comfy/ldm/modules/attention.py

@@ -719,7 +719,7 @@ def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
         )
     return out
 
-def var_attention_pytorch(q, k, v, heads, cu_seqlens_q, cu_seqlens_k, skip_reshape=False, skip_output_reshape=False):
+def var_attention_pytorch(q, k, v, heads, cu_seqlens_q, cu_seqlens_k, skip_reshape=False):
     if not skip_reshape:
         # assumes 2D q, k,v [total_tokens, embed_dim]
         total_tokens, embed_dim = q.shape
@@ -737,10 +737,7 @@ def var_attention_pytorch(q, k, v, heads, cu_seqlens_q, cu_seqlens_k, skip_resha
     v = v.transpose(1, 2)
     out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
 
-    out = out.transpose(1, 2)
-    if not skip_output_reshape:
-        return out.values().reshape(-1, heads * (q.shape[-1]))
-    return out.values()
+    return out.transpose(1, 2).values()
 
 optimized_var_attention = var_attention_pytorch
 optimized_attention = attention_basic

comfy/ldm/seedvr/model.py

@@ -808,7 +808,7 @@ class NaSwinAttention(NaMMAttention):
             q=concat_win(vid_q, txt_q),
             k=concat_win(vid_k, txt_k),
             v=concat_win(vid_v, txt_v),
-            heads=self.heads, skip_reshape=True, skip_output_reshape=True,
+            heads=self.heads, skip_reshape=True,
             cu_seqlens_q=cache_win(
                 "vid_seqlens_q", lambda: safe_pad_operation(all_len_win.cumsum(0), (1, 0)).int()
             ),