diff --git a/py/blendmodes.py b/py/blendmodes.py
index fac31af..8d5eada 100644
--- a/py/blendmodes.py
+++ b/py/blendmodes.py
@@ -7,9 +7,10 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 from colorsys import rgb_to_hsv
+import comfy.model_management
 from blend_modes import difference, normal, screen, soft_light, lighten_only, dodge, \
-                        addition, darken_only, multiply, hard_light, \
-                        grain_extract, grain_merge, divide, overlay
+                         addition, darken_only, multiply, hard_light, \
+                         grain_extract, grain_merge, divide, overlay
 
 def dissolve(backdrop, source, opacity):
     # Normalize the RGB and alpha values to 0-1
@@ -56,7 +57,7 @@ def rgb_to_hsv_via_torch(rgb_numpy: np.ndarray, device=None) -> torch.Tensor:
              The hue (H) will be in the range [0, 1], while S and V will be in the range [0, 1].
     """
     if device is None:
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        device = comfy.model_management.get_torch_device()
     
     rgb = torch.from_numpy(rgb_numpy).float().permute(2, 0, 1).to(device)
     r, g, b = rgb[0], rgb[1], rgb[2]
@@ -99,7 +100,7 @@ def hsv_to_rgb_via_torch(hsv_numpy: np.ndarray, device=None) -> torch.Tensor:
              The RGB values will be in the range [0, 1].
     """
     if device is None:
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        device = comfy.model_management.get_torch_device()
     
     hsv = torch.from_numpy(hsv_numpy).float().permute(2, 0, 1).to(device)
     h, s, v = hsv[0], hsv[1], hsv[2]
diff --git a/py/imagefunc.py b/py/imagefunc.py
index 9a4b2c4..7b060c7 100644
--- a/py/imagefunc.py
+++ b/py/imagefunc.py
@@ -1486,7 +1486,7 @@ def create_mask_from_color_tensor(image:Image, color:str, tolerance:int=0) -> Im
 def load_RMBG_model():
     from .briarmbg import BriaRMBG
     current_directory = os.path.dirname(os.path.abspath(__file__))
-    device = "cuda" if torch.cuda.is_available() else "cpu"
+    device = comfy.model_management.get_torch_device()
     net = BriaRMBG()
     model_path = ""
     try:
@@ -1510,8 +1510,8 @@ def RMBG(image:Image) -> Image:
     im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2, 0, 1)
     im_tensor = torch.divide(torch.unsqueeze(im_tensor, 0), 255.0)
     im_tensor = TF.normalize(im_tensor, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
-    if torch.cuda.is_available():
-        im_tensor = im_tensor.cuda()
+    device = rmbgmodel.get_device() if hasattr(rmbgmodel, 'get_device') else next(rmbgmodel.parameters()).device
+    im_tensor = im_tensor.to(device)
     result = rmbgmodel(im_tensor)
     result = torch.squeeze(F.interpolate(result[0][0], size=(h, w), mode='bilinear'), 0)
     ma = torch.max(result)
@@ -1601,11 +1601,11 @@ def generate_VITMatte(image:Image, trimap:Image, local_files_only:bool=False, de
     if device=="cpu":
         device = torch.device('cpu')
     else:
-        if torch.cuda.is_available():
+        if device == "cuda" and not torch.cuda.is_available():
+            log("vitmatte device is set to cuda, but not available, using ComfyUI default device instead.")
+        device = comfy.model_management.get_torch_device()
+        if device.type == "cpu" and torch.cuda.is_available():
             device = torch.device('cuda')
-        else:
-            log("vitmatte device is set to cuda, but not available, using cpu instead.")
-            device = torch.device('cpu')
     if method == "vitmatte-base-composition-1k":
         model_name = "hustvl/vitmatte-base-composition-1k"
         vit_matte_model = load_VITMatte_base_model(model_name=model_name, local_files_only=local_files_only)
@@ -1621,6 +1621,8 @@ def generate_VITMatte(image:Image, trimap:Image, local_files_only:bool=False, de
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
+    elif torch.accelerator.is_available():
+        torch.accelerator.empty_cache()
     mask = tensor2pil(predictions).convert('L')
     mask = mask.crop(
         (0, 0, image.width, image.height))  # remove padding that the prediction appends (works in 32px tiles)
@@ -2155,6 +2157,8 @@ def clear_memory():
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
+    elif torch.accelerator.is_available():
+        torch.accelerator.empty_cache()
 
 def tensor_info(tensor:object) -> str:
     value = ''
@@ -2206,7 +2210,7 @@ class UformGen2QwenChat:
         #                                     local_files_only=False,  # Set to False to allow downloading if not available locally
         #                                     local_dir_use_symlinks="auto") # or set to True/False based on your symlink preference
         self.model_path = files_for_uform_gen2_qwen
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device = comfy.model_management.get_torch_device()
         self.model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True).to(self.device)
         self.processor = AutoProcessor.from_pretrained(self.model_path, trust_remote_code=True)