diff --git a/comfy/ldm/kandinsky5/model.py b/comfy/ldm/kandinsky5/model.py
index 4e85dfd9a..50d3ec5cd 100644
--- a/comfy/ldm/kandinsky5/model.py
+++ b/comfy/ldm/kandinsky5/model.py
@@ -307,24 +307,23 @@ class Kandinsky5(nn.Module):
             h_start += rope_options.get("shift_y", 0.0)
             w_start += rope_options.get("shift_x", 0.0)
         else:
-            # this is experimental as the original code only had two fixed scales for 512p and 1024p
-            if t == 1: # image model
-                self.rope_scale_factor = (1.0, 1.0, 1.0)
-            else:
+            if self.model_dim == 4096: # pro video model,this is experimental as the original code only had two fixed scales for 512p and 1024p
                 spatial_size = h * w
                 scale_16384 = (1.0, 3.16, 3.16)
                 scale_9216 = (1.0, 2.0, 2.0)
                 if spatial_size <= 6144:
-                    self.rope_scale_factor = scale_9216
+                    rope_scale_factor = scale_9216
                 elif spatial_size >= 14080:
-                    self.rope_scale_factor = scale_16384
+                    rope_scale_factor = scale_16384
                 else:
                     t = (spatial_size - 14080) / (6144 - 14080)
-                    self.rope_scale_factor = tuple(a + (b - a) * t for a, b in zip(scale_16384, scale_9216))
+                    rope_scale_factor = tuple(a + (b - a) * t for a, b in zip(scale_16384, scale_9216))
+            else:
+                rope_scale_factor = self.rope_scale_factor
 
-            t_len = (t_len - 1.0) // self.rope_scale_factor[0] + 1.0
-            h_len = (h_len - 1.0) // self.rope_scale_factor[1] + 1.0
-            w_len = (w_len - 1.0) // self.rope_scale_factor[2] + 1.0
+            t_len = (t_len - 1.0) // rope_scale_factor[0] + 1.0
+            h_len = (h_len - 1.0) // rope_scale_factor[1] + 1.0
+            w_len = (w_len - 1.0) // rope_scale_factor[2] + 1.0
 
         img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype)
         img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1)
@@ -345,8 +344,8 @@ class Kandinsky5(nn.Module):
 
         visual_embed = self.visual_embeddings(x)
         visual_shape = visual_embed.shape[:-1]
-
         visual_embed = visual_embed.flatten(1, -2)
+
         blocks_replace = patches_replace.get("dit", {})
         transformer_options["total_blocks"] = len(self.visual_transformer_blocks)
         transformer_options["block_type"] = "double"