Update app.py

app.py

@@ -312,6 +312,7 @@ class ShapERenderer:
         self.diffusion = diffusion_from_config(load_config('diffusion'))
         print("Shap-E models initialized!")
     
+    @spaces.GPU(duration=80)  
     def generate_views(self, prompt, guidance_scale=15.0, num_steps=64):
         try:
             torch.cuda.empty_cache()  # Clear GPU memory before generation
@@ -322,22 +323,22 @@ class ShapERenderer:
             
             with torch.amp.autocast('cuda'):  # Use automatic mixed precision
                 # spaces duration is 20 seconds, so we need to be careful here
-                with spaces.GPU(duration=20):   
-                    latents = sample_latents(
-                        batch_size=batch_size,
-                        model=self.model,
-                        diffusion=self.diffusion,
-                        guidance_scale=guidance_scale,
-                        model_kwargs=dict(texts=[prompt] * batch_size),
-                    progress=True,
-                    clip_denoised=True,
-                    use_fp16=True,
-                    use_karras=True,
-                    karras_steps=num_steps,
-                    sigma_min=1e-3,
-                    sigma_max=160,
-                    s_churn=0,
-                )
+            
+                latents = sample_latents(
+                    batch_size=batch_size,
+                    model=self.model,
+                    diffusion=self.diffusion,
+                    guidance_scale=guidance_scale,
+                    model_kwargs=dict(texts=[prompt] * batch_size),
+                progress=True,
+                clip_denoised=True,
+                use_fp16=True,
+                use_karras=True,
+                karras_steps=num_steps,
+                sigma_min=1e-3,
+                sigma_max=160,
+                s_churn=0,
+            )
 
             # Render the 6 views we need with specific viewing angles
             size = 320  # Size of each rendered image