Create app py

app.py

@@ -0,0 +1,116 @@
+import gradio as gr
+import torch
+import gc
+from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
+from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextModelWithProjection
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class AIImageGeneratorNSFW:
+    def __init__(self):
+        self.pipeline = None
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        # ¡Aquí va la línea!
+        self.model_id = "segmind/Segmind-DE-XL"  
+        self.is_model_loaded = False
+        logger.info(f"Inicializando en dispositivo: {self.device}")
+
+    def load_model(self):
+        if self.is_model_loaded:
+            return True
+        try:
+            logger.info("Cargando modelo NSFW...")
+            torch_dtype = torch.float16 if self.device == "cuda" else torch.float32
+            
+            tokenizer_1 = CLIPTokenizer.from_pretrained(self.model_id, subfolder="tokenizer", use_fast=False)
+            tokenizer_2 = CLIPTokenizer.from_pretrained(self.model_id, subfolder="tokenizer_2", use_fast=False)
+            
+            text_encoder_1 = CLIPTextModel.from_pretrained(self.model_id, subfolder="text_encoder", torch_dtype=torch_dtype, low_cpu_mem_usage=True)
+            text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(self.model_id, subfolder="text_encoder_2", torch_dtype=torch_dtype, low_cpu_mem_usage=True)
+            
+            self.pipeline = StableDiffusionXLPipeline.from_pretrained(
+                self.model_id,
+                tokenizer=[tokenizer_1, tokenizer_2],
+                text_encoder=[text_encoder_1, text_encoder_2],
+                torch_dtype=torch_dtype,
+                scheduler=EulerDiscreteScheduler.from_pretrained(self.model_id, subfolder="scheduler"),
+                safety_checker=None,
+                use_safetensors=True,
+                variant="fp16" if self.device == "cuda" else None
+            )
+            self.pipeline.to(self.device)
+            self.is_model_loaded = True
+            logger.info("Modelo NSFW cargado correctamente.")
+            return True
+        except Exception as e:
+            logger.error(f"Error cargando modelo NSFW: {e}")
+            return False
+
+    def generate_image(self, prompt, width=1024, height=576, steps=35, guidance_scale=12.0):
+        if not self.is_model_loaded and not self.load_model():
+            return None
+        try:
+            with torch.inference_mode():
+                generator = torch.Generator(self.device).manual_seed(torch.randint(0, 2**32, (1,)).item())
+                result = self.pipeline(
+                    prompt=prompt,
+                    width=(width // 8) * 8,
+                    height=(height // 8) * 8,
+                    num_inference_steps=steps,
+                    guidance_scale=guidance_scale,
+                    generator=generator,
+                    output_type="pil"
+                )
+                gc.collect()
+                return result.images[0]
+        except Exception as e:
+            logger.error(f"Error generando imagen NSFW: {e}")
+            gc.collect()
+            return None
+
+# Instancia global
+generator_nsfw = None
+
+def initialize_generator_nsfw():
+    global generator_nsfw
+    if generator_nsfw is None:
+        generator_nsfw = AIImageGeneratorNSFW()
+    return generator_nsfw
+
+def generate_image_nsfw(prompt, width, height, steps, guidance_scale):
+    gen = initialize_generator_nsfw()
+    if not prompt.strip():
+        return None
+    return gen.generate_image(
+        prompt=prompt,
+        width=int(width),
+        height=int(height),
+        steps=int(steps),
+        guidance_scale=float(guidance_scale)
+    )
+
+def create_nsfw_interface():
+    with gr.Blocks(title="Generador de Imágenes NSFW con IA - Stable Diffusion") as iface:
+        gr.Markdown("# 🎨 Generador NSFW basado en Stable Diffusion\n_Uso responsable y solo para adultos_")
+
+        prompt = gr.Textbox(label="Prompt para la imagen NSFW", placeholder="Describe el contenido explícito...", lines=3)
+        width = gr.Slider(512, 1536, value=1024, step=8, label="Ancho (pixeles)")
+        height = gr.Slider(512, 1536, value=576, step=8, label="Alto (pixeles)")
+        steps = gr.Slider(10, 50, value=35, step=1, label="Pasos de inferencia")
+        guidance_scale = gr.Slider(1.0, 20.0, value=12.0, step=0.1, label="Escala de guía")
+
+        btn_generate = gr.Button("Generar Imagen NSFW")
+        img_output = gr.Image(label="Imagen generada")
+
+        btn_generate.click(
+            fn=generate_image_nsfw,
+            inputs=[prompt, width, height, steps, guidance_scale],
+            outputs=img_output
+        )
+    return iface
+
+if __name__ == "__main__":
+    nsfw_app = create_nsfw_interface()
+    nsfw_app.launch()