diff --git a/tinygrad/mixin/op.py b/tinygrad/mixin/op.py
index b501faac9a..1b86cbda42 100644
--- a/tinygrad/mixin/op.py
+++ b/tinygrad/mixin/op.py
@@ -1057,14 +1057,16 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
     assert not (align_corners and mode != "linear"), "align_corners option can only be set with the interpolating mode linear"
     x, expand = self, list(self.shape)
     for i in range(-1,-len(size)-1,-1):
-      scale = (int(self.shape[i]) - int(align_corners)) / (size[i] - int(align_corners))
-      arr, reshape = type(self).arange(size[i], dtype=dtypes.float32), [1] * self.ndim
+      in_sz, reshape = int(self.shape[i]), [1] * self.ndim
       reshape[i] = expand[i] = size[i]
       if mode == "linear":
-        index = (scale*arr if align_corners else (scale*(arr+0.5))-0.5).clip(0, self.shape[i]-1)
-        low, high, perc = [y.reshape(reshape).expand(expand) for y in (index.floor().int(), index.ceil().int(), index - index.floor())]
+        arr = type(self).arange(size[i])
+        num, den = (arr*(in_sz-1), size[i]-1) if align_corners else ((arr*2+1)*in_sz - size[i], size[i]*2)
+        num = num.clip(0, (in_sz-1)*den)
+        low, high, perc = [y.reshape(reshape).expand(expand) for y in (num//den, (num+den-1)//den, (num % den).cast(dtypes.float32)/den)]
         x = x.gather(i, low).lerp(x.gather(i, high), perc)
       else:
+        scale, arr = in_sz / size[i], type(self).arange(size[i], dtype=dtypes.float32)
         index = (scale*(arr+0.5) if mode=="nearest-exact" else scale*arr).cast(dtypes.int32).reshape(reshape).expand(expand)
         x = x.gather(i, index)
     return x.cast(self.dtype)