Error when setting num_single_layers=0 while training flux-controlnet on a multi-GPU server using a single GPU

### Describe the bug

While training flux-controlnet on a multi-GPU server and restricting the training to a single GPU, setting **_num_single_layers=0_** leads to an error:

[rank0]: Parameter indices which did not receive grad for rank 0: 64 65 72 73 74 75

### Reproduction

`
accelerate launch --gpu_ids='0,' --num_processes=1 --num_machines=1 --main_process_port 28700 train_controlnet_flux.py \
--pretrained_model_name_or_path="black-forest-labs/FLUX.1-schnell" \
--dataset_name="lucataco/fill1k" \
--conditioning_image_column=conditioning_image \
--image_column=image \
--caption_column=text \
--output_dir="logs" \
--mixed_precision="bf16" \
--resolution=512 \
--learning_rate=1e-5 \
--max_train_steps=15000 \
--validation_steps=100 \
--checkpointing_steps=200 \
--validation_image "./example_images/conditioning_image_1.png" "./example_images/conditioning_image_2.png" \
--validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--report_to="tensorboard" \
--num_double_layers=2 \
--num_single_layers=0 \
--seed=42 \
--enable_model_cpu_offload \
--use_8bit_adam \
--use_adafactor \
--gradient_checkpointing \
`

### Logs

```shell
[rank0]: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by
[rank0]: making sure all `forward` function outputs participate in calculating loss.
[rank0]: If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).
[rank0]: Parameter indices which did not receive grad for rank 0: 64 65 72 73 74 75
[rank0]: In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error
```

### System Info

- 🤗 Diffusers version: 0.31.0.dev0
- Platform: Linux-5.14.0-427.33.1.el9_4.x86_64-x86_64-with-glibc2.34
- Running on Google Colab?: No
- Python version: 3.12.4
- PyTorch version (GPU?): 2.4.1+cu121 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.24.7
- Transformers version: 4.45.0
- Accelerate version: 0.33.0
- PEFT version: 0.12.0
- Bitsandbytes version: 0.44.1
- Safetensors version: 0.4.4
- xFormers version: 0.0.28
- Accelerator: NVIDIA RTX A6000, 49140 MiB
NVIDIA RTX A6000, 49140 MiB
NVIDIA RTX A6000, 49140 MiB
- Using GPU in script?:
- Using distributed or parallel set-up in script?: Yes

### Who can help?

@sayakpaul

Start with the provided accelerate command and train_controlnet_flux.py, reproducing the failure with --num_single_layers=0 and a single GPU. Inspect how the training script handles distributed wrapping and unused parameters, then verify the fix by rerunning the command without the reported reduction error.

Vom Indexierungsmodell aus dem Issue-Text verfasst.