diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py
index 5a22ef030..e81bb37d3 100644
--- a/comfy/ldm/modules/diffusionmodules/model.py
+++ b/comfy/ldm/modules/diffusionmodules/model.py
@@ -259,7 +259,10 @@ def slice_attention(q, k, v):
 
     while True:
         try:
-            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+            slice_size = max(1, math.ceil(q.shape[1] / steps))
+            if q.device.type == "mps":
+                max_slice_for_mps = max(1, ((2 ** 31) - 1) // max(1, q.shape[0] * k.shape[2]))
+                slice_size = min(slice_size, max_slice_for_mps)
             for i in range(0, q.shape[1], slice_size):
                 end = i + slice_size
                 s1 = torch.bmm(q[:, i:end], k) * scale
@@ -270,6 +273,15 @@ def slice_attention(q, k, v):
                 r1[:, :, i:end] = torch.bmm(v, s2)
                 del s2
             break
+        except RuntimeError as e:
+            if q.device.type == "mps" and ("INT_MAX" in str(e) or "MPSGraph" in str(e) or "MPSGaph" in str(e)):
+                model_management.soft_empty_cache(True)
+                steps *= 2
+                if steps > 4096:
+                    raise e
+                logging.warning("MPS attention limit reached, increasing steps and trying again {}".format(steps))
+                continue
+            raise e
         except model_management.OOM_EXCEPTION as e:
             model_management.soft_empty_cache(True)
             steps *= 2
diff --git a/comfy/utils.py b/comfy/utils.py
index 1337e2205..ecc2aa20f 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -1105,7 +1105,7 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am
 
         # handle entire input fitting in a single tile
         if all(s.shape[d+2] <= tile[d] for d in range(dims)):
-            output[b:b+1] = function(s).to(output_device)
+            output[b:b+1] = function(s.contiguous()).to(output_device)
             if pbar is not None:
                 pbar.update(1)
             continue
@@ -1125,7 +1125,7 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_am
                 s_in = s_in.narrow(d + 2, pos, l)
                 upscaled.append(round(get_pos(d, pos)))
 
-            ps = function(s_in).to(output_device)
+            ps = function(s_in.contiguous()).to(output_device)
             mask = torch.ones_like(ps)
 
             for d in range(2, dims + 2):