diff --git a/src/maxdiffusion/models/attention_flax.py b/src/maxdiffusion/models/attention_flax.py
index cc1d9ea1..d246cee4 100644
--- a/src/maxdiffusion/models/attention_flax.py
+++ b/src/maxdiffusion/models/attention_flax.py
@@ -128,7 +128,7 @@ def _unflatten_heads(tensor, heads):
   return tensor
 
 
-def _reshape_data_for_flash(tensor, heads):
+def _reshape_data_for_flash(tensor, heads, num_context_shards = 1):
   """
   Reshapes tensors for pallas flash attention adding padding to both seq_len and head_dim.
   Pads seq_len to a multiple of flash_block_size, and ensures the resulting number of
@@ -136,7 +136,17 @@ def _reshape_data_for_flash(tensor, heads):
   """
   if tensor.ndim != 4:
     tensor = _unflatten_heads(tensor, heads)
-  return tensor
+
+  # Pad sequence dimension so it is evenly divisible by the context mesh axis,
+  # which shard_map requires.
+  if num_context_shards <= 1:
+    return tensor
+  rem = tensor.shape[2] % num_context_shards
+  if rem == 0:
+    return tensor
+  pad_width = [(0, 0)] * tensor.ndim
+  pad_width[2] = (0, num_context_shards - rem)
+  return jnp.pad(tensor, pad_width)
 
 
 def _pad_data_for_flash(tensor, heads, flash_block_size, num_shards: int = 1):
@@ -255,9 +265,11 @@ def _tpu_flash_attention(
         use_fused_bwd_kernel=True if attention_kernel == "tokamax_flash" else False,
     )
   num_context_shards = mesh.shape["context"]
-  query = _reshape_data_for_flash(query, heads)
-  key = _reshape_data_for_flash(key, heads)
-  value = _reshape_data_for_flash(value, heads)
+  orig_q_seq_len = query.shape[1]
+  query = _reshape_data_for_flash(query, heads, num_context_shards)
+  key = _reshape_data_for_flash(key, heads, num_context_shards)
+  value = _reshape_data_for_flash(value, heads, num_context_shards)
+
   q_axis_names = nn.logical_to_mesh_axes(axis_names_q)
   kv_axis_names = nn.logical_to_mesh_axes(axis_names_kv)
 
@@ -401,6 +413,8 @@ def ring_scan_body(carry, _):
         f" axis, batch dimension: {query.shape[0]}, devices_in_data_context: {devices_in_data_context}"
     )
   x = wrap_flash_attention(query, key, value)
+  # Trim back to original sequence length after context-axis padding.
+  x = x[:, :, :orig_q_seq_len, :]
   x = _reshape_heads_to_head_dim(x)
 
   return x