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 from gradio_client import Client, handle_file from huggingface_hub import login 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") 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 = str(Translator().translate(prompt, 'English')) + "," + lora_word client = AsyncInferenceClient() 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: print(f"Error generating image: {e}") return None, 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: print(f"Error upscale image: {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 image, seed = await generate_image(prompt, model, "", width, height, scales, steps, seed) if image is None: return [None, None] image_path = "temp_image.png" try: image.save(image_path, format="PNG") except Exception as e: print(f"Error al guardar la imagen: {e}") return [None, None] if process_upscale: upscale_image = get_upscale_finegrain(prompt, image_path, upscale_factor) if upscale_image is None: return [image_path, image_path] upscale_image_path = "upscale_image.png" try: upscale_image.save(upscale_image_path, format="PNG") except Exception as e: print(f"Error al guardar la imagen escalada: {e}") return [image_path, None] return [image_path, upscale_image_path] 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") 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) 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="Escalas", 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) submit_btn = gr.Button("Crear", scale=1) submit_btn.click( fn=lambda: None, inputs=None, outputs=[output_res], queue=False ).then( 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()