Update app.py

app.py

@@ -18,7 +18,7 @@ from utils import process_gradio_source, get_bbox_from_mask, crop_image_from_bb,
     resize_img_and_pad, generate_context_reference_image
 from huggingface_hub import snapshot_download
 
-snapshot_download("Calligrapher2025/Calligrapher")
+
 # Global settings.
 with open(os.path.join(os.path.dirname(__file__), 'path_dict.json'), 'r') as f:
     path_dict = json.load(f)
@@ -29,11 +29,14 @@ os.environ['TMPDIR'] = path_dict['gradio_temp_dir']
 
 # Function of loading pre-trained models.
 def load_models():
-    transformer = FluxTransformer2DModel.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", subfolder="transformer",
+    base_model_path = snapshot_download("black-forest-labs/FLUX.1-Fill-dev")
+    image_encoder_path = snapshot_download("google/siglip-so400m-patch14-384")
+    calligrapher_path = snapshot_download("Calligrapher2025/Calligrapher")
+    transformer = FluxTransformer2DModel.from_pretrained(base_model_path, subfolder="transformer",
                                                          torch_dtype=torch.bfloat16)
-    pipe = CalligrapherPipeline.from_pretrained("Calligrapher2025/Calligrapher", transformer=transformer,
+    pipe = CalligrapherPipeline.from_pretrained(base_model_path, transformer=transformer,
                                                 torch_dtype=torch.bfloat16).to("cuda")
-    model = Calligrapher(pipe, "google/siglip-so400m-patch14-384", calligrapher_path, device="cuda", num_tokens=128)
+    model = Calligrapher(pipe, image_encoder_path, calligrapher_path, device="cuda", num_tokens=128)
     return model