diff --git "a/flowsdvae_50kx512_lgnm1p0/log.txt" "b/flowsdvae_50kx512_lgnm1p0/log.txt" new file mode 100644--- /dev/null +++ "b/flowsdvae_50kx512_lgnm1p0/log.txt" @@ -0,0 +1,1273 @@ +[2025-02-26 18:56:24] Model: DistributedDataParallel( + (module): FlowAE( + (flow): FlowDecoder( + (conv_in): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (mid): Module( + (block_1): ResnetBlock( + (norm1): RMSNorm() + (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (temb_proj): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (norm2): RMSNorm() + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (attn_1): AttnBlock( + (norm): RMSNorm() + (q): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (k): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (v): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (proj_out): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + ) + (block_2): ResnetBlock( + (norm1): RMSNorm() + (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (temb_proj): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (norm2): RMSNorm() + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (up): ModuleList( + (0): Module( + (block): ModuleList( + (0): ResnetBlock( + (norm1): RMSNorm() + (conv1): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (temb_proj): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) + (norm2): RMSNorm() + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (nin_shortcut): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + ) + (1-2): 2 x ResnetBlock( + (norm1): RMSNorm() + (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (temb_proj): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) + (norm2): RMSNorm() + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (attn): ModuleList() + ) + (1): Module( + (block): ModuleList( + (0): ResnetBlock( + (norm1): RMSNorm() + (conv1): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (temb_proj): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (norm2): RMSNorm() + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (nin_shortcut): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + ) + (1-2): 2 x ResnetBlock( + (norm1): RMSNorm() + (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (temb_proj): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (norm2): RMSNorm() + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (attn): ModuleList() + (upsample): Upsample( + (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (2-3): 2 x Module( + (block): ModuleList( + (0-2): 3 x ResnetBlock( + (norm1): RMSNorm() + (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (temb_proj): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (norm2): RMSNorm() + (dropout): Dropout(p=0.0, inplace=False) + (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + (attn): ModuleList() + (upsample): Upsample( + (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + ) + ) + (norm_out): RMSNorm() + (conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + ) + (t_embedder): TimestepEmbedder( + (mlp): Sequential( + (0): Linear(in_features=256, out_features=512, bias=True) + (1): SiLU() + (2): Linear(in_features=512, out_features=512, bias=True) + ) + ) + (y_embedder): Conv2d(4, 512, kernel_size=(1, 1), stride=(1, 1)) + (x_embedder): PatchEmbed( + (proj): Conv2d(3, 512, kernel_size=(8, 8), stride=(8, 8)) + (norm): Identity() + ) + ) +) +[2025-02-26 18:56:24] FlowVAE Parameters: 55.53M +[2025-02-26 18:56:24] FlowVAE Trainable Parameters: 55.01M +[2025-02-26 18:56:24] Optimizer: AdamW, lr=0.0002, beta2=0.95 +[2025-02-26 18:56:24] module.pos_embed.requires_grad : False +[2025-02-26 18:56:24] module.flow.conv_in.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.conv_in.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_1.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_1.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_1.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_1.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_1.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_1.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_1.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_1.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.attn_1.norm.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.attn_1.q.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.attn_1.q.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.attn_1.k.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.attn_1.k.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.attn_1.v.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.attn_1.v.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.attn_1.proj_out.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.attn_1.proj_out.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_2.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_2.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_2.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_2.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_2.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_2.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_2.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.mid.block_2.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.0.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.0.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.0.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.0.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.0.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.0.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.0.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.0.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.0.nin_shortcut.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.0.nin_shortcut.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.1.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.1.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.1.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.1.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.1.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.1.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.1.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.1.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.2.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.2.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.2.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.2.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.2.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.2.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.2.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.0.block.2.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.0.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.0.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.0.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.0.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.0.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.0.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.0.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.0.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.0.nin_shortcut.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.0.nin_shortcut.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.1.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.1.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.1.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.1.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.1.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.1.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.1.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.1.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.2.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.2.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.2.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.2.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.2.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.2.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.2.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.block.2.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.upsample.conv.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.1.upsample.conv.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.0.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.0.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.0.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.0.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.0.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.0.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.0.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.0.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.1.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.1.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.1.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.1.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.1.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.1.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.1.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.1.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.2.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.2.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.2.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.2.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.2.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.2.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.2.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.block.2.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.upsample.conv.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.2.upsample.conv.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.0.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.0.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.0.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.0.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.0.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.0.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.0.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.0.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.1.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.1.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.1.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.1.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.1.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.1.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.1.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.1.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.2.norm1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.2.conv1.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.2.conv1.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.2.temb_proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.2.temb_proj.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.2.norm2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.2.conv2.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.block.2.conv2.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.upsample.conv.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.up.3.upsample.conv.bias.requires_grad : True +[2025-02-26 18:56:24] module.flow.norm_out.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.conv_out.weight.requires_grad : True +[2025-02-26 18:56:24] module.flow.conv_out.bias.requires_grad : True +[2025-02-26 18:56:24] module.t_embedder.mlp.0.weight.requires_grad : True +[2025-02-26 18:56:24] module.t_embedder.mlp.0.bias.requires_grad : True +[2025-02-26 18:56:24] module.t_embedder.mlp.2.weight.requires_grad : True +[2025-02-26 18:56:24] module.t_embedder.mlp.2.bias.requires_grad : True +[2025-02-26 18:56:24] module.y_embedder.weight.requires_grad : True +[2025-02-26 18:56:24] module.y_embedder.bias.requires_grad : True +[2025-02-26 18:56:24] module.x_embedder.proj.weight.requires_grad : True +[2025-02-26 18:56:24] module.x_embedder.proj.bias.requires_grad : True +[2025-02-26 18:56:25] Dataset contains 1,281,168 images /data/checkpoints/LanguageBind/offline_feature/offline_sdvae_256_path/imagenet_train_256 +[2025-02-26 18:56:25] Batch size 64 per gpu, with 512 global batch size +[2025-02-26 18:56:25] Train config: {'ckpt_path': '/data/logs/flow/flowsdvae_50kx512_lgnm1p0/checkpoints/0050000.pt', 'data': {'raw_data_dir': '/data/OpenDataLab___ImageNet-1K/raw/ImageNet-1K/train', 'raw_val_data_dir': '/data/OpenDataLab___ImageNet-1K/raw/ImageNet-1K/val', 'data_path': '/data/checkpoints/LanguageBind/offline_feature/offline_sdvae_256_path/imagenet_train_256', 'fid_reference_file': '/data/checkpoints/VIRTUAL_imagenet256_labeled.npz', 'image_size': 256, 'num_classes': 1000, 'num_workers': 16, 'latent_norm': False, 'latent_multiplier': 0.18215}, 'vae': {'vae_type': 'FlowSDVAE', 'model_path': '/data/checkpoints/stabilityai/sd-vae-ft-ema/vae-ft-ema-560000-ema-pruned.safetensors', 'downsample_ratio': 8, 'multi_latent': False, 'add_y_to_x': False, 'norm_type': 'rmsnorm'}, 'model': {'model_type': 'DiT-S/2', 'use_qknorm': True, 'use_swiglu': True, 'use_rope': True, 'use_rmsnorm': True, 'in_chans': 4, 'use_checkpoint': False}, 'train': {'max_steps': 50000, 'global_batch_size': 512, 'global_seed': 0, 'output_dir': '../logs/flow/flowsdvae_50kx512_lgnm1p0', 'ckpt': None, 'log_every': 50, 'ckpt_every': 10000, 'eval_every': 10000, 'wandb': True, 'seed': 1234, 'precision': 'bf16', 'resume': False}, 'optimizer': {'lr': 0.0002, 'beta2': 0.95}, 'wandb': {'proj_name': 'flow', 'log_name': 'flowsdvae_50kx512_lgnm1p0', 'key': '953e958793b218efb850fa194e85843e2c3bd88b'}, 'scheduler': {'diffusion': False, 'transport': True}, 'diffusion': {'learn_sigma': True, 'diffusion_steps': 1000}, 'transport': {'path_type': 'Linear', 'prediction': 'velocity', 'loss_weight': None, 'sample_eps': None, 'train_eps': None, 'use_cosine_loss': True, 'use_lognorm': True}, 'sample': {'mode': 'ODE', 'sampling_method': 'euler', 'atol': 1e-06, 'rtol': 0.001, 'reverse': False, 'likelihood': False, 'num_sampling_steps': 250, 'cfg_scale': 1.0, 'per_proc_batch_size': 64, 'fid_num': 50000, 'cfg_interval_start': 0.0, 'timestep_shift': 0.0}, 'flowvae_transport': {'path_type': 'Linear', 'prediction': 'velocity', 'loss_weight': None, 'sample_eps': None, 'train_eps': None, 'use_cosine_loss': False, 'use_lognorm': True, 'l2_loss': True, 'shift_lg': True, 'shifted_mu': -1.0, 'timestep_sampling': 'lognorm', 'beta_alpha': None, 'beta_beta': None, 'pareto_alpha': None}, 'flowvae_sample': {'mode': 'ODE', 'sampling_method': 'euler', 'atol': 1e-06, 'rtol': 0.001, 'reverse': False, 'likelihood': False, 'num_sampling_steps': 25, 'cfg_scale': 1.0, 'per_proc_batch_size': 64, 'fid_num': 50000, 'cfg_interval_start': 0.0, 'timestep_shift': 0.0}} +[2025-02-26 18:57:53] (step=0000050) Train Loss: 1.1677, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.57, Grad Norm: 3.1777 +[2025-02-26 18:58:51] (step=0000100) Train Loss: 1.0784, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0085 +[2025-02-26 18:59:49] (step=0000150) Train Loss: 1.0462, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5301 +[2025-02-26 19:00:48] (step=0000200) Train Loss: 1.0347, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4667 +[2025-02-26 19:01:46] (step=0000250) Train Loss: 1.0264, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4137 +[2025-02-26 19:02:45] (step=0000300) Train Loss: 1.0157, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4686 +[2025-02-26 19:03:43] (step=0000350) Train Loss: 1.0012, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4869 +[2025-02-26 19:04:42] (step=0000400) Train Loss: 0.9719, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6841 +[2025-02-26 19:05:40] (step=0000450) Train Loss: 0.9169, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6600 +[2025-02-26 19:06:38] (step=0000500) Train Loss: 0.8309, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1421 +[2025-02-26 19:07:37] (step=0000550) Train Loss: 0.7331, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1747 +[2025-02-26 19:08:35] (step=0000600) Train Loss: 0.6524, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0832 +[2025-02-26 19:09:34] (step=0000650) Train Loss: 0.5604, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4505 +[2025-02-26 19:10:32] (step=0000700) Train Loss: 0.4960, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4502 +[2025-02-26 19:11:31] (step=0000750) Train Loss: 0.4541, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5927 +[2025-02-26 19:12:29] (step=0000800) Train Loss: 0.4068, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.8809 +[2025-02-26 19:13:27] (step=0000850) Train Loss: 0.3694, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.6223 +[2025-02-26 19:14:26] (step=0000900) Train Loss: 0.3279, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5550 +[2025-02-26 19:15:24] (step=0000950) Train Loss: 0.2994, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.7335 +[2025-02-26 19:16:23] (step=0001000) Train Loss: 0.2714, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5146 +[2025-02-26 19:17:21] (step=0001050) Train Loss: 0.2625, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.7393 +[2025-02-26 19:18:20] (step=0001100) Train Loss: 0.2535, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.6187 +[2025-02-26 19:19:18] (step=0001150) Train Loss: 0.2435, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.7065 +[2025-02-26 19:20:16] (step=0001200) Train Loss: 0.2377, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.7565 +[2025-02-26 19:21:15] (step=0001250) Train Loss: 0.2349, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.7492 +[2025-02-26 19:22:13] (step=0001300) Train Loss: 0.2247, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4003 +[2025-02-26 19:23:12] (step=0001350) Train Loss: 0.2206, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5516 +[2025-02-26 19:24:10] (step=0001400) Train Loss: 0.2145, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4276 +[2025-02-26 19:25:09] (step=0001450) Train Loss: 0.2110, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4904 +[2025-02-26 19:26:07] (step=0001500) Train Loss: 0.2068, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4398 +[2025-02-26 19:27:06] (step=0001550) Train Loss: 0.2055, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5075 +[2025-02-26 19:28:04] (step=0001600) Train Loss: 0.2031, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4499 +[2025-02-26 19:29:02] (step=0001650) Train Loss: 0.2001, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5355 +[2025-02-26 19:30:01] (step=0001700) Train Loss: 0.1965, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2738 +[2025-02-26 19:30:59] (step=0001750) Train Loss: 0.2016, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5440 +[2025-02-26 19:31:58] (step=0001800) Train Loss: 0.1918, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2856 +[2025-02-26 19:32:56] (step=0001850) Train Loss: 0.1906, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4321 +[2025-02-26 19:33:55] (step=0001900) Train Loss: 0.1915, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4122 +[2025-02-26 19:34:53] (step=0001950) Train Loss: 0.1836, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1722 +[2025-02-26 19:35:52] (step=0002000) Train Loss: 0.1817, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 1.2351 +[2025-02-26 19:36:50] (step=0002050) Train Loss: 0.1819, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.3526 +[2025-02-26 19:37:49] (step=0002100) Train Loss: 0.1778, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2137 +[2025-02-26 19:38:47] (step=0002150) Train Loss: 0.1774, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2704 +[2025-02-26 19:39:45] (step=0002200) Train Loss: 0.2122, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.9116 +[2025-02-26 19:40:44] (step=0002250) Train Loss: 0.1884, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1750 +[2025-02-26 19:41:42] (step=0002300) Train Loss: 0.1759, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1908 +[2025-02-26 19:42:41] (step=0002350) Train Loss: 0.1729, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2161 +[2025-02-26 19:43:39] (step=0002400) Train Loss: 0.1738, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2957 +[2025-02-26 19:44:38] (step=0002450) Train Loss: 0.1687, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1664 +[2025-02-26 19:45:36] (step=0002500) Train Loss: 0.1681, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2662 +[2025-02-26 19:46:37] (step=0002550) Train Loss: 0.1655, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.83, Grad Norm: 1.0756 +[2025-02-26 19:47:35] (step=0002600) Train Loss: 0.1662, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2684 +[2025-02-26 19:48:33] (step=0002650) Train Loss: 0.1636, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0308 +[2025-02-26 19:49:32] (step=0002700) Train Loss: 0.1632, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2406 +[2025-02-26 19:50:30] (step=0002750) Train Loss: 0.1594, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0963 +[2025-02-26 19:51:29] (step=0002800) Train Loss: 0.1613, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2170 +[2025-02-26 19:52:27] (step=0002850) Train Loss: 0.1563, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1245 +[2025-02-26 19:53:26] (step=0002900) Train Loss: 0.1554, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0606 +[2025-02-26 19:54:24] (step=0002950) Train Loss: 0.1557, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2320 +[2025-02-26 19:55:22] (step=0003000) Train Loss: 0.1524, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0990 +[2025-02-26 19:56:21] (step=0003050) Train Loss: 0.1642, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.3448 +[2025-02-26 19:57:19] (step=0003100) Train Loss: 0.1508, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0058 +[2025-02-26 19:58:18] (step=0003150) Train Loss: 0.1530, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2071 +[2025-02-26 19:59:16] (step=0003200) Train Loss: 0.1485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1553 +[2025-02-26 20:00:15] (step=0003250) Train Loss: 0.1459, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0355 +[2025-02-26 20:01:13] (step=0003300) Train Loss: 0.1437, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1439 +[2025-02-26 20:02:11] (step=0003350) Train Loss: 0.1425, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0909 +[2025-02-26 20:03:10] (step=0003400) Train Loss: 0.1423, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0221 +[2025-02-26 20:04:08] (step=0003450) Train Loss: 0.1385, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0965 +[2025-02-26 20:05:07] (step=0003500) Train Loss: 0.1380, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0547 +[2025-02-26 20:06:05] (step=0003550) Train Loss: 0.1361, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0738 +[2025-02-26 20:07:04] (step=0003600) Train Loss: 0.1359, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0501 +[2025-02-26 20:08:02] (step=0003650) Train Loss: 0.1335, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9318 +[2025-02-26 20:09:00] (step=0003700) Train Loss: 0.1358, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0949 +[2025-02-26 20:09:59] (step=0003750) Train Loss: 0.1326, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9836 +[2025-02-26 20:10:57] (step=0003800) Train Loss: 0.1344, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9917 +[2025-02-26 20:11:56] (step=0003850) Train Loss: 0.1319, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0781 +[2025-02-26 20:12:54] (step=0003900) Train Loss: 0.1311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8763 +[2025-02-26 20:13:53] (step=0003950) Train Loss: 0.1304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9759 +[2025-02-26 20:14:51] (step=0004000) Train Loss: 0.1299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9948 +[2025-02-26 20:15:49] (step=0004050) Train Loss: 0.1301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9458 +[2025-02-26 20:16:48] (step=0004100) Train Loss: 0.1261, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9698 +[2025-02-26 20:17:46] (step=0004150) Train Loss: 0.1251, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9720 +[2025-02-26 20:18:45] (step=0004200) Train Loss: 0.1226, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9055 +[2025-02-26 20:19:43] (step=0004250) Train Loss: 0.1170, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8778 +[2025-02-26 20:20:42] (step=0004300) Train Loss: 0.1163, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9994 +[2025-02-26 20:21:40] (step=0004350) Train Loss: 0.1124, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1151 +[2025-02-26 20:22:38] (step=0004400) Train Loss: 0.1081, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8948 +[2025-02-26 20:23:37] (step=0004450) Train Loss: 0.1038, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9387 +[2025-02-26 20:24:35] (step=0004500) Train Loss: 0.1004, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8796 +[2025-02-26 20:25:34] (step=0004550) Train Loss: 0.0969, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0055 +[2025-02-26 20:26:32] (step=0004600) Train Loss: 0.0958, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8941 +[2025-02-26 20:27:31] (step=0004650) Train Loss: 0.0914, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8125 +[2025-02-26 20:28:29] (step=0004700) Train Loss: 0.0899, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8756 +[2025-02-26 20:29:27] (step=0004750) Train Loss: 0.0877, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7708 +[2025-02-26 20:30:26] (step=0004800) Train Loss: 0.0878, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8610 +[2025-02-26 20:31:24] (step=0004850) Train Loss: 0.0864, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8210 +[2025-02-26 20:32:23] (step=0004900) Train Loss: 0.0862, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8179 +[2025-02-26 20:33:21] (step=0004950) Train Loss: 0.0832, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7719 +[2025-02-26 20:34:20] (step=0005000) Train Loss: 0.0824, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8312 +[2025-02-26 20:35:20] (step=0005050) Train Loss: 0.0812, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.83, Grad Norm: 0.8038 +[2025-02-26 20:36:18] (step=0005100) Train Loss: 0.0810, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8204 +[2025-02-26 20:37:17] (step=0005150) Train Loss: 0.0801, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7568 +[2025-02-26 20:38:15] (step=0005200) Train Loss: 0.0787, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7570 +[2025-02-26 20:39:14] (step=0005250) Train Loss: 0.0766, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7485 +[2025-02-26 20:40:12] (step=0005300) Train Loss: 0.0734, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7775 +[2025-02-26 20:41:11] (step=0005350) Train Loss: 0.0696, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7197 +[2025-02-26 20:42:09] (step=0005400) Train Loss: 0.0660, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7376 +[2025-02-26 20:43:07] (step=0005450) Train Loss: 0.0627, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6649 +[2025-02-26 20:44:06] (step=0005500) Train Loss: 0.0611, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7295 +[2025-02-26 20:45:04] (step=0005550) Train Loss: 0.0581, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6472 +[2025-02-26 20:46:03] (step=0005600) Train Loss: 0.0572, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7288 +[2025-02-26 20:47:01] (step=0005650) Train Loss: 0.0560, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6146 +[2025-02-26 20:48:00] (step=0005700) Train Loss: 0.0549, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7004 +[2025-02-26 20:48:58] (step=0005750) Train Loss: 0.0529, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6683 +[2025-02-26 20:49:56] (step=0005800) Train Loss: 0.0526, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6675 +[2025-02-26 20:50:55] (step=0005850) Train Loss: 0.0514, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6564 +[2025-02-26 20:51:53] (step=0005900) Train Loss: 0.0508, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6822 +[2025-02-26 20:52:52] (step=0005950) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6296 +[2025-02-26 20:53:50] (step=0006000) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6546 +[2025-02-26 20:54:49] (step=0006050) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6397 +[2025-02-26 20:55:47] (step=0006100) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6496 +[2025-02-26 20:56:46] (step=0006150) Train Loss: 0.0477, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6409 +[2025-02-26 20:57:44] (step=0006200) Train Loss: 0.0470, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6386 +[2025-02-26 20:58:42] (step=0006250) Train Loss: 0.0467, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6406 +[2025-02-26 20:59:41] (step=0006300) Train Loss: 0.0468, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6165 +[2025-02-26 21:00:39] (step=0006350) Train Loss: 0.0463, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6278 +[2025-02-26 21:01:38] (step=0006400) Train Loss: 0.0463, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6353 +[2025-02-26 21:02:36] (step=0006450) Train Loss: 0.0455, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6482 +[2025-02-26 21:03:35] (step=0006500) Train Loss: 0.0452, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6489 +[2025-02-26 21:04:33] (step=0006550) Train Loss: 0.0451, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5852 +[2025-02-26 21:05:32] (step=0006600) Train Loss: 0.0447, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6182 +[2025-02-26 21:06:30] (step=0006650) Train Loss: 0.0445, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6311 +[2025-02-26 21:07:28] (step=0006700) Train Loss: 0.0438, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5858 +[2025-02-26 21:08:27] (step=0006750) Train Loss: 0.0438, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6037 +[2025-02-26 21:09:25] (step=0006800) Train Loss: 0.0434, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6180 +[2025-02-26 21:10:24] (step=0006850) Train Loss: 0.0431, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5876 +[2025-02-26 21:11:22] (step=0006900) Train Loss: 0.0430, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6036 +[2025-02-26 21:12:21] (step=0006950) Train Loss: 0.0428, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5928 +[2025-02-26 21:13:19] (step=0007000) Train Loss: 0.0427, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5947 +[2025-02-26 21:14:18] (step=0007050) Train Loss: 0.0427, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5645 +[2025-02-26 21:15:16] (step=0007100) Train Loss: 0.0423, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6150 +[2025-02-26 21:16:14] (step=0007150) Train Loss: 0.0425, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5860 +[2025-02-26 21:17:13] (step=0007200) Train Loss: 0.0419, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5857 +[2025-02-26 21:18:11] (step=0007250) Train Loss: 0.0423, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5941 +[2025-02-26 21:19:10] (step=0007300) Train Loss: 0.0416, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5349 +[2025-02-26 21:20:08] (step=0007350) Train Loss: 0.0420, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5931 +[2025-02-26 21:21:07] (step=0007400) Train Loss: 0.0415, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5604 +[2025-02-26 21:22:05] (step=0007450) Train Loss: 0.0413, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5627 +[2025-02-26 21:23:04] (step=0007500) Train Loss: 0.0410, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5468 +[2025-02-26 21:24:04] (step=0007550) Train Loss: 0.0408, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.83, Grad Norm: 0.5883 +[2025-02-26 21:25:02] (step=0007600) Train Loss: 0.0413, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5753 +[2025-02-26 21:26:01] (step=0007650) Train Loss: 0.0406, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5627 +[2025-02-26 21:26:59] (step=0007700) Train Loss: 0.0402, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5331 +[2025-02-26 21:27:58] (step=0007750) Train Loss: 0.0400, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5391 +[2025-02-26 21:28:56] (step=0007800) Train Loss: 0.0399, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5775 +[2025-02-26 21:29:55] (step=0007850) Train Loss: 0.0401, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5325 +[2025-02-26 21:30:53] (step=0007900) Train Loss: 0.0401, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5621 +[2025-02-26 21:31:52] (step=0007950) Train Loss: 0.0399, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5445 +[2025-02-26 21:32:50] (step=0008000) Train Loss: 0.0395, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5424 +[2025-02-26 21:33:48] (step=0008050) Train Loss: 0.0389, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5370 +[2025-02-26 21:34:47] (step=0008100) Train Loss: 0.0396, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5124 +[2025-02-26 21:35:45] (step=0008150) Train Loss: 0.0393, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5413 +[2025-02-26 21:36:44] (step=0008200) Train Loss: 0.0392, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5512 +[2025-02-26 21:37:42] (step=0008250) Train Loss: 0.0394, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5392 +[2025-02-26 21:38:41] (step=0008300) Train Loss: 0.0388, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5351 +[2025-02-26 21:39:39] (step=0008350) Train Loss: 0.0389, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5256 +[2025-02-26 21:40:38] (step=0008400) Train Loss: 0.0385, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5424 +[2025-02-26 21:41:36] (step=0008450) Train Loss: 0.0387, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5316 +[2025-02-26 21:42:35] (step=0008500) Train Loss: 0.0385, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5277 +[2025-02-26 21:43:33] (step=0008550) Train Loss: 0.0383, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5228 +[2025-02-26 21:44:31] (step=0008600) Train Loss: 0.0385, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5295 +[2025-02-26 21:45:30] (step=0008650) Train Loss: 0.0383, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4940 +[2025-02-26 21:46:28] (step=0008700) Train Loss: 0.0382, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5295 +[2025-02-26 21:47:27] (step=0008750) Train Loss: 0.0384, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4954 +[2025-02-26 21:48:25] (step=0008800) Train Loss: 0.0380, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5355 +[2025-02-26 21:49:24] (step=0008850) Train Loss: 0.0375, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5119 +[2025-02-26 21:50:22] (step=0008900) Train Loss: 0.0379, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4710 +[2025-02-26 21:51:21] (step=0008950) Train Loss: 0.0379, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5259 +[2025-02-26 21:52:19] (step=0009000) Train Loss: 0.0376, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4748 +[2025-02-26 21:53:17] (step=0009050) Train Loss: 0.0378, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5464 +[2025-02-26 21:54:16] (step=0009100) Train Loss: 0.0377, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4889 +[2025-02-26 21:55:14] (step=0009150) Train Loss: 0.0376, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5277 +[2025-02-26 21:56:13] (step=0009200) Train Loss: 0.0372, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4913 +[2025-02-26 21:57:11] (step=0009250) Train Loss: 0.0375, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5009 +[2025-02-26 21:58:10] (step=0009300) Train Loss: 0.0369, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4963 +[2025-02-26 21:59:08] (step=0009350) Train Loss: 0.0378, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5209 +[2025-02-26 22:00:07] (step=0009400) Train Loss: 0.0373, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4771 +[2025-02-26 22:01:05] (step=0009450) Train Loss: 0.0367, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5076 +[2025-02-26 22:02:04] (step=0009500) Train Loss: 0.0365, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4938 +[2025-02-26 22:03:02] (step=0009550) Train Loss: 0.0373, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4801 +[2025-02-26 22:04:00] (step=0009600) Train Loss: 0.0367, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4887 +[2025-02-26 22:04:59] (step=0009650) Train Loss: 0.0369, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4770 +[2025-02-26 22:05:57] (step=0009700) Train Loss: 0.0363, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4807 +[2025-02-26 22:06:56] (step=0009750) Train Loss: 0.0371, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4646 +[2025-02-26 22:07:54] (step=0009800) Train Loss: 0.0365, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4682 +[2025-02-26 22:08:53] (step=0009850) Train Loss: 0.0369, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4791 +[2025-02-26 22:09:51] (step=0009900) Train Loss: 0.0363, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4916 +[2025-02-26 22:10:50] (step=0009950) Train Loss: 0.0366, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4609 +[2025-02-26 22:11:48] (step=0010000) Train Loss: 0.0369, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4774 +[2025-02-26 22:11:50] Saved checkpoint to ../logs/flow/flowsdvae_50kx512_lgnm1p0/checkpoints/0010000.pt +[2025-02-26 22:33:38] (step=0010000), Fid=170.31164961716058, PSNR=9.44866250575781, LPIPS=0.81640625, SSIM=0.026317469775676727 +[2025-02-26 22:34:38] (step=0010050) Train Loss: 0.0367, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.04, Grad Norm: 0.5013 +[2025-02-26 22:35:37] (step=0010100) Train Loss: 0.0364, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4291 +[2025-02-26 22:36:35] (step=0010150) Train Loss: 0.0361, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4602 +[2025-02-26 22:37:34] (step=0010200) Train Loss: 0.0362, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4568 +[2025-02-26 22:38:33] (step=0010250) Train Loss: 0.0362, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4807 +[2025-02-26 22:39:31] (step=0010300) Train Loss: 0.0364, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4714 +[2025-02-26 22:40:30] (step=0010350) Train Loss: 0.0358, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4622 +[2025-02-26 22:41:28] (step=0010400) Train Loss: 0.0364, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4516 +[2025-02-26 22:42:27] (step=0010450) Train Loss: 0.0356, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4458 +[2025-02-26 22:43:25] (step=0010500) Train Loss: 0.0357, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4708 +[2025-02-26 22:44:24] (step=0010550) Train Loss: 0.0358, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4426 +[2025-02-26 22:45:22] (step=0010600) Train Loss: 0.0360, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4497 +[2025-02-26 22:46:21] (step=0010650) Train Loss: 0.0353, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4315 +[2025-02-26 22:47:19] (step=0010700) Train Loss: 0.0357, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4610 +[2025-02-26 22:48:18] (step=0010750) Train Loss: 0.0355, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4308 +[2025-02-26 22:49:16] (step=0010800) Train Loss: 0.0359, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4505 +[2025-02-26 22:50:15] (step=0010850) Train Loss: 0.0354, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4383 +[2025-02-26 22:51:13] (step=0010900) Train Loss: 0.0358, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4666 +[2025-02-26 22:52:12] (step=0010950) Train Loss: 0.0357, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4557 +[2025-02-26 22:53:10] (step=0011000) Train Loss: 0.0356, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4242 +[2025-02-26 22:54:09] (step=0011050) Train Loss: 0.0354, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4325 +[2025-02-26 22:55:07] (step=0011100) Train Loss: 0.0353, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4709 +[2025-02-26 22:56:06] (step=0011150) Train Loss: 0.0351, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4418 +[2025-02-26 22:57:04] (step=0011200) Train Loss: 0.0354, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4240 +[2025-02-26 22:58:03] (step=0011250) Train Loss: 0.0352, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4178 +[2025-02-26 22:59:02] (step=0011300) Train Loss: 0.0351, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4179 +[2025-02-26 23:00:00] (step=0011350) Train Loss: 0.0352, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4571 +[2025-02-26 23:00:59] (step=0011400) Train Loss: 0.0351, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4093 +[2025-02-26 23:01:57] (step=0011450) Train Loss: 0.0347, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4441 +[2025-02-26 23:02:56] (step=0011500) Train Loss: 0.0350, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4392 +[2025-02-26 23:03:54] (step=0011550) Train Loss: 0.0347, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4060 +[2025-02-26 23:04:53] (step=0011600) Train Loss: 0.0350, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4299 +[2025-02-26 23:05:51] (step=0011650) Train Loss: 0.0349, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4347 +[2025-02-26 23:06:50] (step=0011700) Train Loss: 0.0349, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4563 +[2025-02-26 23:07:48] (step=0011750) Train Loss: 0.0346, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4149 +[2025-02-26 23:08:47] (step=0011800) Train Loss: 0.0348, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4436 +[2025-02-26 23:09:45] (step=0011850) Train Loss: 0.0348, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4040 +[2025-02-26 23:10:44] (step=0011900) Train Loss: 0.0347, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4348 +[2025-02-26 23:11:43] (step=0011950) Train Loss: 0.0346, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4401 +[2025-02-26 23:12:41] (step=0012000) Train Loss: 0.0348, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4385 +[2025-02-26 23:13:40] (step=0012050) Train Loss: 0.0343, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4063 +[2025-02-26 23:14:38] (step=0012100) Train Loss: 0.0347, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4200 +[2025-02-26 23:15:37] (step=0012150) Train Loss: 0.0349, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4610 +[2025-02-26 23:16:35] (step=0012200) Train Loss: 0.0341, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3870 +[2025-02-26 23:17:34] (step=0012250) Train Loss: 0.0341, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4315 +[2025-02-26 23:18:32] (step=0012300) Train Loss: 0.0341, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4329 +[2025-02-26 23:19:31] (step=0012350) Train Loss: 0.0345, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4180 +[2025-02-26 23:20:29] (step=0012400) Train Loss: 0.0344, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4179 +[2025-02-26 23:21:28] (step=0012450) Train Loss: 0.0341, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4005 +[2025-02-26 23:22:27] (step=0012500) Train Loss: 0.0341, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4563 +[2025-02-26 23:23:27] (step=0012550) Train Loss: 0.0341, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.4227 +[2025-02-26 23:24:26] (step=0012600) Train Loss: 0.0340, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4120 +[2025-02-26 23:25:24] (step=0012650) Train Loss: 0.0339, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4335 +[2025-02-26 23:26:23] (step=0012700) Train Loss: 0.0343, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3814 +[2025-02-26 23:27:22] (step=0012750) Train Loss: 0.0342, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4332 +[2025-02-26 23:28:20] (step=0012800) Train Loss: 0.0344, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3999 +[2025-02-26 23:29:19] (step=0012850) Train Loss: 0.0341, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3867 +[2025-02-26 23:30:17] (step=0012900) Train Loss: 0.0336, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3999 +[2025-02-26 23:31:16] (step=0012950) Train Loss: 0.0342, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4279 +[2025-02-26 23:32:14] (step=0013000) Train Loss: 0.0340, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3791 +[2025-02-26 23:33:13] (step=0013050) Train Loss: 0.0340, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4120 +[2025-02-26 23:34:11] (step=0013100) Train Loss: 0.0338, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3821 +[2025-02-26 23:35:10] (step=0013150) Train Loss: 0.0343, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4030 +[2025-02-26 23:36:08] (step=0013200) Train Loss: 0.0337, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3947 +[2025-02-26 23:37:07] (step=0013250) Train Loss: 0.0333, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3645 +[2025-02-26 23:38:05] (step=0013300) Train Loss: 0.0335, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4160 +[2025-02-26 23:39:04] (step=0013350) Train Loss: 0.0338, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4107 +[2025-02-26 23:40:02] (step=0013400) Train Loss: 0.0336, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4042 +[2025-02-26 23:41:01] (step=0013450) Train Loss: 0.0337, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3909 +[2025-02-26 23:41:59] (step=0013500) Train Loss: 0.0335, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3777 +[2025-02-26 23:42:58] (step=0013550) Train Loss: 0.0335, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3843 +[2025-02-26 23:43:56] (step=0013600) Train Loss: 0.0337, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3922 +[2025-02-26 23:44:55] (step=0013650) Train Loss: 0.0337, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3905 +[2025-02-26 23:45:54] (step=0013700) Train Loss: 0.0339, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4045 +[2025-02-26 23:46:52] (step=0013750) Train Loss: 0.0336, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3966 +[2025-02-26 23:47:51] (step=0013800) Train Loss: 0.0335, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3900 +[2025-02-26 23:48:49] (step=0013850) Train Loss: 0.0334, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3929 +[2025-02-26 23:49:48] (step=0013900) Train Loss: 0.0334, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3430 +[2025-02-26 23:50:46] (step=0013950) Train Loss: 0.0337, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3721 +[2025-02-26 23:51:45] (step=0014000) Train Loss: 0.0329, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4114 +[2025-02-26 23:52:43] (step=0014050) Train Loss: 0.0332, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3914 +[2025-02-26 23:53:42] (step=0014100) Train Loss: 0.0335, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3653 +[2025-02-26 23:54:40] (step=0014150) Train Loss: 0.0332, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4072 +[2025-02-26 23:55:39] (step=0014200) Train Loss: 0.0333, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3906 +[2025-02-26 23:56:37] (step=0014250) Train Loss: 0.0329, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3887 +[2025-02-26 23:57:36] (step=0014300) Train Loss: 0.0333, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3705 +[2025-02-26 23:58:34] (step=0014350) Train Loss: 0.0331, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4109 +[2025-02-26 23:59:33] (step=0014400) Train Loss: 0.0327, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3543 +[2025-02-27 00:00:31] (step=0014450) Train Loss: 0.0334, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4035 +[2025-02-27 00:01:30] (step=0014500) Train Loss: 0.0331, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3783 +[2025-02-27 00:02:28] (step=0014550) Train Loss: 0.0328, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3547 +[2025-02-27 00:03:27] (step=0014600) Train Loss: 0.0335, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3797 +[2025-02-27 00:04:25] (step=0014650) Train Loss: 0.0333, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3738 +[2025-02-27 00:05:24] (step=0014700) Train Loss: 0.0329, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3677 +[2025-02-27 00:06:23] (step=0014750) Train Loss: 0.0334, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3720 +[2025-02-27 00:07:21] (step=0014800) Train Loss: 0.0329, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3703 +[2025-02-27 00:08:20] (step=0014850) Train Loss: 0.0334, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3658 +[2025-02-27 00:09:18] (step=0014900) Train Loss: 0.0329, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3770 +[2025-02-27 00:10:17] (step=0014950) Train Loss: 0.0331, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3747 +[2025-02-27 00:11:15] (step=0015000) Train Loss: 0.0328, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3683 +[2025-02-27 00:12:16] (step=0015050) Train Loss: 0.0331, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.83, Grad Norm: 0.3684 +[2025-02-27 00:13:14] (step=0015100) Train Loss: 0.0325, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3876 +[2025-02-27 00:14:13] (step=0015150) Train Loss: 0.0327, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3540 +[2025-02-27 00:15:11] (step=0015200) Train Loss: 0.0331, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3854 +[2025-02-27 00:16:10] (step=0015250) Train Loss: 0.0328, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3767 +[2025-02-27 00:17:08] (step=0015300) Train Loss: 0.0330, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3760 +[2025-02-27 00:18:07] (step=0015350) Train Loss: 0.0327, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3666 +[2025-02-27 00:19:05] (step=0015400) Train Loss: 0.0325, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3794 +[2025-02-27 00:20:04] (step=0015450) Train Loss: 0.0330, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3564 +[2025-02-27 00:21:02] (step=0015500) Train Loss: 0.0326, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3714 +[2025-02-27 00:22:01] (step=0015550) Train Loss: 0.0331, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3594 +[2025-02-27 00:22:59] (step=0015600) Train Loss: 0.0327, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3477 +[2025-02-27 00:23:58] (step=0015650) Train Loss: 0.0327, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3669 +[2025-02-27 00:24:56] (step=0015700) Train Loss: 0.0324, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3883 +[2025-02-27 00:25:55] (step=0015750) Train Loss: 0.0327, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3575 +[2025-02-27 00:26:53] (step=0015800) Train Loss: 0.0329, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3555 +[2025-02-27 00:27:52] (step=0015850) Train Loss: 0.0328, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3644 +[2025-02-27 00:28:51] (step=0015900) Train Loss: 0.0325, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3396 +[2025-02-27 00:29:49] (step=0015950) Train Loss: 0.0326, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3446 +[2025-02-27 00:30:48] (step=0016000) Train Loss: 0.0327, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3612 +[2025-02-27 00:31:46] (step=0016050) Train Loss: 0.0323, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3445 +[2025-02-27 00:32:45] (step=0016100) Train Loss: 0.0328, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3616 +[2025-02-27 00:33:43] (step=0016150) Train Loss: 0.0324, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3549 +[2025-02-27 00:34:42] (step=0016200) Train Loss: 0.0329, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3705 +[2025-02-27 00:35:40] (step=0016250) Train Loss: 0.0322, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3557 +[2025-02-27 00:36:39] (step=0016300) Train Loss: 0.0324, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3293 +[2025-02-27 00:37:37] (step=0016350) Train Loss: 0.0325, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3647 +[2025-02-27 00:38:36] (step=0016400) Train Loss: 0.0321, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3438 +[2025-02-27 00:39:34] (step=0016450) Train Loss: 0.0325, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3703 +[2025-02-27 00:40:33] (step=0016500) Train Loss: 0.0326, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3235 +[2025-02-27 00:41:31] (step=0016550) Train Loss: 0.0323, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3657 +[2025-02-27 00:42:30] (step=0016600) Train Loss: 0.0325, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3318 +[2025-02-27 00:43:28] (step=0016650) Train Loss: 0.0326, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3520 +[2025-02-27 00:44:27] (step=0016700) Train Loss: 0.0324, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3375 +[2025-02-27 00:45:25] (step=0016750) Train Loss: 0.0321, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3601 +[2025-02-27 00:46:24] (step=0016800) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3407 +[2025-02-27 00:47:22] (step=0016850) Train Loss: 0.0326, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3245 +[2025-02-27 00:48:21] (step=0016900) Train Loss: 0.0322, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3685 +[2025-02-27 00:49:19] (step=0016950) Train Loss: 0.0323, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3351 +[2025-02-27 00:50:18] (step=0017000) Train Loss: 0.0324, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3465 +[2025-02-27 00:51:16] (step=0017050) Train Loss: 0.0321, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3260 +[2025-02-27 00:52:15] (step=0017100) Train Loss: 0.0325, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3695 +[2025-02-27 00:53:13] (step=0017150) Train Loss: 0.0322, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3406 +[2025-02-27 00:54:12] (step=0017200) Train Loss: 0.0323, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3599 +[2025-02-27 00:55:10] (step=0017250) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3351 +[2025-02-27 00:56:09] (step=0017300) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3619 +[2025-02-27 00:57:07] (step=0017350) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3575 +[2025-02-27 00:58:06] (step=0017400) Train Loss: 0.0323, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3116 +[2025-02-27 00:59:04] (step=0017450) Train Loss: 0.0323, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3325 +[2025-02-27 01:00:03] (step=0017500) Train Loss: 0.0319, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3221 +[2025-02-27 01:01:04] (step=0017550) Train Loss: 0.0321, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.83, Grad Norm: 0.3696 +[2025-02-27 01:02:02] (step=0017600) Train Loss: 0.0318, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3414 +[2025-02-27 01:03:01] (step=0017650) Train Loss: 0.0322, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3339 +[2025-02-27 01:03:59] (step=0017700) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3581 +[2025-02-27 01:04:58] (step=0017750) Train Loss: 0.0321, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3427 +[2025-02-27 01:05:56] (step=0017800) Train Loss: 0.0318, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3511 +[2025-02-27 01:06:55] (step=0017850) Train Loss: 0.0323, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3341 +[2025-02-27 01:07:53] (step=0017900) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3264 +[2025-02-27 01:08:52] (step=0017950) Train Loss: 0.0321, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3472 +[2025-02-27 01:09:50] (step=0018000) Train Loss: 0.0321, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3504 +[2025-02-27 01:10:49] (step=0018050) Train Loss: 0.0323, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3401 +[2025-02-27 01:11:47] (step=0018100) Train Loss: 0.0318, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3451 +[2025-02-27 01:12:46] (step=0018150) Train Loss: 0.0323, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3493 +[2025-02-27 01:13:44] (step=0018200) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3176 +[2025-02-27 01:14:43] (step=0018250) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3323 +[2025-02-27 01:15:41] (step=0018300) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3214 +[2025-02-27 01:16:40] (step=0018350) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3263 +[2025-02-27 01:17:39] (step=0018400) Train Loss: 0.0317, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3544 +[2025-02-27 01:18:37] (step=0018450) Train Loss: 0.0323, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3169 +[2025-02-27 01:19:36] (step=0018500) Train Loss: 0.0318, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3347 +[2025-02-27 01:20:34] (step=0018550) Train Loss: 0.0316, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3064 +[2025-02-27 01:21:33] (step=0018600) Train Loss: 0.0320, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3215 +[2025-02-27 01:22:31] (step=0018650) Train Loss: 0.0317, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3187 +[2025-02-27 01:23:30] (step=0018700) Train Loss: 0.0314, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3384 +[2025-02-27 01:24:28] (step=0018750) Train Loss: 0.0316, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3160 +[2025-02-27 01:25:27] (step=0018800) Train Loss: 0.0316, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3428 +[2025-02-27 01:26:25] (step=0018850) Train Loss: 0.0317, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3141 +[2025-02-27 01:27:24] (step=0018900) Train Loss: 0.0315, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3118 +[2025-02-27 01:28:23] (step=0018950) Train Loss: 0.0319, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3501 +[2025-02-27 01:29:21] (step=0019000) Train Loss: 0.0316, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3264 +[2025-02-27 01:30:20] (step=0019050) Train Loss: 0.0317, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3166 +[2025-02-27 01:31:18] (step=0019100) Train Loss: 0.0315, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3135 +[2025-02-27 01:32:17] (step=0019150) Train Loss: 0.0317, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3220 +[2025-02-27 01:33:15] (step=0019200) Train Loss: 0.0315, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3283 +[2025-02-27 01:34:14] (step=0019250) Train Loss: 0.0313, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3102 +[2025-02-27 01:35:12] (step=0019300) Train Loss: 0.0315, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3078 +[2025-02-27 01:36:11] (step=0019350) Train Loss: 0.0316, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3315 +[2025-02-27 01:37:09] (step=0019400) Train Loss: 0.0315, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3362 +[2025-02-27 01:38:08] (step=0019450) Train Loss: 0.0317, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3390 +[2025-02-27 01:39:07] (step=0019500) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2958 +[2025-02-27 01:40:05] (step=0019550) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3049 +[2025-02-27 01:41:04] (step=0019600) Train Loss: 0.0314, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3206 +[2025-02-27 01:42:02] (step=0019650) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3127 +[2025-02-27 01:43:01] (step=0019700) Train Loss: 0.0315, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2908 +[2025-02-27 01:43:59] (step=0019750) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3393 +[2025-02-27 01:44:58] (step=0019800) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3228 +[2025-02-27 01:45:56] (step=0019850) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3142 +[2025-02-27 01:46:55] (step=0019900) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3038 +[2025-02-27 01:47:53] (step=0019950) Train Loss: 0.0313, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3278 +[2025-02-27 01:48:52] (step=0020000) Train Loss: 0.0317, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3193 +[2025-02-27 01:48:53] Saved checkpoint to ../logs/flow/flowsdvae_50kx512_lgnm1p0/checkpoints/0020000.pt +[2025-02-27 02:07:18] (step=0020000), Fid=42.30319998666408, PSNR=15.246639061260224, LPIPS=0.62890625, SSIM=0.11930602043867111 +[2025-02-27 02:08:18] (step=0020050) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.04, Grad Norm: 0.3125 +[2025-02-27 02:09:17] (step=0020100) Train Loss: 0.0317, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3081 +[2025-02-27 02:10:15] (step=0020150) Train Loss: 0.0314, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3175 +[2025-02-27 02:11:14] (step=0020200) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2899 +[2025-02-27 02:12:12] (step=0020250) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3232 +[2025-02-27 02:13:11] (step=0020300) Train Loss: 0.0313, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3129 +[2025-02-27 02:14:09] (step=0020350) Train Loss: 0.0315, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3068 +[2025-02-27 02:15:08] (step=0020400) Train Loss: 0.0313, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2985 +[2025-02-27 02:16:06] (step=0020450) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3137 +[2025-02-27 02:17:05] (step=0020500) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3100 +[2025-02-27 02:18:03] (step=0020550) Train Loss: 0.0313, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2841 +[2025-02-27 02:19:02] (step=0020600) Train Loss: 0.0314, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3122 +[2025-02-27 02:20:01] (step=0020650) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3070 +[2025-02-27 02:20:59] (step=0020700) Train Loss: 0.0309, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3204 +[2025-02-27 02:21:58] (step=0020750) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3135 +[2025-02-27 02:22:56] (step=0020800) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3094 +[2025-02-27 02:23:55] (step=0020850) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3022 +[2025-02-27 02:24:53] (step=0020900) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3135 +[2025-02-27 02:25:52] (step=0020950) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3130 +[2025-02-27 02:26:50] (step=0021000) Train Loss: 0.0314, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3027 +[2025-02-27 02:27:49] (step=0021050) Train Loss: 0.0309, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3040 +[2025-02-27 02:28:47] (step=0021100) Train Loss: 0.0315, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3127 +[2025-02-27 02:29:46] (step=0021150) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2943 +[2025-02-27 02:30:44] (step=0021200) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2799 +[2025-02-27 02:31:43] (step=0021250) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3284 +[2025-02-27 02:32:41] (step=0021300) Train Loss: 0.0309, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3041 +[2025-02-27 02:33:40] (step=0021350) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2938 +[2025-02-27 02:34:38] (step=0021400) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3088 +[2025-02-27 02:35:37] (step=0021450) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2997 +[2025-02-27 02:36:35] (step=0021500) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3020 +[2025-02-27 02:37:34] (step=0021550) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2835 +[2025-02-27 02:38:33] (step=0021600) Train Loss: 0.0309, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3078 +[2025-02-27 02:39:31] (step=0021650) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2778 +[2025-02-27 02:40:30] (step=0021700) Train Loss: 0.0308, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3113 +[2025-02-27 02:41:28] (step=0021750) Train Loss: 0.0313, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2819 +[2025-02-27 02:42:27] (step=0021800) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2836 +[2025-02-27 02:43:25] (step=0021850) Train Loss: 0.0311, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2906 +[2025-02-27 02:44:24] (step=0021900) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2884 +[2025-02-27 02:45:22] (step=0021950) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2856 +[2025-02-27 02:46:21] (step=0022000) Train Loss: 0.0308, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3008 +[2025-02-27 02:47:19] (step=0022050) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2777 +[2025-02-27 02:48:18] (step=0022100) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2847 +[2025-02-27 02:49:16] (step=0022150) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3081 +[2025-02-27 02:50:15] (step=0022200) Train Loss: 0.0308, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2863 +[2025-02-27 02:51:13] (step=0022250) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3048 +[2025-02-27 02:52:12] (step=0022300) Train Loss: 0.0309, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2813 +[2025-02-27 02:53:10] (step=0022350) Train Loss: 0.0308, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2949 +[2025-02-27 02:54:09] (step=0022400) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3087 +[2025-02-27 02:55:07] (step=0022450) Train Loss: 0.0312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2928 +[2025-02-27 02:56:06] (step=0022500) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2811 +[2025-02-27 02:57:07] (step=0022550) Train Loss: 0.0308, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.83, Grad Norm: 0.2908 +[2025-02-27 02:58:05] (step=0022600) Train Loss: 0.0304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2926 +[2025-02-27 02:59:04] (step=0022650) Train Loss: 0.0309, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2941 +[2025-02-27 03:00:02] (step=0022700) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3210 +[2025-02-27 03:01:01] (step=0022750) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2687 +[2025-02-27 03:01:59] (step=0022800) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3007 +[2025-02-27 03:02:58] (step=0022850) Train Loss: 0.0309, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2878 +[2025-02-27 03:03:56] (step=0022900) Train Loss: 0.0308, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2964 +[2025-02-27 03:04:55] (step=0022950) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2986 +[2025-02-27 03:05:53] (step=0023000) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3004 +[2025-02-27 03:06:52] (step=0023050) Train Loss: 0.0309, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2996 +[2025-02-27 03:07:50] (step=0023100) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2600 +[2025-02-27 03:08:49] (step=0023150) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2992 +[2025-02-27 03:09:47] (step=0023200) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2701 +[2025-02-27 03:10:46] (step=0023250) Train Loss: 0.0309, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2749 +[2025-02-27 03:11:44] (step=0023300) Train Loss: 0.0310, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2768 +[2025-02-27 03:12:43] (step=0023350) Train Loss: 0.0308, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2917 +[2025-02-27 03:13:42] (step=0023400) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2842 +[2025-02-27 03:14:40] (step=0023450) Train Loss: 0.0308, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3039 +[2025-02-27 03:15:39] (step=0023500) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2740 +[2025-02-27 03:16:37] (step=0023550) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2832 +[2025-02-27 03:17:36] (step=0023600) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2581 +[2025-02-27 03:18:34] (step=0023650) Train Loss: 0.0304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2667 +[2025-02-27 03:19:33] (step=0023700) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2908 +[2025-02-27 03:20:31] (step=0023750) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2691 +[2025-02-27 03:21:30] (step=0023800) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2947 +[2025-02-27 03:22:28] (step=0023850) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2823 +[2025-02-27 03:23:27] (step=0023900) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2816 +[2025-02-27 03:24:25] (step=0023950) Train Loss: 0.0304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2884 +[2025-02-27 03:25:24] (step=0024000) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2738 +[2025-02-27 03:26:22] (step=0024050) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2917 +[2025-02-27 03:27:21] (step=0024100) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2707 +[2025-02-27 03:28:19] (step=0024150) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3094 +[2025-02-27 03:29:18] (step=0024200) Train Loss: 0.0304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2849 +[2025-02-27 03:30:16] (step=0024250) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2766 +[2025-02-27 03:31:15] (step=0024300) Train Loss: 0.0306, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3015 +[2025-02-27 03:32:14] (step=0024350) Train Loss: 0.0308, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2850 +[2025-02-27 03:33:12] (step=0024400) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2703 +[2025-02-27 03:34:11] (step=0024450) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3025 +[2025-02-27 03:35:09] (step=0024500) Train Loss: 0.0308, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2748 +[2025-02-27 03:36:08] (step=0024550) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2841 +[2025-02-27 03:37:06] (step=0024600) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2728 +[2025-02-27 03:38:05] (step=0024650) Train Loss: 0.0302, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3010 +[2025-02-27 03:39:03] (step=0024700) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2742 +[2025-02-27 03:40:02] (step=0024750) Train Loss: 0.0304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2694 +[2025-02-27 03:41:00] (step=0024800) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2940 +[2025-02-27 03:41:59] (step=0024850) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2851 +[2025-02-27 03:42:57] (step=0024900) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2822 +[2025-02-27 03:43:56] (step=0024950) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2678 +[2025-02-27 03:44:54] (step=0025000) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2729 +[2025-02-27 03:45:55] (step=0025050) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.2696 +[2025-02-27 03:46:53] (step=0025100) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2773 +[2025-02-27 03:47:52] (step=0025150) Train Loss: 0.0304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2833 +[2025-02-27 03:48:51] (step=0025200) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2842 +[2025-02-27 03:49:49] (step=0025250) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2691 +[2025-02-27 03:50:48] (step=0025300) Train Loss: 0.0302, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2759 +[2025-02-27 03:51:46] (step=0025350) Train Loss: 0.0304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2728 +[2025-02-27 03:52:45] (step=0025400) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2752 +[2025-02-27 03:53:43] (step=0025450) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2871 +[2025-02-27 03:54:42] (step=0025500) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2657 +[2025-02-27 03:55:40] (step=0025550) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2822 +[2025-02-27 03:56:39] (step=0025600) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2698 +[2025-02-27 03:57:37] (step=0025650) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2643 +[2025-02-27 03:58:36] (step=0025700) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2561 +[2025-02-27 03:59:34] (step=0025750) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2686 +[2025-02-27 04:00:33] (step=0025800) Train Loss: 0.0302, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2693 +[2025-02-27 04:01:31] (step=0025850) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2943 +[2025-02-27 04:02:30] (step=0025900) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2551 +[2025-02-27 04:03:28] (step=0025950) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2608 +[2025-02-27 04:04:27] (step=0026000) Train Loss: 0.0302, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2798 +[2025-02-27 04:05:25] (step=0026050) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2797 +[2025-02-27 04:06:24] (step=0026100) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2470 +[2025-02-27 04:07:23] (step=0026150) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2660 +[2025-02-27 04:08:21] (step=0026200) Train Loss: 0.0304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2564 +[2025-02-27 04:09:20] (step=0026250) Train Loss: 0.0302, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2648 +[2025-02-27 04:10:18] (step=0026300) Train Loss: 0.0307, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2775 +[2025-02-27 04:11:17] (step=0026350) Train Loss: 0.0302, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2642 +[2025-02-27 04:12:15] (step=0026400) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2702 +[2025-02-27 04:13:14] (step=0026450) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2437 +[2025-02-27 04:14:12] (step=0026500) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2603 +[2025-02-27 04:15:11] (step=0026550) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2595 +[2025-02-27 04:16:09] (step=0026600) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2540 +[2025-02-27 04:17:08] (step=0026650) Train Loss: 0.0305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2991 +[2025-02-27 04:18:06] (step=0026700) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2599 +[2025-02-27 04:19:05] (step=0026750) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2529 +[2025-02-27 04:20:03] (step=0026800) Train Loss: 0.0304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2455 +[2025-02-27 04:21:02] (step=0026850) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2696 +[2025-02-27 04:22:00] (step=0026900) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2395 +[2025-02-27 04:22:59] (step=0026950) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2610 +[2025-02-27 04:23:57] (step=0027000) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2430 +[2025-02-27 04:24:56] (step=0027050) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2719 +[2025-02-27 04:25:54] (step=0027100) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2555 +[2025-02-27 04:26:53] (step=0027150) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2491 +[2025-02-27 04:27:51] (step=0027200) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2723 +[2025-02-27 04:28:50] (step=0027250) Train Loss: 0.0304, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2728 +[2025-02-27 04:29:49] (step=0027300) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2599 +[2025-02-27 04:30:47] (step=0027350) Train Loss: 0.0302, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2639 +[2025-02-27 04:31:46] (step=0027400) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2933 +[2025-02-27 04:32:44] (step=0027450) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2638 +[2025-02-27 04:33:43] (step=0027500) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2613 +[2025-02-27 04:34:43] (step=0027550) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.83, Grad Norm: 0.2556 +[2025-02-27 04:35:42] (step=0027600) Train Loss: 0.0302, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2634 +[2025-02-27 04:36:40] (step=0027650) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2808 +[2025-02-27 04:37:39] (step=0027700) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2361 +[2025-02-27 04:38:37] (step=0027750) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2652 +[2025-02-27 04:39:36] (step=0027800) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2702 +[2025-02-27 04:40:34] (step=0027850) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2397 +[2025-02-27 04:41:33] (step=0027900) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2578 +[2025-02-27 04:42:31] (step=0027950) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2545 +[2025-02-27 04:43:30] (step=0028000) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2419 +[2025-02-27 04:44:28] (step=0028050) Train Loss: 0.0303, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2594 +[2025-02-27 04:45:27] (step=0028100) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2653 +[2025-02-27 04:46:25] (step=0028150) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2668 +[2025-02-27 04:47:24] (step=0028200) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2553 +[2025-02-27 04:48:22] (step=0028250) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2512 +[2025-02-27 04:49:21] (step=0028300) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2557 +[2025-02-27 04:50:20] (step=0028350) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2633 +[2025-02-27 04:51:18] (step=0028400) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2474 +[2025-02-27 04:52:17] (step=0028450) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2331 +[2025-02-27 04:53:15] (step=0028500) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2666 +[2025-02-27 04:54:14] (step=0028550) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2527 +[2025-02-27 04:55:12] (step=0028600) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2606 +[2025-02-27 04:56:11] (step=0028650) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2679 +[2025-02-27 04:57:09] (step=0028700) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2129 +[2025-02-27 04:58:08] (step=0028750) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2473 +[2025-02-27 04:59:06] (step=0028800) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2473 +[2025-02-27 05:00:05] (step=0028850) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2401 +[2025-02-27 05:01:03] (step=0028900) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2500 +[2025-02-27 05:02:02] (step=0028950) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2356 +[2025-02-27 05:03:00] (step=0029000) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2520 +[2025-02-27 05:03:59] (step=0029050) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2527 +[2025-02-27 05:04:57] (step=0029100) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2626 +[2025-02-27 05:05:56] (step=0029150) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2564 +[2025-02-27 05:06:54] (step=0029200) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2450 +[2025-02-27 05:07:53] (step=0029250) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2626 +[2025-02-27 05:08:51] (step=0029300) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2441 +[2025-02-27 05:09:50] (step=0029350) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2265 +[2025-02-27 05:10:48] (step=0029400) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2455 +[2025-02-27 05:11:47] (step=0029450) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2303 +[2025-02-27 05:12:46] (step=0029500) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2466 +[2025-02-27 05:13:44] (step=0029550) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2300 +[2025-02-27 05:14:43] (step=0029600) Train Loss: 0.0302, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2560 +[2025-02-27 05:15:41] (step=0029650) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2501 +[2025-02-27 05:16:40] (step=0029700) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2384 +[2025-02-27 05:17:38] (step=0029750) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2524 +[2025-02-27 05:18:37] (step=0029800) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2673 +[2025-02-27 05:19:35] (step=0029850) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2543 +[2025-02-27 05:20:34] (step=0029900) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2326 +[2025-02-27 05:21:32] (step=0029950) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2531 +[2025-02-27 05:22:31] (step=0030000) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2355 +[2025-02-27 05:22:33] Saved checkpoint to ../logs/flow/flowsdvae_50kx512_lgnm1p0/checkpoints/0030000.pt +[2025-02-27 05:40:55] (step=0030000), Fid=14.801599353074153, PSNR=20.848283165121078, LPIPS=0.44921875, SSIM=0.32102730870246887 +[2025-02-27 05:41:55] (step=0030050) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.04, Grad Norm: 0.2473 +[2025-02-27 05:42:54] (step=0030100) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2398 +[2025-02-27 05:43:52] (step=0030150) Train Loss: 0.0301, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2388 +[2025-02-27 05:44:51] (step=0030200) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2495 +[2025-02-27 05:45:49] (step=0030250) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2227 +[2025-02-27 05:46:48] (step=0030300) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2570 +[2025-02-27 05:47:46] (step=0030350) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2276 +[2025-02-27 05:48:45] (step=0030400) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2277 +[2025-02-27 05:49:43] (step=0030450) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2308 +[2025-02-27 05:50:42] (step=0030500) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2605 +[2025-02-27 05:51:40] (step=0030550) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2384 +[2025-02-27 05:52:39] (step=0030600) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2387 +[2025-02-27 05:53:37] (step=0030650) Train Loss: 0.0298, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2609 +[2025-02-27 05:54:36] (step=0030700) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2326 +[2025-02-27 05:55:34] (step=0030750) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2609 +[2025-02-27 05:56:33] (step=0030800) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2459 +[2025-02-27 05:57:31] (step=0030850) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2220 +[2025-02-27 05:58:30] (step=0030900) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2506 +[2025-02-27 05:59:28] (step=0030950) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2392 +[2025-02-27 06:00:27] (step=0031000) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2380 +[2025-02-27 06:01:26] (step=0031050) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2332 +[2025-02-27 06:02:24] (step=0031100) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2308 +[2025-02-27 06:03:23] (step=0031150) Train Loss: 0.0300, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2294 +[2025-02-27 06:04:21] (step=0031200) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2378 +[2025-02-27 06:05:20] (step=0031250) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2298 +[2025-02-27 06:06:18] (step=0031300) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2285 +[2025-02-27 06:07:17] (step=0031350) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2429 +[2025-02-27 06:08:15] (step=0031400) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2570 +[2025-02-27 06:09:14] (step=0031450) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2316 +[2025-02-27 06:10:12] (step=0031500) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2492 +[2025-02-27 06:11:11] (step=0031550) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2212 +[2025-02-27 06:12:09] (step=0031600) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2210 +[2025-02-27 06:13:08] (step=0031650) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2282 +[2025-02-27 06:14:06] (step=0031700) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2322 +[2025-02-27 06:15:05] (step=0031750) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2300 +[2025-02-27 06:16:03] (step=0031800) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2563 +[2025-02-27 06:17:02] (step=0031850) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2278 +[2025-02-27 06:18:00] (step=0031900) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2320 +[2025-02-27 06:18:59] (step=0031950) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2340 +[2025-02-27 06:19:57] (step=0032000) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2461 +[2025-02-27 06:20:56] (step=0032050) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2335 +[2025-02-27 06:21:54] (step=0032100) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2509 +[2025-02-27 06:22:53] (step=0032150) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2307 +[2025-02-27 06:23:51] (step=0032200) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2273 +[2025-02-27 06:24:50] (step=0032250) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2335 +[2025-02-27 06:25:48] (step=0032300) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2466 +[2025-02-27 06:26:47] (step=0032350) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2387 +[2025-02-27 06:27:45] (step=0032400) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2174 +[2025-02-27 06:28:44] (step=0032450) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2244 +[2025-02-27 06:29:43] (step=0032500) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2387 +[2025-02-27 06:30:43] (step=0032550) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.83, Grad Norm: 0.2345 +[2025-02-27 06:31:42] (step=0032600) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2260 +[2025-02-27 06:32:40] (step=0032650) Train Loss: 0.0299, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2269 +[2025-02-27 06:33:39] (step=0032700) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2382 +[2025-02-27 06:34:37] (step=0032750) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2353 +[2025-02-27 06:35:36] (step=0032800) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2175 +[2025-02-27 06:36:34] (step=0032850) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2265 +[2025-02-27 06:37:33] (step=0032900) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2182 +[2025-02-27 06:38:31] (step=0032950) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2381 +[2025-02-27 06:39:30] (step=0033000) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2200 +[2025-02-27 06:40:28] (step=0033050) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2331 +[2025-02-27 06:41:27] (step=0033100) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2192 +[2025-02-27 06:42:25] (step=0033150) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2116 +[2025-02-27 06:43:24] (step=0033200) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2246 +[2025-02-27 06:44:22] (step=0033250) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2493 +[2025-02-27 06:45:21] (step=0033300) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2050 +[2025-02-27 06:46:19] (step=0033350) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2316 +[2025-02-27 06:47:18] (step=0033400) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2367 +[2025-02-27 06:48:16] (step=0033450) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2402 +[2025-02-27 06:49:15] (step=0033500) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2354 +[2025-02-27 06:50:14] (step=0033550) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2339 +[2025-02-27 06:51:12] (step=0033600) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2134 +[2025-02-27 06:52:11] (step=0033650) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2220 +[2025-02-27 06:53:09] (step=0033700) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2260 +[2025-02-27 06:54:08] (step=0033750) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2407 +[2025-02-27 06:55:06] (step=0033800) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2191 +[2025-02-27 06:56:05] (step=0033850) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2272 +[2025-02-27 06:57:03] (step=0033900) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2325 +[2025-02-27 06:58:02] (step=0033950) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2307 +[2025-02-27 06:59:00] (step=0034000) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2345 +[2025-02-27 06:59:59] (step=0034050) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2270 +[2025-02-27 07:00:57] (step=0034100) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2294 +[2025-02-27 07:01:56] (step=0034150) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2341 +[2025-02-27 07:02:54] (step=0034200) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2266 +[2025-02-27 07:03:53] (step=0034250) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2154 +[2025-02-27 07:04:51] (step=0034300) Train Loss: 0.0296, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2325 +[2025-02-27 07:05:50] (step=0034350) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2095 +[2025-02-27 07:06:48] (step=0034400) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2256 +[2025-02-27 07:07:47] (step=0034450) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2192 +[2025-02-27 07:08:45] (step=0034500) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2151 +[2025-02-27 07:09:44] (step=0034550) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2177 +[2025-02-27 07:10:42] (step=0034600) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2197 +[2025-02-27 07:11:41] (step=0034650) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2382 +[2025-02-27 07:12:39] (step=0034700) Train Loss: 0.0297, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2095 +[2025-02-27 07:13:38] (step=0034750) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2222 +[2025-02-27 07:14:37] (step=0034800) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1965 +[2025-02-27 07:15:35] (step=0034850) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2174 +[2025-02-27 07:16:34] (step=0034900) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2230 +[2025-02-27 07:17:32] (step=0034950) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2318 +[2025-02-27 07:18:31] (step=0035000) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2325 +[2025-02-27 07:19:31] (step=0035050) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.2371 +[2025-02-27 07:20:30] (step=0035100) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2096 +[2025-02-27 07:21:28] (step=0035150) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2132 +[2025-02-27 07:22:27] (step=0035200) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2319 +[2025-02-27 07:23:25] (step=0035250) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2077 +[2025-02-27 07:24:24] (step=0035300) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2240 +[2025-02-27 07:25:22] (step=0035350) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2045 +[2025-02-27 07:26:21] (step=0035400) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2383 +[2025-02-27 07:27:19] (step=0035450) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2065 +[2025-02-27 07:28:18] (step=0035500) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2345 +[2025-02-27 07:29:16] (step=0035550) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2150 +[2025-02-27 07:30:15] (step=0035600) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2116 +[2025-02-27 07:31:13] (step=0035650) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2133 +[2025-02-27 07:32:12] (step=0035700) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2335 +[2025-02-27 07:33:10] (step=0035750) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2240 +[2025-02-27 07:34:09] (step=0035800) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2315 +[2025-02-27 07:35:07] (step=0035850) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1878 +[2025-02-27 07:36:06] (step=0035900) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2464 +[2025-02-27 07:37:04] (step=0035950) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2149 +[2025-02-27 07:38:03] (step=0036000) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2055 +[2025-02-27 07:39:02] (step=0036050) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2179 +[2025-02-27 07:40:00] (step=0036100) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2011 +[2025-02-27 07:40:59] (step=0036150) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2106 +[2025-02-27 07:41:57] (step=0036200) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2293 +[2025-02-27 07:42:56] (step=0036250) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2118 +[2025-02-27 07:43:54] (step=0036300) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2020 +[2025-02-27 07:44:53] (step=0036350) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2192 +[2025-02-27 07:45:51] (step=0036400) Train Loss: 0.0294, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2071 +[2025-02-27 07:46:50] (step=0036450) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2163 +[2025-02-27 07:47:48] (step=0036500) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2208 +[2025-02-27 07:48:47] (step=0036550) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2003 +[2025-02-27 07:49:45] (step=0036600) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2129 +[2025-02-27 07:50:44] (step=0036650) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2087 +[2025-02-27 07:51:42] (step=0036700) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2389 +[2025-02-27 07:52:41] (step=0036750) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2352 +[2025-02-27 07:53:39] (step=0036800) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2137 +[2025-02-27 07:54:38] (step=0036850) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2210 +[2025-02-27 07:55:36] (step=0036900) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2265 +[2025-02-27 07:56:35] (step=0036950) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2078 +[2025-02-27 07:57:33] (step=0037000) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1979 +[2025-02-27 07:58:32] (step=0037050) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2193 +[2025-02-27 07:59:30] (step=0037100) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2222 +[2025-02-27 08:00:29] (step=0037150) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2067 +[2025-02-27 08:01:27] (step=0037200) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2383 +[2025-02-27 08:02:26] (step=0037250) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2055 +[2025-02-27 08:03:24] (step=0037300) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2293 +[2025-02-27 08:04:23] (step=0037350) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2094 +[2025-02-27 08:05:21] (step=0037400) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2117 +[2025-02-27 08:06:20] (step=0037450) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2127 +[2025-02-27 08:07:18] (step=0037500) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1883 +[2025-02-27 08:08:19] (step=0037550) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.2177 +[2025-02-27 08:09:17] (step=0037600) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2032 +[2025-02-27 08:10:16] (step=0037650) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2009 +[2025-02-27 08:11:14] (step=0037700) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2157 +[2025-02-27 08:12:13] (step=0037750) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2051 +[2025-02-27 08:13:12] (step=0037800) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2115 +[2025-02-27 08:14:10] (step=0037850) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1912 +[2025-02-27 08:15:09] (step=0037900) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2149 +[2025-02-27 08:16:07] (step=0037950) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2044 +[2025-02-27 08:17:06] (step=0038000) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2130 +[2025-02-27 08:18:04] (step=0038050) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2106 +[2025-02-27 08:19:03] (step=0038100) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2120 +[2025-02-27 08:20:01] (step=0038150) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2116 +[2025-02-27 08:21:00] (step=0038200) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2182 +[2025-02-27 08:21:58] (step=0038250) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1921 +[2025-02-27 08:22:57] (step=0038300) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2109 +[2025-02-27 08:23:55] (step=0038350) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2076 +[2025-02-27 08:24:54] (step=0038400) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2119 +[2025-02-27 08:25:52] (step=0038450) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2087 +[2025-02-27 08:26:51] (step=0038500) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2046 +[2025-02-27 08:27:49] (step=0038550) Train Loss: 0.0295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2119 +[2025-02-27 08:28:48] (step=0038600) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2154 +[2025-02-27 08:29:46] (step=0038650) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2054 +[2025-02-27 08:30:45] (step=0038700) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2110 +[2025-02-27 08:31:43] (step=0038750) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2145 +[2025-02-27 08:32:42] (step=0038800) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1801 +[2025-02-27 08:33:40] (step=0038850) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2141 +[2025-02-27 08:34:39] (step=0038900) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2169 +[2025-02-27 08:35:37] (step=0038950) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2088 +[2025-02-27 08:36:36] (step=0039000) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2003 +[2025-02-27 08:37:34] (step=0039050) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1971 +[2025-02-27 08:38:33] (step=0039100) Train Loss: 0.0293, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2080 +[2025-02-27 08:39:31] (step=0039150) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1935 +[2025-02-27 08:40:30] (step=0039200) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1960 +[2025-02-27 08:41:28] (step=0039250) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2308 +[2025-02-27 08:42:27] (step=0039300) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2080 +[2025-02-27 08:43:25] (step=0039350) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2060 +[2025-02-27 08:44:24] (step=0039400) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2034 +[2025-02-27 08:45:22] (step=0039450) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1907 +[2025-02-27 08:46:21] (step=0039500) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2017 +[2025-02-27 08:47:19] (step=0039550) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1913 +[2025-02-27 08:48:18] (step=0039600) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2122 +[2025-02-27 08:49:16] (step=0039650) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2035 +[2025-02-27 08:50:15] (step=0039700) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2028 +[2025-02-27 08:51:13] (step=0039750) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1976 +[2025-02-27 08:52:12] (step=0039800) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1886 +[2025-02-27 08:53:10] (step=0039850) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2094 +[2025-02-27 08:54:09] (step=0039900) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1858 +[2025-02-27 08:55:07] (step=0039950) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1843 +[2025-02-27 08:56:06] (step=0040000) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2052 +[2025-02-27 08:56:09] Saved checkpoint to ../logs/flow/flowsdvae_50kx512_lgnm1p0/checkpoints/0040000.pt +[2025-02-27 09:14:41] (step=0040000), Fid=7.843537747909693, PSNR=23.531876158595086, LPIPS=0.326171875, SSIM=0.5252382755279541 +[2025-02-27 09:15:42] (step=0040050) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.04, Grad Norm: 0.1947 +[2025-02-27 09:16:40] (step=0040100) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1972 +[2025-02-27 09:17:39] (step=0040150) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1947 +[2025-02-27 09:18:38] (step=0040200) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2297 +[2025-02-27 09:19:36] (step=0040250) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1894 +[2025-02-27 09:20:35] (step=0040300) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2050 +[2025-02-27 09:21:33] (step=0040350) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1807 +[2025-02-27 09:22:32] (step=0040400) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2142 +[2025-02-27 09:23:30] (step=0040450) Train Loss: 0.0292, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2009 +[2025-02-27 09:24:29] (step=0040500) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2075 +[2025-02-27 09:25:27] (step=0040550) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1911 +[2025-02-27 09:26:26] (step=0040600) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2009 +[2025-02-27 09:27:24] (step=0040650) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2079 +[2025-02-27 09:28:23] (step=0040700) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1828 +[2025-02-27 09:29:21] (step=0040750) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2045 +[2025-02-27 09:30:20] (step=0040800) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1808 +[2025-02-27 09:31:18] (step=0040850) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1922 +[2025-02-27 09:32:17] (step=0040900) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2081 +[2025-02-27 09:33:15] (step=0040950) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1742 +[2025-02-27 09:34:14] (step=0041000) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2026 +[2025-02-27 09:35:12] (step=0041050) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2044 +[2025-02-27 09:36:11] (step=0041100) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1732 +[2025-02-27 09:37:09] (step=0041150) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2155 +[2025-02-27 09:38:08] (step=0041200) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2091 +[2025-02-27 09:39:07] (step=0041250) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1947 +[2025-02-27 09:40:05] (step=0041300) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2002 +[2025-02-27 09:41:04] (step=0041350) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1946 +[2025-02-27 09:42:02] (step=0041400) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2029 +[2025-02-27 09:43:01] (step=0041450) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2116 +[2025-02-27 09:43:59] (step=0041500) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1895 +[2025-02-27 09:44:58] (step=0041550) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2001 +[2025-02-27 09:45:56] (step=0041600) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1826 +[2025-02-27 09:46:55] (step=0041650) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2072 +[2025-02-27 09:47:53] (step=0041700) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1772 +[2025-02-27 09:48:52] (step=0041750) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2071 +[2025-02-27 09:49:50] (step=0041800) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1985 +[2025-02-27 09:50:49] (step=0041850) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2086 +[2025-02-27 09:51:47] (step=0041900) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1994 +[2025-02-27 09:52:46] (step=0041950) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1890 +[2025-02-27 09:53:44] (step=0042000) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1989 +[2025-02-27 09:54:43] (step=0042050) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1701 +[2025-02-27 09:55:41] (step=0042100) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1789 +[2025-02-27 09:56:40] (step=0042150) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2060 +[2025-02-27 09:57:38] (step=0042200) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2016 +[2025-02-27 09:58:37] (step=0042250) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1885 +[2025-02-27 09:59:36] (step=0042300) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1871 +[2025-02-27 10:00:34] (step=0042350) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1910 +[2025-02-27 10:01:33] (step=0042400) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1910 +[2025-02-27 10:02:31] (step=0042450) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1940 +[2025-02-27 10:03:30] (step=0042500) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1865 +[2025-02-27 10:04:30] (step=0042550) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.1822 +[2025-02-27 10:05:29] (step=0042600) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1957 +[2025-02-27 10:06:27] (step=0042650) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1806 +[2025-02-27 10:07:26] (step=0042700) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1819 +[2025-02-27 10:08:24] (step=0042750) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1885 +[2025-02-27 10:09:23] (step=0042800) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1972 +[2025-02-27 10:10:22] (step=0042850) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1929 +[2025-02-27 10:11:20] (step=0042900) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2066 +[2025-02-27 10:12:19] (step=0042950) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1765 +[2025-02-27 10:13:17] (step=0043000) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2091 +[2025-02-27 10:14:16] (step=0043050) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1853 +[2025-02-27 10:15:14] (step=0043100) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1988 +[2025-02-27 10:16:13] (step=0043150) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1905 +[2025-02-27 10:17:11] (step=0043200) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1913 +[2025-02-27 10:18:10] (step=0043250) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1796 +[2025-02-27 10:19:08] (step=0043300) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1875 +[2025-02-27 10:20:07] (step=0043350) Train Loss: 0.0282, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1810 +[2025-02-27 10:21:05] (step=0043400) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1855 +[2025-02-27 10:22:04] (step=0043450) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1866 +[2025-02-27 10:23:02] (step=0043500) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1865 +[2025-02-27 10:24:01] (step=0043550) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1960 +[2025-02-27 10:24:59] (step=0043600) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2088 +[2025-02-27 10:25:58] (step=0043650) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1714 +[2025-02-27 10:26:56] (step=0043700) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1893 +[2025-02-27 10:27:55] (step=0043750) Train Loss: 0.0282, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2059 +[2025-02-27 10:28:54] (step=0043800) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1916 +[2025-02-27 10:29:52] (step=0043850) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1588 +[2025-02-27 10:30:51] (step=0043900) Train Loss: 0.0282, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1994 +[2025-02-27 10:31:49] (step=0043950) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1823 +[2025-02-27 10:32:48] (step=0044000) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1982 +[2025-02-27 10:33:46] (step=0044050) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2228 +[2025-02-27 10:34:45] (step=0044100) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1748 +[2025-02-27 10:35:43] (step=0044150) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1876 +[2025-02-27 10:36:42] (step=0044200) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1877 +[2025-02-27 10:37:40] (step=0044250) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1960 +[2025-02-27 10:38:39] (step=0044300) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1773 +[2025-02-27 10:39:37] (step=0044350) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1959 +[2025-02-27 10:40:36] (step=0044400) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1777 +[2025-02-27 10:41:34] (step=0044450) Train Loss: 0.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1952 +[2025-02-27 10:42:33] (step=0044500) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1805 +[2025-02-27 10:43:31] (step=0044550) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1890 +[2025-02-27 10:44:30] (step=0044600) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1960 +[2025-02-27 10:45:28] (step=0044650) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1670 +[2025-02-27 10:46:27] (step=0044700) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1916 +[2025-02-27 10:47:25] (step=0044750) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1620 +[2025-02-27 10:48:24] (step=0044800) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1907 +[2025-02-27 10:49:22] (step=0044850) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1794 +[2025-02-27 10:50:21] (step=0044900) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1877 +[2025-02-27 10:51:20] (step=0044950) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1643 +[2025-02-27 10:52:18] (step=0045000) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1885 +[2025-02-27 10:53:19] (step=0045050) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.1907 +[2025-02-27 10:54:17] (step=0045100) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1713 +[2025-02-27 10:55:16] (step=0045150) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2034 +[2025-02-27 10:56:14] (step=0045200) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1777 +[2025-02-27 10:57:13] (step=0045250) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1763 +[2025-02-27 10:58:11] (step=0045300) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1793 +[2025-02-27 10:59:10] (step=0045350) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1826 +[2025-02-27 11:00:08] (step=0045400) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1883 +[2025-02-27 11:01:07] (step=0045450) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2022 +[2025-02-27 11:02:05] (step=0045500) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1863 +[2025-02-27 11:03:04] (step=0045550) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1826 +[2025-02-27 11:04:02] (step=0045600) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1715 +[2025-02-27 11:05:01] (step=0045650) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1773 +[2025-02-27 11:06:00] (step=0045700) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1812 +[2025-02-27 11:06:58] (step=0045750) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1795 +[2025-02-27 11:07:57] (step=0045800) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1883 +[2025-02-27 11:08:55] (step=0045850) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1886 +[2025-02-27 11:09:54] (step=0045900) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1733 +[2025-02-27 11:10:52] (step=0045950) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2000 +[2025-02-27 11:11:51] (step=0046000) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1722 +[2025-02-27 11:12:49] (step=0046050) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1750 +[2025-02-27 11:13:48] (step=0046100) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1897 +[2025-02-27 11:14:46] (step=0046150) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1756 +[2025-02-27 11:15:45] (step=0046200) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1752 +[2025-02-27 11:16:43] (step=0046250) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1816 +[2025-02-27 11:17:42] (step=0046300) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1817 +[2025-02-27 11:18:40] (step=0046350) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1902 +[2025-02-27 11:19:39] (step=0046400) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1815 +[2025-02-27 11:20:37] (step=0046450) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1804 +[2025-02-27 11:21:36] (step=0046500) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1737 +[2025-02-27 11:22:34] (step=0046550) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1775 +[2025-02-27 11:23:33] (step=0046600) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1870 +[2025-02-27 11:24:31] (step=0046650) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1815 +[2025-02-27 11:25:30] (step=0046700) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1844 +[2025-02-27 11:26:28] (step=0046750) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1638 +[2025-02-27 11:27:27] (step=0046800) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1876 +[2025-02-27 11:28:25] (step=0046850) Train Loss: 0.0282, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1578 +[2025-02-27 11:29:24] (step=0046900) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1766 +[2025-02-27 11:30:22] (step=0046950) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1813 +[2025-02-27 11:31:21] (step=0047000) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1639 +[2025-02-27 11:32:19] (step=0047050) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1775 +[2025-02-27 11:33:18] (step=0047100) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1693 +[2025-02-27 11:34:16] (step=0047150) Train Loss: 0.0290, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1745 +[2025-02-27 11:35:15] (step=0047200) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1904 +[2025-02-27 11:36:13] (step=0047250) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1700 +[2025-02-27 11:37:12] (step=0047300) Train Loss: 0.0282, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1778 +[2025-02-27 11:38:10] (step=0047350) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1630 +[2025-02-27 11:39:09] (step=0047400) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1896 +[2025-02-27 11:40:08] (step=0047450) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1713 +[2025-02-27 11:41:06] (step=0047500) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1637 +[2025-02-27 11:42:07] (step=0047550) Train Loss: 0.0282, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.1984 +[2025-02-27 11:43:06] (step=0047600) Train Loss: 0.0289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1781 +[2025-02-27 11:44:04] (step=0047650) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1668 +[2025-02-27 11:45:03] (step=0047700) Train Loss: 0.0279, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1729 +[2025-02-27 11:46:01] (step=0047750) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1877 +[2025-02-27 11:47:00] (step=0047800) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1689 +[2025-02-27 11:47:58] (step=0047850) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1800 +[2025-02-27 11:48:57] (step=0047900) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1658 +[2025-02-27 11:49:55] (step=0047950) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1844 +[2025-02-27 11:50:54] (step=0048000) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1669 +[2025-02-27 11:51:52] (step=0048050) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1721 +[2025-02-27 11:52:51] (step=0048100) Train Loss: 0.0288, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1789 +[2025-02-27 11:53:49] (step=0048150) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1794 +[2025-02-27 11:54:48] (step=0048200) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1615 +[2025-02-27 11:55:46] (step=0048250) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1644 +[2025-02-27 11:56:45] (step=0048300) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1860 +[2025-02-27 11:57:43] (step=0048350) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1893 +[2025-02-27 11:58:42] (step=0048400) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1765 +[2025-02-27 11:59:41] (step=0048450) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1820 +[2025-02-27 12:00:39] (step=0048500) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1937 +[2025-02-27 12:01:38] (step=0048550) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1689 +[2025-02-27 12:02:36] (step=0048600) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1864 +[2025-02-27 12:03:35] (step=0048650) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1774 +[2025-02-27 12:04:33] (step=0048700) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1840 +[2025-02-27 12:05:32] (step=0048750) Train Loss: 0.0282, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1602 +[2025-02-27 12:06:30] (step=0048800) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1717 +[2025-02-27 12:07:29] (step=0048850) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1724 +[2025-02-27 12:08:27] (step=0048900) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1988 +[2025-02-27 12:09:26] (step=0048950) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1558 +[2025-02-27 12:10:24] (step=0049000) Train Loss: 0.0281, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1760 +[2025-02-27 12:11:23] (step=0049050) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1639 +[2025-02-27 12:12:21] (step=0049100) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1633 +[2025-02-27 12:13:20] (step=0049150) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1753 +[2025-02-27 12:14:18] (step=0049200) Train Loss: 0.0287, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1784 +[2025-02-27 12:15:17] (step=0049250) Train Loss: 0.0282, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1709 +[2025-02-27 12:16:16] (step=0049300) Train Loss: 0.0280, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1655 +[2025-02-27 12:17:14] (step=0049350) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1831 +[2025-02-27 12:18:13] (step=0049400) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1676 +[2025-02-27 12:19:11] (step=0049450) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1632 +[2025-02-27 12:20:10] (step=0049500) Train Loss: 0.0279, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1732 +[2025-02-27 12:21:08] (step=0049550) Train Loss: 0.0282, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1733 +[2025-02-27 12:22:07] (step=0049600) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1594 +[2025-02-27 12:23:05] (step=0049650) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1723 +[2025-02-27 12:24:04] (step=0049700) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1617 +[2025-02-27 12:25:02] (step=0049750) Train Loss: 0.0281, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1781 +[2025-02-27 12:26:01] (step=0049800) Train Loss: 0.0282, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1677 +[2025-02-27 12:26:59] (step=0049850) Train Loss: 0.0283, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1834 +[2025-02-27 12:27:58] (step=0049900) Train Loss: 0.0286, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1620 +[2025-02-27 12:28:56] (step=0049950) Train Loss: 0.0284, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1725 +[2025-02-27 12:29:55] (step=0050000) Train Loss: 0.0285, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.1695 +[2025-02-27 12:29:58] Saved checkpoint to ../logs/flow/flowsdvae_50kx512_lgnm1p0/checkpoints/0050000.pt +[2025-02-27 12:48:40] (step=0050000), Fid=4.673616434640735, PSNR=24.476785852956773, LPIPS=0.2578125, SSIM=0.6111387610435486 +[2025-02-27 12:48:41] Done!