Hooks conflicts: Context Parallelism and CPU Offload

### Describe the bug

Enable cpu offload before enabling parallelism will raise shape error after first pipe call. It seems a bug of diffusers that cpu offload is not fully compatible with context parallelism, visa versa.

- cpu offload `before` context parallelism (not work)

```python
pipe.enable_model_cpu_offload(device=device)

# pipe.transformer.set_attention_backend("flash")
pipe.transformer.set_attention_backend("_native_cudnn")
pipe.transformer.enable_parallelism(
config=ContextParallelConfig(ulysses_degree=dist.get_world_size())
)
```

- cpu offload `after` context parallelism (work)

```python
# pipe.transformer.set_attention_backend("flash")
pipe.transformer.set_attention_backend("_native_cudnn")
pipe.transformer.enable_parallelism(
config=ContextParallelConfig(ulysses_degree=dist.get_world_size())
)
pipe.enable_model_cpu_offload(device=device)
```

### Reproduction

```python
import os
import time
import torch
import torch.distributed as dist
from diffusers import (
QwenImagePipeline,
QwenImageTransformer2DModel,
ContextParallelConfig,
)

def maybe_init_distributed():
if not dist.is_initialized():
dist.init_process_group("nccl")
rank = dist.get_rank()
device = torch.device("cuda", rank % torch.cuda.device_count())
torch.cuda.set_device(device)
return rank, device

def maybe_destroy_distributed():
if dist.is_initialized():
dist.destroy_process_group()

rank, device = maybe_init_distributed()

pipe = QwenImagePipeline.from_pretrained(
os.environ.get(
"QWEN_IMAGE_DIR",
"Qwen/Qwen-Image",
),
torch_dtype=torch.bfloat16,
)

# NOTE: Enable cpu offload before enabling parallelism will
# raise shape error after first pipe call, so we enable it after.
# It seems a bug of diffusers that cpu offload is not fully
# compatible with context parallelism, visa versa.
pipe.enable_model_cpu_offload(device=device)

assert isinstance(pipe.transformer, QwenImageTransformer2DModel)
# pipe.transformer.set_attention_backend("flash")
pipe.transformer.set_attention_backend("_native_cudnn")
pipe.transformer.enable_parallelism(
config=ContextParallelConfig(ulysses_degree=dist.get_world_size())
)

# NOTE: Enable cpu offload after enabling parallelism
# pipe.enable_model_cpu_offload(device=device)

# assert isinstance(pipe.vae, AutoencoderKLQwenImage)
# pipe.vae.enable_tiling()

positive_magic = {
"en": ", Ultra HD, 4K, cinematic composition.", # for english prompt
"zh": ", 超清，4K，电影级构图.", # for chinese prompt
}

# Generate image
prompt = """A coffee shop entrance features a chalkboard sign reading "Qwen Coffee 😊 $2 per cup," with a neon light beside it displaying "通义千问". Next to it hangs a poster showing a beautiful Chinese woman, and beneath the poster is written "π≈3.1415926-53589793-23846264-33832795-02384197". Ultra HD, 4K, cinematic composition"""

# using an empty string if you do not have specific concept to remove
negative_prompt = " "

pipe.set_progress_bar_config(disable=rank != 0)

def run_pipe():
# do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
image = pipe(
prompt=prompt + positive_magic["en"],
negative_prompt=negative_prompt,
width=1024,
height=1024,
num_inference_steps=50,
true_cfg_scale=4.0,
generator=torch.Generator(device="cpu").manual_seed(42),
).images[0]

return image

# warmup
_ = run_pipe() # always work

start = time.time()
image = run_pipe() # raise error here if cpu offload is enabled before parallelism
end = time.time()

if rank == 0:
time_cost = end - start
save_path = f"qwen-image.cp{dist.get_world_size()}.png"
print(f"Time cost: {time_cost:.2f}s")
print(f"Saving image to {save_path}")
image.save(save_path)

maybe_destroy_distributed()
```

### Logs

```shell
Error:

[rank0]: Traceback (most recent call last):
[rank0]: File "/workspace/dev/vipshop/cache-dit/examples/parallelism/run_qwen_image_cp_naive.py", line 71, in
[rank0]: start = time.time()
[rank0]: ^^^^^^^^^^
[rank0]: File "/workspace/dev/vipshop/cache-dit/examples/parallelism/run_qwen_image_cp_naive.py", line 54, in run_pipe
[rank0]: # do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py", line 120, in decorate_context
[rank0]: return func(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/workspace/dev/vipshop/diffusers/src/diffusers/pipelines/qwenimage/pipeline_qwenimage.py", line 691, in __call__
[rank0]: noise_pred = self.transformer(
[rank0]: ^^^^^^^^^^^^^^^^^
[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1786, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/usr/local/lib/python3.12/dist-packages/accelerate/hooks.py", line 175, in new_forward
[rank0]: output = module._old_forward(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/workspace/dev/vipshop/diffusers/src/diffusers/models/transformers/transformer_qwenimage.py", line 647, in forward
[rank0]: encoder_hidden_states, hidden_states = block(
[rank0]: ^^^^^^
[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1786, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/workspace/dev/vipshop/diffusers/src/diffusers/models/transformers/transformer_qwenimage.py", line 443, in forward
[rank0]: attn_output = self.attn(
[rank0]: ^^^^^^^^^^
[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
[rank0]: return self._call_impl(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1786, in _call_impl
[rank0]: return forward_call(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/workspace/dev/vipshop/diffusers/src/diffusers/models/attention_processor.py", line 605, in forward
[rank0]: return self.processor(
[rank0]: ^^^^^^^^^^^^^^^
[rank0]: File "/workspace/dev/vipshop/diffusers/src/diffusers/models/transformers/transformer_qwenimage.py", line 322, in __call__
[rank0]: img_query = apply_rotary_emb_qwen(img_query, img_freqs, use_real=False)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/workspace/dev/vipshop/diffusers/src/diffusers/models/transformers/transformer_qwenimage.py", line 139, in apply_rotary_emb_qwen
[rank0]: x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)
[rank0]: ~~~~~~~~~~^~~~~~~~~~~
[rank0]: RuntimeError: The size of tensor a (4096) must match the size of tensor b (2048) at non-singleton dimension 1
```

### System Info

diffusers 0.36.dev0 (latest main branch), pytorch 2.9.0

### Who can help?

@yiyixuxu @DN6