#!/usr/bin/env python

from __future__ import annotations

import requests
import os
import random

import gradio as gr
import numpy as np
import spaces
import torch
from PIL import Image
from io import BytesIO
from diffusers import AutoencoderKL, DiffusionPipeline, AutoPipelineForImage2Image

DESCRIPTION = "# Run any LoRA or SD Model"
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>⚠️ This space is running on the CPU. This demo doesn't work on CPU 😞! Run on a GPU by duplicating this space or test our website for free and unlimited by <a href='https://squaadai.com'>clicking here</a>, which provides these and more options.</p>"

MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1824"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
ENABLE_USE_LORA = os.getenv("ENABLE_USE_LORA", "1") == "1"
ENABLE_USE_VAE = os.getenv("ENABLE_USE_VAE", "1") == "1"
ENABLE_USE_IMG2IMG = os.getenv("ENABLE_USE_VAE", "1") == "1"

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

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


@spaces.GPU
def generate(
    prompt: str,
    negative_prompt: str = "",
    prompt_2: str = "",
    negative_prompt_2: str = "",
    use_negative_prompt: bool = False,
    use_prompt_2: bool = False,
    use_negative_prompt_2: bool = False,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale_base: float = 5.0,
    num_inference_steps_base: int = 25,
    strength_img2img: float = 0.7,
    use_vae: bool = False,
    use_lora: bool = False,
    model = 'stabilityai/stable-diffusion-xl-base-1.0',
    vaecall = 'madebyollin/sdxl-vae-fp16-fix',
    lora = '',
    lora_scale: float = 0.7,
    use_img2img: bool = False,
    url = '',
):
    if torch.cuda.is_available():
            
        if not use_img2img:
            pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16)

            if use_vae:  
                vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
                pipe = DiffusionPipeline.from_pretrained(model, vae=vae, torch_dtype=torch.float16)
            
        if use_img2img:
            pipe = AutoPipelineForImage2Image.from_pretrained(model, torch_dtype=torch.float16)

            if use_vae:  
                vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
                pipe = AutoPipelineForImage2Image.from_pretrained(model, vae=vae, torch_dtype=torch.float16)
                    
            response = requests.get(url)
            init_image = Image.open(BytesIO(response.content)).convert("RGB")
            init_image = init_image.resize((width, height))

        if use_lora:
            pipe.load_lora_weights(lora)
            pipe.fuse_lora(lora_scale)
                                   
        if ENABLE_CPU_OFFLOAD:
            pipe.enable_model_cpu_offload()
            
        else:
            pipe.to(device)

        if USE_TORCH_COMPILE:
            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
        
    generator = torch.Generator().manual_seed(seed)

    if not use_negative_prompt:
        negative_prompt = None  # type: ignore
    if not use_prompt_2:
        prompt_2 = None  # type: ignore
    if not use_negative_prompt_2:
        negative_prompt_2 = None  # type: ignore

    if not use_img2img:
        return pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            prompt_2=prompt_2,
            negative_prompt_2=negative_prompt_2,
            width=width,
            height=height,
            guidance_scale=guidance_scale_base,
            num_inference_steps=num_inference_steps_base,
            generator=generator,
            output_type="pil",
        ).images[0]
    else:
        images = pipe(
            prompt=prompt,
            image=init_image,
            strength=strength_img2img,
            negative_prompt=negative_prompt,
            prompt_2=prompt_2,
            negative_prompt_2=negative_prompt_2,
            width=width,
            height=height,
            guidance_scale=guidance_scale_base,
            num_inference_steps=num_inference_steps_base,
            generator=generator,
            output_type="pil",
        ).images[0]
        return images

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
]

with gr.Blocks(theme=gr.themes.Soft(), css="style.css") as demo:
    gr.HTML(
        "<p><center>📙 For any additional support, join our <a href='https://discord.gg/JprjXpjt9K'>Discord</a></center></p>"
    )
    gr.Markdown(DESCRIPTION, elem_id="description")
    with gr.Group():
        model = gr.Text(label='Model', placeholder='e.g. stabilityai/stable-diffusion-xl-base-1.0')
        vaecall = gr.Text(label='VAE', placeholder='e.g. madebyollin/sdxl-vae-fp16-fix')
        lora = gr.Text(label='LoRA', placeholder='e.g. nerijs/pixel-art-xl')
        lora_scale = gr.Slider(
                info="The closer to 1, the more it will resemble LoRA, but errors may be visible.",
                label="Lora Scale",
                minimum=0.01,
                maximum=1,
                step=0.01,
                value=0.7,
            )
        url = gr.Text(label='URL (Img2Img)', placeholder='e.g https://example.com/image.png')
        with gr.Row():
            prompt = gr.Text(
                placeholder="Input prompt",
                label="Prompt",
                show_label=False,
                max_lines=1,
                container=False,
            )
            run_button = gr.Button("Run", scale=0)
        result = gr.Image(label="Result", show_label=False)
    with gr.Accordion("Advanced options", open=False):
        with gr.Row():
            use_img2img = gr.Checkbox(label='Use Img2Img', value=False, visible=ENABLE_USE_IMG2IMG)
            use_vae = gr.Checkbox(label='Use VAE', value=False, visible=ENABLE_USE_VAE)
            use_lora = gr.Checkbox(label='Use Lora', value=False, visible=ENABLE_USE_LORA)
            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
            use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
            use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
        negative_prompt = gr.Text(
            placeholder="Input Negative Prompt",
            label="Negative prompt",
            max_lines=1,
            visible=False,
        )
        prompt_2 = gr.Text(
            placeholder="Input Prompt 2",
            label="Prompt 2",
            max_lines=1,
            visible=False,
        )
        negative_prompt_2 = gr.Text(
            placeholder="Input Negative Prompt 2",
            label="Negative prompt 2",
            max_lines=1,
            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=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=256,
                maximum=MAX_IMAGE_SIZE,
                step=32,
                value=1024,
            )
            
        with gr.Row():
            guidance_scale_base = gr.Slider(
                info="Scale for classifier-free guidance",
                label="Guidance scale",
                minimum=1,
                maximum=20,
                step=0.1,
                value=5.0,
            )
        with gr.Row():
            num_inference_steps_base = gr.Slider(
                info="Number of denoising steps",
                label="Number of inference steps",
                minimum=10,
                maximum=100,
                step=1,
                value=25,
            )
        with gr.Row():
            strength_img2img = gr.Slider(
                info="Strength for Img2Img",
                label="Strength",
                minimum=0,
                maximum=1,
                step=0.01,
                value=0.7,
            )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=result,
        fn=generate,
        cache_examples=CACHE_EXAMPLES,
    )

    use_negative_prompt.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt,
        queue=False,
        api_name=False,
    )
    use_prompt_2.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_prompt_2,
        outputs=prompt_2,
        queue=False,
        api_name=False,
    )
    use_negative_prompt_2.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt_2,
        outputs=negative_prompt_2,
        queue=False,
        api_name=False,
    )
    use_vae.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_vae,
        outputs=vaecall,
        queue=False,
        api_name=False,
    )
    use_lora.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_lora,
        outputs=lora,
        queue=False,
        api_name=False,
    )
    use_img2img.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_img2img,
        outputs=url,
        queue=False,
        api_name=False,
    )

    gr.on(
        triggers=[
            prompt.submit,
            negative_prompt.submit,
            prompt_2.submit,
            negative_prompt_2.submit,
            run_button.click,
        ],
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=[
            prompt,
            negative_prompt,
            prompt_2,
            negative_prompt_2,
            use_negative_prompt,
            use_prompt_2,
            use_negative_prompt_2,
            seed,
            width,
            height,
            guidance_scale_base,
            num_inference_steps_base,
            strength_img2img,
            use_vae,
            use_lora,
            model,
            vaecall,
            lora,
            lora_scale,
            use_img2img,
            url,
        ],
        outputs=result,
        api_name="run",
    )

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