Update app.py

app.py

@@ -66,7 +66,10 @@ is_cuda = torch.cuda.is_available()
 device = torch.device("cuda" if is_cuda else "cpu")
 print(device)
 clip_model, clip_preprocess = clip.load("ViT-B/32", device=device, jit=False, download_root='./')  # Must set jit=False for training
-clip.model.convert_weights(clip_model)  # Actually this line is unnecessary since clip by default already on float16
+
+if is_cuda:
+    clip.model.convert_weights(clip_model)
+    
 clip_model.eval()
 for p in clip_model.parameters():
     p.requires_grad = False
@@ -101,15 +104,16 @@ print('loading transformer checkpoint from {}'.format(args.resume_trans))
 ckpt = torch.load(args.resume_trans, map_location='cpu')
 trans_encoder.load_state_dict(ckpt['trans'], strict=True)
 trans_encoder.eval()
+
 mean = torch.from_numpy(np.load('./checkpoints/t2m/VQVAEV3_CB1024_CMT_H1024_NRES3/meta/mean.npy'))
 std = torch.from_numpy(np.load('./checkpoints/t2m/VQVAEV3_CB1024_CMT_H1024_NRES3/meta/std.npy'))
+
 if is_cuda:
     net.cuda()
     trans_encoder.cuda()
     mean = mean.cuda()
     std = std.cuda()
 
-
 def render(motions, device_id=0, name='test_vis'):
     frames, njoints, nfeats = motions.shape
     MINS = motions.min(axis=0).min(axis=0)