diff --git "a/flowsdvae_50kx512_lgn0p0/log.txt" "b/flowsdvae_50kx512_lgn0p0/log.txt"
new file mode 100644--- /dev/null
+++ "b/flowsdvae_50kx512_lgn0p0/log.txt"
@@ -0,0 +1,1273 @@
+[2025-02-26 18:55:51] 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 18:55:51] FlowVAE Parameters: 55.53M
+[2025-02-26 18:55:51] FlowVAE Trainable Parameters: 55.01M
+[2025-02-26 18:55:51] Optimizer: AdamW, lr=0.0002, beta2=0.95
+[2025-02-26 18:55:51] module.pos_embed.requires_grad                                        : False
+[2025-02-26 18:55:51] module.flow.conv_in.weight.requires_grad                              : True
+[2025-02-26 18:55:51] module.flow.conv_in.bias.requires_grad                                : True
+[2025-02-26 18:55:51] module.flow.mid.block_1.norm1.weight.requires_grad                    : True
+[2025-02-26 18:55:51] module.flow.mid.block_1.conv1.weight.requires_grad                    : True
+[2025-02-26 18:55:51] module.flow.mid.block_1.conv1.bias.requires_grad                      : True
+[2025-02-26 18:55:51] module.flow.mid.block_1.temb_proj.weight.requires_grad                : True
+[2025-02-26 18:55:51] module.flow.mid.block_1.temb_proj.bias.requires_grad                  : True
+[2025-02-26 18:55:51] module.flow.mid.block_1.norm2.weight.requires_grad                    : True
+[2025-02-26 18:55:51] module.flow.mid.block_1.conv2.weight.requires_grad                    : True
+[2025-02-26 18:55:51] module.flow.mid.block_1.conv2.bias.requires_grad                      : True
+[2025-02-26 18:55:51] module.flow.mid.attn_1.norm.weight.requires_grad                      : True
+[2025-02-26 18:55:51] module.flow.mid.attn_1.q.weight.requires_grad                         : True
+[2025-02-26 18:55:51] module.flow.mid.attn_1.q.bias.requires_grad                           : True
+[2025-02-26 18:55:51] module.flow.mid.attn_1.k.weight.requires_grad                         : True
+[2025-02-26 18:55:51] module.flow.mid.attn_1.k.bias.requires_grad                           : True
+[2025-02-26 18:55:51] module.flow.mid.attn_1.v.weight.requires_grad                         : True
+[2025-02-26 18:55:51] module.flow.mid.attn_1.v.bias.requires_grad                           : True
+[2025-02-26 18:55:51] module.flow.mid.attn_1.proj_out.weight.requires_grad                  : True
+[2025-02-26 18:55:51] module.flow.mid.attn_1.proj_out.bias.requires_grad                    : True
+[2025-02-26 18:55:51] module.flow.mid.block_2.norm1.weight.requires_grad                    : True
+[2025-02-26 18:55:51] module.flow.mid.block_2.conv1.weight.requires_grad                    : True
+[2025-02-26 18:55:51] module.flow.mid.block_2.conv1.bias.requires_grad                      : True
+[2025-02-26 18:55:51] module.flow.mid.block_2.temb_proj.weight.requires_grad                : True
+[2025-02-26 18:55:51] module.flow.mid.block_2.temb_proj.bias.requires_grad                  : True
+[2025-02-26 18:55:51] module.flow.mid.block_2.norm2.weight.requires_grad                    : True
+[2025-02-26 18:55:51] module.flow.mid.block_2.conv2.weight.requires_grad                    : True
+[2025-02-26 18:55:51] module.flow.mid.block_2.conv2.bias.requires_grad                      : True
+[2025-02-26 18:55:51] module.flow.up.0.block.0.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.0.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.0.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.0.block.0.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.0.block.0.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.0.block.0.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.0.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.0.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.0.block.0.nin_shortcut.weight.requires_grad            : True
+[2025-02-26 18:55:51] module.flow.up.0.block.0.nin_shortcut.bias.requires_grad              : True
+[2025-02-26 18:55:51] module.flow.up.0.block.1.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.1.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.1.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.0.block.1.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.0.block.1.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.0.block.1.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.1.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.1.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.0.block.2.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.2.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.2.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.0.block.2.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.0.block.2.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.0.block.2.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.2.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.0.block.2.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.1.block.0.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.0.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.0.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.1.block.0.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.1.block.0.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.1.block.0.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.0.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.0.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.1.block.0.nin_shortcut.weight.requires_grad            : True
+[2025-02-26 18:55:51] module.flow.up.1.block.0.nin_shortcut.bias.requires_grad              : True
+[2025-02-26 18:55:51] module.flow.up.1.block.1.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.1.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.1.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.1.block.1.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.1.block.1.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.1.block.1.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.1.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.1.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.1.block.2.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.2.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.2.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.1.block.2.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.1.block.2.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.1.block.2.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.2.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.block.2.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.1.upsample.conv.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.1.upsample.conv.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.2.block.0.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.0.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.0.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.2.block.0.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.2.block.0.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.2.block.0.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.0.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.0.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.2.block.1.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.1.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.1.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.2.block.1.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.2.block.1.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.2.block.1.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.1.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.1.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.2.block.2.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.2.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.2.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.2.block.2.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.2.block.2.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.2.block.2.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.2.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.block.2.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.2.upsample.conv.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.2.upsample.conv.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.3.block.0.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.0.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.0.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.3.block.0.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.3.block.0.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.3.block.0.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.0.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.0.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.3.block.1.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.1.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.1.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.3.block.1.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.3.block.1.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.3.block.1.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.1.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.1.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.3.block.2.norm1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.2.conv1.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.2.conv1.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.3.block.2.temb_proj.weight.requires_grad               : True
+[2025-02-26 18:55:51] module.flow.up.3.block.2.temb_proj.bias.requires_grad                 : True
+[2025-02-26 18:55:51] module.flow.up.3.block.2.norm2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.2.conv2.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.block.2.conv2.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.up.3.upsample.conv.weight.requires_grad                   : True
+[2025-02-26 18:55:51] module.flow.up.3.upsample.conv.bias.requires_grad                     : True
+[2025-02-26 18:55:51] module.flow.norm_out.weight.requires_grad                             : True
+[2025-02-26 18:55:51] module.flow.conv_out.weight.requires_grad                             : True
+[2025-02-26 18:55:51] module.flow.conv_out.bias.requires_grad                               : True
+[2025-02-26 18:55:51] module.t_embedder.mlp.0.weight.requires_grad                          : True
+[2025-02-26 18:55:51] module.t_embedder.mlp.0.bias.requires_grad                            : True
+[2025-02-26 18:55:51] module.t_embedder.mlp.2.weight.requires_grad                          : True
+[2025-02-26 18:55:51] module.t_embedder.mlp.2.bias.requires_grad                            : True
+[2025-02-26 18:55:51] module.y_embedder.weight.requires_grad                                : True
+[2025-02-26 18:55:51] module.y_embedder.bias.requires_grad                                  : True
+[2025-02-26 18:55:51] module.x_embedder.proj.weight.requires_grad                           : True
+[2025-02-26 18:55:51] module.x_embedder.proj.bias.requires_grad                             : True
+[2025-02-26 18:55:51] Dataset contains 1,281,168 images /data/checkpoints/LanguageBind/offline_feature/offline_sdvae_256_path/imagenet_train_256
+[2025-02-26 18:55:51] Batch size 64 per gpu, with 512 global batch size
+[2025-02-26 18:55:51] Train config: {'ckpt_path': '/data/logs/flow/flowsdvae_50kx512_lgn0p0/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_lgn0p0', '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_lgn0p0', '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': 0.0, '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 18:57:21] (step=0000050) Train Loss: 1.1712, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.56, Grad Norm: 3.3834
+[2025-02-26 18:58:19] (step=0000100) Train Loss: 1.0785, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8843
+[2025-02-26 18:59:17] (step=0000150) Train Loss: 1.0454, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4285
+[2025-02-26 19:00:16] (step=0000200) Train Loss: 1.0348, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4468
+[2025-02-26 19:01:14] (step=0000250) Train Loss: 1.0291, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4099
+[2025-02-26 19:02:13] (step=0000300) Train Loss: 1.0235, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4434
+[2025-02-26 19:03:11] (step=0000350) Train Loss: 1.0143, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4824
+[2025-02-26 19:04:10] (step=0000400) Train Loss: 1.0037, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4715
+[2025-02-26 19:05:08] (step=0000450) Train Loss: 0.9815, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5165
+[2025-02-26 19:06:07] (step=0000500) Train Loss: 0.9345, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6388
+[2025-02-26 19:07:05] (step=0000550) Train Loss: 0.8724, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9232
+[2025-02-26 19:08:04] (step=0000600) Train Loss: 0.7959, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9690
+[2025-02-26 19:09:02] (step=0000650) Train Loss: 0.7267, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1740
+[2025-02-26 19:10:01] (step=0000700) Train Loss: 0.6694, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0340
+[2025-02-26 19:10:59] (step=0000750) Train Loss: 0.5996, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.3735
+[2025-02-26 19:11:58] (step=0000800) Train Loss: 0.5374, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2352
+[2025-02-26 19:12:56] (step=0000850) Train Loss: 0.4971, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.3158
+[2025-02-26 19:13:54] (step=0000900) Train Loss: 0.4580, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4295
+[2025-02-26 19:14:53] (step=0000950) Train Loss: 0.4143, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5257
+[2025-02-26 19:15:51] (step=0001000) Train Loss: 0.3755, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4875
+[2025-02-26 19:16:50] (step=0001050) Train Loss: 0.3387, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.3632
+[2025-02-26 19:17:48] (step=0001100) Train Loss: 0.3289, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.9746
+[2025-02-26 19:18:47] (step=0001150) Train Loss: 0.2998, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4070
+[2025-02-26 19:19:45] (step=0001200) Train Loss: 0.2870, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5307
+[2025-02-26 19:20:44] (step=0001250) Train Loss: 0.2733, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5492
+[2025-02-26 19:21:42] (step=0001300) Train Loss: 0.2640, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2192
+[2025-02-26 19:22:41] (step=0001350) Train Loss: 0.2598, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5224
+[2025-02-26 19:23:39] (step=0001400) Train Loss: 0.2538, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.5951
+[2025-02-26 19:24:37] (step=0001450) Train Loss: 0.2462, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.3309
+[2025-02-26 19:25:36] (step=0001500) Train Loss: 0.2413, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4543
+[2025-02-26 19:26:34] (step=0001550) Train Loss: 0.2375, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2433
+[2025-02-26 19:27:33] (step=0001600) Train Loss: 0.2345, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.3788
+[2025-02-26 19:28:31] (step=0001650) Train Loss: 0.2312, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.3900
+[2025-02-26 19:29:30] (step=0001700) Train Loss: 0.2279, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2632
+[2025-02-26 19:30:28] (step=0001750) Train Loss: 0.2241, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.3286
+[2025-02-26 19:31:27] (step=0001800) Train Loss: 0.2218, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2956
+[2025-02-26 19:32:25] (step=0001850) Train Loss: 0.2243, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.4440
+[2025-02-26 19:33:24] (step=0001900) Train Loss: 0.2161, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1276
+[2025-02-26 19:34:22] (step=0001950) Train Loss: 0.2169, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2553
+[2025-02-26 19:35:20] (step=0002000) Train Loss: 0.2120, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1557
+[2025-02-26 19:36:19] (step=0002050) Train Loss: 0.2101, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2851
+[2025-02-26 19:37:17] (step=0002100) Train Loss: 0.2087, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2670
+[2025-02-26 19:38:16] (step=0002150) Train Loss: 0.2042, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1598
+[2025-02-26 19:39:14] (step=0002200) Train Loss: 0.2039, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1728
+[2025-02-26 19:40:13] (step=0002250) Train Loss: 0.2003, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1459
+[2025-02-26 19:41:11] (step=0002300) Train Loss: 0.2005, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2471
+[2025-02-26 19:42:10] (step=0002350) Train Loss: 0.2024, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.3747
+[2025-02-26 19:43:08] (step=0002400) Train Loss: 0.1970, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0973
+[2025-02-26 19:44:07] (step=0002450) Train Loss: 0.1930, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0713
+[2025-02-26 19:45:05] (step=0002500) Train Loss: 0.1941, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 1.1263
+[2025-02-26 19:46:06] (step=0002550) Train Loss: 0.1975, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 1.2988
+[2025-02-26 19:47:04] (step=0002600) Train Loss: 0.1906, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9962
+[2025-02-26 19:48:03] (step=0002650) Train Loss: 0.1928, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0698
+[2025-02-26 19:49:01] (step=0002700) Train Loss: 0.1897, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0794
+[2025-02-26 19:49:59] (step=0002750) Train Loss: 0.1876, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0473
+[2025-02-26 19:50:57] (step=0002800) Train Loss: 0.1846, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0281
+[2025-02-26 19:51:56] (step=0002850) Train Loss: 0.1822, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0613
+[2025-02-26 19:52:54] (step=0002900) Train Loss: 0.1801, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9453
+[2025-02-26 19:53:52] (step=0002950) Train Loss: 0.1799, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1346
+[2025-02-26 19:54:51] (step=0003000) Train Loss: 0.1814, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0882
+[2025-02-26 19:55:49] (step=0003050) Train Loss: 0.1794, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0397
+[2025-02-26 19:56:47] (step=0003100) Train Loss: 0.1757, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1289
+[2025-02-26 19:57:45] (step=0003150) Train Loss: 0.1755, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1924
+[2025-02-26 19:58:44] (step=0003200) Train Loss: 0.1796, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.2892
+[2025-02-26 19:59:42] (step=0003250) Train Loss: 0.1773, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9413
+[2025-02-26 20:00:40] (step=0003300) Train Loss: 0.1707, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0659
+[2025-02-26 20:01:39] (step=0003350) Train Loss: 0.1685, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0755
+[2025-02-26 20:02:37] (step=0003400) Train Loss: 0.1706, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0872
+[2025-02-26 20:03:35] (step=0003450) Train Loss: 0.1653, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0410
+[2025-02-26 20:04:33] (step=0003500) Train Loss: 0.1636, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9961
+[2025-02-26 20:05:32] (step=0003550) Train Loss: 0.1630, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0162
+[2025-02-26 20:06:30] (step=0003600) Train Loss: 0.1628, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0891
+[2025-02-26 20:07:28] (step=0003650) Train Loss: 0.1617, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0816
+[2025-02-26 20:08:26] (step=0003700) Train Loss: 0.1574, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9545
+[2025-02-26 20:09:25] (step=0003750) Train Loss: 0.1567, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9811
+[2025-02-26 20:10:23] (step=0003800) Train Loss: 0.1549, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9639
+[2025-02-26 20:11:21] (step=0003850) Train Loss: 0.1519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9945
+[2025-02-26 20:12:20] (step=0003900) Train Loss: 0.1498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9989
+[2025-02-26 20:13:18] (step=0003950) Train Loss: 0.1504, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.1051
+[2025-02-26 20:14:16] (step=0004000) Train Loss: 0.1485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0405
+[2025-02-26 20:15:14] (step=0004050) Train Loss: 0.1448, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9179
+[2025-02-26 20:16:13] (step=0004100) Train Loss: 0.1426, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 1.0311
+[2025-02-26 20:17:11] (step=0004150) Train Loss: 0.1388, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9055
+[2025-02-26 20:18:09] (step=0004200) Train Loss: 0.1375, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9221
+[2025-02-26 20:19:07] (step=0004250) Train Loss: 0.1361, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9913
+[2025-02-26 20:20:06] (step=0004300) Train Loss: 0.1345, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8426
+[2025-02-26 20:21:04] (step=0004350) Train Loss: 0.1331, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9036
+[2025-02-26 20:22:02] (step=0004400) Train Loss: 0.1337, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9380
+[2025-02-26 20:23:01] (step=0004450) Train Loss: 0.1295, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8791
+[2025-02-26 20:23:59] (step=0004500) Train Loss: 0.1305, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8110
+[2025-02-26 20:24:57] (step=0004550) Train Loss: 0.1272, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8539
+[2025-02-26 20:25:55] (step=0004600) Train Loss: 0.1231, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8338
+[2025-02-26 20:26:54] (step=0004650) Train Loss: 0.1209, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8962
+[2025-02-26 20:27:52] (step=0004700) Train Loss: 0.1171, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8177
+[2025-02-26 20:28:50] (step=0004750) Train Loss: 0.1141, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8777
+[2025-02-26 20:29:49] (step=0004800) Train Loss: 0.1113, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7652
+[2025-02-26 20:30:47] (step=0004850) Train Loss: 0.1101, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7868
+[2025-02-26 20:31:45] (step=0004900) Train Loss: 0.1090, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7605
+[2025-02-26 20:32:43] (step=0004950) Train Loss: 0.1075, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8023
+[2025-02-26 20:33:42] (step=0005000) Train Loss: 0.1052, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7912
+[2025-02-26 20:34:42] (step=0005050) Train Loss: 0.1052, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.83, Grad Norm: 0.7422
+[2025-02-26 20:35:40] (step=0005100) Train Loss: 0.1066, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.9091
+[2025-02-26 20:36:39] (step=0005150) Train Loss: 0.1051, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7606
+[2025-02-26 20:37:37] (step=0005200) Train Loss: 0.1035, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.8891
+[2025-02-26 20:38:35] (step=0005250) Train Loss: 0.1006, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7033
+[2025-02-26 20:39:33] (step=0005300) Train Loss: 0.0994, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7370
+[2025-02-26 20:40:32] (step=0005350) Train Loss: 0.0973, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7383
+[2025-02-26 20:41:30] (step=0005400) Train Loss: 0.0930, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7372
+[2025-02-26 20:42:28] (step=0005450) Train Loss: 0.0891, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7306
+[2025-02-26 20:43:26] (step=0005500) Train Loss: 0.0873, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7137
+[2025-02-26 20:44:25] (step=0005550) Train Loss: 0.0837, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7352
+[2025-02-26 20:45:23] (step=0005600) Train Loss: 0.0831, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6905
+[2025-02-26 20:46:21] (step=0005650) Train Loss: 0.0828, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7361
+[2025-02-26 20:47:19] (step=0005700) Train Loss: 0.0810, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7271
+[2025-02-26 20:48:18] (step=0005750) Train Loss: 0.0786, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6735
+[2025-02-26 20:49:16] (step=0005800) Train Loss: 0.0788, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7365
+[2025-02-26 20:50:14] (step=0005850) Train Loss: 0.0778, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6675
+[2025-02-26 20:51:13] (step=0005900) Train Loss: 0.0768, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6735
+[2025-02-26 20:52:11] (step=0005950) Train Loss: 0.0756, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6998
+[2025-02-26 20:53:09] (step=0006000) Train Loss: 0.0762, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.7011
+[2025-02-26 20:54:07] (step=0006050) Train Loss: 0.0756, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6587
+[2025-02-26 20:55:06] (step=0006100) Train Loss: 0.0747, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6909
+[2025-02-26 20:56:04] (step=0006150) Train Loss: 0.0739, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6749
+[2025-02-26 20:57:03] (step=0006200) Train Loss: 0.0727, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6262
+[2025-02-26 20:58:01] (step=0006250) Train Loss: 0.0728, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6812
+[2025-02-26 20:58:59] (step=0006300) Train Loss: 0.0726, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6554
+[2025-02-26 20:59:58] (step=0006350) Train Loss: 0.0722, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6504
+[2025-02-26 21:00:56] (step=0006400) Train Loss: 0.0720, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6735
+[2025-02-26 21:01:55] (step=0006450) Train Loss: 0.0709, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6433
+[2025-02-26 21:02:53] (step=0006500) Train Loss: 0.0710, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6374
+[2025-02-26 21:03:52] (step=0006550) Train Loss: 0.0712, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6318
+[2025-02-26 21:04:50] (step=0006600) Train Loss: 0.0704, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6553
+[2025-02-26 21:05:48] (step=0006650) Train Loss: 0.0696, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6206
+[2025-02-26 21:06:47] (step=0006700) Train Loss: 0.0689, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6514
+[2025-02-26 21:07:45] (step=0006750) Train Loss: 0.0689, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6268
+[2025-02-26 21:08:44] (step=0006800) Train Loss: 0.0682, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6214
+[2025-02-26 21:09:42] (step=0006850) Train Loss: 0.0683, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6303
+[2025-02-26 21:10:41] (step=0006900) Train Loss: 0.0674, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6070
+[2025-02-26 21:11:39] (step=0006950) Train Loss: 0.0678, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6468
+[2025-02-26 21:12:37] (step=0007000) Train Loss: 0.0671, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6397
+[2025-02-26 21:13:36] (step=0007050) Train Loss: 0.0674, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6237
+[2025-02-26 21:14:34] (step=0007100) Train Loss: 0.0668, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5956
+[2025-02-26 21:15:33] (step=0007150) Train Loss: 0.0673, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6148
+[2025-02-26 21:16:31] (step=0007200) Train Loss: 0.0661, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6300
+[2025-02-26 21:17:30] (step=0007250) Train Loss: 0.0666, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6339
+[2025-02-26 21:18:28] (step=0007300) Train Loss: 0.0658, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6324
+[2025-02-26 21:19:26] (step=0007350) Train Loss: 0.0660, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6031
+[2025-02-26 21:20:25] (step=0007400) Train Loss: 0.0656, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6481
+[2025-02-26 21:21:23] (step=0007450) Train Loss: 0.0653, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6270
+[2025-02-26 21:22:22] (step=0007500) Train Loss: 0.0649, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5839
+[2025-02-26 21:23:22] (step=0007550) Train Loss: 0.0643, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.83, Grad Norm: 0.6060
+[2025-02-26 21:24:21] (step=0007600) Train Loss: 0.0658, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6260
+[2025-02-26 21:25:19] (step=0007650) Train Loss: 0.0642, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6114
+[2025-02-26 21:26:17] (step=0007700) Train Loss: 0.0635, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5872
+[2025-02-26 21:27:16] (step=0007750) Train Loss: 0.0638, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6252
+[2025-02-26 21:28:14] (step=0007800) Train Loss: 0.0632, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5932
+[2025-02-26 21:29:12] (step=0007850) Train Loss: 0.0637, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6117
+[2025-02-26 21:30:11] (step=0007900) Train Loss: 0.0638, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6034
+[2025-02-26 21:31:09] (step=0007950) Train Loss: 0.0632, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6199
+[2025-02-26 21:32:07] (step=0008000) Train Loss: 0.0632, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6004
+[2025-02-26 21:33:06] (step=0008050) Train Loss: 0.0625, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6051
+[2025-02-26 21:34:04] (step=0008100) Train Loss: 0.0632, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5762
+[2025-02-26 21:35:03] (step=0008150) Train Loss: 0.0628, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6158
+[2025-02-26 21:36:01] (step=0008200) Train Loss: 0.0628, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6084
+[2025-02-26 21:36:59] (step=0008250) Train Loss: 0.0629, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5517
+[2025-02-26 21:37:58] (step=0008300) Train Loss: 0.0619, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.6010
+[2025-02-26 21:38:56] (step=0008350) Train Loss: 0.0624, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5697
+[2025-02-26 21:39:54] (step=0008400) Train Loss: 0.0619, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5912
+[2025-02-26 21:40:53] (step=0008450) Train Loss: 0.0623, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5841
+[2025-02-26 21:41:51] (step=0008500) Train Loss: 0.0617, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5837
+[2025-02-26 21:42:49] (step=0008550) Train Loss: 0.0618, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5456
+[2025-02-26 21:43:48] (step=0008600) Train Loss: 0.0618, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5906
+[2025-02-26 21:44:46] (step=0008650) Train Loss: 0.0614, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5769
+[2025-02-26 21:45:44] (step=0008700) Train Loss: 0.0613, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5734
+[2025-02-26 21:46:43] (step=0008750) Train Loss: 0.0617, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5661
+[2025-02-26 21:47:41] (step=0008800) Train Loss: 0.0609, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5857
+[2025-02-26 21:48:39] (step=0008850) Train Loss: 0.0601, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5555
+[2025-02-26 21:49:38] (step=0008900) Train Loss: 0.0608, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5593
+[2025-02-26 21:50:36] (step=0008950) Train Loss: 0.0609, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5722
+[2025-02-26 21:51:35] (step=0009000) Train Loss: 0.0604, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5581
+[2025-02-26 21:52:33] (step=0009050) Train Loss: 0.0610, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5346
+[2025-02-26 21:53:31] (step=0009100) Train Loss: 0.0608, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5494
+[2025-02-26 21:54:30] (step=0009150) Train Loss: 0.0602, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5710
+[2025-02-26 21:55:28] (step=0009200) Train Loss: 0.0599, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5724
+[2025-02-26 21:56:26] (step=0009250) Train Loss: 0.0600, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5401
+[2025-02-26 21:57:25] (step=0009300) Train Loss: 0.0593, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5459
+[2025-02-26 21:58:23] (step=0009350) Train Loss: 0.0607, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5650
+[2025-02-26 21:59:21] (step=0009400) Train Loss: 0.0602, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5730
+[2025-02-26 22:00:20] (step=0009450) Train Loss: 0.0593, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5623
+[2025-02-26 22:01:18] (step=0009500) Train Loss: 0.0586, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5424
+[2025-02-26 22:02:16] (step=0009550) Train Loss: 0.0602, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5605
+[2025-02-26 22:03:15] (step=0009600) Train Loss: 0.0590, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5285
+[2025-02-26 22:04:13] (step=0009650) Train Loss: 0.0595, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5530
+[2025-02-26 22:05:11] (step=0009700) Train Loss: 0.0584, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5492
+[2025-02-26 22:06:10] (step=0009750) Train Loss: 0.0597, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5357
+[2025-02-26 22:07:08] (step=0009800) Train Loss: 0.0593, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5554
+[2025-02-26 22:08:06] (step=0009850) Train Loss: 0.0595, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4831
+[2025-02-26 22:09:05] (step=0009900) Train Loss: 0.0590, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5406
+[2025-02-26 22:10:03] (step=0009950) Train Loss: 0.0593, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5219
+[2025-02-26 22:11:01] (step=0010000) Train Loss: 0.0599, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.5426
+[2025-02-26 22:11:04] Saved checkpoint to ../logs/flow/flowsdvae_50kx512_lgn0p0/checkpoints/0010000.pt
+[2025-02-26 22:32:49] (step=0010000), Fid=147.19808934595466, PSNR=9.814269090151786, LPIPS=0.7890625, SSIM=0.032099202275276184
+[2025-02-26 22:33:50] (step=0010050) Train Loss: 0.0592, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.04, Grad Norm: 0.5511
+[2025-02-26 22:34:48] (step=0010100) Train Loss: 0.0587, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5441
+[2025-02-26 22:35:47] (step=0010150) Train Loss: 0.0584, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5176
+[2025-02-26 22:36:45] (step=0010200) Train Loss: 0.0588, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5524
+[2025-02-26 22:37:44] (step=0010250) Train Loss: 0.0583, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5230
+[2025-02-26 22:38:42] (step=0010300) Train Loss: 0.0587, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5044
+[2025-02-26 22:39:41] (step=0010350) Train Loss: 0.0578, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5266
+[2025-02-26 22:40:39] (step=0010400) Train Loss: 0.0588, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5358
+[2025-02-26 22:41:38] (step=0010450) Train Loss: 0.0574, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5302
+[2025-02-26 22:42:36] (step=0010500) Train Loss: 0.0579, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5141
+[2025-02-26 22:43:35] (step=0010550) Train Loss: 0.0580, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5238
+[2025-02-26 22:44:33] (step=0010600) Train Loss: 0.0581, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5323
+[2025-02-26 22:45:32] (step=0010650) Train Loss: 0.0573, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5253
+[2025-02-26 22:46:30] (step=0010700) Train Loss: 0.0579, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4970
+[2025-02-26 22:47:29] (step=0010750) Train Loss: 0.0579, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5118
+[2025-02-26 22:48:27] (step=0010800) Train Loss: 0.0581, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5141
+[2025-02-26 22:49:26] (step=0010850) Train Loss: 0.0574, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4966
+[2025-02-26 22:50:24] (step=0010900) Train Loss: 0.0583, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5267
+[2025-02-26 22:51:23] (step=0010950) Train Loss: 0.0579, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5132
+[2025-02-26 22:52:21] (step=0011000) Train Loss: 0.0575, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5090
+[2025-02-26 22:53:20] (step=0011050) Train Loss: 0.0574, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4955
+[2025-02-26 22:54:18] (step=0011100) Train Loss: 0.0574, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5367
+[2025-02-26 22:55:17] (step=0011150) Train Loss: 0.0573, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5039
+[2025-02-26 22:56:15] (step=0011200) Train Loss: 0.0579, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5068
+[2025-02-26 22:57:13] (step=0011250) Train Loss: 0.0572, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5134
+[2025-02-26 22:58:12] (step=0011300) Train Loss: 0.0570, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4999
+[2025-02-26 22:59:10] (step=0011350) Train Loss: 0.0572, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5109
+[2025-02-26 23:00:09] (step=0011400) Train Loss: 0.0572, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4654
+[2025-02-26 23:01:07] (step=0011450) Train Loss: 0.0563, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4921
+[2025-02-26 23:02:06] (step=0011500) Train Loss: 0.0569, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4815
+[2025-02-26 23:03:04] (step=0011550) Train Loss: 0.0570, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5120
+[2025-02-26 23:04:03] (step=0011600) Train Loss: 0.0569, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4514
+[2025-02-26 23:05:01] (step=0011650) Train Loss: 0.0568, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5249
+[2025-02-26 23:06:00] (step=0011700) Train Loss: 0.0568, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4969
+[2025-02-26 23:06:58] (step=0011750) Train Loss: 0.0567, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4766
+[2025-02-26 23:07:57] (step=0011800) Train Loss: 0.0566, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4921
+[2025-02-26 23:08:55] (step=0011850) Train Loss: 0.0569, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4831
+[2025-02-26 23:09:54] (step=0011900) Train Loss: 0.0566, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4546
+[2025-02-26 23:10:52] (step=0011950) Train Loss: 0.0565, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5062
+[2025-02-26 23:11:51] (step=0012000) Train Loss: 0.0568, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5071
+[2025-02-26 23:12:49] (step=0012050) Train Loss: 0.0560, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4684
+[2025-02-26 23:13:48] (step=0012100) Train Loss: 0.0563, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4696
+[2025-02-26 23:14:46] (step=0012150) Train Loss: 0.0570, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4953
+[2025-02-26 23:15:45] (step=0012200) Train Loss: 0.0557, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4948
+[2025-02-26 23:16:43] (step=0012250) Train Loss: 0.0556, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4501
+[2025-02-26 23:17:42] (step=0012300) Train Loss: 0.0557, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4621
+[2025-02-26 23:18:40] (step=0012350) Train Loss: 0.0561, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4842
+[2025-02-26 23:19:39] (step=0012400) Train Loss: 0.0560, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4704
+[2025-02-26 23:20:37] (step=0012450) Train Loss: 0.0561, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.5039
+[2025-02-26 23:21:36] (step=0012500) Train Loss: 0.0558, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4681
+[2025-02-26 23:22:37] (step=0012550) Train Loss: 0.0557, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.4552
+[2025-02-26 23:23:35] (step=0012600) Train Loss: 0.0558, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4630
+[2025-02-26 23:24:34] (step=0012650) Train Loss: 0.0555, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4715
+[2025-02-26 23:25:32] (step=0012700) Train Loss: 0.0561, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4640
+[2025-02-26 23:26:31] (step=0012750) Train Loss: 0.0560, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4392
+[2025-02-26 23:27:29] (step=0012800) Train Loss: 0.0567, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4872
+[2025-02-26 23:28:28] (step=0012850) Train Loss: 0.0560, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4253
+[2025-02-26 23:29:26] (step=0012900) Train Loss: 0.0550, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4741
+[2025-02-26 23:30:25] (step=0012950) Train Loss: 0.0558, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4907
+[2025-02-26 23:31:23] (step=0013000) Train Loss: 0.0555, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4650
+[2025-02-26 23:32:22] (step=0013050) Train Loss: 0.0555, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4628
+[2025-02-26 23:33:20] (step=0013100) Train Loss: 0.0551, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4549
+[2025-02-26 23:34:18] (step=0013150) Train Loss: 0.0562, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4682
+[2025-02-26 23:35:17] (step=0013200) Train Loss: 0.0553, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4391
+[2025-02-26 23:36:15] (step=0013250) Train Loss: 0.0546, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4447
+[2025-02-26 23:37:14] (step=0013300) Train Loss: 0.0550, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4743
+[2025-02-26 23:38:12] (step=0013350) Train Loss: 0.0554, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4542
+[2025-02-26 23:39:11] (step=0013400) Train Loss: 0.0553, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4480
+[2025-02-26 23:40:09] (step=0013450) Train Loss: 0.0552, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4412
+[2025-02-26 23:41:08] (step=0013500) Train Loss: 0.0545, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4450
+[2025-02-26 23:42:06] (step=0013550) Train Loss: 0.0549, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4857
+[2025-02-26 23:43:05] (step=0013600) Train Loss: 0.0550, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4142
+[2025-02-26 23:44:03] (step=0013650) Train Loss: 0.0552, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4722
+[2025-02-26 23:45:02] (step=0013700) Train Loss: 0.0556, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4378
+[2025-02-26 23:46:00] (step=0013750) Train Loss: 0.0552, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4685
+[2025-02-26 23:46:58] (step=0013800) Train Loss: 0.0550, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4647
+[2025-02-26 23:47:57] (step=0013850) Train Loss: 0.0548, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4333
+[2025-02-26 23:48:55] (step=0013900) Train Loss: 0.0547, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4207
+[2025-02-26 23:49:54] (step=0013950) Train Loss: 0.0553, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4822
+[2025-02-26 23:50:52] (step=0014000) Train Loss: 0.0541, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4158
+[2025-02-26 23:51:51] (step=0014050) Train Loss: 0.0547, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4406
+[2025-02-26 23:52:49] (step=0014100) Train Loss: 0.0552, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4389
+[2025-02-26 23:53:48] (step=0014150) Train Loss: 0.0547, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4432
+[2025-02-26 23:54:46] (step=0014200) Train Loss: 0.0548, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4327
+[2025-02-26 23:55:45] (step=0014250) Train Loss: 0.0541, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4662
+[2025-02-26 23:56:43] (step=0014300) Train Loss: 0.0548, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4318
+[2025-02-26 23:57:42] (step=0014350) Train Loss: 0.0543, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4463
+[2025-02-26 23:58:40] (step=0014400) Train Loss: 0.0538, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4424
+[2025-02-26 23:59:38] (step=0014450) Train Loss: 0.0549, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4235
+[2025-02-27 00:00:37] (step=0014500) Train Loss: 0.0545, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4409
+[2025-02-27 00:01:35] (step=0014550) Train Loss: 0.0540, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4204
+[2025-02-27 00:02:34] (step=0014600) Train Loss: 0.0549, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4324
+[2025-02-27 00:03:32] (step=0014650) Train Loss: 0.0547, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4357
+[2025-02-27 00:04:31] (step=0014700) Train Loss: 0.0541, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4478
+[2025-02-27 00:05:29] (step=0014750) Train Loss: 0.0548, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4029
+[2025-02-27 00:06:28] (step=0014800) Train Loss: 0.0543, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4258
+[2025-02-27 00:07:26] (step=0014850) Train Loss: 0.0549, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4319
+[2025-02-27 00:08:25] (step=0014900) Train Loss: 0.0541, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3889
+[2025-02-27 00:09:23] (step=0014950) Train Loss: 0.0545, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4482
+[2025-02-27 00:10:22] (step=0015000) Train Loss: 0.0539, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4482
+[2025-02-27 00:11:22] (step=0015050) Train Loss: 0.0545, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.4191
+[2025-02-27 00:12:21] (step=0015100) Train Loss: 0.0535, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4312
+[2025-02-27 00:13:19] (step=0015150) Train Loss: 0.0541, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4535
+[2025-02-27 00:14:18] (step=0015200) Train Loss: 0.0546, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4143
+[2025-02-27 00:15:16] (step=0015250) Train Loss: 0.0540, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4395
+[2025-02-27 00:16:15] (step=0015300) Train Loss: 0.0542, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4167
+[2025-02-27 00:17:13] (step=0015350) Train Loss: 0.0541, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4339
+[2025-02-27 00:18:12] (step=0015400) Train Loss: 0.0534, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4018
+[2025-02-27 00:19:11] (step=0015450) Train Loss: 0.0542, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4375
+[2025-02-27 00:20:09] (step=0015500) Train Loss: 0.0536, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4176
+[2025-02-27 00:21:08] (step=0015550) Train Loss: 0.0546, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4414
+[2025-02-27 00:22:06] (step=0015600) Train Loss: 0.0540, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4406
+[2025-02-27 00:23:05] (step=0015650) Train Loss: 0.0537, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4265
+[2025-02-27 00:24:03] (step=0015700) Train Loss: 0.0536, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4139
+[2025-02-27 00:25:02] (step=0015750) Train Loss: 0.0539, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4116
+[2025-02-27 00:26:00] (step=0015800) Train Loss: 0.0543, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4265
+[2025-02-27 00:26:59] (step=0015850) Train Loss: 0.0540, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4320
+[2025-02-27 00:27:57] (step=0015900) Train Loss: 0.0537, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4299
+[2025-02-27 00:28:56] (step=0015950) Train Loss: 0.0538, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3717
+[2025-02-27 00:29:54] (step=0016000) Train Loss: 0.0540, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4067
+[2025-02-27 00:30:53] (step=0016050) Train Loss: 0.0533, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4328
+[2025-02-27 00:31:51] (step=0016100) Train Loss: 0.0541, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4099
+[2025-02-27 00:32:50] (step=0016150) Train Loss: 0.0536, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4239
+[2025-02-27 00:33:48] (step=0016200) Train Loss: 0.0543, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4117
+[2025-02-27 00:34:46] (step=0016250) Train Loss: 0.0530, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4217
+[2025-02-27 00:35:45] (step=0016300) Train Loss: 0.0537, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3926
+[2025-02-27 00:36:43] (step=0016350) Train Loss: 0.0539, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4342
+[2025-02-27 00:37:42] (step=0016400) Train Loss: 0.0530, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4114
+[2025-02-27 00:38:40] (step=0016450) Train Loss: 0.0536, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4008
+[2025-02-27 00:39:39] (step=0016500) Train Loss: 0.0538, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4025
+[2025-02-27 00:40:37] (step=0016550) Train Loss: 0.0533, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4214
+[2025-02-27 00:41:36] (step=0016600) Train Loss: 0.0539, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4075
+[2025-02-27 00:42:34] (step=0016650) Train Loss: 0.0539, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4134
+[2025-02-27 00:43:33] (step=0016700) Train Loss: 0.0536, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4201
+[2025-02-27 00:44:31] (step=0016750) Train Loss: 0.0530, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4346
+[2025-02-27 00:45:29] (step=0016800) Train Loss: 0.0532, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4123
+[2025-02-27 00:46:28] (step=0016850) Train Loss: 0.0540, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4097
+[2025-02-27 00:47:26] (step=0016900) Train Loss: 0.0533, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3645
+[2025-02-27 00:48:25] (step=0016950) Train Loss: 0.0536, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4128
+[2025-02-27 00:49:23] (step=0017000) Train Loss: 0.0533, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3862
+[2025-02-27 00:50:22] (step=0017050) Train Loss: 0.0533, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4216
+[2025-02-27 00:51:20] (step=0017100) Train Loss: 0.0536, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3717
+[2025-02-27 00:52:19] (step=0017150) Train Loss: 0.0531, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3964
+[2025-02-27 00:53:17] (step=0017200) Train Loss: 0.0536, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3966
+[2025-02-27 00:54:16] (step=0017250) Train Loss: 0.0531, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4084
+[2025-02-27 00:55:14] (step=0017300) Train Loss: 0.0530, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3884
+[2025-02-27 00:56:13] (step=0017350) Train Loss: 0.0527, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.4024
+[2025-02-27 00:57:11] (step=0017400) Train Loss: 0.0535, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3898
+[2025-02-27 00:58:10] (step=0017450) Train Loss: 0.0535, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3836
+[2025-02-27 00:59:08] (step=0017500) Train Loss: 0.0529, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4058
+[2025-02-27 01:00:09] (step=0017550) Train Loss: 0.0530, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.3930
+[2025-02-27 01:01:07] (step=0017600) Train Loss: 0.0527, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4064
+[2025-02-27 01:02:06] (step=0017650) Train Loss: 0.0533, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4000
+[2025-02-27 01:03:04] (step=0017700) Train Loss: 0.0527, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3927
+[2025-02-27 01:04:03] (step=0017750) Train Loss: 0.0533, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3922
+[2025-02-27 01:05:01] (step=0017800) Train Loss: 0.0527, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3940
+[2025-02-27 01:06:00] (step=0017850) Train Loss: 0.0533, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3943
+[2025-02-27 01:06:58] (step=0017900) Train Loss: 0.0531, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3714
+[2025-02-27 01:07:57] (step=0017950) Train Loss: 0.0533, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3747
+[2025-02-27 01:08:55] (step=0018000) Train Loss: 0.0534, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3992
+[2025-02-27 01:09:54] (step=0018050) Train Loss: 0.0535, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3925
+[2025-02-27 01:10:52] (step=0018100) Train Loss: 0.0528, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3925
+[2025-02-27 01:11:51] (step=0018150) Train Loss: 0.0536, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3997
+[2025-02-27 01:12:49] (step=0018200) Train Loss: 0.0530, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3762
+[2025-02-27 01:13:48] (step=0018250) Train Loss: 0.0529, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3966
+[2025-02-27 01:14:46] (step=0018300) Train Loss: 0.0531, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3990
+[2025-02-27 01:15:45] (step=0018350) Train Loss: 0.0533, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3994
+[2025-02-27 01:16:44] (step=0018400) Train Loss: 0.0524, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3957
+[2025-02-27 01:17:42] (step=0018450) Train Loss: 0.0535, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3792
+[2025-02-27 01:18:41] (step=0018500) Train Loss: 0.0527, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3624
+[2025-02-27 01:19:39] (step=0018550) Train Loss: 0.0525, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3855
+[2025-02-27 01:20:38] (step=0018600) Train Loss: 0.0531, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3911
+[2025-02-27 01:21:36] (step=0018650) Train Loss: 0.0524, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3676
+[2025-02-27 01:22:35] (step=0018700) Train Loss: 0.0522, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.4018
+[2025-02-27 01:23:33] (step=0018750) Train Loss: 0.0524, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3829
+[2025-02-27 01:24:32] (step=0018800) Train Loss: 0.0524, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3795
+[2025-02-27 01:25:30] (step=0018850) Train Loss: 0.0526, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3644
+[2025-02-27 01:26:29] (step=0018900) Train Loss: 0.0522, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3762
+[2025-02-27 01:27:27] (step=0018950) Train Loss: 0.0531, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3958
+[2025-02-27 01:28:26] (step=0019000) Train Loss: 0.0530, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3913
+[2025-02-27 01:29:24] (step=0019050) Train Loss: 0.0527, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3816
+[2025-02-27 01:30:23] (step=0019100) Train Loss: 0.0525, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3818
+[2025-02-27 01:31:21] (step=0019150) Train Loss: 0.0527, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3505
+[2025-02-27 01:32:19] (step=0019200) Train Loss: 0.0526, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3879
+[2025-02-27 01:33:18] (step=0019250) Train Loss: 0.0521, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3536
+[2025-02-27 01:34:16] (step=0019300) Train Loss: 0.0526, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3752
+[2025-02-27 01:35:15] (step=0019350) Train Loss: 0.0525, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3590
+[2025-02-27 01:36:13] (step=0019400) Train Loss: 0.0526, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3926
+[2025-02-27 01:37:12] (step=0019450) Train Loss: 0.0528, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3661
+[2025-02-27 01:38:10] (step=0019500) Train Loss: 0.0517, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3737
+[2025-02-27 01:39:08] (step=0019550) Train Loss: 0.0522, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3862
+[2025-02-27 01:40:07] (step=0019600) Train Loss: 0.0521, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3557
+[2025-02-27 01:41:05] (step=0019650) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3846
+[2025-02-27 01:42:04] (step=0019700) Train Loss: 0.0525, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3692
+[2025-02-27 01:43:02] (step=0019750) Train Loss: 0.0517, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3706
+[2025-02-27 01:44:01] (step=0019800) Train Loss: 0.0521, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3798
+[2025-02-27 01:44:59] (step=0019850) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3774
+[2025-02-27 01:45:57] (step=0019900) Train Loss: 0.0520, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3640
+[2025-02-27 01:46:56] (step=0019950) Train Loss: 0.0521, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3508
+[2025-02-27 01:47:54] (step=0020000) Train Loss: 0.0528, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3621
+[2025-02-27 01:47:56] Saved checkpoint to ../logs/flow/flowsdvae_50kx512_lgn0p0/checkpoints/0020000.pt
+[2025-02-27 02:06:23] (step=0020000), Fid=31.314535057803937, PSNR=16.30665199303627, LPIPS=0.57421875, SSIM=0.14990435540676117
+[2025-02-27 02:07:24] (step=0020050) Train Loss: 0.0521, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.04, Grad Norm: 0.3650
+[2025-02-27 02:08:23] (step=0020100) Train Loss: 0.0528, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3689
+[2025-02-27 02:09:21] (step=0020150) Train Loss: 0.0522, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3556
+[2025-02-27 02:10:20] (step=0020200) Train Loss: 0.0518, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3836
+[2025-02-27 02:11:18] (step=0020250) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3404
+[2025-02-27 02:12:17] (step=0020300) Train Loss: 0.0523, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3829
+[2025-02-27 02:13:15] (step=0020350) Train Loss: 0.0523, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3651
+[2025-02-27 02:14:14] (step=0020400) Train Loss: 0.0522, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3822
+[2025-02-27 02:15:12] (step=0020450) Train Loss: 0.0522, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3753
+[2025-02-27 02:16:11] (step=0020500) Train Loss: 0.0520, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3554
+[2025-02-27 02:17:09] (step=0020550) Train Loss: 0.0523, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3580
+[2025-02-27 02:18:07] (step=0020600) Train Loss: 0.0522, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3752
+[2025-02-27 02:19:06] (step=0020650) Train Loss: 0.0521, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3497
+[2025-02-27 02:20:04] (step=0020700) Train Loss: 0.0518, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3722
+[2025-02-27 02:21:03] (step=0020750) Train Loss: 0.0513, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3702
+[2025-02-27 02:22:01] (step=0020800) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3828
+[2025-02-27 02:23:00] (step=0020850) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3525
+[2025-02-27 02:23:58] (step=0020900) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3682
+[2025-02-27 02:24:57] (step=0020950) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3516
+[2025-02-27 02:25:55] (step=0021000) Train Loss: 0.0524, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3759
+[2025-02-27 02:26:54] (step=0021050) Train Loss: 0.0515, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3599
+[2025-02-27 02:27:52] (step=0021100) Train Loss: 0.0524, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3393
+[2025-02-27 02:28:51] (step=0021150) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3654
+[2025-02-27 02:29:49] (step=0021200) Train Loss: 0.0514, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3628
+[2025-02-27 02:30:48] (step=0021250) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3791
+[2025-02-27 02:31:46] (step=0021300) Train Loss: 0.0513, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3467
+[2025-02-27 02:32:45] (step=0021350) Train Loss: 0.0521, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3455
+[2025-02-27 02:33:43] (step=0021400) Train Loss: 0.0521, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3645
+[2025-02-27 02:34:42] (step=0021450) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3590
+[2025-02-27 02:35:40] (step=0021500) Train Loss: 0.0518, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3694
+[2025-02-27 02:36:39] (step=0021550) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3311
+[2025-02-27 02:37:37] (step=0021600) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3717
+[2025-02-27 02:38:36] (step=0021650) Train Loss: 0.0521, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3685
+[2025-02-27 02:39:34] (step=0021700) Train Loss: 0.0515, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3414
+[2025-02-27 02:40:32] (step=0021750) Train Loss: 0.0523, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.3491
+[2025-02-27 02:41:31] (step=0021800) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3632
+[2025-02-27 02:42:29] (step=0021850) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3429
+[2025-02-27 02:43:28] (step=0021900) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3648
+[2025-02-27 02:44:26] (step=0021950) Train Loss: 0.0520, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3622
+[2025-02-27 02:45:25] (step=0022000) Train Loss: 0.0513, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3576
+[2025-02-27 02:46:23] (step=0022050) Train Loss: 0.0519, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3747
+[2025-02-27 02:47:22] (step=0022100) Train Loss: 0.0513, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3206
+[2025-02-27 02:48:20] (step=0022150) Train Loss: 0.0518, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3483
+[2025-02-27 02:49:19] (step=0022200) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3733
+[2025-02-27 02:50:17] (step=0022250) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3384
+[2025-02-27 02:51:16] (step=0022300) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3668
+[2025-02-27 02:52:14] (step=0022350) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3402
+[2025-02-27 02:53:13] (step=0022400) Train Loss: 0.0518, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3395
+[2025-02-27 02:54:11] (step=0022450) Train Loss: 0.0520, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3459
+[2025-02-27 02:55:10] (step=0022500) Train Loss: 0.0512, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3345
+[2025-02-27 02:56:11] (step=0022550) Train Loss: 0.0514, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.3315
+[2025-02-27 02:57:09] (step=0022600) Train Loss: 0.0508, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3429
+[2025-02-27 02:58:08] (step=0022650) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3425
+[2025-02-27 02:59:06] (step=0022700) Train Loss: 0.0518, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3735
+[2025-02-27 03:00:05] (step=0022750) Train Loss: 0.0513, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3220
+[2025-02-27 03:01:03] (step=0022800) Train Loss: 0.0512, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3529
+[2025-02-27 03:02:02] (step=0022850) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3250
+[2025-02-27 03:03:00] (step=0022900) Train Loss: 0.0517, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3557
+[2025-02-27 03:03:59] (step=0022950) Train Loss: 0.0518, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3427
+[2025-02-27 03:04:57] (step=0023000) Train Loss: 0.0512, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3463
+[2025-02-27 03:05:56] (step=0023050) Train Loss: 0.0517, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3478
+[2025-02-27 03:06:54] (step=0023100) Train Loss: 0.0520, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3636
+[2025-02-27 03:07:53] (step=0023150) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3326
+[2025-02-27 03:08:51] (step=0023200) Train Loss: 0.0518, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3057
+[2025-02-27 03:09:50] (step=0023250) Train Loss: 0.0518, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3454
+[2025-02-27 03:10:48] (step=0023300) Train Loss: 0.0521, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3471
+[2025-02-27 03:11:47] (step=0023350) Train Loss: 0.0515, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3348
+[2025-02-27 03:12:45] (step=0023400) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3640
+[2025-02-27 03:13:44] (step=0023450) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3477
+[2025-02-27 03:14:42] (step=0023500) Train Loss: 0.0514, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3324
+[2025-02-27 03:15:41] (step=0023550) Train Loss: 0.0515, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3395
+[2025-02-27 03:16:39] (step=0023600) Train Loss: 0.0514, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3297
+[2025-02-27 03:17:38] (step=0023650) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3560
+[2025-02-27 03:18:36] (step=0023700) Train Loss: 0.0512, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3287
+[2025-02-27 03:19:35] (step=0023750) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3486
+[2025-02-27 03:20:33] (step=0023800) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3397
+[2025-02-27 03:21:32] (step=0023850) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3363
+[2025-02-27 03:22:30] (step=0023900) Train Loss: 0.0512, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3089
+[2025-02-27 03:23:29] (step=0023950) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3500
+[2025-02-27 03:24:27] (step=0024000) Train Loss: 0.0508, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3029
+[2025-02-27 03:25:26] (step=0024050) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3473
+[2025-02-27 03:26:24] (step=0024100) Train Loss: 0.0515, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3236
+[2025-02-27 03:27:23] (step=0024150) Train Loss: 0.0515, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3583
+[2025-02-27 03:28:21] (step=0024200) Train Loss: 0.0509, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3241
+[2025-02-27 03:29:20] (step=0024250) Train Loss: 0.0516, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3250
+[2025-02-27 03:30:18] (step=0024300) Train Loss: 0.0512, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3318
+[2025-02-27 03:31:17] (step=0024350) Train Loss: 0.0515, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3554
+[2025-02-27 03:32:15] (step=0024400) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3191
+[2025-02-27 03:33:14] (step=0024450) Train Loss: 0.0515, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3342
+[2025-02-27 03:34:12] (step=0024500) Train Loss: 0.0518, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3247
+[2025-02-27 03:35:11] (step=0024550) Train Loss: 0.0515, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3352
+[2025-02-27 03:36:09] (step=0024600) Train Loss: 0.0514, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3264
+[2025-02-27 03:37:08] (step=0024650) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3308
+[2025-02-27 03:38:06] (step=0024700) Train Loss: 0.0509, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3567
+[2025-02-27 03:39:05] (step=0024750) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3397
+[2025-02-27 03:40:03] (step=0024800) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3301
+[2025-02-27 03:41:02] (step=0024850) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3367
+[2025-02-27 03:42:00] (step=0024900) Train Loss: 0.0512, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3354
+[2025-02-27 03:42:59] (step=0024950) Train Loss: 0.0515, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3130
+[2025-02-27 03:43:57] (step=0025000) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3356
+[2025-02-27 03:44:58] (step=0025050) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.3235
+[2025-02-27 03:45:56] (step=0025100) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3407
+[2025-02-27 03:46:55] (step=0025150) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3146
+[2025-02-27 03:47:53] (step=0025200) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3274
+[2025-02-27 03:48:52] (step=0025250) Train Loss: 0.0504, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3353
+[2025-02-27 03:49:50] (step=0025300) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3256
+[2025-02-27 03:50:49] (step=0025350) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3196
+[2025-02-27 03:51:47] (step=0025400) Train Loss: 0.0513, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3394
+[2025-02-27 03:52:46] (step=0025450) Train Loss: 0.0512, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3240
+[2025-02-27 03:53:44] (step=0025500) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3340
+[2025-02-27 03:54:43] (step=0025550) Train Loss: 0.0509, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3361
+[2025-02-27 03:55:42] (step=0025600) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3288
+[2025-02-27 03:56:40] (step=0025650) Train Loss: 0.0514, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3189
+[2025-02-27 03:57:39] (step=0025700) Train Loss: 0.0514, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3307
+[2025-02-27 03:58:37] (step=0025750) Train Loss: 0.0509, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3136
+[2025-02-27 03:59:36] (step=0025800) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3175
+[2025-02-27 04:00:34] (step=0025850) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3247
+[2025-02-27 04:01:33] (step=0025900) Train Loss: 0.0504, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3214
+[2025-02-27 04:02:31] (step=0025950) Train Loss: 0.0509, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3165
+[2025-02-27 04:03:30] (step=0026000) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3345
+[2025-02-27 04:04:28] (step=0026050) Train Loss: 0.0513, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3256
+[2025-02-27 04:05:27] (step=0026100) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2959
+[2025-02-27 04:06:25] (step=0026150) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3168
+[2025-02-27 04:07:24] (step=0026200) Train Loss: 0.0509, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2917
+[2025-02-27 04:08:22] (step=0026250) Train Loss: 0.0508, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3251
+[2025-02-27 04:09:21] (step=0026300) Train Loss: 0.0514, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3277
+[2025-02-27 04:10:20] (step=0026350) Train Loss: 0.0508, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2936
+[2025-02-27 04:11:18] (step=0026400) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3204
+[2025-02-27 04:12:17] (step=0026450) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3154
+[2025-02-27 04:13:15] (step=0026500) Train Loss: 0.0509, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3209
+[2025-02-27 04:14:14] (step=0026550) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3137
+[2025-02-27 04:15:12] (step=0026600) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3375
+[2025-02-27 04:16:11] (step=0026650) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3163
+[2025-02-27 04:17:09] (step=0026700) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3024
+[2025-02-27 04:18:08] (step=0026750) Train Loss: 0.0509, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3126
+[2025-02-27 04:19:06] (step=0026800) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3318
+[2025-02-27 04:20:05] (step=0026850) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3141
+[2025-02-27 04:21:03] (step=0026900) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3564
+[2025-02-27 04:22:02] (step=0026950) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3034
+[2025-02-27 04:23:00] (step=0027000) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3025
+[2025-02-27 04:23:59] (step=0027050) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3308
+[2025-02-27 04:24:57] (step=0027100) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3277
+[2025-02-27 04:25:56] (step=0027150) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2972
+[2025-02-27 04:26:54] (step=0027200) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3127
+[2025-02-27 04:27:53] (step=0027250) Train Loss: 0.0509, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3104
+[2025-02-27 04:28:52] (step=0027300) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3152
+[2025-02-27 04:29:50] (step=0027350) Train Loss: 0.0508, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3259
+[2025-02-27 04:30:49] (step=0027400) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3030
+[2025-02-27 04:31:47] (step=0027450) Train Loss: 0.0504, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3020
+[2025-02-27 04:32:46] (step=0027500) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3250
+[2025-02-27 04:33:46] (step=0027550) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.3212
+[2025-02-27 04:34:45] (step=0027600) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2979
+[2025-02-27 04:35:44] (step=0027650) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3161
+[2025-02-27 04:36:42] (step=0027700) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3295
+[2025-02-27 04:37:41] (step=0027750) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2962
+[2025-02-27 04:38:39] (step=0027800) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3085
+[2025-02-27 04:39:38] (step=0027850) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3104
+[2025-02-27 04:40:36] (step=0027900) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3222
+[2025-02-27 04:41:35] (step=0027950) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3141
+[2025-02-27 04:42:33] (step=0028000) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3319
+[2025-02-27 04:43:32] (step=0028050) Train Loss: 0.0510, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3069
+[2025-02-27 04:44:30] (step=0028100) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2942
+[2025-02-27 04:45:29] (step=0028150) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3101
+[2025-02-27 04:46:27] (step=0028200) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3053
+[2025-02-27 04:47:26] (step=0028250) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3061
+[2025-02-27 04:48:24] (step=0028300) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3049
+[2025-02-27 04:49:23] (step=0028350) Train Loss: 0.0509, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2960
+[2025-02-27 04:50:21] (step=0028400) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3085
+[2025-02-27 04:51:20] (step=0028450) Train Loss: 0.0508, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3066
+[2025-02-27 04:52:18] (step=0028500) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3039
+[2025-02-27 04:53:17] (step=0028550) Train Loss: 0.0504, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3173
+[2025-02-27 04:54:16] (step=0028600) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2721
+[2025-02-27 04:55:14] (step=0028650) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3131
+[2025-02-27 04:56:13] (step=0028700) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3097
+[2025-02-27 04:57:11] (step=0028750) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2990
+[2025-02-27 04:58:10] (step=0028800) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2922
+[2025-02-27 04:59:08] (step=0028850) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3150
+[2025-02-27 05:00:07] (step=0028900) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2954
+[2025-02-27 05:01:05] (step=0028950) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2806
+[2025-02-27 05:02:04] (step=0029000) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3141
+[2025-02-27 05:03:02] (step=0029050) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2997
+[2025-02-27 05:04:01] (step=0029100) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3059
+[2025-02-27 05:04:59] (step=0029150) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3064
+[2025-02-27 05:05:58] (step=0029200) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2864
+[2025-02-27 05:06:56] (step=0029250) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3250
+[2025-02-27 05:07:55] (step=0029300) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2776
+[2025-02-27 05:08:53] (step=0029350) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2921
+[2025-02-27 05:09:52] (step=0029400) Train Loss: 0.0504, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3051
+[2025-02-27 05:10:50] (step=0029450) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3163
+[2025-02-27 05:11:49] (step=0029500) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2850
+[2025-02-27 05:12:47] (step=0029550) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2964
+[2025-02-27 05:13:46] (step=0029600) Train Loss: 0.0511, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2946
+[2025-02-27 05:14:44] (step=0029650) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2977
+[2025-02-27 05:15:43] (step=0029700) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3063
+[2025-02-27 05:16:41] (step=0029750) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2928
+[2025-02-27 05:17:40] (step=0029800) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3346
+[2025-02-27 05:18:38] (step=0029850) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2954
+[2025-02-27 05:19:37] (step=0029900) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2931
+[2025-02-27 05:20:35] (step=0029950) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2878
+[2025-02-27 05:21:34] (step=0030000) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3003
+[2025-02-27 05:21:37] Saved checkpoint to ../logs/flow/flowsdvae_50kx512_lgn0p0/checkpoints/0030000.pt
+[2025-02-27 05:39:58] (step=0030000), Fid=10.225657702291073, PSNR=21.88112188682556, LPIPS=0.3828125, SSIM=0.4094317555427551
+[2025-02-27 05:40:59] (step=0030050) Train Loss: 0.0504, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.04, Grad Norm: 0.3184
+[2025-02-27 05:41:58] (step=0030100) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2858
+[2025-02-27 05:42:56] (step=0030150) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2722
+[2025-02-27 05:43:55] (step=0030200) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3159
+[2025-02-27 05:44:54] (step=0030250) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2901
+[2025-02-27 05:45:52] (step=0030300) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2933
+[2025-02-27 05:46:51] (step=0030350) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3000
+[2025-02-27 05:47:49] (step=0030400) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2791
+[2025-02-27 05:48:48] (step=0030450) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2989
+[2025-02-27 05:49:46] (step=0030500) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3079
+[2025-02-27 05:50:45] (step=0030550) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2997
+[2025-02-27 05:51:44] (step=0030600) Train Loss: 0.0506, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2882
+[2025-02-27 05:52:42] (step=0030650) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2991
+[2025-02-27 05:53:41] (step=0030700) Train Loss: 0.0507, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2926
+[2025-02-27 05:54:39] (step=0030750) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2882
+[2025-02-27 05:55:38] (step=0030800) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2741
+[2025-02-27 05:56:36] (step=0030850) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2839
+[2025-02-27 05:57:35] (step=0030900) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2725
+[2025-02-27 05:58:33] (step=0030950) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3053
+[2025-02-27 05:59:32] (step=0031000) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3046
+[2025-02-27 06:00:30] (step=0031050) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3040
+[2025-02-27 06:01:29] (step=0031100) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2895
+[2025-02-27 06:02:27] (step=0031150) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2944
+[2025-02-27 06:03:26] (step=0031200) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3010
+[2025-02-27 06:04:24] (step=0031250) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2828
+[2025-02-27 06:05:23] (step=0031300) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2856
+[2025-02-27 06:06:21] (step=0031350) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2883
+[2025-02-27 06:07:20] (step=0031400) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3147
+[2025-02-27 06:08:18] (step=0031450) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2896
+[2025-02-27 06:09:17] (step=0031500) Train Loss: 0.0503, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2895
+[2025-02-27 06:10:15] (step=0031550) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2966
+[2025-02-27 06:11:14] (step=0031600) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2835
+[2025-02-27 06:12:13] (step=0031650) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2852
+[2025-02-27 06:13:11] (step=0031700) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2837
+[2025-02-27 06:14:10] (step=0031750) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2960
+[2025-02-27 06:15:08] (step=0031800) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2950
+[2025-02-27 06:16:07] (step=0031850) Train Loss: 0.0505, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2871
+[2025-02-27 06:17:05] (step=0031900) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3023
+[2025-02-27 06:18:04] (step=0031950) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2805
+[2025-02-27 06:19:02] (step=0032000) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3053
+[2025-02-27 06:20:01] (step=0032050) Train Loss: 0.0504, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2837
+[2025-02-27 06:20:59] (step=0032100) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2921
+[2025-02-27 06:21:58] (step=0032150) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2875
+[2025-02-27 06:22:56] (step=0032200) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2793
+[2025-02-27 06:23:55] (step=0032250) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3026
+[2025-02-27 06:24:53] (step=0032300) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2568
+[2025-02-27 06:25:52] (step=0032350) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3005
+[2025-02-27 06:26:50] (step=0032400) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2746
+[2025-02-27 06:27:49] (step=0032450) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2864
+[2025-02-27 06:28:47] (step=0032500) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2869
+[2025-02-27 06:29:48] (step=0032550) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.2933
+[2025-02-27 06:30:47] (step=0032600) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2605
+[2025-02-27 06:31:45] (step=0032650) Train Loss: 0.0504, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2897
+[2025-02-27 06:32:44] (step=0032700) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3000
+[2025-02-27 06:33:43] (step=0032750) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2708
+[2025-02-27 06:34:41] (step=0032800) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2946
+[2025-02-27 06:35:40] (step=0032850) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2517
+[2025-02-27 06:36:38] (step=0032900) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2840
+[2025-02-27 06:37:37] (step=0032950) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2976
+[2025-02-27 06:38:35] (step=0033000) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2959
+[2025-02-27 06:39:34] (step=0033050) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2956
+[2025-02-27 06:40:33] (step=0033100) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2800
+[2025-02-27 06:41:31] (step=0033150) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2714
+[2025-02-27 06:42:30] (step=0033200) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2849
+[2025-02-27 06:43:28] (step=0033250) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2638
+[2025-02-27 06:44:27] (step=0033300) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3013
+[2025-02-27 06:45:26] (step=0033350) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2840
+[2025-02-27 06:46:24] (step=0033400) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3057
+[2025-02-27 06:47:23] (step=0033450) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2585
+[2025-02-27 06:48:22] (step=0033500) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2742
+[2025-02-27 06:49:20] (step=0033550) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2816
+[2025-02-27 06:50:19] (step=0033600) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2782
+[2025-02-27 06:51:17] (step=0033650) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2661
+[2025-02-27 06:52:16] (step=0033700) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2915
+[2025-02-27 06:53:15] (step=0033750) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2641
+[2025-02-27 06:54:13] (step=0033800) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3030
+[2025-02-27 06:55:12] (step=0033850) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2613
+[2025-02-27 06:56:11] (step=0033900) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2873
+[2025-02-27 06:57:09] (step=0033950) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2732
+[2025-02-27 06:58:08] (step=0034000) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2973
+[2025-02-27 06:59:06] (step=0034050) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2529
+[2025-02-27 07:00:05] (step=0034100) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2963
+[2025-02-27 07:01:04] (step=0034150) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2541
+[2025-02-27 07:02:02] (step=0034200) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2987
+[2025-02-27 07:03:01] (step=0034250) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2651
+[2025-02-27 07:03:59] (step=0034300) Train Loss: 0.0501, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2857
+[2025-02-27 07:04:58] (step=0034350) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2814
+[2025-02-27 07:05:57] (step=0034400) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2667
+[2025-02-27 07:06:55] (step=0034450) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2961
+[2025-02-27 07:07:54] (step=0034500) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2522
+[2025-02-27 07:08:52] (step=0034550) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2613
+[2025-02-27 07:09:51] (step=0034600) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2914
+[2025-02-27 07:10:50] (step=0034650) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2825
+[2025-02-27 07:11:48] (step=0034700) Train Loss: 0.0502, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2786
+[2025-02-27 07:12:47] (step=0034750) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2743
+[2025-02-27 07:13:46] (step=0034800) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2904
+[2025-02-27 07:14:44] (step=0034850) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2789
+[2025-02-27 07:15:43] (step=0034900) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2873
+[2025-02-27 07:16:41] (step=0034950) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2606
+[2025-02-27 07:17:40] (step=0035000) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.3020
+[2025-02-27 07:18:41] (step=0035050) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.2706
+[2025-02-27 07:19:39] (step=0035100) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2693
+[2025-02-27 07:20:38] (step=0035150) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2769
+[2025-02-27 07:21:36] (step=0035200) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2612
+[2025-02-27 07:22:35] (step=0035250) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2706
+[2025-02-27 07:23:34] (step=0035300) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2715
+[2025-02-27 07:24:32] (step=0035350) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2802
+[2025-02-27 07:25:31] (step=0035400) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2882
+[2025-02-27 07:26:29] (step=0035450) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2654
+[2025-02-27 07:27:28] (step=0035500) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2746
+[2025-02-27 07:28:26] (step=0035550) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2809
+[2025-02-27 07:29:25] (step=0035600) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2754
+[2025-02-27 07:30:23] (step=0035650) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2829
+[2025-02-27 07:31:22] (step=0035700) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2790
+[2025-02-27 07:32:21] (step=0035750) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2550
+[2025-02-27 07:33:19] (step=0035800) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2852
+[2025-02-27 07:34:18] (step=0035850) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2698
+[2025-02-27 07:35:16] (step=0035900) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2666
+[2025-02-27 07:36:15] (step=0035950) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2677
+[2025-02-27 07:37:13] (step=0036000) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2789
+[2025-02-27 07:38:12] (step=0036050) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2735
+[2025-02-27 07:39:10] (step=0036100) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2684
+[2025-02-27 07:40:09] (step=0036150) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2709
+[2025-02-27 07:41:08] (step=0036200) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2784
+[2025-02-27 07:42:06] (step=0036250) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2740
+[2025-02-27 07:43:05] (step=0036300) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2730
+[2025-02-27 07:44:03] (step=0036350) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2643
+[2025-02-27 07:45:02] (step=0036400) Train Loss: 0.0499, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2809
+[2025-02-27 07:46:00] (step=0036450) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2711
+[2025-02-27 07:46:59] (step=0036500) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2684
+[2025-02-27 07:47:57] (step=0036550) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2663
+[2025-02-27 07:48:56] (step=0036600) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2666
+[2025-02-27 07:49:55] (step=0036650) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2627
+[2025-02-27 07:50:53] (step=0036700) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2638
+[2025-02-27 07:51:52] (step=0036750) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2575
+[2025-02-27 07:52:50] (step=0036800) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2644
+[2025-02-27 07:53:49] (step=0036850) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2812
+[2025-02-27 07:54:47] (step=0036900) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2633
+[2025-02-27 07:55:46] (step=0036950) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2798
+[2025-02-27 07:56:45] (step=0037000) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2674
+[2025-02-27 07:57:43] (step=0037050) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2817
+[2025-02-27 07:58:42] (step=0037100) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2537
+[2025-02-27 07:59:40] (step=0037150) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2764
+[2025-02-27 08:00:39] (step=0037200) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2650
+[2025-02-27 08:01:38] (step=0037250) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2505
+[2025-02-27 08:02:36] (step=0037300) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2849
+[2025-02-27 08:03:35] (step=0037350) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2613
+[2025-02-27 08:04:33] (step=0037400) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2667
+[2025-02-27 08:05:32] (step=0037450) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2686
+[2025-02-27 08:06:30] (step=0037500) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2720
+[2025-02-27 08:07:31] (step=0037550) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.2660
+[2025-02-27 08:08:30] (step=0037600) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2581
+[2025-02-27 08:09:28] (step=0037650) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2665
+[2025-02-27 08:10:27] (step=0037700) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2592
+[2025-02-27 08:11:25] (step=0037750) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2560
+[2025-02-27 08:12:24] (step=0037800) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2617
+[2025-02-27 08:13:22] (step=0037850) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2608
+[2025-02-27 08:14:21] (step=0037900) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2768
+[2025-02-27 08:15:20] (step=0037950) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2653
+[2025-02-27 08:16:18] (step=0038000) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2665
+[2025-02-27 08:17:17] (step=0038050) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2660
+[2025-02-27 08:18:15] (step=0038100) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2603
+[2025-02-27 08:19:14] (step=0038150) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2515
+[2025-02-27 08:20:12] (step=0038200) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2613
+[2025-02-27 08:21:11] (step=0038250) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2615
+[2025-02-27 08:22:09] (step=0038300) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2591
+[2025-02-27 08:23:08] (step=0038350) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2824
+[2025-02-27 08:24:07] (step=0038400) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2497
+[2025-02-27 08:25:05] (step=0038450) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2594
+[2025-02-27 08:26:04] (step=0038500) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2533
+[2025-02-27 08:27:03] (step=0038550) Train Loss: 0.0500, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2591
+[2025-02-27 08:28:01] (step=0038600) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2504
+[2025-02-27 08:29:00] (step=0038650) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2758
+[2025-02-27 08:29:58] (step=0038700) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2703
+[2025-02-27 08:30:57] (step=0038750) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2270
+[2025-02-27 08:31:56] (step=0038800) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2651
+[2025-02-27 08:32:54] (step=0038850) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2390
+[2025-02-27 08:33:53] (step=0038900) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2600
+[2025-02-27 08:34:51] (step=0038950) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2517
+[2025-02-27 08:35:50] (step=0039000) Train Loss: 0.0497, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2521
+[2025-02-27 08:36:49] (step=0039050) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2652
+[2025-02-27 08:37:47] (step=0039100) Train Loss: 0.0498, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2564
+[2025-02-27 08:38:46] (step=0039150) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2800
+[2025-02-27 08:39:45] (step=0039200) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2575
+[2025-02-27 08:40:43] (step=0039250) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2713
+[2025-02-27 08:41:42] (step=0039300) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2717
+[2025-02-27 08:42:40] (step=0039350) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2522
+[2025-02-27 08:43:39] (step=0039400) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2733
+[2025-02-27 08:44:38] (step=0039450) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2715
+[2025-02-27 08:45:36] (step=0039500) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2479
+[2025-02-27 08:46:35] (step=0039550) Train Loss: 0.0496, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2658
+[2025-02-27 08:47:34] (step=0039600) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2626
+[2025-02-27 08:48:32] (step=0039650) Train Loss: 0.0494, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2538
+[2025-02-27 08:49:31] (step=0039700) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2574
+[2025-02-27 08:50:29] (step=0039750) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2528
+[2025-02-27 08:51:28] (step=0039800) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2296
+[2025-02-27 08:52:27] (step=0039850) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2747
+[2025-02-27 08:53:25] (step=0039900) Train Loss: 0.0479, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2453
+[2025-02-27 08:54:24] (step=0039950) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2418
+[2025-02-27 08:55:23] (step=0040000) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2683
+[2025-02-27 08:55:26] Saved checkpoint to ../logs/flow/flowsdvae_50kx512_lgn0p0/checkpoints/0040000.pt
+[2025-02-27 09:14:06] (step=0040000), Fid=4.821858952408604, PSNR=24.05693536863327, LPIPS=0.26953125, SSIM=0.594628095626831
+[2025-02-27 09:15:06] (step=0040050) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.04, Grad Norm: 0.2349
+[2025-02-27 09:16:05] (step=0040100) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2408
+[2025-02-27 09:17:03] (step=0040150) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2557
+[2025-02-27 09:18:02] (step=0040200) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2349
+[2025-02-27 09:19:01] (step=0040250) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2566
+[2025-02-27 09:19:59] (step=0040300) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2606
+[2025-02-27 09:20:58] (step=0040350) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2429
+[2025-02-27 09:21:56] (step=0040400) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2627
+[2025-02-27 09:22:55] (step=0040450) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2626
+[2025-02-27 09:23:53] (step=0040500) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2363
+[2025-02-27 09:24:52] (step=0040550) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2615
+[2025-02-27 09:25:51] (step=0040600) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2447
+[2025-02-27 09:26:49] (step=0040650) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2635
+[2025-02-27 09:27:48] (step=0040700) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2519
+[2025-02-27 09:28:46] (step=0040750) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2436
+[2025-02-27 09:29:45] (step=0040800) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2586
+[2025-02-27 09:30:43] (step=0040850) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2589
+[2025-02-27 09:31:42] (step=0040900) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2663
+[2025-02-27 09:32:40] (step=0040950) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2377
+[2025-02-27 09:33:39] (step=0041000) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2525
+[2025-02-27 09:34:38] (step=0041050) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2546
+[2025-02-27 09:35:36] (step=0041100) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2463
+[2025-02-27 09:36:35] (step=0041150) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2506
+[2025-02-27 09:37:33] (step=0041200) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2615
+[2025-02-27 09:38:32] (step=0041250) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2448
+[2025-02-27 09:39:30] (step=0041300) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2512
+[2025-02-27 09:40:29] (step=0041350) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2518
+[2025-02-27 09:41:28] (step=0041400) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2490
+[2025-02-27 09:42:26] (step=0041450) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2495
+[2025-02-27 09:43:25] (step=0041500) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2683
+[2025-02-27 09:44:23] (step=0041550) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2420
+[2025-02-27 09:45:22] (step=0041600) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2621
+[2025-02-27 09:46:20] (step=0041650) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2455
+[2025-02-27 09:47:19] (step=0041700) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2632
+[2025-02-27 09:48:18] (step=0041750) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2259
+[2025-02-27 09:49:16] (step=0041800) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2556
+[2025-02-27 09:50:15] (step=0041850) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2412
+[2025-02-27 09:51:13] (step=0041900) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2714
+[2025-02-27 09:52:12] (step=0041950) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2477
+[2025-02-27 09:53:10] (step=0042000) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2480
+[2025-02-27 09:54:09] (step=0042050) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2666
+[2025-02-27 09:55:07] (step=0042100) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2349
+[2025-02-27 09:56:06] (step=0042150) Train Loss: 0.0483, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2591
+[2025-02-27 09:57:05] (step=0042200) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2466
+[2025-02-27 09:58:03] (step=0042250) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2581
+[2025-02-27 09:59:02] (step=0042300) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2407
+[2025-02-27 10:00:00] (step=0042350) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2623
+[2025-02-27 10:00:59] (step=0042400) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2505
+[2025-02-27 10:01:57] (step=0042450) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2467
+[2025-02-27 10:02:56] (step=0042500) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2383
+[2025-02-27 10:03:57] (step=0042550) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.2613
+[2025-02-27 10:04:55] (step=0042600) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2570
+[2025-02-27 10:05:54] (step=0042650) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2465
+[2025-02-27 10:06:53] (step=0042700) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2566
+[2025-02-27 10:07:51] (step=0042750) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2418
+[2025-02-27 10:08:50] (step=0042800) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2621
+[2025-02-27 10:09:48] (step=0042850) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2314
+[2025-02-27 10:10:47] (step=0042900) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2485
+[2025-02-27 10:11:45] (step=0042950) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2422
+[2025-02-27 10:12:44] (step=0043000) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2509
+[2025-02-27 10:13:42] (step=0043050) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2372
+[2025-02-27 10:14:41] (step=0043100) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2533
+[2025-02-27 10:15:39] (step=0043150) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2442
+[2025-02-27 10:16:38] (step=0043200) Train Loss: 0.0492, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2413
+[2025-02-27 10:17:37] (step=0043250) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2561
+[2025-02-27 10:18:35] (step=0043300) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2297
+[2025-02-27 10:19:34] (step=0043350) Train Loss: 0.0480, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2588
+[2025-02-27 10:20:32] (step=0043400) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2500
+[2025-02-27 10:21:31] (step=0043450) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2532
+[2025-02-27 10:22:29] (step=0043500) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2477
+[2025-02-27 10:23:28] (step=0043550) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2634
+[2025-02-27 10:24:26] (step=0043600) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2380
+[2025-02-27 10:25:25] (step=0043650) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2494
+[2025-02-27 10:26:23] (step=0043700) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2466
+[2025-02-27 10:27:22] (step=0043750) Train Loss: 0.0480, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2505
+[2025-02-27 10:28:21] (step=0043800) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2618
+[2025-02-27 10:29:19] (step=0043850) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2431
+[2025-02-27 10:30:18] (step=0043900) Train Loss: 0.0478, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2155
+[2025-02-27 10:31:16] (step=0043950) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2501
+[2025-02-27 10:32:15] (step=0044000) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2308
+[2025-02-27 10:33:13] (step=0044050) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2642
+[2025-02-27 10:34:12] (step=0044100) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2241
+[2025-02-27 10:35:11] (step=0044150) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2386
+[2025-02-27 10:36:09] (step=0044200) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2305
+[2025-02-27 10:37:08] (step=0044250) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2568
+[2025-02-27 10:38:06] (step=0044300) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2276
+[2025-02-27 10:39:05] (step=0044350) Train Loss: 0.0483, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2415
+[2025-02-27 10:40:04] (step=0044400) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2237
+[2025-02-27 10:41:02] (step=0044450) Train Loss: 0.0495, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2536
+[2025-02-27 10:42:01] (step=0044500) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2436
+[2025-02-27 10:42:59] (step=0044550) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2496
+[2025-02-27 10:43:58] (step=0044600) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2259
+[2025-02-27 10:44:56] (step=0044650) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2287
+[2025-02-27 10:45:55] (step=0044700) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2495
+[2025-02-27 10:46:54] (step=0044750) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2385
+[2025-02-27 10:47:52] (step=0044800) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2495
+[2025-02-27 10:48:51] (step=0044850) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2304
+[2025-02-27 10:49:49] (step=0044900) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2411
+[2025-02-27 10:50:48] (step=0044950) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2493
+[2025-02-27 10:51:46] (step=0045000) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2414
+[2025-02-27 10:52:47] (step=0045050) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.2363
+[2025-02-27 10:53:46] (step=0045100) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2291
+[2025-02-27 10:54:44] (step=0045150) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2367
+[2025-02-27 10:55:43] (step=0045200) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2297
+[2025-02-27 10:56:42] (step=0045250) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2477
+[2025-02-27 10:57:40] (step=0045300) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2334
+[2025-02-27 10:58:39] (step=0045350) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2445
+[2025-02-27 10:59:37] (step=0045400) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2396
+[2025-02-27 11:00:36] (step=0045450) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2273
+[2025-02-27 11:01:34] (step=0045500) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2428
+[2025-02-27 11:02:33] (step=0045550) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2385
+[2025-02-27 11:03:31] (step=0045600) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2430
+[2025-02-27 11:04:30] (step=0045650) Train Loss: 0.0483, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2316
+[2025-02-27 11:05:29] (step=0045700) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2286
+[2025-02-27 11:06:27] (step=0045750) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2225
+[2025-02-27 11:07:26] (step=0045800) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2429
+[2025-02-27 11:08:24] (step=0045850) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2416
+[2025-02-27 11:09:23] (step=0045900) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2332
+[2025-02-27 11:10:21] (step=0045950) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2332
+[2025-02-27 11:11:20] (step=0046000) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2339
+[2025-02-27 11:12:18] (step=0046050) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2458
+[2025-02-27 11:13:17] (step=0046100) Train Loss: 0.0489, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2305
+[2025-02-27 11:14:15] (step=0046150) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2272
+[2025-02-27 11:15:14] (step=0046200) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2540
+[2025-02-27 11:16:13] (step=0046250) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2155
+[2025-02-27 11:17:11] (step=0046300) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2218
+[2025-02-27 11:18:10] (step=0046350) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2314
+[2025-02-27 11:19:08] (step=0046400) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2367
+[2025-02-27 11:20:07] (step=0046450) Train Loss: 0.0491, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2314
+[2025-02-27 11:21:05] (step=0046500) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2394
+[2025-02-27 11:22:04] (step=0046550) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2380
+[2025-02-27 11:23:02] (step=0046600) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2269
+[2025-02-27 11:24:01] (step=0046650) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2369
+[2025-02-27 11:24:59] (step=0046700) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2287
+[2025-02-27 11:25:58] (step=0046750) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2320
+[2025-02-27 11:26:56] (step=0046800) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2180
+[2025-02-27 11:27:55] (step=0046850) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2318
+[2025-02-27 11:28:54] (step=0046900) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2587
+[2025-02-27 11:29:52] (step=0046950) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2175
+[2025-02-27 11:30:51] (step=0047000) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2393
+[2025-02-27 11:31:49] (step=0047050) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2296
+[2025-02-27 11:32:48] (step=0047100) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2397
+[2025-02-27 11:33:46] (step=0047150) Train Loss: 0.0493, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2184
+[2025-02-27 11:34:45] (step=0047200) Train Loss: 0.0483, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2397
+[2025-02-27 11:35:43] (step=0047250) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2344
+[2025-02-27 11:36:42] (step=0047300) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2163
+[2025-02-27 11:37:41] (step=0047350) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2365
+[2025-02-27 11:38:39] (step=0047400) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2420
+[2025-02-27 11:39:38] (step=0047450) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2312
+[2025-02-27 11:40:36] (step=0047500) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2263
+[2025-02-27 11:41:37] (step=0047550) Train Loss: 0.0479, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.82, Grad Norm: 0.2211
+[2025-02-27 11:42:36] (step=0047600) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2262
+[2025-02-27 11:43:34] (step=0047650) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2494
+[2025-02-27 11:44:33] (step=0047700) Train Loss: 0.0474, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2101
+[2025-02-27 11:45:31] (step=0047750) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2352
+[2025-02-27 11:46:30] (step=0047800) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2409
+[2025-02-27 11:47:29] (step=0047850) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2243
+[2025-02-27 11:48:27] (step=0047900) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2311
+[2025-02-27 11:49:26] (step=0047950) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2436
+[2025-02-27 11:50:24] (step=0048000) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2343
+[2025-02-27 11:51:23] (step=0048050) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2246
+[2025-02-27 11:52:21] (step=0048100) Train Loss: 0.0490, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2475
+[2025-02-27 11:53:20] (step=0048150) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2329
+[2025-02-27 11:54:18] (step=0048200) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2302
+[2025-02-27 11:55:17] (step=0048250) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2240
+[2025-02-27 11:56:15] (step=0048300) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2257
+[2025-02-27 11:57:14] (step=0048350) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2228
+[2025-02-27 11:58:12] (step=0048400) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2540
+[2025-02-27 11:59:11] (step=0048450) Train Loss: 0.0486, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2246
+[2025-02-27 12:00:09] (step=0048500) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2423
+[2025-02-27 12:01:07] (step=0048550) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2278
+[2025-02-27 12:02:06] (step=0048600) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2309
+[2025-02-27 12:03:04] (step=0048650) Train Loss: 0.0483, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2289
+[2025-02-27 12:04:03] (step=0048700) Train Loss: 0.0483, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2360
+[2025-02-27 12:05:01] (step=0048750) Train Loss: 0.0478, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2207
+[2025-02-27 12:05:59] (step=0048800) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2288
+[2025-02-27 12:06:58] (step=0048850) Train Loss: 0.0480, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2311
+[2025-02-27 12:07:56] (step=0048900) Train Loss: 0.0483, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2339
+[2025-02-27 12:08:54] (step=0048950) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2352
+[2025-02-27 12:09:53] (step=0049000) Train Loss: 0.0478, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2446
+[2025-02-27 12:10:51] (step=0049050) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2359
+[2025-02-27 12:11:50] (step=0049100) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2187
+[2025-02-27 12:12:48] (step=0049150) Train Loss: 0.0483, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2424
+[2025-02-27 12:13:46] (step=0049200) Train Loss: 0.0488, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2396
+[2025-02-27 12:14:45] (step=0049250) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2328
+[2025-02-27 12:15:43] (step=0049300) Train Loss: 0.0477, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2339
+[2025-02-27 12:16:42] (step=0049350) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.85, Grad Norm: 0.2224
+[2025-02-27 12:17:40] (step=0049400) Train Loss: 0.0483, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2179
+[2025-02-27 12:18:39] (step=0049450) Train Loss: 0.0485, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2238
+[2025-02-27 12:19:37] (step=0049500) Train Loss: 0.0474, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2229
+[2025-02-27 12:20:35] (step=0049550) Train Loss: 0.0479, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2222
+[2025-02-27 12:21:34] (step=0049600) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2142
+[2025-02-27 12:22:32] (step=0049650) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2351
+[2025-02-27 12:23:30] (step=0049700) Train Loss: 0.0482, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2109
+[2025-02-27 12:24:29] (step=0049750) Train Loss: 0.0477, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2154
+[2025-02-27 12:25:27] (step=0049800) Train Loss: 0.0480, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2107
+[2025-02-27 12:26:26] (step=0049850) Train Loss: 0.0481, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2405
+[2025-02-27 12:27:24] (step=0049900) Train Loss: 0.0487, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2130
+[2025-02-27 12:28:22] (step=0049950) Train Loss: 0.0483, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2261
+[2025-02-27 12:29:21] (step=0050000) Train Loss: 0.0484, Perceptual Loss: 0.0000, Cos Loss: 0.0000, Train Steps/Sec: 0.86, Grad Norm: 0.2073
+[2025-02-27 12:29:24] Saved checkpoint to ../logs/flow/flowsdvae_50kx512_lgn0p0/checkpoints/0050000.pt
+[2025-02-27 12:49:10] (step=0050000), Fid=3.265143203080868, PSNR=24.72016086781025, LPIPS=0.2216796875, SSIM=0.6391960382461548
+[2025-02-27 12:49:10] Done!