diff --git a/timm/optim/adafactor_bv.py b/timm/optim/adafactor_bv.py
index bad982ee..465f8f0e 100644
--- a/timm/optim/adafactor_bv.py
+++ b/timm/optim/adafactor_bv.py
@@ -139,11 +139,11 @@ class AdafactorBigVision(Optimizer):
                     )
 
                     if factored_dims is not None:
-                        d1, d0 = factored_dims
+                        dc, dr = factored_dims
                         row_shape = list(p.grad.shape)
-                        row_shape[d0] = 1
+                        row_shape[dr] = 1
                         col_shape = list(p.grad.shape)
-                        col_shape[d1] = 1
+                        col_shape[dc] = 1
                         state['exp_avg_sq_r'] = p.grad.new_zeros(row_shape)
                         state['exp_avg_sq_c'] = p.grad.new_zeros(col_shape)
                     else:
@@ -226,12 +226,12 @@ def _single_tensor_adafactor(
         # NOTE application of eps (epsilon1) mirrors the optax/big vision/t5x approach
         if exp_avg_sq is None:
             # factorized second moment
-            d1, d0 = _factored_dims(grad.shape, True, min_dim_size_to_factor=min_dim_size_to_factor)
-            exp_avg_sq_r.lerp_(grad_sqr.mean(dim=d0, keepdim=True), one_minus_beta2_t)
-            exp_avg_sq_c.lerp_(grad_sqr.mean(dim=d1, keepdim=True), one_minus_beta2_t)
+            dc, dr = _factored_dims(grad.shape, True, min_dim_size_to_factor=min_dim_size_to_factor)
+            exp_avg_sq_r.lerp_(grad_sqr.mean(dim=dr, keepdim=True), one_minus_beta2_t)
+            exp_avg_sq_c.lerp_(grad_sqr.mean(dim=dc, keepdim=True), one_minus_beta2_t)
 
-            reduced_d1 = d1 - 1 if d1 > d0 else d1
-            row_col_mean = exp_avg_sq_r.mean(dim=reduced_d1, keepdim=True)
+            reduce_dc = dc - 1 if dc > dr else dc
+            row_col_mean = exp_avg_sq_r.mean(dim=reduce_dc, keepdim=True)
             row_factor = (exp_avg_sq_r / row_col_mean).rsqrt()
             col_factor = exp_avg_sq_c.rsqrt()