import gradio as gr
import torch
from PIL import Image
import numpy as np
from diffusers import StableDiffusionDepth2ImgPipeline
from pathlib import Path

dept2img = StableDiffusionDepth2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-2-depth",
    torch_dtype=torch.float16,
).to("cuda")


def pad_image(input_image):
    pad_w, pad_h = np.max(((2, 2), np.ceil(
        np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
    im_padded = Image.fromarray(
        np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
    w, h = im_padded.size
    if w == h:
        return im_padded
    elif w > h:
        new_image = Image.new(im_padded.mode, (w, w), (0, 0, 0))
        new_image.paste(im_padded, (0, (w - h) // 2))
        return new_image
    else:
        new_image = Image.new(im_padded.mode, (h, h), (0, 0, 0))
        new_image.paste(im_padded, ((h - w) // 2, 0))
        return new_image


def predict(input_image, prompt, negative_prompt, steps, num_samples, scale, seed, strength, depth_image=None):
    depth = None
    if depth_image is not None:
        depth_image = pad_image(depth_image)
        depth_image = depth_image.resize((512, 512))
        depth = np.array(depth_image.convert("L"))
        depth = depth.astype(np.float32) / 255.0
        depth = depth[None, None]
        depth = torch.from_numpy(depth)
    init_image = input_image.convert("RGB")
    image = pad_image(init_image)  # resize to integer multiple of 32
    image = image.resize((512, 512))
    result = dept2img(
        image=image,
        prompt=prompt,
        negative_prompt=negative_prompt,
        depth_image=depth,
        seed=seed,
        strength=strength,
        num_inference_steps=steps,
        guidance_scale=scale,
        num_images_per_prompt=num_samples,
    )
    return result['images']


block = gr.Blocks().queue()
with block:
    with gr.Row():
        with gr.Column():
            gr.Markdown("## Stable Diffusion 2 Depth2Img")
            gr.HTML("<p><a href='https://huggingface.co/spaces/radames/stable-diffusion-depth2img?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a></p>")


    with gr.Row():
        with gr.Column():
            input_image = gr.Image(source='upload', type="pil")
            depth_image = gr.Image(
                source='upload', type="pil", label="Depth image Optional", value=None)
            prompt = gr.Textbox(label="Prompt")
            negative_prompt = gr.Textbox(label="Negative Pompt")

            run_button = gr.Button(label="Run")
            with gr.Accordion("Advanced options", open=False):
                num_samples = gr.Slider(
                    label="Images", minimum=1, maximum=4, value=1, step=1)
                steps = gr.Slider(label="Steps", minimum=1,
                                  maximum=50, value=50, step=1)
                scale = gr.Slider(
                    label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1
                )
                strength = gr.Slider(
                    label="Strength", minimum=0.0, maximum=1.0, value=0.9, step=0.01
                )
                seed = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=2147483647,
                    step=1,
                    randomize=True,
                )
        with gr.Column():
            gallery = gr.Gallery(label="Generated images", show_label=False).style(
                grid=[2], height="auto")
    gr.Examples(
        examples=[
            ["./examples/baby.jpg", "high definition photo of a baby astronaut space walking at the international space station with earth seeing from above in the background",
             "", 50, 4, 9.0, 123123123, 0.8, None],
            ["./examples/gol.jpg", "professional photo of a Elmo jumping between two high rises, beautiful colorful city landscape in the background",
             "", 50, 4, 9.0, 1734133747, 0.9, None],
            ["./examples/bag.jpg", "a photo of a bag of cookies in the bathroom", "low light, dark, blurry", 50, 4, 9.0, 1734133747, 0.9, "./examples/depth.jpg"],
            ["./examples/smile_face.jpg", "a hand holding a very spherical orange", "low light, dark, blurry", 50, 4, 6.0, 961736534, 0.5, "./examples/smile_depth.jpg"]

        ],
        inputs=[input_image, prompt, negative_prompt, steps,
                num_samples, scale, seed, strength, depth_image],
        outputs=[gallery],
        fn=predict,
        cache_examples=True,
    )
    run_button.click(fn=predict, inputs=[input_image, prompt, negative_prompt,
                     steps, num_samples, scale, seed, strength, depth_image], outputs=[gallery])


block.launch(show_api=False)