app.py

import os
import gradio as gr
import numpy as np
import random
from huggingface_hub import AsyncInferenceClient, InferenceClient
from PIL import Image
from gradio_client import Client, handle_file
from gradio_imageslider import ImageSlider

MAX_SEED = np.iinfo(np.int32).max
HF_TOKEN = os.environ.get("HF_TOKEN")
HF_TOKEN_UPSCALER = os.environ.get("HF_TOKEN_UPSCALER")

client = AsyncInferenceClient()
llm_client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

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

async def generate_image(prompt, model, lora_word, width, height, scales, steps, seed):
    try:
        if seed == -1:
            seed = random.randint(0, MAX_SEED)
        seed = int(seed)
        text = prompt + "," + lora_word
        image = await client.text_to_image(prompt=text, height=height, width=width, guidance_scale=scales, num_inference_steps=steps, model=model)
        return image, seed
    except Exception as e:
        return f"Error al generar imagen: {e}", None

def get_upscale_finegrain(prompt, img_path, upscale_factor):
    try:
        client = Client("finegrain/finegrain-image-enhancer", hf_token=HF_TOKEN_UPSCALER)
        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")
        return result[1]
    except Exception as e:
        return None

async def gen(prompt, basemodel, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model, process_lora):
    model = enable_lora(lora_model, basemodel) if process_lora else basemodel
    
    improved_prompt = await improve_prompt(prompt)
    combined_prompt = f"{prompt} {improved_prompt}"

    image, seed = await generate_image(combined_prompt, model, "", width, height, scales, steps, seed)
    
    if isinstance(image, str) and image.startswith("Error"):
        return [image, None]
    
    image_path = "temp_image.jpg"
    image.save(image_path, format="JPEG")
    
    if process_upscale:
        upscale_image_path = get_upscale_finegrain(combined_prompt, image_path, upscale_factor)
        if upscale_image_path is not None:
            upscale_image = Image.open(upscale_image_path)
            upscale_image.save("upscale_image.jpg", format="JPEG")
            return [image_path, "upscale_image.jpg"]
        else:
            return [image_path, image_path]
    else:
        return [image_path, image_path]

async def improve_prompt(prompt):
    try:
        instruction = "create a prompt with my idea and translate it into English (improve my text and add it), adding detailed descriptions of character, style, cinematography, cameras, atmosphere, and lighting for the best quality and realism, up max to 300 words."
        formatted_prompt = f"{instruction}: {prompt}"
        response = llm_client.text_generation(formatted_prompt, max_new_tokens=300)
        improved_text = response['generated_text'].strip() if 'generated_text' in response else response.strip()

        return improved_text
    except Exception as e:
        return f"Error mejorando el prompt: {e}"

css = """
#col-container{ margin: 0 auto; max-width: 1024px;}
"""

with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
    with gr.Column(elem_id="col-container"):
        with gr.Row():
            with gr.Column(scale=3):
                output_res = ImageSlider(label="Flux / Upscaled")
            with gr.Column(scale=2):
                prompt = gr.Textbox(label="Descripción de imágen")
                basemodel_choice = gr.Dropdown(label="Modelo", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV"], value="black-forest-labs/FLUX.1-schnell")
                lora_model_choice = gr.Dropdown(label="LORA Realismo", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora"], value="XLabs-AI/flux-RealismLora")
                
                with gr.Row():
                    process_lora = gr.Checkbox(label="Procesar LORA")
                    process_upscale = gr.Checkbox(label="Procesar Escalador")

                upscale_factor = gr.Radio(label="Factor de Escala", choices=[2, 4, 8], value=2)

                improved_prompt = gr.Textbox(label="Prompt Mejorado", interactive=False)

                improve_btn = gr.Button("Mejora mi prompt")
                improve_btn.click(fn=improve_prompt, inputs=[prompt], outputs=improved_prompt)
                
                with gr.Accordion(label="Opciones Avanzadas", open=False):
                    width = gr.Slider(label="Ancho", minimum=512, maximum=1280, step=8, value=1280)
                    height = gr.Slider(label="Alto", minimum=512, maximum=1280, step=8, value=768)
                    scales = gr.Slider(label="Escalado", minimum=1, maximum=20, step=1, value=10)
                    steps = gr.Slider(label="Pasos", minimum=1, maximum=100, step=1, value=20)
                    seed = gr.Number(label="Semilla", value=-1)
    
                btn = gr.Button("Generar")
                btn.click(fn=gen, inputs=[prompt, basemodel_choice, width, height, scales, steps, seed, upscale_factor, process_upscale, lora_model_choice, process_lora], outputs=output_res)
    demo.launch()