diff --git "a/i2v_motion/log.txt" "b/i2v_motion/log.txt"
new file mode 100644--- /dev/null
+++ "b/i2v_motion/log.txt"
@@ -0,0 +1,5861 @@
+[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'], unexpected_keys=['lgm_big.unet.conv_in.weight', 'lgm_big.unet.conv_in.bias', 'lgm_big.unet.down_blocks.0.nets.0.norm1.weight', 'lgm_big.unet.down_blocks.0.nets.0.norm1.bias', 'lgm_big.unet.down_blocks.0.nets.0.conv1.weight', 'lgm_big.unet.down_blocks.0.nets.0.conv1.bias', 'lgm_big.unet.down_blocks.0.nets.0.norm2.weight', 'lgm_big.unet.down_blocks.0.nets.0.norm2.bias', 'lgm_big.unet.down_blocks.0.nets.0.conv2.weight', 'lgm_big.unet.down_blocks.0.nets.0.conv2.bias', 'lgm_big.unet.down_blocks.0.nets.1.norm1.weight', 'lgm_big.unet.down_blocks.0.nets.1.norm1.bias', 'lgm_big.unet.down_blocks.0.nets.1.conv1.weight', 'lgm_big.unet.down_blocks.0.nets.1.conv1.bias', 'lgm_big.unet.down_blocks.0.nets.1.norm2.weight', 'lgm_big.unet.down_blocks.0.nets.1.norm2.bias', 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'lgm_big.unet.up_blocks.3.nets.1.norm1.bias', 'lgm_big.unet.up_blocks.3.nets.1.conv1.weight', 'lgm_big.unet.up_blocks.3.nets.1.conv1.bias', '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: 0.0000300
+[2024-06-08 18:35:56,830] INFO: Step: 882063/1000000 Loss: 0.095 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:36:10,469] INFO: Step: 882064/1000000 Loss: 0.083 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:36:24,066] INFO: Step: 882065/1000000 Loss: 0.079 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:36:37,801] INFO: Step: 882066/1000000 Loss: 0.093 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:36:51,322] INFO: Step: 882067/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:37:05,197] INFO: Step: 882068/1000000 Loss: 0.087 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:37:18,843] INFO: Step: 882069/1000000 Loss: 0.088 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:37:32,808] INFO: Step: 882070/1000000 Loss: 0.094 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:37:46,590] INFO: Step: 882071/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:38:00,327] INFO: Step: 882072/1000000 Loss: 0.101 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:38:14,285] INFO: Step: 882073/1000000 Loss: 0.086 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:38:28,072] INFO: Step: 882074/1000000 Loss: 0.100 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:38:41,791] INFO: Step: 882075/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:38:55,628] INFO: Step: 882076/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:39:09,520] INFO: Step: 882077/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300
+[2024-06-08 18:39:23,185] INFO: Step: 882078/1000000 Loss: 0.092 scale: 65536.0 LR: 0.0000300
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+[2024-06-08 20:13:12,029] INFO: Step: 882486/1000000 Loss: 0.075 scale: 65536.0 LR: 0.0000300
+[2024-06-08 20:13:25,901] INFO: Step: 882487/1000000 Loss: 0.073 scale: 65536.0 LR: 0.0000300
+[2024-06-08 20:13:39,538] INFO: Step: 882488/1000000 Loss: 0.080 scale: 65536.0 LR: 0.0000300
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+[2024-06-08 20:14:48,433] INFO: Step: 882493/1000000 Loss: 0.083 scale: 65536.0 LR: 0.0000300
+[2024-06-08 20:15:02,357] INFO: Step: 882494/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000300
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+[2024-06-08 20:15:43,601] INFO: Step: 882497/1000000 Loss: 0.074 scale: 65536.0 LR: 0.0000300
+[2024-06-08 20:15:57,321] INFO: Step: 882498/1000000 Loss: 0.079 scale: 65536.0 LR: 0.0000300
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+[2024-06-08 20:16:24,972] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00882500.pth
+[2024-06-08 20:16:31,922] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00882500.pth
+[2024-06-08 20:16:44,064] INFO: Step: 882501/1000000 Loss: 0.088 scale: 65536.0 LR: 0.0000300
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+[2024-06-08 22:11:15,822] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00883000.pth
+[2024-06-08 22:11:23,505] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00883000.pth
+[2024-06-08 22:11:35,697] INFO: Step: 883001/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 00:04:23,604] INFO: Step: 883491/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300
+[2024-06-09 00:04:37,420] INFO: Step: 883492/1000000 Loss: 0.091 scale: 65536.0 LR: 0.0000300
+[2024-06-09 00:04:51,308] INFO: Step: 883493/1000000 Loss: 0.084 scale: 65536.0 LR: 0.0000300
+[2024-06-09 00:05:05,168] INFO: Step: 883494/1000000 Loss: 0.080 scale: 65536.0 LR: 0.0000300
+[2024-06-09 00:05:19,141] INFO: Step: 883495/1000000 Loss: 0.077 scale: 65536.0 LR: 0.0000300
+[2024-06-09 00:05:33,048] 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
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+[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
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+[2024-06-09 01:58:58,299] INFO: Step: 883988/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 01:59:25,927] INFO: Step: 883990/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300
+[2024-06-09 01:59:39,683] INFO: Step: 883991/1000000 Loss: 0.077 scale: 65536.0 LR: 0.0000300
+[2024-06-09 01:59:53,373] INFO: Step: 883992/1000000 Loss: 0.087 scale: 65536.0 LR: 0.0000300
+[2024-06-09 02:00:07,097] INFO: Step: 883993/1000000 Loss: 0.084 scale: 65536.0 LR: 0.0000300
+[2024-06-09 02:00:20,746] INFO: Step: 883994/1000000 Loss: 0.093 scale: 65536.0 LR: 0.0000300
+[2024-06-09 02:00:34,632] INFO: Step: 883995/1000000 Loss: 0.076 scale: 65536.0 LR: 0.0000300
+[2024-06-09 02:00:48,439] INFO: Step: 883996/1000000 Loss: 0.083 scale: 65536.0 LR: 0.0000300
+[2024-06-09 02:01:02,377] INFO: Step: 883997/1000000 Loss: 0.075 scale: 65536.0 LR: 0.0000300
+[2024-06-09 02:01:16,205] INFO: Step: 883998/1000000 Loss: 0.080 scale: 65536.0 LR: 0.0000300
+[2024-06-09 02:01:30,032] INFO: Step: 883999/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 02:01:43,901] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00884000.pth
+[2024-06-09 02:01:50,962] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00884000.pth
+[2024-06-09 02:02:03,107] INFO: Step: 884001/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 02:02:30,864] INFO: Step: 884003/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 02:03:12,173] INFO: Step: 884006/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:03:26,052] INFO: Step: 884007/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:03:39,804] INFO: Step: 884008/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:03:53,554] INFO: Step: 884009/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:04:07,315] INFO: Step: 884010/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:04:21,005] INFO: Step: 884011/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:04:34,997] INFO: Step: 884012/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:04:48,803] INFO: Step: 884013/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:05:02,740] INFO: Step: 884014/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:05:16,322] INFO: Step: 884015/1000000 Loss: 0.071 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:05:30,113] INFO: Step: 884016/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 02:05:57,620] INFO: Step: 884018/1000000 Loss: 0.096 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:06:11,526] INFO: Step: 884019/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 02:08:15,526] INFO: Step: 884028/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:08:29,413] INFO: Step: 884029/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300
+[2024-06-09 02:08:43,249] INFO: Step: 884030/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 03:36:16,715] INFO: Step: 884410/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300
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+[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
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+[2024-06-09 03:47:05,872] INFO: Step: 884457/1000000 Loss: 0.073 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 03:52:37,057] INFO: Step: 884481/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300
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+[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 scale: 131072.0 LR: 0.0000300
+[2024-06-09 03:59:35,578] INFO: Step: 884511/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300
+[2024-06-09 03:59:49,356] INFO: Step: 884512/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:00:03,011] INFO: Step: 884513/1000000 Loss: 0.071 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:00:16,916] INFO: Step: 884514/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:00:30,953] INFO: Step: 884515/1000000 Loss: 0.074 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:00:44,633] INFO: Step: 884516/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:00:58,468] INFO: Step: 884517/1000000 Loss: 0.090 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:01:12,315] INFO: Step: 884518/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:01:26,347] INFO: Step: 884519/1000000 Loss: 0.082 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:01:40,249] INFO: Step: 884520/1000000 Loss: 0.072 scale: 131072.0 LR: 0.0000300
+[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
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+[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: 884531/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:04:25,513] INFO: Step: 884532/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:04:39,424] INFO: Step: 884533/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:04:53,188] INFO: Step: 884534/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:05:06,903] INFO: Step: 884535/1000000 Loss: 0.096 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 04:05:48,211] INFO: Step: 884538/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 04:06:15,773] INFO: Step: 884540/1000000 Loss: 0.078 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:06:29,519] INFO: Step: 884541/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 04:06:57,155] INFO: Step: 884543/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:07:10,840] INFO: Step: 884544/1000000 Loss: 0.070 scale: 131072.0 LR: 0.0000300
+[2024-06-09 04:07:24,713] INFO: Step: 884545/1000000 Loss: 0.080 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 04:11:05,007] INFO: Step: 884561/1000000 Loss: 0.083 scale: 131072.0 LR: 0.0000300
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+[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 <All keys matched successfully>
+[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: 65536.0 LR: 0.0000300
+[2024-06-09 08:46:54,204] INFO: Step: 884523/1000000 Loss: 0.095 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:47:07,382] INFO: Step: 884524/1000000 Loss: 0.087 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:47:20,644] INFO: Step: 884525/1000000 Loss: 0.075 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:47:33,634] INFO: Step: 884526/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:47:46,944] INFO: Step: 884527/1000000 Loss: 0.080 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:48:00,270] INFO: Step: 884528/1000000 Loss: 0.068 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:48:13,455] INFO: Step: 884529/1000000 Loss: 0.075 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:48:26,839] INFO: Step: 884530/1000000 Loss: 0.088 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:48:40,124] INFO: Step: 884531/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:48:53,204] INFO: Step: 884532/1000000 Loss: 0.082 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:49:06,487] 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: 65536.0 LR: 0.0000300
+[2024-06-09 08:51:32,025] INFO: Step: 884544/1000000 Loss: 0.088 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:51:45,260] INFO: Step: 884545/1000000 Loss: 0.089 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:51:58,449] INFO: Step: 884546/1000000 Loss: 0.076 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:52:11,705] INFO: Step: 884547/1000000 Loss: 0.071 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:52:25,039] INFO: Step: 884548/1000000 Loss: 0.083 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 08:53:17,863] INFO: Step: 884552/1000000 Loss: 0.086 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:53:31,157] INFO: Step: 884553/1000000 Loss: 0.079 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:53:44,341] 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
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+[2024-06-09 08:54:37,431] INFO: Step: 884558/1000000 Loss: 0.093 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 08:55:03,909] INFO: Step: 884560/1000000 Loss: 0.072 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 08:56:10,142] INFO: Step: 884565/1000000 Loss: 0.087 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 08:57:16,215] INFO: Step: 884570/1000000 Loss: 0.081 scale: 65536.0 LR: 0.0000300
+[2024-06-09 08:57:29,568] INFO: Step: 884571/1000000 Loss: 0.072 scale: 65536.0 LR: 0.0000300
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+[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: 65536.0 LR: 0.0000300
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+[2024-06-09 09:01:01,412] INFO: Step: 884587/1000000 Loss: 0.097 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 10:30:13,113] INFO: Step: 884992/1000000 Loss: 0.068 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 10:30:39,408] INFO: Step: 884994/1000000 Loss: 0.075 scale: 65536.0 LR: 0.0000300
+[2024-06-09 10:30:52,475] 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
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+[2024-06-09 10:31:32,133] INFO: Step: 884998/1000000 Loss: 0.078 scale: 65536.0 LR: 0.0000300
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+[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
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+[2024-06-09 12:22:18,836] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00885500.pth
+[2024-06-09 12:22:24,738] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00885500.pth
+[2024-06-09 12:22:36,860] INFO: Step: 885501/1000000 Loss: 0.083 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 14:12:41,486] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00886000.pth
+[2024-06-09 14:12:48,923] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00886000.pth
+[2024-06-09 14:13:01,039] INFO: Step: 886001/1000000 Loss: 0.085 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 16:02:32,090] INFO: Step: 886496/1000000 Loss: 0.092 scale: 65536.0 LR: 0.0000300
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+[2024-06-09 16:03:25,009] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00886500.pth
+[2024-06-09 16:03:32,140] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00886500.pth
+[2024-06-09 16:03:44,147] INFO: Step: 886501/1000000 Loss: 0.095 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 16:32:46,801] INFO: Step: 886632/1000000 Loss: 0.103 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 17:53:14,880] INFO: Step: 886995/1000000 Loss: 0.087 scale: 131072.0 LR: 0.0000300
+[2024-06-09 17:53:28,287] INFO: Step: 886996/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 17:54:21,444] INFO: Begin to Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00887000.pth
+[2024-06-09 17:54:27,752] INFO: Save model to workspace/i2v_motion/obj_train_motion/checkpoints/non_ema_00887000.pth
+[2024-06-09 17:54:39,959] INFO: Step: 887001/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 17:55:58,925] INFO: Step: 887007/1000000 Loss: 0.063 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 19:32:49,937] INFO: Step: 887444/1000000 Loss: 0.079 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 19:33:16,623] INFO: Step: 887446/1000000 Loss: 0.094 scale: 131072.0 LR: 0.0000300
+[2024-06-09 19:33:29,920] INFO: Step: 887447/1000000 Loss: 0.081 scale: 131072.0 LR: 0.0000300
+[2024-06-09 19:33:43,412] INFO: Step: 887448/1000000 Loss: 0.090 scale: 131072.0 LR: 0.0000300
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+[2024-06-09 19:41:16,300] INFO: Step: 887482/1000000 Loss: 0.075 scale: 131072.0 LR: 0.0000300
+[2024-06-09 19:41:29,442] INFO: Step: 887483/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300
+[2024-06-09 19:41:42,777] INFO: Step: 887484/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300
+[2024-06-09 19:41:55,948] INFO: Step: 887485/1000000 Loss: 0.089 scale: 131072.0 LR: 0.0000300
+[2024-06-09 19:42:09,225] INFO: Step: 887486/1000000 Loss: 0.077 scale: 131072.0 LR: 0.0000300
+[2024-06-09 19:42:22,500] INFO: Step: 887487/1000000 Loss: 0.072 scale: 131072.0 LR: 0.0000300
+[2024-06-09 19:42:35,801] INFO: Step: 887488/1000000 Loss: 0.076 scale: 131072.0 LR: 0.0000300
+[2024-06-09 19:42:49,174] INFO: Step: 887489/1000000 Loss: 0.084 scale: 131072.0 LR: 0.0000300
+[2024-06-09 19:43:02,564] 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
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