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# /// script
# requires-python = ">=3.11"
# dependencies = [
# "huggingface-hub[hf_transfer]",
# "pillow",
# "torch",
# "diffusers",
# "accelerate",
# "transformers",
# "python-slugify",
# ]
#
# ///
import argparse
import os
import torch
from diffusers import AutoPipelineForText2Image
from huggingface_hub import HfApi, login
from slugify import slugify
def main(prompt: str, repo: str, hf_token: str = None):
HF_TOKEN = hf_token or os.environ.get("HF_TOKEN")
if HF_TOKEN:
login(token=HF_TOKEN)
name = slugify(prompt)
filename = f"{name}.png"
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
api = HfApi()
print(f"Loading model: {model_id}...")
pipe = AutoPipelineForText2Image.from_pretrained(
model_id, torch_dtype=torch.float16, variant="fp16"
).to("cuda")
print(f"Generating image for prompt: '{prompt}'...")
image = pipe(prompt=prompt).images[0]
temp_image_path = f"/tmp/{filename}"
image.save(temp_image_path)
print(f"Image saved temporarily to {temp_image_path}")
print(f"Uploading {filename} to dataset repository: {repo}...")
api.upload_file(
path_or_fileobj=temp_image_path,
path_in_repo=filename,
repo_id=repo,
repo_type="dataset",
commit_message=f"add {filename}"
)
repo_url = f"https://huggingface.co/datasets/{repo}/tree/main"
print(f"View it here: {repo_url}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate a single image using HF Jobs.")
parser.add_argument("--prompt", required=True, help="The text prompt to generate an image from.")
parser.add_argument("--repo", required=True, help="Your destination dataset repository ID.")
parser.add_argument("--hf-token", help="Hugging Face API token.")
args = parser.parse_args()
main(prompt=args.prompt, repo=args.repo, hf_token=args.hf_token)
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