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@@ -41,3 +41,7 @@ la_ddwc_phase_2/inference_0000.mp4 filter=lfs diff=lfs merge=lfs -text
 la_ddwc_phase_2/inference_0001.mp4 filter=lfs diff=lfs merge=lfs -text
 la_ddwc_phase_2/inference_0002.mp4 filter=lfs diff=lfs merge=lfs -text
 la_ddwc_phase_2/inference_0003.mp4 filter=lfs diff=lfs merge=lfs -text
+gla_ddwc_phase_2/inference_0000.mp4 filter=lfs diff=lfs merge=lfs -text
+gla_ddwc_phase_2/inference_0001.mp4 filter=lfs diff=lfs merge=lfs -text
+gla_ddwc_phase_2/inference_0002.mp4 filter=lfs diff=lfs merge=lfs -text
+gla_ddwc_phase_2/inference_0003.mp4 filter=lfs diff=lfs merge=lfs -text

gla_ddwc_phase_2/inference_0000.mp4

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+size 326760

gla_ddwc_phase_2/inference_0001.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e9b1058a78d6035b0a645f44d09c4759be2d5242e36fbe03a6de68d8417bf694
+size 1250017

gla_ddwc_phase_2/inference_0002.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:de144291bb0fcc2fb82fea581e51d94859794048bc3c92d1a1d263af3fe6cbac
+size 461301

gla_ddwc_phase_2/inference_0003.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:056b4dcfd4edf3012c36cfa29983d3506bcdda2d99e353ff455d5214dc2bd35b
+size 223736

gla_ddwc_phase_2/model.txt

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+JetWanT2V(
+  (patch_embedding): Conv3d(16, 1536, kernel_size=(1, 2, 2), stride=(1, 2, 2))
+  (text_embedding): Sequential(
+    (0): Linear(in_features=4096, out_features=1536, bias=True)
+    (1): GELU(approximate='tanh')
+    (2): Linear(in_features=1536, out_features=1536, bias=True)
+  )
+  (time_embedding): Sequential(
+    (0): Linear(in_features=256, out_features=1536, bias=True)
+    (1): SiLU()
+    (2): Linear(in_features=1536, out_features=1536, bias=True)
+  )
+  (time_projection): Sequential(
+    (0): SiLU()
+    (1): Linear(in_features=1536, out_features=9216, bias=True)
+  )
+  (blocks): ModuleList(
+    (0-29): 30 x JetWanBlock(
+      (norm1): WanLayerNorm(normalized_shape=(1536,), eps=1e-06, elementwise_affine=False)
+      (self_attn): GatedLinearAttentionWithDynamicDWC(
+        (q): Linear(in_features=1536, out_features=1536, bias=True)
+        (k): Linear(in_features=1536, out_features=1536, bias=True)
+        (v): Linear(in_features=1536, out_features=1536, bias=True)
+        (o): Linear(in_features=1536, out_features=1536, bias=True)
+        (norm_q): WanRMSNorm(dim=1536, eps=1e-06)
+        (norm_k): WanRMSNorm(dim=1536, eps=1e-06)
+        (norm_o): WanRMSNorm(dim=128, eps=1e-06)
+        (g): Linear(in_features=1536, out_features=1536, bias=True)
+        (gate_act): Sigmoid()
+        (dwc): DynamicDWC3D(
+          channels=128, kernel_size=(3, 3, 3)
+          (kernel_generator): Sequential(
+            (conv1): Conv3d(128, 1024, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+            (act1): SiLU()
+            (conv2): Conv3d(1024, 3456, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+          )
+        )
+      )
+      (norm3): WanLayerNorm(normalized_shape=(1536,), eps=1e-06, elementwise_affine=True)
+      (cross_attn): WanT2VCrossAttention(
+        (q): Linear(in_features=1536, out_features=1536, bias=True)
+        (k): Linear(in_features=1536, out_features=1536, bias=True)
+        (v): Linear(in_features=1536, out_features=1536, bias=True)
+        (o): Linear(in_features=1536, out_features=1536, bias=True)
+        (norm_q): WanRMSNorm(dim=1536, eps=1e-06)
+        (norm_k): WanRMSNorm(dim=1536, eps=1e-06)
+      )
+      (norm2): WanLayerNorm(normalized_shape=(1536,), eps=1e-06, elementwise_affine=False)
+      (ffn): Sequential(
+        (0): Linear(in_features=1536, out_features=8960, bias=True)
+        (1): GELU(approximate='tanh')
+        (2): Linear(in_features=8960, out_features=1536, bias=True)
+      )
+    )
+  )
+  (head): WanHead(
+    (norm): WanLayerNorm(normalized_shape=(1536,), eps=1e-06, elementwise_affine=False)
+    (head): Linear(in_features=1536, out_features=64, bias=True)
+  )
+)