Update app.py

app.py

@@ -10,14 +10,23 @@ move_cache()
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
+# Check if a GPU is available and set the appropriate torch_dtype and device
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+    device = "cuda"
+else:
+    torch_dtype = torch.float32
+    device = "cpu"
+
+
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
-    pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Envvi/Inkpunk-Diffusion", torch_dtype=torch.float16)
+    pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Envvi/Inkpunk-Diffusion", torch_dtype=torch_dtype)
     #pipe = DiffusionPipeline.from_pretrained("Envvi/Inkpunk-Diffusion", torch_dtype=torch.float16, variant="fp16")
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
 else: 
-    pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Envvi/Inkpunk-Diffusion", torch_dtype=torch.float16)
+    pipe = StableDiffusionImg2ImgPipeline.from_pretrained("Envvi/Inkpunk-Diffusion", torch_dtype=torch_dtype)
     pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
@@ -81,7 +90,7 @@ with gr.Blocks(css=css) as demo:
 
 
         with gr.Row():
-            base_img = gr.Interface(fn=generate_image, inputs=gr.inputs.Image(type="pil"), outputs=gr.outputs.Image(type="pil"))            
+            base_img = gr.Interface(fn=generate_image, inputs=gr.Image(type="pil"), outputs=gr.Image(type="pil"))            
         
         with gr.Row():