fix(model-loaders): add local_files_only=True to prevent network requests

invokeai/backend/model_manager/load/model_loaders/cogview4.py

@@ -45,12 +45,13 @@ def _load_model(
                 model_path,
                 torch_dtype=dtype,
                 variant=variant,
+                local_files_only=True,
             )
         except OSError as e:
             if variant and "no file named" in str(
                 e
             ):  # try without the variant, just in case user's preferences changed
-                result = load_class.from_pretrained(model_path, torch_dtype=dtype)
+                result = load_class.from_pretrained(model_path, torch_dtype=dtype, local_files_only=True)
             else:
                 raise e
 

invokeai/backend/model_manager/load/model_loaders/flux.py

@@ -122,9 +122,9 @@ def _load_model(
 
         match submodel_type:
             case SubModelType.Tokenizer:
-                return CLIPTokenizer.from_pretrained(Path(config.path) / "tokenizer")
+                return CLIPTokenizer.from_pretrained(Path(config.path) / "tokenizer", local_files_only=True)
             case SubModelType.TextEncoder:
-                return CLIPTextModel.from_pretrained(Path(config.path) / "text_encoder")
+                return CLIPTextModel.from_pretrained(Path(config.path) / "text_encoder", local_files_only=True)
 
         raise ValueError(
             f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
@@ -148,10 +148,12 @@ def _load_model(
             )
         match submodel_type:
             case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
-                return T5TokenizerFast.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
+                return T5TokenizerFast.from_pretrained(
+                    Path(config.path) / "tokenizer_2", max_length=512, local_files_only=True
+                )
             case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 te2_model_path = Path(config.path) / "text_encoder_2"
-                model_config = AutoConfig.from_pretrained(te2_model_path)
+                model_config = AutoConfig.from_pretrained(te2_model_path, local_files_only=True)
                 with accelerate.init_empty_weights():
                     model = AutoModelForTextEncoding.from_config(model_config)
                     model = quantize_model_llm_int8(model, modules_to_not_convert=set())
@@ -192,10 +194,15 @@ def _load_model(
 
         match submodel_type:
             case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
-                return T5TokenizerFast.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
+                return T5TokenizerFast.from_pretrained(
+                    Path(config.path) / "tokenizer_2", max_length=512, local_files_only=True
+                )
             case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 return T5EncoderModel.from_pretrained(
-                    Path(config.path) / "text_encoder_2", torch_dtype="auto", low_cpu_mem_usage=True
+                    Path(config.path) / "text_encoder_2",
+                    torch_dtype="auto",
+                    low_cpu_mem_usage=True,
+                    local_files_only=True,
                 )
 
         raise ValueError(

invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py

@@ -37,12 +37,14 @@ def _load_model(
         repo_variant = config.repo_variant if isinstance(config, Diffusers_Config_Base) else None
         variant = repo_variant.value if repo_variant else None
         try:
-            result: AnyModel = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype, variant=variant)
+            result: AnyModel = model_class.from_pretrained(
+                model_path, torch_dtype=self._torch_dtype, variant=variant, local_files_only=True
+            )
         except OSError as e:
             if variant and "no file named" in str(
                 e
             ):  # try without the variant, just in case user's preferences changed
-                result = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype)
+                result = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype, local_files_only=True)
             else:
                 raise e
         return result

invokeai/backend/model_manager/load/model_loaders/onnx.py

@@ -38,5 +38,6 @@ def _load_model(
             model_path,
             torch_dtype=self._torch_dtype,
             variant=variant,
+            local_files_only=True,
         )
         return result

invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

@@ -80,12 +80,13 @@ def _load_model(
                 model_path,
                 torch_dtype=self._torch_dtype,
                 variant=variant,
+                local_files_only=True,
             )
         except OSError as e:
             if variant and "no file named" in str(
                 e
             ):  # try without the variant, just in case user's preferences changed
-                result = load_class.from_pretrained(model_path, torch_dtype=self._torch_dtype)
+                result = load_class.from_pretrained(model_path, torch_dtype=self._torch_dtype, local_files_only=True)
             else:
                 raise e