diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index cf0dc7f571..53f152eacf 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -465,12 +465,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             #   Should reimplement to use existing model management context etc.
             #
             if "sdxl" in ip_adapter_data.ip_adapter_model:
-                print("using IPAdapterXL")
                 ip_adapter = IPAdapterXL(
                     self, ip_adapter_data.image_encoder_model, ip_adapter_data.ip_adapter_model, self.unet.device
                 )
             elif "plus" in ip_adapter_data.ip_adapter_model:
-                print("using IPAdapterPlus")
                 ip_adapter = IPAdapterPlus(
                     self,  # IPAdapterPlus first arg is StableDiffusionPipeline
                     ip_adapter_data.image_encoder_model,
@@ -479,33 +477,25 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     num_tokens=16,
                 )
             else:
-                print("using IPAdapter")
                 ip_adapter = IPAdapter(
                     self,  # IPAdapter first arg is StableDiffusionPipeline
                     ip_adapter_data.image_encoder_model,
                     ip_adapter_data.ip_adapter_model,
                     self.unet.device,
                 )
-            # IP-Adapter ==> add additional cross-attention layers to UNet model here?
             ip_adapter.set_scale(ip_adapter_data.weight)
-            print("ip_adapter:", ip_adapter)
 
-            # get image embedding from CLIP and ImageProjModel
-            print("getting image embeddings from IP-Adapter...")
-            num_samples = 1  # hardwiring for first pass
+            # Get image embeddings from CLIP and ImageProjModel.
             image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter.get_image_embeds(ip_adapter_data.image)
-            print("image cond   embeds shape:", image_prompt_embeds.shape)
-            print("image uncond embeds shape:", uncond_image_prompt_embeds.shape)
-            bs_embed, seq_len, _ = image_prompt_embeds.shape
-            image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
-            image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-            uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
-            uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-            print("image cond   embeds shape:", image_prompt_embeds.shape)
-            print("image uncond embeds shape:", uncond_image_prompt_embeds.shape)
 
-            # IP-Adapter: run IP-Adapter model here?
-            # and add output as additional cross-attention layers
+            # The following commented block is kept for reference on how to repeat/reshape the image embeddings to
+            # generate a batch of multiple images:
+            # bs_embed, seq_len, _ = image_prompt_embeds.shape
+            # image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
+            # image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+            # uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
+            # uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
+
             text_prompt_embeds = conditioning_data.text_embeddings.embeds
             uncond_text_prompt_embeds = conditioning_data.unconditioned_embeddings.embeds
             print("text embeds shape:", text_prompt_embeds.shape)