app.py

import gradio as gr
from diffusers import DiffusionPipeline

# import torch
# from diffusers import DDPMScheduler, UNet2DModel
# from PIL import Image
# import numpy as np

# pipeline = DiffusionPipeline.from_pretrained("google/ddpm-cat-256")
pipeline = DiffusionPipeline.from_pretrained("google/ddpm-celebahq-256")
# pipeline.to("cuda")


def erzeuge(prompt):
 return pipeline(prompt).images # [0]


with gr.Blocks() as demo:
 with gr.Column(variant="panel"):
 with gr.Row(variant="compact"):
 text = gr.Textbox(
 label="Deine Beschreibung:",
 show_label=False,
 max_lines=1,
 placeholder="Bildbeschrei",
 )
 btn = gr.Button("erzeuge Bild")

 gallery = gr.Gallery(
 label="Erzeugtes Bild", show_label=False, elem_id="gallery"
 )

 btn.click(erzeuge, inputs=[text], outputs=[gallery])
 text.submit(erzeuge, inputs=[text], outputs=[gallery])

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