diff --git a/tinygrad/opt/heuristic.py b/tinygrad/opt/heuristic.py
index 2b6a90df8f..f6ab24ba8b 100644
--- a/tinygrad/opt/heuristic.py
+++ b/tinygrad/opt/heuristic.py
@@ -28,8 +28,7 @@ def hand_coded_optimizations(k:Kernel) -> list[Opt]:
 
   if k.opts.has_local and k.opts.has_shared and all_int(k.sts[0].shape[:k.first_reduce]):
     # are we grouping? (requires local shape support)
-    if not [x for x in k.sts[0].unit_stride_axes() if x >= k.first_upcast and k.sts[0].shape[x]%4 == 0] and \
-      k.first_reduce <= 2 and k.first_reduce < k.shape_len and prod(k.sts[0].shape[:k.first_reduce]) <= 2048:
+    if k.first_reduce <= 2 and k.first_reduce < k.shape_len and prod(k.sts[0].shape[:k.first_reduce]) <= 2048:
       # TODO: use 1024 if it's allowed in a smarter way
       for sz in ([256, 16] if prod(k.sts[0].shape[:k.first_reduce]) <= 32 else [16]):
         try: # may fail due to excessive smem usage
@@ -40,13 +39,11 @@ def hand_coded_optimizations(k:Kernel) -> list[Opt]:
   # upcast float4 images
   for buf_index,buf in enumerate(k.bufs):
     unit_stride_axes_mul_4 = [i for i in k.sts[buf_index].unit_stride_axes(ignore_valid=True) if k.sts[buf_index].shape[i]%4 == 0]
-    if buf.src[0].dtype.__class__ is ImageDType:
-      #assert len(unit_stride_axes_mul_4) >= 1, f"needs a unit stride axis in {k.bufs[buf_index]}"
-      if len(unit_stride_axes_mul_4) and all(x < k.first_upcast for x in unit_stride_axes_mul_4):
-        if unit_stride_axes_mul_4[0] < k.first_reduce:
-          k.apply_opt(Opt(OptOps.UPCAST, unit_stride_axes_mul_4[0], 4))
-        else:
-          k.apply_opt(Opt(OptOps.UNROLL, unit_stride_axes_mul_4[0]-k.first_reduce, 4))
+    if buf.src[0].dtype.__class__ is ImageDType and len(unit_stride_axes_mul_4) and all(x < k.first_upcast for x in unit_stride_axes_mul_4):
+      if unit_stride_axes_mul_4[0] < k.first_reduce:
+        k.apply_opt(Opt(OptOps.UPCAST, unit_stride_axes_mul_4[0], 4))
+      else:
+        k.apply_opt(Opt(OptOps.UNROLL, unit_stride_axes_mul_4[0]-k.first_reduce, 4))
 
   # no more opt if we are grouping
   if k.group_for_reduces: return k.applied_opts