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app.py
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import gradio as gr
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from diffusers import DiffusionPipeline
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#
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# def
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#
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# return output[0]['generated_text']
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#
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#
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return pipeline(prompt).images[0]
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gr.close_all()
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demo = gr.Interface(fn=generate,
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inputs=[gr.Textbox(label="Your prompt")],
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outputs=[gr.Image(label="Result")],
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title="Image Generation with Stable Diffusion",
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description="Generate any image with Stable Diffusion",
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allow_flagging="never",
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examples=["the spirit of a tamagotchi wandering in the city of Vienna","a mecha robot in a favela"])
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# import gradio as gr
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# from diffusers import DiffusionPipeline
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# # get_completion = pipeline("image-to-text",model="nlpconnect/vit-gpt2-image-captioning")
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# pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
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# # def summarize(input):
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# # output = get_completion(input)
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# # return output[0]['generated_text']
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# # def captioner(image):
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# # result = get_completion(image)
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# # return result[0]['generated_text']
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# def generate(prompt):
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# return pipeline(prompt).images[0]
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# gr.close_all()
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# demo = gr.Interface(fn=generate,
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# inputs=[gr.Textbox(label="Your prompt")],
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# outputs=[gr.Image(label="Result")],
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# title="Image Generation with Stable Diffusion",
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# description="Generate any image with Stable Diffusion",
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# allow_flagging="never",
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# examples=["the spirit of a tamagotchi wandering in the city of Vienna","a mecha robot in a favela"])
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# demo.launch()
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import gradio as gr
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gr.Interface.load("models/stabilityai/stable-diffusion-xl-base-1.0").launch()
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