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# 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
# 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)
# print(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 generando imagen: {e}")
# return None, None
# def get_upscale_finegrain(prompt, img_path, upscale_factor):
# try:
# 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")
# return result[1]
# except Exception as e:
# print(f"Error escalando imagen: {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.jpg"
# image.save(image_path, format="JPEG")
# if process_upscale:
# upscale_image_path = get_upscale_finegrain(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:
# print("Error: The scaled image path is None")
# return [image_path, image_path]
# else:
# return [image_path, image_path]
# css = """
# #col-container{ margin: 0 auto; max-width: 1024px;}
# """
# with gr.Blocks(css=css) 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="Image Description")
# basemodel_choice = gr.Dropdown(label="Model", choices=["black-forest-labs/FLUX.1-schnell", "black-forest-labs/FLUX.1-DEV", "enhanceaiteam/Flux-uncensored", "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "Shakker-Labs/FLUX.1-dev-LoRA-add-details", "city96/FLUX.1-dev-gguf"], value="black-forest-labs/FLUX.1-schnell")
# lora_model_choice = gr.Dropdown(label="LoRA", choices=["Shakker-Labs/FLUX.1-dev-LoRA-add-details", "XLabs-AI/flux-RealismLora", "enhanceaiteam/Flux-uncensored"], value="XLabs-AI/flux-RealismLora")
# process_lora = gr.Checkbox(label="LoRA Process")
# process_upscale = gr.Checkbox(label="Scale Process")
# upscale_factor = gr.Radio(label="Scaling Factor", choices=[2, 4, 8], value=2)
# with gr.Accordion(label="Advanced Options", open=False):
# width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=1280)
# height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=768)
# scales = gr.Slider(label="Scale", minimum=1, maximum=20, step=1, value=8)
# steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=8)
# seed = gr.Number(label="Seed", value=-1)
# btn = gr.Button("Generate")
# 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() |