Update app.py

app.py

@@ -13,7 +13,7 @@ torch.cuda.empty_cache()
 def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
     if Model == "SD3":
-        torch.cuda.max_memory_allocated(device=device)
+        #torch.cuda.max_memory_allocated(device=device)
         torch.cuda.empty_cache()
         SD3 = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16).to(device)
         torch.cuda.empty_cache()
@@ -28,13 +28,13 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
     if Model == "FXL":
 
         torch.cuda.empty_cache()
-        torch.cuda.max_memory_allocated(device=device)
+        #torch.cuda.max_memory_allocated(device=device)
         pipe = DiffusionPipeline.from_pretrained("circulus/canvers-fusionXL-v1", torch_dtype=torch.float16)
         pipe.enable_xformers_memory_efficient_attention()
         pipe = pipe.to(device)
         torch.cuda.empty_cache()
 
-        torch.cuda.max_memory_allocated(device=device)
+        #torch.cuda.max_memory_allocated(device=device)
         int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, output_type="latent").images
         pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16") if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
         pipe.enable_xformers_memory_efficient_attention()