Issue with Using Multiple Controls (Depth and Canny) with LoRA on FLUX.1-dev Model

### Describe the bug

When attempting to use multiple control images (Depth and Canny) with LoRA on the FLUX.1-dev model, an error occurs during execution. The documentation indicates that multiple control images in PIL format can be supplied, but the pipeline throws a runtime error. Notably, the pipeline functions correctly with a single control image.

### Expected Behavior
The pipeline should generate the output image without errors when multiple control images (Depth and Canny) are supplied.

### Observed Behavior
The pipeline fails with the error RuntimeError: shape '[1, 16, 64, 2, 64, 2]' is invalid for input of size 524288.

### Reproduction

1. Set up the FLUX.1-dev model with multiple control images using LoRA.
2. Use a Depth control image and a Canny control image.
3. Execute the code with the following snippet:

```python
import os
from huggingface_hub import login
from diffusers import FluxControlPipeline
from image_gen_aux import DepthPreprocessor
from diffusers.utils import load_image
from controlnet_aux import CannyDetector
import numpy as np
import torch

# Set Hugging Face directories
os.environ["HF_HOME"] = "/scratch/pramish_paudel/job_108669/hf"
os.environ["HF_DATASETS_CACHE"] = "/scratch/pramish_paudel/job_1086695/hf"

login(token="")

control_pipe = FluxControlPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
control_pipe.load_lora_weights("black-forest-labs/FLUX.1-Depth-dev-lora", adapter_name="depth")
control_pipe.load_lora_weights("black-forest-labs/FLUX.1-Canny-dev-lora", adapter_name="canny")

control_pipe.set_adapters(["depth", "canny"], adapter_weights=[0.85, 0.85])
control_pipe.enable_model_cpu_offload()

prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")

processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
control_image1 = processor(control_image)[0].convert("RGB")
shape = np.asarray(control_image1).shape[0]

processor = CannyDetector()
control_image2 = processor(control_image, low_threshold=50, high_threshold=200, detect_resolution=shape, image_resolution=shape)

image = control_pipe(
prompt=prompt,
control_image=[control_image1, control_image2],
height=1024,
width=1024,
num_inference_steps=30,
guidance_scale=10.0,
generator=torch.Generator().manual_seed(42),
).images[0]
image.save("output.png")

### Logs

```shell
/lib/python3.12/site-packages/diffusers/pipelines/flux/pipeline_flux_control.py", line 474, in _pack_latents
latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: shape '[1, 16, 64, 2, 64, 2]' is invalid for input of size 524288
```

### System Info

• diffusers version: 0.32.0
• Python version: 3.12
• System: Debian GNU/Linux
• GPU: A6000

### Who can help?

@sayakpaul @yiyixuxu @DN6