diff --git a/ip_adapter/ip_adapter_faceid.py b/ip_adapter/ip_adapter_faceid.py
index 318e131..5fd8a89 100644
--- a/ip_adapter/ip_adapter_faceid.py
+++ b/ip_adapter/ip_adapter_faceid.py
@@ -106,11 +106,12 @@ class ProjPlusModel(torch.nn.Module):
 
 
 class IPAdapterFaceID:
-    def __init__(self, sd_pipe, ip_ckpt, device, lora_rank=128, num_tokens=4):
+    def __init__(self, sd_pipe, ip_ckpt, device, lora_rank=128, num_tokens=4, torch_dtype=torch.float16):
         self.device = device
         self.ip_ckpt = ip_ckpt
         self.lora_rank = lora_rank
         self.num_tokens = num_tokens
+        self.torch_dtype = torch_dtype
 
         self.pipe = sd_pipe.to(self.device)
         self.set_ip_adapter()
@@ -125,7 +126,7 @@ class IPAdapterFaceID:
             cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
             id_embeddings_dim=512,
             num_tokens=self.num_tokens,
-        ).to(self.device, dtype=torch.float16)
+        ).to(self.device, dtype=self.torch_dtype)
         return image_proj_model
 
     def set_ip_adapter(self):
@@ -144,11 +145,11 @@ class IPAdapterFaceID:
             if cross_attention_dim is None:
                 attn_procs[name] = LoRAAttnProcessor(
                     hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=self.lora_rank,
-                ).to(self.device, dtype=torch.float16)
+                ).to(self.device, dtype=self.torch_dtype)
             else:
                 attn_procs[name] = LoRAIPAttnProcessor(
                     hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, rank=self.lora_rank, num_tokens=self.num_tokens,
-                ).to(self.device, dtype=torch.float16)
+                ).to(self.device, dtype=self.torch_dtype)
         unet.set_attn_processor(attn_procs)
 
     def load_ip_adapter(self):
@@ -169,7 +170,7 @@ class IPAdapterFaceID:
     @torch.inference_mode()
     def get_image_embeds(self, faceid_embeds):
         
-        faceid_embeds = faceid_embeds.to(self.device, dtype=torch.float16)
+        faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype)
         image_prompt_embeds = self.image_proj_model(faceid_embeds)
         uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds))
         return image_prompt_embeds, uncond_image_prompt_embeds
@@ -239,19 +240,20 @@ class IPAdapterFaceID:
 
 
 class IPAdapterFaceIDPlus:
-    def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, lora_rank=128, num_tokens=4):
+    def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, lora_rank=128, num_tokens=4, torch_dtype=torch.float16):
         self.device = device
         self.image_encoder_path = image_encoder_path
         self.ip_ckpt = ip_ckpt
         self.lora_rank = lora_rank
         self.num_tokens = num_tokens
+        self.torch_dtype = torch_dtype
 
         self.pipe = sd_pipe.to(self.device)
         self.set_ip_adapter()
 
         # load image encoder
         self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
-            self.device, dtype=torch.float16
+            self.device, dtype=self.torch_dtype
         )
         self.clip_image_processor = CLIPImageProcessor()
         # image proj model
@@ -265,7 +267,7 @@ class IPAdapterFaceIDPlus:
             id_embeddings_dim=512,
             clip_embeddings_dim=self.image_encoder.config.hidden_size,
             num_tokens=self.num_tokens,
-        ).to(self.device, dtype=torch.float16)
+        ).to(self.device, dtype=self.torch_dtype)
         return image_proj_model
 
     def set_ip_adapter(self):
@@ -284,11 +286,11 @@ class IPAdapterFaceIDPlus:
             if cross_attention_dim is None:
                 attn_procs[name] = LoRAAttnProcessor(
                     hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=self.lora_rank,
-                ).to(self.device, dtype=torch.float16)
+                ).to(self.device, dtype=self.torch_dtype)
             else:
                 attn_procs[name] = LoRAIPAttnProcessor(
                     hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, rank=self.lora_rank, num_tokens=self.num_tokens,
-                ).to(self.device, dtype=torch.float16)
+                ).to(self.device, dtype=self.torch_dtype)
         unet.set_attn_processor(attn_procs)
 
     def load_ip_adapter(self):
@@ -311,13 +313,13 @@ class IPAdapterFaceIDPlus:
         if isinstance(face_image, Image.Image):
             pil_image = [face_image]
         clip_image = self.clip_image_processor(images=face_image, return_tensors="pt").pixel_values
-        clip_image = clip_image.to(self.device, dtype=torch.float16)
+        clip_image = clip_image.to(self.device, dtype=self.torch_dtype)
         clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
         uncond_clip_image_embeds = self.image_encoder(
             torch.zeros_like(clip_image), output_hidden_states=True
         ).hidden_states[-2]
         
-        faceid_embeds = faceid_embeds.to(self.device, dtype=torch.float16)
+        faceid_embeds = faceid_embeds.to(self.device, dtype=self.torch_dtype)
         image_prompt_embeds = self.image_proj_model(faceid_embeds, clip_image_embeds, shortcut=shortcut, scale=s_scale)
         uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds), uncond_clip_image_embeds, shortcut=shortcut, scale=s_scale)
         return image_prompt_embeds, uncond_image_prompt_embeds