Update src/facerender/animate.py

src/facerender/animate.py

@@ -129,28 +129,32 @@ class AnimateFromCoeff():
         return checkpoint.get('epoch', 0)
 
     def load_cpk_mapping(self, checkpoint_path, mapping=None, discriminator=None,
-                         optimizer_mapping=None, optimizer_discriminator=None, device='cpu'):
-
-        if checkpoint_path.endswith(".tar"):
-            try:
-                with tarfile.open(checkpoint_path, "r") as tar:
-                    members = tar.getnames()
-                    if not any(name.startswith("storages") for name in members):
-                        print("⚠️ 'storages' klasörü .tar dosyasında bulunamadı. Devam ediliyor...")
-                    else:
-                        print("✔️ 'storages' bulundu.")
-            except Exception as e:
-                print(f"Tar kontrol hatası: {e}")
+                     optimizer_mapping=None, optimizer_discriminator=None, device='cpu'):
+
+    # Eğer .tar ise aç ve klasör haline getir
+    if checkpoint_path.endswith(".tar"):
+        try:
+            print(f"Extracting tar file: {checkpoint_path}")
+            with tarfile.open(checkpoint_path, "r") as tar:
+                extract_dir = checkpoint_path.replace(".tar", "")
+                os.makedirs(extract_dir, exist_ok=True)
+                tar.extractall(path=extract_dir)
+            checkpoint_path = os.path.join(extract_dir, "data.pkl")
+        except Exception as e:
+            print(f"Tar açma hatası: {e}")
+            return 0  # fail gracefully
+
+    # Checkpoint (data.pkl) yükle
+    checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
+
+    if mapping is not None and 'mapping' in checkpoint:
+        mapping.load_state_dict(checkpoint['mapping'])
+    if discriminator is not None and 'discriminator' in checkpoint:
+        discriminator.load_state_dict(checkpoint['discriminator'])
+    if optimizer_mapping is not None and 'optimizer_mapping' in checkpoint:
+        optimizer_mapping.load_state_dict(checkpoint['optimizer_mapping'])
+    if optimizer_discriminator is not None and 'optimizer_discriminator' in checkpoint:
+        optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
+
+    return checkpoint.get('epoch', 0)
 
-        checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
-
-        if mapping is not None and 'mapping' in checkpoint:
-            mapping.load_state_dict(checkpoint['mapping'])
-        if discriminator is not None and 'discriminator' in checkpoint:
-            discriminator.load_state_dict(checkpoint['discriminator'])
-        if optimizer_mapping is not None and 'optimizer_mapping' in checkpoint:
-            optimizer_mapping.load_state_dict(checkpoint['optimizer_mapping'])
-        if optimizer_discriminator is not None and 'optimizer_discriminator' in checkpoint:
-            optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
-
-        return checkpoint.get('epoch', 0)