wan2.1 vae take more gpu memory after compile

### Describe the bug

After `torch.compile` wan2.1 vae consume more GPU memory than `no compilation`, which is unexpected in my opinion.

**compiled**

**no-compile**

### Reproduction

```python
import sys

import torch
from diffusers import AutoencoderKLWan

def compile_wan_vae(compile):
model_id = 'Wan-AI/Wan2.1-T2V-14B-Diffusers'
dtype = torch.float32
device = 'cuda'

torch.cuda.memory._record_memory_history()
vae = AutoencoderKLWan.from_pretrained(
model_id, subfolder="vae", torch_dtype=dtype
).to(device)

if compile:
vae.decoder = torch.compile(vae.decoder)

shape = (1, 16, 13, 120, 120)
with torch.no_grad():
latents = torch.randn(shape, device=device, dtype=dtype)
video = vae.decode(latents, return_dict=False)[0]
torch.cuda.empty_cache()

with torch.no_grad():
for _ in range(3):
latents = torch.randn(shape, device=device, dtype=dtype)
video = vae.decode(latents, return_dict=False)[0]
torch.cuda.memory._dump_snapshot(f"{compile}-compile.pickle")

if __name__ == '__main__':
compile_wan_vae(sys.argv[1] == 'compile')
```

### Logs

```shell

```

### System Info

- 🤗 Diffusers version: 0.34.0
- Platform: Linux-5.10.134-16.1.3.vip.an8.x86_64-x86_64-with-glibc2.39
- Running on Google Colab?: No
- Python version: 3.12.3
- PyTorch version (GPU?): 2.7.1+cu126 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.34.2
- Transformers version: 4.54.0
- Accelerate version: 1.9.0
- PEFT version: 0.16.0
- Bitsandbytes version: not installed
- Safetensors version: 0.5.3
- xFormers version: not installed
- Accelerator: NVIDIA L20, 46068 MiB
NVIDIA L20, 46068 MiB
NVIDIA L20, 46068 MiB
NVIDIA L20, 46068 MiB
NVIDIA L20, 46068 MiB
NVIDIA L20, 46068 MiB
NVIDIA L20, 46068 MiB
NVIDIA L20, 46068 MiB
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

### Who can help?

_No response_

Start with the supplied reproduction using AutoencoderKLWan and compare the torch.cuda memory snapshots for the compiled and uncompiled vae.decoder paths. Investigate how torch.compile affects the decoder during repeated vae.decode calls, then verify that the compiled path no longer consumes unexpectedly more GPU memory on the listed PyTorch and GPU setup.

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