style

src/diffusers/loaders/ip_adapter.py

@@ -33,7 +33,6 @@
 
 
 if is_transformers_available():
-
     from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
     from ..models.attention_processor import (
@@ -283,7 +282,9 @@ def set_ip_adapter_scale(self, scale):
         scale_configs = _maybe_expand_lora_scales(unet, scale, default_scale=0.0)
 
         for attn_name, attn_processor in unet.attn_processors.items():
-            if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor)):
+            if isinstance(
+                attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor)
+            ):
                 if len(scale_configs) != len(attn_processor.scale):
                     raise ValueError(
                         f"Cannot assign {len(scale_configs)} scale_configs to "
@@ -341,7 +342,9 @@ def unload_ip_adapter(self):
             )
             attn_procs[name] = (
                 attn_processor_class
-                if isinstance(value, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor))
+                if isinstance(
+                    value, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0, IPAdapterXFormersAttnProcessor)
+                )
                 else value.__class__()
             )
         self.unet.set_attn_processor(attn_procs)

src/diffusers/loaders/unet.py

@@ -806,12 +806,14 @@ def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=F
                 attn_processor_class = self.attn_processors[name].__class__
                 attn_procs[name] = attn_processor_class()
             else:
-                if ('XFormers' in str(self.attn_processors[name].__class__)):
-                    attn_processor_class = (IPAdapterXFormersAttnProcessor)
+                if "XFormers" in str(self.attn_processors[name].__class__):
+                    attn_processor_class = IPAdapterXFormersAttnProcessor
                 else:
                     attn_processor_class = (
-                    IPAdapterAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else IPAdapterAttnProcessor
-                )
+                        IPAdapterAttnProcessor2_0
+                        if hasattr(F, "scaled_dot_product_attention")
+                        else IPAdapterAttnProcessor
+                    )
                 num_image_text_embeds = []
                 for state_dict in state_dicts:
                     if "proj.weight" in state_dict["image_proj"]:

src/diffusers/models/attention_processor.py

@@ -372,14 +372,18 @@ def set_use_memory_efficient_attention_xformers(
                 )
                 processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)
             elif is_ip_adapter:
-                processor = IPAdapterXFormersAttnProcessor(hidden_size=self.processor.hidden_size,
+                processor = IPAdapterXFormersAttnProcessor(
+                    hidden_size=self.processor.hidden_size,
                     cross_attention_dim=self.processor.cross_attention_dim,
                     scale=self.processor.scale,
                     num_tokens=self.processor.num_tokens,
-                    attention_op=attention_op)
+                    attention_op=attention_op,
+                )
                 processor.load_state_dict(self.processor.state_dict())
                 if hasattr(self.processor, "to_k_ip"):
-                    processor.to(device=self.processor.to_k_ip[0].weight.device, dtype=self.processor.to_k_ip[0].weight.dtype)
+                    processor.to(
+                        device=self.processor.to_k_ip[0].weight.device, dtype=self.processor.to_k_ip[0].weight.dtype
+                    )
             else:
                 processor = XFormersAttnProcessor(attention_op=attention_op)
         else:
@@ -4569,10 +4573,19 @@ class IPAdapterXFormersAttnProcessor(torch.nn.Module):
             the weight scale of image prompt.
         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.
+            [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, hidden_size, cross_attention_dim=None, num_tokens=(4,), scale=1.0, attention_op: Optional[Callable] = None):
+
+    def __init__(
+        self,
+        hidden_size,
+        cross_attention_dim=None,
+        num_tokens=(4,),
+        scale=1.0,
+        attention_op: Optional[Callable] = None,
+    ):
         super().__init__()
 
         self.hidden_size = hidden_size
@@ -4665,7 +4678,9 @@ def __call__(
         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, op=self.attention_op)
+        hidden_states = xformers.ops.memory_efficient_attention(
+            query, key, value, attn_bias=attention_mask, op=self.attention_op
+        )
         hidden_states = hidden_states.to(query.dtype)
         hidden_states = attn.batch_to_head_dim(hidden_states)
 
@@ -4681,7 +4696,9 @@ def __call__(
                         f"({len(ip_hidden_states)})"
                     )
                 else:
-                    for index, (mask, scale, ip_state) in enumerate(zip(ip_adapter_masks, self.scale, ip_hidden_states)):
+                    for index, (mask, scale, ip_state) in enumerate(
+                        zip(ip_adapter_masks, self.scale, ip_hidden_states)
+                    ):
                         if mask is None:
                             continue
                         if not isinstance(mask, torch.Tensor) or mask.ndim != 4:
@@ -4727,7 +4744,9 @@ def __call__(
                             ip_key = attn.head_to_batch_dim(ip_key).contiguous()
                             ip_value = attn.head_to_batch_dim(ip_value).contiguous()
 
-                            _current_ip_hidden_states = xformers.ops.memory_efficient_attention(query, ip_key, ip_value, op=self.attention_op)
+                            _current_ip_hidden_states = xformers.ops.memory_efficient_attention(
+                                query, ip_key, ip_value, op=self.attention_op
+                            )
                             _current_ip_hidden_states = _current_ip_hidden_states.to(query.dtype)
                             _current_ip_hidden_states = attn.batch_to_head_dim(_current_ip_hidden_states)
 
@@ -4747,7 +4766,9 @@ def __call__(
                         ip_key = attn.head_to_batch_dim(ip_key).contiguous()
                         ip_value = attn.head_to_batch_dim(ip_value).contiguous()
 
-                        current_ip_hidden_states = xformers.ops.memory_efficient_attention(query, ip_key, ip_value, op=self.attention_op)
+                        current_ip_hidden_states = xformers.ops.memory_efficient_attention(
+                            query, ip_key, ip_value, op=self.attention_op
+                        )
                         current_ip_hidden_states = current_ip_hidden_states.to(query.dtype)
                         current_ip_hidden_states = attn.batch_to_head_dim(current_ip_hidden_states)