Update app.py

app.py

@@ -9,18 +9,19 @@ from huggingface_hub import hf_hub_download
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 torch.cuda.max_memory_allocated(device=device)
 torch.cuda.empty_cache()
+pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1").to(device)
+pipe.enable_xformers_memory_efficient_attention()
+torch.cuda.empty_cache()
 
 def genie (Prompt, negative_prompt, height, width, scale, steps, seed):
-    pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1").to(device)
-    pipe.enable_xformers_memory_efficient_attention()
-    torch.cuda.empty_cache()
+    
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
     int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
     torch.cuda.empty_cache()
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0").to(device)
-    pipe.enable_xformers_memory_efficient_attention()
+    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0").to(device)
+    refiner.enable_xformers_memory_efficient_attention()
     torch.cuda.empty_cache()
-    image = pipe(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
+    image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
     torch.cuda.empty_cache()
         
     return image