diff --git a/comfy/diffusers_load.py b/comfy/diffusers_load.py
index 56e63a756..14dbf6488 100644
--- a/comfy/diffusers_load.py
+++ b/comfy/diffusers_load.py
@@ -1,6 +1,7 @@
 import os
-
+import torch
 import comfy.sd
+import comfy.utils
 
 def first_file(path, filenames):
     for f in filenames:
@@ -9,28 +10,41 @@ def first_file(path, filenames):
             return p
     return None
 
-def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None):
-    diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"]
+def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None, weight_dtype=torch.float16):
+    """
+    Load Stable Diffusion model components with custom precision.
+
+    :param model_path: Path to the model directory.
+    :param output_vae: Whether to load the VAE model.
+    :param output_clip: Whether to load the CLIP model (text encoder).
+    :param embedding_directory: Path to embedding directory.
+    :param weight_dtype: Data type for model weights (torch.float16, torch.float32, torch.bfloat16).
+    :return: (UNet, CLIP, VAE)
+    """
+
+    diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors",
+                             "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"]
     unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names)
     vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names)
 
-    text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"]
+    text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors",
+                                "pytorch_model.fp16.bin", "pytorch_model.bin"]
     text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names)
     text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names)
 
-    text_encoder_paths = [text_encoder1_path]
-    if text_encoder2_path is not None:
+    text_encoder_paths = [text_encoder1_path] if text_encoder1_path else []
+    if text_encoder2_path:
         text_encoder_paths.append(text_encoder2_path)
 
-    unet = comfy.sd.load_diffusion_model(unet_path)
+    unet = comfy.sd.load_diffusion_model(unet_path, dtype=weight_dtype)
 
     clip = None
-    if output_clip:
-        clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory)
+    if output_clip and text_encoder_paths:
+        clip = comfy.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory, dtype=weight_dtype)
 
     vae = None
-    if output_vae:
-        sd = comfy.utils.load_torch_file(vae_path)
-        vae = comfy.sd.VAE(sd=sd)
+    if output_vae and vae_path:
+        sd = comfy.utils.load_torch_file(vae_path, map_location="cpu") 
+        vae = comfy.sd.VAE(sd=sd).to(dtype=weight_dtype)  
 
     return (unet, clip, vae)