from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny
import torch
import os

try:
    import intel_extension_for_pytorch as ipex
except:
    pass

from PIL import Image
import numpy as np
import gradio as gr
import psutil
import time
from sfast.compilers.stable_diffusion_pipeline_compiler import (
    compile,
    CompilationConfig,
)


SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)
HF_TOKEN = os.environ.get("HF_TOKEN", None)
# check if MPS is available OSX only M1/M2/M3 chips
mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
device = torch.device(
    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
)
torch_device = device
torch_dtype = torch.float16

print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
print(f"TORCH_COMPILE: {TORCH_COMPILE}")
print(f"device: {device}")

if mps_available:
    device = torch.device("mps")
    torch_device = "cpu"
    torch_dtype = torch.float32

if SAFETY_CHECKER == "True":
    pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7")
else:
    pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7", safety_checker=None)

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.unet.to(memory_format=torch.channels_last)
pipe.set_progress_bar_config(disable=True)
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
pipe.fuse_lora()
pipe.to(device=torch_device, dtype=torch_dtype).to(device)


config = CompilationConfig.Default()
config.enable_xformers = True
config.enable_triton = True
config.enable_cuda_graph = True
pipe = compile(pipe, config=config)


def predict(prompt, guidance, steps, seed=1231231):
    generator = torch.manual_seed(seed)
    last_time = time.time()
    results = pipe(
        prompt=prompt,
        generator=generator,
        num_inference_steps=steps,
        guidance_scale=guidance,
        width=512,
        height=512,
        # original_inference_steps=params.lcm_steps,
        output_type="pil",
    )
    print(f"Pipe took {time.time() - last_time} seconds")
    nsfw_content_detected = (
        results.nsfw_content_detected[0]
        if "nsfw_content_detected" in results
        else False
    )
    if nsfw_content_detected:
        gr.Warning("NSFW content detected.")
        return Image.new("RGB", (512, 512))
    return results.images[0]


css = """
#container{
    margin: 0 auto;
    max-width: 40rem;
}
#intro{
    max-width: 100%;
    text-align: center;
    margin: 0 auto;
}


#container{
    margin: 0 auto;
    max-width: 40rem;
}
#intro{
    max-width: 100%;
    text-align: center;
    margin: 0 auto;
}
div.svelte-vt1mxs {
    display: flex;
    position: relative;
    flex-direction: column
}

div.svelte-vt1mxs>*,div.svelte-vt1mxs>.form > * {
    width: var(--size-full)
}

.gap.svelte-vt1mxs {
    gap: var(--layout-gap)
}

.hide.svelte-vt1mxs {
    display: none
}

.compact.svelte-vt1mxs>*,.compact.svelte-vt1mxs .box {
    border-radius: 0
}

.compact.svelte-vt1mxs,.panel.svelte-vt1mxs {
    border: solid var(--panel-border-width) var(--panel-border-color);
    border-radius: var(--container-radius);
    background: var(--panel-background-fill);
    padding: var(--spacing-lg)
}

div#component-24 {
    display: none;
}

div#component-8 {background: #00000024;border: 0;color: #ffffff;backdrop-filter: blur(20px);-webkit-backdrop-filter: blur(20px);border-width: 0 !important;}

span.md.svelte-9tftx4 {
    display: none;
}

.empty.svelte-lk9eg8.large.unpadded_box {
    background: none !important;
}

div#component-26 {
    display: none;
}

div#component-7 {
    background: none;
}

.wrap.default.full.svelte-119qaqt.hide {
    background: none !important;
}

.styler.svelte-iyf88w {
    background: none !important;
}

div#component-3 {
    background: none !important;
    border: 0;
}

input.scroll-hide.svelte-1f354aw {
    overflow: hidden !important;
}

div#component-5 {
    border-radius: 40px 0px 0px 40px;
    background: black !important;
    opacity: 0.9;
}

#component-6 {
    border-radius: 0px 40px 40px 0px;
    background: linear-gradient(358deg, #ff4d0080, #fff0);
    color: #ffffffe3;
    border: 2px #ffffffc2 dashed;
    border-left: 0;
    font-size: 30px;
    letter-spacing:-1px;
    position: relative;
    z-index: 1;
    backdrop-filter: blur(18px);
-webkit-backdrop-filter: blur(18px);
}



div#component-0 {
    max-width: 100% !important;
}

.grid-wrap.svelte-1b19cri.fixed-height {
    max-height: 100% !important;
    overflow: auto;
}

footer.svelte-1ax1toq {
    display: none !important;
}

input.scroll-hide.svelte-1f354aw {
    font-size: 26px;
    padding: 25px;
}

div#component-4 {
    margin-top: 230px;
    margin-bottom: 30px;
}

gradio-app {
    background-color: transparent !important;
    background: url(https://vivawaves.com/wavesweaveslogo.svg) top center no-repeat !important;
  margin-top: 77px;
}

label.svelte-1f354aw {
}

.styler.svelte-iyf88w {
}

body {
    background: url(https://vivawaves.com/vivatodaybg2.jpg);
    background-size: cover;
}

img.svelte-1b19cri {}

.preview.svelte-1b19cri {
    background: #0000004d !important;
    border-radius: 20px;
    padding: 20px;
    overflow: hidden;
}

button.svelte-1030q2h {
    border-radius: 100%;
}

div.svelte-1030q2h svg {
}

svg path {
}

img.svelte-1b19cri {
    border-radius: 10px;
}

.form.svelte-sfqy0y {
    background: #fff0;
    border-width: 0px;
    opacity: 0.8;
}

.gradio-container-3-44-2,.gradio-container-3-44-2 *,.gradio-container-3-44-2 :before,.gradio-container-3-44-2 :after {
    box-sizing: border-box;
    border-width: 0;
    border-style: solid;
}


div#component-13 {
    display: none;
}

footer.svelte-mpyp5e {
    display: none !important;
}

div#intro {
    display: none;
}

div.svelte-15lo0d8 {
    display: flex;
    flex-wrap: wrap;
    gap: 0;
    width: var(--size-full);
    flex-direction: initial;
    justify-content: center;
    align-items: baseline;
}

input.svelte-1f354aw.svelte-1f354aw, textarea.svelte-1f354aw.svelte-1f354aw {
    display: block;
    position: relative;
    outline: none !important;
    box-shadow: var(--input-shadow);
    background: var(--input-background-fill);
    padding: var(--input-padding);
    width: 100%;
    color: var(--body-text-color);
    font-weight: var(--input-text-weight);
    font-size: large;
    line-height: initial;
    border: none;
    text-size-adjust: auto;
    font-size: 23px !important;
}

div#component-24 {
    display: none;
}

div#component-8 {background: #00000024;border: 0;color: #ffffff;backdrop-filter: blur(20px);-webkit-backdrop-filter: blur(20px);border-width: 0 !important;}

span.md.svelte-9tftx4 {
    display: none;
}

.empty.svelte-lk9eg8.large.unpadded_box {
    background: none !important;
}

div#component-26 {
    display: none;
}

div#component-7 {
    background: none;
}

.wrap.default.full.svelte-119qaqt.hide {
    background: none !important;
}

.styler.svelte-iyf88w {
    background: none !important;
}

div#component-3 {
    background: none !important;
    border: 0;
}

input.scroll-hide.svelte-1f354aw {
    overflow: hidden !important;
}

div#component-5 {
    border-radius: 40px;
    background: white !important;
    opacity: 0.9;
}

#component-6 {
    border-radius: 0px 40px 40px 0px;
    background: linear-gradient(358deg, #ff4d0080, #fff0);
    color: #ffffffe3;
    border: 2px #ffffffc2 dashed;
    border-left: 0;
    font-size: 30px;
    letter-spacing:-1px;
    position: relative;
    z-index: 1;
    backdrop-filter: blur(18px);
    -webkit-backdrop-filter: blur(18px);
    display: none;
}



div#component-0 {
    max-width: 100% !important;
}

.grid-wrap.svelte-1b19cri.fixed-height {
    max-height: 100% !important;
    overflow: auto;
}

footer.svelte-1ax1toq {
    display: none !important;
}

input.scroll-hide.svelte-1f354aw {
    font-size: 26px;
    padding: 25px;
}

div#component-4 {
    margin-top: 230px;
    margin-bottom: 30px;
}

gradio-app {
    background-color: transparent !important;
    background: url(https://vivawaves.com/wavesweaveslogo.svg) top center no-repeat !important;
  margin-top: 77px;
}

label.svelte-1f354aw {
}

.styler.svelte-iyf88w {
}

body {
    background: url(https://vivawaves.com/vivatodaybg2.jpg);
    background-size: cover;
}

img.svelte-1b19cri {}

.preview.svelte-1b19cri {
    background: #0000004d !important;
    border-radius: 20px;
    padding: 20px;
    overflow: hidden;
}

button.svelte-1030q2h {
    border-radius: 100%;
}

div.svelte-1030q2h svg {
}

svg path {
}

img.svelte-1b19cri {
    border-radius: 10px;
}

.form.svelte-sfqy0y {
    background: #fff0;
    border-width: 0px;
    opacity: 0.8;
}

.gradio-container-3-44-2,.gradio-container-3-44-2 *,.gradio-container-3-44-2 :before,.gradio-container-3-44-2 :after {
    box-sizing: border-box;
    border-width: 0;
    border-style: solid;
}


div#component-13 {
    display: none;
}

footer.svelte-mpyp5e {
    display: none !important;
}

div#intro {
    display: none;
}

div.svelte-15lo0d8 {
    display: flex;
    flex-wrap: wrap;
    gap: 0 !important;
    width: var(--size-full);
    flex-direction: initial;
    justify-content: center;
    align-items: baseline;
}

input.svelte-1f354aw.svelte-1f354aw, textarea.svelte-1f354aw.svelte-1f354aw {
    display: block;
    position: relative;
    outline: none !important;
    box-shadow: var(--input-shadow);
    background: var(--input-background-fill);
    padding: var(--input-padding);
    width: 100%;
    color: var(--body-text-color);
    font-weight: var(--input-text-weight);
    font-size: large;
    line-height: initial;
    border: none;
    text-size-adjust: auto;
    font-size: 23px !important;
    border-radius: 30px;
    background: white !important;
    text-align: center;
}

div#component-8 {
    margin-bottom: 70px;
}



"""
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="container"):
        gr.Markdown(
            """# SD1.5 Latent Consistency LoRAs
            SD1.5  is loaded with a LCM-LoRA, giving it the super power of doing inference in as little as 4 steps. [Learn more on our blog](#) or [technical report](#).
            """,
            elem_id="intro",
        )
        with gr.Row():
            with gr.Row():
                prompt = gr.Textbox(
                    placeholder="Insert your prompt here:", scale=5, container=False
                )
                generate_bt = gr.Button("Generate", scale=1)

        image = gr.Image(type="filepath")
        with gr.Accordion("Advanced options", open=False):
            guidance = gr.Slider(
                label="Guidance", minimum=0.0, maximum=5, value=0.3, step=0.001
            )
            steps = gr.Slider(label="Steps", value=4, minimum=2, maximum=10, step=1)
            seed = gr.Slider(
                randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1
            )
        with gr.Accordion("Run with diffusers"):
            gr.Markdown(
                """## Running LCM-LoRAs it with `diffusers`
            ```bash
            pip install diffusers==0.23.0
            ```
            
            ```py
            from diffusers import DiffusionPipeline, LCMScheduler

            pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7").to("cuda") 
            pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
            pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5") #yes, it's a normal LoRA

            results = pipe(
                prompt="The spirit of a tamagotchi wandering in the city of Vienna",
                num_inference_steps=4,
                guidance_scale=0.0,
            )
            results.images[0]
            ```
            """
            )

        inputs = [prompt, guidance, steps, seed]
        generate_bt.click(fn=predict, inputs=inputs, outputs=image, show_progress=False)
        prompt.input(fn=predict, inputs=inputs, outputs=image, show_progress=False)
        guidance.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
        steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
        seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)

demo.queue(api_open=False)
demo.launch(show_api=False)