inference.py

from pathlib import Path
from PIL import Image
from transformers import AutoProcessor, AutoModel
import torch

# Load model and processor
model = AutoModel.from_pretrained("cloudqi/cqi_text_to_image_pt_v0", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("cloudqi/cqi_text_to_image_pt_v0", trust_remote_code=True)

def generate_image(prompt):
    inputs = processor(prompt, return_tensors="pt")
    with torch.no_grad():
        output = model(**inputs).images[0]  # assumes .images exists
    return Image.fromarray(output)