diff --git a/timm/optim/kron.py b/timm/optim/kron.py
index e01c9885be..533354ecc8 100644
--- a/timm/optim/kron.py
+++ b/timm/optim/kron.py
@@ -94,7 +94,10 @@ class Kron(torch.optim.Optimizer):
         mu_dtype: Dtype of the momentum accumulator.
         precond_dtype: Dtype of the preconditioner.
         decoupled_decay: AdamW style decoupled weight decay
-        flatten_dim: Flatten dim >= 2 instead of relying on expressions
+        flatten: Flatten dimensions instead of fully relying on expressions for higher rank params
+        flatten_start_dim: Start of flatten range, defaults to 2. Seems good tradeoff for ConvNets.
+        flatten_end_dim: End of flatten range, defaults to -1.
+        stochastic_weight_decay: Enable random modulation of weight decay
         deterministic: Deterministic behaviour across save / load (resume). FIXME slow, needs work
     """
 
@@ -114,7 +117,10 @@ def __init__(
         mu_dtype: Optional[torch.dtype] = None,
         precond_dtype: Optional[torch.dtype] = None,
         decoupled_decay: bool = False,
-        flatten_dim: bool = False,
+        flatten: bool = False,
+        flatten_start_dim: int = 2,
+        flatten_end_dim: int = -1,
+        stochastic_weight_decay: bool = False,
         deterministic: bool = False,
     ):
         if not has_opt_einsum:
@@ -141,7 +147,10 @@ def __init__(
             mu_dtype=mu_dtype,
             precond_dtype=precond_dtype,
             decoupled_decay=decoupled_decay,
-            flatten_dim=flatten_dim,
+            flatten=flatten,
+            flatten_start_dim=flatten_start_dim,
+            flatten_end_dim=flatten_end_dim,
+            stochastic_weight_decay=stochastic_weight_decay,
         )
         super(Kron, self).__init__(params, defaults)
 
@@ -229,8 +238,11 @@ def step(self, closure=None):
 
                 grad = p.grad
                 state = self.state[p]
-                if group['flatten_dim']:
-                    grad = grad.view(grad.size(0), -1)
+
+                flattened = False
+                if group['flatten']:
+                    grad = safe_flatten(grad, group["flatten_start_dim"], group["flatten_end_dim"])
+                    flattened = True
 
                 if len(state) == 0:
                     state["step"] = 0
@@ -341,15 +353,19 @@ def step(self, closure=None):
 
                 # RMS of pre_grad should be 1.0, so let's cap at 1.1
                 pre_grad.mul_(torch.clamp(1.1 / (pre_grad.square().mean().sqrt_() + 1e-8), max=1.0))
-                if group['flatten_dim']:
+                if flattened:
                     pre_grad = pre_grad.view(p.shape)
 
                 # Apply weight decay
-                if group["weight_decay"] != 0:
+                weight_decay = group["weight_decay"]
+                if weight_decay != 0:
+                    if group["stochastic_weight_decay"]:
+                        weight_decay = 2 * self.rng.random() * weight_decay
+
                     if group["decoupled_decay"]:
-                        p.mul_(1. - group["lr"] * group["weight_decay"])
+                        p.mul_(1. - group["lr"] * weight_decay)
                     else:
-                        pre_grad.add_(p, alpha=group["weight_decay"])
+                        pre_grad.add_(p, alpha=weight_decay)
 
                 # Update parameters
                 p.add_(pre_grad, alpha=-group["lr"])
@@ -361,6 +377,20 @@ def step(self, closure=None):
         return loss
 
 
+def safe_flatten(tensor, start_dim=0, end_dim=-1):
+    ndim = tensor.ndim
+
+    # Convert negative end_dim to positive and clip to end
+    end_dim = min(end_dim if end_dim >= 0 else ndim + end_dim, ndim - 1)
+
+    # If tensor has fewer dims than start_dim or start > end, return tensor as is
+    if ndim <= start_dim or start_dim > end_dim:
+        return tensor
+
+    # Now safe to flatten
+    return tensor.flatten(start_dim, end_dim)
+
+
 def _init_Q_exprs(
         t,
         scale,