diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py
index cb64bc61f3..92e9a6d4f5 100644
--- a/src/diffusers/models/embeddings.py
+++ b/src/diffusers/models/embeddings.py
@@ -717,7 +717,14 @@ class HunyuanDiTAttentionPool(nn.Module):
 
 
 class HunyuanCombinedTimestepTextSizeStyleEmbedding(nn.Module):
-    def __init__(self, embedding_dim, pooled_projection_dim=1024, seq_len=256, cross_attention_dim=2048):
+    def __init__(
+        self,
+        embedding_dim,
+        pooled_projection_dim=1024,
+        seq_len=256,
+        cross_attention_dim=2048,
+        use_style_cond_and_image_meta_size=True,
+    ):
         super().__init__()
 
         self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
@@ -726,9 +733,15 @@ class HunyuanCombinedTimestepTextSizeStyleEmbedding(nn.Module):
         self.pooler = HunyuanDiTAttentionPool(
             seq_len, cross_attention_dim, num_heads=8, output_dim=pooled_projection_dim
         )
+
         # Here we use a default learned embedder layer for future extension.
-        self.style_embedder = nn.Embedding(1, embedding_dim)
-        extra_in_dim = 256 * 6 + embedding_dim + pooled_projection_dim
+        self.use_style_cond_and_image_meta_size = use_style_cond_and_image_meta_size
+        if use_style_cond_and_image_meta_size:
+            self.style_embedder = nn.Embedding(1, embedding_dim)
+            extra_in_dim = 256 * 6 + embedding_dim + pooled_projection_dim
+        else:
+            extra_in_dim = pooled_projection_dim
+
         self.extra_embedder = PixArtAlphaTextProjection(
             in_features=extra_in_dim,
             hidden_size=embedding_dim * 4,
@@ -743,16 +756,20 @@ class HunyuanCombinedTimestepTextSizeStyleEmbedding(nn.Module):
         # extra condition1: text
         pooled_projections = self.pooler(encoder_hidden_states)  # (N, 1024)
 
-        # extra condition2: image meta size embdding
-        image_meta_size = get_timestep_embedding(image_meta_size.view(-1), 256, True, 0)
-        image_meta_size = image_meta_size.to(dtype=hidden_dtype)
-        image_meta_size = image_meta_size.view(-1, 6 * 256)  # (N, 1536)
+        if self.use_style_cond_and_image_meta_size:
+            # extra condition2: image meta size embdding
+            image_meta_size = get_timestep_embedding(image_meta_size.view(-1), 256, True, 0)
+            image_meta_size = image_meta_size.to(dtype=hidden_dtype)
+            image_meta_size = image_meta_size.view(-1, 6 * 256)  # (N, 1536)
 
-        # extra condition3: style embedding
-        style_embedding = self.style_embedder(style)  # (N, embedding_dim)
+            # extra condition3: style embedding
+            style_embedding = self.style_embedder(style)  # (N, embedding_dim)
+
+            # Concatenate all extra vectors
+            extra_cond = torch.cat([pooled_projections, image_meta_size, style_embedding], dim=1)
+        else:
+            extra_cond = torch.cat([pooled_projections], dim=1)
 
-        # Concatenate all extra vectors
-        extra_cond = torch.cat([pooled_projections, image_meta_size, style_embedding], dim=1)
         conditioning = timesteps_emb + self.extra_embedder(extra_cond)  # [B, D]
 
         return conditioning
diff --git a/src/diffusers/models/transformers/hunyuan_transformer_2d.py b/src/diffusers/models/transformers/hunyuan_transformer_2d.py
index d67b35586a..8313ffd87a 100644
--- a/src/diffusers/models/transformers/hunyuan_transformer_2d.py
+++ b/src/diffusers/models/transformers/hunyuan_transformer_2d.py
@@ -249,6 +249,8 @@ class HunyuanDiT2DModel(ModelMixin, ConfigMixin):
             The length of the clip text embedding.
         text_len_t5 (`int`, *optional*):
             The length of the T5 text embedding.
+        use_style_cond_and_image_meta_size (`bool`,  *optional*):
+            Whether or not to use style condition and image meta size. True for version <=1.1, False for version >= 1.2
     """
 
     @register_to_config
@@ -270,6 +272,7 @@ class HunyuanDiT2DModel(ModelMixin, ConfigMixin):
         pooled_projection_dim: int = 1024,
         text_len: int = 77,
         text_len_t5: int = 256,
+        use_style_cond_and_image_meta_size: bool = True,
     ):
         super().__init__()
         self.out_channels = in_channels * 2 if learn_sigma else in_channels
@@ -301,6 +304,7 @@ class HunyuanDiT2DModel(ModelMixin, ConfigMixin):
             pooled_projection_dim=pooled_projection_dim,
             seq_len=text_len_t5,
             cross_attention_dim=cross_attention_dim_t5,
+            use_style_cond_and_image_meta_size=use_style_cond_and_image_meta_size,
         )
 
         # HunyuanDiT Blocks