Update app.py

app.py

@@ -118,7 +118,7 @@ thick = debias(thick, "Heavy_Makeup", df, pinverse, device)
 @torch.no_grad()
 @spaces.GPU
 def sample_then_run(net):
-    print(net)
+    device = "cuda"
     # get mean and standard deviation for each principal component
     m = torch.mean(proj, 0)
     standev = torch.std(proj, 0)
@@ -142,6 +142,7 @@ def sample_then_run(net):
 @torch.no_grad()
 @spaces.GPU()
 def inference(net, prompt, negative_prompt, guidance_scale, ddim_steps, seed):
+    device = "cuda"
     mean.to(device)
     std.to(device)
     v.to(device)
@@ -206,12 +207,11 @@ def inference(net, prompt, negative_prompt, guidance_scale, ddim_steps, seed):
 @torch.no_grad()
 @spaces.GPU()
 def edit_inference(net, prompt, negative_prompt, guidance_scale, ddim_steps, seed, start_noise, a1, a2, a3, a4): 
+    device = "cuda"
     mean.to(device)
     std.to(device)
     v.to(device)
-    print(young.device)
     young.to(device)
-    print(young.device)
     pointy.to(device)
     wavy.to(device)
     thick.to(device)
@@ -234,9 +234,6 @@ def edit_inference(net, prompt, negative_prompt, guidance_scale, ddim_steps, see
     pcs_original = weights.shape[1]
     pcs_edits = young.shape[1]
     padding =  torch.zeros((1,pcs_original-pcs_edits)).to(device)
-    print(device)
-    print(padding.device)
-    print(young.device)
     young_pad = torch.cat((young, padding), 1)
     pointy_pad = torch.cat((pointy, padding), 1)
     wavy_pad = torch.cat((wavy, padding), 1)