diff --git a/timm/data/random_erasing.py b/timm/data/random_erasing.py
index c16725ae..e66f7b95 100644
--- a/timm/data/random_erasing.py
+++ b/timm/data/random_erasing.py
@@ -6,12 +6,13 @@ import torch
 def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float32, device='cuda'):
     # NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
     # paths, flip the order so normal is run on CPU if this becomes a problem
-    # ie torch.empty(patch_size, dtype=dtype).normal_().to(device=device)
+    # Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
+    # will revert back to doing normal_() on GPU when it's in next release
     if per_pixel:
         return torch.empty(
-            patch_size, dtype=dtype, device=device).normal_()
+            patch_size, dtype=dtype).normal_().to(device=device)
     elif rand_color:
-        return torch.empty((patch_size[0], 1, 1), dtype=dtype, device=device).normal_()
+        return torch.empty((patch_size[0], 1, 1), dtype=dtype).normal_().to(device=device)
     else:
         return torch.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)
 
diff --git a/timm/data/transforms.py b/timm/data/transforms.py
index 1e1b054a..13a6ff01 100644
--- a/timm/data/transforms.py
+++ b/timm/data/transforms.py
@@ -171,7 +171,6 @@ def transforms_imagenet_train(
     else:
         # if it's a scalar, duplicate for brightness, contrast, and saturation, no hue
         color_jitter = (float(color_jitter),) * 3
-    print(*color_jitter)
 
     tfl = [
         RandomResizedCropAndInterpolation(