Simplify and improve some vae attention code.

comfy/ldm/modules/diffusionmodules/model.py

@@ -331,25 +331,13 @@ class MemoryEfficientAttnBlockPytorch(nn.Module):
 
         # compute attention
         B, C, H, W = q.shape
-        q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
-
         q, k, v = map(
-            lambda t: t.unsqueeze(3)
+            lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
-            .reshape(B, t.shape[1], 1, C)
-            .permute(0, 2, 1, 3)
-            .reshape(B * 1, t.shape[1], C)
-            .contiguous(),
             (q, k, v),
         )
         out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
 
-        out = (
+        out = out.transpose(2, 3).reshape(B, C, H, W)
-            out.unsqueeze(0)
-            .reshape(B, 1, out.shape[1], C)
-            .permute(0, 2, 1, 3)
-            .reshape(B, out.shape[1], C)
-        )
-        out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
         out = self.proj_out(out)
         return x+out