Update app.py

app.py

@@ -1,4 +1,3 @@
-import spaces
 import gradio as gr
 import torch
 from PIL import Image
@@ -6,52 +5,37 @@ from transformers import AutoProcessor
 from longcat_image.models import LongCatImageTransformer2DModel
 from longcat_image.pipelines import LongCatImageEditPipeline
 import numpy as np
-import os
+import spaces
 
-# Global variables for model
-pipe = None
-device = None
+# Load model directly at startup
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model_id = 'meituan-longcat/LongCat-Image-Edit'
 
-def initialize_model():
-    """Initialize the model on first use"""
-    global pipe, device
-    
-    if pipe is not None:
-        return
-    
-    try:
-        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        model_id = 'meituan-longcat/LongCat-Image-Edit'
-        
-        print(f"🔄 Loading model from {model_id}...")
-        
-        # Load text processor
-        text_processor = AutoProcessor.from_pretrained(
-            model_id, 
-            subfolder='tokenizer'
-        )
-        
-        # Load transformer
-        transformer = LongCatImageTransformer2DModel.from_pretrained(
-            model_id, 
-            subfolder='transformer',
-            torch_dtype=torch.bfloat16, 
-            use_safetensors=True
-        ).to(device)
-        
-        # Load pipeline
-        pipe = LongCatImageEditPipeline.from_pretrained(
-            model_id,
-            transformer=transformer,
-            text_processor=text_processor,
-        )
-        pipe.to(device, torch.bfloat16)
-        
-        print(f"✅ Model loaded successfully on {device}")
-        
-    except Exception as e:
-        print(f"❌ Error loading model: {e}")
-        raise
+print(f"🔄 Loading model from {model_id}...")
+
+# Load text processor
+text_processor = AutoProcessor.from_pretrained(
+    model_id, 
+    subfolder='tokenizer'
+)
+
+# Load transformer
+transformer = LongCatImageTransformer2DModel.from_pretrained(
+    model_id, 
+    subfolder='transformer',
+    torch_dtype=torch.bfloat16, 
+    use_safetensors=True
+).to(device)
+
+# Load pipeline
+pipe = LongCatImageEditPipeline.from_pretrained(
+    model_id,
+    transformer=transformer,
+    text_processor=text_processor,
+)
+pipe.to(device, torch.bfloat16)
+
+print(f"✅ Model loaded successfully on {device}")
 
 @spaces.GPU(duration=120)
 def edit_image(
@@ -72,9 +56,6 @@ def edit_image(
         raise gr.Error("Please enter an edit instruction")
     
     try:
-        # Initialize model if not already loaded
-        initialize_model()
-        
         progress(0.1, desc="Preparing image...")
         
         # Convert to RGB if needed
@@ -189,7 +170,7 @@ with gr.Blocks(fill_height=True) as demo:
             <div style="padding: 10px; background-color: #f0f7ff; border-radius: 8px; margin-top: 10px;">
                 <p style="margin: 0; font-size: 12px; color: #555;">
                     ⏱️ <strong>Note:</strong> Zero-GPU provides 120 seconds of GPU time per request. 
-                    First run may take longer as the model loads from Hugging Face Hub.
+                    Model is loaded at startup from Hugging Face Hub.
                     Processing typically takes 30-60 seconds depending on settings.
                 </p>
             </div>
@@ -235,7 +216,7 @@ with gr.Blocks(fill_height=True) as demo:
             seed
         ],
         outputs=output_image,
-        api_name="edit_image"
+        api_visibility="public"
     )
     
     # Footer