add: better warning messages when handling multiple conditionings. (#2804)

* add: better warning messages when handling multiple conditioning.

* fix: handling of controlnet_conditioning_scale

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py

@@ -537,15 +537,27 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline):
                     f" {negative_prompt_embeds.shape}."
                 )
 
-        # Check `image`
+        # `prompt` needs more sophisticated handling when there are multiple
+        # conditionings.
+        if isinstance(self.controlnet, MultiControlNetModel):
+            if isinstance(prompt, list):
+                logger.warning(
+                    f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
+                    " prompts. The conditionings will be fixed across the prompts."
+                )
 
+        # Check `image`
         if isinstance(self.controlnet, ControlNetModel):
             self.check_image(image, prompt, prompt_embeds)
         elif isinstance(self.controlnet, MultiControlNetModel):
             if not isinstance(image, list):
                 raise TypeError("For multiple controlnets: `image` must be type `list`")
 
-            if len(image) != len(self.controlnet.nets):
+            # When `image` is a nested list:
+            # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
+            elif any(isinstance(i, list) for i in image):
+                raise ValueError("A single batch of multiple conditionings are supported at the moment.")
+            elif len(image) != len(self.controlnet.nets):
                 raise ValueError(
                     "For multiple controlnets: `image` must have the same length as the number of controlnets."
                 )
@@ -556,12 +568,14 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline):
             assert False
 
         # Check `controlnet_conditioning_scale`
-
         if isinstance(self.controlnet, ControlNetModel):
             if not isinstance(controlnet_conditioning_scale, float):
                 raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
         elif isinstance(self.controlnet, MultiControlNetModel):
-            if isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
+            if isinstance(controlnet_conditioning_scale, list):
+                if any(isinstance(i, list) for i in controlnet_conditioning_scale):
+                    raise ValueError("A single batch of multiple conditionings are supported at the moment.")
+            elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
                 self.controlnet.nets
             ):
                 raise ValueError(