import gradio as gr
import numpy as np
import random
import torch
import spaces
import re
from diffusers import (
    DiffusionPipeline,
    AutoencoderTiny,
)
from huggingface_hub import hf_hub_download


def feifeimodload():

    dtype = torch.bfloat16
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    pipe = DiffusionPipeline.from_pretrained(
        "aifeifei798/DarkIdol-flux-v1", torch_dtype=dtype
    ).to(device)
    
    pipe.load_lora_weights(
        hf_hub_download("aifeifei798/feifei-flux-lora-v1.1", "feifei-v1.1.safetensors"),
        adapter_name="feifei",
    )

    pipe.vae.enable_slicing()
    pipe.vae.enable_tiling()
    torch.cuda.empty_cache()
    return pipe
    
pipe = feifeimodload()
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, guidancescale=3.5, num_feifei=0.35, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    prompt = re.sub("young woman", " feifei " ,prompt)
    prompt = re.sub("woman", " feifei " ,prompt)
    prompt = re.sub("girl", " feifei " ,prompt)
    prompt = re.sub("model", " feifei " ,prompt)
    pipe.set_adapters(
        ["feifei"],
        adapter_weights=[num_feifei],
    )
    
    pipe.fuse_lora(
        adapter_name=["feifei"],
        lora_scale=1.0,
    )
    image = pipe(
            prompt = "",
            prompt_2 = prompt,
            width = width,
            height = height,
            num_inference_steps = num_inference_steps, 
            generator = generator,
            guidance_scale=guidancescale
    ).images[0] 
    return image, seed
 
examples = [
    "flux,this photo is kpop idol girl"
]

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(f"""# FLUX.1 + feifei-flux-lora """)
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=12,
                placeholder="Enter your prompt",
                value="flux",
                container=False,
            )
            
        run_button = gr.Button("Run")
        
        result = gr.Image(label="Result", show_label=False,height=520)
        
        with gr.Accordion("Advanced Settings", open=False):
            
            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=64,
                    value=896,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=1152,
                )
            
            with gr.Row():
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=8,
                )

            with gr.Row():
                guidancescale = gr.Slider(
                    label="Guidance scale",
                    minimum=0,
                    maximum=10,
                    step=0.1,
                    value=3.5,
                )
                
            with gr.Row():
                num_feifei = gr.Slider(
                    label="FeiFei",
                    minimum=0,
                    maximum=2,
                    step=0.05,
                    value=0.55,
                )
        
        gr.Examples(
            examples = examples,
            fn = infer,
            inputs = [prompt],
            outputs = [result, seed],
            cache_examples=False
        )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn = infer,
        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps, guidancescale, num_feifei],
        outputs = [result, seed]
    )

demo.launch()