app.py

import gradio as gr
from diffusers import DiffusionPipeline

# get_completion = pipeline("image-to-text",model="nlpconnect/vit-gpt2-image-captioning")

# pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")

# def summarize(input):
#     output = get_completion(input)
#     return output[0]['generated_text']

# def captioner(image):
#     result = get_completion(image)
#     return result[0]['generated_text']

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

gr.close_all()
demo = gr.Interface(fn=generate,
                    inputs=[gr.Textbox(label="Your prompt")],
                    outputs=[gr.Image(label="Result")],
                    title="Image Generation with Stable Diffusion",
                    description="Generate any image with Stable Diffusion",
                    allow_flagging="never",
                    examples=["the spirit of a tamagotchi wandering in the city of Vienna","a mecha robot in a favela"])

demo.launch()


# import gradio as gr

# gr.Interface.load("models/stabilityai/stable-diffusion-xl-base-1.0").launch()