app.py

import os
import gradio as gr
import numpy as np
import random
from huggingface_hub import AsyncInferenceClient
from translatepy import Translator
import requests
import re
import asyncio
from PIL import Image

translator = Translator()
HF_TOKEN = os.environ.get("HF_TOKEN", None)
basemodel = "black-forest-labs/FLUX.1-dev"
MAX_SEED = np.iinfo(np.int32).max

CSS = """
footer {
    visibility: hidden;
}
"""

JS = """function () {
  gradioURL = window.location.href
  if (!gradioURL.endsWith('?__theme=dark')) {
    window.location.replace(gradioURL + '?__theme=dark');
  }
}"""

def enable_lora(lora_add):
    if not lora_add:
        return basemodel
    else:
        return lora_add

client = AsyncInferenceClient()

async def generate_image(
    prompt:str,
    model:str,
    lora_word:str,
    width:int=768,
    height:int=1024,
    scales:float=3.5,
    steps:int=24,
    seed:int=-1):

    if seed == -1:
        seed = random.randint(0, MAX_SEED)
    seed = int(seed)
    print(f'prompt:{prompt}')
    
    text = str(translator.translate(prompt, 'English')) + "," + lora_word

    try:
        image = await client.text_to_image(
            prompt=text,
            height=height,
            width=width,
            guidance_scale=scales,
            num_inference_steps=steps,
            model=model,
        )
    except Exception as e:
        raise gr.Error(f"Error in {e}")
    
    return image, seed

def get_upscale_finegrain(prompt, img_path, upscale_factor):
    client = Client("finegrain/finegrain-image-enhancer")
    result = client.predict(
        input_image=handle_file(img_path),
        prompt=prompt,
        negative_prompt="",
        seed=42,
        upscale_factor=upscale_factor,
        controlnet_scale=0.6,
        controlnet_decay=1,
        condition_scale=6,
        tile_width=112,
        tile_height=144,
        denoise_strength=0.35,
        num_inference_steps=18,
        solver="DDIM",
        api_name="/process"
    )
    print(result)
    return result[1]

async def upscale_image(image, upscale_factor):
    try:
        result = get_upscale_finegrain(
            prompt="",
            img_path=image,
            upscale_factor=upscale_factor
        )
    except Exception as e:
        raise gr.Error(f"Error in {e}")
    
    return result

async def gen(
    prompt:str,
    lora_add:str="XLabs-AI/flux-RealismLora",
    lora_word:str="",
    width:int=768,
    height:int=1024,
    scales:float=3.5,
    steps:int=24,
    seed:int=-1,
    upscale_factor:int=2
):
    model = enable_lora(lora_add)
    image, seed = await generate_image(prompt,model,lora_word,width,height,scales,steps,seed)
    
    upscaled_image = await upscale_image(image, upscale_factor)
    return upscaled_image, seed
     
with gr.Blocks(css=CSS, js=JS, theme="Nymbo/Nymbo_Theme") as demo:
    gr.HTML("<h1><center>Flux Lab Light</center></h1>")
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Row():
                img = gr.Image(type="filepath", label='Imagen generada por Flux', height=600)
            with gr.Row():
                prompt = gr.Textbox(label='Ingresa tu prompt (Multi-Idiomas)', placeholder="Ingresa prompt...", scale=6)
                sendBtn = gr.Button(scale=1, variant='primary')
        with gr.Accordion("Opciones avanzadas", open=True):
            with gr.Column(scale=1):
                width = gr.Slider(
                    label="Ancho",
                    minimum=512,
                    maximum=1280,
                    step=8,
                    value=768,
                )
                height = gr.Slider(
                    label="Alto",
                    minimum=512,
                    maximum=1280,
                    step=8,
                    value=1024,
                )
                scales = gr.Slider(
                    label="Guía",
                    minimum=3.5,
                    maximum=7,
                    step=0.1,
                    value=3.5,
                )
                steps = gr.Slider(
                    label="Pasos",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=24,
                )
                seed = gr.Slider(
                    label="Semillas",
                    minimum=-1,
                    maximum=MAX_SEED,
                    step=1,
                    value=-1,
                )
                lora_add = gr.Textbox(
                    label="Agregar Flux LoRA",
                    info="Modelo de LoRA a agregar",
                    lines=1,
                    value="XLabs-AI/flux-RealismLora",
                )
                lora_word = gr.Textbox(
                    label="Palabra clave de LoRA",
                    info="Palabra clave para activar el modelo de LoRA",
                    lines=1,
                    value="",
                )
                upscale_factor = gr.Radio(
                    label="Factor de escalado",
                    choices=[2, 3, 4],
                    value=2,
                )

    gr.on(
        triggers=[
            prompt.submit,
            sendBtn.click,
        ],
        fn=gen,
        inputs=[
            prompt,
            lora_add,
            lora_word,
            width, 
            height, 
            scales, 
            steps, 
            seed,
            upscale_factor
        ],
        outputs=[img, seed]
    )
    
if __name__ == "__main__":
    demo.queue(api_open=False).launch(show_api=False, share=False)