Bug fixes, negative prompting during training, hardened catching

toolkit/config_modules.py

@@ -193,6 +193,9 @@ class TrainConfig:
         self.adapter_assist_name_or_path: Optional[str] = kwargs.get('adapter_assist_name_or_path', None)
         self.noise_multiplier = kwargs.get('noise_multiplier', 1.0)
         self.img_multiplier = kwargs.get('img_multiplier', 1.0)
+        self.negative_prompt = kwargs.get('negative_prompt', None)
+        # multiplier applied to loos on regularization images
+        self.reg_weight = kwargs.get('reg_weight', 1.0)
 
         # dropout that happens before encoding. It functions independently per text encoder
         self.prompt_dropout_prob = kwargs.get('prompt_dropout_prob', 0.0)

toolkit/metadata.py

@@ -77,6 +77,10 @@ def parse_metadata_from_safetensors(meta: OrderedDict) -> OrderedDict:
 
 
 def load_metadata_from_safetensors(file_path: str) -> OrderedDict:
-    with safe_open(file_path, framework="pt") as f:
-        metadata = f.metadata()
-    return parse_metadata_from_safetensors(metadata)
+    try:
+        with safe_open(file_path, framework="pt") as f:
+            metadata = f.metadata()
+        return parse_metadata_from_safetensors(metadata)
+    except Exception as e:
+        print(f"Error loading metadata from {file_path}: {e}")
+        return OrderedDict()

toolkit/network_mixins.py

@@ -254,6 +254,7 @@ class ToolkitModuleMixin:
         multiplier_batch_size = multiplier.size(0)
         if lora_output_batch_size != multiplier_batch_size:
             num_interleaves = lora_output_batch_size // multiplier_batch_size
+            # todo check if this is correct, do we just concat when doing cfg?
             multiplier = multiplier.repeat_interleave(num_interleaves)
 
         x = org_forwarded + broadcast_and_multiply(lora_output, multiplier)
@@ -470,11 +471,11 @@ class ToolkitNetworkMixin:
             self.torch_multiplier = tensor_multiplier.clone().detach()
 
     @property
-    def multiplier(self) -> Union[float, List[float]]:
+    def multiplier(self) -> Union[float, List[float], List[List[float]]]:
         return self._multiplier
 
     @multiplier.setter
-    def multiplier(self, value: Union[float, List[float]]):
+    def multiplier(self, value: Union[float, List[float], List[List[float]]]):
         # it takes time to update all the multipliers, so we only do it if the value has changed
         if self._multiplier == value:
             return