Update app.py

app.py

@@ -10,15 +10,22 @@ device = 'cuda' if torch.cuda.is_available() else 'cpu'
 pipe = DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-real-v3.8.1")
 pipe = pipe.to(device)
 pipe.enable_xformers_memory_efficient_attention()
+torch.cuda.empty_cache()
+
 anime = DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-anime-v3.8.1")
 anime = anime.to(device)
 anime.enable_xformers_memory_efficient_attention()
+torch.cuda.empty_cache()
+
 disney = DiffusionPipeline.from_pretrained("circulus/canvers-disney-v3.8.1", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("circulus/canvers-disney-v3.8.1")
 disney = disney.to(device)
 disney.enable_xformers_memory_efficient_attention()
+torch.cuda.empty_cache()
+
 refiner = 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")
 refiner.enable_xformers_memory_efficient_attention()
 refiner = refiner.to(device)
+torch.cuda.empty_cache()
 
 def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, upscale, high_noise_frac):
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
@@ -26,27 +33,32 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, up
         if upscale == "Yes":
             int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
             image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
+            torch.cuda.empty_cache()
             return image
         else:
             image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+            torch.cuda.empty_cache()
             return image
     
     if Model == "Anime":
         if upscale == "Yes":
             int_image = anime(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
             image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
+            torch.cuda.empty_cache()
             return image
         else:
             image = anime(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
+            torch.cuda.empty_cache()
             return image
     else:
         if upscale == "Yes":
             int_image = disney(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images
             image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
+            torch.cuda.empty_cache()
             return image
         else:
             image = disney(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
-    
+            torch.cuda.empty_cache()
     return image
     
 gr.Interface(fn=genie, inputs=[gr.Radio(['Real', 'Anime', 'Disney'], value='Real', label='Choose Canvers Model'),