resolve merge conflicts.

src/diffusers/commands/custom_blocks.py

@@ -89,8 +89,6 @@ class CustomBlocksCommand(BaseDiffusersCLICommand):
         # automap = self._create_automap(parent_class=parent_class, child_class=child_class)
         # with open(CONFIG, "w") as f:
         #     json.dump(automap, f)
-        with open("requirements.txt", "w") as f:
-            f.write("")
 
     def _choose_block(self, candidates, chosen=None):
         for cls, base in candidates:

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -40,6 +40,7 @@ from .modular_pipeline_utils import (
     InputParam,
     InsertableDict,
     OutputParam,
+    _validate_requirements,
     combine_inputs,
     combine_outputs,
     format_components,
@@ -290,6 +291,7 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
 
     config_name = "modular_config.json"
     model_name = None
+    _requirements: dict[str, str] | None = None
     _workflow_map = None
 
     @classmethod
@@ -382,6 +384,19 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
         trust_remote_code: bool = False,
         **kwargs,
     ):
+        config = cls.load_config(pretrained_model_name_or_path)
+        has_remote_code = "auto_map" in config and cls.__name__ in config["auto_map"]
+        trust_remote_code = resolve_trust_remote_code(
+            trust_remote_code, pretrained_model_name_or_path, has_remote_code
+        )
+        if not (has_remote_code and trust_remote_code):
+            raise ValueError(
+                "Selected model repository does not happear to have any custom code or does not have a valid `config.json` file."
+            )
+
+        if "requirements" in config and config["requirements"] is not None:
+            _ = _validate_requirements(config["requirements"])
+
         hub_kwargs_names = [
             "cache_dir",
             "force_download",
@@ -394,16 +409,6 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
         ]
         hub_kwargs = {name: kwargs.pop(name) for name in hub_kwargs_names if name in kwargs}
 
-        config = cls.load_config(pretrained_model_name_or_path, **hub_kwargs)
-        has_remote_code = "auto_map" in config and cls.__name__ in config["auto_map"]
-        trust_remote_code = resolve_trust_remote_code(
-            trust_remote_code, pretrained_model_name_or_path, has_remote_code
-        )
-        if not has_remote_code and trust_remote_code:
-            raise ValueError(
-                "Selected model repository does not happear to have any custom code or does not have a valid `config.json` file."
-            )
-
         class_ref = config["auto_map"][cls.__name__]
         module_file, class_name = class_ref.split(".")
         module_file = module_file + ".py"
@@ -428,8 +433,13 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
         module = full_mod.rsplit(".", 1)[-1].replace("__dynamic__", "")
         parent_module = self.save_pretrained.__func__.__qualname__.split(".", 1)[0]
         auto_map = {f"{parent_module}": f"{module}.{cls_name}"}
-
         self.register_to_config(auto_map=auto_map)
+
+        # resolve requirements
+        requirements = _validate_requirements(getattr(self, "_requirements", None))
+        if requirements:
+            self.register_to_config(requirements=requirements)
+
         self.save_config(save_directory=save_directory, push_to_hub=push_to_hub, **kwargs)
         config = dict(self.config)
         self._internal_dict = FrozenDict(config)
@@ -1240,6 +1250,14 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
             expected_configs=self.expected_configs,
         )
 
+    @property
+    def _requirements(self) -> dict[str, str]:
+        requirements = {}
+        for block_name, block in self.sub_blocks.items():
+            if getattr(block, "_requirements", None):
+                requirements[block_name] = block._requirements
+        return requirements
+
 
 class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
     """

src/diffusers/modular_pipelines/modular_pipeline_utils.py

@@ -22,10 +22,12 @@ from typing import Any, Literal, Type, Union, get_args, get_origin
 
 import PIL.Image
 import torch
+from packaging.specifiers import InvalidSpecifier, SpecifierSet
 
 from ..configuration_utils import ConfigMixin, FrozenDict
 from ..loaders.single_file_utils import _is_single_file_path_or_url
 from ..utils import DIFFUSERS_LOAD_ID_FIELDS, is_torch_available, logging
+from ..utils.import_utils import _is_package_available
 
 
 if is_torch_available():
@@ -972,6 +974,89 @@ def make_doc_string(
     return output
 
 
+def _validate_requirements(reqs):
+    if reqs is None:
+        normalized_reqs = {}
+    else:
+        if not isinstance(reqs, dict):
+            raise ValueError(
+                "Requirements must be provided as a dictionary mapping package names to version specifiers."
+            )
+        normalized_reqs = _normalize_requirements(reqs)
+
+    if not normalized_reqs:
+        return {}
+
+    final: dict[str, str] = {}
+    for req, specified_ver in normalized_reqs.items():
+        req_available, req_actual_ver = _is_package_available(req)
+        if not req_available:
+            logger.warning(f"{req} was specified in the requirements but wasn't found in the current environment.")
+
+        if specified_ver:
+            try:
+                specifier = SpecifierSet(specified_ver)
+            except InvalidSpecifier as err:
+                raise ValueError(f"Requirement specifier '{specified_ver}' for {req} is invalid.") from err
+
+            if req_actual_ver == "N/A":
+                logger.warning(
+                    f"Version of {req} could not be determined to validate requirement '{specified_ver}'. Things might work unexpected."
+                )
+            elif not specifier.contains(req_actual_ver, prereleases=True):
+                logger.warning(
+                    f"{req} requirement '{specified_ver}' is not satisfied by the installed version {req_actual_ver}. Things might work unexpected."
+                )
+
+        final[req] = specified_ver
+
+    return final
+
+
+def _normalize_requirements(reqs):
+    if not reqs:
+        return {}
+
+    normalized: "OrderedDict[str, str]" = OrderedDict()
+
+    def _accumulate(mapping: dict[str, Any]):
+        for pkg, spec in mapping.items():
+            if isinstance(spec, dict):
+                # This is recursive because blocks are composable. This way, we can merge requirements
+                # from multiple blocks.
+                _accumulate(spec)
+                continue
+
+            pkg_name = str(pkg).strip()
+            if not pkg_name:
+                raise ValueError("Requirement package name cannot be empty.")
+
+            spec_str = "" if spec is None else str(spec).strip()
+            if spec_str and not spec_str.startswith(("<", ">", "=", "!", "~")):
+                spec_str = f"=={spec_str}"
+
+            existing_spec = normalized.get(pkg_name)
+            if existing_spec is not None:
+                if not existing_spec and spec_str:
+                    normalized[pkg_name] = spec_str
+                elif existing_spec and spec_str and existing_spec != spec_str:
+                    try:
+                        combined_spec = SpecifierSet(",".join(filter(None, [existing_spec, spec_str])))
+                    except InvalidSpecifier:
+                        logger.warning(
+                            f"Conflicting requirements for '{pkg_name}' detected: '{existing_spec}' vs '{spec_str}'. Keeping '{existing_spec}'."
+                        )
+                    else:
+                        normalized[pkg_name] = str(combined_spec)
+                continue
+
+            normalized[pkg_name] = spec_str
+
+    _accumulate(reqs)
+
+    return normalized
+
+
 def combine_inputs(*named_input_lists: list[tuple[str, list[InputParam]]]) -> list[InputParam]:
     """
     Combines multiple lists of InputParam objects from different blocks. For duplicate inputs, updates only if current

tests/models/testing_utils/compile.py

@@ -81,7 +81,7 @@ class TorchCompileTesterMixin:
             _ = model(**inputs_dict)
 
     @torch.no_grad()
-    def test_torch_compile_repeated_blocks(self, recompile_limit=1):
+    def test_torch_compile_repeated_blocks(self):
         if self.model_class._repeated_blocks is None:
             pytest.skip("Skipping test as the model class doesn't have `_repeated_blocks` set.")
 
@@ -92,6 +92,7 @@ class TorchCompileTesterMixin:
         model.eval()
         model.compile_repeated_blocks(fullgraph=True)
 
+        recompile_limit = 1
         if self.model_class.__name__ == "UNet2DConditionModel":
             recompile_limit = 2
 

tests/models/transformers/test_models_transformer_wan_vace.py

@@ -147,7 +147,22 @@ class TestWanVACETransformer3DCompile(WanVACETransformer3DTesterConfig, TorchCom
     def test_torch_compile_repeated_blocks(self):
         # WanVACE has two block types (WanTransformerBlock and WanVACETransformerBlock),
         # so we need recompile_limit=2 instead of the default 1.
-        super().test_torch_compile_repeated_blocks(recompile_limit=2)
+        import torch._dynamo
+        import torch._inductor.utils
+
+        init_dict = self.get_init_dict()
+        inputs_dict = self.get_dummy_inputs()
+
+        model = self.model_class(**init_dict).to(torch_device)
+        model.eval()
+        model.compile_repeated_blocks(fullgraph=True)
+
+        with (
+            torch._inductor.utils.fresh_inductor_cache(),
+            torch._dynamo.config.patch(recompile_limit=2),
+        ):
+            _ = model(**inputs_dict)
+            _ = model(**inputs_dict)
 
 
 class TestWanVACETransformer3DBitsAndBytes(WanVACETransformer3DTesterConfig, BitsAndBytesTesterMixin):

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -1,4 +1,6 @@
 import gc
+import json
+import os
 import tempfile
 from typing import Callable
 
@@ -8,6 +10,7 @@ import torch
 import diffusers
 from diffusers import AutoModel, ComponentsManager, ModularPipeline, ModularPipelineBlocks
 from diffusers.guiders import ClassifierFreeGuidance
+from diffusers.modular_pipelines import SequentialPipelineBlocks
 from diffusers.modular_pipelines.modular_pipeline_utils import (
     ComponentSpec,
     ConfigSpec,
@@ -17,7 +20,13 @@ from diffusers.modular_pipelines.modular_pipeline_utils import (
 )
 from diffusers.utils import logging
 
-from ..testing_utils import backend_empty_cache, numpy_cosine_similarity_distance, require_accelerator, torch_device
+from ..testing_utils import (
+    CaptureLogger,
+    backend_empty_cache,
+    numpy_cosine_similarity_distance,
+    require_accelerator,
+    torch_device,
+)
 
 
 class ModularPipelineTesterMixin:
@@ -400,6 +409,56 @@ class ModularGuiderTesterMixin:
         assert max_diff > expected_max_diff, "Output with CFG must be different from normal inference"
 
 
+class TestCustomBlockRequirements:
+    def get_dummy_block_pipe(self):
+        class DummyBlockOne:
+            # keep two arbitrary deps so that we can test warnings.
+            _requirements = {"xyz": ">=0.8.0", "abc": ">=10.0.0"}
+
+        class DummyBlockTwo:
+            # keep two dependencies that will be available during testing.
+            _requirements = {"transformers": ">=4.44.0", "diffusers": ">=0.2.0"}
+
+        pipe = SequentialPipelineBlocks.from_blocks_dict(
+            {"dummy_block_one": DummyBlockOne, "dummy_block_two": DummyBlockTwo}
+        )
+        return pipe
+
+    def test_custom_requirements_save_load(self):
+        pipe = self.get_dummy_block_pipe()
+        with tempfile.TemporaryDirectory() as tmpdir:
+            pipe.save_pretrained(tmpdir)
+            config_path = os.path.join(tmpdir, "modular_config.json")
+            with open(config_path, "r") as f:
+                config = json.load(f)
+
+        assert "requirements" in config
+        requirements = config["requirements"]
+
+        expected_requirements = {
+            "xyz": ">=0.8.0",
+            "abc": ">=10.0.0",
+            "transformers": ">=4.44.0",
+            "diffusers": ">=0.2.0",
+        }
+        assert expected_requirements == requirements
+
+    def test_warnings(self):
+        pipe = self.get_dummy_block_pipe()
+        with tempfile.TemporaryDirectory() as tmpdir:
+            logger = logging.get_logger("diffusers.modular_pipelines.modular_pipeline_utils")
+            logger.setLevel(30)
+
+            with CaptureLogger(logger) as cap_logger:
+                pipe.save_pretrained(tmpdir)
+
+            template = "{req} was specified in the requirements but wasn't found in the current environment"
+            msg_xyz = template.format(req="xyz")
+            msg_abc = template.format(req="abc")
+            assert msg_xyz in str(cap_logger.out)
+            assert msg_abc in str(cap_logger.out)
+
+
 class TestModularModelCardContent:
     def create_mock_block(self, name="TestBlock", description="Test block description"):
         class MockBlock: