diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
index 689febe3e891..cdb86b58b5c8 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
@@ -51,6 +51,12 @@
         >>> image = pipe(prompt).images[0]
         ```
 """
+class NullContext:
+    def __enter__(self):
+        pass
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        pass
 
 
 class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
@@ -540,6 +546,7 @@ def __call__(
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
         callback_steps: int = 1,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        use_progress_bar: bool = True,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -666,7 +673,8 @@ def __call__(
 
         # 7. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
+        state = self.progress_bar(total=num_inference_steps) if use_progress_bar else NullContext()
+        with state as progress_bar:
             for i, t in enumerate(timesteps):
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
@@ -690,7 +698,8 @@ def __call__(
 
                 # call the callback, if provided
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
+                    if use_progress_bar:
+                        progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)