import gradio as gr
import numpy as np
import PIL.Image
from PIL import Image
import random
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, StableDiffusionXLPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
#from diffusers.utils import load_image

import cv2
import torch
import spaces


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

controlnet = ControlNetModel.from_pretrained(
    #"2vXpSwA7/test_controlnet2/CN-anytest_v4-marged_am_dim256.safetensors",
    "xinsir/controlnet-scribble-sdxl-1.0",
    torch_dtype=torch.float16
    #from_tf=False,  
    #variant="safetensors"
)

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "yodayo-ai/holodayo-xl-2.1",
    controlnet=controlnet,
    vae=vae,
    torch_dtype=torch.float16,
)

pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1216

    
@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, image: PIL.Image.Image) -> PIL.Image.Image:
    width, height = image['composite'].size
    ratio = np.sqrt(1024. * 1024. / (width * height))
    new_width, new_height = int(width * ratio), int(height * ratio)
    image = image['composite'].resize((new_width, new_height))
    print(image)

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

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

    output_image = pipe(
        prompt=prompt + ", masterpiece, best quality, very aesthetic, absurdres",
        negative_prompt=negative_prompt,
        image=image,
        controlnet_conditioning_scale=1.0,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=new_width,
        height=new_height,
        generator=generator
    ).images[0]

    return output_image


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

with gr.Blocks(css=css) as demo:

    with gr.Column(elem_id="col-container"):
        gr.Markdown("""
        # Text-to-Image Demo
        using : 
        [Holodayo XL 2.1](https://huggingface.co/yodayo-ai/holodayo-xl-2.1), 
        [scribble](https://huggingface.co/xinsir/controlnet-scribble-sdxl-1.0)
        """)

        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)

        image = gr.ImageEditor(type="pil", image_mode="L", crop_size=(512, 512))
        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",
                value="nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
            )

            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,#832,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,#1216,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=20.0,
                    step=0.1,
                    value=7,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=28,
                    step=1,
                    value=28,
                )

    run_button.click(#lambda x: None, inputs=None, outputs=result).then(
        fn=infer,
        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,image],
        outputs=[result]
    )

demo.queue().launch()