import spaces
import gradio as gr
import numpy as np
import random
import functools
import torch
from diffusers import StableDiffusion3Pipeline
from inference import run
from peft import LoraConfig, get_peft_model, PeftModel

pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium",
                                                torch_dtype=torch.float16)
pipe = pipe.to("cuda")

distill_check = 'yresearch/swd-medium-6-steps'
pipe.transformer = PeftModel.from_pretrained(
    pipe.transformer,
    distill_check,
)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024


@spaces.GPU()
def infer(prompt, seed, randomize_seed):

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

    generator = torch.Generator().manual_seed(seed)
    sigmas = [1.0000, 0.9454, 0.8959, 0.7904, 0.7371, 0.6022]
    scales = [32, 48, 64, 80, 96, 128]

    images = run(
        pipe,
        prompt,
        sigmas=sigmas,
        scales=scales,
        num_inference_steps=6,

        guidance_scale=0.0,
        height=int(scales[0] * 8),
        width=int(scales[0] * 8),
        generator=generator,
    ).images

    return images


examples = [
    "An astronaut riding a green horse",
    'Long-exposure night photography of a starry sky over a mountain range, with light trails.',
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "A portrait of a girl with blonde, tousled hair, blue eyes",
]

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

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(
            f"""
        # ⚡ Scale-wise Distillation ⚡ 
        # ⚡ Image Generation with 6-step SwD ⚡
        This is a demo of [Scale-wise Distillation](https://yandex-research.github.io/invertible-cd/), 
        a diffusion distillation method proposed in [Scale-wise Distillation of Diffusion Models](https://arxiv.org/abs/2406.14539)
        by [Yandex Research](https://github.com/yandex-research).
        Currently running on {power_device}.
        """
        )
        gr.Markdown(
            "If you enjoy the space, feel free to give a ⭐ to the <a href='https://github.com/yandex-research/invertible-cd' target='_blank'>Github Repo</a>. [![GitHub Stars](https://img.shields.io/github/stars/yandex-research/invertible-cd?style=social)](https://github.com/yandex-research/invertible-cd)"
        )

        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)

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

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


        gr.Examples(
            examples=examples,
            inputs=[prompt],
            cache_examples=False
        )
    run_button.click(
        fn=infer,
        inputs=[prompt, seed, randomize_seed],
        outputs=[result]
    )

demo.queue().launch(share=False)