Update app.py

app.py

@@ -18,8 +18,8 @@ import aoti
 # =========================================================
 # MODEL CONFIGURATION
 # =========================================================
-MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"  # المسار الجديد للنموذج
-HF_TOKEN = os.environ.get("HF_TOKEN")  # ضع توكن Hugging Face هنا إذا كان النموذج خاصًا
+MODEL_ID = "Wan-AI/Wan2.2-I2V-A14B-Diffusers"
+HF_TOKEN = os.environ.get("HF_TOKEN")
 
 MAX_DIM = 832
 MIN_DIM = 480
@@ -38,6 +38,7 @@ MAX_DURATION = round(MAX_FRAMES_MODEL / FIXED_FPS, 1)
 # =========================================================
 # LOAD PIPELINE
 # =========================================================
+print("Loading pipeline...")
 pipe = WanImageToVideoPipeline.from_pretrained(
     MODEL_ID,
     transformer=WanTransformer3DModel.from_pretrained(
@@ -60,6 +61,7 @@ pipe = WanImageToVideoPipeline.from_pretrained(
 # =========================================================
 # LOAD LORA ADAPTERS
 # =========================================================
+print("Loading LoRA adapters...")
 pipe.load_lora_weights(
     "Kijai/WanVideo_comfy",
     weight_name="Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
@@ -80,10 +82,12 @@ pipe.unload_lora_weights()
 # =========================================================
 # QUANTIZATION & AOT OPTIMIZATION
 # =========================================================
+print("Applying quantization...")
 quantize_(pipe.text_encoder, Int8WeightOnlyConfig())
 quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig())
 quantize_(pipe.transformer_2, Float8DynamicActivationFloat8WeightConfig())
 
+print("Loading AOTI blocks...")
 aoti.aoti_blocks_load(pipe.transformer, 'zerogpu-aoti/Wan2', variant='fp8da')
 aoti.aoti_blocks_load(pipe.transformer_2, 'zerogpu-aoti/Wan2', variant='fp8da')
 
@@ -196,7 +200,26 @@ def generate_video(
 # =========================================================
 # GRADIO UI
 # =========================================================
-with gr.Blocks() as demo:
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    
+    # --- ADVERTISEMENT BANNER FOR DREAM HUB PRO ---
+    gr.HTML("""
+    <div style="background: linear-gradient(90deg, #4f46e5, #9333ea); color: white; padding: 15px; border-radius: 10px; text-align: center; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(0,0,0,0.1);">
+        <div style="display: flex; align-items: center; justify-content: center; gap: 20px; flex-wrap: wrap;">
+            <div style="text-align: left;">
+                <h3 style="margin: 0; font-weight: bold; font-size: 18px;">✨ New: Dream Hub Pro (All-in-One)</h3>
+                <p style="margin: 5px 0 0 0; opacity: 0.9; font-size: 14px;">Access all your pro tools (Wan2.1, Qwen, Audio, Video Enhance) in one place!</p>
+            </div>
+            <a href="https://huggingface.co/spaces/dream2589632147/Dream-Hub-Pro" target="_blank" style="text-decoration: none;">
+                <button style="background-color: white; color: #4f46e5; border: none; padding: 10px 25px; border-radius: 25px; font-weight: bold; cursor: pointer; transition: all 0.2s; font-size: 15px; box-shadow: 0 2px 5px rgba(0,0,0,0.2);">
+                    🚀 Open Hub Pro Now
+                </button>
+            </a>
+        </div>
+    </div>
+    """)
+    # ---------------------------------------------
+
     gr.Markdown("# 🚀 Dream Wan 2.2 Faster Pro (14B) — Ultra Fast I2V with Lightning LoRA")
     gr.Markdown("Optimized FP8 quantized pipeline with AoT blocks & 4-step fast inference ⚡")
 
@@ -246,4 +269,4 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    demo.queue().launch(mcp_server=True)
+    demo.queue().launch(mcp_server=True)