diff --git a/romatch/models/matcher.py b/romatch/models/matcher.py
index 8108a92..1fac0aa 100644
--- a/romatch/models/matcher.py
+++ b/romatch/models/matcher.py
@@ -1,18 +1,19 @@
-import os
 import math
+import os
+import warnings
+from warnings import warn
+
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
-import warnings
-from warnings import warn
 from PIL import Image
-
 from romatch.utils import get_tuple_transform_ops
+from romatch.utils.kde import kde
 from romatch.utils.local_correlation import local_correlation
 from romatch.utils.utils import cls_to_flow_refine, get_autocast_params
-from romatch.utils.kde import kde
+
 
 class ConvRefiner(nn.Module):
     def __init__(
@@ -654,7 +655,12 @@ def match(
                 test_transform = get_tuple_transform_ops(
                     resize=(hs, ws), normalize=True
                 )
-                im_A, im_B = test_transform((Image.open(im_A_path).convert('RGB'), Image.open(im_B_path).convert('RGB')))
+                if isinstance(im_A_path, (str, os.PathLike)):
+                    im_A, im_B = Image.open(im_A_path).convert("RGB"), Image.open(im_B_path).convert("RGB")
+                else:
+                    im_A, im_B = im_A_path, im_B_path 
+                im_A, im_B = test_transform((im_A, im_B))
+
                 im_A, im_B = im_A[None].to(device), im_B[None].to(device)
                 scale_factor = math.sqrt(self.upsample_res[0] * self.upsample_res[1] / (self.w_resized * self.h_resized))
                 batch = {"im_A": im_A, "im_B": im_B, "corresps": finest_corresps}
diff --git a/romatch/models/model_zoo/__init__.py b/romatch/models/model_zoo/__init__.py
index d0470ca..c22026f 100644
--- a/romatch/models/model_zoo/__init__.py
+++ b/romatch/models/model_zoo/__init__.py
@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Union, Tuple
 import torch
 from .roma_models import roma_model, tiny_roma_v1_model
 
@@ -27,7 +27,7 @@ def tiny_roma_v1_outdoor(device, weights = None, xfeat = None):
 
     return tiny_roma_v1_model(weights = weights, xfeat = xfeat).to(device) 
 
-def roma_outdoor(device, weights=None, dinov2_weights=None, coarse_res: Union[int,tuple[int,int]] = 560, upsample_res: Union[int,tuple[int,int]] = 864, amp_dtype: torch.dtype = torch.float16):
+def roma_outdoor(device, weights=None, dinov2_weights=None, coarse_res: Union[int,Tuple[int,int]] = 560, upsample_res: Union[int,Tuple[int,int]] = 864, amp_dtype: torch.dtype = torch.float16):
     if isinstance(coarse_res, int):
         coarse_res = (coarse_res, coarse_res)
     if isinstance(upsample_res, int):    
@@ -51,7 +51,7 @@ def roma_outdoor(device, weights=None, dinov2_weights=None, coarse_res: Union[in
     print(f"Using coarse resolution {coarse_res}, and upsample res {model.upsample_res}")
     return model
 
-def roma_indoor(device, weights=None, dinov2_weights=None, coarse_res: Union[int,tuple[int,int]] = 560, upsample_res: Union[int,tuple[int,int]] = 864, amp_dtype: torch.dtype = torch.float16):
+def roma_indoor(device, weights=None, dinov2_weights=None, coarse_res: Union[int,Tuple[int,int]] = 560, upsample_res: Union[int,Tuple[int,int]] = 864, amp_dtype: torch.dtype = torch.float16):
     if isinstance(coarse_res, int):
         coarse_res = (coarse_res, coarse_res)
     if isinstance(upsample_res, int):