diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py
index bb5e02b6..99f49810 100644
--- a/comfy/ldm/flux/layers.py
+++ b/comfy/ldm/flux/layers.py
@@ -8,9 +8,8 @@ from torch import Tensor, nn
 from .math import attention, rope
 import comfy.ops
 
-
 class EmbedND(nn.Module):
-    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
+    def __init__(self, dim: int, theta: int, axes_dim: list):
         super().__init__()
         self.dim = dim
         self.theta = theta
@@ -79,7 +78,7 @@ class QKNorm(torch.nn.Module):
         self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
         self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
 
-    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:
         q = self.query_norm(q)
         k = self.key_norm(k)
         return q.to(v), k.to(v)
@@ -118,7 +117,7 @@ class Modulation(nn.Module):
         self.multiplier = 6 if double else 3
         self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
 
-    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
+    def forward(self, vec: Tensor) -> tuple:
         out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
 
         return (
@@ -156,7 +155,7 @@ class DoubleStreamBlock(nn.Module):
             operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
         )
 
-    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor):
         img_mod1, img_mod2 = self.img_mod(vec)
         txt_mod1, txt_mod2 = self.txt_mod(vec)
 
@@ -203,7 +202,7 @@ class SingleStreamBlock(nn.Module):
         hidden_size: int,
         num_heads: int,
         mlp_ratio: float = 4.0,
-        qk_scale: float | None = None,
+        qk_scale: float = None,
         dtype=None,
         device=None,
         operations=None
diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
index e4ef624e..d9bb568a 100644
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -21,7 +21,7 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     return out.float()
 
 
-def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
     xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
     xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
     xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index f77834c1..ae34052d 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -26,7 +26,7 @@ class FluxParams:
     num_heads: int
     depth: int
     depth_single_blocks: int
-    axes_dim: list[int]
+    axes_dim: list
     theta: int
     qkv_bias: bool
     guidance_embed: bool
@@ -92,7 +92,7 @@ class Flux(nn.Module):
         txt_ids: Tensor,
         timesteps: Tensor,
         y: Tensor,
-        guidance: Tensor | None = None,
+        guidance: Tensor = None,
     ) -> Tensor:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")