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app.py

@@ -5,58 +5,28 @@ from diffusers import ZImagePipeline
 import os
 from pathlib import Path
 
-# Global variable to store the pipeline
-pipe = None
+# Load the model directly at startup
+print("Loading Z-Image Turbo model...")
+print("This may take a few minutes on first run while the model downloads...")
 
-def load_model():
-    """
-    Load the Z-Image Turbo model before inference.
-    This ensures the model is downloaded and ready before any generation requests.
-    """
-    global pipe
+try:
+    # Load the pipeline with optimal settings
+    pipe = ZImagePipeline.from_pretrained(
+        "Tongyi-MAI/Z-Image-Turbo",
+        torch_dtype=torch.bfloat16,
+        low_cpu_mem_usage=False,
+    )
     
-    if pipe is not None:
-        return pipe
+    # Move to GPU if available
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    pipe.to(device)
+    print(f"Model loaded on {device}")
     
-    print("Loading Z-Image Turbo model...")
-    print("This may take a few minutes on first run while the model downloads...")
+    print("Model loaded successfully!")
     
-    try:
-        # Load the pipeline with optimal settings
-        pipe = ZImagePipeline.from_pretrained(
-            "Tongyi-MAI/Z-Image-Turbo",
-            torch_dtype=torch.bfloat16,
-            low_cpu_mem_usage=False,
-        )
-        
-        # Move to GPU if available
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        pipe.to(device)
-        print(f"Model loaded on {device}")
-        
-        # Optional: Enable Flash Attention for better efficiency
-        try:
-            pipe.transformer.set_attention_backend("flash")
-            print("Flash Attention enabled")
-        except Exception as e:
-            print(f"Flash Attention not available: {e}")
-            print("Using default attention backend")
-        
-        print("Model loaded successfully!")
-        return pipe
-        
-    except Exception as e:
-        print(f"Error loading model: {e}")
-        raise
-
-# Pre-load the model when the app starts
-print("Initializing model on startup...")
-try:
-    load_model()
-    print("Model initialization complete!")
 except Exception as e:
-    print(f"Warning: Could not pre-load model: {e}")
-    print("Model will be loaded on first generation request")
+    print(f"Error loading model: {e}")
+    pipe = None
 
 @spaces.GPU()
 def generate_image(
@@ -74,10 +44,8 @@ def generate_image(
     """
     global pipe
     
-    # Ensure model is loaded
     if pipe is None:
-        progress(0, desc="Loading model...")
-        load_model()
+        raise gr.Error("Model failed to load on startup. Please restart the application.")
     
     if not prompt.strip():
         raise gr.Error("Please enter a prompt to generate an image.")
@@ -297,6 +265,7 @@ button.primary:active {
 with gr.Blocks(
     title="Z-Image Turbo",
     fill_height=False,
+    footer_links=[{"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}]
 ) as demo:
     
     # Header
@@ -350,12 +319,14 @@ with gr.Blocks(
         fn=generate_image,
         inputs=prompt,
         outputs=output_image,
+        api_visibility="public"
     )
     
     prompt.submit(
         fn=generate_image,
         inputs=prompt,
         outputs=output_image,
+        api_visibility="public"
     )
 
 if __name__ == "__main__":

requirements.txt

@@ -1,16 +1,12 @@
+spaces
+gradio
 torch
-torchvision
-torchaudio
 git+https://github.com/huggingface/diffusers
 git+https://github.com/huggingface/transformers
 sentencepiece
 accelerate
 tokenizers
-spaces
-gradio
 requests
 Pillow
-numpy
-pandas
-matplotlib
-scipy
+torchvision
+torchaudio