The Huggingface example code for stabilityai/stable-diffusion-2-depth (with GPU, CPU and multiple image results)

two_tigers-StableDiffusionDepth2ImgPipeline.py

import torch
import requests
from PIL import Image
from diffusers import StableDiffusionDepth2ImgPipeline

def main():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    dtype = torch.float16 if device == "cuda" else torch.float32

    pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-depth",
    torch_dtype=dtype
    ).to(device)

    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    init_image = Image.open(requests.get(url, stream=True).raw)    
    prompt = "two tigers"
    n_propmt = "bad, deformed, ugly, bad anotomy"
    prompt_to_print = prompt.replace(" ", "_")
    init_image.save(f"init-{prompt_to_print}-Depth2Img.jpg")
    num_of_samples = 2
    result = pipe(prompt=[prompt] * num_of_samples, image=init_image, negative_prompt=[n_propmt] * num_of_samples, strength=0.7)

    images = result["images"]
    for i, image in enumerate(images):       
        prompt_to_print = str(i) + "-" + prompt
        output_file = prompt_to_print.replace(" ", "_") + "-Depth2Img" + ".jpg"
        image.save(output_file)
        print("Saved: " + str(output_file))

if __name__ == '__main__':
    main()