diff --git a/src/refiners/foundationals/clip/text_encoder.py b/src/refiners/foundationals/clip/text_encoder.py
index c35c912..92038ea 100644
--- a/src/refiners/foundationals/clip/text_encoder.py
+++ b/src/refiners/foundationals/clip/text_encoder.py
@@ -1,21 +1,9 @@
 from torch import Tensor, arange, device as Device, dtype as DType
-
-from refiners.fluxion.layers import (
-    ApproximateGeLU,
-    GeLU,
-    Linear,
-    LayerNorm,
-    Embedding,
-    Chain,
-    Sum,
-    SelfAttention,
-    Lambda,
-    Residual,
-)
+import refiners.fluxion.layers as fl
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 
 
-class PositionalTokenEncoder(Sum):
+class PositionalTokenEncoder(fl.Sum):
     structural_attrs = ["vocabulary_size", "positional_embedding_dim"]
 
     def __init__(
@@ -29,15 +17,15 @@ class PositionalTokenEncoder(Sum):
         self.vocabulary_size = vocabulary_size
         self.positional_embedding_dim = positional_embedding_dim
         super().__init__(
-            Embedding(
+            fl.Embedding(
                 num_embeddings=vocabulary_size,
                 embedding_dim=embedding_dim,
                 device=device,
                 dtype=dtype,
             ),
-            Chain(
-                Lambda(func=self.get_position_ids),
-                Embedding(
+            fl.Chain(
+                fl.Lambda(func=self.get_position_ids),
+                fl.Embedding(
                     num_embeddings=positional_embedding_dim,
                     embedding_dim=embedding_dim,
                     device=device,
@@ -54,7 +42,7 @@ class PositionalTokenEncoder(Sum):
         return self.position_ids[:, : x.shape[1]]
 
 
-class FeedForward(Chain):
+class FeedForward(fl.Chain):
     structural_attrs = ["embedding_dim", "feedforward_dim"]
 
     def __init__(
@@ -67,13 +55,13 @@ class FeedForward(Chain):
         self.embedding_dim = embedding_dim
         self.feedforward_dim = feedforward_dim
         super().__init__(
-            Linear(in_features=embedding_dim, out_features=feedforward_dim, device=device, dtype=dtype),
-            GeLU(),
-            Linear(in_features=feedforward_dim, out_features=embedding_dim, device=device, dtype=dtype),
+            fl.Linear(in_features=embedding_dim, out_features=feedforward_dim, device=device, dtype=dtype),
+            fl.GeLU(),
+            fl.Linear(in_features=feedforward_dim, out_features=embedding_dim, device=device, dtype=dtype),
         )
 
 
-class TransformerLayer(Chain):
+class TransformerLayer(fl.Chain):
     structural_attrs = ["embedding_dim", "num_attention_heads", "feedforward_dim", "layer_norm_eps"]
 
     def __init__(
@@ -90,14 +78,14 @@ class TransformerLayer(Chain):
         self.feedforward_dim = feedforward_dim
         self.layer_norm_eps = layer_norm_eps
         super().__init__(
-            Residual(
-                LayerNorm(
+            fl.Residual(
+                fl.LayerNorm(
                     normalized_shape=embedding_dim,
                     eps=layer_norm_eps,
                     device=device,
                     dtype=dtype,
                 ),
-                SelfAttention(
+                fl.SelfAttention(
                     embedding_dim=embedding_dim,
                     num_heads=num_attention_heads,
                     is_causal=True,
@@ -105,8 +93,8 @@ class TransformerLayer(Chain):
                     dtype=dtype,
                 ),
             ),
-            Residual(
-                LayerNorm(
+            fl.Residual(
+                fl.LayerNorm(
                     normalized_shape=embedding_dim,
                     eps=layer_norm_eps,
                     device=device,
@@ -122,7 +110,7 @@ class TransformerLayer(Chain):
         )
 
 
-class CLIPTextEncoder(Chain):
+class CLIPTextEncoder(fl.Chain):
     structural_attrs = [
         "embedding_dim",
         "positional_embedding_dim",
@@ -177,11 +165,11 @@ class CLIPTextEncoder(Chain):
                 )
                 for _ in range(num_layers)
             ),
-            LayerNorm(normalized_shape=embedding_dim, eps=layer_norm_eps, device=device, dtype=dtype),
+            fl.LayerNorm(normalized_shape=embedding_dim, eps=layer_norm_eps, device=device, dtype=dtype),
         )
         if use_quick_gelu:
-            for gelu, parent in self.walk(predicate=lambda m, _: isinstance(m, GeLU)):
-                parent.replace(old_module=gelu, new_module=ApproximateGeLU())
+            for gelu, parent in self.walk(predicate=lambda m, _: isinstance(m, fl.GeLU)):
+                parent.replace(old_module=gelu, new_module=fl.ApproximateGeLU())
 
     def encode(self, text: str) -> Tensor:
         tokens = self.tokenizer(text, sequence_length=self.positional_embedding_dim).to(device=self.device)