diff --git a/comfy/ldm/kandinsky5/model.py b/comfy/ldm/kandinsky5/model.py
index df10e4496..ac9352867 100644
--- a/comfy/ldm/kandinsky5/model.py
+++ b/comfy/ldm/kandinsky5/model.py
@@ -387,13 +387,12 @@ class Kandinsky5(nn.Module):
         transformer_options["block_type"] = "double"
 
         B, _, T, H, W = x.shape
-        if T > 30: # 10 sec generation
+        NABLA_THR = 40 # long (10 sec) generation
+        if T > NABLA_THR:
             assert self.patch_size[0] == 1
 
-            freqs = freqs.view(freqs.shape[0], *visual_shape[1:], *freqs.shape[2:])[0]
-            visual_embed_4d, freqs = fractal_flatten(visual_embed[0], freqs, visual_shape[1:])
-            visual_embed, freqs = visual_embed_4d.unsqueeze(0), freqs.unsqueeze(0)
-
+            freqs = freqs.view(freqs.shape[0], *visual_shape[1:], *freqs.shape[2:])
+            visual_embed, freqs = fractal_flatten(visual_embed, freqs, visual_shape[1:])
             pt, ph, pw = self.patch_size
             T, H, W = T // pt, H // ph, W // pw
 
@@ -447,11 +446,11 @@ class Kandinsky5(nn.Module):
                     transformer_options=transformer_options,
                 )
 
-        if T > 30:
+        if T > NABLA_THR:
             visual_embed = fractal_unflatten(
-                visual_embed[0],
+                visual_embed,
                 visual_shape[1:],
-            ).unsqueeze(0)
+            )
         else:
             visual_embed = visual_embed.reshape(*visual_shape, -1)
 
diff --git a/comfy/ldm/kandinsky5/utils_nabla.py b/comfy/ldm/kandinsky5/utils_nabla.py
index 705b1d75e..5e2bc4076 100644
--- a/comfy/ldm/kandinsky5/utils_nabla.py
+++ b/comfy/ldm/kandinsky5/utils_nabla.py
@@ -7,20 +7,19 @@ from torch.nn.attention.flex_attention import BlockMask, flex_attention
 
 def fractal_flatten(x, rope, shape):
     pixel_size = 8
-    x = local_patching(x, shape, (1, pixel_size, pixel_size), dim=0)
-    rope = local_patching(rope, shape, (1, pixel_size, pixel_size), dim=0)
-    x = x.flatten(0, 1)
-    rope = rope.flatten(0, 1)
+    x = local_patching(x, shape, (1, pixel_size, pixel_size), dim=1)
+    rope = local_patching(rope, shape, (1, pixel_size, pixel_size), dim=1)
+    x = x.flatten(1, 2)
+    rope = rope.flatten(1, 2)
     return x, rope
 
 
 def fractal_unflatten(x, shape):
     pixel_size = 8
-    x = x.reshape(-1, pixel_size**2, x.shape[-1])
-    x = local_merge(x, shape, (1, pixel_size, pixel_size), dim=0)
+    x = x.reshape(x.shape[0], -1, pixel_size**2, x.shape[-1])
+    x = local_merge(x, shape, (1, pixel_size, pixel_size), dim=1)
     return x
 
-
 def local_patching(x, shape, group_size, dim=0):
     duration, height, width = shape
     g1, g2, g3 = group_size