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#!/usr/bin/env python3
import torch
from diffusers import AutoPipelineForText2Image
from huggingface_hub import HfApi
from pathlib import Path
import os
from PIL import Image
import numpy as np
api = HfApi()
pipe = AutoPipelineForText2Image.from_pretrained("warp-diffusion/WuerstchenGeneratorPipeline", torch_dtype=torch.float16).to("cuda")
prompt = [
"An old destroyed car standing on a cliff in norway, cinematic photography",
"Western movie, closeup cinematic photography",
"Pink nike shoe commercial, closeup cinematic photography",
"Croatia, closeup cinematic photography",
"South Tyrol mountains at sunset, closeup cinematic photography",
]
images = pipe(prompt, guidance_scale=8.0, width=1024, height=1024).images
for i, image in enumerate(images):
file_name = f"bb_1_{i}"
path = os.path.join(Path.home(), "images", f"{file_name}.png")
image.save(path)
api.upload_file(
path_or_fileobj=path,
path_in_repo=path.split("/")[-1],
repo_id="patrickvonplaten/images",
repo_type="dataset",
)
print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/{file_name}.png")
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