[Modular] Add single file support to Modular (#12383)

* update * update * update * update * Apply style fixes * update * update * update * update * update --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
2025-12-06 12:34:13 +08:00 · 2025-11-28 22:23:04 +05:30
parent 1b91856d0e
commit b010a8ce0c
11 changed files with 101 additions and 59 deletions
--- 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)
--- 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)
--- 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)
--- 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)
--- a/src/diffusers/loaders/single_file_utils.py
+++ b/src/diffusers/loaders/single_file_utils.py
@@ -389,6 +389,14 @@ def is_valid_url(url):
    return False


+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)
+
+
 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.")
@@ -400,7 +408,6 @@ 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)}"
--- a/src/diffusers/modular_pipelines/modular_pipeline.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline.py
@@ -360,7 +360,7 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
        collection: Optional[str] = None,
    ) -> "ModularPipeline":
        """
-        create a ModularPipeline, optionally accept modular_repo 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")
@@ -1645,8 +1645,8 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
            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
-                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`
@@ -1807,7 +1807,7 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
                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,
@@ -2111,8 +2111,10 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
            **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`,
-               `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 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:
@@ -2378,10 +2380,10 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
          - "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]
@@ -2398,11 +2400,13 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
        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", ...
-                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"), "repo": "path/to/repo", "subfolder": "subfolder",
+                "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,
            }
        """
@@ -2432,10 +2436,10 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
          - "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]
+              - "pretrained_model_name_or_path": Optional[str]
                  The model repository (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]
@@ -2452,11 +2456,20 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
            ComponentSpec: A reconstructed ComponentSpec object.

        Example:
-            >>> spec_dict = { ... "type_hint": ("diffusers", "UNet2DConditionModel"), ... "repo":
-            "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",
-                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"
+            >>> 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
--- a/src/diffusers/modular_pipelines/modular_pipeline_utils.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline_utils.py
@@ -21,6 +21,7 @@ from typing import Any, Dict, List, Literal, Optional, Type, Union
 import torch

 from ..configuration_utils import ConfigMixin, FrozenDict
+from ..loaders.single_file_utils import _is_single_file_path_or_url
 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"
    """

@@ -91,13 +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
-    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})
    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))
@@ -182,8 +190,8 @@ class ComponentSpec:
    @property
    def load_id(self) -> str:
        """
-        Unique identifier for this spec's pretrained load, composed of repo|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"
@@ -197,12 +205,13 @@ class ComponentSpec:
        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/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).
        """
@@ -259,34 +268,45 @@ class ComponentSpec:
    # 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. 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 = 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 = _is_single_file_path_or_url(pretrained_model_name_or_path)
+        if is_single_file and self.type_hint is None:
+            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:
                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
--- a/tests/modular_pipelines/flux/test_modular_pipeline_flux.py
+++ b/tests/modular_pipelines/flux/test_modular_pipeline_flux.py
@@ -36,7 +36,7 @@ from ..test_modular_pipelines_common import ModularPipelineTesterMixin
 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 @@ class TestFluxModularPipelineFast(ModularPipelineTesterMixin):
 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 @@ class TestFluxImg2ImgModularPipelineFast(ModularPipelineTesterMixin):
 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"])
--- a/tests/modular_pipelines/qwen/test_modular_pipeline_qwenimage.py
+++ b/tests/modular_pipelines/qwen/test_modular_pipeline_qwenimage.py
@@ -32,7 +32,7 @@ from ..test_modular_pipelines_common import ModularGuiderTesterMixin, ModularPip
 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 @@ class TestQwenImageModularPipelineFast(ModularPipelineTesterMixin, ModularGuider
 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 @@ class TestQwenImageEditModularPipelineFast(ModularPipelineTesterMixin, ModularGu
 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"])
--- 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
@@ -105,7 +105,7 @@ class SDXLModularIPAdapterTesterMixin:

        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)

@@ -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",
--- a/tests/modular_pipelines/test_modular_pipelines_common.py
+++ b/tests/modular_pipelines/test_modular_pipelines_common.py
@@ -43,9 +43,9 @@ class ModularPipelineTesterMixin:
        )

    @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."
+            "You need to set the attribute `pretrained_model_name_or_path` in the child test class. See existing pipeline tests for reference."
        )

    @property
@@ -103,7 +103,9 @@ class ModularPipelineTesterMixin:
        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