diff --git a/modules/devices.py b/modules/devices.py
index 033a42d5..7511e1dc 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -81,3 +81,7 @@ def autocast(disable=False):
         return contextlib.nullcontext()
 
     return torch.autocast("cuda")
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+def mps_contiguous(input_tensor, device): return input_tensor.contiguous() if device.type == 'mps' else input_tensor
+def mps_contiguous_to(input_tensor, device): return mps_contiguous(input_tensor, device).to(device)
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index a49e2258..a13cf6ac 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -190,7 +190,7 @@ def upscale_without_tiling(model, img):
     img = img[:, :, ::-1]
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(devices.device_esrgan)
+    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_esrgan)
     with torch.no_grad():
         output = model(img)
     output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/scunet_model.py b/modules/scunet_model.py
index 36a996bf..59532274 100644
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@@ -54,9 +54,8 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         img = img[:, :, ::-1]
         img = np.moveaxis(img, 2, 0) / 255
         img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(device)
+        img = devices.mps_contiguous_to(img.unsqueeze(0), device)
 
-        img = img.to(device)
         with torch.no_grad():
             output = model(img)
         output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index facd262d..4253b66d 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -111,7 +111,7 @@ def upscale(
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(devices.device_swinir)
+    img = devices.mps_contiguous_to(img.unsqueeze(0), devices.device_swinir)
     with torch.no_grad(), precision_scope("cuda"):
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old