diff --git a/__init__.py b/__init__.py
index 61b5806..31dea5d 100644
--- a/__init__.py
+++ b/__init__.py
@@ -51,7 +51,7 @@ def concat_first(feat: T, dim=2, scale=1.0) -> T:
     return torch.cat((feat, feat_style), dim=dim)
 
 
-def calc_mean_std(feat, eps: float = 1e-5) -> tuple[T, T]:
+def calc_mean_std(feat, eps: float = 1e-5) -> "tuple[T, T]":
     feat_std = (feat.var(dim=-2, keepdims=True) + eps).sqrt()
     feat_mean = feat.mean(dim=-2, keepdims=True)
     return feat_mean, feat_std
@@ -94,7 +94,7 @@ class SharedAttentionProcessor:
 
 def get_norm_layers(
     layer: nn.Module,
-    norm_layers_: dict[str, list[Union[nn.GroupNorm, nn.LayerNorm]]],
+    norm_layers_: "dict[str, list[Union[nn.GroupNorm, nn.LayerNorm]]]",
     share_layer_norm: bool,
     share_group_norm: bool,
 ):
@@ -119,7 +119,7 @@ def register_norm_forward(
     def forward_(hidden_states: T) -> T:
         n = hidden_states.shape[-2]
         hidden_states = concat_first(hidden_states, dim=-2)
-        hidden_states = orig_forward(hidden_states)
+        hidden_states = orig_forward(hidden_states)  # type: ignore
         return hidden_states[..., :n, :]
 
     norm_layer.forward = forward_  # type: ignore
@@ -141,14 +141,17 @@ def register_shared_norm(
     ]
 
 
-class StyleAlignedSample:
+SHARE_NORM_OPTIONS = ["both", "group", "layer", "disabled"]
+
+
+class StyleAlignWithRefSampler:
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "model": ("MODEL",),
-                "share_norm": (["both", "group", "layer", "disabled"],),
-                "scale": ("FLOAT", {"default": 1, "min": 0, "max": 1.0, "step": 0.1}),
+                "share_norm": (SHARE_NORM_OPTIONS,),
+                "scale": ("FLOAT", {"default": 1, "min": 0, "max": 2.0, "step": 0.1}),
                 "batch_size": ("INT", {"default": 2, "min": 1, "max": 8, "step": 1}),
                 "noise_seed": (
                     "INT",
@@ -175,7 +178,7 @@ class StyleAlignedSample:
     RETURN_TYPES = ("LATENT", "LATENT")
     RETURN_NAMES = ("output", "denoised_output")
     FUNCTION = "patch"
-    CATEGORY = "advanced"
+    CATEGORY = "style_aligned"
 
     def __init__(self) -> None:
         self.args = StyleAlignedArgs()
@@ -192,11 +195,10 @@ class StyleAlignedSample:
         negative: T,
         sampler: T,
         sigmas: T,
-        ref_latent: dict[str, T],
-    ) -> tuple[dict, dict]:
+        ref_latent: "dict[str, T]",
+    ) -> "tuple[dict, dict]":
         m = model.clone()
 
-
         # Concat batch with style latent
         style_latent_tensor = ref_latent["samples"]
         height, width = style_latent_tensor.shape[-2:]
@@ -237,7 +239,7 @@ class StyleAlignedSample:
             disable_pbar=disable_pbar,
             seed=noise_seed,
         )
-        
+
         # remove reference image
         samples = samples[1:]
 
@@ -246,14 +248,51 @@ class StyleAlignedSample:
         if "x0" in x0_output:
             out_denoised = latent.copy()
             x0 = x0_output["x0"][1:]
-            out_denoised["samples"] = m.model.process_latent_out(
-                x0.cpu()
-            )
+            out_denoised["samples"] = m.model.process_latent_out(x0.cpu())
         else:
             out_denoised = out
         return (out, out_denoised)
 
 
+class StyleAlignWithinBatch:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "share_norm": (SHARE_NORM_OPTIONS,),
+                "scale": ("FLOAT", {"default": 1, "min": 0, "max": 1.0, "step": 0.1}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "style_aligned"
+
+    def __init__(self) -> None:
+        self.args = StyleAlignedArgs()
+
+    def patch(
+        self,
+        model: ModelPatcher,
+        share_norm: str,
+        scale: float,
+    ):
+        m = model.clone()
+        share_group_norm = share_norm in ["group", "both"]
+        share_layer_norm = share_norm in ["layer", "both"]
+        register_shared_norm(model, share_group_norm, share_layer_norm)
+        m.set_model_attn1_patch(SharedAttentionProcessor(self.args, scale))
+        return (m,)
+
+
 NODE_CLASS_MAPPINGS = {
-    "StyleAlignedSample": StyleAlignedSample,
+    "StyleAlignWithRefSampler": StyleAlignWithRefSampler,
+    "StyleAlignWithinBatch": StyleAlignWithinBatch,
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "StyleAlignWithRefSampler": "StyleAligned Reference Sampler",
+    "StyleAlignWithinBatch": "StyleAligned Within Batch",
 }