chore: fix

app.py

@@ -42,19 +42,18 @@ parser.add_argument("--decoder_tile_size", type=int, default=64)
 parser.add_argument("--load_8bit_llava", action='store_true', default=False)
 args = parser.parse_args()
 
-if torch.cuda.device_count() > 0:
-    BOOXEL_device = 'cpu'
-
-    # 加载 BOOXEL
-    model, default_setting = create_BOOXEL_model(args.opt, BOOXEL_sign='Q', load_default_setting=True)
-    if args.loading_half_params:
-        model = model.half()
-    if args.use_tile_vae:
-        model.init_tile_vae(encoder_tile_size=args.encoder_tile_size, decoder_tile_size=args.decoder_tile_size)
-    model = model.to(BOOXEL_device)
-    model.first_stage_model.denoise_encoder_s1 = copy.deepcopy(model.first_stage_model.denoise_encoder)
-    model.current_model = 'v0-Q'
-    ckpt_Q, ckpt_F = load_QF_ckpt(args.opt)
+BOOXEL_device = 'cpu'
+
+# 加载 BOOXEL
+model, default_setting = create_BOOXEL_model(args.opt, BOOXEL_sign='Q', load_default_setting=True)
+if args.loading_half_params:
+    model = model.half()
+if args.use_tile_vae:
+    model.init_tile_vae(encoder_tile_size=args.encoder_tile_size, decoder_tile_size=args.decoder_tile_size)
+model = model.to(BOOXEL_device)
+model.first_stage_model.denoise_encoder_s1 = copy.deepcopy(model.first_stage_model.denoise_encoder)
+model.current_model = 'v0-Q'
+ckpt_Q, ckpt_F = load_QF_ckpt(args.opt)
 
 def check_upload(input_image):
     if input_image is None: