Wan2.2 TI2V-5B VRAM OOM at the end

### Describe the bug

After completing 50 steps of progress, the video memory usage skyrocketed from around 8GB to 26GB, resulting in very slow performance

### Reproduction

```
import torch
import numpy as np
from diffusers import WanImageToVideoPipeline, AutoencoderKLWan, ModularPipeline
from diffusers.utils import export_to_video

model_id = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
dtype = torch.bfloat16
device = "cuda:2"

vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, torch_dtype=dtype)
pipe.enable_model_cpu_offload(device=device)

# use default wan image processor to resize and crop the image
image_processor = ModularPipeline.from_pretrained("YiYiXu/WanImageProcessor", trust_remote_code=True)
image = image_processor(
image="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
max_area=1280*704, output="processed_image")

height, width = image.height, image.width
print(f"height: {height}, width: {width}")
num_frames = 121
num_inference_steps = 50
guidance_scale = 5.0

prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."

negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"

output = pipe(
image=image,
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_frames=num_frames,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
).frames[0]
export_to_video(output, "yiyi_test_6_ti2v_5b_output.mp4", fps=24)
```

### Logs

```shell

```

### System Info

0.35.0 dev

### Who can help?

_No response_

Start with the WanImageToVideoPipeline reproduction using enable_model_cpu_offload and monitor VRAM across the 50 denoising steps, especially the final step and output decoding. Trace the pipeline entry point and memory changes to identify what causes usage to rise from about 8GB to 26GB; done means the reproduction completes without the reported end-of-run spike.

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