diff --git a/tinygrad/llops/ops_cpu.py b/tinygrad/llops/ops_cpu.py
index f2d83f5743..a8aefd23cf 100644
--- a/tinygrad/llops/ops_cpu.py
+++ b/tinygrad/llops/ops_cpu.py
@@ -10,7 +10,7 @@ class CPUBuffer(np.ndarray):
   def flip(x, axis): return np.flip(x, axis)
   def amax(x, *args, **kwargs): return np.amax(x, *args, **kwargs)
   def permute(x, order): return x.transpose(order)
-  def custompad(x, padding): return np.pad(x, padding).view(CPUBuffer)
+  def custompad(x, padding): return np.pad(x, padding).view(CPUBuffer) if any(x > 0 or y > 0 for x,y in padding) else x
   def expand(x, new_shape): return np.broadcast_to(x, new_shape).view(CPUBuffer)
 
   @staticmethod
@@ -54,15 +54,13 @@ class CPUBuffer(np.ndarray):
     elif op == MovementOps.FLIP: return x.flip(arg)
     elif op == MovementOps.SLICE:
       padding = [(max(0, -p[0]), max(0, p[1]-x.shape[i])) for i,p in enumerate(arg)]
-      slicee = [(p[0] + padding[i][0], p[1] + padding[i][0]) for i,p in enumerate(arg)]
-      return x.custompad(padding)[tuple([slice(x[0], x[1], None) for x in slicee])]
+      return x.custompad(padding)[tuple(slice(p[0] + padding[i][0], p[1] + padding[i][0], None) for i,p in enumerate(arg))]
     elif op == MovementOps.EXPAND: return x.expand(arg)
     else: raise Exception(f"{op} isn't supported")
 
   def processing_op(x,op,w,C):
     assert op == ProcessingOps.CONV, f"{op} isn't supported"
-    if C.px != 0 or C.py != 0 or C.px_ != 0 or C.py_ != 0:
-      x = x.movement_op(MovementOps.SLICE, ((0, x.shape[0]), (0, x.shape[1]), (-C.py, x.shape[2]+C.py_), (-C.px, x.shape[3]+C.px_)))
+    x = x.movement_op(MovementOps.SLICE, ((0, x.shape[0]), (0, x.shape[1]), (-C.py, x.shape[2]+C.py_), (-C.px, x.shape[3]+C.px_)))
     gx = x.reshape(C.bs,C.groups,C.cin,x.shape[2],x.shape[3])
     tx = np.lib.stride_tricks.as_strided(gx,
       shape=(C.bs, C.groups, C.cin, C.oy, C.ox, C.H, C.W),
diff --git a/tinygrad/llops/ops_torch.py b/tinygrad/llops/ops_torch.py
index 21571cc3ce..49338ad97d 100644
--- a/tinygrad/llops/ops_torch.py
+++ b/tinygrad/llops/ops_torch.py
@@ -23,6 +23,5 @@ class TorchBuffer(torch.Tensor):
 
   def processing_op(x,op,w,C):
     assert op == ProcessingOps.CONV, f"{op} isn't supported"
-    if C.px != C.px_ or C.py != C.py_: padding, x = 0, x.movement_op(MovementOps.SLICE, ((0, x.shape[0]), (0, x.shape[1]), (-C.py, x.shape[2]+C.py_), (-C.px, x.shape[3]+C.px_)))
-    else: padding = (C.py, C.px)
-    return torch.conv2d(x, w, stride=(C.ys, C.xs), groups=C.groups, dilation=(C.dy, C.dx), padding=padding)
+    x = x.movement_op(MovementOps.SLICE, ((0, x.shape[0]), (0, x.shape[1]), (-C.py, x.shape[2]+C.py_), (-C.px, x.shape[3]+C.px_)))
+    return torch.conv2d(x, w, stride=(C.ys, C.xs), groups=C.groups, dilation=(C.dy, C.dx))