[LTX-2.3] Add modular pipeline modules and registration

Adds the LTX-2.3 modular pipeline structure:
- modular_pipelines/ltx2/: encoders, modular_blocks, modular_pipeline
- Registration in __init__.py, auto_pipeline.py, modular_pipeline mapping
- Checkpoint utilities for parity testing
- Supports T2V with CFG guidance (pixel-identical to reference)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: yiyi@huggingface.co

src/diffusers/__init__.py

@@ -434,6 +434,9 @@
             "FluxKontextAutoBlocks",
             "FluxKontextModularPipeline",
             "FluxModularPipeline",
+            "LTX2AutoBlocks",
+            "LTX2Blocks",
+            "LTX2ModularPipeline",
             "HeliosAutoBlocks",
             "HeliosModularPipeline",
             "HeliosPyramidAutoBlocks",
@@ -1195,6 +1198,9 @@
             FluxKontextAutoBlocks,
             FluxKontextModularPipeline,
             FluxModularPipeline,
+            LTX2AutoBlocks,
+            LTX2Blocks,
+            LTX2ModularPipeline,
             HeliosAutoBlocks,
             HeliosModularPipeline,
             HeliosPyramidAutoBlocks,

src/diffusers/modular_pipelines/__init__.py

@@ -70,6 +70,11 @@
         "FluxKontextAutoBlocks",
         "FluxKontextModularPipeline",
     ]
+    _import_structure["ltx2"] = [
+        "LTX2AutoBlocks",
+        "LTX2Blocks",
+        "LTX2ModularPipeline",
+    ]
     _import_structure["flux2"] = [
         "Flux2AutoBlocks",
         "Flux2KleinAutoBlocks",
@@ -103,6 +108,7 @@
     else:
         from .components_manager import ComponentsManager
         from .flux import FluxAutoBlocks, FluxKontextAutoBlocks, FluxKontextModularPipeline, FluxModularPipeline
+        from .ltx2 import LTX2AutoBlocks, LTX2Blocks, LTX2ModularPipeline
         from .flux2 import (
             Flux2AutoBlocks,
             Flux2KleinAutoBlocks,

src/diffusers/modular_pipelines/ltx2/__init__.py

@@ -0,0 +1,52 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["modular_blocks_ltx2"] = ["LTX2Blocks", "LTX2AutoBlocks", "LTX2Stage1Blocks", "LTX2Stage2Blocks", "LTX2FullPipelineBlocks"]
+    _import_structure["modular_blocks_ltx2_upsample"] = ["LTX2UpsampleBlocks", "LTX2UpsampleCoreBlocks"]
+    _import_structure["modular_pipeline"] = [
+        "LTX2ModularPipeline",
+        "LTX2UpsampleModularPipeline",
+    ]
+
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
+    else:
+        from .modular_blocks_ltx2 import LTX2AutoBlocks, LTX2Blocks, LTX2FullPipelineBlocks, LTX2Stage1Blocks, LTX2Stage2Blocks
+        from .modular_blocks_ltx2_upsample import LTX2UpsampleBlocks, LTX2UpsampleCoreBlocks
+        from .modular_pipeline import LTX2ModularPipeline, LTX2UpsampleModularPipeline
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/modular_pipelines/ltx2/_checkpoint_utils.py

@@ -0,0 +1,27 @@
+"""Checkpoint utilities for parity debugging. No effect when _checkpoints is None."""
+from dataclasses import dataclass, field
+
+import torch
+
+
+@dataclass
+class Checkpoint:
+    save: bool = False
+    stop: bool = False
+    load: bool = False
+    data: dict = field(default_factory=dict)
+
+
+def _maybe_checkpoint(checkpoints, name, data):
+    if not checkpoints:
+        return
+    ckpt = checkpoints.get(name)
+    if ckpt is None:
+        return
+    if ckpt.save:
+        ckpt.data.update({
+            k: v.cpu().clone() if isinstance(v, torch.Tensor) else v
+            for k, v in data.items()
+        })
+    if ckpt.stop:
+        raise StopIteration(name)