app.py

import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
from optimum.intel.openvino import OVStableDiffusionPipeline
import torch


model_id = "helenai/Linaqruf-anything-v3.0-ov"

pipe = OVStableDiffusionPipeline.from_pretrained(model_id, compile=False)
pipe.reshape( batch_size=1, height=256, width=256, num_images_per_prompt=1)
pipe.compile()

def infer(prompt,negative_prompt):

    image = pipe(
        prompt = prompt, 
        negative_prompt = negative_prompt,
        width = 256, 
        height = 256,
    ).images[0] 
    
    return image

examples = [
    "A cute kitten, Japanese cartoon style.",
    "A sweet family, dad stands next to mom, mom holds baby girl.",
    "A delicious ceviche cheesecake slice",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""


power_device = "CPU"

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Linaqruf-anything-v3.0-ov 256x256
        Currently running on {power_device}.
        """)
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
         with gr.Row():
            negative_prompt = gr.Text(
                label="negative_prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )           
            run_button = gr.Button("Run", scale=0)
        
        result = gr.Image(label="Result", show_label=False)

        gr.Examples(
            examples = examples,
            inputs = [prompt]
        )

    run_button.click(
        fn = infer,
        inputs = [prompt,negative_prompt],
        outputs = [result]
    )

demo.queue().launch()