Remove unnecessary hasattr checks for scaled_dot_product_attention. W…

…e pin the torch version, so there should be no concern that this function does not exist.

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -198,29 +198,24 @@ def _adjust_memory_efficient_attention(self, latents: torch.Tensor):
             self.disable_attention_slicing()
             return
         elif config.attention_type == "torch-sdp":
-            if hasattr(torch.nn.functional, "scaled_dot_product_attention"):
-                # diffusers enables sdp automatically
-                return
-            else:
-                raise Exception("torch-sdp attention slicing not available")
+            # torch-sdp is the default in diffusers.
+            return
 
         # See https://github.com/invoke-ai/InvokeAI/issues/7049 for context.
         # Bumping torch from 2.2.2 to 2.4.1 caused the sliced attention implementation to produce incorrect results.
         # For now, if a user is on an MPS device and has not explicitly set the attention_type, then we select the
         # non-sliced torch-sdp implementation. This keeps things working on MPS at the cost of increased peak memory
         # utilization.
         if torch.backends.mps.is_available():
-            assert hasattr(torch.nn.functional, "scaled_dot_product_attention")
             return
 
-        # the remainder if this code is called when attention_type=='auto'
+        # The remainder if this code is called when attention_type=='auto'.
         if self.unet.device.type == "cuda":
             if is_xformers_available() and prefer_xformers:
                 self.enable_xformers_memory_efficient_attention()
                 return
-            elif hasattr(torch.nn.functional, "scaled_dot_product_attention"):
-                # diffusers enables sdp automatically
-                return
+            # torch-sdp is the default in diffusers.
+            return
 
         if self.unet.device.type == "cpu" or self.unet.device.type == "mps":
             mem_free = psutil.virtual_memory().free