#!/usr/bin/env python

import os
import random
import uuid

import gradio as gr
import numpy as np
from PIL import Image
import spaces
import torch
from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionXLPipeline, StableDiffusionXLInpaintPipeline, EulerAncestralDiscreteScheduler, DPMSolverSinglestepScheduler
from diffusers.utils import load_image
from huggingface_hub import hf_hub_download

HF_TOKEN = os.getenv("HF_TOKEN")

DESCRIPTION = """
# [Fluently Playground](https://huggingface.co/fluently)

[🦾 New FluentlyXL Final!](https://huggingface.co/fluently/Fluently-XL-Final)
"""
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"

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

USE_TORCH_COMPILE = 0
ENABLE_CPU_OFFLOAD = 0

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


if torch.cuda.is_available():
    pipe_xl_final = StableDiffusionXLPipeline.from_single_file(
        hf_hub_download(repo_id="fluently/Fluently-XL-Final", filename="FluentlyXL-Final.safetensors", token=HF_TOKEN),
        torch_dtype=torch.float16,
        use_safetensors=True,
    )
    pipe_xl_final.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_xl_final.scheduler.config)
    pipe_xl_final.to(device)   

    pipe_anime = StableDiffusionPipeline.from_pretrained(
        "fluently/Fluently-anime",
        torch_dtype=torch.float16,
        use_safetensors=True,
    )
    pipe_anime.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_anime.scheduler.config)
    pipe_anime.to(device)
    
    pipe_epic = StableDiffusionPipeline.from_pretrained(
        "fluently/Fluently-epic",
        torch_dtype=torch.float16,
        use_safetensors=True,
    )
    pipe_epic.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_epic.scheduler.config)
    pipe_epic.to(device)

    pipe_xl_inpaint = StableDiffusionXLInpaintPipeline.from_single_file(
        "https://huggingface.co/fluently/Fluently-XL-v3-inpainting/blob/main/FluentlyXL-v3-inpainting.safetensors",
        torch_dtype=torch.float16,
        use_safetensors=True,
    )
    pipe_xl_inpaint.to(device)

    pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained(
        "fluently/Fluently-v4-inpainting",
        torch_dtype=torch.float16,
        use_safetensors=True,
    )
    #pipe_inpaint.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_inpaint.scheduler.config)
    pipe_inpaint.to(device)

    pipe_xl = StableDiffusionXLPipeline.from_pretrained(
        "fluently/Fluently-XL-v4",
        torch_dtype=torch.float16,
        use_safetensors=True,
    )
    pipe_xl.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_xl.scheduler.config)
    pipe_xl.to(device)
    
    pipe_xl_lightning = StableDiffusionXLPipeline.from_pretrained(
        "fluently/Fluently-XL-v3-lightning",
        torch_dtype=torch.float16,
        use_safetensors=True,
    )
    pipe_xl_lightning.scheduler = DPMSolverSinglestepScheduler.from_config(pipe_xl_lightning.scheduler.config, use_karras_sigmas=False, timestep_spacing="trailing", lower_order_final=True)
    pipe_xl_lightning.to(device)

    print("Loaded on Device!")



def save_image(img):
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name)
    return unique_name


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

def get_model(model):
    if model == "Fluently v4 inpaint" or model == "Fluently XL v3 inpaint":
        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(interactive=True)
    if model == "Fluently XL v3 Lightning":
        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(interactive=False)
    else:
        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(interactive=True)


@spaces.GPU(duration=70, enable_queue=True)
def generate(
    model,
    inpaint_image,
    mask_image,
    blur_factor,
    strength,
    prompt: str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3,
    randomize_seed: bool = False,
    progress=gr.Progress(track_tqdm=True),
):

    
    seed = int(randomize_seed_fn(seed, randomize_seed))

    if not use_negative_prompt:
        negative_prompt = ""  # type: ignore
    if model == "Fluently XL Final":
        images = pipe_xl_final(
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=width,
            height=height,
            guidance_scale=guidance_scale,
            num_inference_steps=25,
            num_images_per_prompt=1,
            output_type="pil",
        ).images
    elif model == "Fluently Anime":
        images = pipe_anime(
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=width,
            height=height,
            guidance_scale=guidance_scale,
            num_inference_steps=30,
            num_images_per_prompt=1,
            output_type="pil",
        ).images
    elif model == "Fluently Epic":
        images = pipe_epic(
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=width,
            height=height,
            guidance_scale=guidance_scale,
            num_inference_steps=30,
            num_images_per_prompt=1,
            output_type="pil",
        ).images
    elif model == "Fluently XL v4":
        images = pipe_xl(
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=width,
            height=height,
            guidance_scale=guidance_scale,
            num_inference_steps=25,
            num_images_per_prompt=1,
            output_type="pil",
        ).images
    elif model == "Fluently XL v3 Lightning":
        images = pipe_xl_lightning(
            prompt=prompt,
            negative_prompt=negative_prompt,
            width=width,
            height=height,
            guidance_scale=2,
            num_inference_steps=5,
            num_images_per_prompt=1,
            output_type="pil",
        ).images
    elif model == "Fluently v4 inpaint":
        blurred_mask = pipe_inpaint.mask_processor.blur(mask_image, blur_factor=blur_factor)
        images = pipe_inpaint(
            prompt=prompt,
            image=inpaint_image,
            mask_image=blurred_mask,
            negative_prompt=negative_prompt,
            width=width,
            height=height,
            guidance_scale=guidance_scale,
            num_inference_steps=30,
            strength=strength,
            num_images_per_prompt=1,
            output_type="pil",
        ).images
    else:
        blurred_mask = pipe_inpaint.mask_processor.blur(mask_image, blur_factor=blur_factor)
        images = pipe_xl_inpaint(
            prompt=prompt,
            image=inpaint_image,
            mask_image=blurred_mask,
            negative_prompt=negative_prompt,
            width=width,
            height=height,
            guidance_scale=guidance_scale,
            num_inference_steps=25,
            strength=strength,
            num_images_per_prompt=1,
            output_type="pil",
        ).images

    image_paths = [save_image(img) for img in images]
    print(image_paths)
    return image_paths, seed



        


examples = [
    "neon holography crystal cat",
    "a cat eating a piece of cheese",
    "an astronaut riding a horse in space",
    "a cartoon of a boy playing with a tiger",
    "a cute robot artist painting on an easel, concept art",
    "a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone"
]

css = '''
.gradio-container{max-width: 560px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''
with gr.Blocks(title="Fluently Playground", css=css) as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=False,
    )

    with gr.Row():
        model = gr.Radio(
            label="Model",
            choices=["Fluently XL Final", "Fluently XL v4", "Fluently XL v3 Lightning", "Fluently Anime", "Fluently Epic", "Fluently XL v3 inpaint", "Fluently v4 inpaint"],
            value="Fluently XL v3 Lightning",
            interactive=True,
        )

    md_mask = gr.Markdown("""
    ⚠️ To generate an inpaint mask, go [here](https://huggingface.co/spaces/stevhliu/inpaint-mask-maker).
    """, visible=False)
    inpaint_image = gr.Image(label="Inpaint Image", interactive=True, scale=5, visible=False, type="pil")
    mask_image = gr.Image(label="Mask Image", interactive=True, scale=5, visible=False, type="pil")

    blur_factor = gr.Slider(label="Mask Blur Factor", minimum=0, maximum=100, value=4, step=1, interactive=True, visible=False)
    strength = gr.Slider(label="Denoising Strength", minimum=0.00, maximum=1.00, value=0.70, step=0.01, interactive=True, visible=False)
    
    with gr.Group():
        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.Gallery(label="Result", columns=1, preview=True, show_label=False)
    with gr.Accordion("Advanced options", open=False):
        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
        negative_prompt = gr.Text(
            label="Negative prompt",
            max_lines=6,
            lines=5,
            value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation""",
            placeholder="Enter a negative prompt",
            visible=True,
        )
        seed = gr.Slider(
            label="Seed",
            minimum=0,
            maximum=MAX_SEED,
            step=1,
            value=0,
            visible=True
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        with gr.Row(visible=True):
            width = gr.Slider(
                label="Width",
                minimum=256,
                maximum=2048,
                step=8,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=256,
                maximum=2048,
                step=8,
                value=1024,
            )
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=0.1,
                maximum=20.0,
                step=0.1,
                value=6,
                interactive=False,
            )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=[result, seed],
        fn=generate,
        cache_examples=False,
    )

    use_negative_prompt.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt,
        api_name=False,
    )

    model.change(
        fn=get_model,
        inputs=model,
        outputs=[md_mask, inpaint_image, mask_image, blur_factor, strength, guidance_scale],
        api_name=False,
    )
    

    gr.on(
        triggers=[
            prompt.submit,
            negative_prompt.submit,
            run_button.click,
        ],
        fn=generate,
        inputs=[
            model,
            inpaint_image,
            mask_image,
            blur_factor,
            strength,
            prompt,
            negative_prompt,
            use_negative_prompt,
            seed,
            width,
            height,
            guidance_scale,
            randomize_seed,
        ],
        outputs=[result, seed],
        api_name="run",
    )
    
if __name__ == "__main__":
    demo.queue(max_size=50).launch(show_api=False, debug=False)