up

Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -1707,6 +1707,8 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
             _blocks_class_name=self._blocks.__class__.__name__ if self._blocks is not None else None
         )
 
+        self._pretrained_model_name_or_path = pretrained_model_name_or_path
+
     @property
     def default_call_parameters(self) -> dict[str, Any]:
         """
@@ -2254,6 +2256,11 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
                 new_component_spec = current_component_spec
                 if hasattr(self, name) and getattr(self, name) is not None:
                     logger.warning(f"ModularPipeline.update_components: setting {name} to None (spec unchanged)")
+            elif (
+                current_component_spec.default_creation_method == "from_pretrained"
+                and getattr(component, "_diffusers_load_id", None) is None
+            ):
+                new_component_spec = ComponentSpec(name=name, type_hint=type(component))
             else:
                 new_component_spec = ComponentSpec.from_component(name, component)
 
@@ -2325,17 +2332,49 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
                     elif "default" in value:
                         # check if the default is specified
                         component_load_kwargs[key] = value["default"]
+            # Only pass trust_remote_code to components from the same repo as the pipeline.
+            # When a user passes trust_remote_code=True, they intend to trust code from the
+            # pipeline's repo, not from external repos referenced in modular_model_index.json.
+            trust_remote_code_stripped = False
+            if (
+                "trust_remote_code" in component_load_kwargs
+                and self._pretrained_model_name_or_path is not None
+                and spec.pretrained_model_name_or_path != self._pretrained_model_name_or_path
+            ):
+                component_load_kwargs.pop("trust_remote_code")
+                trust_remote_code_stripped = True
+
+            if not spec.pretrained_model_name_or_path:
+                logger.info(f"Skipping component `{name}`: no pretrained model path specified.")
+                continue
+
             try:
                 components_to_register[name] = spec.load(**component_load_kwargs)
             except Exception:
-                logger.warning(
-                    f"\nFailed to create component {name}:\n"
-                    f"- Component spec: {spec}\n"
-                    f"- load() called with kwargs: {component_load_kwargs}\n"
-                    "If this component is not required for your workflow you can safely ignore this message.\n\n"
-                    "Traceback:\n"
-                    f"{traceback.format_exc()}"
-                )
+                tb = traceback.format_exc()
+                if trust_remote_code_stripped and "trust_remote_code" in tb:
+                    warning_msg = (
+                        f"Failed to load component `{name}` from external repository "
+                        f"`{spec.pretrained_model_name_or_path}`.\n\n"
+                        f"`trust_remote_code=True` was not forwarded to `{name}` because it comes from "
+                        f"a different repository than the pipeline (`{self._pretrained_model_name_or_path}`). "
+                        f"For safety, `trust_remote_code` is only forwarded to components from the same "
+                        f"repository as the pipeline.\n\n"
+                        f"You need to load this component manually with `trust_remote_code=True` and pass it "
+                        f"to the pipeline via `pipe.update_components()`. For example, if it is a custom model:\n\n"
+                        f'  {name} = AutoModel.from_pretrained("{spec.pretrained_model_name_or_path}", trust_remote_code=True)\n'
+                        f"  pipe.update_components({name}={name})\n"
+                    )
+                else:
+                    warning_msg = (
+                        f"Failed to create component {name}:\n"
+                        f"- Component spec: {spec}\n"
+                        f"- load() called with kwargs: {component_load_kwargs}\n"
+                        "If this component is not required for your workflow you can safely ignore this message.\n\n"
+                        "Traceback:\n"
+                        f"{tb}"
+                    )
+                logger.warning(warning_msg)
 
         # Register all components at once
         self.register_components(**components_to_register)

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -687,6 +687,18 @@ class TestLoadComponentsSkipBehavior:
         assert pipe.unet is not None
         assert getattr(pipe, "vae", None) is None
 
+    def test_load_components_selective_loading_incremental(self):
+        """Loading a subset of components should not affect already-loaded components."""
+        pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
+
+        pipe.load_components(names="unet", torch_dtype=torch.float32)
+        pipe.load_components(names="text_encoder", torch_dtype=torch.float32)
+
+        assert hasattr(pipe, "unet")
+        assert pipe.unet is not None
+        assert hasattr(pipe, "text_encoder")
+        assert pipe.text_encoder is not None
+
     def test_load_components_skips_invalid_pretrained_path(self):
         pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
 
@@ -749,6 +761,36 @@ class TestCustomModelSavePretrained:
         for key in original_state_dict:
             assert torch.equal(original_state_dict[key], loaded_state_dict[key]), f"Mismatch in {key}"
 
+    def test_save_pretrained_updates_index_for_model_with_no_load_id(self, tmp_path):
+        """testing the workflow of update the pipeline with a custom model and save the pipeline,
+        the modular_model_index.json should point to the save directory."""
+        import json
+
+        from diffusers import UNet2DConditionModel
+
+        pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
+        pipe.load_components(torch_dtype=torch.float32)
+
+        unet = UNet2DConditionModel.from_pretrained(
+            "hf-internal-testing/tiny-stable-diffusion-xl-pipe", subfolder="unet"
+        )
+        assert not hasattr(unet, "_diffusers_load_id")
+
+        pipe.update_components(unet=unet)
+
+        save_dir = str(tmp_path / "my-pipeline")
+        pipe.save_pretrained(save_dir)
+
+        with open(os.path.join(save_dir, "modular_model_index.json")) as f:
+            index = json.load(f)
+
+        _library, _cls, unet_spec = index["unet"]
+        assert unet_spec["pretrained_model_name_or_path"] == save_dir
+        assert unet_spec["subfolder"] == "unet"
+
+        _library, _cls, vae_spec = index["vae"]
+        assert vae_spec["pretrained_model_name_or_path"] == "hf-internal-testing/tiny-stable-diffusion-xl-pipe"
+
     def test_save_pretrained_overwrite_modular_index(self, tmp_path):
         """With overwrite_modular_index=True, all component references should point to the save directory."""
         import json

tests/modular_pipelines/test_modular_pipelines_custom_blocks.py

@@ -192,6 +192,156 @@ class TestModularCustomBlocks:
         assert len(pipe.components) == 1
         assert pipe.component_names[0] == "transformer"
 
+    def test_trust_remote_code_not_propagated_to_external_repo(self):
+        """When a modular pipeline repo references a component from an external repo that has custom
+        code (auto_map in config), calling load_components(trust_remote_code=True) should NOT
+        propagate trust_remote_code to that external component. The external component should fail
+        to load."""
+
+        from diffusers import ModularPipeline
+
+        CUSTOM_MODEL_CODE = (
+            "import torch\n"
+            "from diffusers import ModelMixin, ConfigMixin\n"
+            "from diffusers.configuration_utils import register_to_config\n"
+            "\n"
+            "class CustomModel(ModelMixin, ConfigMixin):\n"
+            "    @register_to_config\n"
+            "    def __init__(self, hidden_size=8):\n"
+            "        super().__init__()\n"
+            "        self.linear = torch.nn.Linear(hidden_size, hidden_size)\n"
+            "\n"
+            "    def forward(self, x):\n"
+            "        return self.linear(x)\n"
+        )
+
+        with tempfile.TemporaryDirectory() as external_repo_dir, tempfile.TemporaryDirectory() as pipeline_repo_dir:
+            # Step 1: Create an external model repo with custom code (requires trust_remote_code)
+            with open(os.path.join(external_repo_dir, "modeling.py"), "w") as f:
+                f.write(CUSTOM_MODEL_CODE)
+
+            config = {
+                "_class_name": "CustomModel",
+                "_diffusers_version": "0.0.0",
+                "auto_map": {"AutoModel": "modeling.CustomModel"},
+                "hidden_size": 8,
+            }
+            with open(os.path.join(external_repo_dir, "config.json"), "w") as f:
+                json.dump(config, f)
+
+            torch.save({}, os.path.join(external_repo_dir, "diffusion_pytorch_model.bin"))
+
+            # Step 2: Create a custom block that references the external repo.
+            # Define both the class (for direct use) and its code string (for block.py).
+            class ExternalRefBlock(ModularPipelineBlocks):
+                @property
+                def expected_components(self):
+                    return [
+                        ComponentSpec(
+                            "custom_model",
+                            AutoModel,
+                            pretrained_model_name_or_path=external_repo_dir,
+                        )
+                    ]
+
+                @property
+                def inputs(self) -> List[InputParam]:
+                    return [InputParam("prompt", type_hint=str, required=True)]
+
+                @property
+                def intermediate_inputs(self) -> List[InputParam]:
+                    return []
+
+                @property
+                def intermediate_outputs(self) -> List[OutputParam]:
+                    return [OutputParam("output", type_hint=str)]
+
+                def __call__(self, components, state: PipelineState) -> PipelineState:
+                    block_state = self.get_block_state(state)
+                    block_state.output = "test"
+                    self.set_block_state(state, block_state)
+                    return components, state
+
+            EXTERNAL_REF_BLOCK_CODE_STR = (
+                "from typing import List\n"
+                "from diffusers import AutoModel\n"
+                "from diffusers.modular_pipelines import (\n"
+                "    ComponentSpec,\n"
+                "    InputParam,\n"
+                "    ModularPipelineBlocks,\n"
+                "    OutputParam,\n"
+                "    PipelineState,\n"
+                ")\n"
+                "\n"
+                "class ExternalRefBlock(ModularPipelineBlocks):\n"
+                "    @property\n"
+                "    def expected_components(self):\n"
+                "        return [\n"
+                "            ComponentSpec(\n"
+                '                "custom_model",\n'
+                "                AutoModel,\n"
+                f'                pretrained_model_name_or_path="{external_repo_dir}",\n'
+                "            )\n"
+                "        ]\n"
+                "\n"
+                "    @property\n"
+                "    def inputs(self) -> List[InputParam]:\n"
+                '        return [InputParam("prompt", type_hint=str, required=True)]\n'
+                "\n"
+                "    @property\n"
+                "    def intermediate_inputs(self) -> List[InputParam]:\n"
+                "        return []\n"
+                "\n"
+                "    @property\n"
+                "    def intermediate_outputs(self) -> List[OutputParam]:\n"
+                '        return [OutputParam("output", type_hint=str)]\n'
+                "\n"
+                "    def __call__(self, components, state: PipelineState) -> PipelineState:\n"
+                "        block_state = self.get_block_state(state)\n"
+                '        block_state.output = "test"\n'
+                "        self.set_block_state(state, block_state)\n"
+                "        return components, state\n"
+            )
+
+            # Save the block config, write block.py, then load back via from_pretrained
+            block = ExternalRefBlock()
+            block.save_pretrained(pipeline_repo_dir)
+
+            # auto_map will reference the module name derived from ExternalRefBlock.__module__,
+            # which is "test_modular_pipelines_custom_blocks". Write the code file with that name.
+            code_path = os.path.join(pipeline_repo_dir, "test_modular_pipelines_custom_blocks.py")
+            with open(code_path, "w") as f:
+                f.write(EXTERNAL_REF_BLOCK_CODE_STR)
+
+            block = ModularPipelineBlocks.from_pretrained(pipeline_repo_dir, trust_remote_code=True)
+            pipe = block.init_pipeline()
+            pipe.save_pretrained(pipeline_repo_dir)
+
+            # Step 3: Load the pipeline from the saved directory.
+            loaded_pipe = ModularPipeline.from_pretrained(pipeline_repo_dir, trust_remote_code=True)
+
+            assert loaded_pipe._pretrained_model_name_or_path == pipeline_repo_dir
+            assert loaded_pipe._component_specs["custom_model"].pretrained_model_name_or_path == external_repo_dir
+            assert getattr(loaded_pipe, "custom_model", None) is None
+
+            # Step 4a: load_components WITHOUT trust_remote_code.
+            # It should still fail
+            loaded_pipe.load_components()
+            assert getattr(loaded_pipe, "custom_model", None) is None
+
+            # Step 4b: load_components with trust_remote_code=True.
+            # trust_remote_code should be stripped for the external component, so it fails.
+            # The warning should contain guidance about manually loading with trust_remote_code.
+            loaded_pipe.load_components(trust_remote_code=True)
+            assert getattr(loaded_pipe, "custom_model", None) is None
+
+            # Step 4c: Manually load with AutoModel and update_components — this should work.
+            from diffusers import AutoModel
+
+            custom_model = AutoModel.from_pretrained(external_repo_dir, trust_remote_code=True)
+            loaded_pipe.update_components(custom_model=custom_model)
+            assert getattr(loaded_pipe, "custom_model", None) is not None
+
     def test_custom_block_loads_from_hub(self):
         repo_id = "hf-internal-testing/tiny-modular-diffusers-block"
         block = ModularPipelineBlocks.from_pretrained(repo_id, trust_remote_code=True)