diff --git a/src/diffusers/pipelines/flux/pipeline_flux.py b/src/diffusers/pipelines/flux/pipeline_flux.py
index c1a7010d919a..7210451c9ffc 100644
--- a/src/diffusers/pipelines/flux/pipeline_flux.py
+++ b/src/diffusers/pipelines/flux/pipeline_flux.py
@@ -379,6 +379,7 @@ def check_inputs(
         pooled_prompt_embeds=None,
         callback_on_step_end_tensor_inputs=None,
         max_sequence_length=None,
+        latents=None,
     ):
         if height % 8 != 0 or width % 8 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
@@ -417,6 +418,23 @@ def check_inputs(
         if max_sequence_length is not None and max_sequence_length > 512:
             raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
 
+        if latents is not None:
+            if not isinstance(latents, torch.Tensor):
+                raise ValueError(f"`latents` has to be of type `torch.Tensor` but is {type(latents)}")
+ 
+            batch_size, num_patches, channels = latents.shape
+            if channels != self.transformer.config.in_channels:
+                raise ValueError(
+                    f"Number of channels in `latents` ({channels}) does not match the number of channels expected by"
+                    f" the transformer ({self.transformer.config.in_channels=})."
+                )
+
+            if num_patches != (height // self.vae_scale_factor) * (width // self.vae_scale_factor):
+                raise ValueError(
+                    f"Number of patches in `latents` ({num_patches}) does not match the number of patches expected by"
+                    f" the transformer ({(height // self.vae_scale_factor) * (width // self.vae_scale_factor)=})."
+                )
+
     @staticmethod
     def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
         latent_image_ids = torch.zeros(height // 2, width // 2, 3)
@@ -465,15 +483,18 @@ def prepare_latents(
         generator,
         latents=None,
     ):
+        if latents is not None:
+            if latents.ndim == 4:
+                # Packing the latents to be of shape (batch_size, num_patches, channels)
+                latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
+            latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width, device, dtype)
+            return latents.to(device=device, dtype=dtype), latent_image_ids
+
         height = 2 * (int(height) // self.vae_scale_factor)
         width = 2 * (int(width) // self.vae_scale_factor)
 
         shape = (batch_size, num_channels_latents, height, width)
 
-        if latents is not None:
-            latent_image_ids = self._prepare_latent_image_ids(batch_size, height, width, device, dtype)
-            return latents.to(device=device, dtype=dtype), latent_image_ids
-
         if isinstance(generator, list) and len(generator) != batch_size:
             raise ValueError(
                 f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
@@ -561,7 +582,10 @@ def __call__(
             latents (`torch.FloatTensor`, *optional*):
                 Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                 generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
+                tensor will ge generated by sampling using the supplied random `generator`. Note: This pipeline expects 
+                `latents` to be in a packed format. If you're providing custom latents, make sure to use the 
+                `_pack_latents` method to prepare them. Packed latents should be a 3D tensor of shape 
+                (batch_size, num_patches, channels).
             prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                 provided, text embeddings will be generated from `prompt` input argument.
@@ -609,6 +633,7 @@ def __call__(
             pooled_prompt_embeds=pooled_prompt_embeds,
             callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
             max_sequence_length=max_sequence_length,
+            latents=latents,
         )
 
         self._guidance_scale = guidance_scale
@@ -656,6 +681,7 @@ def __call__(
             latents,
         )
 
+
         # 5. Prepare timesteps
         sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
         image_seq_len = latents.shape[1]