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.close_all()
demo = gr.load(name="models/stabilityai/stable-diffusion-xl-base-1.0",
               title='PicassoBot Large',
               description='Because paint splatters are so last century')

# demo.queue(concurrency_count=1)
demo.launch()