add: a warning message when using xformers in a PT 2.0 env. (#3365)

* add: a warning message when using xformers in a PT 2.0 env.

* Apply suggestions from code review
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

---------
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/diffusers/models/attention_processor.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import warnings
 from typing import Callable, Optional, Union
 
 import torch
@@ -72,7 +73,8 @@ class Attention(nn.Module):
         self.upcast_attention = upcast_attention
         self.upcast_softmax = upcast_softmax
 
-        self.scale = dim_head**-0.5 if scale_qk else 1.0
+        self.scale_qk = scale_qk
+        self.scale = dim_head**-0.5 if self.scale_qk else 1.0
 
         self.heads = heads
         # for slice_size > 0 the attention score computation
@@ -140,7 +142,7 @@ class Attention(nn.Module):
         # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
         if processor is None:
             processor = (
-                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and scale_qk else AttnProcessor()
+                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
             )
         self.set_processor(processor)
 
@@ -176,6 +178,11 @@ class Attention(nn.Module):
                     "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
                     " only available for GPU "
                 )
+            elif hasattr(F, "scaled_dot_product_attention") and self.scale_qk:
+                warnings.warn(
+                    "You have specified using flash attention using xFormers but you have PyTorch 2.0 already installed. "
+                    "We will default to PyTorch's native efficient flash attention implementation provided by PyTorch 2.0."
+                )
             else:
                 try:
                     # Make sure we can run the memory efficient attention
@@ -229,7 +236,15 @@ class Attention(nn.Module):
                 if hasattr(self.processor, "to_k_custom_diffusion"):
                     processor.to(self.processor.to_k_custom_diffusion.weight.device)
             else:
-                processor = AttnProcessor()
+                # set attention processor
+                # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
+                # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
+                # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
+                processor = (
+                    AttnProcessor2_0()
+                    if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
+                    else AttnProcessor()
+                )
 
         self.set_processor(processor)
 
@@ -244,7 +259,13 @@ class Attention(nn.Module):
         elif self.added_kv_proj_dim is not None:
             processor = AttnAddedKVProcessor()
         else:
-            processor = AttnProcessor()
+            # set attention processor
+            # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
+            # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
+            # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
+            processor = (
+                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
+            )
 
         self.set_processor(processor)