24 lines
707 B
Python
24 lines
707 B
Python
from diffusers.models import AutoencoderKL
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from diffusers import StableDiffusionPipeline
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import gradio as gr
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model = "CompVis/stable-diffusion-v1-4"
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vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
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pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)
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def text2image(prompt):
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image = pipe(prompt).images[0]
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return image
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demo = gr.Interface(fn=text2image,
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inputs='text',
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outputs='image',
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title = "text2image",
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examples = ['a photo of an astronaut riding a horse on mars'])
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if __name__ == "__main__":
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demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0", server_port = 7015)
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