How to use batch prediction in Diffusers.StableDiffusionXLImg2ImgPipeline library

Question

I'm currently exploring the StableDiffusion Image to Image library within HuggingFace. My goal is to generate images similar to the ones I have stored in a folder. Currently, I'm using the following code snippet:

import torch
from diffusers.utils import load_image
from diffusers import StableDiffusionXLImg2ImgPipeline

pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)

pipe = pipe.to("cuda")
url = "MyImages\ImageList\998.jpg"

init_image = load_image(url).convert("RGB")
prompt = "Give me a similar image like this"
image = pipe(prompt, image=init_image).images

this code requires me to generate each image manually, one by one. I can write a for loop like this -

all_images = os.listdir('MyImages\ImageList\')
for img in all_images:
  ...
  ...

I'm considering the possibility of processing images in batches rather than individually. Is there a method or a library within HuggingFace that allows for batch processing of images to generate similar ones?