❓ [Question] Is it possible to export unet's tensorrt engine as a file in stable diffusion?

❓ Question

Hello. I am currently trying to infer the stable diffusion XL inpaint model using your package.
model link : https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1

I referred to your example code and modified it as follows.

import torch

from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image
import torch_tensorrt

model_id = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
device = "cuda"

# Instantiate Stable Diffusion Pipeline with FP16 weights
pipe = AutoPipelineForInpainting.from_pretrained(
    model_id, variant="fp16", torch_dtype=torch.float16
)

pipe = pipe.to(device)
backend = "torch_tensorrt"

# Optimize the UNet portion with Torch-TensorRT
pipe.unet = torch.compile(
    pipe.unet,
    backend=backend,
    options={
        "truncate_long_and_double": True,
        "precision": torch.float16,
    },
    dynamic=False,
)

# %%
# Inference
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

image = load_image(img_url).resize((1024, 1024))
mask_image = load_image(mask_url).resize((1024, 1024))

prompt = "a tiger sitting on a park bench"

image = pipe(
  prompt=prompt,
  image=image,
  mask_image=mask_image,
  guidance_scale=8.0,
  num_inference_steps=20,
  strength=0.99,
  ).images[0]


image.save("inpaint-result.png")

On my gpu machine the conversion to tensorrt takes over 15 minutes. Since I can't do this conversion every time, I'm trying to find a way to save it in file format such as ".trt" file and use it.

When looking in your documentation, it was difficult to find such a feature. Do you support these features? If so, please let me know.

What you have already tried

Described above

Environment

docker container : nvcr.io/nvidia/pytorch:23.11-py3
gpu : p40

Additional context