better error handle

lama_cleaner/helper.py

@@ -14,9 +14,11 @@ from torch.hub import download_url_to_file, get_dir
 
 
 def switch_mps_device(model_name, device):
-    if model_name not in MPS_SUPPORT_MODELS and (device == "mps" or device == torch.device('mps')):
+    if model_name not in MPS_SUPPORT_MODELS and (
+        device == "mps" or device == torch.device("mps")
+    ):
         logger.info(f"{model_name} not support mps, switch to cpu")
-        return torch.device('cpu')
+        return torch.device("cpu")
     return device
 
 
@@ -51,12 +53,14 @@ def load_jit_model(url_or_path, device):
         model_path = url_or_path
     else:
         model_path = download_model(url_or_path)
-    logger.info(f"Load model from: {model_path}")
+    logger.info(f"Loading model from: {model_path}")
     try:
-        model = torch.jit.load(model_path).to(device)
-    except:
+        model = torch.jit.load(model_path, map_location="cpu").to(device)
+    except Exception as e:
         logger.error(
-            f"Failed to load {model_path}, delete model and restart lama-cleaner"
+            f"Failed to load {model_path}, please delete model and restart lama-cleaner.\n"
+            f"If you still have errors, please try download model manually first https://lama-cleaner-docs.vercel.app/install/download_model_manually.\n"
+            f"If all above operations doesn't work, please submit an issue at https://github.com/Sanster/lama-cleaner/issues and include a screenshot of the error:\n{e}"
         )
         exit(-1)
     model.eval()

lama_cleaner/model/lama.py

@@ -3,9 +3,12 @@ import os
 import cv2
 import numpy as np
 import torch
-from loguru import logger
 
-from lama_cleaner.helper import download_model, norm_img, get_cache_path_by_url
+from lama_cleaner.helper import (
+    norm_img,
+    get_cache_path_by_url,
+    load_jit_model,
+)
 from lama_cleaner.model.base import InpaintModel
 from lama_cleaner.schema import Config
 
@@ -20,20 +23,7 @@ class LaMa(InpaintModel):
     pad_mod = 8
 
     def init_model(self, device, **kwargs):
-        if os.environ.get("LAMA_MODEL"):
-            model_path = os.environ.get("LAMA_MODEL")
-            if not os.path.exists(model_path):
-                raise FileNotFoundError(
-                    f"lama torchscript model not found: {model_path}"
-                )
-        else:
-            model_path = download_model(LAMA_MODEL_URL)
-        logger.info(f"Load LaMa model from: {model_path}")
-        model = torch.jit.load(model_path, map_location="cpu")
-        model = model.to(device)
-        model.eval()
-        self.model = model
-        self.model_path = model_path
+        self.model = load_jit_model(LAMA_MODEL_URL, device).eval()
 
     @staticmethod
     def is_downloaded() -> bool: