import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline
from torch import autocast  # Usando autocast para otimizar operações em float16

# Verifica se a GPU está disponível
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"  # Modelo otimizado para velocidade

# Usando float16 para otimizar a execução na GPU
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Carregando o modelo com otimizações
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype).to(device)

# Max seed
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 512  # Dimensões menores para acelerar

# Função de inferência otimizada
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    # Randomiza a semente, se necessário
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

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

    # Usando autocast para acelerar o cálculo com float16 em GPUs
    with autocast("cuda"):
        # Geração da imagem com um número reduzido de passos (para acelerar)
        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]

    return image, seed


# Exemplos para o Gradio
examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 2k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template")

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

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

        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=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # Dimensões reduzidas para otimizar
                )

                height = gr.Slider(
                    label="Height",
                    minimum=576,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Dimensões reduzidas para otimizar
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.5,  # Valor adequado para controle
                )

                num_inference_steps = gr.Slider(
                    label="Inference steps",
                    minimum=1,
                    maximum=30,  # Menos passos para otimizar a velocidade
                    step=1,
                    value=20,  # Um valor equilibrado
                )

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

    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],
    )

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