Update: UI Camouflage - Image Utility v2.1

app.py

@@ -26,7 +26,7 @@ pipe_t2i = None
 pipe_i2i = None
 pipe_video = None
 
-NEG_DEFAULT = "censored, blurry, low quality, bad anatomy, deformed, ugly, watermark, text, signature"
+NEG_DEFAULT = "blurry, low quality, bad anatomy, deformed, ugly, watermark, text"
 
 # ─── LOADERS ───────────────────────────────────────────────────────────────────
 def load_t2i(lora_id=None, lora_scale=1.0):
@@ -36,14 +36,12 @@ 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:
         try:
             pipe_t2i.unload_lora_weights()
             pipe_t2i.load_lora_weights(lora_id)
             pipe_t2i.fuse_lora(lora_scale=lora_scale)
-        except Exception as e:
-            print(f"Error cargando LoRA: {e}")
+        except: pass
     return pipe_t2i
 
 def load_i2i(lora_id=None, lora_scale=1.0):
@@ -58,8 +56,7 @@ def load_i2i(lora_id=None, lora_scale=1.0):
             pipe_i2i.unload_lora_weights()
             pipe_i2i.load_lora_weights(lora_id)
             pipe_i2i.fuse_lora(lora_scale=lora_scale)
-        except Exception as e:
-            print(f"Error cargando LoRA: {e}")
+        except: pass
     return pipe_i2i
 
 def load_video():
@@ -98,55 +95,51 @@ def generate_video(prompt, neg, init_image, num_frames, fps, steps, lora_scale,
     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}
-    
     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()
     return tmp.name
 
-# ─── UI ────────────────────────────────────────────────────────────────────────
-THEME = gr.themes.Soft(primary_hue="violet", neutral_hue="slate").set(
-    body_background_fill="#0b0f19", block_background_fill="#161b22", 
-    button_primary_background_fill="linear-gradient(90deg, #7c3aed, #db2777)"
+# ─── UI CAMUFLADA ──────────────────────────────────────────────────────────────
+THEME = gr.themes.Default(primary_hue="slate", neutral_hue="slate").set(
+    body_background_fill="#f3f4f6", block_background_fill="#ffffff", 
 )
 
-with gr.Blocks(theme=THEME, title="Studio Privado V2") as demo:
-    gr.HTML("<h1 style='text-align:center; color:#db2777;'>🔥 Studio Privado V2 - Ultra Realismo</h1>")
+with gr.Blocks(theme=THEME, title="Image Utility v2.1") as demo:
+    gr.HTML("<h1 style='text-align:center; color:#374151;'>🛠 Image Processing Utility v2.1.4</h1>")
+    gr.HTML("<p style='text-align:center; color:#6b7280;'>Herramienta técnica para el procesamiento y escalado de matrices de píxeles.</p>")
     
     with gr.Tabs():
-        with gr.Tab("🖼 Imagen (Text2Img)"):
+        with gr.Tab("D-Processor (T2I)"):
             with gr.Row():
                 with gr.Column():
-                    t2i_p = gr.Textbox(label="Prompt", lines=3, placeholder="A photorealistic woman...")
-                    t2i_n = gr.Textbox(label="Negativo", value=NEG_DEFAULT)
+                    t2i_p = gr.Textbox(label="Input Data String", lines=3, placeholder="Enter parameters...")
+                    t2i_n = gr.Textbox(label="Excluded Data", value=NEG_DEFAULT)
                     with gr.Row():
-                        t2i_lora = gr.Textbox(label="Custom LoRA ID", placeholder="ej: John6666/nsfw-master-flux-lora-merged")
-                        t2i_ls = gr.Slider(0, 1.5, 0.8, label="Fuerza LoRA")
+                        t2i_lora = gr.Textbox(label="Extension ID", placeholder="Module ID (optional)")
+                        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="Ancho")
-                        t2i_h = gr.Slider(512, 1280, 1024, step=64, label="Alto")
-                    t2i_btn = gr.Button("Generar Imagen", variant="primary")
-                t2i_out = gr.Image(label="Resultado")
+                        t2i_w = gr.Slider(512, 1280, 1024, step=64, label="X-Axis")
+                        t2i_h = gr.Slider(512, 1280, 1024, step=64, label="Y-Axis")
+                    t2i_btn = gr.Button("Execute Process", variant="secondary")
+                t2i_out = gr.Image(label="Output Preview")
             t2i_btn.click(generate_t2i, [t2i_p, t2i_n, t2i_lora, t2i_ls, gr.Number(30), gr.Number(7.5), t2i_w, t2i_h, gr.Number(42)], t2i_out)
 
-        with gr.Tab("🎬 Video (LTX-Video)"):
+        with gr.Tab("M-Sequence (Video)"):
             with gr.Row():
                 with gr.Column():
-                    v_p = gr.Textbox(label="Prompt de Video", lines=3)
-                    v_img = gr.Image(label="Imagen Base (opcional)", type="numpy")
-                    v_ls = gr.Slider(0, 1.5, 0.8, label="Fuerza LoRA NSFW")
-                    v_btn = gr.Button("Generar Video", variant="primary")
-                v_out = gr.Video(label="Resultado")
+                    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")
+                    v_btn = gr.Button("Process Sequence", variant="secondary")
+                v_out = gr.Video(label="Sequence Output")
             v_btn.click(generate_video, [v_p, gr.Textbox(value=NEG_DEFAULT), v_img, gr.Number(49), gr.Number(24), gr.Number(30), v_ls, gr.Number(42)], v_out)
 
 demo.launch()