560 - Loss: 0.1923, step: 103100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:42:34,127 - Loss: 0.2094, step: 103200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:43:29,933 - Loss: 0.2040, step: 103300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:44:24,308 - Loss: 0.2106, step: 103400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:45:19,513 - Loss: 0.2126, step: 103500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:46:14,952 - Loss: 0.1778, step: 103600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:47:11,185 - Loss: 0.2057, step: 103700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:48:07,611 - Loss: 0.1893, step: 103800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:49:03,852 - Loss: 0.2207, step: 103900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:49:59,215 - Loss: 0.2073, step: 104000
+INFO - dpsgd_diffusion.py - 2024-10-24 16:49:59,236 - Saving snapshot checkpoint and sampling single batch at iteration 104000.
+WARNING - image.py - 2024-10-24 16:49:59,827 - 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 16:50:18,410 - FID at iteration 104000: 41.093928
+INFO - dpsgd_diffusion.py - 2024-10-24 16:51:14,795 - Loss: 0.2001, step: 104100
+INFO - dpsgd_diffusion.py - 2024-10-24 16:52:10,024 - Loss: 0.1866, step: 104200
+INFO - dpsgd_diffusion.py - 2024-10-24 16:53:05,062 - Loss: 0.2090, step: 104300
+INFO - dpsgd_diffusion.py - 2024-10-24 16:54:01,109 - Loss: 0.2093, step: 104400
+INFO - dpsgd_diffusion.py - 2024-10-24 16:54:56,571 - Loss: 0.2035, step: 104500
+INFO - dpsgd_diffusion.py - 2024-10-24 16:55:52,053 - Loss: 0.1951, step: 104600
+INFO - dpsgd_diffusion.py - 2024-10-24 16:56:47,656 - Loss: 0.2225, step: 104700
+INFO - dpsgd_diffusion.py - 2024-10-24 16:57:44,204 - Loss: 0.1887, step: 104800
+INFO - dpsgd_diffusion.py - 2024-10-24 16:58:39,568 - Loss: 0.2056, step: 104900
+INFO - dpsgd_diffusion.py - 2024-10-24 16:59:12,527 - Eps-value after 41 epochs: 6.1837
+INFO - dpsgd_diffusion.py - 2024-10-24 16:59:35,046 - Loss: 0.2043, step: 105000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:00:29,812 - Loss: 0.2137, step: 105100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:01:24,656 - Loss: 0.2403, step: 105200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:02:20,666 - Loss: 0.2055, step: 105300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:03:15,587 - Loss: 0.2106, step: 105400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:04:12,532 - Loss: 0.1924, step: 105500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:05:06,966 - Loss: 0.2042, step: 105600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:06:01,899 - Loss: 0.2050, step: 105700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:06:56,728 - Loss: 0.1899, step: 105800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:07:51,668 - Loss: 0.1894, step: 105900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:08:46,505 - Loss: 0.2094, step: 106000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:08:46,551 - Saving snapshot checkpoint and sampling single batch at iteration 106000.
+WARNING - image.py - 2024-10-24 17:08:47,142 - 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 17:09:05,548 - FID at iteration 106000: 40.866611
+INFO - dpsgd_diffusion.py - 2024-10-24 17:10:02,295 - Loss: 0.2057, step: 106100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:10:57,405 - Loss: 0.2238, step: 106200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:11:52,112 - Loss: 0.1969, step: 106300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:12:48,332 - Loss: 0.2047, step: 106400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:13:42,834 - Loss: 0.1945, step: 106500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:14:37,319 - Loss: 0.2232, step: 106600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:15:33,500 - Loss: 0.1958, step: 106700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:16:29,736 - Loss: 0.1882, step: 106800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:17:25,187 - Loss: 0.1766, step: 106900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:18:19,315 - Loss: 0.1863, step: 107000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:19:12,772 - Loss: 0.1944, step: 107100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:20:08,117 - Loss: 0.2249, step: 107200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:21:03,489 - Loss: 0.2045, step: 107300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:21:57,843 - Loss: 0.2202, step: 107400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:22:53,999 - Loss: 0.2196, step: 107500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:23:04,872 - Eps-value after 42 epochs: 6.2614
+INFO - dpsgd_diffusion.py - 2024-10-24 17:23:50,691 - Loss: 0.1876, step: 107600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:24:45,717 - Loss: 0.2027, step: 107700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:25:41,376 - Loss: 0.2034, step: 107800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:26:36,023 - Loss: 0.2087, step: 107900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:27:30,724 - Loss: 0.1894, step: 108000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:27:30,772 - Saving snapshot checkpoint and sampling single batch at iteration 108000.
+WARNING - image.py - 2024-10-24 17:27:31,365 - 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 17:27:51,895 - FID at iteration 108000: 40.634974
+INFO - dpsgd_diffusion.py - 2024-10-24 17:28:49,137 - Loss: 0.1969, step: 108100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:29:46,660 - Loss: 0.2073, step: 108200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:30:41,911 - Loss: 0.1896, step: 108300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:31:37,253 - Loss: 0.1984, step: 108400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:32:31,822 - Loss: 0.1932, step: 108500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:33:27,512 - Loss: 0.1883, step: 108600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:34:22,401 - Loss: 0.2192, step: 108700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:35:18,300 - Loss: 0.1905, step: 108800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:36:12,905 - Loss: 0.1966, step: 108900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:37:07,999 - Loss: 0.2198, step: 109000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:38:03,478 - Loss: 0.1950, step: 109100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:38:58,399 - Loss: 0.2008, step: 109200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:39:52,426 - Loss: 0.2126, step: 109300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:40:46,625 - Loss: 0.1994, step: 109400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:41:41,170 - Loss: 0.2176, step: 109500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:42:36,290 - Loss: 0.1996, step: 109600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:43:32,843 - Loss: 0.1901, step: 109700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:44:28,708 - Loss: 0.2032, step: 109800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:45:24,443 - Loss: 0.1893, step: 109900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:46:19,299 - Loss: 0.2146, step: 110000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:46:19,342 - Saving snapshot checkpoint and sampling single batch at iteration 110000.
+WARNING - image.py - 2024-10-24 17:46:19,932 - 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 17:46:38,373 - FID at iteration 110000: 40.624851
+INFO - dpsgd_diffusion.py - 2024-10-24 17:47:22,595 - Eps-value after 43 epochs: 6.3392
+INFO - dpsgd_diffusion.py - 2024-10-24 17:47:33,673 - Loss: 0.2111, step: 110100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:48:29,348 - Loss: 0.2096, step: 110200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:49:24,441 - Loss: 0.1981, step: 110300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:50:19,341 - Loss: 0.1960, step: 110400
+INFO - dpsgd_diffusion.py - 2024-10-24 17:51:16,554 - Loss: 0.2241, step: 110500
+INFO - dpsgd_diffusion.py - 2024-10-24 17:52:12,399 - Loss: 0.2160, step: 110600
+INFO - dpsgd_diffusion.py - 2024-10-24 17:53:07,798 - Loss: 0.1908, step: 110700
+INFO - dpsgd_diffusion.py - 2024-10-24 17:54:03,283 - Loss: 0.2049, step: 110800
+INFO - dpsgd_diffusion.py - 2024-10-24 17:54:59,523 - Loss: 0.1974, step: 110900
+INFO - dpsgd_diffusion.py - 2024-10-24 17:55:55,953 - Loss: 0.2083, step: 111000
+INFO - dpsgd_diffusion.py - 2024-10-24 17:56:51,231 - Loss: 0.1943, step: 111100
+INFO - dpsgd_diffusion.py - 2024-10-24 17:57:46,310 - Loss: 0.2022, step: 111200
+INFO - dpsgd_diffusion.py - 2024-10-24 17:58:41,390 - Loss: 0.2019, step: 111300
+INFO - dpsgd_diffusion.py - 2024-10-24 17:59:36,727 - Loss: 0.1995, step: 111400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:00:30,873 - Loss: 0.1847, step: 111500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:01:25,364 - Loss: 0.2116, step: 111600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:02:20,965 - Loss: 0.1986, step: 111700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:03:17,317 - Loss: 0.1989, step: 111800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:04:11,392 - Loss: 0.2006, step: 111900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:05:06,302 - Loss: 0.2224, step: 112000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:05:06,324 - Saving snapshot checkpoint and sampling single batch at iteration 112000.
+WARNING - image.py - 2024-10-24 18:05:06,913 - 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 18:05:27,608 - FID at iteration 112000: 39.749004
+INFO - dpsgd_diffusion.py - 2024-10-24 18:06:22,613 - Loss: 0.2094, step: 112100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:07:19,597 - Loss: 0.2030, step: 112200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:08:14,799 - Loss: 0.1953, step: 112300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:09:08,397 - Loss: 0.2001, step: 112400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:10:04,487 - Loss: 0.1856, step: 112500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:10:58,919 - Loss: 0.1993, step: 112600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:11:20,580 - Eps-value after 44 epochs: 6.4155
+INFO - dpsgd_diffusion.py - 2024-10-24 18:11:53,518 - Loss: 0.2421, step: 112700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:12:48,858 - Loss: 0.2034, step: 112800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:13:43,566 - Loss: 0.1873, step: 112900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:14:38,158 - Loss: 0.1969, step: 113000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:15:33,861 - Loss: 0.1994, step: 113100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:16:28,888 - Loss: 0.2020, step: 113200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:17:25,036 - Loss: 0.2117, step: 113300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:18:22,111 - Loss: 0.1973, step: 113400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:19:18,753 - Loss: 0.1946, step: 113500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:20:13,863 - Loss: 0.2026, step: 113600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:21:10,263 - Loss: 0.1995, step: 113700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:22:06,456 - Loss: 0.1770, step: 113800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:23:03,011 - Loss: 0.2240, step: 113900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:23:59,145 - Loss: 0.1824, step: 114000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:23:59,158 - Saving snapshot checkpoint and sampling single batch at iteration 114000.
+WARNING - image.py - 2024-10-24 18:23:59,746 - 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 18:24:19,026 - FID at iteration 114000: 39.347467
+INFO - dpsgd_diffusion.py - 2024-10-24 18:25:15,500 - Loss: 0.2232, step: 114100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:26:10,821 - Loss: 0.1816, step: 114200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:27:06,914 - Loss: 0.2051, step: 114300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:28:01,645 - Loss: 0.2154, step: 114400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:28:57,551 - Loss: 0.2037, step: 114500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:29:52,038 - Loss: 0.2048, step: 114600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:30:47,737 - Loss: 0.2280, step: 114700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:31:44,316 - Loss: 0.1928, step: 114800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:32:41,375 - Loss: 0.2195, step: 114900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:33:37,815 - Loss: 0.1928, step: 115000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:34:33,678 - Loss: 0.2045, step: 115100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:35:29,151 - Loss: 0.2117, step: 115200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:35:29,191 - Eps-value after 45 epochs: 6.4913
+INFO - dpsgd_diffusion.py - 2024-10-24 18:36:24,154 - Loss: 0.2138, step: 115300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:37:20,004 - Loss: 0.2121, step: 115400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:38:15,510 - Loss: 0.1908, step: 115500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:39:11,903 - Loss: 0.2127, step: 115600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:40:09,051 - Loss: 0.2085, step: 115700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:41:05,681 - Loss: 0.2062, step: 115800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:42:00,703 - Loss: 0.2192, step: 115900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:42:55,858 - Loss: 0.2153, step: 116000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:42:55,905 - Saving snapshot checkpoint and sampling single batch at iteration 116000.
+WARNING - image.py - 2024-10-24 18:42:56,496 - 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 18:43:15,064 - FID at iteration 116000: 40.201483
+INFO - dpsgd_diffusion.py - 2024-10-24 18:44:10,901 - Loss: 0.1728, step: 116100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:45:06,845 - Loss: 0.2096, step: 116200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:46:02,138 - Loss: 0.2098, step: 116300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:46:58,286 - Loss: 0.2034, step: 116400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:47:53,420 - Loss: 0.1979, step: 116500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:48:50,449 - Loss: 0.2077, step: 116600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:49:47,547 - Loss: 0.2043, step: 116700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:50:42,811 - Loss: 0.2071, step: 116800
+INFO - dpsgd_diffusion.py - 2024-10-24 18:51:39,796 - Loss: 0.2007, step: 116900
+INFO - dpsgd_diffusion.py - 2024-10-24 18:52:35,757 - Loss: 0.2154, step: 117000
+INFO - dpsgd_diffusion.py - 2024-10-24 18:53:31,910 - Loss: 0.2173, step: 117100
+INFO - dpsgd_diffusion.py - 2024-10-24 18:54:27,029 - Loss: 0.1993, step: 117200
+INFO - dpsgd_diffusion.py - 2024-10-24 18:55:22,019 - Loss: 0.2064, step: 117300
+INFO - dpsgd_diffusion.py - 2024-10-24 18:56:16,609 - Loss: 0.2048, step: 117400
+INFO - dpsgd_diffusion.py - 2024-10-24 18:57:11,145 - Loss: 0.2222, step: 117500
+INFO - dpsgd_diffusion.py - 2024-10-24 18:58:07,115 - Loss: 0.1887, step: 117600
+INFO - dpsgd_diffusion.py - 2024-10-24 18:59:01,523 - Loss: 0.1944, step: 117700
+INFO - dpsgd_diffusion.py - 2024-10-24 18:59:34,918 - Eps-value after 46 epochs: 6.5672
+INFO - dpsgd_diffusion.py - 2024-10-24 18:59:58,145 - Loss: 0.2005, step: 117800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:00:54,841 - Loss: 0.2065, step: 117900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:01:50,309 - Loss: 0.2205, step: 118000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:01:50,351 - Saving snapshot checkpoint and sampling single batch at iteration 118000.
+WARNING - image.py - 2024-10-24 19:01:50,942 - 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 19:02:09,620 - FID at iteration 118000: 39.003030
+INFO - dpsgd_diffusion.py - 2024-10-24 19:03:05,550 - Loss: 0.2086, step: 118100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:04:01,192 - Loss: 0.1921, step: 118200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:04:57,164 - Loss: 0.1987, step: 118300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:05:52,256 - Loss: 0.2022, step: 118400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:06:47,681 - Loss: 0.1895, step: 118500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:07:44,804 - Loss: 0.2052, step: 118600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:08:40,055 - Loss: 0.2166, step: 118700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:09:36,189 - Loss: 0.1974, step: 118800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:10:30,444 - Loss: 0.2209, step: 118900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:11:25,697 - Loss: 0.2270, step: 119000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:12:21,582 - Loss: 0.2140, step: 119100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:13:17,588 - Loss: 0.1908, step: 119200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:14:12,312 - Loss: 0.2075, step: 119300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:15:07,754 - Loss: 0.2003, step: 119400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:16:03,171 - Loss: 0.2079, step: 119500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:16:57,734 - Loss: 0.2032, step: 119600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:17:52,762 - Loss: 0.2070, step: 119700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:18:46,988 - Loss: 0.1989, step: 119800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:19:41,594 - Loss: 0.2045, step: 119900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:20:36,501 - Loss: 0.1951, step: 120000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:20:36,520 - Saving snapshot checkpoint and sampling single batch at iteration 120000.
+WARNING - image.py - 2024-10-24 19:20:37,121 - 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 19:20:55,581 - FID at iteration 120000: 38.831261
+INFO - dpsgd_diffusion.py - 2024-10-24 19:21:51,530 - Loss: 0.1963, step: 120100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:22:46,801 - Loss: 0.1792, step: 120200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:23:41,132 - Loss: 0.2024, step: 120300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:23:51,972 - Eps-value after 47 epochs: 6.6413
+INFO - dpsgd_diffusion.py - 2024-10-24 19:24:36,524 - Loss: 0.1865, step: 120400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:25:31,235 - Loss: 0.1909, step: 120500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:26:26,550 - Loss: 0.2266, step: 120600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:27:21,578 - Loss: 0.1897, step: 120700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:28:17,748 - Loss: 0.2030, step: 120800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:29:14,154 - Loss: 0.1857, step: 120900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:30:09,078 - Loss: 0.2313, step: 121000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:31:04,095 - Loss: 0.2166, step: 121100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:31:59,842 - Loss: 0.1951, step: 121200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:32:54,516 - Loss: 0.1867, step: 121300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:33:49,573 - Loss: 0.2195, step: 121400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:34:44,522 - Loss: 0.1832, step: 121500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:35:38,201 - Loss: 0.1954, step: 121600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:36:31,973 - Loss: 0.2074, step: 121700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:37:27,288 - Loss: 0.2083, step: 121800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:38:23,250 - Loss: 0.1926, step: 121900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:39:18,762 - Loss: 0.2023, step: 122000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:39:18,804 - Saving snapshot checkpoint and sampling single batch at iteration 122000.
+WARNING - image.py - 2024-10-24 19:39:19,396 - 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 19:39:37,942 - FID at iteration 122000: 39.064039
+INFO - dpsgd_diffusion.py - 2024-10-24 19:40:32,252 - Loss: 0.1904, step: 122100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:41:26,474 - Loss: 0.2097, step: 122200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:42:21,134 - Loss: 0.2102, step: 122300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:43:16,985 - Loss: 0.2137, step: 122400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:44:13,226 - Loss: 0.1841, step: 122500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:45:09,684 - Loss: 0.1969, step: 122600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:46:04,087 - Loss: 0.2211, step: 122700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:46:59,743 - Loss: 0.2079, step: 122800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:47:45,896 - Eps-value after 48 epochs: 6.7152
+INFO - dpsgd_diffusion.py - 2024-10-24 19:47:57,369 - Loss: 0.1944, step: 122900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:48:52,244 - Loss: 0.1937, step: 123000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:49:48,277 - Loss: 0.2166, step: 123100
+INFO - dpsgd_diffusion.py - 2024-10-24 19:50:44,483 - Loss: 0.1970, step: 123200
+INFO - dpsgd_diffusion.py - 2024-10-24 19:51:39,051 - Loss: 0.1874, step: 123300
+INFO - dpsgd_diffusion.py - 2024-10-24 19:52:33,307 - Loss: 0.1842, step: 123400
+INFO - dpsgd_diffusion.py - 2024-10-24 19:53:27,246 - Loss: 0.2018, step: 123500
+INFO - dpsgd_diffusion.py - 2024-10-24 19:54:22,777 - Loss: 0.1999, step: 123600
+INFO - dpsgd_diffusion.py - 2024-10-24 19:55:18,328 - Loss: 0.2009, step: 123700
+INFO - dpsgd_diffusion.py - 2024-10-24 19:56:13,831 - Loss: 0.2140, step: 123800
+INFO - dpsgd_diffusion.py - 2024-10-24 19:57:08,736 - Loss: 0.2237, step: 123900
+INFO - dpsgd_diffusion.py - 2024-10-24 19:58:04,047 - Loss: 0.2258, step: 124000
+INFO - dpsgd_diffusion.py - 2024-10-24 19:58:04,059 - Saving snapshot checkpoint and sampling single batch at iteration 124000.
+WARNING - image.py - 2024-10-24 19:58:04,650 - 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 19:58:23,171 - FID at iteration 124000: 38.007075
+INFO - dpsgd_diffusion.py - 2024-10-24 19:59:19,847 - Loss: 0.1919, step: 124100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:00:13,148 - Loss: 0.1870, step: 124200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:01:09,321 - Loss: 0.2094, step: 124300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:02:04,421 - Loss: 0.2154, step: 124400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:02:59,753 - Loss: 0.2265, step: 124500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:03:55,173 - Loss: 0.1907, step: 124600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:04:51,324 - Loss: 0.1995, step: 124700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:05:46,705 - Loss: 0.1965, step: 124800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:06:40,645 - Loss: 0.2124, step: 124900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:07:35,263 - Loss: 0.1900, step: 125000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:08:30,597 - Loss: 0.2147, step: 125100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:09:26,637 - Loss: 0.1962, step: 125200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:10:23,176 - Loss: 0.2149, step: 125300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:11:18,909 - Loss: 0.1892, step: 125400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:11:41,361 - Eps-value after 49 epochs: 6.7892
+INFO - dpsgd_diffusion.py - 2024-10-24 20:12:14,054 - Loss: 0.2102, step: 125500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:13:11,778 - Loss: 0.2011, step: 125600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:14:07,382 - Loss: 0.2162, step: 125700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:15:02,345 - Loss: 0.2117, step: 125800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:15:57,704 - Loss: 0.2000, step: 125900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:16:52,605 - Loss: 0.2028, step: 126000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:16:52,618 - Saving snapshot checkpoint and sampling single batch at iteration 126000.
+WARNING - image.py - 2024-10-24 20:16:53,210 - 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 20:17:11,861 - FID at iteration 126000: 37.883580
+INFO - dpsgd_diffusion.py - 2024-10-24 20:18:08,144 - Loss: 0.1849, step: 126100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:19:04,205 - Loss: 0.1903, step: 126200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:19:59,558 - Loss: 0.2145, step: 126300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:20:55,243 - Loss: 0.1957, step: 126400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:21:50,355 - Loss: 0.2066, step: 126500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:22:46,482 - Loss: 0.1900, step: 126600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:23:41,565 - Loss: 0.1783, step: 126700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:24:36,381 - Loss: 0.1960, step: 126800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:25:31,372 - Loss: 0.2061, step: 126900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:26:26,952 - Loss: 0.2073, step: 127000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:27:22,370 - Loss: 0.2222, step: 127100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:28:18,814 - Loss: 0.2137, step: 127200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:29:13,245 - Loss: 0.1802, step: 127300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:30:08,840 - Loss: 0.1937, step: 127400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:31:04,414 - Loss: 0.1932, step: 127500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:32:00,507 - Loss: 0.2162, step: 127600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:32:56,672 - Loss: 0.1987, step: 127700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:33:51,932 - Loss: 0.1905, step: 127800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:34:48,275 - Loss: 0.2107, step: 127900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:35:43,813 - Loss: 0.2064, step: 128000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:35:43,854 - Eps-value after 50 epochs: 6.8616
+INFO - dpsgd_diffusion.py - 2024-10-24 20:35:43,868 - Saving snapshot checkpoint and sampling single batch at iteration 128000.
+WARNING - image.py - 2024-10-24 20:35:44,458 - 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 20:36:02,930 - FID at iteration 128000: 37.524513
+INFO - dpsgd_diffusion.py - 2024-10-24 20:36:58,413 - Loss: 0.2224, step: 128100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:37:53,188 - Loss: 0.1910, step: 128200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:38:49,293 - Loss: 0.2189, step: 128300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:39:45,127 - Loss: 0.1868, step: 128400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:40:41,153 - Loss: 0.1984, step: 128500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:41:36,596 - Loss: 0.2043, step: 128600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:42:33,302 - Loss: 0.2078, step: 128700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:43:28,583 - Loss: 0.2098, step: 128800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:44:24,223 - Loss: 0.2120, step: 128900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:45:18,547 - Loss: 0.2022, step: 129000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:46:12,705 - Loss: 0.1978, step: 129100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:47:07,311 - Loss: 0.1907, step: 129200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:48:03,437 - Loss: 0.1817, step: 129300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:48:59,441 - Loss: 0.2282, step: 129400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:49:53,520 - Loss: 0.1953, step: 129500
+INFO - dpsgd_diffusion.py - 2024-10-24 20:50:48,304 - Loss: 0.1960, step: 129600
+INFO - dpsgd_diffusion.py - 2024-10-24 20:51:43,970 - Loss: 0.1983, step: 129700
+INFO - dpsgd_diffusion.py - 2024-10-24 20:52:40,529 - Loss: 0.2146, step: 129800
+INFO - dpsgd_diffusion.py - 2024-10-24 20:53:36,308 - Loss: 0.1973, step: 129900
+INFO - dpsgd_diffusion.py - 2024-10-24 20:54:31,974 - Loss: 0.1977, step: 130000
+INFO - dpsgd_diffusion.py - 2024-10-24 20:54:31,987 - Saving snapshot checkpoint and sampling single batch at iteration 130000.
+WARNING - image.py - 2024-10-24 20:54:32,575 - 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 20:54:50,893 - FID at iteration 130000: 37.775610
+INFO - dpsgd_diffusion.py - 2024-10-24 20:55:47,588 - Loss: 0.2002, step: 130100
+INFO - dpsgd_diffusion.py - 2024-10-24 20:56:42,209 - Loss: 0.2140, step: 130200
+INFO - dpsgd_diffusion.py - 2024-10-24 20:57:36,997 - Loss: 0.1918, step: 130300
+INFO - dpsgd_diffusion.py - 2024-10-24 20:58:32,654 - Loss: 0.1808, step: 130400
+INFO - dpsgd_diffusion.py - 2024-10-24 20:59:30,580 - Loss: 0.2000, step: 130500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:00:03,345 - Eps-value after 51 epochs: 6.9337
+INFO - dpsgd_diffusion.py - 2024-10-24 21:00:26,365 - Loss: 0.2053, step: 130600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:01:22,547 - Loss: 0.2201, step: 130700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:02:17,291 - Loss: 0.1799, step: 130800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:03:13,476 - Loss: 0.2096, step: 130900
+INFO - dpsgd_diffusion.py - 2024-10-24 21:04:09,953 - Loss: 0.1923, step: 131000
+INFO - dpsgd_diffusion.py - 2024-10-24 21:05:05,145 - Loss: 0.1958, step: 131100
+INFO - dpsgd_diffusion.py - 2024-10-24 21:05:59,832 - Loss: 0.1836, step: 131200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:06:54,455 - Loss: 0.1887, step: 131300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:07:48,773 - Loss: 0.1960, step: 131400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:08:44,111 - Loss: 0.1963, step: 131500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:09:40,913 - Loss: 0.1882, step: 131600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:10:36,956 - Loss: 0.2125, step: 131700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:11:32,040 - Loss: 0.2062, step: 131800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:12:28,015 - Loss: 0.2236, step: 131900
+INFO - dpsgd_diffusion.py - 2024-10-24 21:13:25,597 - Loss: 0.2187, step: 132000
+INFO - dpsgd_diffusion.py - 2024-10-24 21:13:25,645 - Saving snapshot checkpoint and sampling single batch at iteration 132000.
+WARNING - image.py - 2024-10-24 21:13:26,906 - 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 21:13:45,633 - FID at iteration 132000: 37.419046
+INFO - dpsgd_diffusion.py - 2024-10-24 21:14:41,983 - Loss: 0.2001, step: 132100
+INFO - dpsgd_diffusion.py - 2024-10-24 21:15:38,084 - Loss: 0.1965, step: 132200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:16:32,673 - Loss: 0.2128, step: 132300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:17:28,020 - Loss: 0.1840, step: 132400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:18:23,575 - Loss: 0.1948, step: 132500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:19:18,670 - Loss: 0.1947, step: 132600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:20:16,245 - Loss: 0.1938, step: 132700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:21:09,618 - Loss: 0.1902, step: 132800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:22:06,346 - Loss: 0.1966, step: 132900
+INFO - dpsgd_diffusion.py - 2024-10-24 21:23:01,828 - Loss: 0.1930, step: 133000
+INFO - dpsgd_diffusion.py - 2024-10-24 21:23:56,630 - Loss: 0.1957, step: 133100
+INFO - dpsgd_diffusion.py - 2024-10-24 21:24:07,497 - Eps-value after 52 epochs: 7.0058
+INFO - dpsgd_diffusion.py - 2024-10-24 21:24:51,934 - Loss: 0.1801, step: 133200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:25:47,722 - Loss: 0.1949, step: 133300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:26:43,559 - Loss: 0.1941, step: 133400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:27:38,629 - Loss: 0.1971, step: 133500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:28:34,258 - Loss: 0.2157, step: 133600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:29:28,904 - Loss: 0.1830, step: 133700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:30:26,496 - Loss: 0.2020, step: 133800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:31:22,439 - Loss: 0.2115, step: 133900
+INFO - dpsgd_diffusion.py - 2024-10-24 21:32:18,372 - Loss: 0.1963, step: 134000
+INFO - dpsgd_diffusion.py - 2024-10-24 21:32:18,419 - Saving snapshot checkpoint and sampling single batch at iteration 134000.
+WARNING - image.py - 2024-10-24 21:32:19,384 - 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 21:32:38,374 - FID at iteration 134000: 37.185481
+INFO - dpsgd_diffusion.py - 2024-10-24 21:33:34,169 - Loss: 0.2107, step: 134100
+INFO - dpsgd_diffusion.py - 2024-10-24 21:34:29,495 - Loss: 0.2048, step: 134200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:35:26,368 - Loss: 0.2114, step: 134300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:36:22,849 - Loss: 0.2015, step: 134400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:37:19,997 - Loss: 0.2024, step: 134500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:38:15,404 - Loss: 0.1893, step: 134600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:39:11,287 - Loss: 0.2042, step: 134700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:40:07,797 - Loss: 0.2016, step: 134800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:41:04,207 - Loss: 0.1884, step: 134900
+INFO - dpsgd_diffusion.py - 2024-10-24 21:41:59,704 - Loss: 0.1986, step: 135000
+INFO - dpsgd_diffusion.py - 2024-10-24 21:42:54,682 - Loss: 0.2095, step: 135100
+INFO - dpsgd_diffusion.py - 2024-10-24 21:43:52,284 - Loss: 0.1927, step: 135200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:44:48,383 - Loss: 0.1678, step: 135300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:45:43,469 - Loss: 0.1924, step: 135400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:46:38,838 - Loss: 0.1778, step: 135500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:47:34,239 - Loss: 0.1864, step: 135600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:48:17,518 - Eps-value after 53 epochs: 7.0770
+INFO - dpsgd_diffusion.py - 2024-10-24 21:48:28,887 - Loss: 0.2074, step: 135700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:49:23,678 - Loss: 0.1982, step: 135800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:50:20,749 - Loss: 0.1905, step: 135900
+INFO - dpsgd_diffusion.py - 2024-10-24 21:51:15,884 - Loss: 0.1956, step: 136000
+INFO - dpsgd_diffusion.py - 2024-10-24 21:51:15,907 - Saving snapshot checkpoint and sampling single batch at iteration 136000.
+WARNING - image.py - 2024-10-24 21:51:16,496 - 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 21:51:34,388 - FID at iteration 136000: 36.702892
+INFO - dpsgd_diffusion.py - 2024-10-24 21:52:31,240 - Loss: 0.1903, step: 136100
+INFO - dpsgd_diffusion.py - 2024-10-24 21:53:26,030 - Loss: 0.1920, step: 136200
+INFO - dpsgd_diffusion.py - 2024-10-24 21:54:22,241 - Loss: 0.1833, step: 136300
+INFO - dpsgd_diffusion.py - 2024-10-24 21:55:17,806 - Loss: 0.1953, step: 136400
+INFO - dpsgd_diffusion.py - 2024-10-24 21:56:11,918 - Loss: 0.2182, step: 136500
+INFO - dpsgd_diffusion.py - 2024-10-24 21:57:07,721 - Loss: 0.1831, step: 136600
+INFO - dpsgd_diffusion.py - 2024-10-24 21:58:04,918 - Loss: 0.2180, step: 136700
+INFO - dpsgd_diffusion.py - 2024-10-24 21:59:00,582 - Loss: 0.1868, step: 136800
+INFO - dpsgd_diffusion.py - 2024-10-24 21:59:55,951 - Loss: 0.1934, step: 136900
+INFO - dpsgd_diffusion.py - 2024-10-24 22:00:50,563 - Loss: 0.2130, step: 137000
+INFO - dpsgd_diffusion.py - 2024-10-24 22:01:45,648 - Loss: 0.2235, step: 137100
+INFO - dpsgd_diffusion.py - 2024-10-24 22:02:41,192 - Loss: 0.2145, step: 137200
+INFO - dpsgd_diffusion.py - 2024-10-24 22:03:36,892 - Loss: 0.2048, step: 137300
+INFO - dpsgd_diffusion.py - 2024-10-24 22:04:32,356 - Loss: 0.1829, step: 137400
+INFO - dpsgd_diffusion.py - 2024-10-24 22:05:28,158 - Loss: 0.2009, step: 137500
+INFO - dpsgd_diffusion.py - 2024-10-24 22:06:22,193 - Loss: 0.1955, step: 137600
+INFO - dpsgd_diffusion.py - 2024-10-24 22:07:19,808 - Loss: 0.2008, step: 137700
+INFO - dpsgd_diffusion.py - 2024-10-24 22:08:16,089 - Loss: 0.1967, step: 137800
+INFO - dpsgd_diffusion.py - 2024-10-24 22:09:11,193 - Loss: 0.1990, step: 137900
+INFO - dpsgd_diffusion.py - 2024-10-24 22:10:06,848 - Loss: 0.1888, step: 138000
+INFO - dpsgd_diffusion.py - 2024-10-24 22:10:06,861 - Saving snapshot checkpoint and sampling single batch at iteration 138000.
+WARNING - image.py - 2024-10-24 22:10:07,449 - 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 22:10:25,382 - FID at iteration 138000: 37.081725
+INFO - dpsgd_diffusion.py - 2024-10-24 22:11:22,133 - Loss: 0.2040, step: 138100
+INFO - dpsgd_diffusion.py - 2024-10-24 22:12:18,516 - Loss: 0.1948, step: 138200
+INFO - dpsgd_diffusion.py - 2024-10-24 22:12:40,306 - Eps-value after 54 epochs: 7.1472
+INFO - dpsgd_diffusion.py - 2024-10-24 22:13:12,538 - Loss: 0.1935, step: 138300
+INFO - dpsgd_diffusion.py - 2024-10-24 22:14:07,777 - Loss: 0.2060, step: 138400
+INFO - dpsgd_diffusion.py - 2024-10-24 22:15:03,002 - Loss: 0.1893, step: 138500
+INFO - dpsgd_diffusion.py - 2024-10-24 22:15:58,591 - Loss: 0.2128, step: 138600
+INFO - dpsgd_diffusion.py - 2024-10-24 22:16:54,474 - Loss: 0.1973, step: 138700
+INFO - dpsgd_diffusion.py - 2024-10-24 22:17:51,211 - Loss: 0.1953, step: 138800
+INFO - dpsgd_diffusion.py - 2024-10-24 22:18:47,153 - Loss: 0.1970, step: 138900
+INFO - dpsgd_diffusion.py - 2024-10-24 22:19:43,869 - Loss: 0.1813, step: 139000
+INFO - dpsgd_diffusion.py - 2024-10-24 22:20:39,405 - Loss: 0.1972, step: 139100
+INFO - dpsgd_diffusion.py - 2024-10-24 22:21:37,114 - Loss: 0.1877, step: 139200
+INFO - dpsgd_diffusion.py - 2024-10-24 22:22:33,815 - Loss: 0.1953, step: 139300
+INFO - dpsgd_diffusion.py - 2024-10-24 22:23:29,385 - Loss: 0.1927, step: 139400
+INFO - dpsgd_diffusion.py - 2024-10-24 22:24:24,232 - Loss: 0.1930, step: 139500
+INFO - dpsgd_diffusion.py - 2024-10-24 22:25:19,504 - Loss: 0.1972, step: 139600
+INFO - dpsgd_diffusion.py - 2024-10-24 22:26:14,499 - Loss: 0.1796, step: 139700
+INFO - dpsgd_diffusion.py - 2024-10-24 22:27:09,688 - Loss: 0.2019, step: 139800
+INFO - dpsgd_diffusion.py - 2024-10-24 22:28:03,662 - Loss: 0.2002, step: 139900
+INFO - dpsgd_diffusion.py - 2024-10-24 22:28:59,103 - Loss: 0.2065, step: 140000
+INFO - dpsgd_diffusion.py - 2024-10-24 22:28:59,116 - Saving snapshot checkpoint and sampling single batch at iteration 140000.
+WARNING - image.py - 2024-10-24 22:28:59,705 - 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 22:29:17,886 - FID at iteration 140000: 36.246629
+INFO - dpsgd_diffusion.py - 2024-10-24 22:30:14,540 - Loss: 0.1963, step: 140100
+INFO - dpsgd_diffusion.py - 2024-10-24 22:31:10,929 - Loss: 0.1779, step: 140200
+INFO - dpsgd_diffusion.py - 2024-10-24 22:32:05,986 - Loss: 0.2030, step: 140300
+INFO - dpsgd_diffusion.py - 2024-10-24 22:33:00,960 - Loss: 0.2097, step: 140400
+INFO - dpsgd_diffusion.py - 2024-10-24 22:33:57,273 - Loss: 0.1966, step: 140500
+INFO - dpsgd_diffusion.py - 2024-10-24 22:34:52,323 - Loss: 0.1774, step: 140600
+INFO - dpsgd_diffusion.py - 2024-10-24 22:35:47,514 - Loss: 0.2092, step: 140700
+INFO - dpsgd_diffusion.py - 2024-10-24 22:36:42,419 - Loss: 0.1723, step: 140800
+INFO - dpsgd_diffusion.py - 2024-10-24 22:36:42,460 - Eps-value after 55 epochs: 7.2175
+INFO - dpsgd_diffusion.py - 2024-10-24 22:37:37,779 - Loss: 0.2122, step: 140900
+INFO - dpsgd_diffusion.py - 2024-10-24 22:38:34,872 - Loss: 0.2156, step: 141000
+INFO - dpsgd_diffusion.py - 2024-10-24 22:39:29,691 - Loss: 0.1971, step: 141100
+INFO - dpsgd_diffusion.py - 2024-10-24 22:40:26,227 - Loss: 0.2024, step: 141200
+INFO - dpsgd_diffusion.py - 2024-10-24 22:41:20,819 - Loss: 0.1857, step: 141300
+INFO - dpsgd_diffusion.py - 2024-10-24 22:42:17,025 - Loss: 0.1792, step: 141400
+INFO - dpsgd_diffusion.py - 2024-10-24 22:43:13,433 - Loss: 0.2062, step: 141500
+INFO - dpsgd_diffusion.py - 2024-10-24 22:44:08,979 - Loss: 0.2101, step: 141600
+INFO - dpsgd_diffusion.py - 2024-10-24 22:45:04,411 - Loss: 0.2002, step: 141700
+INFO - dpsgd_diffusion.py - 2024-10-24 22:46:01,570 - Loss: 0.2216, step: 141800
+INFO - dpsgd_diffusion.py - 2024-10-24 22:46:56,476 - Loss: 0.2023, step: 141900
+INFO - dpsgd_diffusion.py - 2024-10-24 22:47:51,674 - Loss: 0.2100, step: 142000
+INFO - dpsgd_diffusion.py - 2024-10-24 22:47:51,687 - Saving snapshot checkpoint and sampling single batch at iteration 142000.
+WARNING - image.py - 2024-10-24 22:47:52,276 - 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 22:48:10,266 - FID at iteration 142000: 35.877323
+INFO - dpsgd_diffusion.py - 2024-10-24 22:49:08,467 - Loss: 0.1830, step: 142100
+INFO - dpsgd_diffusion.py - 2024-10-24 22:50:04,929 - Loss: 0.1887, step: 142200
+INFO - dpsgd_diffusion.py - 2024-10-24 22:51:00,921 - Loss: 0.2028, step: 142300
+INFO - dpsgd_diffusion.py - 2024-10-24 22:51:57,498 - Loss: 0.2188, step: 142400
+INFO - dpsgd_diffusion.py - 2024-10-24 22:52:52,699 - Loss: 0.1895, step: 142500
+INFO - dpsgd_diffusion.py - 2024-10-24 22:53:49,776 - Loss: 0.2073, step: 142600
+INFO - dpsgd_diffusion.py - 2024-10-24 22:54:45,501 - Loss: 0.1928, step: 142700
+INFO - dpsgd_diffusion.py - 2024-10-24 22:55:41,641 - Loss: 0.1843, step: 142800
+INFO - dpsgd_diffusion.py - 2024-10-24 22:56:37,019 - Loss: 0.1999, step: 142900
+INFO - dpsgd_diffusion.py - 2024-10-24 22:57:33,927 - Loss: 0.1977, step: 143000
+INFO - dpsgd_diffusion.py - 2024-10-24 22:58:29,685 - Loss: 0.1948, step: 143100
+INFO - dpsgd_diffusion.py - 2024-10-24 22:59:25,292 - Loss: 0.1854, step: 143200
+INFO - dpsgd_diffusion.py - 2024-10-24 23:00:20,391 - Loss: 0.2060, step: 143300
+INFO - dpsgd_diffusion.py - 2024-10-24 23:00:53,358 - Eps-value after 56 epochs: 7.2877
+INFO - dpsgd_diffusion.py - 2024-10-24 23:01:15,046 - Loss: 0.1916, step: 143400
+INFO - dpsgd_diffusion.py - 2024-10-24 23:02:10,216 - Loss: 0.1781, step: 143500
+INFO - dpsgd_diffusion.py - 2024-10-24 23:03:06,894 - Loss: 0.2008, step: 143600
+INFO - dpsgd_diffusion.py - 2024-10-24 23:04:00,449 - Loss: 0.2133, step: 143700
+INFO - dpsgd_diffusion.py - 2024-10-24 23:04:56,691 - Loss: 0.1864, step: 143800
+INFO - dpsgd_diffusion.py - 2024-10-24 23:05:52,760 - Loss: 0.1814, step: 143900
+INFO - dpsgd_diffusion.py - 2024-10-24 23:06:48,411 - Loss: 0.1869, step: 144000
+INFO - dpsgd_diffusion.py - 2024-10-24 23:06:48,433 - Saving snapshot checkpoint and sampling single batch at iteration 144000.
+WARNING - image.py - 2024-10-24 23:06:49,022 - 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 23:07:07,659 - FID at iteration 144000: 35.958271
+INFO - dpsgd_diffusion.py - 2024-10-24 23:08:04,494 - Loss: 0.1923, step: 144100
+INFO - dpsgd_diffusion.py - 2024-10-24 23:09:01,623 - Loss: 0.2073, step: 144200
+INFO - dpsgd_diffusion.py - 2024-10-24 23:09:56,445 - Loss: 0.1940, step: 144300
+INFO - dpsgd_diffusion.py - 2024-10-24 23:10:51,339 - Loss: 0.1792, step: 144400
+INFO - dpsgd_diffusion.py - 2024-10-24 23:11:47,886 - Loss: 0.2182, step: 144500
+INFO - dpsgd_diffusion.py - 2024-10-24 23:12:43,228 - Loss: 0.2004, step: 144600
+INFO - dpsgd_diffusion.py - 2024-10-24 23:13:37,603 - Loss: 0.2019, step: 144700
+INFO - dpsgd_diffusion.py - 2024-10-24 23:14:32,919 - Loss: 0.1769, step: 144800
+INFO - dpsgd_diffusion.py - 2024-10-24 23:15:27,618 - Loss: 0.1967, step: 144900
+INFO - dpsgd_diffusion.py - 2024-10-24 23:16:23,917 - Loss: 0.1852, step: 145000
+INFO - dpsgd_diffusion.py - 2024-10-24 23:17:21,693 - Loss: 0.1999, step: 145100
+INFO - dpsgd_diffusion.py - 2024-10-24 23:18:17,640 - Loss: 0.2105, step: 145200
+INFO - dpsgd_diffusion.py - 2024-10-24 23:19:12,333 - Loss: 0.2015, step: 145300
+INFO - dpsgd_diffusion.py - 2024-10-24 23:20:08,081 - Loss: 0.2223, step: 145400
+INFO - dpsgd_diffusion.py - 2024-10-24 23:21:04,153 - Loss: 0.2133, step: 145500
+INFO - dpsgd_diffusion.py - 2024-10-24 23:21:58,511 - Loss: 0.2044, step: 145600
+INFO - dpsgd_diffusion.py - 2024-10-24 23:22:54,548 - Loss: 0.2128, step: 145700
+INFO - dpsgd_diffusion.py - 2024-10-24 23:23:49,800 - Loss: 0.1962, step: 145800
+INFO - dpsgd_diffusion.py - 2024-10-24 23:24:44,927 - Loss: 0.1908, step: 145900
+INFO - dpsgd_diffusion.py - 2024-10-24 23:24:55,815 - Eps-value after 57 epochs: 7.3565
+INFO - dpsgd_diffusion.py - 2024-10-24 23:25:39,056 - Loss: 0.1994, step: 146000
+INFO - dpsgd_diffusion.py - 2024-10-24 23:25:39,103 - Saving snapshot checkpoint and sampling single batch at iteration 146000.
+WARNING - image.py - 2024-10-24 23:25:39,694 - 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 23:25:58,084 - FID at iteration 146000: 35.901987
+INFO - dpsgd_diffusion.py - 2024-10-24 23:26:54,997 - Loss: 0.1926, step: 146100
+INFO - dpsgd_diffusion.py - 2024-10-24 23:27:50,107 - Loss: 0.2060, step: 146200
+INFO - dpsgd_diffusion.py - 2024-10-24 23:28:45,192 - Loss: 0.1833, step: 146300
+INFO - dpsgd_diffusion.py - 2024-10-24 23:29:41,275 - Loss: 0.1884, step: 146400
+INFO - dpsgd_diffusion.py - 2024-10-24 23:30:39,153 - Loss: 0.2069, step: 146500
+INFO - dpsgd_diffusion.py - 2024-10-24 23:31:34,780 - Loss: 0.2001, step: 146600
+INFO - dpsgd_diffusion.py - 2024-10-24 23:32:30,930 - Loss: 0.2065, step: 146700
+INFO - dpsgd_diffusion.py - 2024-10-24 23:33:26,583 - Loss: 0.2140, step: 146800
+INFO - dpsgd_diffusion.py - 2024-10-24 23:34:22,278 - Loss: 0.2048, step: 146900
+INFO - dpsgd_diffusion.py - 2024-10-24 23:35:17,660 - Loss: 0.2064, step: 147000
+INFO - dpsgd_diffusion.py - 2024-10-24 23:36:14,967 - Loss: 0.1831, step: 147100
+INFO - dpsgd_diffusion.py - 2024-10-24 23:37:08,594 - Loss: 0.1895, step: 147200
+INFO - dpsgd_diffusion.py - 2024-10-24 23:38:05,018 - Loss: 0.1968, step: 147300
+INFO - dpsgd_diffusion.py - 2024-10-24 23:38:59,533 - Loss: 0.1961, step: 147400
+INFO - dpsgd_diffusion.py - 2024-10-24 23:39:56,057 - Loss: 0.2236, step: 147500
+INFO - dpsgd_diffusion.py - 2024-10-24 23:40:52,101 - Loss: 0.1769, step: 147600
+INFO - dpsgd_diffusion.py - 2024-10-24 23:41:49,530 - Loss: 0.2037, step: 147700
+INFO - dpsgd_diffusion.py - 2024-10-24 23:42:45,259 - Loss: 0.1792, step: 147800
+INFO - dpsgd_diffusion.py - 2024-10-24 23:43:40,526 - Loss: 0.2147, step: 147900
+INFO - dpsgd_diffusion.py - 2024-10-24 23:44:37,670 - Loss: 0.2013, step: 148000
+INFO - dpsgd_diffusion.py - 2024-10-24 23:44:37,684 - Saving snapshot checkpoint and sampling single batch at iteration 148000.
+WARNING - image.py - 2024-10-24 23:44:38,274 - 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 23:44:56,651 - FID at iteration 148000: 35.533266
+INFO - dpsgd_diffusion.py - 2024-10-24 23:45:52,234 - Loss: 0.1949, step: 148100
+INFO - dpsgd_diffusion.py - 2024-10-24 23:46:49,333 - Loss: 0.2044, step: 148200
+INFO - dpsgd_diffusion.py - 2024-10-24 23:47:46,334 - Loss: 0.1953, step: 148300
+INFO - dpsgd_diffusion.py - 2024-10-24 23:48:42,125 - Loss: 0.1935, step: 148400
+INFO - dpsgd_diffusion.py - 2024-10-24 23:49:26,196 - Eps-value after 58 epochs: 7.4249
+INFO - dpsgd_diffusion.py - 2024-10-24 23:49:37,630 - Loss: 0.2132, step: 148500
+INFO - dpsgd_diffusion.py - 2024-10-24 23:50:33,720 - Loss: 0.2308, step: 148600
+INFO - dpsgd_diffusion.py - 2024-10-24 23:51:30,326 - Loss: 0.1975, step: 148700
+INFO - dpsgd_diffusion.py - 2024-10-24 23:52:26,005 - Loss: 0.2030, step: 148800
+INFO - dpsgd_diffusion.py - 2024-10-24 23:53:20,925 - Loss: 0.2317, step: 148900
+INFO - dpsgd_diffusion.py - 2024-10-24 23:54:16,132 - Loss: 0.1949, step: 149000
+INFO - dpsgd_diffusion.py - 2024-10-24 23:55:11,024 - Loss: 0.1988, step: 149100
+INFO - dpsgd_diffusion.py - 2024-10-24 23:56:06,185 - Loss: 0.1934, step: 149200
+INFO - dpsgd_diffusion.py - 2024-10-24 23:57:00,277 - Loss: 0.2142, step: 149300
+INFO - dpsgd_diffusion.py - 2024-10-24 23:57:56,791 - Loss: 0.1880, step: 149400
+INFO - dpsgd_diffusion.py - 2024-10-24 23:58:53,057 - Loss: 0.1831, step: 149500
+INFO - dpsgd_diffusion.py - 2024-10-24 23:59:48,496 - Loss: 0.2123, step: 149600
+INFO - dpsgd_diffusion.py - 2024-10-25 00:00:42,869 - Loss: 0.1903, step: 149700
+INFO - dpsgd_diffusion.py - 2024-10-25 00:01:40,813 - Loss: 0.1812, step: 149800
+INFO - dpsgd_diffusion.py - 2024-10-25 00:02:37,128 - Loss: 0.2217, step: 149900
+INFO - dpsgd_diffusion.py - 2024-10-25 00:03:34,175 - Loss: 0.1962, step: 150000
+INFO - dpsgd_diffusion.py - 2024-10-25 00:03:34,221 - Saving snapshot checkpoint and sampling single batch at iteration 150000.
+WARNING - image.py - 2024-10-25 00:03:34,812 - 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 - 2024-10-25 00:03:53,939 - FID at iteration 150000: 35.953616
+INFO - dpsgd_diffusion.py - 2024-10-25 00:04:48,893 - Loss: 0.2038, step: 150100
+INFO - dpsgd_diffusion.py - 2024-10-25 00:05:44,660 - Loss: 0.1880, step: 150200
+INFO - dpsgd_diffusion.py - 2024-10-25 00:06:39,277 - Loss: 0.2087, step: 150300
+INFO - dpsgd_diffusion.py - 2024-10-25 00:07:34,942 - Loss: 0.1725, step: 150400
+INFO - dpsgd_diffusion.py - 2024-10-25 00:08:30,951 - Loss: 0.1897, step: 150500
+INFO - dpsgd_diffusion.py - 2024-10-25 00:09:25,614 - Loss: 0.2172, step: 150600
+INFO - dpsgd_diffusion.py - 2024-10-25 00:10:19,532 - Loss: 0.1947, step: 150700
+INFO - dpsgd_diffusion.py - 2024-10-25 00:11:14,718 - Loss: 0.1984, step: 150800
+INFO - dpsgd_diffusion.py - 2024-10-25 00:12:08,842 - Loss: 0.1811, step: 150900
+INFO - dpsgd_diffusion.py - 2024-10-25 00:13:05,246 - Loss: 0.1939, step: 151000
+INFO - dpsgd_diffusion.py - 2024-10-25 00:13:27,759 - Eps-value after 59 epochs: 7.4933
+INFO - dpsgd_diffusion.py - 2024-10-25 00:14:01,096 - Loss: 0.1961, step: 151100
+INFO - dpsgd_diffusion.py - 2024-10-25 00:14:56,896 - Loss: 0.2065, step: 151200
+INFO - dpsgd_diffusion.py - 2024-10-25 00:15:51,534 - Loss: 0.2083, step: 151300
+INFO - dpsgd_diffusion.py - 2024-10-25 00:16:46,858 - Loss: 0.2166, step: 151400
+INFO - dpsgd_diffusion.py - 2024-10-25 00:17:42,692 - Loss: 0.2032, step: 151500
+INFO - dpsgd_diffusion.py - 2024-10-25 00:18:38,054 - Loss: 0.1934, step: 151600
+INFO - dpsgd_diffusion.py - 2024-10-25 00:19:34,895 - Loss: 0.1901, step: 151700
+INFO - dpsgd_diffusion.py - 2024-10-25 00:20:30,416 - Loss: 0.1901, step: 151800
+INFO - dpsgd_diffusion.py - 2024-10-25 00:21:27,698 - Loss: 0.1856, step: 151900
+INFO - dpsgd_diffusion.py - 2024-10-25 00:22:22,884 - Loss: 0.2178, step: 152000
+INFO - dpsgd_diffusion.py - 2024-10-25 00:22:22,906 - Saving snapshot checkpoint and sampling single batch at iteration 152000.
+WARNING - image.py - 2024-10-25 00:22:23,496 - 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 - 2024-10-25 00:22:41,968 - FID at iteration 152000: 35.182919
+INFO - dpsgd_diffusion.py - 2024-10-25 00:23:39,702 - Loss: 0.1985, step: 152100
+INFO - dpsgd_diffusion.py - 2024-10-25 00:24:36,026 - Loss: 0.1899, step: 152200
+INFO - dpsgd_diffusion.py - 2024-10-25 00:25:32,556 - Loss: 0.1865, step: 152300
+INFO - dpsgd_diffusion.py - 2024-10-25 00:26:27,592 - Loss: 0.1987, step: 152400
+INFO - dpsgd_diffusion.py - 2024-10-25 00:27:24,102 - Loss: 0.1911, step: 152500
+INFO - dpsgd_diffusion.py - 2024-10-25 00:28:18,390 - Loss: 0.1833, step: 152600
+INFO - dpsgd_diffusion.py - 2024-10-25 00:29:14,004 - Loss: 0.2010, step: 152700
+INFO - dpsgd_diffusion.py - 2024-10-25 00:30:10,198 - Loss: 0.1740, step: 152800
+INFO - dpsgd_diffusion.py - 2024-10-25 00:31:06,198 - Loss: 0.2043, step: 152900
+INFO - dpsgd_diffusion.py - 2024-10-25 00:32:02,380 - Loss: 0.1719, step: 153000
+INFO - dpsgd_diffusion.py - 2024-10-25 00:32:57,679 - Loss: 0.2271, step: 153100
+INFO - dpsgd_diffusion.py - 2024-10-25 00:33:52,701 - Loss: 0.2022, step: 153200
+INFO - dpsgd_diffusion.py - 2024-10-25 00:34:48,446 - Loss: 0.2122, step: 153300
+INFO - dpsgd_diffusion.py - 2024-10-25 00:35:42,728 - Loss: 0.2112, step: 153400
+INFO - dpsgd_diffusion.py - 2024-10-25 00:36:37,821 - Loss: 0.1892, step: 153500
+INFO - dpsgd_diffusion.py - 2024-10-25 00:37:33,057 - Loss: 0.2004, step: 153600
+INFO - dpsgd_diffusion.py - 2024-10-25 00:37:33,101 - Eps-value after 60 epochs: 7.5617
+INFO - dpsgd_diffusion.py - 2024-10-25 00:38:29,383 - Loss: 0.1990, step: 153700
+INFO - dpsgd_diffusion.py - 2024-10-25 00:39:25,137 - Loss: 0.2049, step: 153800
+INFO - dpsgd_diffusion.py - 2024-10-25 00:40:20,185 - Loss: 0.1863, step: 153900
+INFO - dpsgd_diffusion.py - 2024-10-25 00:41:14,668 - Loss: 0.1914, step: 154000
+INFO - dpsgd_diffusion.py - 2024-10-25 00:41:14,714 - Saving snapshot checkpoint and sampling single batch at iteration 154000.
+WARNING - image.py - 2024-10-25 00:41:15,304 - 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 - 2024-10-25 00:41:33,133 - FID at iteration 154000: 35.418983
+INFO - dpsgd_diffusion.py - 2024-10-25 00:42:31,258 - Loss: 0.1944, step: 154100
+INFO - dpsgd_diffusion.py - 2024-10-25 00:43:28,634 - Loss: 0.2087, step: 154200
+INFO - dpsgd_diffusion.py - 2024-10-25 00:44:22,922 - Loss: 0.2055, step: 154300
+INFO - dpsgd_diffusion.py - 2024-10-25 00:45:19,919 - Loss: 0.2021, step: 154400
+INFO - dpsgd_diffusion.py - 2024-10-25 00:46:15,453 - Loss: 0.2068, step: 154500
+INFO - dpsgd_diffusion.py - 2024-10-25 00:47:10,894 - Loss: 0.1968, step: 154600
+INFO - dpsgd_diffusion.py - 2024-10-25 00:48:04,977 - Loss: 0.1961, step: 154700
+INFO - dpsgd_diffusion.py - 2024-10-25 00:48:59,702 - Loss: 0.2168, step: 154800
+INFO - dpsgd_diffusion.py - 2024-10-25 00:49:55,139 - Loss: 0.1934, step: 154900
+INFO - dpsgd_diffusion.py - 2024-10-25 00:50:49,342 - Loss: 0.2090, step: 155000
+INFO - dpsgd_diffusion.py - 2024-10-25 00:51:44,649 - Loss: 0.2196, step: 155100
+INFO - dpsgd_diffusion.py - 2024-10-25 00:52:41,023 - Loss: 0.2028, step: 155200
+INFO - dpsgd_diffusion.py - 2024-10-25 00:53:36,014 - Loss: 0.1908, step: 155300
+INFO - dpsgd_diffusion.py - 2024-10-25 00:54:31,297 - Loss: 0.1959, step: 155400
+INFO - dpsgd_diffusion.py - 2024-10-25 00:55:26,150 - Loss: 0.2003, step: 155500
+INFO - dpsgd_diffusion.py - 2024-10-25 00:56:22,635 - Loss: 0.1858, step: 155600
+INFO - dpsgd_diffusion.py - 2024-10-25 00:57:17,585 - Loss: 0.1973, step: 155700
+INFO - dpsgd_diffusion.py - 2024-10-25 00:58:13,503 - Loss: 0.2030, step: 155800
+INFO - dpsgd_diffusion.py - 2024-10-25 00:59:08,054 - Loss: 0.2204, step: 155900
+INFO - dpsgd_diffusion.py - 2024-10-25 01:00:03,595 - Loss: 0.2093, step: 156000
+INFO - dpsgd_diffusion.py - 2024-10-25 01:00:03,607 - Saving snapshot checkpoint and sampling single batch at iteration 156000.
+WARNING - image.py - 2024-10-25 01:00:04,200 - 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 - 2024-10-25 01:00:22,059 - FID at iteration 156000: 34.729950
+INFO - dpsgd_diffusion.py - 2024-10-25 01:01:18,617 - Loss: 0.1880, step: 156100
+INFO - dpsgd_diffusion.py - 2024-10-25 01:01:52,999 - Eps-value after 61 epochs: 7.6287
+INFO - dpsgd_diffusion.py - 2024-10-25 01:02:15,064 - Loss: 0.1853, step: 156200
+INFO - dpsgd_diffusion.py - 2024-10-25 01:03:10,690 - Loss: 0.2026, step: 156300
+INFO - dpsgd_diffusion.py - 2024-10-25 01:04:06,786 - Loss: 0.2025, step: 156400
+INFO - dpsgd_diffusion.py - 2024-10-25 01:05:02,733 - Loss: 0.1951, step: 156500
+INFO - dpsgd_diffusion.py - 2024-10-25 01:05:58,324 - Loss: 0.1993, step: 156600
+INFO - dpsgd_diffusion.py - 2024-10-25 01:06:53,362 - Loss: 0.1942, step: 156700
+INFO - dpsgd_diffusion.py - 2024-10-25 01:07:48,930 - Loss: 0.1951, step: 156800
+INFO - dpsgd_diffusion.py - 2024-10-25 01:08:45,339 - Loss: 0.2032, step: 156900
+INFO - dpsgd_diffusion.py - 2024-10-25 01:09:41,469 - Loss: 0.2069, step: 157000
+INFO - dpsgd_diffusion.py - 2024-10-25 01:10:38,060 - Loss: 0.1828, step: 157100
+INFO - dpsgd_diffusion.py - 2024-10-25 01:11:33,958 - Loss: 0.2013, step: 157200
+INFO - dpsgd_diffusion.py - 2024-10-25 01:12:28,785 - Loss: 0.2058, step: 157300
+INFO - dpsgd_diffusion.py - 2024-10-25 01:13:25,098 - Loss: 0.1949, step: 157400
+INFO - dpsgd_diffusion.py - 2024-10-25 01:14:21,388 - Loss: 0.1996, step: 157500
+INFO - dpsgd_diffusion.py - 2024-10-25 01:15:15,668 - Loss: 0.1999, step: 157600
+INFO - dpsgd_diffusion.py - 2024-10-25 01:16:12,188 - Loss: 0.1871, step: 157700
+INFO - dpsgd_diffusion.py - 2024-10-25 01:17:08,331 - Loss: 0.2043, step: 157800
+INFO - dpsgd_diffusion.py - 2024-10-25 01:18:04,204 - Loss: 0.1889, step: 157900
+INFO - dpsgd_diffusion.py - 2024-10-25 01:19:01,208 - Loss: 0.1988, step: 158000
+INFO - dpsgd_diffusion.py - 2024-10-25 01:19:01,251 - Saving snapshot checkpoint and sampling single batch at iteration 158000.
+WARNING - image.py - 2024-10-25 01:19:01,838 - 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 - 2024-10-25 01:19:19,702 - FID at iteration 158000: 35.053994
+INFO - dpsgd_diffusion.py - 2024-10-25 01:20:16,005 - Loss: 0.2064, step: 158100
+INFO - dpsgd_diffusion.py - 2024-10-25 01:21:11,798 - Loss: 0.1999, step: 158200
+INFO - dpsgd_diffusion.py - 2024-10-25 01:22:07,134 - Loss: 0.1962, step: 158300
+INFO - dpsgd_diffusion.py - 2024-10-25 01:23:02,265 - Loss: 0.2156, step: 158400
+INFO - dpsgd_diffusion.py - 2024-10-25 01:23:57,496 - Loss: 0.2119, step: 158500
+INFO - dpsgd_diffusion.py - 2024-10-25 01:24:52,141 - Loss: 0.2073, step: 158600
+INFO - dpsgd_diffusion.py - 2024-10-25 01:25:47,764 - Loss: 0.1953, step: 158700
+INFO - dpsgd_diffusion.py - 2024-10-25 01:25:58,723 - Eps-value after 62 epochs: 7.6953
+INFO - dpsgd_diffusion.py - 2024-10-25 01:26:42,301 - Loss: 0.1865, step: 158800
+INFO - dpsgd_diffusion.py - 2024-10-25 01:27:38,199 - Loss: 0.1903, step: 158900
+INFO - dpsgd_diffusion.py - 2024-10-25 01:28:34,388 - Loss: 0.1848, step: 159000
+INFO - dpsgd_diffusion.py - 2024-10-25 01:29:29,777 - Loss: 0.1927, step: 159100
+INFO - dpsgd_diffusion.py - 2024-10-25 01:30:26,731 - Loss: 0.1923, step: 159200
+INFO - dpsgd_diffusion.py - 2024-10-25 01:31:22,569 - Loss: 0.1885, step: 159300
+INFO - dpsgd_diffusion.py - 2024-10-25 01:32:18,349 - Loss: 0.2035, step: 159400
+INFO - dpsgd_diffusion.py - 2024-10-25 01:33:12,582 - Loss: 0.1841, step: 159500
+INFO - dpsgd_diffusion.py - 2024-10-25 01:34:06,907 - Loss: 0.1896, step: 159600
+INFO - dpsgd_diffusion.py - 2024-10-25 01:35:03,064 - Loss: 0.2061, step: 159700
+INFO - dpsgd_diffusion.py - 2024-10-25 01:35:57,355 - Loss: 0.2168, step: 159800
+INFO - dpsgd_diffusion.py - 2024-10-25 01:36:52,128 - Loss: 0.1860, step: 159900
+INFO - dpsgd_diffusion.py - 2024-10-25 01:37:47,228 - Loss: 0.2004, step: 160000
+INFO - dpsgd_diffusion.py - 2024-10-25 01:37:47,246 - Saving snapshot checkpoint and sampling single batch at iteration 160000.
+WARNING - image.py - 2024-10-25 01:37:47,834 - 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 - 2024-10-25 01:38:05,702 - FID at iteration 160000: 34.736929
+INFO - dpsgd_diffusion.py - 2024-10-25 01:39:01,983 - Loss: 0.1882, step: 160100
+INFO - dpsgd_diffusion.py - 2024-10-25 01:39:57,865 - Loss: 0.1950, step: 160200
+INFO - dpsgd_diffusion.py - 2024-10-25 01:40:53,215 - Loss: 0.2067, step: 160300
+INFO - dpsgd_diffusion.py - 2024-10-25 01:41:48,337 - Loss: 0.1726, step: 160400
+INFO - dpsgd_diffusion.py - 2024-10-25 01:42:44,663 - Loss: 0.1943, step: 160500
+INFO - dpsgd_diffusion.py - 2024-10-25 01:43:39,816 - Loss: 0.1833, step: 160600
+INFO - dpsgd_diffusion.py - 2024-10-25 01:44:33,774 - Loss: 0.2109, step: 160700
+INFO - dpsgd_diffusion.py - 2024-10-25 01:45:28,268 - Loss: 0.1943, step: 160800
+INFO - dpsgd_diffusion.py - 2024-10-25 01:46:25,235 - Loss: 0.1975, step: 160900
+INFO - dpsgd_diffusion.py - 2024-10-25 01:47:20,586 - Loss: 0.1831, step: 161000
+INFO - dpsgd_diffusion.py - 2024-10-25 01:48:15,380 - Loss: 0.2046, step: 161100
+INFO - dpsgd_diffusion.py - 2024-10-25 01:49:10,814 - Loss: 0.1942, step: 161200
+INFO - dpsgd_diffusion.py - 2024-10-25 01:49:55,231 - Eps-value after 63 epochs: 7.7619
+INFO - dpsgd_diffusion.py - 2024-10-25 01:50:06,768 - Loss: 0.1979, step: 161300
+INFO - dpsgd_diffusion.py - 2024-10-25 01:51:02,022 - Loss: 0.1907, step: 161400
+INFO - dpsgd_diffusion.py - 2024-10-25 01:51:57,223 - Loss: 0.1922, step: 161500
+INFO - dpsgd_diffusion.py - 2024-10-25 01:52:53,771 - Loss: 0.1934, step: 161600
+INFO - dpsgd_diffusion.py - 2024-10-25 01:53:49,669 - Loss: 0.1920, step: 161700
+INFO - dpsgd_diffusion.py - 2024-10-25 01:54:44,262 - Loss: 0.2050, step: 161800
+INFO - dpsgd_diffusion.py - 2024-10-25 01:55:40,577 - Loss: 0.1961, step: 161900
+INFO - dpsgd_diffusion.py - 2024-10-25 01:56:36,474 - Loss: 0.1884, step: 162000
+INFO - dpsgd_diffusion.py - 2024-10-25 01:56:36,522 - Saving snapshot checkpoint and sampling single batch at iteration 162000.
+WARNING - image.py - 2024-10-25 01:56:37,110 - 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 - 2024-10-25 01:56:55,054 - FID at iteration 162000: 34.819965
+INFO - dpsgd_diffusion.py - 2024-10-25 01:57:51,246 - Loss: 0.1944, step: 162100
+INFO - dpsgd_diffusion.py - 2024-10-25 01:58:46,739 - Loss: 0.1981, step: 162200
+INFO - dpsgd_diffusion.py - 2024-10-25 01:59:41,407 - Loss: 0.2028, step: 162300
+INFO - dpsgd_diffusion.py - 2024-10-25 02:00:38,081 - Loss: 0.1911, step: 162400
+INFO - dpsgd_diffusion.py - 2024-10-25 02:01:34,073 - Loss: 0.1948, step: 162500
+INFO - dpsgd_diffusion.py - 2024-10-25 02:02:31,217 - Loss: 0.2105, step: 162600
+INFO - dpsgd_diffusion.py - 2024-10-25 02:03:27,604 - Loss: 0.2022, step: 162700
+INFO - dpsgd_diffusion.py - 2024-10-25 02:04:23,062 - Loss: 0.2064, step: 162800
+INFO - dpsgd_diffusion.py - 2024-10-25 02:05:19,072 - Loss: 0.2079, step: 162900
+INFO - dpsgd_diffusion.py - 2024-10-25 02:06:15,025 - Loss: 0.1853, step: 163000
+INFO - dpsgd_diffusion.py - 2024-10-25 02:07:09,863 - Loss: 0.1831, step: 163100
+INFO - dpsgd_diffusion.py - 2024-10-25 02:08:04,842 - Loss: 0.2007, step: 163200
+INFO - dpsgd_diffusion.py - 2024-10-25 02:08:58,234 - Loss: 0.1925, step: 163300
+INFO - dpsgd_diffusion.py - 2024-10-25 02:09:52,508 - Loss: 0.2056, step: 163400
+INFO - dpsgd_diffusion.py - 2024-10-25 02:10:48,553 - Loss: 0.1840, step: 163500
+INFO - dpsgd_diffusion.py - 2024-10-25 02:11:46,583 - Loss: 0.1977, step: 163600
+INFO - dpsgd_diffusion.py - 2024-10-25 02:12:42,195 - Loss: 0.2030, step: 163700
+INFO - dpsgd_diffusion.py - 2024-10-25 02:13:36,772 - Loss: 0.1933, step: 163800
+INFO - dpsgd_diffusion.py - 2024-10-25 02:13:57,567 - Eps-value after 64 epochs: 7.8285
+INFO - dpsgd_diffusion.py - 2024-10-25 02:14:31,612 - Loss: 0.1745, step: 163900
+INFO - dpsgd_diffusion.py - 2024-10-25 02:15:27,222 - Loss: 0.1843, step: 164000
+INFO - dpsgd_diffusion.py - 2024-10-25 02:15:27,234 - Saving snapshot checkpoint and sampling single batch at iteration 164000.
+WARNING - image.py - 2024-10-25 02:15:27,824 - 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 - 2024-10-25 02:15:46,694 - FID at iteration 164000: 34.461797
+INFO - dpsgd_diffusion.py - 2024-10-25 02:16:41,955 - Loss: 0.2001, step: 164100
+INFO - dpsgd_diffusion.py - 2024-10-25 02:17:36,661 - Loss: 0.2036, step: 164200
+INFO - dpsgd_diffusion.py - 2024-10-25 02:18:31,109 - Loss: 0.1947, step: 164300
+INFO - dpsgd_diffusion.py - 2024-10-25 02:19:25,168 - Loss: 0.1951, step: 164400
+INFO - dpsgd_diffusion.py - 2024-10-25 02:20:19,887 - Loss: 0.1998, step: 164500
+INFO - dpsgd_diffusion.py - 2024-10-25 02:21:14,288 - Loss: 0.2084, step: 164600
+INFO - dpsgd_diffusion.py - 2024-10-25 02:22:10,952 - Loss: 0.1940, step: 164700
+INFO - dpsgd_diffusion.py - 2024-10-25 02:23:06,893 - Loss: 0.1806, step: 164800
+INFO - dpsgd_diffusion.py - 2024-10-25 02:24:02,162 - Loss: 0.1891, step: 164900
+INFO - dpsgd_diffusion.py - 2024-10-25 02:24:58,341 - Loss: 0.1973, step: 165000
+INFO - dpsgd_diffusion.py - 2024-10-25 02:25:54,032 - Loss: 0.1963, step: 165100
+INFO - dpsgd_diffusion.py - 2024-10-25 02:26:48,081 - Loss: 0.1786, step: 165200
+INFO - dpsgd_diffusion.py - 2024-10-25 02:27:42,903 - Loss: 0.2196, step: 165300
+INFO - dpsgd_diffusion.py - 2024-10-25 02:28:39,228 - Loss: 0.2025, step: 165400
+INFO - dpsgd_diffusion.py - 2024-10-25 02:29:35,350 - Loss: 0.2030, step: 165500
+INFO - dpsgd_diffusion.py - 2024-10-25 02:30:30,518 - Loss: 0.1786, step: 165600
+INFO - dpsgd_diffusion.py - 2024-10-25 02:31:26,814 - Loss: 0.2130, step: 165700
+INFO - dpsgd_diffusion.py - 2024-10-25 02:32:21,256 - Loss: 0.1885, step: 165800
+INFO - dpsgd_diffusion.py - 2024-10-25 02:33:16,343 - Loss: 0.2150, step: 165900
+INFO - dpsgd_diffusion.py - 2024-10-25 02:34:11,792 - Loss: 0.1932, step: 166000
+INFO - dpsgd_diffusion.py - 2024-10-25 02:34:11,806 - Saving snapshot checkpoint and sampling single batch at iteration 166000.
+WARNING - image.py - 2024-10-25 02:34:12,394 - 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 - 2024-10-25 02:34:30,862 - FID at iteration 166000: 35.183153
+INFO - dpsgd_diffusion.py - 2024-10-25 02:35:27,256 - Loss: 0.1870, step: 166100
+INFO - dpsgd_diffusion.py - 2024-10-25 02:36:22,453 - Loss: 0.2013, step: 166200
+INFO - dpsgd_diffusion.py - 2024-10-25 02:37:17,072 - Loss: 0.2081, step: 166300
+INFO - dpsgd_diffusion.py - 2024-10-25 02:38:11,695 - Loss: 0.2177, step: 166400
+INFO - dpsgd_diffusion.py - 2024-10-25 02:38:11,739 - Eps-value after 65 epochs: 7.8944
+INFO - dpsgd_diffusion.py - 2024-10-25 02:39:07,136 - Loss: 0.1840, step: 166500
+INFO - dpsgd_diffusion.py - 2024-10-25 02:40:01,474 - Loss: 0.1915, step: 166600
+INFO - dpsgd_diffusion.py - 2024-10-25 02:40:57,554 - Loss: 0.1993, step: 166700
+INFO - dpsgd_diffusion.py - 2024-10-25 02:41:53,131 - Loss: 0.1996, step: 166800
+INFO - dpsgd_diffusion.py - 2024-10-25 02:42:47,481 - Loss: 0.2070, step: 166900
+INFO - dpsgd_diffusion.py - 2024-10-25 02:43:43,083 - Loss: 0.1779, step: 167000
+INFO - dpsgd_diffusion.py - 2024-10-25 02:44:38,450 - Loss: 0.1927, step: 167100
+INFO - dpsgd_diffusion.py - 2024-10-25 02:45:34,602 - Loss: 0.1912, step: 167200
+INFO - dpsgd_diffusion.py - 2024-10-25 02:46:30,147 - Loss: 0.2052, step: 167300
+INFO - dpsgd_diffusion.py - 2024-10-25 02:47:27,463 - Loss: 0.1872, step: 167400
+INFO - dpsgd_diffusion.py - 2024-10-25 02:48:24,155 - Loss: 0.2016, step: 167500
+INFO - dpsgd_diffusion.py - 2024-10-25 02:49:21,477 - Loss: 0.2010, step: 167600
+INFO - dpsgd_diffusion.py - 2024-10-25 02:50:17,039 - Loss: 0.2150, step: 167700
+INFO - dpsgd_diffusion.py - 2024-10-25 02:51:11,301 - Loss: 0.2255, step: 167800
+INFO - dpsgd_diffusion.py - 2024-10-25 02:52:07,252 - Loss: 0.1873, step: 167900
+INFO - dpsgd_diffusion.py - 2024-10-25 02:53:01,903 - Loss: 0.2110, step: 168000
+INFO - dpsgd_diffusion.py - 2024-10-25 02:53:01,925 - Saving snapshot checkpoint and sampling single batch at iteration 168000.
+WARNING - image.py - 2024-10-25 02:53:02,514 - 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 - 2024-10-25 02:53:21,486 - FID at iteration 168000: 33.838132
+INFO - dpsgd_diffusion.py - 2024-10-25 02:54:17,119 - Loss: 0.2014, step: 168100
+INFO - dpsgd_diffusion.py - 2024-10-25 02:55:13,408 - Loss: 0.1924, step: 168200
+INFO - dpsgd_diffusion.py - 2024-10-25 02:56:08,582 - Loss: 0.1968, step: 168300
+INFO - dpsgd_diffusion.py - 2024-10-25 02:57:04,332 - Loss: 0.2041, step: 168400
+INFO - dpsgd_diffusion.py - 2024-10-25 02:57:58,531 - Loss: 0.1962, step: 168500
+INFO - dpsgd_diffusion.py - 2024-10-25 02:58:54,385 - Loss: 0.1931, step: 168600
+INFO - dpsgd_diffusion.py - 2024-10-25 02:59:49,969 - Loss: 0.2084, step: 168700
+INFO - dpsgd_diffusion.py - 2024-10-25 03:00:43,871 - Loss: 0.2045, step: 168800
+INFO - dpsgd_diffusion.py - 2024-10-25 03:01:39,184 - Loss: 0.1971, step: 168900
+INFO - dpsgd_diffusion.py - 2024-10-25 03:02:11,792 - Eps-value after 66 epochs: 7.9592
+INFO - dpsgd_diffusion.py - 2024-10-25 03:02:33,424 - Loss: 0.1888, step: 169000
+INFO - dpsgd_diffusion.py - 2024-10-25 03:03:28,999 - Loss: 0.1936, step: 169100
+INFO - dpsgd_diffusion.py - 2024-10-25 03:04:23,921 - Loss: 0.1946, step: 169200
+INFO - dpsgd_diffusion.py - 2024-10-25 03:05:19,300 - Loss: 0.1924, step: 169300
+INFO - dpsgd_diffusion.py - 2024-10-25 03:06:13,538 - Loss: 0.1850, step: 169400
+INFO - dpsgd_diffusion.py - 2024-10-25 03:07:09,002 - Loss: 0.2231, step: 169500
+INFO - dpsgd_diffusion.py - 2024-10-25 03:08:04,678 - Loss: 0.1820, step: 169600
+INFO - dpsgd_diffusion.py - 2024-10-25 03:09:00,564 - Loss: 0.1808, step: 169700
+INFO - dpsgd_diffusion.py - 2024-10-25 03:09:55,551 - Loss: 0.2034, step: 169800
+INFO - dpsgd_diffusion.py - 2024-10-25 03:10:49,574 - Loss: 0.2164, step: 169900
+INFO - dpsgd_diffusion.py - 2024-10-25 03:11:45,366 - Loss: 0.1811, step: 170000
+INFO - dpsgd_diffusion.py - 2024-10-25 03:11:45,379 - Saving snapshot checkpoint and sampling single batch at iteration 170000.
+WARNING - image.py - 2024-10-25 03:11:45,968 - 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 - 2024-10-25 03:12:04,179 - FID at iteration 170000: 33.937373
+INFO - dpsgd_diffusion.py - 2024-10-25 03:13:01,610 - Loss: 0.1817, step: 170100
+INFO - dpsgd_diffusion.py - 2024-10-25 03:13:57,670 - Loss: 0.2007, step: 170200
+INFO - dpsgd_diffusion.py - 2024-10-25 03:14:51,334 - Loss: 0.1916, step: 170300
+INFO - dpsgd_diffusion.py - 2024-10-25 03:15:45,502 - Loss: 0.1955, step: 170400
+INFO - dpsgd_diffusion.py - 2024-10-25 03:16:40,218 - Loss: 0.2027, step: 170500
+INFO - dpsgd_diffusion.py - 2024-10-25 03:17:35,376 - Loss: 0.1891, step: 170600
+INFO - dpsgd_diffusion.py - 2024-10-25 03:18:31,869 - Loss: 0.2149, step: 170700
+INFO - dpsgd_diffusion.py - 2024-10-25 03:19:26,880 - Loss: 0.2029, step: 170800
+INFO - dpsgd_diffusion.py - 2024-10-25 03:20:22,046 - Loss: 0.2170, step: 170900
+INFO - dpsgd_diffusion.py - 2024-10-25 03:21:17,879 - Loss: 0.1945, step: 171000
+INFO - dpsgd_diffusion.py - 2024-10-25 03:22:13,473 - Loss: 0.1966, step: 171100
+INFO - dpsgd_diffusion.py - 2024-10-25 03:23:08,505 - Loss: 0.2001, step: 171200
+INFO - dpsgd_diffusion.py - 2024-10-25 03:24:04,487 - Loss: 0.1937, step: 171300
+INFO - dpsgd_diffusion.py - 2024-10-25 03:25:00,109 - Loss: 0.1813, step: 171400
+INFO - dpsgd_diffusion.py - 2024-10-25 03:25:55,188 - Loss: 0.2060, step: 171500
+INFO - dpsgd_diffusion.py - 2024-10-25 03:26:05,740 - Eps-value after 67 epochs: 8.0239
+INFO - dpsgd_diffusion.py - 2024-10-25 03:26:49,805 - Loss: 0.1945, step: 171600
+INFO - dpsgd_diffusion.py - 2024-10-25 03:27:46,114 - Loss: 0.2068, step: 171700
+INFO - dpsgd_diffusion.py - 2024-10-25 03:28:41,993 - Loss: 0.2031, step: 171800
+INFO - dpsgd_diffusion.py - 2024-10-25 03:29:36,834 - Loss: 0.2104, step: 171900
+INFO - dpsgd_diffusion.py - 2024-10-25 03:30:32,935 - Loss: 0.2125, step: 172000
+INFO - dpsgd_diffusion.py - 2024-10-25 03:30:32,947 - Saving snapshot checkpoint and sampling single batch at iteration 172000.
+WARNING - image.py - 2024-10-25 03:30:33,537 - 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 - 2024-10-25 03:30:51,782 - FID at iteration 172000: 34.196715
+INFO - dpsgd_diffusion.py - 2024-10-25 03:31:48,152 - Loss: 0.1948, step: 172100
+INFO - dpsgd_diffusion.py - 2024-10-25 03:32:45,387 - Loss: 0.1958, step: 172200
+INFO - dpsgd_diffusion.py - 2024-10-25 03:33:42,138 - Loss: 0.2040, step: 172300
+INFO - dpsgd_diffusion.py - 2024-10-25 03:34:38,680 - Loss: 0.1894, step: 172400
+INFO - dpsgd_diffusion.py - 2024-10-25 03:35:34,993 - Loss: 0.2021, step: 172500
+INFO - dpsgd_diffusion.py - 2024-10-25 03:36:30,067 - Loss: 0.1975, step: 172600
+INFO - dpsgd_diffusion.py - 2024-10-25 03:37:25,581 - Loss: 0.2073, step: 172700
+INFO - dpsgd_diffusion.py - 2024-10-25 03:38:20,305 - Loss: 0.2131, step: 172800
+INFO - dpsgd_diffusion.py - 2024-10-25 03:39:16,258 - Loss: 0.2176, step: 172900
+INFO - dpsgd_diffusion.py - 2024-10-25 03:40:10,409 - Loss: 0.2169, step: 173000
+INFO - dpsgd_diffusion.py - 2024-10-25 03:41:06,744 - Loss: 0.1867, step: 173100
+INFO - dpsgd_diffusion.py - 2024-10-25 03:42:03,248 - Loss: 0.1804, step: 173200
+INFO - dpsgd_diffusion.py - 2024-10-25 03:42:59,460 - Loss: 0.1846, step: 173300
+INFO - dpsgd_diffusion.py - 2024-10-25 03:43:53,790 - Loss: 0.1973, step: 173400
+INFO - dpsgd_diffusion.py - 2024-10-25 03:44:48,224 - Loss: 0.1885, step: 173500
+INFO - dpsgd_diffusion.py - 2024-10-25 03:45:43,307 - Loss: 0.1898, step: 173600
+INFO - dpsgd_diffusion.py - 2024-10-25 03:46:39,515 - Loss: 0.1815, step: 173700
+INFO - dpsgd_diffusion.py - 2024-10-25 03:47:35,830 - Loss: 0.1879, step: 173800
+INFO - dpsgd_diffusion.py - 2024-10-25 03:48:30,786 - Loss: 0.2013, step: 173900
+INFO - dpsgd_diffusion.py - 2024-10-25 03:49:26,468 - Loss: 0.1863, step: 174000
+INFO - dpsgd_diffusion.py - 2024-10-25 03:49:26,545 - Saving snapshot checkpoint and sampling single batch at iteration 174000.
+WARNING - image.py - 2024-10-25 03:49:27,134 - 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 - 2024-10-25 03:49:46,492 - FID at iteration 174000: 33.980441
+INFO - dpsgd_diffusion.py - 2024-10-25 03:50:29,951 - Eps-value after 68 epochs: 8.0887
+INFO - dpsgd_diffusion.py - 2024-10-25 03:50:41,468 - Loss: 0.2073, step: 174100
+INFO - dpsgd_diffusion.py - 2024-10-25 03:51:36,677 - Loss: 0.1969, step: 174200
+INFO - dpsgd_diffusion.py - 2024-10-25 03:52:31,857 - Loss: 0.1982, step: 174300
+INFO - dpsgd_diffusion.py - 2024-10-25 03:53:27,458 - Loss: 0.1927, step: 174400
+INFO - dpsgd_diffusion.py - 2024-10-25 03:54:22,286 - Loss: 0.1968, step: 174500
+INFO - dpsgd_diffusion.py - 2024-10-25 03:55:19,268 - Loss: 0.2019, step: 174600
+INFO - dpsgd_diffusion.py - 2024-10-25 03:56:14,719 - Loss: 0.1881, step: 174700
+INFO - dpsgd_diffusion.py - 2024-10-25 03:57:12,115 - Loss: 0.2059, step: 174800
+INFO - dpsgd_diffusion.py - 2024-10-25 03:58:07,630 - Loss: 0.1951, step: 174900
+INFO - dpsgd_diffusion.py - 2024-10-25 03:59:02,968 - Loss: 0.2021, step: 175000
+INFO - dpsgd_diffusion.py - 2024-10-25 03:59:57,151 - Loss: 0.1875, step: 175100
+INFO - dpsgd_diffusion.py - 2024-10-25 04:00:52,913 - Loss: 0.2050, step: 175200
+INFO - dpsgd_diffusion.py - 2024-10-25 04:01:47,522 - Loss: 0.1853, step: 175300
+INFO - dpsgd_diffusion.py - 2024-10-25 04:02:44,270 - Loss: 0.1812, step: 175400
+INFO - dpsgd_diffusion.py - 2024-10-25 04:03:38,721 - Loss: 0.1904, step: 175500
+INFO - dpsgd_diffusion.py - 2024-10-25 04:04:34,326 - Loss: 0.1921, step: 175600
+INFO - dpsgd_diffusion.py - 2024-10-25 04:05:29,865 - Loss: 0.1854, step: 175700
+INFO - dpsgd_diffusion.py - 2024-10-25 04:06:26,169 - Loss: 0.1883, step: 175800
+INFO - dpsgd_diffusion.py - 2024-10-25 04:07:22,694 - Loss: 0.2057, step: 175900
+INFO - dpsgd_diffusion.py - 2024-10-25 04:08:18,991 - Loss: 0.2191, step: 176000
+INFO - dpsgd_diffusion.py - 2024-10-25 04:08:19,010 - Saving snapshot checkpoint and sampling single batch at iteration 176000.
+WARNING - image.py - 2024-10-25 04:08:19,739 - 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 - 2024-10-25 04:08:38,151 - FID at iteration 176000: 33.701380
+INFO - dpsgd_diffusion.py - 2024-10-25 04:09:34,366 - Loss: 0.1844, step: 176100
+INFO - dpsgd_diffusion.py - 2024-10-25 04:10:30,270 - Loss: 0.2143, step: 176200
+INFO - dpsgd_diffusion.py - 2024-10-25 04:11:24,951 - Loss: 0.2036, step: 176300
+INFO - dpsgd_diffusion.py - 2024-10-25 04:12:21,079 - Loss: 0.1976, step: 176400
+INFO - dpsgd_diffusion.py - 2024-10-25 04:13:15,640 - Loss: 0.1937, step: 176500
+INFO - dpsgd_diffusion.py - 2024-10-25 04:14:10,531 - Loss: 0.1983, step: 176600
+INFO - dpsgd_diffusion.py - 2024-10-25 04:14:31,795 - Eps-value after 69 epochs: 8.1535
+INFO - dpsgd_diffusion.py - 2024-10-25 04:15:03,661 - Loss: 0.1893, step: 176700
+INFO - dpsgd_diffusion.py - 2024-10-25 04:15:59,033 - Loss: 0.1800, step: 176800
+INFO - dpsgd_diffusion.py - 2024-10-25 04:16:54,070 - Loss: 0.1967, step: 176900
+INFO - dpsgd_diffusion.py - 2024-10-25 04:17:50,606 - Loss: 0.1944, step: 177000
+INFO - dpsgd_diffusion.py - 2024-10-25 04:18:45,431 - Loss: 0.2038, step: 177100
+INFO - dpsgd_diffusion.py - 2024-10-25 04:19:40,399 - Loss: 0.2049, step: 177200
+INFO - dpsgd_diffusion.py - 2024-10-25 04:20:36,364 - Loss: 0.2173, step: 177300
+INFO - dpsgd_diffusion.py - 2024-10-25 04:21:32,053 - Loss: 0.1891, step: 177400
+INFO - dpsgd_diffusion.py - 2024-10-25 04:22:27,186 - Loss: 0.1975, step: 177500
+INFO - dpsgd_diffusion.py - 2024-10-25 04:23:22,374 - Loss: 0.1957, step: 177600
+INFO - dpsgd_diffusion.py - 2024-10-25 04:24:18,721 - Loss: 0.2040, step: 177700
+INFO - dpsgd_diffusion.py - 2024-10-25 04:25:13,664 - Loss: 0.1976, step: 177800
+INFO - dpsgd_diffusion.py - 2024-10-25 04:26:07,527 - Loss: 0.1994, step: 177900
+INFO - dpsgd_diffusion.py - 2024-10-25 04:27:01,553 - Loss: 0.2087, step: 178000
+INFO - dpsgd_diffusion.py - 2024-10-25 04:27:01,567 - Saving snapshot checkpoint and sampling single batch at iteration 178000.
+WARNING - image.py - 2024-10-25 04:27:02,155 - 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 - 2024-10-25 04:27:20,752 - FID at iteration 178000: 33.879008
+INFO - dpsgd_diffusion.py - 2024-10-25 04:28:15,343 - Loss: 0.1809, step: 178100
+INFO - dpsgd_diffusion.py - 2024-10-25 04:29:10,218 - Loss: 0.2114, step: 178200
+INFO - dpsgd_diffusion.py - 2024-10-25 04:30:04,864 - Loss: 0.2096, step: 178300
+INFO - dpsgd_diffusion.py - 2024-10-25 04:30:59,203 - Loss: 0.1939, step: 178400
+INFO - dpsgd_diffusion.py - 2024-10-25 04:31:53,871 - Loss: 0.2051, step: 178500
+INFO - dpsgd_diffusion.py - 2024-10-25 04:32:48,911 - Loss: 0.1629, step: 178600
+INFO - dpsgd_diffusion.py - 2024-10-25 04:33:44,360 - Loss: 0.2161, step: 178700
+INFO - dpsgd_diffusion.py - 2024-10-25 04:34:40,527 - Loss: 0.1789, step: 178800
+INFO - dpsgd_diffusion.py - 2024-10-25 04:35:36,462 - Loss: 0.1859, step: 178900
+INFO - dpsgd_diffusion.py - 2024-10-25 04:36:31,896 - Loss: 0.1970, step: 179000
+INFO - dpsgd_diffusion.py - 2024-10-25 04:37:29,207 - Loss: 0.1958, step: 179100
+INFO - dpsgd_diffusion.py - 2024-10-25 04:38:24,936 - Loss: 0.1976, step: 179200
+INFO - dpsgd_diffusion.py - 2024-10-25 04:38:24,977 - Eps-value after 70 epochs: 8.2174
+INFO - dpsgd_diffusion.py - 2024-10-25 04:39:21,271 - Loss: 0.1993, step: 179300
+INFO - dpsgd_diffusion.py - 2024-10-25 04:40:18,537 - Loss: 0.2002, step: 179400
+INFO - dpsgd_diffusion.py - 2024-10-25 04:41:15,450 - Loss: 0.1893, step: 179500
+INFO - dpsgd_diffusion.py - 2024-10-25 04:42:10,740 - Loss: 0.1803, step: 179600
+INFO - dpsgd_diffusion.py - 2024-10-25 04:43:06,363 - Loss: 0.1899, step: 179700
+INFO - dpsgd_diffusion.py - 2024-10-25 04:44:01,930 - Loss: 0.1997, step: 179800
+INFO - dpsgd_diffusion.py - 2024-10-25 04:44:56,989 - Loss: 0.1873, step: 179900
+INFO - dpsgd_diffusion.py - 2024-10-25 04:45:54,171 - Loss: 0.1904, step: 180000
+INFO - dpsgd_diffusion.py - 2024-10-25 04:45:54,185 - Saving snapshot checkpoint and sampling single batch at iteration 180000.
+WARNING - image.py - 2024-10-25 04:45:54,776 - 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 - 2024-10-25 04:46:13,107 - FID at iteration 180000: 33.596968
+INFO - dpsgd_diffusion.py - 2024-10-25 04:47:09,852 - Loss: 0.2020, step: 180100
+INFO - dpsgd_diffusion.py - 2024-10-25 04:48:05,893 - Loss: 0.1956, step: 180200
+INFO - dpsgd_diffusion.py - 2024-10-25 04:49:01,419 - Loss: 0.2135, step: 180300
+INFO - dpsgd_diffusion.py - 2024-10-25 04:49:56,234 - Loss: 0.1973, step: 180400
+INFO - dpsgd_diffusion.py - 2024-10-25 04:50:52,309 - Loss: 0.1865, step: 180500
+INFO - dpsgd_diffusion.py - 2024-10-25 04:51:49,073 - Loss: 0.1794, step: 180600
+INFO - dpsgd_diffusion.py - 2024-10-25 04:52:45,683 - Loss: 0.2146, step: 180700
+INFO - dpsgd_diffusion.py - 2024-10-25 04:53:41,313 - Loss: 0.1914, step: 180800
+INFO - dpsgd_diffusion.py - 2024-10-25 04:54:36,780 - Loss: 0.1969, step: 180900
+INFO - dpsgd_diffusion.py - 2024-10-25 04:55:31,568 - Loss: 0.1955, step: 181000
+INFO - dpsgd_diffusion.py - 2024-10-25 04:56:28,575 - Loss: 0.1877, step: 181100
+INFO - dpsgd_diffusion.py - 2024-10-25 04:57:23,755 - Loss: 0.1769, step: 181200
+INFO - dpsgd_diffusion.py - 2024-10-25 04:58:20,245 - Loss: 0.2100, step: 181300
+INFO - dpsgd_diffusion.py - 2024-10-25 04:59:15,147 - Loss: 0.1874, step: 181400
+INFO - dpsgd_diffusion.py - 2024-10-25 05:00:09,196 - Loss: 0.1915, step: 181500
+INFO - dpsgd_diffusion.py - 2024-10-25 05:01:04,710 - Loss: 0.1985, step: 181600
+INFO - dpsgd_diffusion.py - 2024-10-25 05:01:59,514 - Loss: 0.1992, step: 181700
+INFO - dpsgd_diffusion.py - 2024-10-25 05:02:33,298 - Eps-value after 71 epochs: 8.2804
+INFO - dpsgd_diffusion.py - 2024-10-25 05:02:54,943 - Loss: 0.1637, step: 181800
+INFO - dpsgd_diffusion.py - 2024-10-25 05:03:49,521 - Loss: 0.2012, step: 181900
+INFO - dpsgd_diffusion.py - 2024-10-25 05:04:43,101 - Loss: 0.2022, step: 182000
+INFO - dpsgd_diffusion.py - 2024-10-25 05:04:43,143 - Saving snapshot checkpoint and sampling single batch at iteration 182000.
+WARNING - image.py - 2024-10-25 05:04:43,733 - 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 - 2024-10-25 05:05:02,481 - FID at iteration 182000: 33.015619
+INFO - dpsgd_diffusion.py - 2024-10-25 05:05:57,856 - Loss: 0.1991, step: 182100
+INFO - dpsgd_diffusion.py - 2024-10-25 05:06:53,448 - Loss: 0.1913, step: 182200
+INFO - dpsgd_diffusion.py - 2024-10-25 05:07:49,517 - Loss: 0.1920, step: 182300
+INFO - dpsgd_diffusion.py - 2024-10-25 05:08:44,609 - Loss: 0.1813, step: 182400
+INFO - dpsgd_diffusion.py - 2024-10-25 05:09:40,307 - Loss: 0.1826, step: 182500
+INFO - dpsgd_diffusion.py - 2024-10-25 05:10:34,860 - Loss: 0.2246, step: 182600
+INFO - dpsgd_diffusion.py - 2024-10-25 05:11:30,355 - Loss: 0.1972, step: 182700
+INFO - dpsgd_diffusion.py - 2024-10-25 05:12:26,555 - Loss: 0.1926, step: 182800
+INFO - dpsgd_diffusion.py - 2024-10-25 05:13:22,202 - Loss: 0.2060, step: 182900
+INFO - dpsgd_diffusion.py - 2024-10-25 05:14:17,824 - Loss: 0.1765, step: 183000
+INFO - dpsgd_diffusion.py - 2024-10-25 05:15:13,766 - Loss: 0.1925, step: 183100
+INFO - dpsgd_diffusion.py - 2024-10-25 05:16:09,015 - Loss: 0.2063, step: 183200
+INFO - dpsgd_diffusion.py - 2024-10-25 05:17:04,201 - Loss: 0.1975, step: 183300
+INFO - dpsgd_diffusion.py - 2024-10-25 05:17:59,035 - Loss: 0.2012, step: 183400
+INFO - dpsgd_diffusion.py - 2024-10-25 05:18:55,491 - Loss: 0.2161, step: 183500
+INFO - dpsgd_diffusion.py - 2024-10-25 05:19:50,862 - Loss: 0.1889, step: 183600
+INFO - dpsgd_diffusion.py - 2024-10-25 05:20:45,976 - Loss: 0.1995, step: 183700
+INFO - dpsgd_diffusion.py - 2024-10-25 05:21:42,579 - Loss: 0.2003, step: 183800
+INFO - dpsgd_diffusion.py - 2024-10-25 05:22:37,368 - Loss: 0.2144, step: 183900
+INFO - dpsgd_diffusion.py - 2024-10-25 05:23:32,893 - Loss: 0.2103, step: 184000
+INFO - dpsgd_diffusion.py - 2024-10-25 05:23:32,912 - Saving snapshot checkpoint and sampling single batch at iteration 184000.
+WARNING - image.py - 2024-10-25 05:23:33,502 - 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 - 2024-10-25 05:23:52,857 - FID at iteration 184000: 33.034726
+INFO - dpsgd_diffusion.py - 2024-10-25 05:24:48,794 - Loss: 0.1891, step: 184100
+INFO - dpsgd_diffusion.py - 2024-10-25 05:25:44,772 - Loss: 0.1886, step: 184200
+INFO - dpsgd_diffusion.py - 2024-10-25 05:26:40,304 - Loss: 0.2027, step: 184300
+INFO - dpsgd_diffusion.py - 2024-10-25 05:26:51,199 - Eps-value after 72 epochs: 8.3434
+INFO - dpsgd_diffusion.py - 2024-10-25 05:27:35,619 - Loss: 0.2068, step: 184400
+INFO - dpsgd_diffusion.py - 2024-10-25 05:28:30,580 - Loss: 0.1751, step: 184500
+INFO - dpsgd_diffusion.py - 2024-10-25 05:29:25,834 - Loss: 0.1864, step: 184600
+INFO - dpsgd_diffusion.py - 2024-10-25 05:30:21,566 - Loss: 0.2123, step: 184700
+INFO - dpsgd_diffusion.py - 2024-10-25 05:31:16,443 - Loss: 0.2158, step: 184800
+INFO - dpsgd_diffusion.py - 2024-10-25 05:32:11,706 - Loss: 0.2080, step: 184900
+INFO - dpsgd_diffusion.py - 2024-10-25 05:33:08,410 - Loss: 0.1992, step: 185000
+INFO - dpsgd_diffusion.py - 2024-10-25 05:34:04,593 - Loss: 0.1905, step: 185100
+INFO - dpsgd_diffusion.py - 2024-10-25 05:34:59,759 - Loss: 0.1831, step: 185200
+INFO - dpsgd_diffusion.py - 2024-10-25 05:35:56,107 - Loss: 0.1910, step: 185300
+INFO - dpsgd_diffusion.py - 2024-10-25 05:36:51,630 - Loss: 0.1991, step: 185400
+INFO - dpsgd_diffusion.py - 2024-10-25 05:37:45,856 - Loss: 0.1851, step: 185500
+INFO - dpsgd_diffusion.py - 2024-10-25 05:38:40,484 - Loss: 0.1780, step: 185600
+INFO - dpsgd_diffusion.py - 2024-10-25 05:39:35,537 - Loss: 0.2045, step: 185700
+INFO - dpsgd_diffusion.py - 2024-10-25 05:40:30,213 - Loss: 0.2109, step: 185800
+INFO - dpsgd_diffusion.py - 2024-10-25 05:41:26,512 - Loss: 0.2277, step: 185900
+INFO - dpsgd_diffusion.py - 2024-10-25 05:42:23,756 - Loss: 0.1920, step: 186000
+INFO - dpsgd_diffusion.py - 2024-10-25 05:42:23,770 - Saving snapshot checkpoint and sampling single batch at iteration 186000.
+WARNING - image.py - 2024-10-25 05:42:24,359 - 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 - 2024-10-25 05:42:42,797 - FID at iteration 186000: 32.978028
+INFO - dpsgd_diffusion.py - 2024-10-25 05:43:38,483 - Loss: 0.2020, step: 186100
+INFO - dpsgd_diffusion.py - 2024-10-25 05:44:33,287 - Loss: 0.1851, step: 186200
+INFO - dpsgd_diffusion.py - 2024-10-25 05:45:29,369 - Loss: 0.1955, step: 186300
+INFO - dpsgd_diffusion.py - 2024-10-25 05:46:23,529 - Loss: 0.2040, step: 186400
+INFO - dpsgd_diffusion.py - 2024-10-25 05:47:17,681 - Loss: 0.1937, step: 186500
+INFO - dpsgd_diffusion.py - 2024-10-25 05:48:14,107 - Loss: 0.1811, step: 186600
+INFO - dpsgd_diffusion.py - 2024-10-25 05:49:10,421 - Loss: 0.1793, step: 186700
+INFO - dpsgd_diffusion.py - 2024-10-25 05:50:06,021 - Loss: 0.2085, step: 186800
+INFO - dpsgd_diffusion.py - 2024-10-25 05:50:50,334 - Eps-value after 73 epochs: 8.4064
+INFO - dpsgd_diffusion.py - 2024-10-25 05:51:01,830 - Loss: 0.1862, step: 186900
+INFO - dpsgd_diffusion.py - 2024-10-25 05:51:57,483 - Loss: 0.2127, step: 187000
+INFO - dpsgd_diffusion.py - 2024-10-25 05:52:53,270 - Loss: 0.2043, step: 187100
+INFO - dpsgd_diffusion.py - 2024-10-25 05:53:47,205 - Loss: 0.2129, step: 187200
+INFO - dpsgd_diffusion.py - 2024-10-25 05:54:42,313 - Loss: 0.1934, step: 187300
+INFO - dpsgd_diffusion.py - 2024-10-25 05:55:36,344 - Loss: 0.1994, step: 187400
+INFO - dpsgd_diffusion.py - 2024-10-25 05:56:30,959 - Loss: 0.1877, step: 187500
+INFO - dpsgd_diffusion.py - 2024-10-25 05:57:25,740 - Loss: 0.2035, step: 187600
+INFO - dpsgd_diffusion.py - 2024-10-25 05:58:20,341 - Loss: 0.1845, step: 187700
+INFO - dpsgd_diffusion.py - 2024-10-25 05:59:16,047 - Loss: 0.1837, step: 187800
+INFO - dpsgd_diffusion.py - 2024-10-25 06:00:13,048 - Loss: 0.2009, step: 187900
+INFO - dpsgd_diffusion.py - 2024-10-25 06:01:08,621 - Loss: 0.2016, step: 188000
+INFO - dpsgd_diffusion.py - 2024-10-25 06:01:08,635 - Saving snapshot checkpoint and sampling single batch at iteration 188000.
+WARNING - image.py - 2024-10-25 06:01:09,225 - 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 - 2024-10-25 06:01:27,546 - FID at iteration 188000: 32.435031
+INFO - dpsgd_diffusion.py - 2024-10-25 06:02:21,880 - Loss: 0.1972, step: 188100
+INFO - dpsgd_diffusion.py - 2024-10-25 06:03:17,767 - Loss: 0.1924, step: 188200
+INFO - dpsgd_diffusion.py - 2024-10-25 06:04:14,270 - Loss: 0.1918, step: 188300
+INFO - dpsgd_diffusion.py - 2024-10-25 06:05:10,237 - Loss: 0.1936, step: 188400
+INFO - dpsgd_diffusion.py - 2024-10-25 06:06:05,577 - Loss: 0.1858, step: 188500
+INFO - dpsgd_diffusion.py - 2024-10-25 06:07:01,164 - Loss: 0.2004, step: 188600
+INFO - dpsgd_diffusion.py - 2024-10-25 06:07:58,089 - Loss: 0.1785, step: 188700
+INFO - dpsgd_diffusion.py - 2024-10-25 06:08:53,014 - Loss: 0.1849, step: 188800
+INFO - dpsgd_diffusion.py - 2024-10-25 06:09:49,514 - Loss: 0.1881, step: 188900
+INFO - dpsgd_diffusion.py - 2024-10-25 06:10:44,377 - Loss: 0.2025, step: 189000
+INFO - dpsgd_diffusion.py - 2024-10-25 06:11:39,578 - Loss: 0.2044, step: 189100
+INFO - dpsgd_diffusion.py - 2024-10-25 06:12:34,035 - Loss: 0.1961, step: 189200
+INFO - dpsgd_diffusion.py - 2024-10-25 06:13:28,832 - Loss: 0.1727, step: 189300
+INFO - dpsgd_diffusion.py - 2024-10-25 06:14:24,236 - Loss: 0.2206, step: 189400
+INFO - dpsgd_diffusion.py - 2024-10-25 06:14:47,213 - Eps-value after 74 epochs: 8.4694
+INFO - dpsgd_diffusion.py - 2024-10-25 06:15:20,324 - Loss: 0.1849, step: 189500
+INFO - dpsgd_diffusion.py - 2024-10-25 06:16:14,829 - Loss: 0.2106, step: 189600
+INFO - dpsgd_diffusion.py - 2024-10-25 06:17:10,332 - Loss: 0.1899, step: 189700
+INFO - dpsgd_diffusion.py - 2024-10-25 06:18:05,641 - Loss: 0.1865, step: 189800
+INFO - dpsgd_diffusion.py - 2024-10-25 06:19:01,395 - Loss: 0.1951, step: 189900
+INFO - dpsgd_diffusion.py - 2024-10-25 06:19:57,336 - Loss: 0.1949, step: 190000
+INFO - dpsgd_diffusion.py - 2024-10-25 06:19:57,377 - Saving snapshot checkpoint and sampling single batch at iteration 190000.
+WARNING - image.py - 2024-10-25 06:19:57,966 - 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 - 2024-10-25 06:20:16,431 - FID at iteration 190000: 32.842012
+INFO - dpsgd_diffusion.py - 2024-10-25 06:21:11,968 - Loss: 0.1763, step: 190100
+INFO - dpsgd_diffusion.py - 2024-10-25 06:22:07,648 - Loss: 0.1849, step: 190200
+INFO - dpsgd_diffusion.py - 2024-10-25 06:23:03,571 - Loss: 0.1975, step: 190300
+INFO - dpsgd_diffusion.py - 2024-10-25 06:23:59,286 - Loss: 0.1971, step: 190400
+INFO - dpsgd_diffusion.py - 2024-10-25 06:24:54,788 - Loss: 0.1728, step: 190500
+INFO - dpsgd_diffusion.py - 2024-10-25 06:25:50,282 - Loss: 0.1949, step: 190600
+INFO - dpsgd_diffusion.py - 2024-10-25 06:26:46,195 - Loss: 0.1875, step: 190700
+INFO - dpsgd_diffusion.py - 2024-10-25 06:27:42,688 - Loss: 0.2070, step: 190800
+INFO - dpsgd_diffusion.py - 2024-10-25 06:28:38,727 - Loss: 0.2127, step: 190900
+INFO - dpsgd_diffusion.py - 2024-10-25 06:29:34,370 - Loss: 0.1879, step: 191000
+INFO - dpsgd_diffusion.py - 2024-10-25 06:30:29,398 - Loss: 0.1996, step: 191100
+INFO - dpsgd_diffusion.py - 2024-10-25 06:31:25,719 - Loss: 0.2005, step: 191200
+INFO - dpsgd_diffusion.py - 2024-10-25 06:32:21,881 - Loss: 0.1882, step: 191300
+INFO - dpsgd_diffusion.py - 2024-10-25 06:33:16,304 - Loss: 0.1904, step: 191400
+INFO - dpsgd_diffusion.py - 2024-10-25 06:34:10,534 - Loss: 0.1772, step: 191500
+INFO - dpsgd_diffusion.py - 2024-10-25 06:35:05,306 - Loss: 0.1961, step: 191600
+INFO - dpsgd_diffusion.py - 2024-10-25 06:36:01,813 - Loss: 0.2028, step: 191700
+INFO - dpsgd_diffusion.py - 2024-10-25 06:36:57,661 - Loss: 0.2071, step: 191800
+INFO - dpsgd_diffusion.py - 2024-10-25 06:37:53,817 - Loss: 0.1927, step: 191900
+INFO - dpsgd_diffusion.py - 2024-10-25 06:38:49,782 - Loss: 0.2035, step: 192000
+INFO - dpsgd_diffusion.py - 2024-10-25 06:38:49,823 - Eps-value after 75 epochs: 8.5320
+INFO - dpsgd_diffusion.py - 2024-10-25 06:38:49,838 - Saving snapshot checkpoint and sampling single batch at iteration 192000.
+WARNING - image.py - 2024-10-25 06:38:50,428 - 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 - 2024-10-25 06:39:09,026 - FID at iteration 192000: 32.404499
+INFO - dpsgd_diffusion.py - 2024-10-25 06:40:05,297 - Loss: 0.1888, step: 192100
+INFO - dpsgd_diffusion.py - 2024-10-25 06:40:59,810 - Loss: 0.2014, step: 192200
+INFO - dpsgd_diffusion.py - 2024-10-25 06:41:55,106 - Loss: 0.2052, step: 192300
+INFO - dpsgd_diffusion.py - 2024-10-25 06:42:50,971 - Loss: 0.1765, step: 192400
+INFO - dpsgd_diffusion.py - 2024-10-25 06:43:47,474 - Loss: 0.2034, step: 192500
+INFO - dpsgd_diffusion.py - 2024-10-25 06:44:43,445 - Loss: 0.1882, step: 192600
+INFO - dpsgd_diffusion.py - 2024-10-25 06:45:39,467 - Loss: 0.1998, step: 192700
+INFO - dpsgd_diffusion.py - 2024-10-25 06:46:34,360 - Loss: 0.1871, step: 192800
+INFO - dpsgd_diffusion.py - 2024-10-25 06:47:28,778 - Loss: 0.2072, step: 192900
+INFO - dpsgd_diffusion.py - 2024-10-25 06:48:23,196 - Loss: 0.1967, step: 193000
+INFO - dpsgd_diffusion.py - 2024-10-25 06:49:17,517 - Loss: 0.2160, step: 193100
+INFO - dpsgd_diffusion.py - 2024-10-25 06:50:11,796 - Loss: 0.2170, step: 193200
+INFO - dpsgd_diffusion.py - 2024-10-25 06:51:06,815 - Loss: 0.2055, step: 193300
+INFO - dpsgd_diffusion.py - 2024-10-25 06:52:02,550 - Loss: 0.1851, step: 193400
+INFO - dpsgd_diffusion.py - 2024-10-25 06:52:57,356 - Loss: 0.1933, step: 193500
+INFO - dpsgd_diffusion.py - 2024-10-25 06:53:52,187 - Loss: 0.2013, step: 193600
+INFO - dpsgd_diffusion.py - 2024-10-25 06:54:46,595 - Loss: 0.2093, step: 193700
+INFO - dpsgd_diffusion.py - 2024-10-25 06:55:42,957 - Loss: 0.1960, step: 193800
+INFO - dpsgd_diffusion.py - 2024-10-25 06:56:40,486 - Loss: 0.2099, step: 193900
+INFO - dpsgd_diffusion.py - 2024-10-25 06:57:36,305 - Loss: 0.2038, step: 194000
+INFO - dpsgd_diffusion.py - 2024-10-25 06:57:36,318 - Saving snapshot checkpoint and sampling single batch at iteration 194000.
+WARNING - image.py - 2024-10-25 06:57:36,906 - 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 - 2024-10-25 06:57:55,401 - FID at iteration 194000: 31.993029
+INFO - dpsgd_diffusion.py - 2024-10-25 06:58:49,909 - Loss: 0.2005, step: 194100
+INFO - dpsgd_diffusion.py - 2024-10-25 06:59:44,403 - Loss: 0.1826, step: 194200
+INFO - dpsgd_diffusion.py - 2024-10-25 07:00:40,239 - Loss: 0.1969, step: 194300
+INFO - dpsgd_diffusion.py - 2024-10-25 07:01:35,990 - Loss: 0.2179, step: 194400
+INFO - dpsgd_diffusion.py - 2024-10-25 07:02:31,963 - Loss: 0.1943, step: 194500
+INFO - dpsgd_diffusion.py - 2024-10-25 07:03:06,568 - Eps-value after 76 epochs: 8.5933
+INFO - dpsgd_diffusion.py - 2024-10-25 07:03:29,246 - Loss: 0.2379, step: 194600
+INFO - dpsgd_diffusion.py - 2024-10-25 07:04:23,335 - Loss: 0.1873, step: 194700
+INFO - dpsgd_diffusion.py - 2024-10-25 07:05:18,720 - Loss: 0.1765, step: 194800
+INFO - dpsgd_diffusion.py - 2024-10-25 07:06:13,708 - Loss: 0.1888, step: 194900
+INFO - dpsgd_diffusion.py - 2024-10-25 07:07:08,432 - Loss: 0.1703, step: 195000
+INFO - dpsgd_diffusion.py - 2024-10-25 07:08:03,660 - Loss: 0.1888, step: 195100
+INFO - dpsgd_diffusion.py - 2024-10-25 07:08:58,037 - Loss: 0.2116, step: 195200
+INFO - dpsgd_diffusion.py - 2024-10-25 07:09:52,510 - Loss: 0.1904, step: 195300
+INFO - dpsgd_diffusion.py - 2024-10-25 07:10:46,547 - Loss: 0.1933, step: 195400
+INFO - dpsgd_diffusion.py - 2024-10-25 07:11:40,327 - Loss: 0.1954, step: 195500
+INFO - dpsgd_diffusion.py - 2024-10-25 07:12:37,039 - Loss: 0.2148, step: 195600
+INFO - dpsgd_diffusion.py - 2024-10-25 07:13:32,177 - Loss: 0.1829, step: 195700
+INFO - dpsgd_diffusion.py - 2024-10-25 07:14:28,441 - Loss: 0.1929, step: 195800
+INFO - dpsgd_diffusion.py - 2024-10-25 07:15:25,635 - Loss: 0.1825, step: 195900
+INFO - dpsgd_diffusion.py - 2024-10-25 07:16:21,898 - Loss: 0.2008, step: 196000
+INFO - dpsgd_diffusion.py - 2024-10-25 07:16:21,945 - Saving snapshot checkpoint and sampling single batch at iteration 196000.
+WARNING - image.py - 2024-10-25 07:16:22,534 - 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 - 2024-10-25 07:16:40,978 - FID at iteration 196000: 31.869146
+INFO - dpsgd_diffusion.py - 2024-10-25 07:17:37,399 - Loss: 0.1976, step: 196100
+INFO - dpsgd_diffusion.py - 2024-10-25 07:18:33,127 - Loss: 0.1807, step: 196200
+INFO - dpsgd_diffusion.py - 2024-10-25 07:19:29,204 - Loss: 0.1759, step: 196300
+INFO - dpsgd_diffusion.py - 2024-10-25 07:20:24,423 - Loss: 0.2093, step: 196400
+INFO - dpsgd_diffusion.py - 2024-10-25 07:21:19,468 - Loss: 0.2109, step: 196500
+INFO - dpsgd_diffusion.py - 2024-10-25 07:22:13,714 - Loss: 0.1831, step: 196600
+INFO - dpsgd_diffusion.py - 2024-10-25 07:23:08,640 - Loss: 0.1917, step: 196700
+INFO - dpsgd_diffusion.py - 2024-10-25 07:24:02,772 - Loss: 0.2022, step: 196800
+INFO - dpsgd_diffusion.py - 2024-10-25 07:24:59,498 - Loss: 0.2002, step: 196900
+INFO - dpsgd_diffusion.py - 2024-10-25 07:25:55,433 - Loss: 0.2029, step: 197000
+INFO - dpsgd_diffusion.py - 2024-10-25 07:26:51,732 - Loss: 0.2000, step: 197100
+INFO - dpsgd_diffusion.py - 2024-10-25 07:27:02,636 - Eps-value after 77 epochs: 8.6546
+INFO - dpsgd_diffusion.py - 2024-10-25 07:27:47,306 - Loss: 0.1983, step: 197200
+INFO - dpsgd_diffusion.py - 2024-10-25 07:28:41,770 - Loss: 0.1813, step: 197300
+INFO - dpsgd_diffusion.py - 2024-10-25 07:29:38,288 - Loss: 0.1934, step: 197400
+INFO - dpsgd_diffusion.py - 2024-10-25 07:30:31,650 - Loss: 0.1907, step: 197500
+INFO - dpsgd_diffusion.py - 2024-10-25 07:31:27,168 - Loss: 0.1978, step: 197600
+INFO - dpsgd_diffusion.py - 2024-10-25 07:32:22,436 - Loss: 0.2194, step: 197700
+INFO - dpsgd_diffusion.py - 2024-10-25 07:33:18,443 - Loss: 0.1847, step: 197800
+INFO - dpsgd_diffusion.py - 2024-10-25 07:34:12,798 - Loss: 0.2175, step: 197900
+INFO - dpsgd_diffusion.py - 2024-10-25 07:35:06,946 - Loss: 0.1820, step: 198000
+INFO - dpsgd_diffusion.py - 2024-10-25 07:35:06,988 - Saving snapshot checkpoint and sampling single batch at iteration 198000.
+WARNING - image.py - 2024-10-25 07:35:07,577 - 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 - 2024-10-25 07:35:26,233 - FID at iteration 198000: 31.746842
+INFO - dpsgd_diffusion.py - 2024-10-25 07:36:21,266 - Loss: 0.2076, step: 198100
+INFO - dpsgd_diffusion.py - 2024-10-25 07:37:15,638 - Loss: 0.1984, step: 198200
+INFO - dpsgd_diffusion.py - 2024-10-25 07:38:11,308 - Loss: 0.1981, step: 198300
+INFO - dpsgd_diffusion.py - 2024-10-25 07:39:07,214 - Loss: 0.2123, step: 198400
+INFO - dpsgd_diffusion.py - 2024-10-25 07:40:04,777 - Loss: 0.1996, step: 198500
+INFO - dpsgd_diffusion.py - 2024-10-25 07:41:00,459 - Loss: 0.1954, step: 198600
+INFO - dpsgd_diffusion.py - 2024-10-25 07:41:56,437 - Loss: 0.1742, step: 198700
+INFO - dpsgd_diffusion.py - 2024-10-25 07:42:52,600 - Loss: 0.1892, step: 198800
+INFO - dpsgd_diffusion.py - 2024-10-25 07:43:48,429 - Loss: 0.1912, step: 198900
+INFO - dpsgd_diffusion.py - 2024-10-25 07:44:45,410 - Loss: 0.1912, step: 199000
+INFO - dpsgd_diffusion.py - 2024-10-25 07:45:41,162 - Loss: 0.1863, step: 199100
+INFO - dpsgd_diffusion.py - 2024-10-25 07:46:38,123 - Loss: 0.1873, step: 199200
+INFO - dpsgd_diffusion.py - 2024-10-25 07:47:33,693 - Loss: 0.1979, step: 199300
+INFO - dpsgd_diffusion.py - 2024-10-25 07:48:29,193 - Loss: 0.1865, step: 199400
+INFO - dpsgd_diffusion.py - 2024-10-25 07:49:25,164 - Loss: 0.1859, step: 199500
+INFO - dpsgd_diffusion.py - 2024-10-25 07:50:20,678 - Loss: 0.2016, step: 199600
+INFO - dpsgd_diffusion.py - 2024-10-25 07:51:06,020 - Eps-value after 78 epochs: 8.7158
+INFO - dpsgd_diffusion.py - 2024-10-25 07:51:17,533 - Loss: 0.1990, step: 199700
+INFO - dpsgd_diffusion.py - 2024-10-25 07:52:13,931 - Loss: 0.1852, step: 199800
+INFO - dpsgd_diffusion.py - 2024-10-25 07:53:09,877 - Loss: 0.2079, step: 199900
+INFO - dpsgd_diffusion.py - 2024-10-25 07:54:05,700 - Loss: 0.1986, step: 200000
+INFO - dpsgd_diffusion.py - 2024-10-25 07:54:05,720 - Saving snapshot checkpoint and sampling single batch at iteration 200000.
+WARNING - image.py - 2024-10-25 07:54:06,309 - 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 - 2024-10-25 07:54:25,051 - FID at iteration 200000: 32.220153
+INFO - dpsgd_diffusion.py - 2024-10-25 07:54:25,208 - Saving  checkpoint at iteration 200000
+INFO - dpsgd_diffusion.py - 2024-10-25 07:55:21,930 - Loss: 0.2073, step: 200100
+INFO - dpsgd_diffusion.py - 2024-10-25 07:56:16,832 - Loss: 0.2004, step: 200200
+INFO - dpsgd_diffusion.py - 2024-10-25 07:57:11,499 - Loss: 0.1881, step: 200300
+INFO - dpsgd_diffusion.py - 2024-10-25 07:58:07,178 - Loss: 0.1819, step: 200400
+INFO - dpsgd_diffusion.py - 2024-10-25 07:59:02,799 - Loss: 0.1778, step: 200500
+INFO - dpsgd_diffusion.py - 2024-10-25 07:59:57,790 - Loss: 0.1935, step: 200600
+INFO - dpsgd_diffusion.py - 2024-10-25 08:00:53,815 - Loss: 0.1997, step: 200700
+INFO - dpsgd_diffusion.py - 2024-10-25 08:01:50,372 - Loss: 0.1955, step: 200800
+INFO - dpsgd_diffusion.py - 2024-10-25 08:02:45,289 - Loss: 0.2042, step: 200900
+INFO - dpsgd_diffusion.py - 2024-10-25 08:03:40,470 - Loss: 0.2138, step: 201000
+INFO - dpsgd_diffusion.py - 2024-10-25 08:04:34,779 - Loss: 0.1789, step: 201100
+INFO - dpsgd_diffusion.py - 2024-10-25 08:05:29,612 - Loss: 0.1992, step: 201200
+INFO - dpsgd_diffusion.py - 2024-10-25 08:06:26,370 - Loss: 0.1986, step: 201300
+INFO - dpsgd_diffusion.py - 2024-10-25 08:07:22,701 - Loss: 0.2034, step: 201400
+INFO - dpsgd_diffusion.py - 2024-10-25 08:08:18,657 - Loss: 0.1914, step: 201500
+INFO - dpsgd_diffusion.py - 2024-10-25 08:09:14,292 - Loss: 0.1740, step: 201600
+INFO - dpsgd_diffusion.py - 2024-10-25 08:10:10,421 - Loss: 0.1901, step: 201700
+INFO - dpsgd_diffusion.py - 2024-10-25 08:11:05,980 - Loss: 0.1720, step: 201800
+INFO - dpsgd_diffusion.py - 2024-10-25 08:12:01,753 - Loss: 0.2050, step: 201900
+INFO - dpsgd_diffusion.py - 2024-10-25 08:12:56,870 - Loss: 0.1936, step: 202000
+INFO - dpsgd_diffusion.py - 2024-10-25 08:12:56,884 - Saving snapshot checkpoint and sampling single batch at iteration 202000.
+WARNING - image.py - 2024-10-25 08:12:57,473 - 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 - 2024-10-25 08:13:16,250 - FID at iteration 202000: 31.467116
+INFO - dpsgd_diffusion.py - 2024-10-25 08:14:13,988 - Loss: 0.1824, step: 202100
+INFO - dpsgd_diffusion.py - 2024-10-25 08:15:10,164 - Loss: 0.1911, step: 202200
+INFO - dpsgd_diffusion.py - 2024-10-25 08:15:32,062 - Eps-value after 79 epochs: 8.7771
+INFO - dpsgd_diffusion.py - 2024-10-25 08:16:04,425 - Loss: 0.2086, step: 202300
+INFO - dpsgd_diffusion.py - 2024-10-25 08:16:59,524 - Loss: 0.1954, step: 202400
+INFO - dpsgd_diffusion.py - 2024-10-25 08:17:54,890 - Loss: 0.1941, step: 202500
+INFO - dpsgd_diffusion.py - 2024-10-25 08:18:49,578 - Loss: 0.1954, step: 202600
+INFO - dpsgd_diffusion.py - 2024-10-25 08:19:43,561 - Loss: 0.1856, step: 202700
+INFO - dpsgd_diffusion.py - 2024-10-25 08:20:37,578 - Loss: 0.2087, step: 202800
+INFO - dpsgd_diffusion.py - 2024-10-25 08:21:32,977 - Loss: 0.1803, step: 202900
+INFO - dpsgd_diffusion.py - 2024-10-25 08:22:29,133 - Loss: 0.2232, step: 203000
+INFO - dpsgd_diffusion.py - 2024-10-25 08:23:24,377 - Loss: 0.1707, step: 203100
+INFO - dpsgd_diffusion.py - 2024-10-25 08:24:20,378 - Loss: 0.1899, step: 203200
+INFO - dpsgd_diffusion.py - 2024-10-25 08:25:15,509 - Loss: 0.1846, step: 203300
+INFO - dpsgd_diffusion.py - 2024-10-25 08:26:11,549 - Loss: 0.2080, step: 203400
+INFO - dpsgd_diffusion.py - 2024-10-25 08:27:07,402 - Loss: 0.1943, step: 203500
+INFO - dpsgd_diffusion.py - 2024-10-25 08:28:04,354 - Loss: 0.1927, step: 203600
+INFO - dpsgd_diffusion.py - 2024-10-25 08:28:59,399 - Loss: 0.1873, step: 203700
+INFO - dpsgd_diffusion.py - 2024-10-25 08:29:54,542 - Loss: 0.2063, step: 203800
+INFO - dpsgd_diffusion.py - 2024-10-25 08:30:49,312 - Loss: 0.2070, step: 203900
+INFO - dpsgd_diffusion.py - 2024-10-25 08:31:44,820 - Loss: 0.1927, step: 204000
+INFO - dpsgd_diffusion.py - 2024-10-25 08:31:44,869 - Saving snapshot checkpoint and sampling single batch at iteration 204000.
+WARNING - image.py - 2024-10-25 08:31:45,458 - 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 - 2024-10-25 08:32:04,178 - FID at iteration 204000: 31.567103
+INFO - dpsgd_diffusion.py - 2024-10-25 08:32:59,956 - Loss: 0.1949, step: 204100
+INFO - dpsgd_diffusion.py - 2024-10-25 08:33:56,375 - Loss: 0.1752, step: 204200
+INFO - dpsgd_diffusion.py - 2024-10-25 08:34:51,701 - Loss: 0.1879, step: 204300
+INFO - dpsgd_diffusion.py - 2024-10-25 08:35:47,196 - Loss: 0.1817, step: 204400
+INFO - dpsgd_diffusion.py - 2024-10-25 08:36:43,116 - Loss: 0.1841, step: 204500
+INFO - dpsgd_diffusion.py - 2024-10-25 08:37:38,792 - Loss: 0.1879, step: 204600
+INFO - dpsgd_diffusion.py - 2024-10-25 08:38:36,196 - Loss: 0.1942, step: 204700
+INFO - dpsgd_diffusion.py - 2024-10-25 08:39:31,125 - Loss: 0.1968, step: 204800
+INFO - dpsgd_diffusion.py - 2024-10-25 08:39:31,169 - Eps-value after 80 epochs: 8.8383
+INFO - dpsgd_diffusion.py - 2024-10-25 08:40:27,835 - Loss: 0.1805, step: 204900
+INFO - dpsgd_diffusion.py - 2024-10-25 08:41:21,888 - Loss: 0.1950, step: 205000
+INFO - dpsgd_diffusion.py - 2024-10-25 08:42:16,675 - Loss: 0.1931, step: 205100
+INFO - dpsgd_diffusion.py - 2024-10-25 08:43:14,196 - Loss: 0.1958, step: 205200
+INFO - dpsgd_diffusion.py - 2024-10-25 08:44:09,600 - Loss: 0.1943, step: 205300
+INFO - dpsgd_diffusion.py - 2024-10-25 08:45:05,177 - Loss: 0.1876, step: 205400
+INFO - dpsgd_diffusion.py - 2024-10-25 08:46:00,866 - Loss: 0.2088, step: 205500
+INFO - dpsgd_diffusion.py - 2024-10-25 08:46:56,228 - Loss: 0.2027, step: 205600
+INFO - dpsgd_diffusion.py - 2024-10-25 08:47:51,594 - Loss: 0.1968, step: 205700
+INFO - dpsgd_diffusion.py - 2024-10-25 08:48:48,004 - Loss: 0.1961, step: 205800
+INFO - dpsgd_diffusion.py - 2024-10-25 08:49:44,820 - Loss: 0.1751, step: 205900
+INFO - dpsgd_diffusion.py - 2024-10-25 08:50:42,057 - Loss: 0.1711, step: 206000
+INFO - dpsgd_diffusion.py - 2024-10-25 08:50:42,104 - Saving snapshot checkpoint and sampling single batch at iteration 206000.
+WARNING - image.py - 2024-10-25 08:50:42,692 - 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 - 2024-10-25 08:51:01,271 - FID at iteration 206000: 31.320252
+INFO - dpsgd_diffusion.py - 2024-10-25 08:51:56,855 - Loss: 0.1793, step: 206100
+INFO - dpsgd_diffusion.py - 2024-10-25 08:52:52,031 - Loss: 0.1958, step: 206200
+INFO - dpsgd_diffusion.py - 2024-10-25 08:53:48,431 - Loss: 0.1891, step: 206300
+INFO - dpsgd_diffusion.py - 2024-10-25 08:54:44,390 - Loss: 0.1872, step: 206400
+INFO - dpsgd_diffusion.py - 2024-10-25 08:55:41,126 - Loss: 0.1939, step: 206500
+INFO - dpsgd_diffusion.py - 2024-10-25 08:56:37,078 - Loss: 0.1769, step: 206600
+INFO - dpsgd_diffusion.py - 2024-10-25 08:57:32,117 - Loss: 0.2067, step: 206700
+INFO - dpsgd_diffusion.py - 2024-10-25 08:58:27,308 - Loss: 0.2097, step: 206800
+INFO - dpsgd_diffusion.py - 2024-10-25 08:59:23,300 - Loss: 0.1824, step: 206900
+INFO - dpsgd_diffusion.py - 2024-10-25 09:00:19,745 - Loss: 0.1946, step: 207000
+INFO - dpsgd_diffusion.py - 2024-10-25 09:01:14,615 - Loss: 0.1981, step: 207100
+INFO - dpsgd_diffusion.py - 2024-10-25 09:02:09,847 - Loss: 0.1946, step: 207200
+INFO - dpsgd_diffusion.py - 2024-10-25 09:03:05,376 - Loss: 0.1880, step: 207300
+INFO - dpsgd_diffusion.py - 2024-10-25 09:03:38,767 - Eps-value after 81 epochs: 8.8990
+INFO - dpsgd_diffusion.py - 2024-10-25 09:04:01,760 - Loss: 0.2020, step: 207400
+INFO - dpsgd_diffusion.py - 2024-10-25 09:04:57,933 - Loss: 0.1946, step: 207500
+INFO - dpsgd_diffusion.py - 2024-10-25 09:05:54,378 - Loss: 0.1857, step: 207600
+INFO - dpsgd_diffusion.py - 2024-10-25 09:06:50,131 - Loss: 0.1783, step: 207700
+INFO - dpsgd_diffusion.py - 2024-10-25 09:07:45,142 - Loss: 0.1996, step: 207800
+INFO - dpsgd_diffusion.py - 2024-10-25 09:08:40,932 - Loss: 0.1959, step: 207900
+INFO - dpsgd_diffusion.py - 2024-10-25 09:09:36,445 - Loss: 0.1991, step: 208000
+INFO - dpsgd_diffusion.py - 2024-10-25 09:09:36,463 - Saving snapshot checkpoint and sampling single batch at iteration 208000.
+WARNING - image.py - 2024-10-25 09:09:37,052 - 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 - 2024-10-25 09:09:56,110 - FID at iteration 208000: 31.440134
+INFO - dpsgd_diffusion.py - 2024-10-25 09:10:51,352 - Loss: 0.1955, step: 208100
+INFO - dpsgd_diffusion.py - 2024-10-25 09:11:45,070 - Loss: 0.1932, step: 208200
+INFO - dpsgd_diffusion.py - 2024-10-25 09:12:41,314 - Loss: 0.1798, step: 208300
+INFO - dpsgd_diffusion.py - 2024-10-25 09:13:35,918 - Loss: 0.1802, step: 208400
+INFO - dpsgd_diffusion.py - 2024-10-25 09:14:31,235 - Loss: 0.1977, step: 208500
+INFO - dpsgd_diffusion.py - 2024-10-25 09:15:27,184 - Loss: 0.1876, step: 208600
+INFO - dpsgd_diffusion.py - 2024-10-25 09:16:22,835 - Loss: 0.2018, step: 208700
+INFO - dpsgd_diffusion.py - 2024-10-25 09:17:19,639 - Loss: 0.1940, step: 208800
+INFO - dpsgd_diffusion.py - 2024-10-25 09:18:16,844 - Loss: 0.1951, step: 208900
+INFO - dpsgd_diffusion.py - 2024-10-25 09:19:14,090 - Loss: 0.1936, step: 209000
+INFO - dpsgd_diffusion.py - 2024-10-25 09:20:09,701 - Loss: 0.1824, step: 209100
+INFO - dpsgd_diffusion.py - 2024-10-25 09:21:06,331 - Loss: 0.2101, step: 209200
+INFO - dpsgd_diffusion.py - 2024-10-25 09:22:02,351 - Loss: 0.1931, step: 209300
+INFO - dpsgd_diffusion.py - 2024-10-25 09:22:56,980 - Loss: 0.1978, step: 209400
+INFO - dpsgd_diffusion.py - 2024-10-25 09:23:51,813 - Loss: 0.1762, step: 209500
+INFO - dpsgd_diffusion.py - 2024-10-25 09:24:45,976 - Loss: 0.1743, step: 209600
+INFO - dpsgd_diffusion.py - 2024-10-25 09:25:41,478 - Loss: 0.1949, step: 209700
+INFO - dpsgd_diffusion.py - 2024-10-25 09:26:37,044 - Loss: 0.1960, step: 209800
+INFO - dpsgd_diffusion.py - 2024-10-25 09:27:33,593 - Loss: 0.1875, step: 209900
+INFO - dpsgd_diffusion.py - 2024-10-25 09:27:44,768 - Eps-value after 82 epochs: 8.9585
+INFO - dpsgd_diffusion.py - 2024-10-25 09:28:29,930 - Loss: 0.1849, step: 210000
+INFO - dpsgd_diffusion.py - 2024-10-25 09:28:29,943 - Saving snapshot checkpoint and sampling single batch at iteration 210000.
+WARNING - image.py - 2024-10-25 09:28:30,533 - 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 - 2024-10-25 09:28:48,987 - FID at iteration 210000: 31.415304
+INFO - dpsgd_diffusion.py - 2024-10-25 09:29:45,154 - Loss: 0.2038, step: 210100
+INFO - dpsgd_diffusion.py - 2024-10-25 09:30:41,084 - Loss: 0.1996, step: 210200
+INFO - dpsgd_diffusion.py - 2024-10-25 09:31:38,240 - Loss: 0.1938, step: 210300
+INFO - dpsgd_diffusion.py - 2024-10-25 09:32:34,031 - Loss: 0.1991, step: 210400
+INFO - dpsgd_diffusion.py - 2024-10-25 09:33:30,076 - Loss: 0.1903, step: 210500
+INFO - dpsgd_diffusion.py - 2024-10-25 09:34:24,438 - Loss: 0.1887, step: 210600
+INFO - dpsgd_diffusion.py - 2024-10-25 09:35:19,925 - Loss: 0.2001, step: 210700
+INFO - dpsgd_diffusion.py - 2024-10-25 09:36:15,784 - Loss: 0.1990, step: 210800
+INFO - dpsgd_diffusion.py - 2024-10-25 09:37:10,849 - Loss: 0.1881, step: 210900
+INFO - dpsgd_diffusion.py - 2024-10-25 09:38:05,975 - Loss: 0.1737, step: 211000
+INFO - dpsgd_diffusion.py - 2024-10-25 09:38:59,981 - Loss: 0.1959, step: 211100
+INFO - dpsgd_diffusion.py - 2024-10-25 09:39:55,584 - Loss: 0.1985, step: 211200
+INFO - dpsgd_diffusion.py - 2024-10-25 09:40:52,037 - Loss: 0.1901, step: 211300
+INFO - dpsgd_diffusion.py - 2024-10-25 09:41:46,405 - Loss: 0.2114, step: 211400
+INFO - dpsgd_diffusion.py - 2024-10-25 09:42:41,213 - Loss: 0.1931, step: 211500
+INFO - dpsgd_diffusion.py - 2024-10-25 09:43:35,975 - Loss: 0.1943, step: 211600
+INFO - dpsgd_diffusion.py - 2024-10-25 09:44:31,912 - Loss: 0.2002, step: 211700
+INFO - dpsgd_diffusion.py - 2024-10-25 09:45:27,528 - Loss: 0.1997, step: 211800
+INFO - dpsgd_diffusion.py - 2024-10-25 09:46:23,916 - Loss: 0.1899, step: 211900
+INFO - dpsgd_diffusion.py - 2024-10-25 09:47:19,388 - Loss: 0.2127, step: 212000
+INFO - dpsgd_diffusion.py - 2024-10-25 09:47:19,436 - Saving snapshot checkpoint and sampling single batch at iteration 212000.
+WARNING - image.py - 2024-10-25 09:47:20,026 - 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 - 2024-10-25 09:47:38,597 - FID at iteration 212000: 30.957309
+INFO - dpsgd_diffusion.py - 2024-10-25 09:48:36,056 - Loss: 0.1865, step: 212100
+INFO - dpsgd_diffusion.py - 2024-10-25 09:49:31,669 - Loss: 0.1657, step: 212200
+INFO - dpsgd_diffusion.py - 2024-10-25 09:50:27,962 - Loss: 0.1934, step: 212300
+INFO - dpsgd_diffusion.py - 2024-10-25 09:51:22,690 - Loss: 0.1983, step: 212400
+INFO - dpsgd_diffusion.py - 2024-10-25 09:52:07,198 - Eps-value after 83 epochs: 9.0180
+INFO - dpsgd_diffusion.py - 2024-10-25 09:52:18,095 - Loss: 0.2000, step: 212500
+INFO - dpsgd_diffusion.py - 2024-10-25 09:53:12,624 - Loss: 0.1823, step: 212600
+INFO - dpsgd_diffusion.py - 2024-10-25 09:54:09,304 - Loss: 0.2252, step: 212700
+INFO - dpsgd_diffusion.py - 2024-10-25 09:55:04,837 - Loss: 0.1904, step: 212800
+INFO - dpsgd_diffusion.py - 2024-10-25 09:55:59,808 - Loss: 0.2036, step: 212900
+INFO - dpsgd_diffusion.py - 2024-10-25 09:56:55,169 - Loss: 0.2012, step: 213000
+INFO - dpsgd_diffusion.py - 2024-10-25 09:57:50,908 - Loss: 0.1942, step: 213100
+INFO - dpsgd_diffusion.py - 2024-10-25 09:58:46,091 - Loss: 0.1744, step: 213200
+INFO - dpsgd_diffusion.py - 2024-10-25 09:59:43,799 - Loss: 0.1890, step: 213300
+INFO - dpsgd_diffusion.py - 2024-10-25 10:00:38,851 - Loss: 0.1937, step: 213400
+INFO - dpsgd_diffusion.py - 2024-10-25 10:01:34,656 - Loss: 0.2184, step: 213500
+INFO - dpsgd_diffusion.py - 2024-10-25 10:02:30,935 - Loss: 0.1781, step: 213600
+INFO - dpsgd_diffusion.py - 2024-10-25 10:03:26,604 - Loss: 0.1953, step: 213700
+INFO - dpsgd_diffusion.py - 2024-10-25 10:04:23,223 - Loss: 0.1926, step: 213800
+INFO - dpsgd_diffusion.py - 2024-10-25 10:05:18,589 - Loss: 0.1864, step: 213900
+INFO - dpsgd_diffusion.py - 2024-10-25 10:06:15,259 - Loss: 0.2179, step: 214000
+INFO - dpsgd_diffusion.py - 2024-10-25 10:06:15,271 - Saving snapshot checkpoint and sampling single batch at iteration 214000.
+WARNING - image.py - 2024-10-25 10:06:15,861 - 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 - 2024-10-25 10:06:34,339 - FID at iteration 214000: 30.904684
+INFO - dpsgd_diffusion.py - 2024-10-25 10:07:30,196 - Loss: 0.1943, step: 214100
+INFO - dpsgd_diffusion.py - 2024-10-25 10:08:24,662 - Loss: 0.2026, step: 214200
+INFO - dpsgd_diffusion.py - 2024-10-25 10:09:18,463 - Loss: 0.1842, step: 214300
+INFO - dpsgd_diffusion.py - 2024-10-25 10:10:13,237 - Loss: 0.1954, step: 214400
+INFO - dpsgd_diffusion.py - 2024-10-25 10:11:09,790 - Loss: 0.1910, step: 214500
+INFO - dpsgd_diffusion.py - 2024-10-25 10:12:04,535 - Loss: 0.1824, step: 214600
+INFO - dpsgd_diffusion.py - 2024-10-25 10:13:00,042 - Loss: 0.1866, step: 214700
+INFO - dpsgd_diffusion.py - 2024-10-25 10:13:56,221 - Loss: 0.1990, step: 214800
+INFO - dpsgd_diffusion.py - 2024-10-25 10:14:53,025 - Loss: 0.2054, step: 214900
+INFO - dpsgd_diffusion.py - 2024-10-25 10:15:48,826 - Loss: 0.1942, step: 215000
+INFO - dpsgd_diffusion.py - 2024-10-25 10:16:10,541 - Eps-value after 84 epochs: 9.0775
+INFO - dpsgd_diffusion.py - 2024-10-25 10:16:43,208 - Loss: 0.1946, step: 215100
+INFO - dpsgd_diffusion.py - 2024-10-25 10:17:39,652 - Loss: 0.1920, step: 215200
+INFO - dpsgd_diffusion.py - 2024-10-25 10:18:35,535 - Loss: 0.1976, step: 215300
+INFO - dpsgd_diffusion.py - 2024-10-25 10:19:31,946 - Loss: 0.1986, step: 215400
+INFO - dpsgd_diffusion.py - 2024-10-25 10:20:26,823 - Loss: 0.1877, step: 215500
+INFO - dpsgd_diffusion.py - 2024-10-25 10:21:21,812 - Loss: 0.1844, step: 215600
+INFO - dpsgd_diffusion.py - 2024-10-25 10:22:17,045 - Loss: 0.1975, step: 215700
+INFO - dpsgd_diffusion.py - 2024-10-25 10:23:13,161 - Loss: 0.1765, step: 215800
+INFO - dpsgd_diffusion.py - 2024-10-25 10:24:09,230 - Loss: 0.1847, step: 215900
+INFO - dpsgd_diffusion.py - 2024-10-25 10:25:05,049 - Loss: 0.1904, step: 216000
+INFO - dpsgd_diffusion.py - 2024-10-25 10:25:05,068 - Saving snapshot checkpoint and sampling single batch at iteration 216000.
+WARNING - image.py - 2024-10-25 10:25:05,658 - 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 - 2024-10-25 10:25:24,045 - FID at iteration 216000: 30.561976
+INFO - dpsgd_diffusion.py - 2024-10-25 10:26:20,151 - Loss: 0.2239, step: 216100
+INFO - dpsgd_diffusion.py - 2024-10-25 10:27:14,703 - Loss: 0.1799, step: 216200
+INFO - dpsgd_diffusion.py - 2024-10-25 10:28:11,424 - Loss: 0.1909, step: 216300
+INFO - dpsgd_diffusion.py - 2024-10-25 10:29:06,655 - Loss: 0.1930, step: 216400
+INFO - dpsgd_diffusion.py - 2024-10-25 10:30:01,467 - Loss: 0.1896, step: 216500
+INFO - dpsgd_diffusion.py - 2024-10-25 10:30:55,987 - Loss: 0.1817, step: 216600
+INFO - dpsgd_diffusion.py - 2024-10-25 10:31:52,099 - Loss: 0.1995, step: 216700
+INFO - dpsgd_diffusion.py - 2024-10-25 10:32:48,099 - Loss: 0.1958, step: 216800
+INFO - dpsgd_diffusion.py - 2024-10-25 10:33:42,819 - Loss: 0.1996, step: 216900
+INFO - dpsgd_diffusion.py - 2024-10-25 10:34:39,182 - Loss: 0.1922, step: 217000
+INFO - dpsgd_diffusion.py - 2024-10-25 10:35:35,819 - Loss: 0.1874, step: 217100
+INFO - dpsgd_diffusion.py - 2024-10-25 10:36:31,842 - Loss: 0.1933, step: 217200
+INFO - dpsgd_diffusion.py - 2024-10-25 10:37:27,832 - Loss: 0.2091, step: 217300
+INFO - dpsgd_diffusion.py - 2024-10-25 10:38:23,348 - Loss: 0.1837, step: 217400
+INFO - dpsgd_diffusion.py - 2024-10-25 10:39:19,999 - Loss: 0.1681, step: 217500
+INFO - dpsgd_diffusion.py - 2024-10-25 10:40:14,117 - Loss: 0.2074, step: 217600
+INFO - dpsgd_diffusion.py - 2024-10-25 10:40:14,158 - Eps-value after 85 epochs: 9.1370
+INFO - dpsgd_diffusion.py - 2024-10-25 10:41:10,303 - Loss: 0.1874, step: 217700
+INFO - dpsgd_diffusion.py - 2024-10-25 10:42:06,446 - Loss: 0.1947, step: 217800
+INFO - dpsgd_diffusion.py - 2024-10-25 10:43:01,264 - Loss: 0.2260, step: 217900
+INFO - dpsgd_diffusion.py - 2024-10-25 10:43:56,875 - Loss: 0.1815, step: 218000
+INFO - dpsgd_diffusion.py - 2024-10-25 10:43:56,889 - Saving snapshot checkpoint and sampling single batch at iteration 218000.
+WARNING - image.py - 2024-10-25 10:43:57,479 - 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 - 2024-10-25 10:44:15,910 - FID at iteration 218000: 30.227274
+INFO - dpsgd_diffusion.py - 2024-10-25 10:45:12,605 - Loss: 0.1916, step: 218100
+INFO - dpsgd_diffusion.py - 2024-10-25 10:46:08,730 - Loss: 0.1940, step: 218200
+INFO - dpsgd_diffusion.py - 2024-10-25 10:47:04,182 - Loss: 0.1770, step: 218300
+INFO - dpsgd_diffusion.py - 2024-10-25 10:48:00,653 - Loss: 0.2094, step: 218400
+INFO - dpsgd_diffusion.py - 2024-10-25 10:48:55,248 - Loss: 0.1940, step: 218500
+INFO - dpsgd_diffusion.py - 2024-10-25 10:49:51,357 - Loss: 0.1894, step: 218600
+INFO - dpsgd_diffusion.py - 2024-10-25 10:50:47,284 - Loss: 0.1941, step: 218700
+INFO - dpsgd_diffusion.py - 2024-10-25 10:51:41,728 - Loss: 0.1817, step: 218800
+INFO - dpsgd_diffusion.py - 2024-10-25 10:52:38,155 - Loss: 0.1802, step: 218900
+INFO - dpsgd_diffusion.py - 2024-10-25 10:53:32,877 - Loss: 0.1711, step: 219000
+INFO - dpsgd_diffusion.py - 2024-10-25 10:54:29,303 - Loss: 0.1780, step: 219100
+INFO - dpsgd_diffusion.py - 2024-10-25 10:55:26,316 - Loss: 0.2180, step: 219200
+INFO - dpsgd_diffusion.py - 2024-10-25 10:56:22,345 - Loss: 0.2037, step: 219300
+INFO - dpsgd_diffusion.py - 2024-10-25 10:57:19,053 - Loss: 0.2093, step: 219400
+INFO - dpsgd_diffusion.py - 2024-10-25 10:58:14,198 - Loss: 0.1881, step: 219500
+INFO - dpsgd_diffusion.py - 2024-10-25 10:59:10,180 - Loss: 0.1989, step: 219600
+INFO - dpsgd_diffusion.py - 2024-10-25 11:00:05,488 - Loss: 0.1857, step: 219700
+INFO - dpsgd_diffusion.py - 2024-10-25 11:01:00,817 - Loss: 0.2072, step: 219800
+INFO - dpsgd_diffusion.py - 2024-10-25 11:01:55,721 - Loss: 0.1894, step: 219900
+INFO - dpsgd_diffusion.py - 2024-10-25 11:02:50,600 - Loss: 0.1770, step: 220000
+INFO - dpsgd_diffusion.py - 2024-10-25 11:02:50,614 - Saving snapshot checkpoint and sampling single batch at iteration 220000.
+WARNING - image.py - 2024-10-25 11:02:51,203 - 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 - 2024-10-25 11:03:09,613 - FID at iteration 220000: 30.694313
+INFO - dpsgd_diffusion.py - 2024-10-25 11:04:05,761 - Loss: 0.1847, step: 220100
+INFO - dpsgd_diffusion.py - 2024-10-25 11:04:40,417 - Eps-value after 86 epochs: 9.1966
+INFO - dpsgd_diffusion.py - 2024-10-25 11:05:02,377 - Loss: 0.1959, step: 220200
+INFO - dpsgd_diffusion.py - 2024-10-25 11:05:58,512 - Loss: 0.2022, step: 220300
+INFO - dpsgd_diffusion.py - 2024-10-25 11:06:53,281 - Loss: 0.1879, step: 220400
+INFO - dpsgd_diffusion.py - 2024-10-25 11:07:48,847 - Loss: 0.2172, step: 220500
+INFO - dpsgd_diffusion.py - 2024-10-25 11:08:44,210 - Loss: 0.2062, step: 220600
+INFO - dpsgd_diffusion.py - 2024-10-25 11:09:40,236 - Loss: 0.1929, step: 220700
+INFO - dpsgd_diffusion.py - 2024-10-25 11:10:35,694 - Loss: 0.2000, step: 220800
+INFO - dpsgd_diffusion.py - 2024-10-25 11:11:31,095 - Loss: 0.2009, step: 220900
+INFO - dpsgd_diffusion.py - 2024-10-25 11:12:25,139 - Loss: 0.1838, step: 221000
+INFO - dpsgd_diffusion.py - 2024-10-25 11:13:22,056 - Loss: 0.1950, step: 221100
+INFO - dpsgd_diffusion.py - 2024-10-25 11:14:17,709 - Loss: 0.1879, step: 221200
+INFO - dpsgd_diffusion.py - 2024-10-25 11:15:14,280 - Loss: 0.1942, step: 221300
+INFO - dpsgd_diffusion.py - 2024-10-25 11:16:08,977 - Loss: 0.2066, step: 221400
+INFO - dpsgd_diffusion.py - 2024-10-25 11:17:04,126 - Loss: 0.1920, step: 221500
+INFO - dpsgd_diffusion.py - 2024-10-25 11:18:01,382 - Loss: 0.1885, step: 221600
+INFO - dpsgd_diffusion.py - 2024-10-25 11:18:57,524 - Loss: 0.1863, step: 221700
+INFO - dpsgd_diffusion.py - 2024-10-25 11:19:52,674 - Loss: 0.1930, step: 221800
+INFO - dpsgd_diffusion.py - 2024-10-25 11:20:49,619 - Loss: 0.1894, step: 221900
+INFO - dpsgd_diffusion.py - 2024-10-25 11:21:43,897 - Loss: 0.1727, step: 222000
+INFO - dpsgd_diffusion.py - 2024-10-25 11:21:43,911 - Saving snapshot checkpoint and sampling single batch at iteration 222000.
+WARNING - image.py - 2024-10-25 11:21:44,501 - 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 - 2024-10-25 11:22:02,908 - FID at iteration 222000: 30.519734
+INFO - dpsgd_diffusion.py - 2024-10-25 11:22:57,864 - Loss: 0.1912, step: 222100
+INFO - dpsgd_diffusion.py - 2024-10-25 11:23:53,218 - Loss: 0.2004, step: 222200
+INFO - dpsgd_diffusion.py - 2024-10-25 11:24:48,237 - Loss: 0.1874, step: 222300
+INFO - dpsgd_diffusion.py - 2024-10-25 11:25:43,612 - Loss: 0.1813, step: 222400
+INFO - dpsgd_diffusion.py - 2024-10-25 11:26:40,227 - Loss: 0.2001, step: 222500
+INFO - dpsgd_diffusion.py - 2024-10-25 11:27:36,820 - Loss: 0.1896, step: 222600
+INFO - dpsgd_diffusion.py - 2024-10-25 11:28:33,331 - Loss: 0.1989, step: 222700
+INFO - dpsgd_diffusion.py - 2024-10-25 11:28:44,462 - Eps-value after 87 epochs: 9.2561
+INFO - dpsgd_diffusion.py - 2024-10-25 11:29:29,310 - Loss: 0.1780, step: 222800
+INFO - dpsgd_diffusion.py - 2024-10-25 11:30:23,565 - Loss: 0.1934, step: 222900
+INFO - dpsgd_diffusion.py - 2024-10-25 11:31:20,420 - Loss: 0.1734, step: 223000
+INFO - dpsgd_diffusion.py - 2024-10-25 11:32:15,092 - Loss: 0.1947, step: 223100
+INFO - dpsgd_diffusion.py - 2024-10-25 11:33:10,878 - Loss: 0.2186, step: 223200
+INFO - dpsgd_diffusion.py - 2024-10-25 11:34:06,076 - Loss: 0.1944, step: 223300
+INFO - dpsgd_diffusion.py - 2024-10-25 11:35:02,355 - Loss: 0.2100, step: 223400
+INFO - dpsgd_diffusion.py - 2024-10-25 11:35:56,049 - Loss: 0.1986, step: 223500
+INFO - dpsgd_diffusion.py - 2024-10-25 11:36:52,013 - Loss: 0.1931, step: 223600
+INFO - dpsgd_diffusion.py - 2024-10-25 11:37:48,990 - Loss: 0.1960, step: 223700
+INFO - dpsgd_diffusion.py - 2024-10-25 11:38:45,109 - Loss: 0.1853, step: 223800
+INFO - dpsgd_diffusion.py - 2024-10-25 11:39:40,108 - Loss: 0.1978, step: 223900
+INFO - dpsgd_diffusion.py - 2024-10-25 11:40:34,387 - Loss: 0.1751, step: 224000
+INFO - dpsgd_diffusion.py - 2024-10-25 11:40:34,409 - Saving snapshot checkpoint and sampling single batch at iteration 224000.
+WARNING - image.py - 2024-10-25 11:40:34,999 - 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 - 2024-10-25 11:40:53,377 - FID at iteration 224000: 29.991754
+INFO - dpsgd_diffusion.py - 2024-10-25 11:41:49,208 - Loss: 0.1937, step: 224100
+INFO - dpsgd_diffusion.py - 2024-10-25 11:42:44,820 - Loss: 0.1950, step: 224200
+INFO - dpsgd_diffusion.py - 2024-10-25 11:43:40,484 - Loss: 0.1935, step: 224300
+INFO - dpsgd_diffusion.py - 2024-10-25 11:44:35,295 - Loss: 0.1908, step: 224400
+INFO - dpsgd_diffusion.py - 2024-10-25 11:45:29,435 - Loss: 0.1765, step: 224500
+INFO - dpsgd_diffusion.py - 2024-10-25 11:46:25,505 - Loss: 0.1912, step: 224600
+INFO - dpsgd_diffusion.py - 2024-10-25 11:47:19,916 - Loss: 0.2100, step: 224700
+INFO - dpsgd_diffusion.py - 2024-10-25 11:48:15,890 - Loss: 0.2043, step: 224800
+INFO - dpsgd_diffusion.py - 2024-10-25 11:49:12,070 - Loss: 0.1953, step: 224900
+INFO - dpsgd_diffusion.py - 2024-10-25 11:50:07,768 - Loss: 0.1744, step: 225000
+INFO - dpsgd_diffusion.py - 2024-10-25 11:51:03,220 - Loss: 0.1913, step: 225100
+INFO - dpsgd_diffusion.py - 2024-10-25 11:51:59,107 - Loss: 0.2104, step: 225200
+INFO - dpsgd_diffusion.py - 2024-10-25 11:52:43,010 - Eps-value after 88 epochs: 9.3142
+INFO - dpsgd_diffusion.py - 2024-10-25 11:52:53,875 - Loss: 0.1935, step: 225300
+INFO - dpsgd_diffusion.py - 2024-10-25 11:53:49,379 - Loss: 0.1971, step: 225400
+INFO - dpsgd_diffusion.py - 2024-10-25 11:54:46,807 - Loss: 0.1891, step: 225500
+INFO - dpsgd_diffusion.py - 2024-10-25 11:55:42,809 - Loss: 0.2106, step: 225600
+INFO - dpsgd_diffusion.py - 2024-10-25 11:56:37,335 - Loss: 0.1821, step: 225700
+INFO - dpsgd_diffusion.py - 2024-10-25 11:57:32,741 - Loss: 0.2037, step: 225800
+INFO - dpsgd_diffusion.py - 2024-10-25 11:58:28,498 - Loss: 0.1612, step: 225900
+INFO - dpsgd_diffusion.py - 2024-10-25 11:59:24,920 - Loss: 0.1910, step: 226000
+INFO - dpsgd_diffusion.py - 2024-10-25 11:59:24,934 - Saving snapshot checkpoint and sampling single batch at iteration 226000.
+WARNING - image.py - 2024-10-25 11:59:25,524 - 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 - 2024-10-25 11:59:43,932 - FID at iteration 226000: 30.227351
+INFO - dpsgd_diffusion.py - 2024-10-25 12:00:39,702 - Loss: 0.2054, step: 226100
+INFO - dpsgd_diffusion.py - 2024-10-25 12:01:36,791 - Loss: 0.2024, step: 226200
+INFO - dpsgd_diffusion.py - 2024-10-25 12:02:33,230 - Loss: 0.2082, step: 226300
+INFO - dpsgd_diffusion.py - 2024-10-25 12:03:28,624 - Loss: 0.2186, step: 226400
+INFO - dpsgd_diffusion.py - 2024-10-25 12:04:25,685 - Loss: 0.1811, step: 226500
+INFO - dpsgd_diffusion.py - 2024-10-25 12:05:20,100 - Loss: 0.1924, step: 226600
+INFO - dpsgd_diffusion.py - 2024-10-25 12:06:16,377 - Loss: 0.1952, step: 226700
+INFO - dpsgd_diffusion.py - 2024-10-25 12:07:11,355 - Loss: 0.1929, step: 226800
+INFO - dpsgd_diffusion.py - 2024-10-25 12:08:07,228 - Loss: 0.2168, step: 226900
+INFO - dpsgd_diffusion.py - 2024-10-25 12:09:03,377 - Loss: 0.1901, step: 227000
+INFO - dpsgd_diffusion.py - 2024-10-25 12:09:59,920 - Loss: 0.1947, step: 227100
+INFO - dpsgd_diffusion.py - 2024-10-25 12:10:55,891 - Loss: 0.1754, step: 227200
+INFO - dpsgd_diffusion.py - 2024-10-25 12:11:51,310 - Loss: 0.1978, step: 227300
+INFO - dpsgd_diffusion.py - 2024-10-25 12:12:46,246 - Loss: 0.1759, step: 227400
+INFO - dpsgd_diffusion.py - 2024-10-25 12:13:41,111 - Loss: 0.1828, step: 227500
+INFO - dpsgd_diffusion.py - 2024-10-25 12:14:36,449 - Loss: 0.1830, step: 227600
+INFO - dpsgd_diffusion.py - 2024-10-25 12:15:31,634 - Loss: 0.1924, step: 227700
+INFO - dpsgd_diffusion.py - 2024-10-25 12:16:27,456 - Loss: 0.2011, step: 227800
+INFO - dpsgd_diffusion.py - 2024-10-25 12:16:49,680 - Eps-value after 89 epochs: 9.3720
+INFO - dpsgd_diffusion.py - 2024-10-25 12:17:22,141 - Loss: 0.2069, step: 227900
+INFO - dpsgd_diffusion.py - 2024-10-25 12:18:17,568 - Loss: 0.1918, step: 228000
+INFO - dpsgd_diffusion.py - 2024-10-25 12:18:17,616 - Saving snapshot checkpoint and sampling single batch at iteration 228000.
+WARNING - image.py - 2024-10-25 12:18:18,204 - 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 - 2024-10-25 12:18:37,091 - FID at iteration 228000: 30.006635
+INFO - dpsgd_diffusion.py - 2024-10-25 12:19:32,964 - Loss: 0.2081, step: 228100
+INFO - dpsgd_diffusion.py - 2024-10-25 12:20:29,019 - Loss: 0.1841, step: 228200
+INFO - dpsgd_diffusion.py - 2024-10-25 12:21:25,608 - Loss: 0.1943, step: 228300
+INFO - dpsgd_diffusion.py - 2024-10-25 12:22:20,795 - Loss: 0.1877, step: 228400
+INFO - dpsgd_diffusion.py - 2024-10-25 12:23:15,997 - Loss: 0.1879, step: 228500
+INFO - dpsgd_diffusion.py - 2024-10-25 12:24:11,720 - Loss: 0.1885, step: 228600
+INFO - dpsgd_diffusion.py - 2024-10-25 12:25:08,361 - Loss: 0.1861, step: 228700
+INFO - dpsgd_diffusion.py - 2024-10-25 12:26:05,526 - Loss: 0.1905, step: 228800
+INFO - dpsgd_diffusion.py - 2024-10-25 12:27:00,693 - Loss: 0.1901, step: 228900
+INFO - dpsgd_diffusion.py - 2024-10-25 12:27:55,715 - Loss: 0.1980, step: 229000
+INFO - dpsgd_diffusion.py - 2024-10-25 12:28:52,456 - Loss: 0.1838, step: 229100
+INFO - dpsgd_diffusion.py - 2024-10-25 12:29:49,246 - Loss: 0.2008, step: 229200
+INFO - dpsgd_diffusion.py - 2024-10-25 12:30:44,089 - Loss: 0.1784, step: 229300
+INFO - dpsgd_diffusion.py - 2024-10-25 12:31:39,138 - Loss: 0.1995, step: 229400
+INFO - dpsgd_diffusion.py - 2024-10-25 12:32:35,780 - Loss: 0.1974, step: 229500
+INFO - dpsgd_diffusion.py - 2024-10-25 12:33:32,938 - Loss: 0.2034, step: 229600
+INFO - dpsgd_diffusion.py - 2024-10-25 12:34:29,389 - Loss: 0.1776, step: 229700
+INFO - dpsgd_diffusion.py - 2024-10-25 12:35:26,933 - Loss: 0.1857, step: 229800
+INFO - dpsgd_diffusion.py - 2024-10-25 12:36:22,698 - Loss: 0.1894, step: 229900
+INFO - dpsgd_diffusion.py - 2024-10-25 12:37:19,191 - Loss: 0.1848, step: 230000
+INFO - dpsgd_diffusion.py - 2024-10-25 12:37:19,205 - Saving snapshot checkpoint and sampling single batch at iteration 230000.
+WARNING - image.py - 2024-10-25 12:37:19,796 - 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 - 2024-10-25 12:37:38,712 - FID at iteration 230000: 30.438195
+INFO - dpsgd_diffusion.py - 2024-10-25 12:38:33,440 - Loss: 0.1974, step: 230100
+INFO - dpsgd_diffusion.py - 2024-10-25 12:39:27,532 - Loss: 0.1724, step: 230200
+INFO - dpsgd_diffusion.py - 2024-10-25 12:40:25,134 - Loss: 0.1922, step: 230300
+INFO - dpsgd_diffusion.py - 2024-10-25 12:41:19,485 - Loss: 0.1858, step: 230400
+INFO - dpsgd_diffusion.py - 2024-10-25 12:41:19,526 - Eps-value after 90 epochs: 9.4298
+INFO - dpsgd_diffusion.py - 2024-10-25 12:42:15,620 - Loss: 0.1858, step: 230500
+INFO - dpsgd_diffusion.py - 2024-10-25 12:43:12,423 - Loss: 0.1724, step: 230600
+INFO - dpsgd_diffusion.py - 2024-10-25 12:44:09,183 - Loss: 0.2097, step: 230700
+INFO - dpsgd_diffusion.py - 2024-10-25 12:45:03,324 - Loss: 0.1952, step: 230800
+INFO - dpsgd_diffusion.py - 2024-10-25 12:45:59,284 - Loss: 0.1968, step: 230900
+INFO - dpsgd_diffusion.py - 2024-10-25 12:46:55,245 - Loss: 0.1772, step: 231000
+INFO - dpsgd_diffusion.py - 2024-10-25 12:47:50,106 - Loss: 0.1899, step: 231100
+INFO - dpsgd_diffusion.py - 2024-10-25 12:48:46,085 - Loss: 0.1916, step: 231200
+INFO - dpsgd_diffusion.py - 2024-10-25 12:49:41,349 - Loss: 0.1882, step: 231300
+INFO - dpsgd_diffusion.py - 2024-10-25 12:50:35,984 - Loss: 0.1870, step: 231400
+INFO - dpsgd_diffusion.py - 2024-10-25 12:51:30,693 - Loss: 0.2044, step: 231500
+INFO - dpsgd_diffusion.py - 2024-10-25 12:52:26,506 - Loss: 0.1753, step: 231600
+INFO - dpsgd_diffusion.py - 2024-10-25 12:53:22,921 - Loss: 0.1882, step: 231700
+INFO - dpsgd_diffusion.py - 2024-10-25 12:54:19,454 - Loss: 0.1884, step: 231800
+INFO - dpsgd_diffusion.py - 2024-10-25 12:55:15,912 - Loss: 0.1944, step: 231900
+INFO - dpsgd_diffusion.py - 2024-10-25 12:56:10,916 - Loss: 0.1894, step: 232000
+INFO - dpsgd_diffusion.py - 2024-10-25 12:56:10,939 - Saving snapshot checkpoint and sampling single batch at iteration 232000.
+WARNING - image.py - 2024-10-25 12:56:11,528 - 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 - 2024-10-25 12:56:30,518 - FID at iteration 232000: 30.022328
+INFO - dpsgd_diffusion.py - 2024-10-25 12:57:25,894 - Loss: 0.2012, step: 232100
+INFO - dpsgd_diffusion.py - 2024-10-25 12:58:21,302 - Loss: 0.1887, step: 232200
+INFO - dpsgd_diffusion.py - 2024-10-25 12:59:16,987 - Loss: 0.1963, step: 232300
+INFO - dpsgd_diffusion.py - 2024-10-25 13:00:13,761 - Loss: 0.1901, step: 232400
+INFO - dpsgd_diffusion.py - 2024-10-25 13:01:08,508 - Loss: 0.1873, step: 232500
+INFO - dpsgd_diffusion.py - 2024-10-25 13:02:03,620 - Loss: 0.1987, step: 232600
+INFO - dpsgd_diffusion.py - 2024-10-25 13:02:59,952 - Loss: 0.2049, step: 232700
+INFO - dpsgd_diffusion.py - 2024-10-25 13:03:56,203 - Loss: 0.1808, step: 232800
+INFO - dpsgd_diffusion.py - 2024-10-25 13:04:50,132 - Loss: 0.1947, step: 232900
+INFO - dpsgd_diffusion.py - 2024-10-25 13:05:24,028 - Eps-value after 91 epochs: 9.4876
+INFO - dpsgd_diffusion.py - 2024-10-25 13:05:46,019 - Loss: 0.2092, step: 233000
+INFO - dpsgd_diffusion.py - 2024-10-25 13:06:42,527 - Loss: 0.1934, step: 233100
+INFO - dpsgd_diffusion.py - 2024-10-25 13:07:39,419 - Loss: 0.1911, step: 233200
+INFO - dpsgd_diffusion.py - 2024-10-25 13:08:34,331 - Loss: 0.1939, step: 233300
+INFO - dpsgd_diffusion.py - 2024-10-25 13:09:30,923 - Loss: 0.2041, step: 233400
+INFO - dpsgd_diffusion.py - 2024-10-25 13:10:27,377 - Loss: 0.1896, step: 233500
+INFO - dpsgd_diffusion.py - 2024-10-25 13:11:22,740 - Loss: 0.1922, step: 233600
+INFO - dpsgd_diffusion.py - 2024-10-25 13:12:20,600 - Loss: 0.1861, step: 233700
+INFO - dpsgd_diffusion.py - 2024-10-25 13:13:16,587 - Loss: 0.1781, step: 233800
+INFO - dpsgd_diffusion.py - 2024-10-25 13:14:10,752 - Loss: 0.1917, step: 233900
+INFO - dpsgd_diffusion.py - 2024-10-25 13:15:06,045 - Loss: 0.1809, step: 234000
+INFO - dpsgd_diffusion.py - 2024-10-25 13:15:06,089 - Saving snapshot checkpoint and sampling single batch at iteration 234000.
+WARNING - image.py - 2024-10-25 13:15:06,677 - 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 - 2024-10-25 13:15:25,613 - FID at iteration 234000: 29.786383
+INFO - dpsgd_diffusion.py - 2024-10-25 13:16:19,931 - Loss: 0.1893, step: 234100
+INFO - dpsgd_diffusion.py - 2024-10-25 13:17:15,681 - Loss: 0.1858, step: 234200
+INFO - dpsgd_diffusion.py - 2024-10-25 13:18:10,186 - Loss: 0.2002, step: 234300
+INFO - dpsgd_diffusion.py - 2024-10-25 13:19:05,201 - Loss: 0.1903, step: 234400
+INFO - dpsgd_diffusion.py - 2024-10-25 13:19:59,832 - Loss: 0.2069, step: 234500
+INFO - dpsgd_diffusion.py - 2024-10-25 13:20:55,634 - Loss: 0.2009, step: 234600
+INFO - dpsgd_diffusion.py - 2024-10-25 13:21:50,178 - Loss: 0.1947, step: 234700
+INFO - dpsgd_diffusion.py - 2024-10-25 13:22:46,395 - Loss: 0.1894, step: 234800
+INFO - dpsgd_diffusion.py - 2024-10-25 13:23:43,495 - Loss: 0.1926, step: 234900
+INFO - dpsgd_diffusion.py - 2024-10-25 13:24:39,914 - Loss: 0.1860, step: 235000
+INFO - dpsgd_diffusion.py - 2024-10-25 13:25:35,277 - Loss: 0.1848, step: 235100
+INFO - dpsgd_diffusion.py - 2024-10-25 13:26:29,379 - Loss: 0.1913, step: 235200
+INFO - dpsgd_diffusion.py - 2024-10-25 13:27:25,542 - Loss: 0.1892, step: 235300
+INFO - dpsgd_diffusion.py - 2024-10-25 13:28:20,523 - Loss: 0.2099, step: 235400
+INFO - dpsgd_diffusion.py - 2024-10-25 13:29:16,856 - Loss: 0.1760, step: 235500
+INFO - dpsgd_diffusion.py - 2024-10-25 13:29:28,432 - Eps-value after 92 epochs: 9.5453
+INFO - dpsgd_diffusion.py - 2024-10-25 13:30:13,458 - Loss: 0.1796, step: 235600
+INFO - dpsgd_diffusion.py - 2024-10-25 13:31:07,511 - Loss: 0.1882, step: 235700
+INFO - dpsgd_diffusion.py - 2024-10-25 13:32:03,490 - Loss: 0.1965, step: 235800
+INFO - dpsgd_diffusion.py - 2024-10-25 13:32:59,024 - Loss: 0.1807, step: 235900
+INFO - dpsgd_diffusion.py - 2024-10-25 13:33:55,351 - Loss: 0.1966, step: 236000
+INFO - dpsgd_diffusion.py - 2024-10-25 13:33:55,430 - Saving snapshot checkpoint and sampling single batch at iteration 236000.
+WARNING - image.py - 2024-10-25 13:33:56,287 - 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 - 2024-10-25 13:34:14,955 - FID at iteration 236000: 29.973371
+INFO - dpsgd_diffusion.py - 2024-10-25 13:35:10,738 - Loss: 0.1825, step: 236100
+INFO - dpsgd_diffusion.py - 2024-10-25 13:36:05,215 - Loss: 0.1731, step: 236200
+INFO - dpsgd_diffusion.py - 2024-10-25 13:37:00,331 - Loss: 0.1944, step: 236300
+INFO - dpsgd_diffusion.py - 2024-10-25 13:37:54,213 - Loss: 0.1904, step: 236400
+INFO - dpsgd_diffusion.py - 2024-10-25 13:38:49,517 - Loss: 0.1853, step: 236500
+INFO - dpsgd_diffusion.py - 2024-10-25 13:39:45,740 - Loss: 0.2070, step: 236600
+INFO - dpsgd_diffusion.py - 2024-10-25 13:40:40,434 - Loss: 0.1945, step: 236700
+INFO - dpsgd_diffusion.py - 2024-10-25 13:41:37,121 - Loss: 0.1728, step: 236800
+INFO - dpsgd_diffusion.py - 2024-10-25 13:42:35,598 - Loss: 0.1840, step: 236900
+INFO - dpsgd_diffusion.py - 2024-10-25 13:43:30,905 - Loss: 0.2188, step: 237000
+INFO - dpsgd_diffusion.py - 2024-10-25 13:44:26,246 - Loss: 0.1985, step: 237100
+INFO - dpsgd_diffusion.py - 2024-10-25 13:45:21,270 - Loss: 0.1970, step: 237200
+INFO - dpsgd_diffusion.py - 2024-10-25 13:46:16,969 - Loss: 0.1770, step: 237300
+INFO - dpsgd_diffusion.py - 2024-10-25 13:47:14,032 - Loss: 0.1891, step: 237400
+INFO - dpsgd_diffusion.py - 2024-10-25 13:48:10,753 - Loss: 0.1836, step: 237500
+INFO - dpsgd_diffusion.py - 2024-10-25 13:49:08,040 - Loss: 0.1872, step: 237600
+INFO - dpsgd_diffusion.py - 2024-10-25 13:50:03,263 - Loss: 0.1893, step: 237700
+INFO - dpsgd_diffusion.py - 2024-10-25 13:50:58,373 - Loss: 0.1809, step: 237800
+INFO - dpsgd_diffusion.py - 2024-10-25 13:51:53,507 - Loss: 0.1934, step: 237900
+INFO - dpsgd_diffusion.py - 2024-10-25 13:52:48,366 - Loss: 0.2029, step: 238000
+INFO - dpsgd_diffusion.py - 2024-10-25 13:52:48,379 - Saving snapshot checkpoint and sampling single batch at iteration 238000.
+WARNING - image.py - 2024-10-25 13:52:48,967 - 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 - 2024-10-25 13:53:07,319 - FID at iteration 238000: 30.097013
+INFO - dpsgd_diffusion.py - 2024-10-25 13:53:52,563 - Eps-value after 93 epochs: 9.6031
+INFO - dpsgd_diffusion.py - 2024-10-25 13:54:04,064 - Loss: 0.1840, step: 238100
+INFO - dpsgd_diffusion.py - 2024-10-25 13:54:59,086 - Loss: 0.1998, step: 238200
+INFO - dpsgd_diffusion.py - 2024-10-25 13:55:54,276 - Loss: 0.1880, step: 238300
+INFO - dpsgd_diffusion.py - 2024-10-25 13:56:50,218 - Loss: 0.1769, step: 238400
+INFO - dpsgd_diffusion.py - 2024-10-25 13:57:46,676 - Loss: 0.2178, step: 238500
+INFO - dpsgd_diffusion.py - 2024-10-25 13:58:41,033 - Loss: 0.2001, step: 238600
+INFO - dpsgd_diffusion.py - 2024-10-25 13:59:36,880 - Loss: 0.2012, step: 238700
+INFO - dpsgd_diffusion.py - 2024-10-25 14:00:34,845 - Loss: 0.1889, step: 238800
+INFO - dpsgd_diffusion.py - 2024-10-25 14:01:29,344 - Loss: 0.1942, step: 238900
+INFO - dpsgd_diffusion.py - 2024-10-25 14:02:23,915 - Loss: 0.1910, step: 239000
+INFO - dpsgd_diffusion.py - 2024-10-25 14:03:19,551 - Loss: 0.2205, step: 239100
+INFO - dpsgd_diffusion.py - 2024-10-25 14:04:14,148 - Loss: 0.1971, step: 239200
+INFO - dpsgd_diffusion.py - 2024-10-25 14:05:07,939 - Loss: 0.1916, step: 239300
+INFO - dpsgd_diffusion.py - 2024-10-25 14:06:03,466 - Loss: 0.2020, step: 239400
+INFO - dpsgd_diffusion.py - 2024-10-25 14:07:00,129 - Loss: 0.1891, step: 239500
+INFO - dpsgd_diffusion.py - 2024-10-25 14:07:55,734 - Loss: 0.1939, step: 239600
+INFO - dpsgd_diffusion.py - 2024-10-25 14:08:52,291 - Loss: 0.1941, step: 239700
+INFO - dpsgd_diffusion.py - 2024-10-25 14:09:46,346 - Loss: 0.1917, step: 239800
+INFO - dpsgd_diffusion.py - 2024-10-25 14:10:40,813 - Loss: 0.1856, step: 239900
+INFO - dpsgd_diffusion.py - 2024-10-25 14:11:35,022 - Loss: 0.2090, step: 240000
+INFO - dpsgd_diffusion.py - 2024-10-25 14:11:35,043 - Saving snapshot checkpoint and sampling single batch at iteration 240000.
+WARNING - image.py - 2024-10-25 14:11:35,633 - 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 - 2024-10-25 14:11:53,513 - FID at iteration 240000: 29.717175
+INFO - dpsgd_diffusion.py - 2024-10-25 14:12:50,359 - Loss: 0.2000, step: 240100
+INFO - dpsgd_diffusion.py - 2024-10-25 14:13:45,623 - Loss: 0.1776, step: 240200
+INFO - dpsgd_diffusion.py - 2024-10-25 14:14:41,232 - Loss: 0.1888, step: 240300
+INFO - dpsgd_diffusion.py - 2024-10-25 14:15:37,654 - Loss: 0.1752, step: 240400
+INFO - dpsgd_diffusion.py - 2024-10-25 14:16:32,955 - Loss: 0.1813, step: 240500
+INFO - dpsgd_diffusion.py - 2024-10-25 14:17:28,954 - Loss: 0.1769, step: 240600
+INFO - dpsgd_diffusion.py - 2024-10-25 14:17:50,521 - Eps-value after 94 epochs: 9.6609
+INFO - dpsgd_diffusion.py - 2024-10-25 14:18:25,401 - Loss: 0.1845, step: 240700
+INFO - dpsgd_diffusion.py - 2024-10-25 14:19:22,287 - Loss: 0.1829, step: 240800
+INFO - dpsgd_diffusion.py - 2024-10-25 14:20:16,469 - Loss: 0.2016, step: 240900
+INFO - dpsgd_diffusion.py - 2024-10-25 14:21:10,538 - Loss: 0.1882, step: 241000
+INFO - dpsgd_diffusion.py - 2024-10-25 14:22:05,112 - Loss: 0.1787, step: 241100
+INFO - dpsgd_diffusion.py - 2024-10-25 14:23:00,744 - Loss: 0.1983, step: 241200
+INFO - dpsgd_diffusion.py - 2024-10-25 14:23:57,462 - Loss: 0.1862, step: 241300
+INFO - dpsgd_diffusion.py - 2024-10-25 14:24:51,860 - Loss: 0.1812, step: 241400
+INFO - dpsgd_diffusion.py - 2024-10-25 14:25:47,335 - Loss: 0.1767, step: 241500
+INFO - dpsgd_diffusion.py - 2024-10-25 14:26:42,577 - Loss: 0.2077, step: 241600
+INFO - dpsgd_diffusion.py - 2024-10-25 14:27:36,813 - Loss: 0.2018, step: 241700
+INFO - dpsgd_diffusion.py - 2024-10-25 14:28:31,344 - Loss: 0.2027, step: 241800
+INFO - dpsgd_diffusion.py - 2024-10-25 14:29:27,249 - Loss: 0.1735, step: 241900
+INFO - dpsgd_diffusion.py - 2024-10-25 14:30:22,105 - Loss: 0.1817, step: 242000
+INFO - dpsgd_diffusion.py - 2024-10-25 14:30:22,118 - Saving snapshot checkpoint and sampling single batch at iteration 242000.
+WARNING - image.py - 2024-10-25 14:30:22,708 - 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 - 2024-10-25 14:30:41,379 - FID at iteration 242000: 29.194818
+INFO - dpsgd_diffusion.py - 2024-10-25 14:31:36,417 - Loss: 0.1747, step: 242100
+INFO - dpsgd_diffusion.py - 2024-10-25 14:32:29,934 - Loss: 0.2068, step: 242200
+INFO - dpsgd_diffusion.py - 2024-10-25 14:33:25,283 - Loss: 0.1830, step: 242300
+INFO - dpsgd_diffusion.py - 2024-10-25 14:34:21,006 - Loss: 0.1922, step: 242400
+INFO - dpsgd_diffusion.py - 2024-10-25 14:35:16,206 - Loss: 0.1869, step: 242500
+INFO - dpsgd_diffusion.py - 2024-10-25 14:36:12,020 - Loss: 0.2003, step: 242600
+INFO - dpsgd_diffusion.py - 2024-10-25 14:37:08,257 - Loss: 0.2089, step: 242700
+INFO - dpsgd_diffusion.py - 2024-10-25 14:38:04,603 - Loss: 0.2115, step: 242800
+INFO - dpsgd_diffusion.py - 2024-10-25 14:39:00,579 - Loss: 0.1874, step: 242900
+INFO - dpsgd_diffusion.py - 2024-10-25 14:39:55,899 - Loss: 0.1874, step: 243000
+INFO - dpsgd_diffusion.py - 2024-10-25 14:40:50,810 - Loss: 0.2049, step: 243100
+INFO - dpsgd_diffusion.py - 2024-10-25 14:41:45,782 - Loss: 0.2024, step: 243200
+INFO - dpsgd_diffusion.py - 2024-10-25 14:41:45,823 - Eps-value after 95 epochs: 9.7178
+INFO - dpsgd_diffusion.py - 2024-10-25 14:42:43,049 - Loss: 0.1929, step: 243300
+INFO - dpsgd_diffusion.py - 2024-10-25 14:43:37,936 - Loss: 0.2046, step: 243400
+INFO - dpsgd_diffusion.py - 2024-10-25 14:44:32,930 - Loss: 0.1810, step: 243500
+INFO - dpsgd_diffusion.py - 2024-10-25 14:45:28,009 - Loss: 0.2034, step: 243600
+INFO - dpsgd_diffusion.py - 2024-10-25 14:46:24,859 - Loss: 0.2092, step: 243700
+INFO - dpsgd_diffusion.py - 2024-10-25 14:47:20,349 - Loss: 0.1829, step: 243800
+INFO - dpsgd_diffusion.py - 2024-10-25 14:48:14,854 - Loss: 0.1918, step: 243900
+INFO - dpsgd_diffusion.py - 2024-10-25 14:49:10,048 - Loss: 0.1784, step: 244000
+INFO - dpsgd_diffusion.py - 2024-10-25 14:49:10,061 - Saving snapshot checkpoint and sampling single batch at iteration 244000.
+WARNING - image.py - 2024-10-25 14:49:10,651 - 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 - 2024-10-25 14:49:28,982 - FID at iteration 244000: 29.033753
+INFO - dpsgd_diffusion.py - 2024-10-25 14:50:23,894 - Loss: 0.1852, step: 244100
+INFO - dpsgd_diffusion.py - 2024-10-25 14:51:18,397 - Loss: 0.1903, step: 244200
+INFO - dpsgd_diffusion.py - 2024-10-25 14:52:15,060 - Loss: 0.1981, step: 244300
+INFO - dpsgd_diffusion.py - 2024-10-25 14:53:10,101 - Loss: 0.1686, step: 244400
+INFO - dpsgd_diffusion.py - 2024-10-25 14:54:05,426 - Loss: 0.2009, step: 244500
+INFO - dpsgd_diffusion.py - 2024-10-25 14:54:59,760 - Loss: 0.1872, step: 244600
+INFO - dpsgd_diffusion.py - 2024-10-25 14:55:55,644 - Loss: 0.1872, step: 244700
+INFO - dpsgd_diffusion.py - 2024-10-25 14:56:50,565 - Loss: 0.1775, step: 244800
+INFO - dpsgd_diffusion.py - 2024-10-25 14:57:46,919 - Loss: 0.1897, step: 244900
+INFO - dpsgd_diffusion.py - 2024-10-25 14:58:41,157 - Loss: 0.1994, step: 245000
+INFO - dpsgd_diffusion.py - 2024-10-25 14:59:37,250 - Loss: 0.1951, step: 245100
+INFO - dpsgd_diffusion.py - 2024-10-25 15:00:32,533 - Loss: 0.1782, step: 245200
+INFO - dpsgd_diffusion.py - 2024-10-25 15:01:26,347 - Loss: 0.1854, step: 245300
+INFO - dpsgd_diffusion.py - 2024-10-25 15:02:21,415 - Loss: 0.1873, step: 245400
+INFO - dpsgd_diffusion.py - 2024-10-25 15:03:17,457 - Loss: 0.2067, step: 245500
+INFO - dpsgd_diffusion.py - 2024-10-25 15:04:11,507 - Loss: 0.2133, step: 245600
+INFO - dpsgd_diffusion.py - 2024-10-25 15:05:06,937 - Loss: 0.1819, step: 245700
+INFO - dpsgd_diffusion.py - 2024-10-25 15:05:40,686 - Eps-value after 96 epochs: 9.7739
+INFO - dpsgd_diffusion.py - 2024-10-25 15:06:03,648 - Loss: 0.1807, step: 245800
+INFO - dpsgd_diffusion.py - 2024-10-25 15:06:59,235 - Loss: 0.1910, step: 245900
+INFO - dpsgd_diffusion.py - 2024-10-25 15:07:55,358 - Loss: 0.1780, step: 246000
+INFO - dpsgd_diffusion.py - 2024-10-25 15:07:55,371 - Saving snapshot checkpoint and sampling single batch at iteration 246000.
+WARNING - image.py - 2024-10-25 15:07:55,973 - 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 - 2024-10-25 15:08:14,354 - FID at iteration 246000: 29.363702
+INFO - dpsgd_diffusion.py - 2024-10-25 15:09:10,648 - Loss: 0.2044, step: 246100
+INFO - dpsgd_diffusion.py - 2024-10-25 15:10:06,476 - Loss: 0.2011, step: 246200
+INFO - dpsgd_diffusion.py - 2024-10-25 15:11:03,224 - Loss: 0.2133, step: 246300
+INFO - dpsgd_diffusion.py - 2024-10-25 15:11:59,472 - Loss: 0.1985, step: 246400
+INFO - dpsgd_diffusion.py - 2024-10-25 15:12:55,696 - Loss: 0.1977, step: 246500
+INFO - dpsgd_diffusion.py - 2024-10-25 15:13:50,649 - Loss: 0.2012, step: 246600
+INFO - dpsgd_diffusion.py - 2024-10-25 15:14:48,407 - Loss: 0.2015, step: 246700
+INFO - dpsgd_diffusion.py - 2024-10-25 15:15:44,028 - Loss: 0.1995, step: 246800
+INFO - dpsgd_diffusion.py - 2024-10-25 15:16:40,120 - Loss: 0.1871, step: 246900
+INFO - dpsgd_diffusion.py - 2024-10-25 15:17:36,486 - Loss: 0.1887, step: 247000
+INFO - dpsgd_diffusion.py - 2024-10-25 15:18:31,735 - Loss: 0.1855, step: 247100
+INFO - dpsgd_diffusion.py - 2024-10-25 15:19:27,595 - Loss: 0.1863, step: 247200
+INFO - dpsgd_diffusion.py - 2024-10-25 15:20:23,708 - Loss: 0.1989, step: 247300
+INFO - dpsgd_diffusion.py - 2024-10-25 15:21:20,626 - Loss: 0.1855, step: 247400
+INFO - dpsgd_diffusion.py - 2024-10-25 15:22:16,974 - Loss: 0.1938, step: 247500
+INFO - dpsgd_diffusion.py - 2024-10-25 15:23:12,928 - Loss: 0.1940, step: 247600
+INFO - dpsgd_diffusion.py - 2024-10-25 15:24:08,524 - Loss: 0.1970, step: 247700
+INFO - dpsgd_diffusion.py - 2024-10-25 15:25:05,311 - Loss: 0.2016, step: 247800
+INFO - dpsgd_diffusion.py - 2024-10-25 15:26:00,321 - Loss: 0.2035, step: 247900
+INFO - dpsgd_diffusion.py - 2024-10-25 15:26:54,505 - Loss: 0.1733, step: 248000
+INFO - dpsgd_diffusion.py - 2024-10-25 15:26:54,525 - Saving snapshot checkpoint and sampling single batch at iteration 248000.
+WARNING - image.py - 2024-10-25 15:26:55,116 - 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 - 2024-10-25 15:27:13,569 - FID at iteration 248000: 29.091327
+INFO - dpsgd_diffusion.py - 2024-10-25 15:28:08,270 - Loss: 0.1749, step: 248100
+INFO - dpsgd_diffusion.py - 2024-10-25 15:29:03,161 - Loss: 0.1812, step: 248200
+INFO - dpsgd_diffusion.py - 2024-10-25 15:29:57,767 - Loss: 0.1850, step: 248300
+INFO - dpsgd_diffusion.py - 2024-10-25 15:30:08,673 - Eps-value after 97 epochs: 9.8299
+INFO - dpsgd_diffusion.py - 2024-10-25 15:30:53,834 - Loss: 0.1964, step: 248400
+INFO - dpsgd_diffusion.py - 2024-10-25 15:31:50,193 - Loss: 0.1883, step: 248500
+INFO - dpsgd_diffusion.py - 2024-10-25 15:32:46,379 - Loss: 0.2001, step: 248600
+INFO - dpsgd_diffusion.py - 2024-10-25 15:33:39,822 - Loss: 0.2015, step: 248700
+INFO - dpsgd_diffusion.py - 2024-10-25 15:34:34,334 - Loss: 0.1934, step: 248800
+INFO - dpsgd_diffusion.py - 2024-10-25 15:35:29,417 - Loss: 0.1885, step: 248900
+INFO - dpsgd_diffusion.py - 2024-10-25 15:36:24,391 - Loss: 0.2020, step: 249000
+INFO - dpsgd_diffusion.py - 2024-10-25 15:37:18,761 - Loss: 0.1934, step: 249100
+INFO - dpsgd_diffusion.py - 2024-10-25 15:38:13,698 - Loss: 0.2020, step: 249200
+INFO - dpsgd_diffusion.py - 2024-10-25 15:39:08,516 - Loss: 0.1864, step: 249300
+INFO - dpsgd_diffusion.py - 2024-10-25 15:40:03,422 - Loss: 0.1930, step: 249400
+INFO - dpsgd_diffusion.py - 2024-10-25 15:40:59,404 - Loss: 0.1703, step: 249500
+INFO - dpsgd_diffusion.py - 2024-10-25 15:41:53,667 - Loss: 0.2019, step: 249600
+INFO - dpsgd_diffusion.py - 2024-10-25 15:42:47,546 - Loss: 0.1793, step: 249700
+INFO - dpsgd_diffusion.py - 2024-10-25 15:43:41,235 - Loss: 0.1818, step: 249800
+INFO - dpsgd_diffusion.py - 2024-10-25 15:44:38,366 - Loss: 0.1845, step: 249900
+INFO - dpsgd_diffusion.py - 2024-10-25 15:45:34,450 - Loss: 0.1893, step: 250000
+INFO - dpsgd_diffusion.py - 2024-10-25 15:45:34,465 - Saving snapshot checkpoint and sampling single batch at iteration 250000.
+WARNING - image.py - 2024-10-25 15:45:35,194 - 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 - 2024-10-25 15:45:53,622 - FID at iteration 250000: 29.059185
+INFO - dpsgd_diffusion.py - 2024-10-25 15:46:50,899 - Loss: 0.2033, step: 250100
+INFO - dpsgd_diffusion.py - 2024-10-25 15:47:44,968 - Loss: 0.1881, step: 250200
+INFO - dpsgd_diffusion.py - 2024-10-25 15:48:40,136 - Loss: 0.2026, step: 250300
+INFO - dpsgd_diffusion.py - 2024-10-25 15:49:35,552 - Loss: 0.1960, step: 250400
+INFO - dpsgd_diffusion.py - 2024-10-25 15:50:30,517 - Loss: 0.2018, step: 250500
+INFO - dpsgd_diffusion.py - 2024-10-25 15:51:26,336 - Loss: 0.1980, step: 250600
+INFO - dpsgd_diffusion.py - 2024-10-25 15:52:22,308 - Loss: 0.1991, step: 250700
+INFO - dpsgd_diffusion.py - 2024-10-25 15:53:18,534 - Loss: 0.1949, step: 250800
+INFO - dpsgd_diffusion.py - 2024-10-25 15:54:03,342 - Eps-value after 98 epochs: 9.8860
+INFO - dpsgd_diffusion.py - 2024-10-25 15:54:14,144 - Loss: 0.1737, step: 250900
+INFO - dpsgd_diffusion.py - 2024-10-25 15:55:08,443 - Loss: 0.1907, step: 251000
+INFO - dpsgd_diffusion.py - 2024-10-25 15:56:05,322 - Loss: 0.1778, step: 251100
+INFO - dpsgd_diffusion.py - 2024-10-25 15:57:01,809 - Loss: 0.1841, step: 251200
+INFO - dpsgd_diffusion.py - 2024-10-25 15:57:57,678 - Loss: 0.1963, step: 251300
+INFO - dpsgd_diffusion.py - 2024-10-25 15:58:52,388 - Loss: 0.2075, step: 251400
+INFO - dpsgd_diffusion.py - 2024-10-25 15:59:47,802 - Loss: 0.2122, step: 251500
+INFO - dpsgd_diffusion.py - 2024-10-25 16:00:42,646 - Loss: 0.1979, step: 251600
+INFO - dpsgd_diffusion.py - 2024-10-25 16:01:38,046 - Loss: 0.1925, step: 251700
+INFO - dpsgd_diffusion.py - 2024-10-25 16:02:34,274 - Loss: 0.1791, step: 251800
+INFO - dpsgd_diffusion.py - 2024-10-25 16:03:29,841 - Loss: 0.1879, step: 251900
+INFO - dpsgd_diffusion.py - 2024-10-25 16:04:25,184 - Loss: 0.1981, step: 252000
+INFO - dpsgd_diffusion.py - 2024-10-25 16:04:25,198 - Saving snapshot checkpoint and sampling single batch at iteration 252000.
+WARNING - image.py - 2024-10-25 16:04:25,788 - 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 - 2024-10-25 16:04:44,286 - FID at iteration 252000: 28.730869
+INFO - dpsgd_diffusion.py - 2024-10-25 16:05:39,817 - Loss: 0.1847, step: 252100
+INFO - dpsgd_diffusion.py - 2024-10-25 16:06:36,418 - Loss: 0.1895, step: 252200
+INFO - dpsgd_diffusion.py - 2024-10-25 16:07:31,946 - Loss: 0.1821, step: 252300
+INFO - dpsgd_diffusion.py - 2024-10-25 16:08:28,182 - Loss: 0.2098, step: 252400
+INFO - dpsgd_diffusion.py - 2024-10-25 16:09:23,941 - Loss: 0.1962, step: 252500
+INFO - dpsgd_diffusion.py - 2024-10-25 16:10:19,109 - Loss: 0.1881, step: 252600
+INFO - dpsgd_diffusion.py - 2024-10-25 16:11:13,289 - Loss: 0.1731, step: 252700
+INFO - dpsgd_diffusion.py - 2024-10-25 16:12:07,949 - Loss: 0.1855, step: 252800
+INFO - dpsgd_diffusion.py - 2024-10-25 16:13:03,623 - Loss: 0.1977, step: 252900
+INFO - dpsgd_diffusion.py - 2024-10-25 16:13:59,002 - Loss: 0.1852, step: 253000
+INFO - dpsgd_diffusion.py - 2024-10-25 16:14:54,243 - Loss: 0.1826, step: 253100
+INFO - dpsgd_diffusion.py - 2024-10-25 16:15:49,686 - Loss: 0.1792, step: 253200
+INFO - dpsgd_diffusion.py - 2024-10-25 16:16:45,715 - Loss: 0.1936, step: 253300
+INFO - dpsgd_diffusion.py - 2024-10-25 16:17:41,791 - Loss: 0.1819, step: 253400
+INFO - dpsgd_diffusion.py - 2024-10-25 16:18:03,934 - Eps-value after 99 epochs: 9.9421
+INFO - dpsgd_diffusion.py - 2024-10-25 16:18:36,703 - Loss: 0.1994, step: 253500
+INFO - dpsgd_diffusion.py - 2024-10-25 16:19:33,956 - Loss: 0.1929, step: 253600
+INFO - dpsgd_diffusion.py - 2024-10-25 16:20:29,009 - Loss: 0.1853, step: 253700
+INFO - dpsgd_diffusion.py - 2024-10-25 16:21:25,084 - Loss: 0.1787, step: 253800
+INFO - dpsgd_diffusion.py - 2024-10-25 16:22:21,458 - Loss: 0.1849, step: 253900
+INFO - dpsgd_diffusion.py - 2024-10-25 16:23:17,264 - Loss: 0.1759, step: 254000
+INFO - dpsgd_diffusion.py - 2024-10-25 16:23:17,277 - Saving snapshot checkpoint and sampling single batch at iteration 254000.
+WARNING - image.py - 2024-10-25 16:23:17,867 - 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 - 2024-10-25 16:23:36,315 - FID at iteration 254000: 28.407127
+INFO - dpsgd_diffusion.py - 2024-10-25 16:24:32,927 - Loss: 0.1965, step: 254100
+INFO - dpsgd_diffusion.py - 2024-10-25 16:25:29,456 - Loss: 0.1929, step: 254200
+INFO - dpsgd_diffusion.py - 2024-10-25 16:26:26,428 - Loss: 0.2053, step: 254300
+INFO - dpsgd_diffusion.py - 2024-10-25 16:27:23,142 - Loss: 0.1825, step: 254400
+INFO - dpsgd_diffusion.py - 2024-10-25 16:28:18,654 - Loss: 0.1745, step: 254500
+INFO - dpsgd_diffusion.py - 2024-10-25 16:29:13,640 - Loss: 0.1887, step: 254600
+INFO - dpsgd_diffusion.py - 2024-10-25 16:30:09,396 - Loss: 0.1963, step: 254700
+INFO - dpsgd_diffusion.py - 2024-10-25 16:31:05,545 - Loss: 0.1848, step: 254800
+INFO - dpsgd_diffusion.py - 2024-10-25 16:32:01,702 - Loss: 0.2031, step: 254900
+INFO - dpsgd_diffusion.py - 2024-10-25 16:32:57,637 - Loss: 0.1931, step: 255000
+INFO - dpsgd_diffusion.py - 2024-10-25 16:33:52,575 - Loss: 0.1957, step: 255100
+INFO - dpsgd_diffusion.py - 2024-10-25 16:34:48,091 - Loss: 0.2015, step: 255200
+INFO - dpsgd_diffusion.py - 2024-10-25 16:35:44,610 - Loss: 0.1939, step: 255300
+INFO - dpsgd_diffusion.py - 2024-10-25 16:36:42,130 - Loss: 0.1817, step: 255400
+INFO - dpsgd_diffusion.py - 2024-10-25 16:37:37,778 - Loss: 0.2096, step: 255500
+INFO - dpsgd_diffusion.py - 2024-10-25 16:38:33,522 - Loss: 0.1897, step: 255600
+INFO - dpsgd_diffusion.py - 2024-10-25 16:39:30,274 - Loss: 0.2039, step: 255700
+INFO - dpsgd_diffusion.py - 2024-10-25 16:40:25,956 - Loss: 0.1886, step: 255800
+INFO - dpsgd_diffusion.py - 2024-10-25 16:41:21,412 - Loss: 0.2064, step: 255900
+INFO - dpsgd_diffusion.py - 2024-10-25 16:42:16,795 - Loss: 0.1973, step: 256000
+INFO - dpsgd_diffusion.py - 2024-10-25 16:42:16,839 - Eps-value after 100 epochs: 9.9981
+INFO - dpsgd_diffusion.py - 2024-10-25 16:42:17,599 - Saving final checkpoint.
+INFO - dpsgd_diffusion.py - 2024-10-25 16:42:17,602 - start to generate 60000 samples
+INFO - dpsgd_diffusion.py - 2024-10-25 16:51:19,142 - Generation Finished!INFO - dataset_loader.py - 2024-10-27 00:11:33,923 - delta is reset as 5.800209926283058e-07
+INFO - evaluator.py - 2024-10-27 00:12:02,140 - Epoch: 0 Train acc: 68.70363636363636 Val acc: 74.9802326894838 Test acc72.49774571686204; Train loss: 0.0024303967091170225 Val loss: 0.0007218172438977115
+INFO - evaluator.py - 2024-10-27 00:12:16,016 - Epoch: 1 Train acc: 83.68727272727273 Val acc: 89.83395459166384 Test acc90.00100190361687; Train loss: 0.0014803617108951916 Val loss: 0.0002965760876684556
+INFO - evaluator.py - 2024-10-27 00:12:29,488 - Epoch: 2 Train acc: 87.17818181818183 Val acc: 89.12233141308032 Test acc90.36168720569081; Train loss: 0.0011756244334307584 Val loss: 0.00025379199753046763
+INFO - evaluator.py - 2024-10-27 00:12:43,442 - Epoch: 3 Train acc: 89.04545454545455 Val acc: 87.19078278549645 Test acc85.76795912233243; Train loss: 0.0010145390415733512 Val loss: 0.0003127548067257066
+INFO - evaluator.py - 2024-10-27 00:12:57,298 - Epoch: 4 Train acc: 90.53818181818181 Val acc: 90.34790466508528 Test acc90.46688708546237; Train loss: 0.0008995416210456328 Val loss: 0.00023677416030503957
+INFO - evaluator.py - 2024-10-27 00:13:10,858 - Epoch: 5 Train acc: 91.27818181818182 Val acc: 91.02564102564102 Test acc91.61406672678089; Train loss: 0.0008252146783200178 Val loss: 0.0002215949240531219
+INFO - evaluator.py - 2024-10-27 00:13:24,560 - Epoch: 6 Train acc: 91.90363636363637 Val acc: 91.64689935615046 Test acc91.69421901613065; Train loss: 0.0007734862243587321 Val loss: 0.00021905284200322757
+INFO - evaluator.py - 2024-10-27 00:13:38,197 - Epoch: 7 Train acc: 92.59818181818181 Val acc: 84.15226476900486 Test acc86.23384430417794; Train loss: 0.0007189417933875864 Val loss: 0.0003670668209472996
+INFO - evaluator.py - 2024-10-27 00:13:52,322 - Epoch: 8 Train acc: 92.90363636363637 Val acc: 91.31367897887722 Test acc91.31349564171927; Train loss: 0.000679678949713707 Val loss: 0.0002171456499058258
+INFO - evaluator.py - 2024-10-27 00:14:06,606 - Epoch: 9 Train acc: 93.46363636363635 Val acc: 89.42731277533039 Test acc88.31780382727182; Train loss: 0.0006338547858324917 Val loss: 0.0002609855755970032
+INFO - evaluator.py - 2024-10-27 00:14:20,481 - Epoch: 10 Train acc: 93.82363636363637 Val acc: 89.19010504913588 Test acc89.91083057809838; Train loss: 0.000601332746852528 Val loss: 0.0002593422862118844
+INFO - evaluator.py - 2024-10-27 00:14:34,026 - Epoch: 11 Train acc: 94.09818181818181 Val acc: 88.7947588388117 Test acc87.26580502955615; Train loss: 0.0005765658090060408 Val loss: 0.00028996663579398946
+INFO - evaluator.py - 2024-10-27 00:14:47,973 - Epoch: 12 Train acc: 94.50181818181818 Val acc: 90.53992996724274 Test acc90.18635407273821; Train loss: 0.0005420600163665685 Val loss: 0.00024060546463119988
+INFO - evaluator.py - 2024-10-27 00:15:01,906 - Epoch: 13 Train acc: 94.49272727272728 Val acc: 89.8283067886592 Test acc90.6021440737401; Train loss: 0.0005318580955266952 Val loss: 0.0002590025328356412
+INFO - evaluator.py - 2024-10-27 00:15:15,401 - Epoch: 14 Train acc: 95.01272727272728 Val acc: 87.81768891901051 Test acc86.32902514778078; Train loss: 0.0004970399444753473 Val loss: 0.00030951999404747364
+INFO - evaluator.py - 2024-10-27 00:15:28,693 - Epoch: 15 Train acc: 95.46727272727273 Val acc: 89.59674686546933 Test acc89.8958020238453; Train loss: 0.0004498855140398849 Val loss: 0.00026237609059099937
+INFO - evaluator.py - 2024-10-27 00:15:42,729 - Epoch: 16 Train acc: 95.4090909090909 Val acc: 88.17350050830227 Test acc86.6295962328424; Train loss: 0.00044497794637625866 Val loss: 0.00028981727828928863
+INFO - evaluator.py - 2024-10-27 00:15:56,717 - Epoch: 17 Train acc: 95.85636363636364 Val acc: 69.79554953123235 Test acc72.54784089770564; Train loss: 0.0004013825319030068 Val loss: 0.0012677324083475397
+INFO - evaluator.py - 2024-10-27 00:16:10,746 - Epoch: 18 Train acc: 96.33454545454545 Val acc: 89.7379419405851 Test acc89.09928864843202; Train loss: 0.0003690207826820287 Val loss: 0.0002642137590847513
+INFO - evaluator.py - 2024-10-27 00:16:23,871 - Epoch: 19 Train acc: 96.56181818181818 Val acc: 78.89980797469785 Test acc81.52990682296362; Train loss: 0.00033712383176792753 Val loss: 0.000661987703182893
+INFO - evaluator.py - 2024-10-27 00:16:37,393 - Epoch: 20 Train acc: 98.63818181818182 Val acc: 90.79408110245114 Test acc91.06301973750125; Train loss: 0.0001444783188317987 Val loss: 0.0003230589874636638
+INFO - evaluator.py - 2024-10-27 00:16:51,309 - Epoch: 21 Train acc: 99.42727272727274 Val acc: 90.81667231446967 Test acc90.45686804929366; Train loss: 6.549822095019574e-05 Val loss: 0.00043737294174891523
+INFO - evaluator.py - 2024-10-27 00:17:05,389 - Epoch: 22 Train acc: 99.66363636363637 Val acc: 88.61402914266351 Test acc87.07544334235047; Train loss: 3.8446388005765833e-05 Val loss: 0.0007741162093712034
+INFO - evaluator.py - 2024-10-27 00:17:19,121 - Epoch: 23 Train acc: 99.67636363636365 Val acc: 84.58714560036145 Test acc82.12102995691815; Train loss: 3.4577838985503397e-05 Val loss: 0.0013328898320639185
+INFO - evaluator.py - 2024-10-27 00:17:32,670 - Epoch: 24 Train acc: 99.6290909090909 Val acc: 90.27448322602508 Test acc89.53511672177137; Train loss: 3.673182395181026e-05 Val loss: 0.0007109427659454743
+INFO - evaluator.py - 2024-10-27 00:17:46,230 - Epoch: 25 Train acc: 99.7 Val acc: 90.2236529989834 Test acc89.4549644324216; Train loss: 3.27443075731439e-05 Val loss: 0.0007945985884554949
+INFO - evaluator.py - 2024-10-27 00:18:00,291 - Epoch: 26 Train acc: 99.71636363636364 Val acc: 89.69275951654807 Test acc88.85382226229837; Train loss: 2.9285607309165327e-05 Val loss: 0.0008637221347244799
+INFO - evaluator.py - 2024-10-27 00:18:14,421 - Epoch: 27 Train acc: 99.69454545454546 Val acc: 84.07319552694003 Test acc81.73028754633805; Train loss: 3.372830631931058e-05 Val loss: 0.0016910769327284994
+INFO - evaluator.py - 2024-10-27 00:18:28,074 - Epoch: 28 Train acc: 99.82181818181817 Val acc: 89.75488534959901 Test acc88.68850816551448; Train loss: 2.0220740109851415e-05 Val loss: 0.0010789318985822147
+INFO - evaluator.py - 2024-10-27 00:18:41,519 - Epoch: 29 Train acc: 99.67272727272727 Val acc: 91.30803117587259 Test acc91.14317202685102; Train loss: 3.2612264273286033e-05 Val loss: 0.0007108364598866602
+INFO - evaluator.py - 2024-10-27 00:18:55,312 - Epoch: 30 Train acc: 99.71272727272728 Val acc: 89.54591663842764 Test acc88.41298467087466; Train loss: 2.91033403201833e-05 Val loss: 0.0010297314387909319
+INFO - evaluator.py - 2024-10-27 00:19:09,404 - Epoch: 31 Train acc: 99.72181818181818 Val acc: 91.25720094883091 Test acc91.14818154493538; Train loss: 3.108918924408499e-05 Val loss: 0.0008316385595189586
+INFO - evaluator.py - 2024-10-27 00:19:23,482 - Epoch: 32 Train acc: 99.76 Val acc: 91.14989269174292 Test acc90.95281033964532; Train loss: 2.7252525140383197e-05 Val loss: 0.0007468967582901279
+INFO - evaluator.py - 2024-10-27 00:19:36,979 - Epoch: 33 Train acc: 99.68 Val acc: 87.45622952671411 Test acc85.60264502554854; Train loss: 3.3333935153628275e-05 Val loss: 0.0012909441691501711
+INFO - evaluator.py - 2024-10-27 00:19:50,669 - Epoch: 34 Train acc: 99.80909090909091 Val acc: 90.98045860160397 Test acc90.75242961627092; Train loss: 1.9776868892553134e-05 Val loss: 0.0008587659576794771
+INFO - evaluator.py - 2024-10-27 00:20:04,389 - Epoch: 35 Train acc: 99.82545454545455 Val acc: 89.56850785044618 Test acc88.723574792105; Train loss: 1.7618474829545118e-05 Val loss: 0.001145965188798912
+INFO - evaluator.py - 2024-10-27 00:20:18,614 - Epoch: 36 Train acc: 99.72909090909091 Val acc: 90.84491132949283 Test acc91.02795311091073; Train loss: 2.622131646990469e-05 Val loss: 0.0009161636652440711
+INFO - evaluator.py - 2024-10-27 00:20:32,587 - Epoch: 37 Train acc: 99.72909090909091 Val acc: 91.12165367671976 Test acc91.0580102194169; Train loss: 2.873340979514813e-05 Val loss: 0.0007972082895948754
+INFO - evaluator.py - 2024-10-27 00:20:46,530 - Epoch: 38 Train acc: 99.89090909090909 Val acc: 90.43262171015475 Test acc90.83759142370505; Train loss: 1.3580330143701179e-05 Val loss: 0.0009523078430632097
+INFO - evaluator.py - 2024-10-27 00:21:00,158 - Epoch: 39 Train acc: 99.74 Val acc: 90.5851123912798 Test acc90.34164913335337; Train loss: 2.873095074663235e-05 Val loss: 0.0009251216347172047
+INFO - evaluator.py - 2024-10-27 00:21:14,007 - Epoch: 40 Train acc: 99.94363636363637 Val acc: 91.0595278436688 Test acc90.97284841198277; Train loss: 7.631442022367e-06 Val loss: 0.000865824351428687
+INFO - evaluator.py - 2024-10-27 00:21:27,872 - Epoch: 41 Train acc: 99.98727272727272 Val acc: 90.76019428442336 Test acc90.43182045887185; Train loss: 3.0299219573697137e-06 Val loss: 0.000983802478191988
+INFO - evaluator.py - 2024-10-27 00:21:41,459 - Epoch: 42 Train acc: 99.99818181818182 Val acc: 90.91833276855303 Test acc90.62218214607755; Train loss: 1.8989844781158766e-06 Val loss: 0.0009911074640251856
+INFO - evaluator.py - 2024-10-27 00:21:54,812 - Epoch: 43 Train acc: 99.9890909090909 Val acc: 90.86185473850672 Test acc90.48692515779983; Train loss: 1.7642170078199177e-06 Val loss: 0.0010351710039169172
+INFO - evaluator.py - 2024-10-27 00:22:08,112 - Epoch: 44 Train acc: 99.99818181818182 Val acc: 90.30837004405286 Test acc89.81564973449554; Train loss: 9.865133535806142e-07 Val loss: 0.0011743122267208704
+INFO - evaluator.py - 2024-10-27 00:22:22,453 - Epoch: 45 Train acc: 99.99818181818182 Val acc: 90.71501186038631 Test acc90.29155395250977; Train loss: 8.070884818028968e-07 Val loss: 0.0011550522006784714
+INFO - evaluator.py - 2024-10-27 00:22:36,348 - Epoch: 46 Train acc: 99.99454545454546 Val acc: 90.65853383033999 Test acc90.22142069932872; Train loss: 6.446401429663885e-07 Val loss: 0.001130915504114299
+INFO - evaluator.py - 2024-10-27 00:22:49,572 - Epoch: 47 Train acc: 99.99818181818182 Val acc: 90.64723822433074 Test acc90.17633503656948; Train loss: 5.603633610979324e-07 Val loss: 0.0011173913091855386
+INFO - evaluator.py - 2024-10-27 00:23:02,735 - Epoch: 48 Train acc: 99.99818181818182 Val acc: 90.99175420761324 Test acc90.83759142370505; Train loss: 7.755829201714319e-07 Val loss: 0.001248189261925634
+INFO - evaluator.py - 2024-10-27 00:23:16,770 - Epoch: 49 Train acc: 100.0 Val acc: 90.95221958658082 Test acc90.6171726279932; Train loss: 5.078686721731173e-07 Val loss: 0.00130433117173209
+INFO - evaluator.py - 2024-10-27 00:23:16,775 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnet is 91.64689935615046 and 91.69421901613065
+INFO - evaluator.py - 2024-10-27 00:23:16,775 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnet is 91.64689935615046 and 91.69421901613065
+INFO - evaluator.py - 2024-10-27 00:23:16,775 - The best acc test dataset from resnet is 91.69421901613065
+INFO - evaluator.py - 2024-10-27 00:23:37,332 - Epoch: 0 Train acc: 62.42727272727273 Val acc: 73.12775330396477 Test acc74.58671475804027; Train loss: 0.0024903582139448686 Val loss: 0.0005329483769700229
+INFO - evaluator.py - 2024-10-27 00:23:57,539 - Epoch: 1 Train acc: 81.41090909090909 Val acc: 87.39410369366315 Test acc86.89009117322914; Train loss: 0.0015929997780106285 Val loss: 0.0003237176110458202
+INFO - evaluator.py - 2024-10-27 00:24:17,882 - Epoch: 2 Train acc: 87.09454545454545 Val acc: 88.29210437139953 Test acc87.32090972848412; Train loss: 0.0011926044236529958 Val loss: 0.0002998405764308418
+INFO - evaluator.py - 2024-10-27 00:24:38,657 - Epoch: 3 Train acc: 88.78 Val acc: 90.39873489212697 Test acc89.90081154192967; Train loss: 0.0010438958712599494 Val loss: 0.00024499383724625875
+INFO - evaluator.py - 2024-10-27 00:24:59,470 - Epoch: 4 Train acc: 89.70363636363636 Val acc: 90.6754772393539 Test acc90.36669672377518; Train loss: 0.0009602487469261343 Val loss: 0.00023805991776256418
+INFO - evaluator.py - 2024-10-27 00:25:20,085 - Epoch: 5 Train acc: 90.68545454545455 Val acc: 91.03693663165029 Test acc90.70233443542732; Train loss: 0.0008836556987328963 Val loss: 0.00023056240010689737
+INFO - evaluator.py - 2024-10-27 00:25:40,942 - Epoch: 6 Train acc: 91.24363636363636 Val acc: 90.3140178470575 Test acc89.42490732391543; Train loss: 0.0008312672856179151 Val loss: 0.0002464895995810761
+INFO - evaluator.py - 2024-10-27 00:26:01,604 - Epoch: 7 Train acc: 91.76545454545455 Val acc: 90.71501186038631 Test acc89.73048792706142; Train loss: 0.000783142678304152 Val loss: 0.00024145100916709843
+INFO - evaluator.py - 2024-10-27 00:26:22,063 - Epoch: 8 Train acc: 92.42181818181818 Val acc: 91.39274822094205 Test acc91.63911431720268; Train loss: 0.0007269964497197758 Val loss: 0.00021223488664487035
+INFO - evaluator.py - 2024-10-27 00:26:42,352 - Epoch: 9 Train acc: 92.83454545454546 Val acc: 90.79972890545578 Test acc90.17633503656948; Train loss: 0.0006924345925450325 Val loss: 0.0002371246245746813
+INFO - evaluator.py - 2024-10-27 00:27:02,650 - Epoch: 10 Train acc: 93.20545454545454 Val acc: 90.0598667118491 Test acc88.84881274421402; Train loss: 0.0006640531539916992 Val loss: 0.0002525238191678836
+INFO - evaluator.py - 2024-10-27 00:27:22,964 - Epoch: 11 Train acc: 93.68909090909091 Val acc: 89.98079746978426 Test acc88.88888888888889; Train loss: 0.0006201868026094003 Val loss: 0.0002562983398530142
+INFO - evaluator.py - 2024-10-27 00:27:43,711 - Epoch: 12 Train acc: 93.78909090909092 Val acc: 85.2818253699311 Test acc83.2882476705741; Train loss: 0.0006041381948373534 Val loss: 0.00040759386739447524
+INFO - evaluator.py - 2024-10-27 00:28:04,581 - Epoch: 13 Train acc: 94.17272727272727 Val acc: 88.80605444482097 Test acc87.14557659553151; Train loss: 0.0005755439393899657 Val loss: 0.0002854062585349704
+INFO - evaluator.py - 2024-10-27 00:28:24,779 - Epoch: 14 Train acc: 94.52545454545455 Val acc: 88.52931209759404 Test acc86.96523394449454; Train loss: 0.0005390997661785646 Val loss: 0.0002980777890914095
+INFO - evaluator.py - 2024-10-27 00:28:45,429 - Epoch: 15 Train acc: 94.71090909090908 Val acc: 91.70337738619678 Test acc91.49383829275624; Train loss: 0.000526584410125559 Val loss: 0.00021610301634719464
+INFO - evaluator.py - 2024-10-27 00:29:06,134 - Epoch: 16 Train acc: 94.83454545454545 Val acc: 91.43228284197448 Test acc90.86764853221119; Train loss: 0.0005061450558629903 Val loss: 0.00022244717688879885
+INFO - evaluator.py - 2024-10-27 00:29:26,955 - Epoch: 17 Train acc: 95.06545454545454 Val acc: 85.27617756692646 Test acc82.98266706742811; Train loss: 0.00048635949099605735 Val loss: 0.0004921711903028834
+INFO - evaluator.py - 2024-10-27 00:29:47,684 - Epoch: 18 Train acc: 95.39999999999999 Val acc: 91.33062238789111 Test acc90.65724877266807; Train loss: 0.00045381121852181174 Val loss: 0.00024293132751631384
+INFO - evaluator.py - 2024-10-27 00:30:08,189 - Epoch: 19 Train acc: 95.52363636363637 Val acc: 89.36518694227945 Test acc90.3516681695221; Train loss: 0.00044041233536872 Val loss: 0.0002663797185230589
+INFO - evaluator.py - 2024-10-27 00:30:29,014 - Epoch: 20 Train acc: 97.00545454545455 Val acc: 91.85586806732181 Test acc91.8895902214207; Train loss: 0.00030606385791166264 Val loss: 0.00026007096612303996
+INFO - evaluator.py - 2024-10-27 00:30:49,699 - Epoch: 21 Train acc: 97.5690909090909 Val acc: 91.82762905229865 Test acc91.47880973850316; Train loss: 0.0002559151677922769 Val loss: 0.00031751463618720705
+INFO - evaluator.py - 2024-10-27 00:31:10,347 - Epoch: 22 Train acc: 97.61818181818181 Val acc: 90.44391731616402 Test acc89.46498346859032; Train loss: 0.0002402896040881222 Val loss: 0.0004303552522560196
+INFO - evaluator.py - 2024-10-27 00:31:31,122 - Epoch: 23 Train acc: 97.80545454545455 Val acc: 91.13859708573365 Test acc90.25648732591924; Train loss: 0.00022408024275844747 Val loss: 0.00043721312077775535
+INFO - evaluator.py - 2024-10-27 00:31:51,673 - Epoch: 24 Train acc: 98.0109090909091 Val acc: 90.76019428442336 Test acc90.0110209397856; Train loss: 0.00020171768599274483 Val loss: 0.00047337538937747807
+INFO - evaluator.py - 2024-10-27 00:32:12,347 - Epoch: 25 Train acc: 98.16727272727273 Val acc: 87.80639331300124 Test acc86.19376815950307; Train loss: 0.00018667209534482522 Val loss: 0.0007874705583298125
+INFO - evaluator.py - 2024-10-27 00:32:32,652 - Epoch: 26 Train acc: 98.30727272727273 Val acc: 90.31966565006213 Test acc89.28965033563772; Train loss: 0.00017282825032757088 Val loss: 0.0005810259493490969
+INFO - evaluator.py - 2024-10-27 00:32:53,552 - Epoch: 27 Train acc: 98.4509090909091 Val acc: 91.56218231108099 Test acc91.55395250976856; Train loss: 0.00016145558678968386 Val loss: 0.00047140020368382796
+INFO - evaluator.py - 2024-10-27 00:33:14,390 - Epoch: 28 Train acc: 98.49272727272728 Val acc: 89.94126284875183 Test acc88.73359382827371; Train loss: 0.00015095726449719884 Val loss: 0.0006956551777337562
+INFO - evaluator.py - 2024-10-27 00:33:34,986 - Epoch: 29 Train acc: 98.61636363636363 Val acc: 87.84592793403367 Test acc86.13866346057509; Train loss: 0.00014069193375550887 Val loss: 0.0009302867848523307
+INFO - evaluator.py - 2024-10-27 00:33:55,428 - Epoch: 30 Train acc: 98.61454545454545 Val acc: 91.31932678188186 Test acc90.64222021841499; Train loss: 0.0001360116449438713 Val loss: 0.0005683834207646392
+INFO - evaluator.py - 2024-10-27 00:34:15,689 - Epoch: 31 Train acc: 98.74909090909091 Val acc: 89.4103693663165 Test acc88.22262298366897; Train loss: 0.0001265914453024214 Val loss: 0.0008162321424155696
+INFO - evaluator.py - 2024-10-27 00:34:36,295 - Epoch: 32 Train acc: 98.88727272727273 Val acc: 90.9296283745623 Test acc90.22142069932872; Train loss: 0.0001131767581470988 Val loss: 0.0006495996321660172
+INFO - evaluator.py - 2024-10-27 00:34:57,016 - Epoch: 33 Train acc: 98.83090909090909 Val acc: 88.95289732294137 Test acc87.78679491032962; Train loss: 0.00011896625624292277 Val loss: 0.0008973230598444968
+INFO - evaluator.py - 2024-10-27 00:35:17,806 - Epoch: 34 Train acc: 98.90363636363637 Val acc: 90.1332881509093 Test acc89.26961226330027; Train loss: 0.0001116425827822902 Val loss: 0.0007812068191178435
+INFO - evaluator.py - 2024-10-27 00:35:37,928 - Epoch: 35 Train acc: 99.04181818181819 Val acc: 89.37648254828872 Test acc88.16250876665664; Train loss: 9.878251142376525e-05 Val loss: 0.0009031515531885708
+INFO - evaluator.py - 2024-10-27 00:35:58,044 - Epoch: 36 Train acc: 99.09818181818181 Val acc: 90.24059640799729 Test acc89.36980262498747; Train loss: 9.49112871746448e-05 Val loss: 0.0007653012190055782
+INFO - evaluator.py - 2024-10-27 00:36:18,748 - Epoch: 37 Train acc: 99.08545454545454 Val acc: 89.76618095560826 Test acc88.71856527402065; Train loss: 9.453872499818152e-05 Val loss: 0.000821258164550926
+INFO - evaluator.py - 2024-10-27 00:36:39,525 - Epoch: 38 Train acc: 99.05090909090909 Val acc: 91.06517564667345 Test acc90.48191563971545; Train loss: 9.802765895358541e-05 Val loss: 0.0006684707653370332
+INFO - evaluator.py - 2024-10-27 00:37:00,151 - Epoch: 39 Train acc: 99.2 Val acc: 91.13859708573365 Test acc90.6672678088368; Train loss: 8.162473736500199e-05 Val loss: 0.0007033829976916381
+INFO - evaluator.py - 2024-10-27 00:37:20,314 - Epoch: 40 Train acc: 99.42363636363636 Val acc: 91.08211905568734 Test acc90.59713455565574; Train loss: 6.038573968478225e-05 Val loss: 0.0007039513053913434
+INFO - evaluator.py - 2024-10-27 00:37:40,570 - Epoch: 41 Train acc: 99.54181818181819 Val acc: 90.95786738958546 Test acc90.44183949504058; Train loss: 5.052392838776789e-05 Val loss: 0.0007349686859341566
+INFO - evaluator.py - 2024-10-27 00:38:00,997 - Epoch: 42 Train acc: 99.55636363636363 Val acc: 91.04258443465491 Test acc90.44684901312495; Train loss: 4.555962291567332e-05 Val loss: 0.0007364834538559638
+INFO - evaluator.py - 2024-10-27 00:38:21,729 - Epoch: 43 Train acc: 99.60909090909091 Val acc: 91.07647125268271 Test acc90.52199178439034; Train loss: 4.3469495741142466e-05 Val loss: 0.0007429961281335517
+INFO - evaluator.py - 2024-10-27 00:38:42,381 - Epoch: 44 Train acc: 99.50909090909092 Val acc: 90.38179148311308 Test acc89.52509768560265; Train loss: 5.0684894049878826e-05 Val loss: 0.0008528289263846038
+INFO - evaluator.py - 2024-10-27 00:39:02,500 - Epoch: 45 Train acc: 99.5890909090909 Val acc: 90.15587936292782 Test acc89.19947901011923; Train loss: 4.39442790388553e-05 Val loss: 0.0009202269777717879
+INFO - evaluator.py - 2024-10-27 00:39:22,667 - Epoch: 46 Train acc: 99.59454545454545 Val acc: 90.78278549644189 Test acc90.11121130147279; Train loss: 4.472748974909667e-05 Val loss: 0.0008172596677823025
+INFO - evaluator.py - 2024-10-27 00:39:43,304 - Epoch: 47 Train acc: 99.62363636363636 Val acc: 90.1163447418954 Test acc89.33974551648132; Train loss: 4.08278251267885e-05 Val loss: 0.0009168584602883371
+INFO - evaluator.py - 2024-10-27 00:40:04,161 - Epoch: 48 Train acc: 99.69636363636364 Val acc: 90.76019428442336 Test acc90.03105901212304; Train loss: 3.4996192071544516e-05 Val loss: 0.0008381004186992414
+INFO - evaluator.py - 2024-10-27 00:40:24,631 - Epoch: 49 Train acc: 99.63454545454546 Val acc: 90.66418163334463 Test acc90.0611161206292; Train loss: 3.922747593182562e-05 Val loss: 0.0008563769479047292
+INFO - evaluator.py - 2024-10-27 00:40:24,634 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from wrn is 91.85586806732181 and 91.8895902214207
+INFO - evaluator.py - 2024-10-27 00:40:24,634 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from wrn is 91.85586806732181 and 91.8895902214207
+INFO - evaluator.py - 2024-10-27 00:40:24,635 - The best acc test dataset from wrn is 91.8895902214207
+INFO - evaluator.py - 2024-10-27 00:41:53,750 - Epoch: 0 Train acc: 66.9309090909091 Val acc: 80.22139387778154 Test acc79.60625187856928; Train loss: 0.002817657520012422 Val loss: 0.0004582317430502033
+INFO - evaluator.py - 2024-10-27 00:43:22,479 - Epoch: 1 Train acc: 82.58181818181818 Val acc: 87.50705975375578 Test acc87.96212804328223; Train loss: 0.0015582973496480422 Val loss: 0.0002958279527621957
+INFO - evaluator.py - 2024-10-27 00:44:51,770 - Epoch: 2 Train acc: 86.33090909090909 Val acc: 88.29775217440415 Test acc87.60144274120829; Train loss: 0.001266168702732433 Val loss: 0.00029179071684577403
+INFO - evaluator.py - 2024-10-27 00:46:20,364 - Epoch: 3 Train acc: 88.22 Val acc: 89.6645205015249 Test acc89.58521190261497; Train loss: 0.0010917766454544935 Val loss: 0.0002559001499564583
+INFO - evaluator.py - 2024-10-27 00:47:49,493 - Epoch: 4 Train acc: 90.23454545454545 Val acc: 89.38777815429798 Test acc90.02604949403867; Train loss: 0.0009302010167728771 Val loss: 0.0002764841213638899
+INFO - evaluator.py - 2024-10-27 00:49:18,273 - Epoch: 5 Train acc: 91.01818181818182 Val acc: 84.75093188749577 Test acc82.46167718665464; Train loss: 0.0008559999959035353 Val loss: 0.0003820591783248865
+INFO - evaluator.py - 2024-10-27 00:50:46,984 - Epoch: 6 Train acc: 92.07454545454546 Val acc: 90.61899920930759 Test acc90.4518585312093; Train loss: 0.0007582109509543939 Val loss: 0.00023854784200410447
+INFO - evaluator.py - 2024-10-27 00:52:15,650 - Epoch: 7 Train acc: 92.88545454545455 Val acc: 90.39308708912233 Test acc89.835687806833; Train loss: 0.0006904172351414507 Val loss: 0.0002442082550301332
+INFO - evaluator.py - 2024-10-27 00:53:44,192 - Epoch: 8 Train acc: 93.51818181818182 Val acc: 89.65322489551565 Test acc89.71044985472398; Train loss: 0.0006326050438664176 Val loss: 0.00025532614975396785
+INFO - evaluator.py - 2024-10-27 00:55:12,690 - Epoch: 9 Train acc: 94.17454545454545 Val acc: 77.66858691968824 Test acc74.15088668470094; Train loss: 0.0005807349859313531 Val loss: 0.000706649582008716
+INFO - evaluator.py - 2024-10-27 00:56:41,080 - Epoch: 10 Train acc: 94.47272727272727 Val acc: 88.99807974697842 Test acc90.15629696423204; Train loss: 0.0005387524626471779 Val loss: 0.00028184054009755235
+INFO - evaluator.py - 2024-10-27 00:58:09,668 - Epoch: 11 Train acc: 94.90727272727273 Val acc: 90.12199254490002 Test acc89.61025949303676; Train loss: 0.0005011076359586282 Val loss: 0.0002591653818412513
+INFO - evaluator.py - 2024-10-27 00:59:38,364 - Epoch: 12 Train acc: 95.5690909090909 Val acc: 89.82265898565458 Test acc88.46307985171826; Train loss: 0.00044201739213683386 Val loss: 0.00028741819638660853
+INFO - evaluator.py - 2024-10-27 01:01:06,945 - Epoch: 13 Train acc: 95.84181818181818 Val acc: 88.36552581045973 Test acc86.80993888387937; Train loss: 0.0004040985084392808 Val loss: 0.0003263515465598611
+INFO - evaluator.py - 2024-10-27 01:02:35,360 - Epoch: 14 Train acc: 96.46727272727273 Val acc: 88.73263300576076 Test acc87.1856527402064; Train loss: 0.0003554410396651788 Val loss: 0.0003329441885510286
+INFO - evaluator.py - 2024-10-27 01:04:03,828 - Epoch: 15 Train acc: 96.83272727272727 Val acc: 84.33299446515305 Test acc81.79541128143472; Train loss: 0.0003198348577726971 Val loss: 0.000568576787442632
+INFO - evaluator.py - 2024-10-27 01:05:32,261 - Epoch: 16 Train acc: 97.11999999999999 Val acc: 87.99277081215408 Test acc86.35908225628694; Train loss: 0.0002927734580568292 Val loss: 0.0003791542093214929
+INFO - evaluator.py - 2024-10-27 01:07:00,665 - Epoch: 17 Train acc: 97.57454545454546 Val acc: 82.7290184118378 Test acc79.92185151788398; Train loss: 0.0002427592000670054 Val loss: 0.0006907311055965374
+INFO - evaluator.py - 2024-10-27 01:08:29,169 - Epoch: 18 Train acc: 97.71636363636364 Val acc: 81.4808539478143 Test acc78.32882476705741; Train loss: 0.00023218109622936357 Val loss: 0.0007695435036862083
+INFO - evaluator.py - 2024-10-27 01:09:57,772 - Epoch: 19 Train acc: 98.04545454545455 Val acc: 89.47814300237208 Test acc89.61025949303676; Train loss: 0.00019657064495100216 Val loss: 0.0003441263747244895
+INFO - evaluator.py - 2024-10-27 01:11:26,432 - Epoch: 20 Train acc: 99.5690909090909 Val acc: 90.43826951315938 Test acc90.10119226530408; Train loss: 5.328300279903818e-05 Val loss: 0.00040598634803388047
+INFO - evaluator.py - 2024-10-27 01:12:54,886 - Epoch: 21 Train acc: 99.97636363636364 Val acc: 90.6415904213261 Test acc90.0611161206292; Train loss: 8.802596680206163e-06 Val loss: 0.0004975220974752301
+INFO - evaluator.py - 2024-10-27 01:14:23,486 - Epoch: 22 Train acc: 99.99636363636364 Val acc: 90.55687337625665 Test acc90.23143973549745; Train loss: 3.2749107876125806e-06 Val loss: 0.0005697551574004676
+INFO - evaluator.py - 2024-10-27 01:15:52,018 - Epoch: 23 Train acc: 100.0 Val acc: 90.53992996724274 Test acc90.0110209397856; Train loss: 1.783758896503555e-06 Val loss: 0.0006564871094448478
+INFO - evaluator.py - 2024-10-27 01:17:20,501 - Epoch: 24 Train acc: 100.0 Val acc: 90.51169095221958 Test acc89.8958020238453; Train loss: 1.220296730852252e-06 Val loss: 0.0007387020352815055
+INFO - evaluator.py - 2024-10-27 01:18:48,829 - Epoch: 25 Train acc: 100.0 Val acc: 90.63029481531683 Test acc90.02604949403867; Train loss: 6.752414706253066e-07 Val loss: 0.0007908520834009404
+INFO - evaluator.py - 2024-10-27 01:20:17,546 - Epoch: 26 Train acc: 100.0 Val acc: 90.2970744380436 Test acc89.49003105901213; Train loss: 5.727646317444106e-07 Val loss: 0.0008974161260372822
+INFO - evaluator.py - 2024-10-27 01:21:45,912 - Epoch: 27 Train acc: 100.0 Val acc: 90.28577883203434 Test acc89.5050596132652; Train loss: 3.0319793257314646e-07 Val loss: 0.000950467764260768
+INFO - evaluator.py - 2024-10-27 01:23:14,535 - Epoch: 28 Train acc: 100.0 Val acc: 90.07681012086299 Test acc89.05420298567279; Train loss: 2.979128586131e-07 Val loss: 0.001042749755034861
+INFO - evaluator.py - 2024-10-27 01:24:42,900 - Epoch: 29 Train acc: 100.0 Val acc: 90.3140178470575 Test acc89.58020238453061; Train loss: 1.9807530331738896e-07 Val loss: 0.0010349870461953314
+INFO - evaluator.py - 2024-10-27 01:26:11,356 - Epoch: 30 Train acc: 100.0 Val acc: 90.26883542302045 Test acc89.54012623985572; Train loss: 2.3142359161531866e-07 Val loss: 0.0010805351780213085
+INFO - evaluator.py - 2024-10-27 01:27:39,720 - Epoch: 31 Train acc: 100.0 Val acc: 90.33096125607139 Test acc89.70043081855525; Train loss: 1.780085581933897e-07 Val loss: 0.0011151530749916585
+INFO - evaluator.py - 2024-10-27 01:29:08,173 - Epoch: 32 Train acc: 100.0 Val acc: 90.32531345306676 Test acc89.55014527602445; Train loss: 1.8157542491404788e-07 Val loss: 0.0011659352863951213
+INFO - evaluator.py - 2024-10-27 01:30:36,665 - Epoch: 33 Train acc: 99.99818181818182 Val acc: 90.04292330283519 Test acc89.0892696122633; Train loss: 4.284799560578408e-07 Val loss: 0.0012377323901519982
+INFO - evaluator.py - 2024-10-27 01:32:05,031 - Epoch: 34 Train acc: 99.19636363636364 Val acc: 85.43996385406078 Test acc82.89249574190963; Train loss: 9.411487102516648e-05 Val loss: 0.0018107234845925735
+INFO - evaluator.py - 2024-10-27 01:33:33,545 - Epoch: 35 Train acc: 99.77636363636364 Val acc: 89.22399186716368 Test acc87.83689009117323; Train loss: 2.573113289439458e-05 Val loss: 0.001163928936689328
+INFO - evaluator.py - 2024-10-27 01:35:01,946 - Epoch: 36 Train acc: 99.79272727272728 Val acc: 89.17316164012199 Test acc87.8769662358481; Train loss: 2.275829397589074e-05 Val loss: 0.001117441420715084
+INFO - evaluator.py - 2024-10-27 01:36:30,142 - Epoch: 37 Train acc: 99.74909090909091 Val acc: 87.84592793403367 Test acc86.29896803927463; Train loss: 2.8080483769537585e-05 Val loss: 0.00127583355011138
+INFO - evaluator.py - 2024-10-27 01:37:58,310 - Epoch: 38 Train acc: 99.88909090909091 Val acc: 88.24692194736248 Test acc86.88508165514477; Train loss: 1.2858073514177125e-05 Val loss: 0.0012876044440832323
+INFO - evaluator.py - 2024-10-27 01:39:26,541 - Epoch: 39 Train acc: 99.80545454545454 Val acc: 90.51733875522422 Test acc89.93086865043583; Train loss: 2.1528667001588142e-05 Val loss: 0.0007612564146862076
+INFO - evaluator.py - 2024-10-27 01:40:54,927 - Epoch: 40 Train acc: 99.90181818181819 Val acc: 90.68677284536315 Test acc90.00100190361687; Train loss: 1.0975941089220049e-05 Val loss: 0.0008371011875287889
+INFO - evaluator.py - 2024-10-27 01:42:23,442 - Epoch: 41 Train acc: 99.9890909090909 Val acc: 90.65853383033999 Test acc90.20138262699129; Train loss: 2.6644425313861573e-06 Val loss: 0.0008615283330227714
+INFO - evaluator.py - 2024-10-27 01:43:52,138 - Epoch: 42 Train acc: 99.99454545454546 Val acc: 90.60770360329832 Test acc90.0611161206292; Train loss: 1.5110974747751077e-06 Val loss: 0.0009258352274761676
+INFO - evaluator.py - 2024-10-27 01:45:20,686 - Epoch: 43 Train acc: 99.99818181818182 Val acc: 90.65853383033999 Test acc90.07113515679792; Train loss: 8.83029154796201e-07 Val loss: 0.0009600896761536692
+INFO - evaluator.py - 2024-10-27 01:46:49,033 - Epoch: 44 Train acc: 100.0 Val acc: 90.54557777024738 Test acc89.90582106001402; Train loss: 4.986775012763577e-07 Val loss: 0.001003999505004788
+INFO - evaluator.py - 2024-10-27 01:48:17,324 - Epoch: 45 Train acc: 100.0 Val acc: 90.59640799728905 Test acc90.00100190361687; Train loss: 5.330537751764165e-07 Val loss: 0.0010259191854975257
+INFO - evaluator.py - 2024-10-27 01:49:45,568 - Epoch: 46 Train acc: 99.99818181818182 Val acc: 90.50604314921496 Test acc89.92585913235148; Train loss: 6.320963500167471e-07 Val loss: 0.0010656682930328773
+INFO - evaluator.py - 2024-10-27 01:51:14,058 - Epoch: 47 Train acc: 100.0 Val acc: 90.46086072517791 Test acc89.80062118024246; Train loss: 3.4802212547798593e-07 Val loss: 0.0010868625716006137
+INFO - evaluator.py - 2024-10-27 01:52:42,272 - Epoch: 48 Train acc: 99.99818181818182 Val acc: 90.42132610414548 Test acc89.67538322813346; Train loss: 3.6313261426502407e-07 Val loss: 0.001124000113037068
+INFO - evaluator.py - 2024-10-27 01:54:10,695 - Epoch: 49 Train acc: 100.0 Val acc: 90.34790466508528 Test acc89.5551547941088; Train loss: 3.239955397227525e-07 Val loss: 0.0011401285993760688
+INFO - evaluator.py - 2024-10-27 01:54:10,702 - The best acc of synthetic images on sensitive val and the corresponding acc on test dataset from resnext is 90.68677284536315 and 90.00100190361687
+INFO - evaluator.py - 2024-10-27 01:54:10,702 - The best acc of synthetic images on noisy sensitive val and the corresponding acc on test dataset from resnext is 90.68677284536315 and 90.00100190361687
+INFO - evaluator.py - 2024-10-27 01:54:10,702 - The best acc test dataset from resnext is 90.4518585312093
+INFO - evaluator.py - 2024-10-27 01:54:10,702 - The best acc of accuracy (using synthetic images as the validation set) of synthetic images from resnet, wrn, and resnext are [91.69421901613065, 91.8895902214207, 90.00100190361687].
+INFO - evaluator.py - 2024-10-27 01:54:10,702 - The average and std of accuracy of synthetic images are 91.19 and 0.85
+INFO - dataset_loader.py - 2024-10-28 19:02:37,574 - delta is reset as 5.800209926283058e-07
+INFO - evaluator.py - 2024-10-28 19:04:42,932 - Epoch: 0 Train acc: 69.74727272727273 Val acc: 80.52072743702699 Test acc82.11101092074942; Train loss: 0.002360796746340665 Val loss: 0.00041161084375744496
+INFO - evaluator.py - 2024-10-28 19:05:56,236 - Epoch: 1 Train acc: 84.18181818181819 Val acc: 90.0598667118491 Test acc89.76555455365194; Train loss: 0.0014114365366372195 Val loss: 0.00024678522848643975
+INFO - evaluator.py - 2024-10-28 19:07:11,650 - Epoch: 2 Train acc: 87.31090909090909 Val acc: 90.63594261832148 Test acc90.93277226730788; Train loss: 0.0011456798488443548 Val loss: 0.00024787135532477827
+INFO - evaluator.py - 2024-10-28 19:08:30,754 - Epoch: 3 Train acc: 89.26363636363637 Val acc: 88.83429345984412 Test acc87.88197575393248; Train loss: 0.0009917437060312792 Val loss: 0.0002724514897766725
+INFO - evaluator.py - 2024-10-28 19:09:52,857 - Epoch: 4 Train acc: 90.74545454545454 Val acc: 88.7043939907376 Test acc87.64151888588317; Train loss: 0.0008747367669235576 Val loss: 0.00027496498307440627
+INFO - evaluator.py - 2024-10-28 19:11:14,282 - Epoch: 5 Train acc: 91.78545454545454 Val acc: 91.47746526601152 Test acc91.02294359282637; Train loss: 0.000774853574145924 Val loss: 0.00022479574750752473
+INFO - evaluator.py - 2024-10-28 19:12:34,707 - Epoch: 6 Train acc: 92.58727272727273 Val acc: 88.42200384050605 Test acc89.98597334936379; Train loss: 0.0007146742991425775 Val loss: 0.0002638943014502539
+INFO - evaluator.py - 2024-10-28 19:13:51,742 - Epoch: 7 Train acc: 93.1509090909091 Val acc: 89.59109906246469 Test acc90.6672678088368; Train loss: 0.0006669812845912847 Val loss: 0.00024941628829350005
+INFO - evaluator.py - 2024-10-28 19:15:07,598 - Epoch: 8 Train acc: 93.57272727272728 Val acc: 89.58545125946007 Test acc88.52820358681495; Train loss: 0.0006263670768250118 Val loss: 0.00026181327846243357
+INFO - evaluator.py - 2024-10-28 19:16:25,741 - Epoch: 9 Train acc: 94.10181818181819 Val acc: 82.65559697277759 Test acc80.50796513375414; Train loss: 0.0005832589559934356 Val loss: 0.00040997624882032653
+INFO - evaluator.py - 2024-10-28 19:17:44,842 - Epoch: 10 Train acc: 94.45636363636363 Val acc: 61.53846153846154 Test acc57.71966736799919; Train loss: 0.0005402244328097863 Val loss: 0.0012313514715612884
+INFO - evaluator.py - 2024-10-28 19:19:05,233 - Epoch: 11 Train acc: 94.89272727272727 Val acc: 86.91404043826951 Test acc88.68349864743011; Train loss: 0.0004988778998905962 Val loss: 0.0003138990536471834
+INFO - evaluator.py - 2024-10-28 19:20:24,517 - Epoch: 12 Train acc: 95.12 Val acc: 90.84491132949283 Test acc91.04298166516381; Train loss: 0.0004775282005017454 Val loss: 0.00023065470634551152
+INFO - evaluator.py - 2024-10-28 19:21:42,962 - Epoch: 13 Train acc: 95.33454545454545 Val acc: 79.40811024511464 Test acc76.25488428013225; Train loss: 0.00046091820922764866 Val loss: 0.0005406050945047361
+INFO - dataset_loader.py - 2024-10-28 19:23:04,661 - delta is reset as 5.800209926283058e-07
+INFO - evaluator.py - 2024-10-28 22:07:52,030 - The FID of synthetic images is 28.83750267529979
+INFO - evaluator.py - 2024-10-28 22:07:52,032 - The Inception Score of synthetic images is 2.2382640838623047
+INFO - evaluator.py - 2024-10-28 22:07:52,032 - The Precision and Recall of synthetic images is 0.6089062690734863 and 0.15193216502666473
+INFO - evaluator.py - 2024-10-28 22:07:52,032 - The FLD of synthetic images is 4.542505741119385
+INFO - evaluator.py - 2024-10-28 22:07:52,032 - The ImageReward of synthetic images is -1.3833920695268747
+INFO - dataset_loader.py - 2024-10-28 22:46:41,565 - delta is reset as 5.800209926283058e-07
+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
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