Add support for XFormers in SD3

src/diffusers/models/attention_processor.py

@@ -358,6 +358,14 @@ def set_use_memory_efficient_attention_xformers(
             self.processor,
             (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor),
         )
+        is_joint_processor = hasattr(self, "processor") and isinstance(
+            self.processor,
+            (
+                JointAttnProcessor2_0,
+                XFormersJointAttnProcessor,
+            ),
+        )
+
         if use_memory_efficient_attention_xformers:
             if is_added_kv_processor and is_custom_diffusion:
                 raise NotImplementedError(
@@ -420,6 +428,8 @@ def set_use_memory_efficient_attention_xformers(
                     processor.to(
                         device=self.processor.to_k_ip[0].weight.device, dtype=self.processor.to_k_ip[0].weight.dtype
                     )
+            elif is_joint_processor:
+                processor = XFormersJointAttnProcessor(attention_op=attention_op)
             else:
                 processor = XFormersAttnProcessor(attention_op=attention_op)
         else:
@@ -1685,6 +1695,91 @@ def __call__(
         return hidden_states, encoder_hidden_states
 
 
+class XFormersJointAttnProcessor:
+    r"""
+    Processor for implementing memory efficient attention using xFormers.
+
+    Args:
+        attention_op (`Callable`, *optional*, defaults to `None`):
+            The base
+            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
+            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
+            operator.
+    """
+
+    def __init__(self, attention_op: Optional[Callable] = None):
+        self.attention_op = attention_op
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        *args,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+
+        # `sample` projections.
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        query = attn.head_to_batch_dim(query).contiguous()
+        key = attn.head_to_batch_dim(key).contiguous()
+        value = attn.head_to_batch_dim(value).contiguous()
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # `context` projections.
+        if encoder_hidden_states is not None:
+            encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
+            encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
+            encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
+
+            encoder_hidden_states_query_proj = attn.head_to_batch_dim(encoder_hidden_states_query_proj).contiguous()
+            encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj).contiguous()
+            encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj).contiguous()
+
+            if attn.norm_added_q is not None:
+                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
+            if attn.norm_added_k is not None:
+                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
+
+            query = torch.cat([query, encoder_hidden_states_query_proj], dim=1)
+            key = torch.cat([key, encoder_hidden_states_key_proj], dim=1)
+            value = torch.cat([value, encoder_hidden_states_value_proj], dim=1)
+
+        hidden_states = xformers.ops.memory_efficient_attention(
+            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
+        )
+        hidden_states = hidden_states.to(query.dtype)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        if encoder_hidden_states is not None:
+            # Split the attention outputs.
+            hidden_states, encoder_hidden_states = (
+                hidden_states[:, : residual.shape[1]],
+                hidden_states[:, residual.shape[1] :],
+            )
+            if not attn.context_pre_only:
+                encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if encoder_hidden_states is not None:
+            return hidden_states, encoder_hidden_states
+        else:
+            return hidden_states
+
+
 class AllegroAttnProcessor2_0:
     r"""
     Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is

tests/models/transformers/test_models_transformer_sd3.py

@@ -18,6 +18,7 @@
 import torch
 
 from diffusers import SD3Transformer2DModel
+from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
     torch_device,
@@ -80,6 +81,20 @@ def prepare_init_args_and_inputs_for_common(self):
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
 
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_enable_works(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+
+        model.enable_xformers_memory_efficient_attention()
+
+        assert (
+            model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor"
+        ), "xformers is not enabled"
+
     @unittest.skip("SD3Transformer2DModel uses a dedicated attention processor. This test doesn't apply")
     def test_set_attn_processor_for_determinism(self):
         pass
@@ -140,6 +155,20 @@ def prepare_init_args_and_inputs_for_common(self):
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
 
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_enable_works(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+
+        model.enable_xformers_memory_efficient_attention()
+
+        assert (
+            model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor"
+        ), "xformers is not enabled"
+
     @unittest.skip("SD3Transformer2DModel uses a dedicated attention processor. This test doesn't apply")
     def test_set_attn_processor_for_determinism(self):
         pass