diff --git a/ip_adapter.py b/ip_adapter.py
index bab71c0..9a55020 100644
--- a/ip_adapter.py
+++ b/ip_adapter.py
@@ -78,16 +78,15 @@ def load_state_dict(self, state_dict):
             self.to_kvs[i].weight.data = state_dict[key]
     
 class IPAdapterModel(torch.nn.Module):
-    def __init__(self, state_dict, plus, cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
+    def __init__(self, state_dict, plus, cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4, sdxl_plus=False):
         super().__init__()
-        self.device = "cuda"
         self.plus = plus
         if self.plus:
             self.image_proj_model = Resampler(
-                dim=cross_attention_dim,
+                dim=1280 if sdxl_plus else cross_attention_dim,
                 depth=4,
                 dim_head=64,
-                heads=12,
+                heads=20 if sdxl_plus else 12,
                 num_queries=clip_extra_context_tokens,
                 embedding_dim=clip_embeddings_dim,
                 output_dim=cross_attention_dim,
@@ -138,8 +137,8 @@ def INPUT_TYPES(s):
     CATEGORY = "loaders"
 
     def adapter(self, model, image, clip_vision, weight, model_name, dtype, mask=None):
-        self.dtype = torch.float32 if dtype == "fp32" else torch.float16
-        device = "cuda"
+        device = comfy.model_management.get_torch_device()
+        self.dtype = torch.float32 if dtype == "fp32" or device.type == "mps" else torch.float16
         self.weight = weight # ip_adapter scale
 
         ip_state_dict = torch.load(os.path.join(CURRENT_DIR, os.path.join(CURRENT_DIR, "models", model_name)), map_location="cpu")
@@ -148,6 +147,9 @@ def adapter(self, model, image, clip_vision, weight, model_name, dtype, mask=Non
         # cross_attention_dim is equal to text_encoder output
         self.cross_attention_dim = ip_state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[1]
 
+        self.sdxl = self.cross_attention_dim == 2048
+        self.sdxl_plus = self.sdxl and self.plus
+
         # number of tokens of ip_adapter embedding
         if self.plus:
             self.clip_extra_context_tokens = ip_state_dict["image_proj"]["latents"].shape[1]
@@ -156,16 +158,14 @@ def adapter(self, model, image, clip_vision, weight, model_name, dtype, mask=Non
 
         cond, uncond, outputs = self.clip_vision_encode(clip_vision, image, self.plus)
         self.clip_embeddings_dim = cond.shape[-1]
-
-        # sd_v1-2: 1024, sd_xl: 2048
-        self.sdxl = self.cross_attention_dim == 2048
         
         self.ipadapter = IPAdapterModel(
             ip_state_dict,
             plus = self.plus,
             cross_attention_dim = self.cross_attention_dim,
             clip_embeddings_dim = self.clip_embeddings_dim,
-            clip_extra_context_tokens = self.clip_extra_context_tokens
+            clip_extra_context_tokens = self.clip_extra_context_tokens,
+            sdxl_plus = self.sdxl_plus
         )
 
         self.ipadapter.to(device, dtype=self.dtype)
@@ -178,6 +178,11 @@ def adapter(self, model, image, clip_vision, weight, model_name, dtype, mask=Non
         
         new_model = model.clone()
 
+        if mask is not None:
+            if mask.dim() == 3:
+                mask = mask[0]
+            mask = mask.to(device)
+
         '''
         patch_name of sdv1-2: ("input" or "output" or "middle", block_id)
         patch_name of sdxl: ("input" or "output" or "middle", block_id, transformer_index)
@@ -189,7 +194,7 @@ def adapter(self, model, image, clip_vision, weight, model_name, dtype, mask=Non
             "dtype": self.dtype,
             "cond": self.image_emb,
             "uncond": self.uncond_image_emb,
-            "mask": mask if mask is None else mask.to(device)
+            "mask": mask
         }
 
         if not self.sdxl: