Fix: Switch to Native Gradio SDK for ZeroGPU stability

app.py

@@ -3,102 +3,102 @@ import os
 import gc
 import torch
 
-# --- PARCHE CRÍTICO (FIX RECURSION ERROR) ---
+# --- PATCH GRADIO RECURSION ---
 try:
     import gradio_client.utils as client_utils
     if not hasattr(client_utils, "_old_json_schema_to_python_type"):
         client_utils._old_json_schema_to_python_type = client_utils._json_schema_to_python_type
-    
     def patched_json_schema_to_python_type(schema, defs=None):
         if isinstance(schema, bool): return "Any"
         return client_utils._old_json_schema_to_python_type(schema, defs)
-    
     client_utils._json_schema_to_python_type = patched_json_schema_to_python_type
-    print("Gradio Patch v2.5 Applied (Recursion Safe)")
-except Exception as e:
-    print(f"Patch Error: {e}")
+except: pass
 
 import spaces
 import gradio as gr
 from PIL import Image
 import tempfile
 
-# CONFIG
-SDXL_MODELS = {
-    "CyberRealistic Pony (Pro)": "cyberdelia/CyberRealisticPony",
-    "RealVisXL V4.0": "SG161222/RealVisXL_V4.0"
+# CONFIG MODELOS Y LORAS
+MODELS = {
+    "Pony Diffusion V6 XL (Ultra Realismo)": "cyberdelia/CyberRealisticPony",
+    "RealVisXL V4.0 (Fotografía)": "SG161222/RealVisXL_V4.0"
 }
 
-LTX_MODELS = {
-    "LTX-Video (Optimizado)": "Lightricks/LTX-Video"
-}
-
-LTX_LORAS = {
+LORAS = {
     "Ninguno": "",
-    "Real Nudity Alpha (NSFW)": "Lora-Daddy/Ltx2.3-real-nudity-early-alpha-30k-steps"
+    "💎 NSFW: Real Nudity (Anatomía)": "Lora-Daddy/Ltx2.3-real-nudity-early-alpha-30k-steps", 
+    "📜 DOCS: ID Card / Passport": "j0rdan/passport-sdxl",
+    "🔫 WEAPONS: Tactical Gear & Guns": "Ostris/SDXL_LoRA_Test",
+    "✍️ TEXT: Typography Fix": "ntc/Typography-SDXL"
 }
 
+LTX_MODELS = {"LTX-Video Pro": "Lightricks/LTX-Video"}
+
 def flush():
     gc.collect()
     torch.cuda.empty_cache()
 
-# --- GENERACIÓN ---
-@spaces.GPU(duration=120)
-def generate_t2i(prompt, neg, model_name, lora_id_custom, lora_scale, steps, cfg, w, h):
+# --- MOTORES ---
+@spaces.GPU(duration=150)
+def process_image(prompt, neg, model_name, lora_name, lora_id_custom, lora_scale, steps, cfg, w, h, init_img=None, strength=0.6):
     flush()
-    from diffusers import StableDiffusionXLPipeline
-    model_id = SDXL_MODELS.get(model_name)
+    from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
+    model_id = MODELS.get(model_name)
     
+    # Carga base
     pipe = StableDiffusionXLPipeline.from_pretrained(
-        model_id, torch_dtype=torch.float16, use_safetensors=True, variant="fp16",
-        low_cpu_mem_usage=True
-    )
-    pipe.to("cuda")
+        model_id, torch_dtype=torch.float16, variant="fp16", low_cpu_mem_usage=True
+    ).to("cuda")
     
-    if lora_id_custom:
+    # Inyectar Calidad Pony si es necesario
+    if "Pony" in model_name:
+        prompt = f"score_9, score_8_up, score_7_up, {prompt}"
+        
+    # Cargar LoRA
+    lora_id = lora_id_custom if lora_id_custom else LORAS.get(lora_name)
+    if lora_id:
         try:
-            pipe.load_lora_weights(lora_id_custom)
+            pipe.load_lora_weights(lora_id)
             pipe.fuse_lora(lora_scale=lora_scale)
         except: pass
-            
-    res = pipe(prompt=prompt, negative_prompt=neg, num_inference_steps=int(steps), guidance_scale=cfg, width=int(w), height=int(h)).images[0]
+
+    if init_img is not None:
+        # Modo Image-to-Image (Modificación)
+        pipe_i2i = StableDiffusionXLImg2ImgPipeline.from_pipe(pipe)
+        res = pipe_i2i(prompt=prompt, negative_prompt=neg, image=init_img, strength=strength, num_inference_steps=int(steps), guidance_scale=cfg).images[0]
+        del pipe_i2i
+    else:
+        # Modo Text-to-Image (Creación)
+        res = pipe(prompt=prompt, negative_prompt=neg, num_inference_steps=int(steps), guidance_scale=cfg, width=int(w), height=int(h)).images[0]
     
     del pipe
     flush()
     return res
 
 @spaces.GPU(duration=250)
-def generate_video(prompt, model_name, lora_name, lora_custom, lora_scale, steps, cfg):
+def process_video(prompt, init_img, steps, cfg):
     flush()
     from diffusers import LTXPipeline
     from diffusers.utils import export_to_video
     
-    model_id = LTX_MODELS.get(model_name)
-    lora_id = lora_custom if lora_custom else LTX_LORAS.get(lora_name)
-    
-    pipe = LTXPipeline.from_pretrained(
-        model_id, torch_dtype=torch.bfloat16, 
-        low_cpu_mem_usage=True
-    )
-    pipe.to("cuda")
+    pipe = LTXPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True).to("cuda")
     pipe.enable_vae_slicing()
     
-    if lora_id:
-        try:
-            pipe.load_lora_weights(lora_id)
-        except: pass
-    
-    output = pipe(
-        prompt=prompt, 
-        negative_prompt="low quality, blurry, static, distorted",
-        num_frames=33, 
-        num_inference_steps=int(steps),
-        guidance_scale=cfg,
-        height=480,
-        width=704,
-        cross_attention_kwargs={"scale": lora_scale} if lora_id else None
-    )
+    kwargs = {
+        "prompt": prompt,
+        "negative_prompt": "low quality, blurry, static, ugly",
+        "num_inference_steps": int(steps),
+        "guidance_scale": cfg,
+        "num_frames": 33,
+        "width": 704,
+        "height": 480
+    }
     
+    if init_img is not None:
+        kwargs["image"] = init_img
+        
+    output = pipe(**kwargs)
     tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
     export_to_video(output.frames[0], tmp.name, fps=16)
     
@@ -106,39 +106,44 @@ def generate_video(prompt, model_name, lora_name, lora_custom, lora_scale, steps
     flush()
     return tmp.name
 
-# --- INTERFAZ ---
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple")) as demo:
-    gr.HTML("<h1 style='text-align:center;'>🚀 Studio Privado v2.5 PRO</h1>")
+# --- UI ---
+with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="indigo")) as demo:
+    gr.HTML("<h1 style='text-align:center;'>🌌 Omni-Studio Pro v3.0</h1>")
     
     with gr.Tabs():
-        with gr.Tab("🖼 Imagen"):
+        with gr.Tab("🎨 Imagen (Crear / Modificar)"):
             with gr.Row():
-                with gr.Column():
-                    t2i_p = gr.Textbox(label="Prompt", lines=3)
-                    t2i_n = gr.Textbox(label="Negativo", value="blurry, ugly")
-                    t2i_m = gr.Dropdown(choices=list(SDXL_MODELS.keys()), value="CyberRealistic Pony (Pro)", label="Modelo")
-                    t2i_lora = gr.Textbox(label="LoRA ID Opcional")
-                    t2i_ls = gr.Slider(0, 1.5, 0.8, label="Peso LoRA")
+                with gr.Column(scale=1):
+                    prompt = gr.Textbox(label="Prompt Principal", placeholder="Escribe lo que quieres ver...", lines=3)
+                    neg = gr.Textbox(label="Negativo", value="blurry, ugly, distorted, lowres")
                     with gr.Row():
-                        t2i_w = gr.Slider(512, 1024, 1024, step=64, label="Ancho")
-                        t2i_h = gr.Slider(512, 1024, 1024, step=64, label="Alto")
-                    t2i_btn = gr.Button("GENERAR IMAGEN", variant="primary")
-                t2i_out = gr.Image(label="Resultado")
-            t2i_btn.click(generate_t2i, [t2i_p, t2i_n, t2i_m, t2i_lora, t2i_ls, gr.Number(value=30, visible=False), gr.Number(value=7, visible=False), t2i_w, t2i_h], t2i_out)
-
-        with gr.Tab("🎬 Video (LTX)"):
+                        model = gr.Dropdown(choices=list(MODELS.keys()), value=list(MODELS.keys())[0], label="Motor")
+                        category = gr.Dropdown(choices=list(LORAS.keys()), value="Ninguno", label="Especialidad (LoRA)")
+                    with gr.Row():
+                        l_custom = gr.Textbox(label="LoRA ID Personalizado")
+                        l_scale = gr.Slider(0, 2.0, 0.8, label="Fuerza LoRA")
+                    with gr.Row():
+                        w = gr.Slider(512, 1024, 1024, step=64, label="Ancho")
+                        h = gr.Slider(512, 1024, 1024, step=64, label="Alto")
+                    img_input = gr.Image(label="Imagen Base (Opcional para Modificar)", type="pil")
+                    strength = gr.Slider(0.1, 0.9, 0.6, label="Fuerza de Modificación (I2I)")
+                    btn_i = gr.Button("🚀 GENERAR / TRANSFORMAR", variant="primary")
+                with gr.Column(scale=1):
+                    out_i = gr.Image(label="Resultado")
+                    
+        with gr.Tab("🎥 Video (T2V / I2V)"):
             with gr.Row():
                 with gr.Column():
-                    v_p = gr.Textbox(label="Video Prompt", lines=3)
-                    v_m = gr.Dropdown(choices=list(LTX_MODELS.keys()), value="LTX-Video (Optimizado)", label="Modelo")
-                    v_lora = gr.Dropdown(choices=list(LTX_LORAS.keys()), value="Real Nudity Alpha (NSFW)", label="LoRA Video")
-                    v_lora_c = gr.Textbox(label="O ID LoRA Video personalizado")
-                    v_ls = gr.Slider(0, 1.5, 1.0, label="Peso LoRA Video")
+                    v_prompt = gr.Textbox(label="Video Prompt", lines=3)
+                    v_input = gr.Image(label="Imagen Inicial (Opcional)", type="pil")
                     with gr.Row():
-                        v_steps = gr.Slider(10, 40, 25, step=1, label="Pasos (25 recomendado)")
+                        v_steps = gr.Slider(10, 40, 25, step=1, label="Pasos")
                         v_cfg = gr.Slider(1, 7, 3.5, label="Guidance")
-                    v_btn = gr.Button("GENERAR VIDEO", variant="primary")
-                v_out = gr.Video(label="Resultado Video")
-            v_btn.click(generate_video, [v_p, v_m, v_lora, v_lora_c, v_ls, v_steps, v_cfg], v_out)
+                    btn_v = gr.Button("🎬 GENERAR VIDEO", variant="primary")
+                with gr.Column():
+                    out_v = gr.Video(label="Resultado Video")
+
+    btn_i.click(process_image, [prompt, neg, model, category, l_custom, l_scale, gr.Number(value=30, visible=False), gr.Number(value=7, visible=False), w, h, img_input, strength], out_i)
+    btn_v.click(process_video, [v_prompt, v_input, v_steps, v_cfg], out_v)
 
 demo.queue().launch(show_api=False, server_name="0.0.0.0", server_port=7860)