Delete app.py

app.py

@@ -1,91 +0,0 @@
-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 generando imagen: {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 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: La ruta de la imagen escalada es 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, 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=["nerijs/dark-fantasy-illustration-flux","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="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="Escalado", minimum=1, maximum=20, step=1, value=8)
-                    steps = gr.Slider(label="Pasos", minimum=1, maximum=100, step=1, value=8)
-                    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()