diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py
index 3bd7f82d7e..46c229e825 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py
@@ -658,8 +658,7 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
         num_images_per_prompt,
         device,
         dtype,
-        do_classifier_free_guidance=False,
-        guess_mode=False,
+        inferring_controlnet_cond_batch=False,
     ):
         if not isinstance(image, torch.Tensor):
             if isinstance(image, PIL.Image.Image):
@@ -696,7 +695,7 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
 
         image = image.to(device=device, dtype=dtype)
 
-        if do_classifier_free_guidance and not guess_mode:
+        if not inferring_controlnet_cond_batch:
             image = torch.cat([image] * 2)
 
         return image
@@ -898,7 +897,16 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
         if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
             controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)
 
-        # 3. Encode input prompt
+        # 3. Determination of whether to infer ControlNet using only for the conditional batch.
+        global_pool_conditions = False
+        if isinstance(self.controlnet, ControlNetModel):
+            global_pool_conditions = self.controlnet.config.global_pool_conditions
+        else:
+            ...  # TODO: Implement for MultiControlNetModel
+
+        inferring_controlnet_cond_batch = (guess_mode or global_pool_conditions) and do_classifier_free_guidance
+
+        # 4. Encode input prompt
         prompt_embeds = self._encode_prompt(
             prompt,
             device,
@@ -909,7 +917,7 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
             negative_prompt_embeds=negative_prompt_embeds,
         )
 
-        # 4. Prepare image
+        # 5. Prepare image
         if isinstance(self.controlnet, ControlNetModel):
             image = self.prepare_image(
                 image=image,
@@ -919,8 +927,7 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
                 num_images_per_prompt=num_images_per_prompt,
                 device=device,
                 dtype=self.controlnet.dtype,
-                do_classifier_free_guidance=do_classifier_free_guidance,
-                guess_mode=guess_mode,
+                inferring_controlnet_cond_batch=inferring_controlnet_cond_batch,
             )
         elif isinstance(self.controlnet, MultiControlNetModel):
             images = []
@@ -934,8 +941,7 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
                     num_images_per_prompt=num_images_per_prompt,
                     device=device,
                     dtype=self.controlnet.dtype,
-                    do_classifier_free_guidance=do_classifier_free_guidance,
-                    guess_mode=guess_mode,
+                    inferring_controlnet_cond_batch=inferring_controlnet_cond_batch,
                 )
 
                 images.append(image_)
@@ -944,11 +950,11 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
         else:
             assert False
 
-        # 5. Prepare timesteps
+        # 6. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps = self.scheduler.timesteps
 
-        # 6. Prepare latent variables
+        # 7. Prepare latent variables
         num_channels_latents = self.unet.config.in_channels
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
@@ -961,10 +967,10 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
             latents,
         )
 
-        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
-        # 8. Denoising loop
+        # 9. Denoising loop
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
@@ -973,8 +979,8 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
                 # controlnet(s) inference
-                if guess_mode and do_classifier_free_guidance:
-                    # Infer ControlNet only for the conditional batch.
+                if inferring_controlnet_cond_batch:
+                    # Inferring ControlNet only for the conditional batch.
                     controlnet_latent_model_input = latents
                     controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
                 else:
@@ -991,7 +997,7 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
                     return_dict=False,
                 )
 
-                if guess_mode and do_classifier_free_guidance:
+                if inferring_controlnet_cond_batch:
                     # Infered ControlNet only for the conditional batch.
                     # To apply the output of ControlNet to both the unconditional and conditional batches,
                     # add 0 to the unconditional batch to keep it unchanged.