attention_kwargs -> cross_attention_kwargs.

Author: sayakpaul

src/diffusers/models/transformers/sana_transformer.py

@@ -364,22 +364,22 @@ def forward(
         timestep: torch.LongTensor,
         encoder_attention_mask: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
     ) -> Union[Tuple[torch.Tensor, ...], Transformer2DModelOutput]:
-        if attention_kwargs is not None:
-            attention_kwargs = attention_kwargs.copy()
-            lora_scale = attention_kwargs.pop("scale", 1.0)
+        if cross_attention_kwargs is not None:
+            cross_attention_kwargs = cross_attention_kwargs.copy()
+            lora_scale = cross_attention_kwargs.pop("scale", 1.0)
         else:
             lora_scale = 1.0
 
         if USE_PEFT_BACKEND:
             # weight the lora layers by setting `lora_scale` for each PEFT layer
             scale_lora_layers(self, lora_scale)
         else:
-            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
+            if cross_attention_kwargs is not None and cross_attention_kwargs.get("scale", None) is not None:
                 logger.warning(
-                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
+                    "Passing `scale` via `cross_attention_kwargs` when not using the PEFT backend is ineffective."
                 )
 
         # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.

src/diffusers/pipelines/sana/pipeline_sana.py

@@ -574,8 +574,8 @@ def guidance_scale(self):
         return self._guidance_scale
 
     @property
-    def attention_kwargs(self):
-        return self._attention_kwargs
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
 
     @property
     def do_classifier_free_guidance(self):
@@ -613,7 +613,7 @@ def __call__(
         return_dict: bool = True,
         clean_caption: bool = True,
         use_resolution_binning: bool = True,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 300,
@@ -686,7 +686,7 @@ def __call__(
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
-            attention_kwargs: TODO
+            cross_attention_kwargs: TODO
             clean_caption (`bool`, *optional*, defaults to `True`):
                 Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
                 be installed. If the dependencies are not installed, the embeddings will be created from the raw
@@ -747,7 +747,7 @@ def __call__(
         )
 
         self._guidance_scale = guidance_scale
-        self._attention_kwargs = attention_kwargs
+        self._cross_attention_kwargs = cross_attention_kwargs
         self._interrupt = False
 
         # 2. Default height and width to transformer
@@ -759,7 +759,9 @@ def __call__(
             batch_size = prompt_embeds.shape[0]
 
         device = self._execution_device
-        lora_scale = self.attention_kwargs.get("scale", None) if self.attention_kwargs is not None else None
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
 
         # 3. Encode input prompt
         (
@@ -829,7 +831,7 @@ def __call__(
                     encoder_attention_mask=prompt_attention_mask,
                     timestep=timestep,
                     return_dict=False,
-                    attention_kwargs=self.attention_kwargs,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
                 )[0]
                 noise_pred = noise_pred.float()