Why Qwen-Image inference speed may slower than comfyui

### Describe the bug

I have tested diffusers and Comfyui with the same parameters and check the input shapes
The parameters is

I check the shapes of text embeds and vae, they are all the same. The attention is use the same pytorch attention. And I use the same bfloat16 version model.
But the speed is 2.39it/s in diffusers vs 2.7it /s in comfyui

I do a log of effort but can not find the place where influence the speed.

My diffusers version is 0.36.0.dev0

### Reproduction

import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import torch
from PIL import Image
from diffusers import QwenImagePipeline
pipeline = QwenImagePipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=torch.bfloat16, device_map='cuda')
prompt = """女孩"""
inputs = {
"prompt": prompt,
# "negative_prompt": " ",
# "generator": torch.manual_seed(42),
"generator": torch.Generator(device='cuda').manual_seed(1125488487853216),
"width": 1216,
'height': 832,
"true_cfg_scale": 1,
"num_inference_steps": 20,
"guidance_scale": 1.0,
"num_images_per_prompt": 1,
}
with torch.inference_mode():
output = pipeline(**inputs)
output_image = output.images[0]
output_image.save('output.png')

### Logs

```shell

```

### System Info

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

- 🤗 Diffusers version: 0.36.0.dev0
- Platform: Linux-5.15.0-160-generic-x86_64-with-glibc2.35
- Running on Google Colab?: No
- Python version: 3.12.3
- PyTorch version (GPU?): 2.8.0+cu128 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.34.0
- Transformers version: 4.57.1
- Accelerate version: 1.11.0
- PEFT version: 0.17.1
- Bitsandbytes version: not installed
- Safetensors version: 0.6.2
- xFormers version: not installed
- Accelerator: NVIDIA A800-SXM4-80GB, 81920 MiB
NVIDIA A800-SXM4-80GB, 81920 MiB
NVIDIA A800-SXM4-80GB, 81920 MiB
NVIDIA A800-SXM4-80GB, 81920 MiB
NVIDIA A800-SXM4-80GB, 81920 MiB
NVIDIA A800-SXM4-80GB, 81920 MiB
NVIDIA A800-SXM4-80GB, 81920 MiB
NVIDIA A800-SXM4-80GB, 81920 MiB
- Using GPU in script?:
- Using distributed or parallel set-up in script?:

### Who can help?

_No response_

Start by running the provided QwenImagePipeline reproduction with the stated Python, PyTorch, GPU, and model settings, then compare its timing with ComfyUI. Inspect the QwenImagePipeline inference path and the reported input shapes; done means identifying and documenting the source of the throughput difference or confirming the relevant benchmark conditions.

Written by the indexing model from the issue text.