change lora mixin

src/diffusers/__init__.py

@@ -294,17 +294,10 @@ else:
     )
     _import_structure["modular_pipelines"].extend(
         [
-            "AutoPipelineBlocks",
             "ComponentsManager",
             "ComponentSpec",
-            "ConditionalPipelineBlocks",
-            "ConfigSpec",
-            "InputParam",
-            "LoopSequentialPipelineBlocks",
             "ModularPipeline",
             "ModularPipelineBlocks",
-            "OutputParam",
-            "SequentialPipelineBlocks",
         ]
     )
     _import_structure["optimization"] = [
@@ -1070,19 +1063,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ZImageTransformer2DModel,
             attention_backend,
         )
-        from .modular_pipelines import (
-            AutoPipelineBlocks,
-            ComponentsManager,
-            ComponentSpec,
-            ConditionalPipelineBlocks,
-            ConfigSpec,
-            InputParam,
-            LoopSequentialPipelineBlocks,
-            ModularPipeline,
-            ModularPipelineBlocks,
-            OutputParam,
-            SequentialPipelineBlocks,
-        )
+        from .modular_pipelines import ComponentsManager, ComponentSpec, ModularPipeline, ModularPipelineBlocks
         from .optimization import (
             get_constant_schedule,
             get_constant_schedule_with_warmup,

src/diffusers/modular_pipelines/__init__.py

@@ -33,7 +33,6 @@ else:
         "ModularPipeline",
         "AutoPipelineBlocks",
         "SequentialPipelineBlocks",
-        "ConditionalPipelineBlocks",
         "LoopSequentialPipelineBlocks",
         "PipelineState",
         "BlockState",
@@ -106,7 +105,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .modular_pipeline import (
             AutoPipelineBlocks,
             BlockState,
-            ConditionalPipelineBlocks,
             LoopSequentialPipelineBlocks,
             ModularPipeline,
             ModularPipelineBlocks,

src/diffusers/pipelines/ltx2/pipeline_ltx2_image2video.py

@@ -22,7 +22,7 @@ from transformers import Gemma3ForConditionalGeneration, GemmaTokenizer, GemmaTo
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PipelineImageInput
-from ...loaders import FromSingleFileMixin, LTXVideoLoraLoaderMixin
+from ...loaders import FromSingleFileMixin, LTX2LoraLoaderMixin
 from ...models.autoencoders import AutoencoderKLLTX2Audio, AutoencoderKLLTX2Video
 from ...models.transformers import LTX2VideoTransformer3DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
@@ -48,7 +48,7 @@ EXAMPLE_DOC_STRING = """
     Examples:
         ```py
         >>> import torch
-        >>> from diffusers import LTX2ImageToVideoPipeline
+        >>> from diffusers import LTX2Pipeline
         >>> from diffusers.pipelines.ltx2.export_utils import encode_video
         >>> from diffusers.utils import load_image
 
@@ -62,7 +62,7 @@ EXAMPLE_DOC_STRING = """
         >>> negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
 
         >>> frame_rate = 24.0
-        >>> video, audio = pipe(
+        >>> video = pipe(
         ...     image=image,
         ...     prompt=prompt,
         ...     negative_prompt=negative_prompt,
@@ -202,7 +202,7 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
     return noise_cfg
 
 
-class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixin):
+class LTX2ImageToVideoPipeline(DiffusionPipeline, FromSingleFileMixin, LTX2LoraLoaderMixin):
     r"""
     Pipeline for image-to-video generation.
 

src/diffusers/utils/dummy_pt_objects.py

@@ -1905,21 +1905,6 @@ def attention_backend(*args, **kwargs):
     requires_backends(attention_backend, ["torch"])
 
 
-class AutoPipelineBlocks(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
 class ComponentsManager(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -1950,66 +1935,6 @@ class ComponentSpec(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
-class ConditionalPipelineBlocks(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class ConfigSpec(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class InputParam(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class LoopSequentialPipelineBlocks(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
 class ModularPipeline(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -2040,36 +1965,6 @@ class ModularPipelineBlocks(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
-class OutputParam(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
-class SequentialPipelineBlocks(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
 def get_constant_schedule(*args, **kwargs):
     requires_backends(get_constant_schedule, ["torch"])