Update app.py

app.py

@@ -56,7 +56,7 @@ def download_image(url):
 #device = "cpu" #"cuda" if torch.cuda.is_available() else "cpu"
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-print("The model will be running on :: ", device, "device")
+print("The model will be running on :: ", device, " ~device")
 # Convert model parameters and buffers to CPU or Cuda
 
 model_id_or_path = "CompVis/stable-diffusion-v1-4"
@@ -65,12 +65,11 @@ pipe = StableDiffusionInpaintingPipeline.from_pretrained(
     revision="fp16", 
     torch_dtype=torch.double, #float16
     use_auth_token=auth_token
-)
+).to(device)
+#pipe = pipe.to(device)
 #self.register_buffer('n_', ...)
 print ("torch.backends.mps.is_available: ", torch.backends.mps.is_available())
 
-pipe = pipe.to(device)
-
 model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, complex_trans_conv=True)
 
 model = model.to(torch.device(device))
@@ -79,8 +78,8 @@ model.eval() #.half()
 
 weightsPATH = './clipseg/weights/rd64-uni.pth'
 
-state = {'model': model.state_dict()}
-torch.save(state, weightsPATH)
+#state = {'model': model.state_dict()}
+#torch.save(state, weightsPATH)
 
 model.load_state_dict(torch.load(weightsPATH, map_location=torch.device(device)), strict=False) #False
 #model.load_state_dict(torch.load(weightsPATH)['model'])
@@ -89,8 +88,7 @@ print ("Torch load(model) : ", model)
 print ("Weights : ")
 # print weights
 for k, v in model.named_parameters():
-    print(k, v)
-    
+    print(k, v)    
 
 imgRes = 256