diff --git "a/i2v_motion/log.txt" "b/i2v_motion/log.txt" deleted file mode 100644--- "a/i2v_motion/log.txt" +++ /dev/null @@ -1,5861 +0,0 @@ -[2024-06-08 18:16:59,280] INFO: {'__name__': 'Config: VideoLDM Decoder', 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'max_words': 1000, 'num_workers': 16, 'prefetch_factor': 2, 'resolution': [256, 256], 'vit_out_dim': 1024, 'vit_resolution': [224, 224], 'depth_clamp': 10.0, 'misc_size': 384, 'depth_std': 20.0, 'frame_lens': [24], 'sample_fps': [8], 'vid_dataset': {'type': 'ObjDataset', 'data_list': './Obj/data/6.8_with_motion/train.txt', 'max_words': 1000, 'resolution': [256, 256], 'vit_resolution': [224, 224], 'get_first_frame': True}, 'img_dataset': {'type': 'ImageBaseDataset', 'data_list': ['laion_400m'], 'max_words': 1000, 'resolution': [448, 256]}, 'batch_sizes': {'1': 256, '4': 4, '8': 4, '16': 4, '24': 20}, 'Diffusion': {'type': 'DiffusionDDIM', 'schedule': 'cosine', 'schedule_param': {'num_timesteps': 1000, 'cosine_s': 0.008, 'zero_terminal_snr': True}, 'mean_type': 'v', 'loss_type': 'mse', 'var_type': 'fixed_small', 'rescale_timesteps': False, 'noise_strength': 0.1, 'ddim_timesteps': 50, 'with_latent_motion_loss': False}, 'ddim_timesteps': 50, 'use_div_loss': False, 'p_zero': 0.0, 'guide_scale': 3.0, 'vit_mean': [0.48145466, 0.4578275, 0.40821073], 'vit_std': [0.26862954, 0.26130258, 0.27577711], 'scale_factor': 0.18215, 'use_checkpoint': True, 'use_sharded_ddp': False, 'use_fsdp': False, 'use_fp16': True, 'temporal_attention': True, 'UNet': {'type': 'UNetSD_I2VGen', 'in_dim': 4, 'dim': 320, 'y_dim': 1024, 'context_dim': 1024, 'out_dim': 4, 'dim_mult': [1, 2, 4, 4], 'num_heads': 8, 'head_dim': 64, 'num_res_blocks': 2, 'attn_scales': [1.0, 0.5, 0.25], 'dropout': 0.1, 'temporal_attention': True, 'temporal_attn_times': 1, 'use_checkpoint': True, 'use_fps_condition': False, 'use_sim_mask': False, 'upper_len': 128, 'concat_dim': 4, 'default_fps': 8, 'use_camera_condition': False, 'use_lgm_refine': False, 'with_motion': True, 'with_latent_motion': False}, 'guidances': [], 'auto_encoder': {'type': 'AutoencoderKL', 'ddconfig': {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0, 'video_kernel_size': [3, 1, 1]}, 'embed_dim': 4, 'pretrained': './pretrained_models/modelscope_t2v/VQGAN_autoencoder.pth'}, 'embedder': {'type': 'FrozenOpenCLIPTtxtVisualEmbedder', 'layer': 'penultimate', 'pretrained': 'pretrained_models/modelscope_i2v/I2VGen-XL/open_clip_pytorch_model.bin', 'vit_resolution': [224, 224]}, 'ema_decay': 0.9999, 'num_steps': 1000000, 'lr': 3e-05, 'weight_decay': 0.0, 'betas': (0.9, 0.999), 'eps': 1e-08, 'chunk_size': 4, 'decoder_bs': 4, 'alpha': 0.7, 'save_ckp_interval': 500, 'warmup_steps': 10, 'decay_mode': 'cosine', 'use_ema': True, 'load_from': None, 'Pretrain': {'type': 'pretrain_specific_strategies', 'fix_weight': False, 'grad_scale': 0.5, 'resume_checkpoint': './pretrained_models/i2v_882000.pth', 'sd_keys_path': 'pretrained_models/modelscope_i2v/I2VGen-XL/stable_diffusion_image_key_temporal_attention_x1.json'}, 'viz_interval': 500, 'visual_train': {'type': 'VisualTrainTextImageToVideo', 'partial_keys': [['y', 'image', 'local_image', 'fps', 'camera_data', 'gs_data']], 'use_offset_noise': True, 'guide_scale': 6.0}, 'visual_inference': {'type': 'VisualGeneratedVideos'}, 'inference_list_path': '', 'log_interval': 1, 'log_dir': 'workspace/i2v_motion/obj_train_motion', 'seed': 6666, 'negative_prompt': 'static, 3d asset, Distorted, discontinuous, Ugly, blurry, low resolution, disfigured, disconnected limbs, Ugly faces, incomplete arms', 'with_motion': True, 'motion_magnitude': None, 'with_latent_motion': False, 'latent_motion_magnitude': None, 'ENABLE': True, 'DATASET': 'webvid10m', 'TASK_TYPE': 'train_i2v_entrance', 'noise_strength': 0.1, 'use_zero_infer': True, 'cfg_file': './configs/obj_train_motion.yaml', 'init_method': 'tcp://localhost:9999', 'debug': False, 'opts': [], 'pmi_rank': 0, 'pmi_world_size': 1, 'gpus_per_machine': 4, 'world_size': 4, 'gpu': 0, 'rank': 0, 'log_file': 'workspace/i2v_motion/obj_train_motion/log.txt'} -[2024-06-08 18:16:59,282] INFO: Save all the file in to dir workspace/i2v_motion/obj_train_motion -[2024-06-08 18:16:59,282] INFO: Going into i2v_img_fullid_vidcom function on 0 gpu -[2024-06-08 18:16:59,292] INFO: Current worker with max_frames=24, batch_size=20, sample_fps=8 -[2024-06-08 18:18:04,658] INFO: Loaded ViT-H-14 model config. -[2024-06-08 18:18:58,387] INFO: Loading pretrained ViT-H-14 weights (pretrained_models/modelscope_i2v/I2VGen-XL/open_clip_pytorch_model.bin). -[2024-06-08 18:19:35,489] INFO: Restored from ./pretrained_models/modelscope_t2v/VQGAN_autoencoder.pth -[2024-06-08 18:19:55,218] INFO: load a fixed model with _IncompatibleKeys(missing_keys=['motion_embedding.0.weight', 'motion_embedding.0.bias', 'motion_embedding.2.weight', 'motion_embedding.2.bias'], 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'lgm_big.unet.up_blocks.3.nets.1.norm2.weight', 'lgm_big.unet.up_blocks.3.nets.1.norm2.bias', 'lgm_big.unet.up_blocks.3.nets.1.conv2.weight', 'lgm_big.unet.up_blocks.3.nets.1.conv2.bias', 'lgm_big.unet.up_blocks.3.nets.1.shortcut.weight', 'lgm_big.unet.up_blocks.3.nets.1.shortcut.bias', 'lgm_big.unet.up_blocks.3.nets.2.norm1.weight', 'lgm_big.unet.up_blocks.3.nets.2.norm1.bias', 'lgm_big.unet.up_blocks.3.nets.2.conv1.weight', 'lgm_big.unet.up_blocks.3.nets.2.conv1.bias', 'lgm_big.unet.up_blocks.3.nets.2.norm2.weight', 'lgm_big.unet.up_blocks.3.nets.2.norm2.bias', 'lgm_big.unet.up_blocks.3.nets.2.conv2.weight', 'lgm_big.unet.up_blocks.3.nets.2.conv2.bias', 'lgm_big.unet.up_blocks.3.nets.2.shortcut.weight', 'lgm_big.unet.up_blocks.3.nets.2.shortcut.bias', 'lgm_big.unet.up_blocks.3.upsample.weight', 'lgm_big.unet.up_blocks.3.upsample.bias', 'lgm_big.unet.up_blocks.4.nets.0.norm1.weight', 'lgm_big.unet.up_blocks.4.nets.0.norm1.bias', 'lgm_big.unet.up_blocks.4.nets.0.conv1.weight', 'lgm_big.unet.up_blocks.4.nets.0.conv1.bias', 'lgm_big.unet.up_blocks.4.nets.0.norm2.weight', 'lgm_big.unet.up_blocks.4.nets.0.norm2.bias', 'lgm_big.unet.up_blocks.4.nets.0.conv2.weight', 'lgm_big.unet.up_blocks.4.nets.0.conv2.bias', 'lgm_big.unet.up_blocks.4.nets.0.shortcut.weight', 'lgm_big.unet.up_blocks.4.nets.0.shortcut.bias', 'lgm_big.unet.up_blocks.4.nets.1.norm1.weight', 'lgm_big.unet.up_blocks.4.nets.1.norm1.bias', 'lgm_big.unet.up_blocks.4.nets.1.conv1.weight', 'lgm_big.unet.up_blocks.4.nets.1.conv1.bias', 'lgm_big.unet.up_blocks.4.nets.1.norm2.weight', 'lgm_big.unet.up_blocks.4.nets.1.norm2.bias', 'lgm_big.unet.up_blocks.4.nets.1.conv2.weight', 'lgm_big.unet.up_blocks.4.nets.1.conv2.bias', 'lgm_big.unet.up_blocks.4.nets.1.shortcut.weight', 'lgm_big.unet.up_blocks.4.nets.1.shortcut.bias', 'lgm_big.unet.up_blocks.4.nets.2.norm1.weight', 'lgm_big.unet.up_blocks.4.nets.2.norm1.bias', 'lgm_big.unet.up_blocks.4.nets.2.conv1.weight', 'lgm_big.unet.up_blocks.4.nets.2.conv1.bias', 'lgm_big.unet.up_blocks.4.nets.2.norm2.weight', 'lgm_big.unet.up_blocks.4.nets.2.norm2.bias', 'lgm_big.unet.up_blocks.4.nets.2.conv2.weight', 'lgm_big.unet.up_blocks.4.nets.2.conv2.bias', 'lgm_big.unet.up_blocks.4.nets.2.shortcut.weight', 'lgm_big.unet.up_blocks.4.nets.2.shortcut.bias', 'lgm_big.unet.norm_out.weight', 'lgm_big.unet.norm_out.bias', 'lgm_big.unet.conv_out.weight', 'lgm_big.unet.conv_out.bias', 'lgm_big.conv.weight', 'lgm_big.conv.bias', 'camera_embedding.0.weight', 'camera_embedding.0.bias', 'camera_embedding.2.weight', 'camera_embedding.2.bias']) -[2024-06-08 18:19:55,462] INFO: Successfully load step 882000 model from ./pretrained_models/i2v_882000.pth -[2024-06-08 18:19:55,463] INFO: load a fixed model with 823M parameters -[2024-06-08 18:21:19,331] INFO: Step: 882000/1000000 Loss: 0.119 scale: 65536.0 LR: 0.0000030 -[2024-06-08 18:21:19,331] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00882000.pth -[2024-06-08 18:21:26,052] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00882000.pth -[2024-06-08 18:21:39,516] INFO: Step: 882001/1000000 Loss: 0.114 scale: 65536.0 LR: 0.0000060 -[2024-06-08 18:21:53,425] INFO: Step: 882002/1000000 Loss: 0.099 scale: 65536.0 LR: 0.0000090 -[2024-06-08 18:22:07,313] INFO: Step: 882003/1000000 Loss: 0.099 scale: 65536.0 LR: 0.0000120 -[2024-06-08 18:22:21,168] INFO: Step: 882004/1000000 Loss: 0.109 scale: 65536.0 LR: 0.0000150 -[2024-06-08 18:22:34,933] INFO: Step: 882005/1000000 Loss: 0.105 scale: 65536.0 LR: 0.0000180 -[2024-06-08 18:22:48,756] INFO: Step: 882006/1000000 Loss: 0.100 scale: 65536.0 LR: 0.0000210 -[2024-06-08 18:23:02,621] INFO: Step: 882007/1000000 Loss: 0.096 scale: 65536.0 LR: 0.0000240 -[2024-06-08 18:23:16,721] INFO: Step: 882008/1000000 Loss: 0.091 scale: 65536.0 LR: 0.0000270 -[2024-06-08 18:23:30,716] INFO: Step: 882009/1000000 Loss: 0.103 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:23:44,773] INFO: Step: 882010/1000000 Loss: 0.109 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:23:58,724] INFO: Step: 882011/1000000 Loss: 0.099 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:24:12,472] INFO: Step: 882012/1000000 Loss: 0.094 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:24:26,342] INFO: Step: 882013/1000000 Loss: 0.090 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:24:40,258] INFO: Step: 882014/1000000 Loss: 0.099 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:24:54,118] INFO: Step: 882015/1000000 Loss: 0.105 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:25:07,925] INFO: Step: 882016/1000000 Loss: 0.095 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:25:21,663] INFO: Step: 882017/1000000 Loss: 0.098 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:25:35,407] INFO: Step: 882018/1000000 Loss: 0.090 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:25:49,194] INFO: Step: 882019/1000000 Loss: 0.092 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:26:03,143] INFO: Step: 882020/1000000 Loss: 0.095 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:26:16,867] INFO: Step: 882021/1000000 Loss: 0.087 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:26:30,538] INFO: Step: 882022/1000000 Loss: 0.080 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:26:44,377] INFO: Step: 882023/1000000 Loss: 0.094 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:26:58,229] INFO: Step: 882024/1000000 Loss: 0.109 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:27:12,053] INFO: Step: 882025/1000000 Loss: 0.088 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:27:25,745] INFO: Step: 882026/1000000 Loss: 0.097 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:27:39,680] INFO: Step: 882027/1000000 Loss: 0.095 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:27:53,574] INFO: Step: 882028/1000000 Loss: 0.089 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:28:07,381] INFO: Step: 882029/1000000 Loss: 0.097 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:28:21,211] INFO: Step: 882030/1000000 Loss: 0.099 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:28:35,127] INFO: Step: 882031/1000000 Loss: 0.091 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:28:48,854] INFO: Step: 882032/1000000 Loss: 0.088 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:29:02,648] INFO: Step: 882033/1000000 Loss: 0.098 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:29:16,451] INFO: Step: 882034/1000000 Loss: 0.091 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:29:30,088] INFO: Step: 882035/1000000 Loss: 0.093 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:29:43,785] INFO: Step: 882036/1000000 Loss: 0.094 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:29:57,610] INFO: Step: 882037/1000000 Loss: 0.093 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:30:11,513] INFO: Step: 882038/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:30:25,374] INFO: Step: 882039/1000000 Loss: 0.091 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:30:39,264] INFO: Step: 882040/1000000 Loss: 0.093 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:30:53,015] INFO: Step: 882041/1000000 Loss: 0.091 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:31:06,900] INFO: Step: 882042/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:31:20,783] INFO: Step: 882043/1000000 Loss: 0.089 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:31:34,721] INFO: Step: 882044/1000000 Loss: 0.091 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:31:48,494] INFO: Step: 882045/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:32:02,337] INFO: Step: 882046/1000000 Loss: 0.084 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:32:16,074] INFO: Step: 882047/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:32:29,951] INFO: Step: 882048/1000000 Loss: 0.096 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:32:43,764] INFO: Step: 882049/1000000 Loss: 0.084 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:32:57,505] INFO: Step: 882050/1000000 Loss: 0.089 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:33:11,224] INFO: Step: 882051/1000000 Loss: 0.097 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:33:25,113] INFO: Step: 882052/1000000 Loss: 0.084 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:33:38,821] INFO: Step: 882053/1000000 Loss: 0.092 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:33:52,828] INFO: Step: 882054/1000000 Loss: 0.094 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:34:06,587] INFO: Step: 882055/1000000 Loss: 0.083 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:34:20,520] INFO: Step: 882056/1000000 Loss: 0.094 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:34:34,340] INFO: Step: 882057/1000000 Loss: 0.090 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:34:48,102] INFO: Step: 882058/1000000 Loss: 0.073 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:35:01,843] INFO: Step: 882059/1000000 Loss: 0.092 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:35:15,520] INFO: Step: 882060/1000000 Loss: 0.092 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:35:29,264] INFO: Step: 882061/1000000 Loss: 0.087 scale: 65536.0 LR: 0.0000300 -[2024-06-08 18:35:43,073] INFO: Step: 882062/1000000 Loss: 0.097 scale: 65536.0 LR: 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INFO: Step: 883496/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300 -[2024-06-09 00:05:46,835] INFO: Step: 883497/1000000 Loss: 0.074 scale: 65536.0 LR: 0.0000300 -[2024-06-09 00:06:00,541] INFO: Step: 883498/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300 -[2024-06-09 00:06:14,396] INFO: Step: 883499/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300 -[2024-06-09 00:06:28,240] INFO: Step: 883500/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300 -[2024-06-09 00:06:28,241] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00883500.pth -[2024-06-09 00:06:35,293] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00883500.pth -[2024-06-09 00:06:47,516] INFO: Step: 883501/1000000 Loss: 0.089 scale: 65536.0 LR: 0.0000300 -[2024-06-09 00:07:01,397] INFO: Step: 883502/1000000 Loss: 0.074 scale: 65536.0 LR: 0.0000300 -[2024-06-09 00:07:15,012] INFO: Step: 883503/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300 -[2024-06-09 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-[2024-06-09 03:17:11,540] INFO: Step: 884327/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:17:25,218] INFO: Step: 884328/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:17:38,885] INFO: Step: 884329/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:17:52,500] INFO: Step: 884330/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:18:06,390] INFO: Step: 884331/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:18:20,309] INFO: Step: 884332/1000000 Loss: 0.088 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:18:34,088] INFO: Step: 884333/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:18:47,829] INFO: Step: 884334/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:19:01,760] INFO: Step: 884335/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:19:15,739] INFO: Step: 884336/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:19:29,436] INFO: Step: 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INFO: Step: 884389/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:31:40,913] INFO: Step: 884390/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:31:54,647] INFO: Step: 884391/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:32:08,348] INFO: Step: 884392/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:32:22,280] INFO: Step: 884393/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:32:36,172] INFO: Step: 884394/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:32:49,921] INFO: Step: 884395/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:33:03,612] INFO: Step: 884396/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:33:17,470] INFO: Step: 884397/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:33:31,311] INFO: Step: 884398/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:33:45,096] INFO: Step: 884399/1000000 Loss: 0.072 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-[2024-06-09 03:36:16,715] INFO: Step: 884410/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:36:30,596] INFO: Step: 884411/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:36:44,199] INFO: Step: 884412/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:36:58,010] INFO: Step: 884413/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:37:11,969] INFO: Step: 884414/1000000 Loss: 0.087 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:37:25,908] INFO: Step: 884415/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:37:39,695] INFO: Step: 884416/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:37:53,381] INFO: Step: 884417/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:38:07,074] INFO: Step: 884418/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:38:21,037] INFO: Step: 884419/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:38:34,967] INFO: Step: 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03:43:24,454] INFO: Step: 884441/1000000 Loss: 0.088 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:43:38,182] INFO: Step: 884442/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:43:52,096] INFO: Step: 884443/1000000 Loss: 0.069 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:44:06,092] INFO: Step: 884444/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:44:19,801] INFO: Step: 884445/1000000 Loss: 0.091 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:44:33,500] INFO: Step: 884446/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:44:47,338] INFO: Step: 884447/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:45:01,183] INFO: Step: 884448/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:45:15,205] INFO: Step: 884449/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:45:28,996] INFO: Step: 884450/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:45:43,003] INFO: Step: 884451/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:45:56,759] INFO: Step: 884452/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:46:10,597] INFO: Step: 884453/1000000 Loss: 0.092 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:46:24,298] INFO: Step: 884454/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:46:38,244] INFO: Step: 884455/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:46:52,089] INFO: Step: 884456/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:47:05,872] INFO: Step: 884457/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:47:19,607] INFO: Step: 884458/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:47:33,377] INFO: Step: 884459/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:47:47,089] INFO: Step: 884460/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:48:01,003] INFO: Step: 884461/1000000 Loss: 0.076 scale: 131072.0 LR: 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INFO: Step: 884472/1000000 Loss: 0.090 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:50:46,584] INFO: Step: 884473/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:51:00,449] INFO: Step: 884474/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:51:14,298] INFO: Step: 884475/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:51:28,116] INFO: Step: 884476/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:51:41,955] INFO: Step: 884477/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:51:55,557] INFO: Step: 884478/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:52:09,523] INFO: Step: 884479/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:52:23,345] INFO: Step: 884480/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:52:37,057] INFO: Step: 884481/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:52:50,908] INFO: Step: 884482/1000000 Loss: 0.087 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:53:04,692] INFO: Step: 884483/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:53:18,454] INFO: Step: 884484/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:53:32,221] INFO: Step: 884485/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:53:45,952] INFO: Step: 884486/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:53:59,720] INFO: Step: 884487/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:54:13,534] INFO: Step: 884488/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:54:27,284] INFO: Step: 884489/1000000 Loss: 0.093 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:54:41,036] INFO: Step: 884490/1000000 Loss: 0.088 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:54:54,912] INFO: Step: 884491/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:55:08,680] INFO: Step: 884492/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:55:22,473] INFO: Step: 884493/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:55:36,265] INFO: Step: 884494/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:55:50,051] INFO: Step: 884495/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:56:03,876] INFO: Step: 884496/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:56:17,739] INFO: Step: 884497/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:56:31,607] INFO: Step: 884498/1000000 Loss: 0.092 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:56:45,227] INFO: Step: 884499/1000000 Loss: 0.064 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:56:58,989] INFO: Step: 884500/1000000 Loss: 0.087 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:56:58,990] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00884500.pth -[2024-06-09 03:57:06,140] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00884500.pth -[2024-06-09 03:57:18,296] INFO: Step: 884501/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:57:32,020] INFO: Step: 884502/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:57:45,703] INFO: Step: 884503/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:57:59,398] INFO: Step: 884504/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:58:13,215] INFO: Step: 884505/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:58:26,830] INFO: Step: 884506/1000000 Loss: 0.092 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:58:40,482] INFO: Step: 884507/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:58:54,216] INFO: Step: 884508/1000000 Loss: 0.094 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:59:08,015] INFO: Step: 884509/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 03:59:21,733] INFO: Step: 884510/1000000 Loss: 0.089 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-[2024-06-09 04:01:53,949] INFO: Step: 884521/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:02:07,640] INFO: Step: 884522/1000000 Loss: 0.072 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:02:21,342] INFO: Step: 884523/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:02:35,053] INFO: Step: 884524/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:02:48,775] INFO: Step: 884525/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:03:02,619] INFO: Step: 884526/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:03:16,454] INFO: Step: 884527/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:03:30,028] INFO: Step: 884528/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:03:43,865] INFO: Step: 884529/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:03:57,726] INFO: Step: 884530/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:04:11,766] INFO: Step: 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0.0000300 -[2024-06-09 04:32:53,972] INFO: Step: 884656/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 04:33:35,685] INFO: Step: 884657/1000000 Loss: 0.070 scale: 131072.0 LR: 0.0000300 -[2024-06-09 08:37:03,310] INFO: {'__name__': 'Config: VideoLDM Decoder', 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'max_words': 1000, 'num_workers': 16, 'prefetch_factor': 2, 'resolution': [256, 256], 'vit_out_dim': 1024, 'vit_resolution': [224, 224], 'depth_clamp': 10.0, 'misc_size': 384, 'depth_std': 20.0, 'frame_lens': [24], 'sample_fps': [8], 'vid_dataset': {'type': 'ObjDataset', 'data_list': './Obj/data/6.8_with_motion/train.txt', 'max_words': 1000, 'resolution': [256, 256], 'vit_resolution': [224, 224], 'get_first_frame': True}, 'img_dataset': {'type': 'ImageBaseDataset', 'data_list': ['laion_400m'], 'max_words': 1000, 'resolution': [448, 256]}, 'batch_sizes': {'1': 256, '4': 4, '8': 4, '16': 4, '24': 20}, 'Diffusion': {'type': 'DiffusionDDIM', 'schedule': 'cosine', 'schedule_param': {'num_timesteps': 1000, 'cosine_s': 0.008, 'zero_terminal_snr': True}, 'mean_type': 'v', 'loss_type': 'mse', 'var_type': 'fixed_small', 'rescale_timesteps': False, 'noise_strength': 0.1, 'ddim_timesteps': 50, 'with_latent_motion_loss': False}, 'ddim_timesteps': 50, 'use_div_loss': False, 'p_zero': 0.0, 'guide_scale': 3.0, 'vit_mean': [0.48145466, 0.4578275, 0.40821073], 'vit_std': [0.26862954, 0.26130258, 0.27577711], 'scale_factor': 0.18215, 'use_checkpoint': True, 'use_sharded_ddp': False, 'use_fsdp': False, 'use_fp16': True, 'temporal_attention': True, 'UNet': {'type': 'UNetSD_I2VGen', 'in_dim': 4, 'dim': 320, 'y_dim': 1024, 'context_dim': 1024, 'out_dim': 4, 'dim_mult': [1, 2, 4, 4], 'num_heads': 8, 'head_dim': 64, 'num_res_blocks': 2, 'attn_scales': [1.0, 0.5, 0.25], 'dropout': 0.1, 'temporal_attention': True, 'temporal_attn_times': 1, 'use_checkpoint': True, 'use_fps_condition': False, 'use_sim_mask': False, 'upper_len': 128, 'concat_dim': 4, 'default_fps': 8, 'use_camera_condition': False, 'use_lgm_refine': False, 'with_motion': True, 'with_latent_motion': False}, 'guidances': [], 'auto_encoder': {'type': 'AutoencoderKL', 'ddconfig': {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0, 'video_kernel_size': [3, 1, 1]}, 'embed_dim': 4, 'pretrained': './pretrained_models/modelscope_t2v/VQGAN_autoencoder.pth'}, 'embedder': {'type': 'FrozenOpenCLIPTtxtVisualEmbedder', 'layer': 'penultimate', 'pretrained': 'pretrained_models/modelscope_i2v/I2VGen-XL/open_clip_pytorch_model.bin', 'vit_resolution': [224, 224]}, 'ema_decay': 0.9999, 'num_steps': 1000000, 'lr': 3e-05, 'weight_decay': 0.0, 'betas': (0.9, 0.999), 'eps': 1e-08, 'chunk_size': 4, 'decoder_bs': 4, 'alpha': 0.7, 'save_ckp_interval': 500, 'warmup_steps': 10, 'decay_mode': 'cosine', 'use_ema': True, 'load_from': None, 'Pretrain': {'type': 'pretrain_specific_strategies', 'fix_weight': False, 'grad_scale': 0.5, 'resume_checkpoint': './workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00884500.pth', 'sd_keys_path': 'pretrained_models/modelscope_i2v/I2VGen-XL/stable_diffusion_image_key_temporal_attention_x1.json'}, 'viz_interval': 500, 'visual_train': {'type': 'VisualTrainTextImageToVideo', 'partial_keys': [['y', 'image', 'local_image', 'fps', 'camera_data', 'gs_data']], 'use_offset_noise': True, 'guide_scale': 6.0}, 'visual_inference': {'type': 'VisualGeneratedVideos'}, 'inference_list_path': '', 'log_interval': 1, 'log_dir': 'workspace/i2v_motion/obj_train_motion', 'seed': 6666, 'negative_prompt': 'static, 3d asset, Distorted, discontinuous, Ugly, blurry, low resolution, disfigured, disconnected limbs, Ugly faces, incomplete arms', 'with_motion': True, 'motion_magnitude': None, 'with_latent_motion': False, 'latent_motion_magnitude': None, 'ENABLE': True, 'DATASET': 'webvid10m', 'TASK_TYPE': 'train_i2v_entrance', 'noise_strength': 0.1, 'use_zero_infer': True, 'cfg_file': './configs/obj_train_motion.yaml', 'init_method': 'tcp://localhost:9999', 'debug': False, 'opts': [], 'pmi_rank': 0, 'pmi_world_size': 1, 'gpus_per_machine': 3, 'world_size': 3, 'gpu': 0, 'rank': 0, 'log_file': 'workspace/i2v_motion/obj_train_motion/log.txt'} -[2024-06-09 08:37:03,310] INFO: Save all the file in to dir workspace/i2v_motion/obj_train_motion -[2024-06-09 08:37:03,311] INFO: Going into i2v_img_fullid_vidcom function on 0 gpu -[2024-06-09 08:37:03,334] INFO: Current worker with max_frames=24, batch_size=20, sample_fps=8 -[2024-06-09 08:38:10,929] INFO: {'__name__': 'Config: VideoLDM Decoder', 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'max_words': 1000, 'num_workers': 16, 'prefetch_factor': 2, 'resolution': [256, 256], 'vit_out_dim': 1024, 'vit_resolution': [224, 224], 'depth_clamp': 10.0, 'misc_size': 384, 'depth_std': 20.0, 'frame_lens': [24], 'sample_fps': [8], 'vid_dataset': {'type': 'ObjDataset', 'data_list': './Obj/data/6.8_with_motion/train.txt', 'max_words': 1000, 'resolution': [256, 256], 'vit_resolution': [224, 224], 'get_first_frame': True}, 'img_dataset': {'type': 'ImageBaseDataset', 'data_list': ['laion_400m'], 'max_words': 1000, 'resolution': [448, 256]}, 'batch_sizes': {'1': 256, '4': 4, '8': 4, '16': 4, '24': 20}, 'Diffusion': {'type': 'DiffusionDDIM', 'schedule': 'cosine', 'schedule_param': {'num_timesteps': 1000, 'cosine_s': 0.008, 'zero_terminal_snr': True}, 'mean_type': 'v', 'loss_type': 'mse', 'var_type': 'fixed_small', 'rescale_timesteps': False, 'noise_strength': 0.1, 'ddim_timesteps': 50, 'with_latent_motion_loss': False}, 'ddim_timesteps': 50, 'use_div_loss': False, 'p_zero': 0.0, 'guide_scale': 3.0, 'vit_mean': [0.48145466, 0.4578275, 0.40821073], 'vit_std': [0.26862954, 0.26130258, 0.27577711], 'scale_factor': 0.18215, 'use_checkpoint': True, 'use_sharded_ddp': False, 'use_fsdp': False, 'use_fp16': True, 'temporal_attention': True, 'UNet': {'type': 'UNetSD_I2VGen', 'in_dim': 4, 'dim': 320, 'y_dim': 1024, 'context_dim': 1024, 'out_dim': 4, 'dim_mult': [1, 2, 4, 4], 'num_heads': 8, 'head_dim': 64, 'num_res_blocks': 2, 'attn_scales': [1.0, 0.5, 0.25], 'dropout': 0.1, 'temporal_attention': True, 'temporal_attn_times': 1, 'use_checkpoint': True, 'use_fps_condition': False, 'use_sim_mask': False, 'upper_len': 128, 'concat_dim': 4, 'default_fps': 8, 'use_camera_condition': False, 'use_lgm_refine': False, 'with_motion': True, 'with_latent_motion': False}, 'guidances': [], 'auto_encoder': {'type': 'AutoencoderKL', 'ddconfig': {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0, 'video_kernel_size': [3, 1, 1]}, 'embed_dim': 4, 'pretrained': './pretrained_models/modelscope_t2v/VQGAN_autoencoder.pth'}, 'embedder': {'type': 'FrozenOpenCLIPTtxtVisualEmbedder', 'layer': 'penultimate', 'pretrained': 'pretrained_models/modelscope_i2v/I2VGen-XL/open_clip_pytorch_model.bin', 'vit_resolution': [224, 224]}, 'ema_decay': 0.9999, 'num_steps': 1000000, 'lr': 3e-05, 'weight_decay': 0.0, 'betas': (0.9, 0.999), 'eps': 1e-08, 'chunk_size': 4, 'decoder_bs': 4, 'alpha': 0.7, 'save_ckp_interval': 500, 'warmup_steps': 10, 'decay_mode': 'cosine', 'use_ema': True, 'load_from': None, 'Pretrain': {'type': 'pretrain_specific_strategies', 'fix_weight': False, 'grad_scale': 0.5, 'resume_checkpoint': './workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00884500.pth', 'sd_keys_path': 'pretrained_models/modelscope_i2v/I2VGen-XL/stable_diffusion_image_key_temporal_attention_x1.json'}, 'viz_interval': 500, 'visual_train': {'type': 'VisualTrainTextImageToVideo', 'partial_keys': [['y', 'image', 'local_image', 'fps', 'camera_data', 'gs_data']], 'use_offset_noise': True, 'guide_scale': 6.0}, 'visual_inference': {'type': 'VisualGeneratedVideos'}, 'inference_list_path': '', 'log_interval': 1, 'log_dir': 'workspace/i2v_motion/obj_train_motion', 'seed': 6666, 'negative_prompt': 'static, 3d asset, Distorted, discontinuous, Ugly, blurry, low resolution, disfigured, disconnected limbs, Ugly faces, incomplete arms', 'with_motion': True, 'motion_magnitude': None, 'with_latent_motion': False, 'latent_motion_magnitude': None, 'ENABLE': True, 'DATASET': 'webvid10m', 'TASK_TYPE': 'train_i2v_entrance', 'noise_strength': 0.1, 'use_zero_infer': True, 'cfg_file': './configs/obj_train_motion.yaml', 'init_method': 'tcp://localhost:9999', 'debug': False, 'opts': [], 'pmi_rank': 0, 'pmi_world_size': 1, 'gpus_per_machine': 3, 'world_size': 3, 'gpu': 0, 'rank': 0, 'log_file': 'workspace/i2v_motion/obj_train_motion/log.txt'} -[2024-06-09 08:38:10,929] INFO: Save all the file in to dir workspace/i2v_motion/obj_train_motion -[2024-06-09 08:38:10,929] INFO: Going into i2v_img_fullid_vidcom function on 0 gpu -[2024-06-09 08:38:10,940] INFO: Current worker with max_frames=24, batch_size=20, sample_fps=8 -[2024-06-09 08:39:15,378] INFO: Loaded ViT-H-14 model config. -[2024-06-09 08:39:38,483] INFO: Loading pretrained ViT-H-14 weights (pretrained_models/modelscope_i2v/I2VGen-XL/open_clip_pytorch_model.bin). -[2024-06-09 08:40:13,364] INFO: Restored from ./pretrained_models/modelscope_t2v/VQGAN_autoencoder.pth -[2024-06-09 08:40:27,019] INFO: load a fixed model with -[2024-06-09 08:40:27,128] INFO: Successfully load step 884500 model from ./workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00884500.pth -[2024-06-09 08:40:27,128] INFO: load a fixed model with 823M parameters -[2024-06-09 08:41:42,211] INFO: Step: 884500/1000000 Loss: 0.089 scale: 65536.0 LR: 0.0000030 -[2024-06-09 08:41:42,211] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00884500.pth -[2024-06-09 08:41:50,132] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00884500.pth -[2024-06-09 08:42:03,167] INFO: Step: 884501/1000000 Loss: 0.079 scale: 65536.0 LR: 0.0000060 -[2024-06-09 08:42:16,296] INFO: Step: 884502/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000090 -[2024-06-09 08:42:29,432] INFO: Step: 884503/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000120 -[2024-06-09 08:42:42,447] INFO: Step: 884504/1000000 Loss: 0.087 scale: 65536.0 LR: 0.0000150 -[2024-06-09 08:42:55,578] INFO: Step: 884505/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000180 -[2024-06-09 08:43:08,728] INFO: Step: 884506/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000210 -[2024-06-09 08:43:21,875] INFO: Step: 884507/1000000 Loss: 0.076 scale: 65536.0 LR: 0.0000240 -[2024-06-09 08:43:35,035] INFO: Step: 884508/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000270 -[2024-06-09 08:43:48,459] INFO: Step: 884509/1000000 Loss: 0.086 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:44:01,763] INFO: Step: 884510/1000000 Loss: 0.077 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:44:14,985] INFO: Step: 884511/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:44:28,327] INFO: Step: 884512/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:44:41,474] INFO: Step: 884513/1000000 Loss: 0.084 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:44:54,874] INFO: Step: 884514/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:45:08,232] INFO: Step: 884515/1000000 Loss: 0.083 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:45:21,355] INFO: Step: 884516/1000000 Loss: 0.077 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:45:34,601] INFO: Step: 884517/1000000 Loss: 0.079 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:45:47,896] INFO: Step: 884518/1000000 Loss: 0.088 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:46:01,108] INFO: Step: 884519/1000000 Loss: 0.076 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:46:14,291] INFO: Step: 884520/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:46:27,626] INFO: Step: 884521/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:46:41,018] INFO: Step: 884522/1000000 Loss: 0.074 scale: 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INFO: Step: 884533/1000000 Loss: 0.077 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:49:19,765] INFO: Step: 884534/1000000 Loss: 0.087 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:49:33,000] INFO: Step: 884535/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:49:46,339] INFO: Step: 884536/1000000 Loss: 0.080 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:49:59,383] INFO: Step: 884537/1000000 Loss: 0.074 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:50:12,630] INFO: Step: 884538/1000000 Loss: 0.070 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:50:25,695] INFO: Step: 884539/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:50:38,917] INFO: Step: 884540/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:50:52,196] INFO: Step: 884541/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:51:05,389] INFO: Step: 884542/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:51:18,760] INFO: Step: 884543/1000000 Loss: 0.086 scale: 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INFO: Step: 884554/1000000 Loss: 0.091 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:53:57,482] INFO: Step: 884555/1000000 Loss: 0.084 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:54:10,708] INFO: Step: 884556/1000000 Loss: 0.079 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:54:23,937] INFO: Step: 884557/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:54:37,431] INFO: Step: 884558/1000000 Loss: 0.093 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:54:50,605] INFO: Step: 884559/1000000 Loss: 0.076 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:55:03,909] INFO: Step: 884560/1000000 Loss: 0.072 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:55:17,216] INFO: Step: 884561/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:55:30,532] INFO: Step: 884562/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:55:43,948] INFO: Step: 884563/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:55:57,017] INFO: Step: 884564/1000000 Loss: 0.080 scale: 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INFO: Step: 884575/1000000 Loss: 0.091 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:58:35,631] INFO: Step: 884576/1000000 Loss: 0.084 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:58:48,860] INFO: Step: 884577/1000000 Loss: 0.087 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:59:02,124] INFO: Step: 884578/1000000 Loss: 0.074 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:59:15,496] INFO: Step: 884579/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:59:28,566] INFO: Step: 884580/1000000 Loss: 0.080 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:59:41,732] INFO: Step: 884581/1000000 Loss: 0.071 scale: 65536.0 LR: 0.0000300 -[2024-06-09 08:59:54,914] INFO: Step: 884582/1000000 Loss: 0.075 scale: 65536.0 LR: 0.0000300 -[2024-06-09 09:00:08,129] INFO: Step: 884583/1000000 Loss: 0.094 scale: 65536.0 LR: 0.0000300 -[2024-06-09 09:00:21,403] INFO: Step: 884584/1000000 Loss: 0.071 scale: 65536.0 LR: 0.0000300 -[2024-06-09 09:00:34,802] INFO: Step: 884585/1000000 Loss: 0.077 scale: 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INFO: Step: 884995/1000000 Loss: 0.089 scale: 65536.0 LR: 0.0000300 -[2024-06-09 10:31:05,678] INFO: Step: 884996/1000000 Loss: 0.080 scale: 65536.0 LR: 0.0000300 -[2024-06-09 10:31:18,972] INFO: Step: 884997/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000300 -[2024-06-09 10:31:32,133] INFO: Step: 884998/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300 -[2024-06-09 10:31:45,413] INFO: Step: 884999/1000000 Loss: 0.080 scale: 65536.0 LR: 0.0000300 -[2024-06-09 10:31:58,602] INFO: Step: 885000/1000000 Loss: 0.087 scale: 65536.0 LR: 0.0000300 -[2024-06-09 10:31:58,603] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00885000.pth -[2024-06-09 10:32:04,453] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00885000.pth -[2024-06-09 10:32:16,601] INFO: Step: 885001/1000000 Loss: 0.072 scale: 65536.0 LR: 0.0000300 -[2024-06-09 10:32:29,896] INFO: Step: 885002/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300 -[2024-06-09 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-[2024-06-09 12:25:41,872] INFO: Step: 885515/1000000 Loss: 0.084 scale: 65536.0 LR: 0.0000300 -[2024-06-09 12:25:55,055] INFO: Step: 885516/1000000 Loss: 0.072 scale: 65536.0 LR: 0.0000300 -[2024-06-09 12:26:08,131] INFO: Step: 885517/1000000 Loss: 0.070 scale: 65536.0 LR: 0.0000300 -[2024-06-09 12:26:21,493] INFO: Step: 885518/1000000 Loss: 0.084 scale: 65536.0 LR: 0.0000300 -[2024-06-09 12:26:34,714] INFO: Step: 885519/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300 -[2024-06-09 12:26:47,858] INFO: Step: 885520/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300 -[2024-06-09 12:27:01,065] INFO: Step: 885521/1000000 Loss: 0.075 scale: 65536.0 LR: 0.0000300 -[2024-06-09 12:27:14,075] INFO: Step: 885522/1000000 Loss: 0.077 scale: 65536.0 LR: 0.0000300 -[2024-06-09 12:27:27,271] INFO: Step: 885523/1000000 Loss: 0.080 scale: 65536.0 LR: 0.0000300 -[2024-06-09 12:27:40,474] INFO: Step: 885524/1000000 Loss: 0.076 scale: 65536.0 LR: 0.0000300 -[2024-06-09 12:27:53,785] INFO: Step: 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-[2024-06-09 16:16:35,377] INFO: Step: 886559/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:16:48,609] INFO: Step: 886560/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:17:01,845] INFO: Step: 886561/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:17:15,235] INFO: Step: 886562/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:17:28,594] INFO: Step: 886563/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:17:41,833] INFO: Step: 886564/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:17:55,113] INFO: Step: 886565/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:18:08,334] INFO: Step: 886566/1000000 Loss: 0.096 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:18:21,571] INFO: Step: 886567/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:18:34,800] INFO: Step: 886568/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:18:48,285] INFO: Step: 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-[2024-06-09 16:53:21,866] INFO: Step: 886725/1000000 Loss: 0.095 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:53:35,275] INFO: Step: 886726/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:53:48,473] INFO: Step: 886727/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:54:01,923] INFO: Step: 886728/1000000 Loss: 0.093 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:54:15,131] INFO: Step: 886729/1000000 Loss: 0.068 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:54:28,498] INFO: Step: 886730/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:54:41,790] INFO: Step: 886731/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:54:55,210] INFO: Step: 886732/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:55:08,477] INFO: Step: 886733/1000000 Loss: 0.091 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:55:21,643] INFO: Step: 886734/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 16:55:34,912] INFO: Step: 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-[2024-06-09 17:11:48,910] INFO: Step: 886808/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:12:02,370] INFO: Step: 886809/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:12:15,563] INFO: Step: 886810/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:12:28,803] INFO: Step: 886811/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:12:42,347] INFO: Step: 886812/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:12:55,619] INFO: Step: 886813/1000000 Loss: 0.069 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:13:08,929] INFO: Step: 886814/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:13:22,119] INFO: Step: 886815/1000000 Loss: 0.087 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:13:35,335] INFO: Step: 886816/1000000 Loss: 0.072 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:13:48,659] INFO: Step: 886817/1000000 Loss: 0.090 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:14:01,801] INFO: Step: 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-[2024-06-09 17:30:12,749] INFO: Step: 886891/1000000 Loss: 0.069 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:30:26,105] INFO: Step: 886892/1000000 Loss: 0.069 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:30:39,384] INFO: Step: 886893/1000000 Loss: 0.095 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:30:52,651] INFO: Step: 886894/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:31:05,906] INFO: Step: 886895/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:31:19,123] INFO: Step: 886896/1000000 Loss: 0.092 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:31:32,384] INFO: Step: 886897/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:31:45,889] INFO: Step: 886898/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:31:59,427] INFO: Step: 886899/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:32:12,590] INFO: Step: 886900/1000000 Loss: 0.088 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:32:25,905] INFO: Step: 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17:37:04,964] INFO: Step: 886922/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:37:18,326] INFO: Step: 886923/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:37:31,460] INFO: Step: 886924/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:37:44,850] INFO: Step: 886925/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:37:57,946] INFO: Step: 886926/1000000 Loss: 0.072 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:38:11,310] INFO: Step: 886927/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:38:24,536] INFO: Step: 886928/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:38:37,872] INFO: Step: 886929/1000000 Loss: 0.071 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:38:51,026] INFO: Step: 886930/1000000 Loss: 0.069 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:39:04,272] INFO: Step: 886931/1000000 Loss: 0.088 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:39:17,625] INFO: Step: 886932/1000000 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INFO: Step: 886953/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:44:09,525] INFO: Step: 886954/1000000 Loss: 0.087 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:44:22,910] INFO: Step: 886955/1000000 Loss: 0.092 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:44:36,152] INFO: Step: 886956/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:44:49,453] INFO: Step: 886957/1000000 Loss: 0.097 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:45:02,807] INFO: Step: 886958/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:45:16,079] INFO: Step: 886959/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:45:29,240] INFO: Step: 886960/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:45:42,563] INFO: Step: 886961/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:45:55,948] INFO: Step: 886962/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:46:09,020] INFO: Step: 886963/1000000 Loss: 0.082 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-[2024-06-09 17:48:35,015] INFO: Step: 886974/1000000 Loss: 0.088 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:48:48,471] INFO: Step: 886975/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:49:01,676] INFO: Step: 886976/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:49:14,879] INFO: Step: 886977/1000000 Loss: 0.094 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:49:28,274] INFO: Step: 886978/1000000 Loss: 0.088 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:49:41,432] INFO: Step: 886979/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:49:54,767] INFO: Step: 886980/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:50:08,166] INFO: Step: 886981/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:50:21,425] INFO: Step: 886982/1000000 Loss: 0.103 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:50:34,856] INFO: Step: 886983/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:50:48,181] INFO: Step: 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-[2024-06-09 17:57:18,993] INFO: Step: 887013/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:57:32,365] INFO: Step: 887014/1000000 Loss: 0.068 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:57:45,510] INFO: Step: 887015/1000000 Loss: 0.095 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:57:58,721] INFO: Step: 887016/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:58:12,053] INFO: Step: 887017/1000000 Loss: 0.070 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:58:25,463] INFO: Step: 887018/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:58:38,850] INFO: Step: 887019/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:58:52,125] INFO: Step: 887020/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:59:05,464] INFO: Step: 887021/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:59:18,864] INFO: Step: 887022/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 17:59:32,045] INFO: Step: 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18:04:11,327] INFO: Step: 887044/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:04:24,596] INFO: Step: 887045/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:04:37,897] INFO: Step: 887046/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:04:51,128] INFO: Step: 887047/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:05:04,596] INFO: Step: 887048/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:05:17,755] INFO: Step: 887049/1000000 Loss: 0.096 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:05:30,971] INFO: Step: 887050/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:05:44,198] INFO: Step: 887051/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:05:57,438] INFO: Step: 887052/1000000 Loss: 0.068 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:06:10,721] INFO: Step: 887053/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:06:23,805] INFO: Step: 887054/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:06:36,978] INFO: Step: 887055/1000000 Loss: 0.071 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:06:50,282] INFO: Step: 887056/1000000 Loss: 0.088 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:07:03,601] INFO: Step: 887057/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:07:16,918] INFO: Step: 887058/1000000 Loss: 0.094 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:07:30,121] INFO: Step: 887059/1000000 Loss: 0.067 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:07:43,325] INFO: Step: 887060/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:07:56,627] INFO: Step: 887061/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:08:10,060] INFO: Step: 887062/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:08:23,313] INFO: Step: 887063/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:08:36,511] INFO: Step: 887064/1000000 Loss: 0.083 scale: 131072.0 LR: 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INFO: Step: 887075/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:11:15,824] INFO: Step: 887076/1000000 Loss: 0.088 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:11:29,145] INFO: Step: 887077/1000000 Loss: 0.091 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:11:42,441] INFO: Step: 887078/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:11:55,957] INFO: Step: 887079/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:12:08,951] INFO: Step: 887080/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:12:22,171] INFO: Step: 887081/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:12:35,402] INFO: Step: 887082/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:12:48,917] INFO: Step: 887083/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:13:02,493] INFO: Step: 887084/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:13:15,661] INFO: Step: 887085/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:13:28,901] INFO: Step: 887086/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:13:41,775] INFO: Step: 887087/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:13:55,102] INFO: Step: 887088/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:14:08,429] INFO: Step: 887089/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:14:21,705] INFO: Step: 887090/1000000 Loss: 0.066 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:14:35,000] INFO: Step: 887091/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:14:48,256] INFO: Step: 887092/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:15:01,608] INFO: Step: 887093/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:15:15,172] INFO: Step: 887094/1000000 Loss: 0.090 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:15:28,447] INFO: Step: 887095/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:15:41,589] INFO: Step: 887096/1000000 Loss: 0.072 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:15:54,833] INFO: Step: 887097/1000000 Loss: 0.068 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:16:08,214] INFO: Step: 887098/1000000 Loss: 0.094 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:16:21,568] INFO: Step: 887099/1000000 Loss: 0.091 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:16:35,031] INFO: Step: 887100/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:16:48,252] INFO: Step: 887101/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:17:01,709] INFO: Step: 887102/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:17:15,058] INFO: Step: 887103/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:17:28,339] INFO: Step: 887104/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:17:41,606] INFO: Step: 887105/1000000 Loss: 0.091 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:17:55,158] INFO: Step: 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18:22:35,577] INFO: Step: 887127/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:22:48,886] INFO: Step: 887128/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:23:02,216] INFO: Step: 887129/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:23:15,366] INFO: Step: 887130/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:23:28,690] INFO: Step: 887131/1000000 Loss: 0.071 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:23:41,970] INFO: Step: 887132/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:23:55,286] INFO: Step: 887133/1000000 Loss: 0.091 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:24:08,647] INFO: Step: 887134/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:24:21,747] INFO: Step: 887135/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:24:35,180] INFO: Step: 887136/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:24:48,487] INFO: Step: 887137/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:25:01,989] INFO: Step: 887138/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:25:15,505] INFO: Step: 887139/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:25:28,765] INFO: Step: 887140/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:25:42,137] INFO: Step: 887141/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:25:55,513] INFO: Step: 887142/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:26:08,965] INFO: Step: 887143/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:26:22,381] INFO: Step: 887144/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:26:35,673] INFO: Step: 887145/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:26:48,966] INFO: Step: 887146/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:27:02,351] INFO: Step: 887147/1000000 Loss: 0.072 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:27:15,642] INFO: Step: 887148/1000000 Loss: 0.087 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:27:28,941] INFO: Step: 887149/1000000 Loss: 0.068 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:27:42,315] INFO: Step: 887150/1000000 Loss: 0.069 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:27:55,689] INFO: Step: 887151/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:28:09,009] INFO: Step: 887152/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:28:22,439] INFO: Step: 887153/1000000 Loss: 0.093 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:28:35,747] INFO: Step: 887154/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:28:49,013] INFO: Step: 887155/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:29:02,182] INFO: Step: 887156/1000000 Loss: 0.091 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:29:15,439] INFO: Step: 887157/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:29:28,612] INFO: Step: 887158/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:29:41,836] INFO: Step: 887159/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:29:55,107] INFO: Step: 887160/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:30:08,458] INFO: Step: 887161/1000000 Loss: 0.088 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:30:21,915] INFO: Step: 887162/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:30:35,214] INFO: Step: 887163/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:30:48,522] INFO: Step: 887164/1000000 Loss: 0.069 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:31:02,028] INFO: Step: 887165/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:31:15,257] INFO: Step: 887166/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:31:28,569] INFO: Step: 887167/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:31:41,938] INFO: Step: 887168/1000000 Loss: 0.082 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-[2024-06-09 18:34:08,320] INFO: Step: 887179/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:34:21,626] INFO: Step: 887180/1000000 Loss: 0.071 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:34:34,944] INFO: Step: 887181/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:34:48,370] INFO: Step: 887182/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:35:01,717] INFO: Step: 887183/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:35:14,937] INFO: Step: 887184/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:35:28,419] INFO: Step: 887185/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:35:41,659] INFO: Step: 887186/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:35:55,130] INFO: Step: 887187/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:36:08,435] INFO: Step: 887188/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 18:36:21,698] INFO: Step: 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-[2024-06-09 19:10:55,371] INFO: Step: 887345/1000000 Loss: 0.087 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:11:08,506] INFO: Step: 887346/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:11:21,861] INFO: Step: 887347/1000000 Loss: 0.100 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:11:35,092] INFO: Step: 887348/1000000 Loss: 0.064 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:11:48,282] INFO: Step: 887349/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:12:01,909] INFO: Step: 887350/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:12:15,050] INFO: Step: 887351/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:12:28,313] INFO: Step: 887352/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:12:41,533] INFO: Step: 887353/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:12:54,749] INFO: Step: 887354/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:13:08,186] INFO: Step: 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-[2024-06-09 19:29:17,332] INFO: Step: 887428/1000000 Loss: 0.072 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:29:30,574] INFO: Step: 887429/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:29:43,752] INFO: Step: 887430/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:29:56,974] INFO: Step: 887431/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:30:10,206] INFO: Step: 887432/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:30:23,530] INFO: Step: 887433/1000000 Loss: 0.064 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:30:36,799] INFO: Step: 887434/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:30:50,200] INFO: Step: 887435/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:31:03,565] INFO: Step: 887436/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:31:16,884] INFO: Step: 887437/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:31:30,063] INFO: Step: 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19:36:10,062] INFO: Step: 887459/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:36:23,345] INFO: Step: 887460/1000000 Loss: 0.094 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:36:36,828] INFO: Step: 887461/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:36:50,075] INFO: Step: 887462/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:37:03,245] INFO: Step: 887463/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:37:16,663] INFO: Step: 887464/1000000 Loss: 0.067 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:37:29,907] INFO: Step: 887465/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:37:43,188] INFO: Step: 887466/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:37:56,472] INFO: Step: 887467/1000000 Loss: 0.066 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:38:09,804] INFO: Step: 887468/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:38:23,154] INFO: Step: 887469/1000000 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INFO: Step: 887490/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:43:16,062] INFO: Step: 887491/1000000 Loss: 0.071 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:43:29,271] INFO: Step: 887492/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:43:42,733] INFO: Step: 887493/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:43:56,055] INFO: Step: 887494/1000000 Loss: 0.094 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:44:09,408] INFO: Step: 887495/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:44:22,725] INFO: Step: 887496/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:44:36,084] INFO: Step: 887497/1000000 Loss: 0.085 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:44:49,310] INFO: Step: 887498/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:45:02,546] INFO: Step: 887499/1000000 Loss: 0.093 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:45:15,820] INFO: Step: 887500/1000000 Loss: 0.090 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:45:15,821] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00887500.pth -[2024-06-09 19:45:23,214] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00887500.pth -[2024-06-09 19:45:35,454] INFO: Step: 887501/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:45:48,580] INFO: Step: 887502/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:46:01,906] INFO: Step: 887503/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:46:15,160] INFO: Step: 887504/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:46:28,488] INFO: Step: 887505/1000000 Loss: 0.087 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:46:41,803] INFO: Step: 887506/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:46:55,060] INFO: Step: 887507/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300 -[2024-06-09 19:47:08,297] INFO: Step: 887508/1000000 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-[2024-06-09 20:14:42,098] INFO: Step: 887633/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 20:14:55,393] INFO: Step: 887634/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300 -[2024-06-09 20:15:08,558] INFO: Step: 887635/1000000 Loss: 0.093 scale: 131072.0 LR: 0.0000300 -[2024-06-09 20:15:21,702] INFO: Step: 887636/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300 -[2024-06-09 20:15:34,973] INFO: Step: 887637/1000000 Loss: 0.070 scale: 131072.0 LR: 0.0000300 -[2024-06-09 20:15:47,916] INFO: Step: 887638/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300 -[2024-06-09 20:16:01,121] INFO: Step: 887639/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300 -[2024-06-09 20:16:14,106] INFO: Step: 887640/1000000 Loss: 0.086 scale: 131072.0 LR: 0.0000300 -[2024-06-09 20:16:27,239] INFO: Step: 887641/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300 -[2024-06-09 20:16:40,519] INFO: Step: 887642/1000000 Loss: 0.100 scale: 131072.0 LR: 0.0000300 -[2024-06-09 20:16:53,705] INFO: Step: 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