Fix AuraFlow attn processors applying norm_added_q to key projection (#13533)

Both AuraFlowAttnProcessor2_0 and FusedAuraFlowAttnProcessor2_0 were
calling attn.norm_added_q on encoder_hidden_states_key_proj while
guarded by a check on attn.norm_added_k. This applies the query
normalization layer to the key, which is a copy-paste error.

Consistent with every other attention processor in this file that
defines both norm_added_q and norm_added_k (e.g. FluxAttnProcessor,
CogVideoXAttnProcessor, HunyuanAttnProcessor), where norm_added_k is
applied to the added key projection.

Author: Ricardo-M-L

src/diffusers/models/attention_processor.py

@@ -2140,7 +2140,7 @@ def __call__(
             if attn.norm_added_q is not None:
                 encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
             if attn.norm_added_k is not None:
-                encoder_hidden_states_key_proj = attn.norm_added_q(encoder_hidden_states_key_proj)
+                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
 
             query = torch.cat([encoder_hidden_states_query_proj, query], dim=1)
             key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
@@ -2237,7 +2237,7 @@ def __call__(
             if attn.norm_added_q is not None:
                 encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
             if attn.norm_added_k is not None:
-                encoder_hidden_states_key_proj = attn.norm_added_q(encoder_hidden_states_key_proj)
+                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
 
             query = torch.cat([encoder_hidden_states_query_proj, query], dim=1)
             key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)