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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()
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