diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index d88313f455..e9416c8057 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -34,6 +34,7 @@ from .diffusion import (
     BasicConditioningInfo,
 )
 from ..util import normalize_device, auto_detect_slice_size
+from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
 
 
 @dataclass
@@ -357,6 +358,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         additional_guidance: List[Callable] = None,
         callback: Callable[[PipelineIntermediateState], None] = None,
         control_data: List[ControlNetData] = None,
+        ip_adapter_image: Optional[PIL.Image] = None,
         mask: Optional[torch.Tensor] = None,
         masked_latents: Optional[torch.Tensor] = None,
         seed: Optional[int] = None,
@@ -408,6 +410,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 conditioning_data,
                 additional_guidance=additional_guidance,
                 control_data=control_data,
+                ip_adapter_image=ip_adapter_image,
                 callback=callback,
             )
         finally:
@@ -427,8 +430,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         *,
         additional_guidance: List[Callable] = None,
         control_data: List[ControlNetData] = None,
+        ip_adapter_image: Optional[PIL.Image] = None,
         callback: Callable[[PipelineIntermediateState], None] = None,
     ):
+
         self._adjust_memory_efficient_attention(latents)
         if additional_guidance is None:
             additional_guidance = []
@@ -439,6 +444,55 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if timesteps.shape[0] == 0:
             return latents, attention_map_saver
 
+        print("ip_adapter_image: ", type(ip_adapter_image))
+        if ip_adapter_image is not None:
+            # initialize IPAdapter
+            print("   width:", ip_adapter_image.width, " height:", ip_adapter_image.height)
+            clip_image_encoder_path = "ip_adapter_models_sd_1.5/image_encoder/"
+            ip_adapter_model_path = "ip_adapter_models_sd_1.5/ip-adapter_sd15.bin"
+            # FIXME:
+            #   WARNING!
+            #   IPAdapter constructor modifies UNet model in-place
+            #   Adds additional cross-attention layers to UNet model for image embedding
+            #   need to figure out how to only do this if UNet hasn't already been modified by prior IPAdapter
+            #   and how to undo if ip_adapter_image is removed
+            #   use existing model management context etc?
+            #
+            ip_adapter = IPAdapter(self,                    # IPAdapter first arg is StableDiffusionPipeline
+                                   clip_image_encoder_path, # hardwiring to manually downloaded dir for first pass
+                                   ip_adapter_model_path,   # hardwiring to manually downloaded loc for first pass
+                                   "cuda")                  # hardwiring CUDA GPU for first pass
+            # IP-Adapter ==> add additional cross-attention layers to UNet model here?
+            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
+            image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter.get_image_embeds(ip_adapter_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
+            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)
+            concat_prompt_embeds = torch.cat([text_prompt_embeds, image_prompt_embeds], dim=1)
+            concat_uncond_prompt_embeds = torch.cat([uncond_text_prompt_embeds, uncond_image_prompt_embeds], dim=1)
+            print("concat embeds shape:", concat_prompt_embeds.shape)
+            conditioning_data.text_embeddings.embeds = concat_prompt_embeds
+            conditioning_data.unconditioned_embeddings.embeds = concat_uncond_prompt_embeds
+        else:
+            image_prompt_embeds = None
+            uncond_image_prompt_embeds = None
+
         extra_conditioning_info = conditioning_data.extra
         with self.invokeai_diffuser.custom_attention_context(
             self.invokeai_diffuser.model,