from diffusers import (
    StableDiffusionPipeline,
    DPMSolverMultistepScheduler,
    DiffusionPipeline,
)
import gradio as gr
import torch
from PIL import Image
import time
import psutil
import random
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


start_time = time.time()
current_steps = 25

SAFETY_CHECKER = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=torch.float16)

UPSCALER = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16)
UPSCALER.to("cuda")
UPSCALER.enable_xformers_memory_efficient_attention()


class Model:
    def __init__(self, name, path=""):
        self.name = name
        self.path = path

        if path != "":
            self.pipe_t2i = StableDiffusionPipeline.from_pretrained(
                path, torch_dtype=torch.float16, safety_checker=SAFETY_CHECKER
            )
            self.pipe_t2i.scheduler = DPMSolverMultistepScheduler.from_config(
                self.pipe_t2i.scheduler.config
            )
        else:
            self.pipe_t2i = None


models = [
    Model("Stable Diffusion v1-4", "CompVis/stable-diffusion-v1-4"),
    # Model("Stable Diffusion v1-5", "runwayml/stable-diffusion-v1-5"),
    # Model("anything-v4.0", "andite/anything-v4.0"),
]

MODELS = {m.name: m for m in models}

device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"


def error_str(error, title="Error"):
    return (
        f"""#### {title}
            {error}"""
        if error
        else ""
    )


def inference(
    model_name,
    prompt,
    guidance,
    steps,
    seed=0,
    neg_prompt="",
):

    print(psutil.virtual_memory())  # print memory usage

    if seed == 0:
        seed = random.randint(0, 2147483647)

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

    try:
        low_res_image, up_res_image = txt_to_img(
            model_name,
            prompt,
            neg_prompt,
            guidance,
            steps,
            generator,
        )
        return low_res_image, up_res_image, f"Done. Seed: {seed}",
    except Exception as e:
        return None, None, error_str(e)


def txt_to_img(
    model_name,
    prompt,
    neg_prompt,
    guidance,
    steps,
    generator,
):
    pipe = MODELS[model_name].pipe_t2i

    if torch.cuda.is_available():
        pipe = pipe.to("cuda")
        pipe.enable_xformers_memory_efficient_attention()

    low_res_latents = pipe(
        prompt,
        negative_prompt=neg_prompt,
        num_inference_steps=int(steps),
        guidance_scale=guidance,
        generator=generator,
        output_type="latent",
    ).images

    with torch.no_grad():
        low_res_image = pipe.decode_latents(low_res_latents)
        low_res_image = pipe.numpy_to_pil(low_res_image)

    up_res_image = UPSCALER(
        prompt=prompt,
        negative_prompt=neg_prompt,
        image=low_res_latents,
        num_inference_steps=20,
        guidance_scale=0,
        generator=generator,
    ).images

    pipe.to("cpu")
    torch.cuda.empty_cache()

    return low_res_image[0], up_res_image[0]


def replace_nsfw_images(results):
    for i in range(len(results.images)):
        if results.nsfw_content_detected[i]:
            results.images[i] = Image.open("nsfw.png")
    return results.images


with gr.Blocks(css="style.css") as demo:
    gr.HTML(
        f"""
            <div class="finetuned-diffusion-div">
              <div>
                <h1>Stable Diffusion Latent Upscaler</h1>
              </div>
              <p>
               Demo for <a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler">Latent Diffusion Upscaling</a>
              </p>
              <p>
               Running on <b>{device}</b>
              </p>
              <p>You can also duplicate this space and upgrade to gpu by going to settings:<br>
              <a style="display:inline-block" href="https://huggingface.co/spaces/patrickvonplaten/finetuned_diffusion?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>
            </div>
        """
    )
    with gr.Row():

        with gr.Column(scale=55):
            with gr.Group():
                model_name = gr.Dropdown(
                    label="Model",
                    choices=[m.name for m in models],
                    value=models[0].name,
                )

                with gr.Row():
                    prompt = gr.Textbox(
                        label="Prompt",
                        show_label=False,
                        max_lines=2,
                        placeholder="Enter prompt.",
                    ).style(container=False)
                    generate = gr.Button(value="Generate").style(
                        rounded=(False, True, True, False)
                    )

            low_res_image = gr.Image(label="512px Image", shape=(512, 512))
            error_output = gr.Markdown()

        with gr.Column(scale=45):
            with gr.Tab("Options"):
                with gr.Group():
                    neg_prompt = gr.Textbox(
                        label="Negative prompt",
                        placeholder="What to exclude from the image",
                    )

                    with gr.Row():
                        guidance = gr.Slider(
                            label="Guidance scale", value=7.5, maximum=15
                        )
                        steps = gr.Slider(
                            label="Steps",
                            value=current_steps,
                            minimum=2,
                            maximum=75,
                            step=1,
                        )

                    seed = gr.Slider(
                        0, 2147483647, label="Seed (0 = random)", value=0, step=1
                    )

            up_res_image = gr.Image(label="1024px Image", shape=(1024, 1024))

    inputs = [
        model_name,
        prompt,
        guidance,
        steps,
        seed,
        neg_prompt,
    ]
    outputs = [low_res_image, up_res_image, error_output]
    prompt.submit(inference, inputs=inputs, outputs=outputs)
    generate.click(inference, inputs=inputs, outputs=outputs)

#    ex = gr.Examples(
#        [
#            [models[0].name, "a photo of an astronaut high resolution, unreal engine, ultra realistic", 7.5, 50, 33, ""]
#        ],
#        inputs=[model_name, prompt, guidance, steps, seed, neg_prompt],
#        outputs=outputs,
#        fn=inference,
#        cache_examples=False,
#    )

    gr.HTML(
        """
    <div style="border-top: 1px solid #303030;">
      <br>
      <p>Models by 🤗 Hugging Face and others. ❤️</p>
      <p>This space uses the <a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver++</a> sampler by <a href="https://arxiv.org/abs/2206.00927">Cheng Lu, et al.</a>.</p>
      <p>This is a Demo Space For:<br>
      <a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler">Stability AI's Latent Upscaler</a>
    </div>
    """
    )

print(f"Space built in {time.time() - start_time:.2f} seconds")

demo.queue(concurrency_count=1)
demo.launch()