diff --git a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
index 23f4886f06c1..f7c78d7af576 100644
--- a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
+++ b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import inspect
-from typing import Callable, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import numpy as np
 import PIL
@@ -569,6 +569,7 @@ def __call__(
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -626,6 +627,10 @@ def __call__(
             callback_steps (`int`, *optional*, defaults to 1):
                 The frequency at which the `callback` function will be called. If not specified, the callback will be
                 called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
         Examples:
 
         Returns:
@@ -687,7 +692,12 @@ def __call__(
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
                 # predict the noise residual
-                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                ).sample
 
                 # perform guidance
                 if do_classifier_free_guidance:
diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
index ba124ffecbee..a7929e13a5f9 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import inspect
-from typing import Callable, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import numpy as np
 import PIL
@@ -577,6 +577,7 @@ def __call__(
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -634,6 +635,10 @@ def __call__(
             callback_steps (`int`, *optional*, defaults to 1):
                 The frequency at which the `callback` function will be called. If not specified, the callback will be
                 called at every step.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
         Examples:
 
         Returns:
@@ -695,7 +700,12 @@ def __call__(
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
                 # predict the noise residual
-                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                ).sample
 
                 # perform guidance
                 if do_classifier_free_guidance: