Fix running LoRA with xformers (#2286)

* Fix running LoRA with xformers

* support disabling xformers

* reformat

* Add test

src/diffusers/models/cross_attention.py

@@ -105,6 +105,10 @@ class CrossAttention(nn.Module):
     def set_use_memory_efficient_attention_xformers(
         self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
     ):
+        is_lora = hasattr(self, "processor") and isinstance(
+            self.processor, (LoRACrossAttnProcessor, LoRAXFormersCrossAttnProcessor)
+        )
+
         if use_memory_efficient_attention_xformers:
             if self.added_kv_proj_dim is not None:
                 # TODO(Anton, Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP
@@ -138,7 +142,26 @@ class CrossAttention(nn.Module):
                 except Exception as e:
                     raise e
 
+            if is_lora:
+                processor = LoRAXFormersCrossAttnProcessor(
+                    hidden_size=self.processor.hidden_size,
+                    cross_attention_dim=self.processor.cross_attention_dim,
+                    rank=self.processor.rank,
+                    attention_op=attention_op,
+                )
+                processor.load_state_dict(self.processor.state_dict())
+                processor.to(self.processor.to_q_lora.up.weight.device)
+            else:
                 processor = XFormersCrossAttnProcessor(attention_op=attention_op)
+        else:
+            if is_lora:
+                processor = LoRACrossAttnProcessor(
+                    hidden_size=self.processor.hidden_size,
+                    cross_attention_dim=self.processor.cross_attention_dim,
+                    rank=self.processor.rank,
+                )
+                processor.load_state_dict(self.processor.state_dict())
+                processor.to(self.processor.to_q_lora.up.weight.device)
             else:
                 processor = CrossAttnProcessor()
 
@@ -324,6 +347,10 @@ class LoRACrossAttnProcessor(nn.Module):
     def __init__(self, hidden_size, cross_attention_dim=None, rank=4):
         super().__init__()
 
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.rank = rank
+
         self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank)
         self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)
         self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)
@@ -437,9 +464,14 @@ class XFormersCrossAttnProcessor:
 
 
 class LoRAXFormersCrossAttnProcessor(nn.Module):
-    def __init__(self, hidden_size, cross_attention_dim, rank=4):
+    def __init__(self, hidden_size, cross_attention_dim, rank=4, attention_op: Optional[Callable] = None):
         super().__init__()
 
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.rank = rank
+        self.attention_op = attention_op
+
         self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank)
         self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)
         self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank)
@@ -462,7 +494,9 @@ class LoRAXFormersCrossAttnProcessor(nn.Module):
         key = attn.head_to_batch_dim(key).contiguous()
         value = attn.head_to_batch_dim(value).contiguous()
 
-        hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
+        hidden_states = xformers.ops.memory_efficient_attention(
+            query, key, value, attn_bias=attention_mask, op=self.attention_op
+        )
         hidden_states = attn.batch_to_head_dim(hidden_states)
 
         # linear proj
@@ -595,4 +629,6 @@ AttnProcessor = Union[
     SlicedAttnProcessor,
     CrossAttnAddedKVProcessor,
     SlicedAttnAddedKVProcessor,
+    LoRACrossAttnProcessor,
+    LoRAXFormersCrossAttnProcessor,
 ]

tests/models/test_models_unet_2d_condition.py

@@ -412,6 +412,35 @@ class UNet2DConditionModelTests(ModelTesterMixin, unittest.TestCase):
         assert (sample - new_sample).abs().max() < 1e-4
         assert (sample - old_sample).abs().max() < 1e-4
 
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_lora_xformers_on_off(self):
+        # enable deterministic behavior for gradient checkpointing
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        init_dict["attention_head_dim"] = (8, 16)
+
+        torch.manual_seed(0)
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        lora_attn_procs = create_lora_layers(model)
+        model.set_attn_processor(lora_attn_procs)
+
+        # default
+        with torch.no_grad():
+            sample = model(**inputs_dict).sample
+
+            model.enable_xformers_memory_efficient_attention()
+            on_sample = model(**inputs_dict).sample
+
+            model.disable_xformers_memory_efficient_attention()
+            off_sample = model(**inputs_dict).sample
+
+        assert (sample - on_sample).abs().max() < 1e-4
+        assert (sample - off_sample).abs().max() < 1e-4
+
 
 @slow
 class UNet2DConditionModelIntegrationTests(unittest.TestCase):