diff --git a/examples/community/interpolate_stable_diffusion.py b/examples/community/interpolate_stable_diffusion.py
index bbb1b0f9e6..de1c6f687a 100644
--- a/examples/community/interpolate_stable_diffusion.py
+++ b/examples/community/interpolate_stable_diffusion.py
@@ -278,7 +278,7 @@ class StableDiffusionWalkPipeline(DiffusionPipeline):
         if do_classifier_free_guidance:
             uncond_tokens: List[str]
             if negative_prompt is None:
-                uncond_tokens = [""]
+                uncond_tokens = [""] * batch_size
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
                     f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
@@ -307,7 +307,7 @@ class StableDiffusionWalkPipeline(DiffusionPipeline):
 
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
             uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
 
             # For classifier free guidance, we need to do two forward passes.
diff --git a/examples/community/speech_to_image_diffusion.py b/examples/community/speech_to_image_diffusion.py
index 8b24914cb7..1a9d296e81 100644
--- a/examples/community/speech_to_image_diffusion.py
+++ b/examples/community/speech_to_image_diffusion.py
@@ -148,7 +148,7 @@ class SpeechToImagePipeline(DiffusionPipeline):
         if do_classifier_free_guidance:
             uncond_tokens: List[str]
             if negative_prompt is None:
-                uncond_tokens = [""]
+                uncond_tokens = [""] * batch_size
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
                     f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
@@ -177,7 +177,7 @@ class SpeechToImagePipeline(DiffusionPipeline):
 
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
             uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
 
             # For classifier free guidance, we need to do two forward passes.
diff --git a/examples/community/wildcard_stable_diffusion.py b/examples/community/wildcard_stable_diffusion.py
index 79cb4feb1e..b0f6375d50 100644
--- a/examples/community/wildcard_stable_diffusion.py
+++ b/examples/community/wildcard_stable_diffusion.py
@@ -295,7 +295,7 @@ class WildcardStableDiffusionPipeline(DiffusionPipeline):
         if do_classifier_free_guidance:
             uncond_tokens: List[str]
             if negative_prompt is None:
-                uncond_tokens = [""]
+                uncond_tokens = [""] * batch_size
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
                     f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
@@ -324,7 +324,7 @@ class WildcardStableDiffusionPipeline(DiffusionPipeline):
 
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
             uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
 
             # For classifier free guidance, we need to do two forward passes.
diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
index 3c1eb734a4..1ccc87804e 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
@@ -297,7 +297,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         if do_classifier_free_guidance:
             uncond_tokens: List[str]
             if negative_prompt is None:
-                uncond_tokens = [""]
+                uncond_tokens = [""] * batch_size
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
                     f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
@@ -326,7 +326,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
 
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
             uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
 
             # For classifier free guidance, we need to do two forward passes.
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 e61fb27acc..8284bac850 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
@@ -295,7 +295,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         if do_classifier_free_guidance:
             uncond_tokens: List[str]
             if negative_prompt is None:
-                uncond_tokens = [""]
+                uncond_tokens = [""] * batch_size
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
                     f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
@@ -319,7 +319,9 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
             uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
 
             # duplicate unconditional embeddings for each generation per prompt
-            uncond_embeddings = uncond_embeddings.repeat_interleave(batch_size * num_images_per_prompt, dim=0)
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
 
             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
index bbe6ee6083..c200892ef6 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
@@ -302,7 +302,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
         if do_classifier_free_guidance:
             uncond_tokens: List[str]
             if negative_prompt is None:
-                uncond_tokens = [""]
+                uncond_tokens = [""] * batch_size
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
                     f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
@@ -331,7 +331,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
 
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
             uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
 
             # For classifier free guidance, we need to do two forward passes.
diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py
index 6db3624177..5c06b74bfa 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py
@@ -284,7 +284,7 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
         if do_classifier_free_guidance:
             uncond_tokens: List[str]
             if negative_prompt is None:
-                uncond_tokens = [""]
+                uncond_tokens = [""] * batch_size
             elif type(prompt) is not type(negative_prompt):
                 raise TypeError(
                     f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
@@ -312,7 +312,9 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
             uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
 
             # duplicate unconditional embeddings for each generation per prompt
-            uncond_embeddings = uncond_embeddings.repeat_interleave(batch_size * num_images_per_prompt, dim=0)
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
 
             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch