From bea02ccba39e90153e0502eb47a730bebd6d6242 Mon Sep 17 00:00:00 2001
From: DN6 <dhruv.nair@gmail.com>
Date: Thu, 18 Sep 2025 23:31:07 +0530
Subject: [PATCH 01/10] update

---
 src/diffusers/loaders/single_file_utils.py    |  9 ++-
 .../modular_pipelines/modular_pipeline.py     | 60 ++++++++++---------
 .../modular_pipeline_utils.py                 | 43 ++++++++-----
 3 files changed, 67 insertions(+), 45 deletions(-)

diff --git a/src/diffusers/loaders/single_file_utils.py b/src/diffusers/loaders/single_file_utils.py
index ef6c41e3ce97..2b67f91dc205 100644
--- a/src/diffusers/loaders/single_file_utils.py
+++ b/src/diffusers/loaders/single_file_utils.py
@@ -387,6 +387,14 @@ def is_valid_url(url):
     return False
 
 
+def _validate_single_file_path(pretrained_model_name_or_path):
+    if os.path.isfile(pretrained_model_name_or_path):
+        return True
+
+    repo_id, weight_name = _extract_repo_id_and_weights_name(pretrained_model_name_or_path)
+    return bool(repo_id and weight_name)
+
+
 def _extract_repo_id_and_weights_name(pretrained_model_name_or_path):
     if not is_valid_url(pretrained_model_name_or_path):
         raise ValueError("Invalid `pretrained_model_name_or_path` provided. Please set it to a valid URL.")
@@ -398,7 +406,6 @@ def _extract_repo_id_and_weights_name(pretrained_model_name_or_path):
         pretrained_model_name_or_path = pretrained_model_name_or_path.replace(prefix, "")
     match = re.match(pattern, pretrained_model_name_or_path)
     if not match:
-        logger.warning("Unable to identify the repo_id and weights_name from the provided URL.")
         return repo_id, weights_name
 
     repo_id = f"{match.group(1)}/{match.group(2)}"
diff --git a/src/diffusers/modular_pipelines/modular_pipeline.py b/src/diffusers/modular_pipelines/modular_pipeline.py
index 78226a49b122..6e858587947c 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline.py
@@ -22,7 +22,7 @@
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
-from huggingface_hub import create_repo
+from huggingface_hub import create_pretrained_model_name_or_path
 from huggingface_hub.utils import validate_hf_hub_args
 from tqdm.auto import tqdm
 from typing_extensions import Self
@@ -325,7 +325,7 @@ def from_pretrained(
         )
         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."
+                "Selected model pretrained_model_name_or_pathsitory does not happear to have any custom code or does not have a valid `config.json` file."
             )
 
         class_ref = config["auto_map"][cls.__name__]
@@ -366,7 +366,7 @@ def init_pipeline(
         collection: Optional[str] = None,
     ) -> "ModularPipeline":
         """
-        create a ModularPipeline, optionally accept modular_repo to load from hub.
+        create a ModularPipeline, optionally accept modular_pretrained_model_name_or_path to load from hub.
         """
         pipeline_class_name = MODULAR_PIPELINE_MAPPING.get(self.model_name, ModularPipeline.__name__)
         diffusers_module = importlib.import_module("diffusers")
@@ -1481,7 +1481,7 @@ def __init__(
             pretrained_model_name_or_path: Path to a pretrained pipeline configuration. Can be None if the pipeline
                     does not require any additional loading config. If provided, will first try to load component specs
                     (only for from_pretrained components) and config values from `modular_model_index.json`, then
-                    fallback to `model_index.json` for compatibility with standard non-modular repositories.
+                    fallback to `model_index.json` for compatibility with standard non-modular pretrained_model_name_or_pathsitories.
             components_manager:
                 Optional ComponentsManager for managing multiple component cross different pipelines and apply
                 offloading strategies.
@@ -1494,7 +1494,7 @@ def __init__(
             pipeline = ModularPipeline(blocks=my_custom_blocks)
 
             # Initialize from pretrained configuration
-            pipeline = ModularPipeline(blocks=my_blocks, pretrained_model_name_or_path="my-repo/modular-pipeline")
+            pipeline = ModularPipeline(blocks=my_blocks, pretrained_model_name_or_path="my-pretrained_model_name_or_path/modular-pipeline")
 
             # Initialize with components manager
             pipeline = ModularPipeline(
@@ -1528,7 +1528,7 @@ def __init__(
         self._component_specs = {spec.name: deepcopy(spec) for spec in self.blocks.expected_components}
         self._config_specs = {spec.name: deepcopy(spec) for spec in self.blocks.expected_configs}
 
-        # update component_specs and config_specs from modular_repo
+        # update component_specs and config_specs from modular_pretrained_model_name_or_path
         if pretrained_model_name_or_path is not None:
             cache_dir = kwargs.pop("cache_dir", None)
             force_download = kwargs.pop("force_download", False)
@@ -1573,7 +1573,7 @@ def __init__(
 
                     config_dict = DiffusionPipeline.load_config(pretrained_model_name_or_path, **load_config_kwargs)
                 except EnvironmentError as e:
-                    logger.debug(f" model_index.json not found in the repo: {e}")
+                    logger.debug(f" model_index.json not found in the pretrained_model_name_or_path: {e}")
                     config_dict = None
 
                 # update component_specs and config_specs based on model_index.json
@@ -1582,7 +1582,7 @@ def __init__(
                         if name in self._component_specs and isinstance(value, (tuple, list)) and len(value) == 2:
                             library, class_name = value
                             component_spec_dict = {
-                                "repo": pretrained_model_name_or_path,
+                                "pretrained_model_name_or_path": pretrained_model_name_or_path,
                                 "subfolder": name,
                                 "type_hint": (library, class_name),
                             }
@@ -1633,13 +1633,13 @@ def from_pretrained(
         **kwargs,
     ):
         """
-        Load a ModularPipeline from a huggingface hub repo.
+        Load a ModularPipeline from a huggingface hub pretrained_model_name_or_path.
 
         Args:
             pretrained_model_name_or_path (`str` or `os.PathLike`, optional):
                 Path to a pretrained pipeline configuration. It will first try to load config from
                 `modular_model_index.json`, then fallback to `model_index.json` for compatibility with standard
-                non-modular repositories. If the repo does not contain any pipeline config, it will be set to None
+                non-modular pretrained_model_name_or_pathsitories. If the pretrained_model_name_or_path does not contain any pipeline config, it will be set to None
                 during initialization.
             trust_remote_code (`bool`, optional):
                 Whether to trust remote code when loading the pipeline, need to be set to True if you want to create
@@ -1679,7 +1679,7 @@ def from_pretrained(
             # try to load modular_model_index.json
             config_dict = cls.load_config(pretrained_model_name_or_path, **load_config_kwargs)
         except EnvironmentError as e:
-            logger.debug(f" modular_model_index.json not found in the repo: {e}")
+            logger.debug(f" modular_model_index.json not found in the pretrained_model_name_or_path: {e}")
             config_dict = None
 
         if config_dict is not None:
@@ -1692,7 +1692,7 @@ def from_pretrained(
 
                 config_dict = DiffusionPipeline.load_config(pretrained_model_name_or_path, **load_config_kwargs)
             except EnvironmentError as e:
-                logger.debug(f" model_index.json not found in the repo: {e}")
+                logger.debug(f" model_index.json not found in the pretrained_model_name_or_path: {e}")
 
             if config_dict is not None:
                 logger.debug(" try to determine the modular pipeline class from model_index.json")
@@ -1731,11 +1731,15 @@ def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub:
             private = kwargs.pop("private", None)
             create_pr = kwargs.pop("create_pr", False)
             token = kwargs.pop("token", None)
-            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
-            repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
+            pretrained_model_name_or_path_id = kwargs.pop(
+                "pretrained_model_name_or_path_id", save_directory.split(os.path.sep)[-1]
+            )
+            pretrained_model_name_or_path_id = create_pretrained_model_name_or_path(
+                pretrained_model_name_or_path_id, exist_ok=True, private=private, token=token
+            ).pretrained_model_name_or_path_id
 
             # Create a new empty model card and eventually tag it
-            model_card = load_or_create_model_card(repo_id, token=token, is_pipeline=True)
+            model_card = load_or_create_model_card(pretrained_model_name_or_path_id, token=token, is_pipeline=True)
             model_card = populate_model_card(model_card)
             model_card.save(os.path.join(save_directory, "README.md"))
 
@@ -1745,7 +1749,7 @@ def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub:
         if push_to_hub:
             self._upload_folder(
                 save_directory,
-                repo_id,
+                pretrained_model_name_or_path_id,
                 token=token,
                 commit_message=commit_message,
                 create_pr=create_pr,
@@ -1809,7 +1813,7 @@ def register_components(self, **kwargs):
                 library, class_name = None, None
 
             # extract the loading spec from the updated component spec that'll be used as part of modular_model_index.json config
-            # e.g. {"repo": "stabilityai/stable-diffusion-2-1",
+            # e.g. {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-2-1",
             #       "type_hint": ("diffusers", "UNet2DConditionModel"),
             #       "subfolder": "unet",
             #       "variant": None,
@@ -2113,7 +2117,7 @@ def load_components(self, names: Optional[Union[List[str], str]] = None, **kwarg
             **kwargs: additional kwargs to be passed to `from_pretrained()`.Can be:
              - a single value to be applied to all components to be loaded, e.g. torch_dtype=torch.bfloat16
              - a dict, e.g. torch_dtype={"unet": torch.bfloat16, "default": torch.float32}
-             - if potentially override ComponentSpec if passed a different loading field in kwargs, e.g. `repo`,
+             - if potentially override ComponentSpec if passed a different loading field in kwargs, e.g. `pretrained_model_name_or_path`,
                `variant`, `revision`, etc.
         """
 
@@ -2377,10 +2381,10 @@ def _component_spec_to_dict(component_spec: ComponentSpec) -> Any:
           - "type_hint": Tuple[str, str]
               Library name and class name of the component. (e.g. ("diffusers", "UNet2DConditionModel"))
           - All loading fields defined by `component_spec.loading_fields()`, typically:
-              - "repo": Optional[str]
-                  The model repository (e.g., "stabilityai/stable-diffusion-xl").
+              - "pretrained_model_name_or_path": Optional[str]
+                  The model pretrained_model_name_or_pathsitory (e.g., "stabilityai/stable-diffusion-xl").
               - "subfolder": Optional[str]
-                  A subfolder within the repo where this component lives.
+                  A subfolder within the pretrained_model_name_or_path where this component lives.
               - "variant": Optional[str]
                   An optional variant identifier for the model.
               - "revision": Optional[str]
@@ -2397,11 +2401,11 @@ def _component_spec_to_dict(component_spec: ComponentSpec) -> Any:
         Example:
             >>> from diffusers.pipelines.modular_pipeline_utils import ComponentSpec >>> from diffusers import
             UNet2DConditionModel >>> spec = ComponentSpec(
-                ... name="unet", ... type_hint=UNet2DConditionModel, ... config=None, ... repo="path/to/repo", ...
+                ... name="unet", ... type_hint=UNet2DConditionModel, ... config=None, ... pretrained_model_name_or_path="path/to/pretrained_model_name_or_path", ...
                 subfolder="subfolder", ... variant=None, ... revision=None, ...
                 default_creation_method="from_pretrained",
             ... ) >>> ModularPipeline._component_spec_to_dict(spec) {
-                "type_hint": ("diffusers", "UNet2DConditionModel"), "repo": "path/to/repo", "subfolder": "subfolder",
+                "type_hint": ("diffusers", "UNet2DConditionModel"), "pretrained_model_name_or_path": "path/to/pretrained_model_name_or_path", "subfolder": "subfolder",
                 "variant": None, "revision": None,
             }
         """
@@ -2431,10 +2435,10 @@ def _dict_to_component_spec(
           - "type_hint": Tuple[str, str]
               Library name and class name of the component. (e.g. ("diffusers", "UNet2DConditionModel"))
           - All loading fields defined by `component_spec.loading_fields()`, typically:
-              - "repo": Optional[str]
-                  The model repository (e.g., "stabilityai/stable-diffusion-xl").
+              - "pretrained_model_name_or_path": Optional[str]
+                  The model pretrained_model_name_or_pathsitory (e.g., "stabilityai/stable-diffusion-xl").
               - "subfolder": Optional[str]
-                  A subfolder within the repo where this component lives.
+                  A subfolder within the pretrained_model_name_or_path where this component lives.
               - "variant": Optional[str]
                   An optional variant identifier for the model.
               - "revision": Optional[str]
@@ -2451,10 +2455,10 @@ def _dict_to_component_spec(
             ComponentSpec: A reconstructed ComponentSpec object.
 
         Example:
-            >>> spec_dict = { ... "type_hint": ("diffusers", "UNet2DConditionModel"), ... "repo":
+            >>> spec_dict = { ... "type_hint": ("diffusers", "UNet2DConditionModel"), ... "pretrained_model_name_or_path":
             "stabilityai/stable-diffusion-xl", ... "subfolder": "unet", ... "variant": None, ... "revision": None, ...
             } >>> ModularPipeline._dict_to_component_spec("unet", spec_dict) ComponentSpec(
-                name="unet", type_hint=UNet2DConditionModel, config=None, repo="stabilityai/stable-diffusion-xl",
+                name="unet", type_hint=UNet2DConditionModel, config=None, pretrained_model_name_or_path="stabilityai/stable-diffusion-xl",
                 subfolder="unet", variant=None, revision=None, default_creation_method="from_pretrained"
             )
         """
diff --git a/src/diffusers/modular_pipelines/modular_pipeline_utils.py b/src/diffusers/modular_pipelines/modular_pipeline_utils.py
index b15126868634..7d969c66b7da 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline_utils.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline_utils.py
@@ -21,6 +21,7 @@
 import torch
 
 from ..configuration_utils import ConfigMixin, FrozenDict
+from ..loaders.single_file_utils import _validate_single_file_path
 from ..utils import is_torch_available, logging
 
 
@@ -80,10 +81,10 @@ class ComponentSpec:
         type_hint: Type of the component (e.g. UNet2DConditionModel)
         description: Optional description of the component
         config: Optional config dict for __init__ creation
-        repo: Optional repo path for from_pretrained creation
-        subfolder: Optional subfolder in repo
-        variant: Optional variant in repo
-        revision: Optional revision in repo
+        pretrained_model_name_or_path: Optional pretrained_model_name_or_path path for from_pretrained creation
+        subfolder: Optional subfolder in pretrained_model_name_or_path
+        variant: Optional variant in pretrained_model_name_or_path
+        revision: Optional revision in pretrained_model_name_or_path
         default_creation_method: Preferred creation method - "from_config" or "from_pretrained"
     """
 
@@ -92,7 +93,7 @@ class ComponentSpec:
     description: Optional[str] = None
     config: Optional[FrozenDict] = None
     # YiYi Notes: should we change it to pretrained_model_name_or_path for consistency? a bit long for a field name
-    repo: Optional[Union[str, List[str]]] = field(default=None, metadata={"loading": True})
+    pretrained_model_name_or_path: Optional[Union[str, List[str]]] = field(default=None, metadata={"loading": True})
     subfolder: Optional[str] = field(default="", metadata={"loading": True})
     variant: Optional[str] = field(default=None, metadata={"loading": True})
     revision: Optional[str] = field(default=None, metadata={"loading": True})
@@ -182,7 +183,7 @@ def loading_fields(cls) -> List[str]:
     @property
     def load_id(self) -> str:
         """
-        Unique identifier for this spec's pretrained load, composed of repo|subfolder|variant|revision (no empty
+        Unique identifier for this spec's pretrained load, composed of pretrained_model_name_or_path|subfolder|variant|revision (no empty
         segments).
         """
         if self.default_creation_method == "from_config":
@@ -197,12 +198,12 @@ def decode_load_id(cls, load_id: str) -> Dict[str, Optional[str]]:
         Decode a load_id string back into a dictionary of loading fields and values.
 
         Args:
-            load_id: The load_id string to decode, format: "repo|subfolder|variant|revision"
+            load_id: The load_id string to decode, format: "pretrained_model_name_or_path|subfolder|variant|revision"
                      where None values are represented as "null"
 
         Returns:
             Dict mapping loading field names to their values. e.g. {
-                "repo": "path/to/repo", "subfolder": "subfolder", "variant": "variant", "revision": "revision"
+                "pretrained_model_name_or_path": "path/to/pretrained_model_name_or_path", "subfolder": "subfolder", "variant": "variant", "revision": "revision"
             } If a segment value is "null", it's replaced with None. Returns None if load_id is "null" (indicating
             component not created with `load` method).
         """
@@ -260,33 +261,43 @@ def create(self, config: Optional[Union[FrozenDict, Dict[str, Any]]] = None, **k
     def load(self, **kwargs) -> Any:
         """Load component using from_pretrained."""
 
-        # select loading fields from kwargs passed from user: e.g. repo, subfolder, variant, revision, note the list could change
+        # select loading fields from kwargs passed from user: e.g. pretrained_model_name_or_path, subfolder, variant, revision, note the list could change
         passed_loading_kwargs = {key: kwargs.pop(key) for key in self.loading_fields() if key in kwargs}
         # merge loading field value in the spec with user passed values to create load_kwargs
         load_kwargs = {key: passed_loading_kwargs.get(key, getattr(self, key)) for key in self.loading_fields()}
-        # repo is a required argument for from_pretrained, a.k.a. pretrained_model_name_or_path
-        repo = load_kwargs.pop("repo", None)
-        if repo is None:
+        # pretrained_model_name_or_path is a required argument for from_pretrained, a.k.a. pretrained_model_name_or_path
+        pretrained_model_name_or_path = load_kwargs.pop("pretrained_model_name_or_path", None)
+        if pretrained_model_name_or_path is None:
             raise ValueError(
-                "`repo` info is required when using `load` method (you can directly set it in `repo` field of the ComponentSpec or pass it as an argument)"
+                "`pretrained_model_name_or_path` info is required when using `load` method (you can directly set it in `pretrained_model_name_or_path` field of the ComponentSpec or pass it as an argument)"
             )
+        is_single_file = _validate_single_file_path(pretrained_model_name_or_path)
+        if is_single_file and self.type_hint is None:
+            raise ValueError("type_hint is required when loading a single file model")
 
         if self.type_hint is None:
             try:
                 from diffusers import AutoModel
 
-                component = AutoModel.from_pretrained(repo, **load_kwargs, **kwargs)
+                component = AutoModel.from_pretrained(pretrained_model_name_or_path, **load_kwargs, **kwargs)
             except Exception as e:
                 raise ValueError(f"Unable to load {self.name} without `type_hint`: {e}")
             # update type_hint if AutoModel load successfully
             self.type_hint = component.__class__
         else:
+            # determine load method
+            load_method = (
+                getattr(self.type_hint, "from_single_file")
+                if is_single_file
+                else getattr(self.type_hint, "from_pretrained")
+            )
+
             try:
-                component = self.type_hint.from_pretrained(repo, **load_kwargs, **kwargs)
+                component = load_method(pretrained_model_name_or_path, **load_kwargs, **kwargs)
             except Exception as e:
                 raise ValueError(f"Unable to load {self.name} using load method: {e}")
 
-        self.repo = repo
+        self.pretrained_model_name_or_path = pretrained_model_name_or_path
         for k, v in load_kwargs.items():
             setattr(self, k, v)
         component._diffusers_load_id = self.load_id

From af48d815d89b1d7b70fddb544d2e447bd621fcf0 Mon Sep 17 00:00:00 2001
From: DN6 <dhruv.nair@gmail.com>
Date: Wed, 24 Sep 2025 16:31:07 +0530
Subject: [PATCH 02/10] update

---
 .../modular_pipelines/modular_pipeline.py     | 32 ++++++++-----------
 .../modular_pipeline_utils.py                 |  2 +-
 2 files changed, 15 insertions(+), 19 deletions(-)

diff --git a/src/diffusers/modular_pipelines/modular_pipeline.py b/src/diffusers/modular_pipelines/modular_pipeline.py
index 6e858587947c..f1ebcd1014c2 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline.py
@@ -22,7 +22,7 @@
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
-from huggingface_hub import create_pretrained_model_name_or_path
+from huggingface_hub import create_repo
 from huggingface_hub.utils import validate_hf_hub_args
 from tqdm.auto import tqdm
 from typing_extensions import Self
@@ -325,7 +325,7 @@ def from_pretrained(
         )
         if not (has_remote_code and trust_remote_code):
             raise ValueError(
-                "Selected model pretrained_model_name_or_pathsitory does not happear to have any custom code or does not have a valid `config.json` file."
+                "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__]
@@ -1481,7 +1481,7 @@ def __init__(
             pretrained_model_name_or_path: Path to a pretrained pipeline configuration. Can be None if the pipeline
                     does not require any additional loading config. If provided, will first try to load component specs
                     (only for from_pretrained components) and config values from `modular_model_index.json`, then
-                    fallback to `model_index.json` for compatibility with standard non-modular pretrained_model_name_or_pathsitories.
+                    fallback to `model_index.json` for compatibility with standard non-modular repositories.
             components_manager:
                 Optional ComponentsManager for managing multiple component cross different pipelines and apply
                 offloading strategies.
@@ -1494,7 +1494,7 @@ def __init__(
             pipeline = ModularPipeline(blocks=my_custom_blocks)
 
             # Initialize from pretrained configuration
-            pipeline = ModularPipeline(blocks=my_blocks, pretrained_model_name_or_path="my-pretrained_model_name_or_path/modular-pipeline")
+            pipeline = ModularPipeline(blocks=my_blocks, pretrained_model_name_or_path="my-repo/modular-pipeline")
 
             # Initialize with components manager
             pipeline = ModularPipeline(
@@ -1528,7 +1528,7 @@ def __init__(
         self._component_specs = {spec.name: deepcopy(spec) for spec in self.blocks.expected_components}
         self._config_specs = {spec.name: deepcopy(spec) for spec in self.blocks.expected_configs}
 
-        # update component_specs and config_specs from modular_pretrained_model_name_or_path
+        # update component_specs and config_specs from modular_repo
         if pretrained_model_name_or_path is not None:
             cache_dir = kwargs.pop("cache_dir", None)
             force_download = kwargs.pop("force_download", False)
@@ -1573,7 +1573,7 @@ def __init__(
 
                     config_dict = DiffusionPipeline.load_config(pretrained_model_name_or_path, **load_config_kwargs)
                 except EnvironmentError as e:
-                    logger.debug(f" model_index.json not found in the pretrained_model_name_or_path: {e}")
+                    logger.debug(f" model_index.json not found in the repo: {e}")
                     config_dict = None
 
                 # update component_specs and config_specs based on model_index.json
@@ -1633,7 +1633,7 @@ def from_pretrained(
         **kwargs,
     ):
         """
-        Load a ModularPipeline from a huggingface hub pretrained_model_name_or_path.
+        Load a ModularPipeline from a huggingface hub repo.
 
         Args:
             pretrained_model_name_or_path (`str` or `os.PathLike`, optional):
@@ -1679,7 +1679,7 @@ def from_pretrained(
             # try to load modular_model_index.json
             config_dict = cls.load_config(pretrained_model_name_or_path, **load_config_kwargs)
         except EnvironmentError as e:
-            logger.debug(f" modular_model_index.json not found in the pretrained_model_name_or_path: {e}")
+            logger.debug(f" modular_model_index.json not found in the repo: {e}")
             config_dict = None
 
         if config_dict is not None:
@@ -1692,7 +1692,7 @@ def from_pretrained(
 
                 config_dict = DiffusionPipeline.load_config(pretrained_model_name_or_path, **load_config_kwargs)
             except EnvironmentError as e:
-                logger.debug(f" model_index.json not found in the pretrained_model_name_or_path: {e}")
+                logger.debug(f" model_index.json not found in the repo: {e}")
 
             if config_dict is not None:
                 logger.debug(" try to determine the modular pipeline class from model_index.json")
@@ -1731,15 +1731,11 @@ def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub:
             private = kwargs.pop("private", None)
             create_pr = kwargs.pop("create_pr", False)
             token = kwargs.pop("token", None)
-            pretrained_model_name_or_path_id = kwargs.pop(
-                "pretrained_model_name_or_path_id", save_directory.split(os.path.sep)[-1]
-            )
-            pretrained_model_name_or_path_id = create_pretrained_model_name_or_path(
-                pretrained_model_name_or_path_id, exist_ok=True, private=private, token=token
-            ).pretrained_model_name_or_path_id
+            repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
+            repo_id = create_repo(repo_id, exist_ok=True, private=private, token=token).repo_id
 
             # Create a new empty model card and eventually tag it
-            model_card = load_or_create_model_card(pretrained_model_name_or_path_id, token=token, is_pipeline=True)
+            model_card = load_or_create_model_card(repo_id, token=token, is_pipeline=True)
             model_card = populate_model_card(model_card)
             model_card.save(os.path.join(save_directory, "README.md"))
 
@@ -1749,7 +1745,7 @@ def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub:
         if push_to_hub:
             self._upload_folder(
                 save_directory,
-                pretrained_model_name_or_path_id,
+                repo_id,
                 token=token,
                 commit_message=commit_message,
                 create_pr=create_pr,
@@ -2405,7 +2401,7 @@ def _component_spec_to_dict(component_spec: ComponentSpec) -> Any:
                 subfolder="subfolder", ... variant=None, ... revision=None, ...
                 default_creation_method="from_pretrained",
             ... ) >>> ModularPipeline._component_spec_to_dict(spec) {
-                "type_hint": ("diffusers", "UNet2DConditionModel"), "pretrained_model_name_or_path": "path/to/pretrained_model_name_or_path", "subfolder": "subfolder",
+                "type_hint": ("diffusers", "UNet2DConditionModel"), "pretrained_model_name_or_path": "path/to/repo", "subfolder": "subfolder",
                 "variant": None, "revision": None,
             }
         """
diff --git a/src/diffusers/modular_pipelines/modular_pipeline_utils.py b/src/diffusers/modular_pipelines/modular_pipeline_utils.py
index 7d969c66b7da..aa22d963c683 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline_utils.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline_utils.py
@@ -203,7 +203,7 @@ def decode_load_id(cls, load_id: str) -> Dict[str, Optional[str]]:
 
         Returns:
             Dict mapping loading field names to their values. e.g. {
-                "pretrained_model_name_or_path": "path/to/pretrained_model_name_or_path", "subfolder": "subfolder", "variant": "variant", "revision": "revision"
+                "pretrained_model_name_or_path": "path/to/repo", "subfolder": "subfolder", "variant": "variant", "revision": "revision"
             } If a segment value is "null", it's replaced with None. Returns None if load_id is "null" (indicating
             component not created with `load` method).
         """

From 0a7bde92008f7c5e1dee9bd1d9ff552495e8b164 Mon Sep 17 00:00:00 2001
From: DN6 <dhruv.nair@gmail.com>
Date: Wed, 24 Sep 2025 16:33:09 +0530
Subject: [PATCH 03/10] update

---
 src/diffusers/modular_pipelines/modular_pipeline.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/modular_pipelines/modular_pipeline.py b/src/diffusers/modular_pipelines/modular_pipeline.py
index f1ebcd1014c2..0b316676afd9 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline.py
@@ -366,7 +366,7 @@ def init_pipeline(
         collection: Optional[str] = None,
     ) -> "ModularPipeline":
         """
-        create a ModularPipeline, optionally accept modular_pretrained_model_name_or_path to load from hub.
+        create a ModularPipeline, optionally accept pretrained_model_name_or_path to load from hub.
         """
         pipeline_class_name = MODULAR_PIPELINE_MAPPING.get(self.model_name, ModularPipeline.__name__)
         diffusers_module = importlib.import_module("diffusers")
@@ -1639,7 +1639,7 @@ def from_pretrained(
             pretrained_model_name_or_path (`str` or `os.PathLike`, optional):
                 Path to a pretrained pipeline configuration. It will first try to load config from
                 `modular_model_index.json`, then fallback to `model_index.json` for compatibility with standard
-                non-modular pretrained_model_name_or_pathsitories. If the pretrained_model_name_or_path does not contain any pipeline config, it will be set to None
+                non-modular repositories. If the pretrained_model_name_or_path does not contain any pipeline config, it will be set to None
                 during initialization.
             trust_remote_code (`bool`, optional):
                 Whether to trust remote code when loading the pipeline, need to be set to True if you want to create

From c1c0e9a481e2e5f09d9792fb606e10996463f280 Mon Sep 17 00:00:00 2001
From: DN6 <dhruv.nair@gmail.com>
Date: Mon, 29 Sep 2025 12:35:55 +0530
Subject: [PATCH 04/10] update

---
 src/diffusers/modular_pipelines/modular_pipeline.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/diffusers/modular_pipelines/modular_pipeline.py b/src/diffusers/modular_pipelines/modular_pipeline.py
index 0b316676afd9..27b8ab0e42d1 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline.py
@@ -2397,7 +2397,7 @@ def _component_spec_to_dict(component_spec: ComponentSpec) -> Any:
         Example:
             >>> from diffusers.pipelines.modular_pipeline_utils import ComponentSpec >>> from diffusers import
             UNet2DConditionModel >>> spec = ComponentSpec(
-                ... name="unet", ... type_hint=UNet2DConditionModel, ... config=None, ... pretrained_model_name_or_path="path/to/pretrained_model_name_or_path", ...
+                ... name="unet", ... type_hint=UNet2DConditionModel, ... config=None, ... pretrained_model_name_or_path="path/to/repo", ...
                 subfolder="subfolder", ... variant=None, ... revision=None, ...
                 default_creation_method="from_pretrained",
             ... ) >>> ModularPipeline._component_spec_to_dict(spec) {
@@ -2432,7 +2432,7 @@ def _dict_to_component_spec(
               Library name and class name of the component. (e.g. ("diffusers", "UNet2DConditionModel"))
           - All loading fields defined by `component_spec.loading_fields()`, typically:
               - "pretrained_model_name_or_path": Optional[str]
-                  The model pretrained_model_name_or_pathsitory (e.g., "stabilityai/stable-diffusion-xl").
+                  The model repository (e.g., "stabilityai/stable-diffusion-xl").
               - "subfolder": Optional[str]
                   A subfolder within the pretrained_model_name_or_path where this component lives.
               - "variant": Optional[str]

From a707e314ad76f01df93761286d3a237c0778b983 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Mon, 20 Oct 2025 09:44:50 +0000
Subject: [PATCH 05/10] Apply style fixes

---
 .../modular_pipelines/modular_pipeline.py     | 30 ++++++++++---------
 .../modular_pipeline_utils.py                 |  7 +++--
 2 files changed, 20 insertions(+), 17 deletions(-)

diff --git a/src/diffusers/modular_pipelines/modular_pipeline.py b/src/diffusers/modular_pipelines/modular_pipeline.py
index 27b8ab0e42d1..8bd37e7a78b3 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline.py
@@ -1639,8 +1639,8 @@ def from_pretrained(
             pretrained_model_name_or_path (`str` or `os.PathLike`, optional):
                 Path to a pretrained pipeline configuration. It will first try to load config from
                 `modular_model_index.json`, then fallback to `model_index.json` for compatibility with standard
-                non-modular repositories. If the pretrained_model_name_or_path does not contain any pipeline config, it will be set to None
-                during initialization.
+                non-modular repositories. If the pretrained_model_name_or_path does not contain any pipeline config, it
+                will be set to None during initialization.
             trust_remote_code (`bool`, optional):
                 Whether to trust remote code when loading the pipeline, need to be set to True if you want to create
                 pipeline blocks based on the custom code in `pretrained_model_name_or_path`
@@ -2113,8 +2113,8 @@ def load_components(self, names: Optional[Union[List[str], str]] = None, **kwarg
             **kwargs: additional kwargs to be passed to `from_pretrained()`.Can be:
              - a single value to be applied to all components to be loaded, e.g. torch_dtype=torch.bfloat16
              - a dict, e.g. torch_dtype={"unet": torch.bfloat16, "default": torch.float32}
-             - if potentially override ComponentSpec if passed a different loading field in kwargs, e.g. `pretrained_model_name_or_path`,
-               `variant`, `revision`, etc.
+             - if potentially override ComponentSpec if passed a different loading field in kwargs, e.g.
+               `pretrained_model_name_or_path`, `variant`, `revision`, etc.
         """
 
         if names is None:
@@ -2397,12 +2397,12 @@ def _component_spec_to_dict(component_spec: ComponentSpec) -> Any:
         Example:
             >>> from diffusers.pipelines.modular_pipeline_utils import ComponentSpec >>> from diffusers import
             UNet2DConditionModel >>> spec = ComponentSpec(
-                ... name="unet", ... type_hint=UNet2DConditionModel, ... config=None, ... pretrained_model_name_or_path="path/to/repo", ...
-                subfolder="subfolder", ... variant=None, ... revision=None, ...
-                default_creation_method="from_pretrained",
+                ... name="unet", ... type_hint=UNet2DConditionModel, ... config=None, ...
+                pretrained_model_name_or_path="path/to/repo", ... subfolder="subfolder", ... variant=None, ...
+                revision=None, ... default_creation_method="from_pretrained",
             ... ) >>> ModularPipeline._component_spec_to_dict(spec) {
-                "type_hint": ("diffusers", "UNet2DConditionModel"), "pretrained_model_name_or_path": "path/to/repo", "subfolder": "subfolder",
-                "variant": None, "revision": None,
+                "type_hint": ("diffusers", "UNet2DConditionModel"), "pretrained_model_name_or_path": "path/to/repo",
+                "subfolder": "subfolder", "variant": None, "revision": None,
             }
         """
         if component_spec.default_creation_method != "from_pretrained":
@@ -2451,11 +2451,13 @@ def _dict_to_component_spec(
             ComponentSpec: A reconstructed ComponentSpec object.
 
         Example:
-            >>> spec_dict = { ... "type_hint": ("diffusers", "UNet2DConditionModel"), ... "pretrained_model_name_or_path":
-            "stabilityai/stable-diffusion-xl", ... "subfolder": "unet", ... "variant": None, ... "revision": None, ...
-            } >>> ModularPipeline._dict_to_component_spec("unet", spec_dict) ComponentSpec(
-                name="unet", type_hint=UNet2DConditionModel, config=None, pretrained_model_name_or_path="stabilityai/stable-diffusion-xl",
-                subfolder="unet", variant=None, revision=None, default_creation_method="from_pretrained"
+            >>> spec_dict = { ... "type_hint": ("diffusers", "UNet2DConditionModel"), ...
+            "pretrained_model_name_or_path": "stabilityai/stable-diffusion-xl", ... "subfolder": "unet", ... "variant":
+            None, ... "revision": None, ... } >>> ModularPipeline._dict_to_component_spec("unet", spec_dict)
+            ComponentSpec(
+                name="unet", type_hint=UNet2DConditionModel, config=None,
+                pretrained_model_name_or_path="stabilityai/stable-diffusion-xl", subfolder="unet", variant=None,
+                revision=None, default_creation_method="from_pretrained"
             )
         """
         # make a shallow copy so we can pop() safely
diff --git a/src/diffusers/modular_pipelines/modular_pipeline_utils.py b/src/diffusers/modular_pipelines/modular_pipeline_utils.py
index aa22d963c683..379cabc44789 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline_utils.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline_utils.py
@@ -183,8 +183,8 @@ def loading_fields(cls) -> List[str]:
     @property
     def load_id(self) -> str:
         """
-        Unique identifier for this spec's pretrained load, composed of pretrained_model_name_or_path|subfolder|variant|revision (no empty
-        segments).
+        Unique identifier for this spec's pretrained load, composed of
+        pretrained_model_name_or_path|subfolder|variant|revision (no empty segments).
         """
         if self.default_creation_method == "from_config":
             return "null"
@@ -203,7 +203,8 @@ def decode_load_id(cls, load_id: str) -> Dict[str, Optional[str]]:
 
         Returns:
             Dict mapping loading field names to their values. e.g. {
-                "pretrained_model_name_or_path": "path/to/repo", "subfolder": "subfolder", "variant": "variant", "revision": "revision"
+                "pretrained_model_name_or_path": "path/to/repo", "subfolder": "subfolder", "variant": "variant",
+                "revision": "revision"
             } If a segment value is "null", it's replaced with None. Returns None if load_id is "null" (indicating
             component not created with `load` method).
         """

From 8733fef39dd41a2220732ed0f7b0db4f3736a153 Mon Sep 17 00:00:00 2001
From: Dhruv Nair <dhruv.nair@gmail.com>
Date: Thu, 23 Oct 2025 18:56:14 +0200
Subject: [PATCH 06/10] update

---
 docs/source/en/modular_diffusers/guiders.md   |  2 +-
 .../en/modular_diffusers/modular_pipeline.md  |  4 +--
 docs/source/zh/modular_diffusers/guiders.md   |  2 +-
 .../zh/modular_diffusers/modular_pipeline.md  |  4 +--
 src/diffusers/loaders/single_file_utils.py    |  6 ++--
 .../modular_pipelines/modular_pipeline.py     | 30 ++++++++++---------
 .../modular_pipeline_utils.py                 | 27 +++++++++++------
 ...st_modular_pipeline_stable_diffusion_xl.py |  8 +++--
 8 files changed, 48 insertions(+), 35 deletions(-)

diff --git a/docs/source/en/modular_diffusers/guiders.md b/docs/source/en/modular_diffusers/guiders.md
index fd0d27844205..6abe4fad2736 100644
--- a/docs/source/en/modular_diffusers/guiders.md
+++ b/docs/source/en/modular_diffusers/guiders.md
@@ -159,7 +159,7 @@ Change the [`~ComponentSpec.default_creation_method`] to `from_pretrained` and u
 ```py
 guider_spec = t2i_pipeline.get_component_spec("guider")
 guider_spec.default_creation_method="from_pretrained"
-guider_spec.repo="YiYiXu/modular-loader-t2i-guider"
+guider_spec.pretrained_model_name_or_path="YiYiXu/modular-loader-t2i-guider"
 guider_spec.subfolder="pag_guider"
 pag_guider = guider_spec.load()
 t2i_pipeline.update_components(guider=pag_guider)
diff --git a/docs/source/en/modular_diffusers/modular_pipeline.md b/docs/source/en/modular_diffusers/modular_pipeline.md
index 0e0a7bd75d51..34cd8f72b5b7 100644
--- a/docs/source/en/modular_diffusers/modular_pipeline.md
+++ b/docs/source/en/modular_diffusers/modular_pipeline.md
@@ -313,14 +313,14 @@ unet_spec
 ComponentSpec(
     name='unet',
     type_hint=<class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>,
-    repo='RunDiffusion/Juggernaut-XL-v9',
+    pretrained_model_name_or_path='RunDiffusion/Juggernaut-XL-v9',
     subfolder='unet',
     variant='fp16',
     default_creation_method='from_pretrained'
 )
 
 # modify to load from a different repository
-unet_spec.repo = "stabilityai/stable-diffusion-xl-base-1.0"
+unet_spec.pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
 
 # load component with modified spec
 unet = unet_spec.load(torch_dtype=torch.float16)
diff --git a/docs/source/zh/modular_diffusers/guiders.md b/docs/source/zh/modular_diffusers/guiders.md
index 1006460a2bec..50436f90c4a5 100644
--- a/docs/source/zh/modular_diffusers/guiders.md
+++ b/docs/source/zh/modular_diffusers/guiders.md
@@ -157,7 +157,7 @@ guider.push_to_hub("YiYiXu/modular-loader-t2i-guider", subfolder="pag_guider")
 ```py
 guider_spec = t2i_pipeline.get_component_spec("guider")
 guider_spec.default_creation_method="from_pretrained"
-guider_spec.repo="YiYiXu/modular-loader-t2i-guider"
+guider_spec.pretrained_model_name_or_path="YiYiXu/modular-loader-t2i-guider"
 guider_spec.subfolder="pag_guider"
 pag_guider = guider_spec.load()
 t2i_pipeline.update_components(guider=pag_guider)
diff --git a/docs/source/zh/modular_diffusers/modular_pipeline.md b/docs/source/zh/modular_diffusers/modular_pipeline.md
index daf61ecf40d9..a57fdf227506 100644
--- a/docs/source/zh/modular_diffusers/modular_pipeline.md
+++ b/docs/source/zh/modular_diffusers/modular_pipeline.md
@@ -313,14 +313,14 @@ unet_spec
 ComponentSpec(
     name='unet',
     type_hint=<class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'>,
-    repo='RunDiffusion/Juggernaut-XL-v9',
+    pretrained_model_name_or_path='RunDiffusion/Juggernaut-XL-v9',
     subfolder='unet',
     variant='fp16',
     default_creation_method='from_pretrained'
 )
 
 # 修改以从不同的仓库加载
-unet_spec.repo = "stabilityai/stable-diffusion-xl-base-1.0"
+unet_spec.pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
 
 # 使用修改后的规范加载组件
 unet = unet_spec.load(torch_dtype=torch.float16)
diff --git a/src/diffusers/loaders/single_file_utils.py b/src/diffusers/loaders/single_file_utils.py
index 2b67f91dc205..f88bb9407988 100644
--- a/src/diffusers/loaders/single_file_utils.py
+++ b/src/diffusers/loaders/single_file_utils.py
@@ -387,9 +387,9 @@ def is_valid_url(url):
     return False
 
 
-def _validate_single_file_path(pretrained_model_name_or_path):
-    if os.path.isfile(pretrained_model_name_or_path):
-        return True
+def _is_single_file_path_or_url(pretrained_model_name_or_path):
+    if not os.path.isfile(pretrained_model_name_or_path) or not is_valid_url(pretrained_model_name_or_path):
+        return False
 
     repo_id, weight_name = _extract_repo_id_and_weights_name(pretrained_model_name_or_path)
     return bool(repo_id and weight_name)
diff --git a/src/diffusers/modular_pipelines/modular_pipeline.py b/src/diffusers/modular_pipelines/modular_pipeline.py
index 0b316676afd9..8e686d740f93 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline.py
@@ -1639,8 +1639,8 @@ def from_pretrained(
             pretrained_model_name_or_path (`str` or `os.PathLike`, optional):
                 Path to a pretrained pipeline configuration. It will first try to load config from
                 `modular_model_index.json`, then fallback to `model_index.json` for compatibility with standard
-                non-modular repositories. If the pretrained_model_name_or_path does not contain any pipeline config, it will be set to None
-                during initialization.
+                non-modular repositories. If the pretrained_model_name_or_path does not contain any pipeline config, it
+                will be set to None during initialization.
             trust_remote_code (`bool`, optional):
                 Whether to trust remote code when loading the pipeline, need to be set to True if you want to create
                 pipeline blocks based on the custom code in `pretrained_model_name_or_path`
@@ -2113,8 +2113,8 @@ def load_components(self, names: Optional[Union[List[str], str]] = None, **kwarg
             **kwargs: additional kwargs to be passed to `from_pretrained()`.Can be:
              - a single value to be applied to all components to be loaded, e.g. torch_dtype=torch.bfloat16
              - a dict, e.g. torch_dtype={"unet": torch.bfloat16, "default": torch.float32}
-             - if potentially override ComponentSpec if passed a different loading field in kwargs, e.g. `pretrained_model_name_or_path`,
-               `variant`, `revision`, etc.
+             - if potentially override ComponentSpec if passed a different loading field in kwargs, e.g.
+               `pretrained_model_name_or_path`, `variant`, `revision`, etc.
         """
 
         if names is None:
@@ -2397,12 +2397,12 @@ def _component_spec_to_dict(component_spec: ComponentSpec) -> Any:
         Example:
             >>> from diffusers.pipelines.modular_pipeline_utils import ComponentSpec >>> from diffusers import
             UNet2DConditionModel >>> spec = ComponentSpec(
-                ... name="unet", ... type_hint=UNet2DConditionModel, ... config=None, ... pretrained_model_name_or_path="path/to/pretrained_model_name_or_path", ...
-                subfolder="subfolder", ... variant=None, ... revision=None, ...
-                default_creation_method="from_pretrained",
+                ... name="unet", ... type_hint=UNet2DConditionModel, ... config=None, ...
+                pretrained_model_name_or_path="path/to/pretrained_model_name_or_path", ... subfolder="subfolder", ...
+                variant=None, ... revision=None, ... default_creation_method="from_pretrained",
             ... ) >>> ModularPipeline._component_spec_to_dict(spec) {
-                "type_hint": ("diffusers", "UNet2DConditionModel"), "pretrained_model_name_or_path": "path/to/repo", "subfolder": "subfolder",
-                "variant": None, "revision": None,
+                "type_hint": ("diffusers", "UNet2DConditionModel"), "pretrained_model_name_or_path": "path/to/repo",
+                "subfolder": "subfolder", "variant": None, "revision": None,
             }
         """
         if component_spec.default_creation_method != "from_pretrained":
@@ -2451,11 +2451,13 @@ def _dict_to_component_spec(
             ComponentSpec: A reconstructed ComponentSpec object.
 
         Example:
-            >>> spec_dict = { ... "type_hint": ("diffusers", "UNet2DConditionModel"), ... "pretrained_model_name_or_path":
-            "stabilityai/stable-diffusion-xl", ... "subfolder": "unet", ... "variant": None, ... "revision": None, ...
-            } >>> ModularPipeline._dict_to_component_spec("unet", spec_dict) ComponentSpec(
-                name="unet", type_hint=UNet2DConditionModel, config=None, pretrained_model_name_or_path="stabilityai/stable-diffusion-xl",
-                subfolder="unet", variant=None, revision=None, default_creation_method="from_pretrained"
+            >>> spec_dict = { ... "type_hint": ("diffusers", "UNet2DConditionModel"), ...
+            "pretrained_model_name_or_path": "stabilityai/stable-diffusion-xl", ... "subfolder": "unet", ... "variant":
+            None, ... "revision": None, ... } >>> ModularPipeline._dict_to_component_spec("unet", spec_dict)
+            ComponentSpec(
+                name="unet", type_hint=UNet2DConditionModel, config=None,
+                pretrained_model_name_or_path="stabilityai/stable-diffusion-xl", subfolder="unet", variant=None,
+                revision=None, default_creation_method="from_pretrained"
             )
         """
         # make a shallow copy so we can pop() safely
diff --git a/src/diffusers/modular_pipelines/modular_pipeline_utils.py b/src/diffusers/modular_pipelines/modular_pipeline_utils.py
index aa22d963c683..aa421a53727b 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline_utils.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline_utils.py
@@ -21,7 +21,7 @@
 import torch
 
 from ..configuration_utils import ConfigMixin, FrozenDict
-from ..loaders.single_file_utils import _validate_single_file_path
+from ..loaders.single_file_utils import _is_single_file_path_or_url
 from ..utils import is_torch_available, logging
 
 
@@ -92,13 +92,20 @@ class ComponentSpec:
     type_hint: Optional[Type] = None
     description: Optional[str] = None
     config: Optional[FrozenDict] = None
-    # YiYi Notes: should we change it to pretrained_model_name_or_path for consistency? a bit long for a field name
     pretrained_model_name_or_path: Optional[Union[str, List[str]]] = field(default=None, metadata={"loading": True})
     subfolder: Optional[str] = field(default="", metadata={"loading": True})
     variant: Optional[str] = field(default=None, metadata={"loading": True})
     revision: Optional[str] = field(default=None, metadata={"loading": True})
     default_creation_method: Literal["from_config", "from_pretrained"] = "from_pretrained"
 
+    # Deprecated
+    repo: Optional[Union[str, List[str]]] = field(default=None, metadata={"loading": False})
+
+    def __post_init__(self):
+        repo_value = self.repo
+        if repo_value is not None and self.pretrained_model_name_or_path is None:
+            object.__setattr__(self, "pretrained_model_name_or_path", repo_value)
+
     def __hash__(self):
         """Make ComponentSpec hashable, using load_id as the hash value."""
         return hash((self.name, self.load_id, self.default_creation_method))
@@ -183,8 +190,8 @@ def loading_fields(cls) -> List[str]:
     @property
     def load_id(self) -> str:
         """
-        Unique identifier for this spec's pretrained load, composed of pretrained_model_name_or_path|subfolder|variant|revision (no empty
-        segments).
+        Unique identifier for this spec's pretrained load, composed of
+        pretrained_model_name_or_path|subfolder|variant|revision (no empty segments).
         """
         if self.default_creation_method == "from_config":
             return "null"
@@ -203,7 +210,8 @@ def decode_load_id(cls, load_id: str) -> Dict[str, Optional[str]]:
 
         Returns:
             Dict mapping loading field names to their values. e.g. {
-                "pretrained_model_name_or_path": "path/to/repo", "subfolder": "subfolder", "variant": "variant", "revision": "revision"
+                "pretrained_model_name_or_path": "path/to/repo", "subfolder": "subfolder", "variant": "variant",
+                "revision": "revision"
             } If a segment value is "null", it's replaced with None. Returns None if load_id is "null" (indicating
             component not created with `load` method).
         """
@@ -260,20 +268,21 @@ def create(self, config: Optional[Union[FrozenDict, Dict[str, Any]]] = None, **k
     # YiYi TODO: add guard for type of model, if it is supported by from_pretrained
     def load(self, **kwargs) -> Any:
         """Load component using from_pretrained."""
-
         # select loading fields from kwargs passed from user: e.g. pretrained_model_name_or_path, subfolder, variant, revision, note the list could change
         passed_loading_kwargs = {key: kwargs.pop(key) for key in self.loading_fields() if key in kwargs}
         # merge loading field value in the spec with user passed values to create load_kwargs
         load_kwargs = {key: passed_loading_kwargs.get(key, getattr(self, key)) for key in self.loading_fields()}
-        # pretrained_model_name_or_path is a required argument for from_pretrained, a.k.a. pretrained_model_name_or_path
+
         pretrained_model_name_or_path = load_kwargs.pop("pretrained_model_name_or_path", None)
         if pretrained_model_name_or_path is None:
             raise ValueError(
                 "`pretrained_model_name_or_path` info is required when using `load` method (you can directly set it in `pretrained_model_name_or_path` field of the ComponentSpec or pass it as an argument)"
             )
-        is_single_file = _validate_single_file_path(pretrained_model_name_or_path)
+        is_single_file = _is_single_file_path_or_url(pretrained_model_name_or_path)
         if is_single_file and self.type_hint is None:
-            raise ValueError("type_hint is required when loading a single file model")
+            raise ValueError(
+                f"`type_hint` is required when loading a single file model but is missing for component: {self.name}"
+            )
 
         if self.type_hint is None:
             try:
diff --git a/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py b/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py
index d05f818135ab..87730812f60a 100644
--- a/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py
+++ b/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py
@@ -51,7 +51,7 @@ class SDXLModularTests:
 
     pipeline_class = StableDiffusionXLModularPipeline
     pipeline_blocks_class = StableDiffusionXLAutoBlocks
-    repo = "hf-internal-testing/tiny-sdxl-modular"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-sdxl-modular"
     params = frozenset(
         [
             "prompt",
@@ -66,7 +66,9 @@ class SDXLModularTests:
     batch_params = frozenset(["prompt", "negative_prompt", "image", "mask_image"])
 
     def get_pipeline(self, components_manager=None, torch_dtype=torch.float32):
-        pipeline = self.pipeline_blocks_class().init_pipeline(self.repo, components_manager=components_manager)
+        pipeline = self.pipeline_blocks_class().init_pipeline(
+            self.pretrained_model_name_or_path, components_manager=components_manager
+        )
         pipeline.load_components(torch_dtype=torch_dtype)
         return pipeline
 
@@ -157,7 +159,7 @@ def test_ip_adapter(self, expected_max_diff: float = 1e-4, expected_pipe_slice=N
 
         blocks = self.pipeline_blocks_class()
         _ = blocks.sub_blocks.pop("ip_adapter")
-        pipe = blocks.init_pipeline(self.repo)
+        pipe = blocks.init_pipeline(self.pretrained_model_name_or_path)
         pipe.load_components(torch_dtype=torch.float32)
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)

From d391ace1cab3fc0c123bfec31f5ad463100c11e6 Mon Sep 17 00:00:00 2001
From: DN6 <dhruv.nair@gmail.com>
Date: Fri, 28 Nov 2025 15:52:22 +0530
Subject: [PATCH 07/10] update

---
 src/diffusers/modular_pipelines/modular_pipeline.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/diffusers/modular_pipelines/modular_pipeline.py b/src/diffusers/modular_pipelines/modular_pipeline.py
index bb76e2787533..a6336de71a52 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline.py
@@ -2405,9 +2405,9 @@ def _component_spec_to_dict(component_spec: ComponentSpec) -> Any:
                 variant=None, ... revision=None, ... default_creation_method="from_pretrained",
             ... ) >>> ModularPipeline._component_spec_to_dict(spec) {
                 "type_hint": ("diffusers", "UNet2DConditionModel"), "pretrained_model_name_or_path": "path/to/repo",
-                "subfolder": "subfolder", "variant": None, "revision": None,
-                "type_hint": ("diffusers", "UNet2DConditionModel"), "pretrained_model_name_or_path": "path/to/repo",
-                "subfolder": "subfolder", "variant": None, "revision": None,
+                "subfolder": "subfolder", "variant": None, "revision": None, "type_hint": ("diffusers",
+                "UNet2DConditionModel"), "pretrained_model_name_or_path": "path/to/repo", "subfolder": "subfolder",
+                "variant": None, "revision": None,
             }
         """
         if component_spec.default_creation_method != "from_pretrained":

From 2cd7256ecb19354c8eb27ffa12cd4b9a8864a5da Mon Sep 17 00:00:00 2001
From: DN6 <dhruv.nair@gmail.com>
Date: Fri, 28 Nov 2025 18:29:02 +0530
Subject: [PATCH 08/10] update

---
 .../test_modular_pipeline_stable_diffusion_xl.py            | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py b/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py
index b58236949c02..7b55933e4caf 100644
--- a/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py
+++ b/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py
@@ -278,7 +278,7 @@ class TestSDXLModularPipelineFast(
 
     pipeline_class = StableDiffusionXLModularPipeline
     pipeline_blocks_class = StableDiffusionXLAutoBlocks
-    repo = "hf-internal-testing/tiny-sdxl-modular"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-sdxl-modular"
     params = frozenset(
         [
             "prompt",
@@ -325,7 +325,7 @@ class TestSDXLImg2ImgModularPipelineFast(
 
     pipeline_class = StableDiffusionXLModularPipeline
     pipeline_blocks_class = StableDiffusionXLAutoBlocks
-    repo = "hf-internal-testing/tiny-sdxl-modular"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-sdxl-modular"
     params = frozenset(
         [
             "prompt",
@@ -378,7 +378,7 @@ class SDXLInpaintingModularPipelineFastTests(
 
     pipeline_class = StableDiffusionXLModularPipeline
     pipeline_blocks_class = StableDiffusionXLAutoBlocks
-    repo = "hf-internal-testing/tiny-sdxl-modular"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-sdxl-modular"
     params = frozenset(
         [
             "prompt",

From 207847552dbf790c11d291820c1b0b69499715d5 Mon Sep 17 00:00:00 2001
From: DN6 <dhruv.nair@gmail.com>
Date: Fri, 28 Nov 2025 18:52:39 +0530
Subject: [PATCH 09/10] update

---
 tests/modular_pipelines/test_modular_pipelines_common.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tests/modular_pipelines/test_modular_pipelines_common.py b/tests/modular_pipelines/test_modular_pipelines_common.py
index 327ae77d59a5..5c1c4051ab95 100644
--- a/tests/modular_pipelines/test_modular_pipelines_common.py
+++ b/tests/modular_pipelines/test_modular_pipelines_common.py
@@ -43,7 +43,7 @@ def pipeline_class(self) -> Union[Callable, ModularPipeline]:
         )
 
     @property
-    def repo(self) -> str:
+    def pretrained_model_name_or_path(self) -> str:
         raise NotImplementedError(
             "You need to set the attribute `repo` in the child test class. See existing pipeline tests for reference."
         )
@@ -103,7 +103,9 @@ def teardown_method(self):
         backend_empty_cache(torch_device)
 
     def get_pipeline(self, components_manager=None, torch_dtype=torch.float32):
-        pipeline = self.pipeline_blocks_class().init_pipeline(self.repo, components_manager=components_manager)
+        pipeline = self.pipeline_blocks_class().init_pipeline(
+            self.pretrained_model_name_or_path, components_manager=components_manager
+        )
         pipeline.load_components(torch_dtype=torch_dtype)
         pipeline.set_progress_bar_config(disable=None)
         return pipeline

From 15fb54de111e91b50fa6fc08789161cbbd6994b2 Mon Sep 17 00:00:00 2001
From: DN6 <dhruv.nair@gmail.com>
Date: Fri, 28 Nov 2025 21:34:32 +0530
Subject: [PATCH 10/10] update

---
 tests/modular_pipelines/flux/test_modular_pipeline_flux.py  | 6 +++---
 .../qwen/test_modular_pipeline_qwenimage.py                 | 6 +++---
 tests/modular_pipelines/test_modular_pipelines_common.py    | 2 +-
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/tests/modular_pipelines/flux/test_modular_pipeline_flux.py b/tests/modular_pipelines/flux/test_modular_pipeline_flux.py
index b136763e08bc..854b5218c617 100644
--- a/tests/modular_pipelines/flux/test_modular_pipeline_flux.py
+++ b/tests/modular_pipelines/flux/test_modular_pipeline_flux.py
@@ -36,7 +36,7 @@
 class TestFluxModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = FluxModularPipeline
     pipeline_blocks_class = FluxAutoBlocks
-    repo = "hf-internal-testing/tiny-flux-modular"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-modular"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale"])
     batch_params = frozenset(["prompt"])
@@ -62,7 +62,7 @@ def test_float16_inference(self):
 class TestFluxImg2ImgModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = FluxModularPipeline
     pipeline_blocks_class = FluxAutoBlocks
-    repo = "hf-internal-testing/tiny-flux-modular"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-modular"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
     batch_params = frozenset(["prompt", "image"])
@@ -128,7 +128,7 @@ def test_float16_inference(self):
 class TestFluxKontextModularPipelineFast(ModularPipelineTesterMixin):
     pipeline_class = FluxKontextModularPipeline
     pipeline_blocks_class = FluxKontextAutoBlocks
-    repo = "hf-internal-testing/tiny-flux-kontext-pipe"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux-kontext-pipe"
 
     params = frozenset(["prompt", "height", "width", "guidance_scale", "image"])
     batch_params = frozenset(["prompt", "image"])
diff --git a/tests/modular_pipelines/qwen/test_modular_pipeline_qwenimage.py b/tests/modular_pipelines/qwen/test_modular_pipeline_qwenimage.py
index 772fa19927fb..8d7600781b24 100644
--- a/tests/modular_pipelines/qwen/test_modular_pipeline_qwenimage.py
+++ b/tests/modular_pipelines/qwen/test_modular_pipeline_qwenimage.py
@@ -32,7 +32,7 @@
 class TestQwenImageModularPipelineFast(ModularPipelineTesterMixin, ModularGuiderTesterMixin):
     pipeline_class = QwenImageModularPipeline
     pipeline_blocks_class = QwenImageAutoBlocks
-    repo = "hf-internal-testing/tiny-qwenimage-modular"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-qwenimage-modular"
 
     params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image", "mask_image"])
     batch_params = frozenset(["prompt", "negative_prompt", "image", "mask_image"])
@@ -58,7 +58,7 @@ def test_inference_batch_single_identical(self):
 class TestQwenImageEditModularPipelineFast(ModularPipelineTesterMixin, ModularGuiderTesterMixin):
     pipeline_class = QwenImageEditModularPipeline
     pipeline_blocks_class = QwenImageEditAutoBlocks
-    repo = "hf-internal-testing/tiny-qwenimage-edit-modular"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-qwenimage-edit-modular"
 
     params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image", "mask_image"])
     batch_params = frozenset(["prompt", "negative_prompt", "image", "mask_image"])
@@ -84,7 +84,7 @@ def test_guider_cfg(self):
 class TestQwenImageEditPlusModularPipelineFast(ModularPipelineTesterMixin, ModularGuiderTesterMixin):
     pipeline_class = QwenImageEditPlusModularPipeline
     pipeline_blocks_class = QwenImageEditPlusAutoBlocks
-    repo = "hf-internal-testing/tiny-qwenimage-edit-plus-modular"
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-qwenimage-edit-plus-modular"
 
     # No `mask_image` yet.
     params = frozenset(["prompt", "height", "width", "negative_prompt", "attention_kwargs", "image"])
diff --git a/tests/modular_pipelines/test_modular_pipelines_common.py b/tests/modular_pipelines/test_modular_pipelines_common.py
index 5c1c4051ab95..a33951dac538 100644
--- a/tests/modular_pipelines/test_modular_pipelines_common.py
+++ b/tests/modular_pipelines/test_modular_pipelines_common.py
@@ -45,7 +45,7 @@ def pipeline_class(self) -> Union[Callable, ModularPipeline]:
     @property
     def pretrained_model_name_or_path(self) -> str:
         raise NotImplementedError(
-            "You need to set the attribute `repo` in the child test class. See existing pipeline tests for reference."
+            "You need to set the attribute `pretrained_model_name_or_path` in the child test class. See existing pipeline tests for reference."
         )
 
     @property