Fix: Disable SSR and remove API silencer for stable ZeroGPU connection

app.py

@@ -1,7 +1,7 @@
 import sys
 import os
 
-# --- INYECCIÓN ATÓMICA REFORZADA ---
+# --- INYECCIÓN ATÓMICA REFORZADA (HfFolder + audioop) ---
 try:
     import huggingface_hub
     class MockHfFolder:
@@ -22,17 +22,10 @@ try:
 except:
     from unittest.mock import MagicMock
     sys.modules["audioop"] = MagicMock()
-# ---------------------------------------------
-
-import gradio as gr
-
-# --- SILENCIADOR DE API ---
-def fake_get_api_info(self, *args, **kwargs):
-    return {"components": [], "endpoints": []}
-gr.Blocks.get_api_info = fake_get_api_info
-# ----------------------------------------------------
+# -------------------------------------------------------
 
 import spaces
+import gradio as gr
 import torch
 import numpy as np
 from PIL import Image
@@ -64,36 +57,26 @@ def load_video():
 def generate_t2i(prompt, neg, lora_id, lora_scale, w, h, init_img):
     is_img2img = init_img is not None
     pipe = load_t2i(is_img2img).to("cuda")
-    
     if lora_id and len(lora_id.strip()) > 5:
         try:
             pipe.load_lora_weights(lora_id.strip())
             pipe.fuse_lora(lora_scale=lora_scale)
         except: pass
-
-    kwargs = {
-        "prompt": prompt, "negative_prompt": neg, "num_inference_steps": 30, 
-        "guidance_scale": 7.0, "generator": torch.Generator("cuda").manual_seed(42)
-    }
-    
+    kwargs = {"prompt": prompt, "negative_prompt": neg, "num_inference_steps": 30, "guidance_scale": 7.0}
     if is_img2img:
         if isinstance(init_img, dict):
-            init_img = init_img["composite"] if "composite" in init_image else init_img["background"]
+            init_img = init_img["composite"] if "composite" in init_img else init_img["background"]
         kwargs["image"] = Image.fromarray(init_img).convert("RGB").resize((int(w), int(h)))
-        kwargs["strength"] = 0.6 # Balance entre original y prompt
+        kwargs["strength"] = 0.6
     else:
-        kwargs["width"] = int(w)
-        kwargs["height"] = int(h)
-
-    img = pipe(**kwargs).images[0]
-    return img
+        kwargs["width"], kwargs["height"] = int(w), int(h)
+    return pipe(**kwargs).images[0]
 
 @spaces.GPU(duration=200)
 def generate_video(prompt, init_image, lora_scale):
     from diffusers.utils import export_to_video
     pipe = load_video().to("cuda")
-    kwargs = {"prompt": prompt, "negative_prompt": NEG_DEFAULT, "num_frames": 49, 
-              "num_inference_steps": 30, "generator": torch.Generator("cuda").manual_seed(42)}
+    kwargs = {"prompt": prompt, "negative_prompt": NEG_DEFAULT, "num_frames": 49, "num_inference_steps": 30}
     if init_image is not None:
         if isinstance(init_image, dict):
             init_image = init_image["composite"] if "composite" in init_image else init_image["background"]
@@ -105,7 +88,7 @@ def generate_video(prompt, init_image, lora_scale):
     export_to_video(output.frames[0], tmp.name, fps=24)
     return tmp.name
 
-with gr.Blocks(title="Image Utility v2.1") as demo:
+with gr.Blocks(title="Image Utility v2.1", ssr=False) as demo:
     gr.HTML("<h1 style='text-align:center;'>🛠 Image Processing Utility v2.1.4</h1>")
     with gr.Tabs():
         with gr.Tab("D-Processor (Image/T2I)"):
@@ -122,7 +105,6 @@ with gr.Blocks(title="Image Utility v2.1") as demo:
                     t2i_btn = gr.Button("Execute Process", variant="primary")
                 t2i_out = gr.Image(label="Output Preview")
             t2i_btn.click(generate_t2i, [t2i_p, t2i_n, t2i_lora, t2i_ls, t2i_w, t2i_h, t2i_img], t2i_out)
-
         with gr.Tab("M-Sequence (Video)"):
             with gr.Row():
                 with gr.Column():
@@ -133,4 +115,4 @@ with gr.Blocks(title="Image Utility v2.1") as demo:
                 v_out = gr.Video(label="Sequence Output")
             v_btn.click(generate_video, [v_p, v_img, v_ls], v_out)
 
-demo.queue().launch(show_api=False)
+demo.queue().launch(show_api=True)