import torch
from diffusers import DiffusionPipeline  # type: ignore
import gradio as gr

generator = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
# move to GPU if available
if torch.cuda.is_available():
    generator = generator.to("cuda")

def generate(prompts):
  images = generator(list(prompts)).images  # type: ignore
  return [images]

demo = gr.Interface(generate,
             "textbox",
             "image",
             batch=True,
             max_batch_size=4  # Set the batch size based on your CPU/GPU memory
)

if __name__ == "__main__":
    demo.launch()