#!/usr/bin/env python

import random

import gradio as gr
import numpy as np
import PIL.Image
import torch
from diffusers import DDPMScheduler, StableDiffusionXLAdapterPipeline, T2IAdapter

DESCRIPTION = "# T2I-Adapter-SDXL Sketch"

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    model_id = "stabilityai/stable-diffusion-xl-base-1.0"
    adapter = T2IAdapter.from_pretrained(
        "Adapter/t2iadapter",
        subfolder="sketch_sdxl_1.0",
        torch_dtype=torch.float16,
        adapter_type="full_adapter_xl",
    )
    scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler")
    pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
        model_id,
        adapter=adapter,
        safety_checker=None,
        torch_dtype=torch.float16,
        variant="fp16",
        scheduler=scheduler,
    )
    pipe.to(device)
else:
    pipe = None

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


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


def run(
    image: PIL.Image.Image,
    prompt: str,
    negative_prompt: str,
    num_steps=50,
    guidance_scale=7.5,
    seed=0,
) -> PIL.Image.Image:
    # Convert the input image, which is a boolean image, to a grayscale image whose value is 0 or 255.
    image = image.convert("L")

    generator = torch.Generator(device=device).manual_seed(seed)
    out = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        image=image,
        num_inference_steps=num_steps,
        generator=generator,
        guidance_scale=guidance_scale,
    ).images[0]
    return out


with gr.Blocks() as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Row():
        with gr.Column():
            image = gr.Image(
                source="canvas",
                tool="sketch",
                type="pil",
                image_mode="1",
                invert_colors=True,
                shape=(1024, 1024),
                brush_radius=20,
                height=600,
            )
            prompt = gr.Textbox(label="Prompt")
            run_button = gr.Button("Run")
            with gr.Accordion("Advanced options", open=False):
                negative_prompt = gr.Textbox(
                    label="Negative prompt", value="extra digit, fewer digits, cropped, worst quality, low quality"
                )
                num_steps = gr.Slider(
                    label="Number of steps",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=50,
                )
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.1,
                    maximum=30.0,
                    step=0.1,
                    value=7.5,
                )
                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.Column():
            result = gr.Image(label="Result", height=600)

    inputs = [
        image,
        prompt,
        negative_prompt,
        num_steps,
        guidance_scale,
        seed,
    ]
    prompt.submit(
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=run,
        inputs=inputs,
        outputs=result,
        api_name=False,
    )
    run_button.click(
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=run,
        inputs=inputs,
        outputs=result,
        api_name="run",
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch()