import requests
import torch
from PIL import Image
from io import BytesIO
import time

from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("/home/patrick_huggingface_co/stable-diffusion-2-1-unclip-i2i-l", torch_dtype=torch.float16)
pipe.to("cuda")

url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/image%20(13).png"
#url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/image%20(10).png"
#url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/image%20(10).png"
#url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/image%20(10).png"

response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
# init_image = init_image.resize((768, 512))

images = pipe(4 * [init_image]).images

for i in range(len(images)):
    images[i].save(f"fantasy_landscape_{i}.png")