diff --git a/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py b/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py
index 6c072c482020..cee6c6af1273 100644
--- a/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py
+++ b/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py
@@ -16,6 +16,7 @@
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
+import PIL
 import torch
 from transformers import (
     CLIPTextModel,
@@ -389,10 +390,31 @@ def encode_prompt(
 
         return prompt_embeds, pooled_prompt_embeds, text_ids
 
+    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
+    def check_image(self, image, prompt, prompt_embeds):
+        image_is_pil = isinstance(image, PIL.Image.Image)
+        if image_is_pil:
+            image_batch_size = 1
+        else:
+            image_batch_size = len(image)
+
+        if prompt is not None and isinstance(prompt, str):
+            prompt_batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            prompt_batch_size = len(prompt)
+        elif prompt_embeds is not None:
+            prompt_batch_size = prompt_embeds.shape[0]
+
+        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
+            raise ValueError(
+                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
+            )
+
     def check_inputs(
         self,
         prompt,
         prompt_2,
+        image,
         height,
         width,
         prompt_embeds=None,
@@ -429,6 +451,30 @@ def check_inputs(
         elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
             raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
 
+        if (
+            isinstance(self.controlnet, FluxControlNetModel)
+        ):
+            self.check_image(image, prompt, prompt_embeds)
+        elif (
+            isinstance(self.controlnet, FluxMultiControlNetModel)
+        ):
+            if not isinstance(image, list):
+                raise TypeError("For multiple controlnets: `image` must be type `list`")
+
+            # 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(
+                    f"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets."
+                )
+
+            for image_ in image:
+                self.check_image(image_, prompt, prompt_embeds)
+        else:
+            assert False
+
         if prompt_embeds is not None and pooled_prompt_embeds is None:
             raise ValueError(
                 "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
@@ -523,18 +569,20 @@ def prepare_image(
         do_classifier_free_guidance=False,
         guess_mode=False,
     ):
-        if isinstance(image, torch.Tensor):
-            pass
-        else:
-            image = self.image_processor.preprocess(image, height=height, width=width)
-
+        
+        image = self.image_processor.preprocess(image, height=height, width=width)
         image_batch_size = image.shape[0]
 
         if image_batch_size == 1:
-            repeat_by = batch_size
-        else:
+            repeat_by = batch_size*num_images_per_prompt
+        elif image_batch_size == batch_size:
             # image batch size is the same as prompt batch size
             repeat_by = num_images_per_prompt
+        else:
+            raise ValueError(
+                "`image_batch_size` must be either 1 or equal to the prompt " + \
+                f"batch size, which is {batch_size}."
+            )
 
         image = image.repeat_interleave(repeat_by, dim=0)
 
@@ -678,6 +726,7 @@ def __call__(
         self.check_inputs(
             prompt,
             prompt_2,
+            control_image,
             height,
             width,
             prompt_embeds=prompt_embeds,
@@ -726,7 +775,7 @@ def __call__(
                 image=control_image,
                 width=width,
                 height=height,
-                batch_size=batch_size * num_images_per_prompt,
+                batch_size=batch_size,
                 num_images_per_prompt=num_images_per_prompt,
                 device=device,
                 dtype=self.vae.dtype,
@@ -762,7 +811,7 @@ def __call__(
                     image=control_image_,
                     width=width,
                     height=height,
-                    batch_size=batch_size * num_images_per_prompt,
+                    batch_size=batch_size,
                     num_images_per_prompt=num_images_per_prompt,
                     device=device,
                     dtype=self.vae.dtype,