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

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.to(device=torch_device, dtype=torch_dtype).to(device)
pipe.unet.to(memory_format=torch.channels_last)
pipe.set_progress_bar_config(disable=True)

# check if computer has less than 64GB of RAM using sys or os
if psutil.virtual_memory().total < 64 * 1024**3:
    pipe.enable_attention_slicing()

if TORCH_COMPILE:
    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
    pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)

# Load LCM LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
pipe.fuse_lora()


def predict(prompt, negative_prompt, guidance, steps, seed=1231231):
    generator = torch.manual_seed(seed)
    last_time = time.time()
    results = pipe(
        prompt=prompt,
        generator=generator,
        negative_prompt=negative_prompt,
        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;
}
"""
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",
        )
        
        prompt = gr.Textbox(
            placeholder="Insert your prompt here:", lines= 4, container=False
        )
        negative_prompt = gr.Textbox(
            placeholder="Insert your negative prompt here:", lines= 4, container=False
        )
        generate_bt = gr.Button("Generate", scale=1)

        image = gr.Image(type="filepath")
        with gr.Accordion("Advanced options", open=True):
            guidance = gr.Slider(
                label="Guidance", minimum=0.0, maximum=5, value=1.5, step=0.001
            )
            steps = gr.Slider(label="Steps", value=8, minimum=1, maximum=50, step=1)
            seed = gr.Slider(
                randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1
            )

        inputs = [prompt, negative_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)
        negative_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()
demo.launch()