Fix for InstructPix2PixPipeline to allow for prompt embeds to be passed in...

Fix for InstructPix2PixPipeline to allow for prompt embeds to be passed in without prompts.  (#2456)

* fix check inputs to allow prompt embeds in instruct pix2pix

* linting

* add reference comment to check inputs

* remove comment

* style changes

---------
Co-authored-by: Will Berman <wlbberman@gmail.com>

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py

@@ -246,13 +246,19 @@ class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline):
             (nsfw) content, according to the `safety_checker`.
         """
         # 0. Check inputs
-        self.check_inputs(prompt, callback_steps)
+        self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)
 
         if image is None:
             raise ValueError("`image` input cannot be undefined.")
 
         # 1. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
         device = self._execution_device
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
@@ -640,10 +646,9 @@ class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline):
         image = image.cpu().permute(0, 2, 3, 1).float().numpy()
         return image
 
-    def check_inputs(self, prompt, callback_steps):
-        if not isinstance(prompt, str) and not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
+    def check_inputs(
+        self, prompt, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None
+    ):
         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
         ):
@@ -652,6 +657,32 @@ class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline):
                 f" {type(callback_steps)}."
             )
 
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
     def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
         shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)