multi controlnet error for flux when using 2 controlnet with different layer length

### Describe the bug

in flux multicontrolnet when i using 2 controlnet(https://huggingface.co/promeai/FLUX.1-controlnet-lineart-promeai and https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny/blob/main/config.json)
the lineart controlnet has 4 double layers and the canny controlnet has 5 double layers, we think the following code will have a negative impact on the effect.
Because in the transformer calculation, we directly took the length, and due to the calculation logic in Figure 1, the length of prome should be 4, but it was classified into the column with a length of 5.

### Reproduction
```python
import torch
from diffusers.utils import load_image
from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
from diffusers.models.controlnet_flux import FluxControlNetModel, FluxMultiControlNetModel

base_model = 'black-forest-labs/FLUX.1-dev'
# load controlnet models
controlnet_model_canny = 'InstantX/FLUX.1-dev-Controlnet-Canny'
controlnet_canny = FluxControlNetModel.from_pretrained(controlnet_model_canny, torch_dtype=torch.bfloat16)
controlnet_model_lineart = 'promeai/FLUX.1-controlnet-lineart-promeai'
controlnet_lineart = FluxControlNetModel.from_pretrained(controlnet_model_lineart, torch_dtype=torch.bfloat16)

controlnet_canny_lineart = FluxMultiControlNetModel([controlnet_canny, controlnet_lineart])

pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet_canny_lineart, torch_dtype=torch.bfloat16)
pipe.to("cuda")

control_image_canny = load_image("one canny image")
control_image_lineart = load_image("one lineart image")

prompt = "A girl in city, 25 years old, cool, futuristic"
image = pipe(
prompt,
control_image=[control_image_canny, control_image_lineart],
controlnet_conditioning_scale=[0.6, 0.6],
num_inference_steps=28,
guidance_scale=3.5,
).images[0]
image.save("image.jpg")

```

### Logs

_No response_

### System Info

Copy-and-paste the text below in your GitHub issue and FILL OUT the two last points.

- 🤗 Diffusers version: 0.31.0
- Platform: Linux-5.15.0-105-generic-x86_64-with-glibc2.31
- Running on Google Colab?: No
- Python version: 3.10.15
- PyTorch version (GPU?): 2.5.1+cu124 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.26.2
- Transformers version: 4.46.2
- Accelerate version: 1.1.1
- PEFT version: 0.13.2
- Bitsandbytes version: not installed
- Safetensors version: 0.4.5
- xFormers version: not installed
- Accelerator: NVIDIA A100-SXM4-80GB, 81920 MiB
NVIDIA A100-SXM4-80GB, 81920 MiB
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

### Who can help?

@sayakpaul

Start with the reproduction and inspect FluxMultiControlNetModel in diffusers/models/controlnet_flux.py and the FluxControlNetPipeline entry point in diffusers/pipelines/flux/pipeline_flux_controlnet.py. Compare the handling of the two provided ControlNet configurations with different double-layer lengths. Done means the multi-ControlNet case no longer groups or processes the shorter model incorrectly and the reproduction completes with the expected conditioning behavior.

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