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app.py
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import gradio as gr
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from
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import torch
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from transformers.utils import move_cache
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import logging
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#
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# Mover la cach茅 si es necesario
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move_cache()
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# Cargar el modelo base y los pesos LoRA
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model_name = "stabilityai/stable-diffusion-xl-base-1.0"
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lora_weights = "ZB-Tech/Text-to-Image"
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pipeline = DiffusionPipeline.from_pretrained(model_name, lora_backend="cuda")
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pipeline.load_lora_weights(lora_weights)
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# Mover el modelo a la GPU si est谩 disponible
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipeline = pipeline.to(device)
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# Definir la funci贸n de generaci贸n de imagen
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def generate_image(prompt):
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# Crear la interfaz de Gradio
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iface = gr.Interface(
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fn=generate_image,
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inputs=gr.Textbox(lines=5, label="Descripci贸n de la imagen", placeholder="Introduce el texto aqu铆..."),
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForTextToImageGeneration
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import torch
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# Cargar el tokenizador y el modelo
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tokenizer = AutoTokenizer.from_pretrained("ZB-Tech/Text-to-Image")
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model = AutoModelForTextToImageGeneration.from_pretrained("ZB-Tech/Text-to-Image")
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# Definir la funci贸n de generaci贸n de imagen
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def generate_image(prompt):
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inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
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with torch.no_grad():
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output = model.generate(**inputs)
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return output
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# Crear la interfaz de Gradio
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iface = gr.Interface(
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fn=generate_image,
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inputs=gr.Textbox(lines=5, label="Descripci贸n de la imagen", placeholder="Introduce el texto aqu铆..."),
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