diff --git a/invokeai/backend/model_manager/load/model_loaders/flux.py b/invokeai/backend/model_manager/load/model_loaders/flux.py
index 58264ebc25..ebc3333eea 100644
--- a/invokeai/backend/model_manager/load/model_loaders/flux.py
+++ b/invokeai/backend/model_manager/load/model_loaders/flux.py
@@ -64,7 +64,7 @@ class FluxVAELoader(ModelLoader):
         with SilenceWarnings():
             model = AutoEncoder(params)
             sd = load_file(model_path)
-            model.load_state_dict(sd, strict=False, assign=True)
+            model.load_state_dict(sd, assign=True)
 
         return model
 
@@ -83,11 +83,11 @@ class ClipCheckpointModel(ModelLoader):
 
         match submodel_type:
             case SubModelType.Tokenizer:
-                return CLIPTokenizer.from_pretrained(config.path, max_length=77)
+                return CLIPTokenizer.from_pretrained(config.path)
             case SubModelType.TextEncoder:
                 return CLIPTextModel.from_pretrained(config.path)
 
-        raise ValueError("Only Tokenizer and TextEncoder submodels are currently supported.")
+        raise ValueError(f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}")
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.T5Encoder8b)
@@ -108,7 +108,7 @@ class T5Encoder8bCheckpointModel(ModelLoader):
             case SubModelType.TextEncoder2:
                 return FastQuantizedTransformersModel.from_pretrained(Path(config.path) / "text_encoder_2")
 
-        raise ValueError("Only Tokenizer and TextEncoder submodels are currently supported.")
+        raise ValueError(f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}")
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.T5Encoder)
@@ -131,7 +131,7 @@ class T5EncoderCheckpointModel(ModelLoader):
                     Path(config.path) / "text_encoder_2"
                 )  # TODO: Fix hf subfolder install
 
-        raise ValueError("Only Tokenizer and TextEncoder submodels are currently supported.")
+        raise ValueError(f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}")
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.Main, format=ModelFormat.Checkpoint)
@@ -154,7 +154,7 @@ class FluxCheckpointModel(ModelLoader):
             case SubModelType.Transformer:
                 return self._load_from_singlefile(config, flux_conf)
 
-        raise ValueError("Only Transformer submodels are currently supported.")
+        raise ValueError(f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}")
 
     def _load_from_singlefile(
         self,
@@ -162,7 +162,6 @@ class FluxCheckpointModel(ModelLoader):
         flux_conf: Any,
     ) -> AnyModel:
         assert isinstance(config, MainCheckpointConfig)
-        params = None
         model_path = Path(config.path)
         dataclass_fields = {f.name for f in fields(FluxParams)}
         filtered_data = {k: v for k, v in flux_conf["params"].items() if k in dataclass_fields}
@@ -171,7 +170,7 @@ class FluxCheckpointModel(ModelLoader):
         with SilenceWarnings():
             model = Flux(params)
             sd = load_file(model_path)
-            model.load_state_dict(sd, strict=False, assign=True)
+            model.load_state_dict(sd, assign=True)
         return model
 
 
@@ -195,7 +194,7 @@ class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
             case SubModelType.Transformer:
                 return self._load_from_singlefile(config, flux_conf)
 
-        raise ValueError("Only Transformer submodels are currently supported.")
+        raise ValueError(f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}")
 
     def _load_from_singlefile(
         self,
@@ -203,7 +202,6 @@ class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
         flux_conf: Any,
     ) -> AnyModel:
         assert isinstance(config, MainBnbQuantized4bCheckpointConfig)
-        params = None
         model_path = Path(config.path)
         dataclass_fields = {f.name for f in fields(FluxParams)}
         filtered_data = {k: v for k, v in flux_conf["params"].items() if k in dataclass_fields}
@@ -214,5 +212,5 @@ class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
                 model = Flux(params)
                 model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=torch.bfloat16)
             sd = load_file(model_path)
-            model.load_state_dict(sd, strict=False, assign=True)
+            model.load_state_dict(sd, assign=True)
         return model
diff --git a/invokeai/backend/model_manager/probe.py b/invokeai/backend/model_manager/probe.py
index a3364da769..778dd583e5 100644
--- a/invokeai/backend/model_manager/probe.py
+++ b/invokeai/backend/model_manager/probe.py
@@ -224,6 +224,7 @@ class ModelProbe(object):
 
         for key in [str(k) for k in ckpt.keys()]:
             if key.startswith(("cond_stage_model.", "first_stage_model.", "model.diffusion_model.", "double_blocks.")):
+                # Keys starting with double_blocks are associated with Flux models
                 return ModelType.Main
             elif key.startswith(("encoder.conv_in", "decoder.conv_in")):
                 return ModelType.VAE