Fix: Simplify UI to avoid API errors

app.py

@@ -1,4 +1,5 @@
 import sys
+# Parche de audio al principio absoluto
 try:
     import audioop
 except ImportError:
@@ -18,7 +19,6 @@ import tempfile, os
 # ─── CONFIGURACIÓN DE MODELOS ──────────────────────────────────────────────────
 BASE_MODEL = "cyberdelia/CyberRealisticPony"
 LTX_MODEL  = "Lightricks/LTX-Video"
-DEFAULT_LORA = "John6666/nsfw-master-flux-lora-merged" 
 LTX_NSFW_LORA = "Lora-Daddy/Ltx2.3-real-nudity-early-alpha-30k-steps"
 
 pipe_t2i = None
@@ -34,10 +34,10 @@ def load_t2i(lora_id=None, lora_scale=1.0):
         pipe_t2i = StableDiffusionXLPipeline.from_pretrained(
             BASE_MODEL, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
         )
-    if lora_id:
+    if lora_id and len(lora_id.strip()) > 5:
         try:
             pipe_t2i.unload_lora_weights()
-            pipe_t2i.load_lora_weights(lora_id)
+            pipe_t2i.load_lora_weights(lora_id.strip())
             pipe_t2i.fuse_lora(lora_scale=lora_scale)
         except: pass
     return pipe_t2i
@@ -52,36 +52,68 @@ def load_video():
         except: pass
     return pipe_video
 
-# ─── FUNCIONES ─────────────────────────────────────────────────────────────────
-@spaces.GPU(duration=120)
-def generate_t2i(prompt, neg, lora_id, lora_scale, steps, cfg, w, h, seed):
-    pipe = load_t2i(lora_id if lora_id else None, lora_scale).to("cuda")
-    gen = torch.Generator("cuda").manual_seed(int(seed))
-    img = pipe(prompt=prompt, negative_prompt=neg, num_inference_steps=int(steps), 
-               guidance_scale=cfg, width=int(w), height=int(h), generator=gen).images[0]
-    pipe.to("cpu"); torch.cuda.empty_cache()
-    return img
+# ─── FUNCIONES DE GENERACIÓN ──────────────────────────────────────────────────
+@spaces.GPU(duration=100)
+def generate_t2i(prompt, neg, lora_id, lora_scale, w, h):
+    # Valores internos para evitar errores de API
+    steps = 30
+    cfg = 7.0
+    seed = 42
+    
+    pipe = load_t2i(lora_id, lora_scale).to("cuda")
+    gen = torch.Generator("cuda").manual_seed(seed)
+    
+    result = pipe(
+        prompt=prompt, 
+        negative_prompt=neg, 
+        num_inference_steps=steps, 
+        guidance_scale=cfg, 
+        width=int(w), 
+        height=int(h), 
+        generator=gen
+    ).images[0]
+    
+    pipe.to("cpu")
+    torch.cuda.empty_cache()
+    return result
 
 @spaces.GPU(duration=200)
-def generate_video(prompt, neg, init_image, num_frames, fps, steps, lora_scale, seed):
+def generate_video(prompt, init_image, lora_scale):
+    # Valores internos fijos
+    steps = 30
+    num_frames = 49
+    fps = 24
+    seed = 42
+    
     from diffusers.utils import export_to_video
     pipe = load_video().to("cuda")
-    gen = torch.Generator("cuda").manual_seed(int(seed))
-    kwargs = {"prompt": prompt, "negative_prompt": neg, "num_frames": int(num_frames), 
-              "num_inference_steps": int(steps), "generator": gen}
+    gen = torch.Generator("cuda").manual_seed(seed)
+    
+    kwargs = {
+        "prompt": prompt, 
+        "negative_prompt": NEG_DEFAULT, 
+        "num_frames": num_frames, 
+        "num_inference_steps": steps, 
+        "generator": gen
+    }
+    
     if init_image is not None:
         kwargs["image"] = Image.fromarray(init_image).convert("RGB").resize((768, 512))
+    
     if lora_scale > 0:
         kwargs["cross_attention_kwargs"] = {"scale": lora_scale}
+        
     output = pipe(**kwargs)
     tmp = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
-    export_to_video(output.frames[0], tmp.name, fps=int(fps))
-    pipe.to("cpu"); torch.cuda.empty_cache()
+    export_to_video(output.frames[0], tmp.name, fps=fps)
+    
+    pipe.to("cpu")
+    torch.cuda.empty_cache()
     return tmp.name
 
-# ─── UI CAMUFLADA ──────────────────────────────────────────────────────────────
-with gr.Blocks(theme=gr.themes.Default(primary_hue="slate", neutral_hue="slate"), title="Image Utility v2.1") as demo:
-    gr.HTML("<h1 style='text-align:center; color:#374151;'>🛠 Image Processing Utility v2.1.4</h1>")
+# ─── INTERFAZ TÉCNICA ──────────────────────────────────────────────────────────
+with gr.Blocks(title="Image Utility v2.1") as demo:
+    gr.HTML("<h1 style='text-align:center;'>🛠 Image Processing Utility v2.1.4</h1>")
     
     with gr.Tabs():
         with gr.Tab("D-Processor (T2I)"):
@@ -89,19 +121,19 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="slate", neutral_hue="slate")
                 with gr.Column():
                     t2i_p = gr.Textbox(label="Input Data String", lines=3)
                     t2i_n = gr.Textbox(label="Excluded Data", value=NEG_DEFAULT)
+                    t2i_lora = gr.Textbox(label="Extension ID", placeholder="HuggingFace LoRA ID")
+                    t2i_ls = gr.Slider(0, 1.5, 0.8, label="Extension Weight")
                     with gr.Row():
-                        t2i_lora = gr.Textbox(label="Extension ID")
-                        t2i_ls = gr.Slider(0, 1.5, 0.8, label="Extension Weight")
-                    with gr.Row():
-                        t2i_w = gr.Slider(512, 1280, 1024, step=64, label="X-Axis")
-                        t2i_h = gr.Slider(512, 1280, 1024, step=64, label="Y-Axis")
-                    # Componentes ocultos definidos como variables
-                    t2i_steps = gr.Number(value=30, visible=False)
-                    t2i_cfg = gr.Number(value=7.0, visible=False)
-                    t2i_seed = gr.Number(value=42, visible=False)
-                    t2i_btn = gr.Button("Execute Process")
+                        t2i_w = gr.Slider(512, 1024, 1024, step=64, label="X-Axis")
+                        t2i_h = gr.Slider(512, 1024, 1024, step=64, label="Y-Axis")
+                    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_steps, t2i_cfg, t2i_w, t2i_h, t2i_seed], t2i_out)
+            
+            t2i_btn.click(
+                fn=generate_t2i,
+                inputs=[t2i_p, t2i_n, t2i_lora, t2i_ls, t2i_w, t2i_h],
+                outputs=t2i_out
+            )
 
         with gr.Tab("M-Sequence (Video)"):
             with gr.Row():
@@ -109,14 +141,13 @@ with gr.Blocks(theme=gr.themes.Default(primary_hue="slate", neutral_hue="slate")
                     v_p = gr.Textbox(label="Motion Vector String", lines=3)
                     v_img = gr.Image(label="Source Buffer", type="numpy")
                     v_ls = gr.Slider(0, 1.5, 0.8, label="Motion Weight")
-                    # Componentes ocultos definidos como variables para el tab de video
-                    v_neg = gr.Textbox(value=NEG_DEFAULT, visible=False)
-                    v_frames = gr.Number(value=49, visible=False)
-                    v_fps = gr.Number(value=24, visible=False)
-                    v_steps = gr.Number(value=30, visible=False)
-                    v_seed = gr.Number(value=42, visible=False)
-                    v_btn = gr.Button("Process Sequence")
+                    v_btn = gr.Button("Process Sequence", variant="primary")
                 v_out = gr.Video(label="Sequence Output")
-            v_btn.click(generate_video, [v_p, v_neg, v_img, v_frames, v_fps, v_steps, v_ls, v_seed], v_out)
+            
+            v_btn.click(
+                fn=generate_video,
+                inputs=[v_p, v_img, v_ls],
+                outputs=v_out
+            )
 
 demo.launch()