add more tests

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -1640,7 +1640,14 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
                 blocks_class_name = self.default_blocks_name
             if blocks_class_name is not None:
                 diffusers_module = importlib.import_module("diffusers")
-                blocks_class = getattr(diffusers_module, blocks_class_name)
+                blocks_class = getattr(diffusers_module, blocks_class_name, None)
+                # If the blocks_class is not found or is a base class (e.g. SequentialPipelineBlocks saved by from_blocks_dict) with empty block_classes
+                # fall back to default_blocks_name
+                if blocks_class is None or not blocks_class.block_classes:
+                    blocks_class_name = self.default_blocks_name
+                    blocks_class = getattr(diffusers_module, blocks_class_name)
+
+            if blocks_class is not None:
                 blocks = blocks_class()
             else:
                 logger.warning(f"`blocks` is `None`, no default blocks class found for {self.__class__.__name__}")
@@ -2245,6 +2252,10 @@ 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 not (
+                hasattr(component, "_diffusers_load_id") and component._diffusers_load_id is not None
+            ):
+                new_component_spec = ComponentSpec(name=name, type_hint=type(component))
             else:
                 new_component_spec = ComponentSpec.from_component(name, component)
 

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):
+        """When a component without _diffusers_load_id (custom/local model) is saved,
+        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
@@ -776,3 +818,27 @@ class TestCustomModelSavePretrained:
 
         assert loaded_pipe.unet is not None
         assert loaded_pipe.vae is not None
+
+
+class TestModularPipelineInitFallback:
+    """Test that ModularPipeline.__init__ falls back to default_blocks_name when
+    _blocks_class_name is a base class (e.g. SequentialPipelineBlocks saved by from_blocks_dict)."""
+
+    def test_init_fallback_when_blocks_class_name_is_base_class(self, tmp_path):
+        # 1. Load pipeline and get a workflow (returns a base SequentialPipelineBlocks)
+        pipe = ModularPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
+        t2i_blocks = pipe.blocks.get_workflow("text2image")
+        assert t2i_blocks.__class__.__name__ == "SequentialPipelineBlocks"
+
+        # 2. Use init_pipeline to create a new pipeline from the workflow blocks
+        t2i_pipe = t2i_blocks.init_pipeline("hf-internal-testing/tiny-stable-diffusion-xl-pipe")
+
+        # 3. Save and reload — the saved config will have _blocks_class_name="SequentialPipelineBlocks"
+        save_dir = str(tmp_path / "pipeline")
+        t2i_pipe.save_pretrained(save_dir)
+        loaded_pipe = ModularPipeline.from_pretrained(save_dir)
+
+        # 4. Verify it fell back to default_blocks_name and has correct blocks
+        assert loaded_pipe.__class__.__name__ == pipe.__class__.__name__
+        assert loaded_pipe._blocks.__class__.__name__ == pipe._blocks.__class__.__name__
+        assert len(loaded_pipe._blocks.sub_blocks) == len(pipe._blocks.sub_blocks)