Delete app.py

app.py

@@ -1,48 +0,0 @@
-import os
-import uuid
-import gradio as gr
-import torch
-from PIL import Image
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
-
-# Configuração do modelo e pipeline
-model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-
-# Carrega o pipeline do modelo
-scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
-pipe = StableDiffusionXLPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
-pipe.to("cuda")  # Usa GPU para acelerar o processamento
-
-# Função para geração de imagens
-def generate_image(prompt: str, height: int = 576, width: int = 1024, seed: int = None) -> Image.Image:
-    if not seed:
-        seed = random.randint(0, 99999)
-    
-    # Configurar seed para reprodutibilidade
-    generator = torch.manual_seed(seed)
-    
-    # Gerar a imagem
-    image = pipe(prompt, height=height, width=width, num_inference_steps=50, guidance_scale=7.5, generator=generator).images[0]
-    
-    # Retorna a imagem gerada
-    return image
-
-# Interface Gradio
-with gr.Blocks() as demo:
-    gr.Markdown("## Gerador de Imagens com Stable Diffusion XL")
-    
-    with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(label="Texto (Prompt)", placeholder="Descreva a imagem que deseja gerar...")
-            seed = gr.Number(label="Seed (opcional)", value=None)
-            generate_button = gr.Button("Gerar Imagem")
-        
-        with gr.Column():
-            output_image = gr.Image(label="Imagem Gerada", type="pil")
-
-    # Conectar botão à função
-    generate_button.click(fn=generate_image, inputs=[prompt, gr.Number(value=576), gr.Number(value=1024), seed], outputs=output_image)
-
-# Executar o app
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)