app.py

import gradio as gr
import numpy as np
import random
import time
from optimum.intel import OVStableDiffusionXLPipeline
import torch
from diffusers import EulerDiscreteScheduler
from diffusers import LCMScheduler

model_id = "None1145/noobai-XL-Vpred-0.65s-openvino"

prev_height = 1216
prev_width = 832

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

def reload_model(new_model_id):
    global pipe, model_id, prev_height, prev_width
    model_id = new_model_id
    try:
        print(f"{model_id}...")
        pipe = OVStableDiffusionXLPipeline.from_pretrained(model_id, compile=False)
        if model_id == "None1145/noobai-XL-Vpred-0.65s-openvino":
            scheduler_args = {"prediction_type": "v_prediction", "rescale_betas_zero_snr": True}
            pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, **scheduler_args)
            # pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, **scheduler_args)
            # pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
            # pipe.fuse_lora()
        # pipe.to("gpu")
        pipe.reshape(batch_size=1, height=prev_height, width=prev_width, num_images_per_prompt=1)
        pipe.compile()
        print(f"{model_id}!!!")
        return f"Model successfully loaded: {model_id}"
    except Exception as e:
        return f"Failed to load model: {str(e)}"
reload_model(model_id)
        
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    global prev_width, prev_height, pipe

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    if prev_width != width or prev_height != height:
        pipe.reshape(batch_size=1, height=height, width=width, num_images_per_prompt=1)
        pipe.compile()
        prev_width = width
        prev_height = height

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    filename = f"./tmp/{time.time()}.jpg"
    image.save(filename)

    return image, seed


examples = ["murasame \(senren\), senren banka",]
with gr.Blocks() as img:
    gr.Markdown("# OpenVINO Text to Image")
    
    with gr.Column(elem_id="col-container"):
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=60,
                step=1,
                value=28,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        file_output = gr.File(label="Download your image")
        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=False,
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=512,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=832,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=512,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1216,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=5.0,
                )

        gr.Examples(examples=examples, inputs=[prompt])
    
    gr.Markdown("### Model Reload")
    with gr.Row():
        new_model_id = gr.Text(label="New Model ID", placeholder="Enter model ID", value=model_id)
        reload_button = gr.Button("Reload Model", variant="primary")
        reload_status = gr.Text(label="Status", interactive=False)

    reload_button.click(
        fn=reload_model,
        inputs=new_model_id,
        outputs=reload_status,
    )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed, file_output],
    )

if __name__ == "__main__":
    img.launch()