huggingface
diff --git a/‎src/diffusers/__init__.py‎
Lines changed: 72 additions & 7 deletions b/‎src/diffusers/__init__.py‎
Lines changed: 72 additions & 7 deletions
diff --git a/‎src/diffusers/commands/custom_blocks.py‎
Lines changed: 133 additions & 0 deletions b/‎src/diffusers/commands/custom_blocks.py‎
Lines changed: 133 additions & 0 deletions
diff --git a/‎src/diffusers/commands/diffusers_cli.py‎
Lines changed: 2 additions & 0 deletions b/‎src/diffusers/commands/diffusers_cli.py‎
Lines changed: 2 additions & 0 deletions
@@ -34,10 +34,12 @@
 
 _import_structure = {
     "configuration_utils": ["ConfigMixin"],
+    "guiders": [],
     "hooks": [],
     "loaders": ["FromOriginalModelMixin"],
     "models": [],
     "pipelines": [],
+    "modular_pipelines": [],
     "quantizers.quantization_config": [],
     "schedulers": [],
     "utils": [
@@ -130,12 +132,26 @@
     _import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")]
 
 else:
+    _import_structure["guiders"].extend(
+        [
+            "AdaptiveProjectedGuidance",
+            "AutoGuidance",
+            "ClassifierFreeGuidance",
+            "ClassifierFreeZeroStarGuidance",
+            "SkipLayerGuidance",
+            "SmoothedEnergyGuidance",
+            "TangentialClassifierFreeGuidance",
+        ]
+    )
     _import_structure["hooks"].extend(
         [
             "FasterCacheConfig",
             "HookRegistry",
             "PyramidAttentionBroadcastConfig",
+            "LayerSkipConfig",
+            "SmoothedEnergyGuidanceConfig",
             "apply_faster_cache",
+            "apply_layer_skip",
             "apply_pyramid_attention_broadcast",
         ]
     )
@@ -239,13 +255,21 @@
             "KarrasVePipeline",
             "LDMPipeline",
             "LDMSuperResolutionPipeline",
-            "ModularPipeline",
             "PNDMPipeline",
             "RePaintPipeline",
             "ScoreSdeVePipeline",
             "StableDiffusionMixin",
         ]
     )
+    _import_structure["modular_pipelines"].extend(
+        [
+            "ModularLoader",
+            "ModularPipeline",
+            "ModularPipelineBlocks",
+            "ComponentSpec",
+            "ComponentsManager",
+        ]
+    )
     _import_structure["quantizers"] = ["DiffusersQuantizer"]
     _import_structure["schedulers"].extend(
         [
@@ -494,12 +518,10 @@
             "StableDiffusionXLImg2ImgPipeline",
             "StableDiffusionXLInpaintPipeline",
             "StableDiffusionXLInstructPix2PixPipeline",
-            "StableDiffusionXLModularPipeline",
             "StableDiffusionXLPAGImg2ImgPipeline",
             "StableDiffusionXLPAGInpaintPipeline",
             "StableDiffusionXLPAGPipeline",
             "StableDiffusionXLPipeline",
-            "StableDiffusionXLAutoPipeline",
             "StableUnCLIPImg2ImgPipeline",
             "StableUnCLIPPipeline",
             "StableVideoDiffusionPipeline",
@@ -526,6 +548,24 @@
         ]
     )
 
+
+try:
+    if not (is_torch_available() and is_transformers_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from .utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _import_structure["utils.dummy_torch_and_transformers_objects"] = [
+        name for name in dir(dummy_torch_and_transformers_objects) if not name.startswith("_")
+    ]
+
+else:
+    _import_structure["modular_pipelines"].extend(
+        [
+            "StableDiffusionXLAutoPipeline",
+            "StableDiffusionXLModularLoader",
+        ]
+    )
 try:
     if not (is_torch_available() and is_transformers_available() and is_opencv_available()):
         raise OptionalDependencyNotAvailable()
@@ -731,10 +771,22 @@
     except OptionalDependencyNotAvailable:
         from .utils.dummy_pt_objects import *  # noqa F403
     else:
+        from .guiders import (
+            AdaptiveProjectedGuidance,
+            AutoGuidance,
+            ClassifierFreeGuidance,
+            ClassifierFreeZeroStarGuidance,
+            SkipLayerGuidance,
+            SmoothedEnergyGuidance,
+            TangentialClassifierFreeGuidance,
+        )
         from .hooks import (
             FasterCacheConfig,
             HookRegistry,
+            LayerSkipConfig,
             PyramidAttentionBroadcastConfig,
+            SmoothedEnergyGuidanceConfig,
+            apply_layer_skip,
             apply_faster_cache,
             apply_pyramid_attention_broadcast,
         )
@@ -837,12 +889,18 @@
             KarrasVePipeline,
             LDMPipeline,
             LDMSuperResolutionPipeline,
-            ModularPipeline,
             PNDMPipeline,
             RePaintPipeline,
             ScoreSdeVePipeline,
             StableDiffusionMixin,
         )
+        from .modular_pipelines import (
+            ModularLoader,
+            ModularPipeline,
+            ModularPipelineBlocks,
+            ComponentSpec,
+            ComponentsManager,
+        )
         from .quantizers import DiffusersQuantizer
         from .schedulers import (
             AmusedScheduler,
@@ -1070,12 +1128,10 @@
             StableDiffusionXLImg2ImgPipeline,
             StableDiffusionXLInpaintPipeline,
             StableDiffusionXLInstructPix2PixPipeline,
-            StableDiffusionXLModularPipeline,
             StableDiffusionXLPAGImg2ImgPipeline,
             StableDiffusionXLPAGInpaintPipeline,
             StableDiffusionXLPAGPipeline,
             StableDiffusionXLPipeline,
-            StableDiffusionXLAutoPipeline,
             StableUnCLIPImg2ImgPipeline,
             StableUnCLIPPipeline,
             StableVideoDiffusionPipeline,
@@ -1100,7 +1156,16 @@
             WuerstchenDecoderPipeline,
             WuerstchenPriorPipeline,
         )
-
+    try:
+        if not (is_torch_available() and is_transformers_available()):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
+    else:
+        from .modular_pipelines import (
+            StableDiffusionXLAutoPipeline,
+            StableDiffusionXLModularLoader,
+        )
     try:
         if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
             raise OptionalDependencyNotAvailable()
 
@@ -0,0 +1,133 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Usage example:
+    TODO
+"""
+
+import ast
+from argparse import ArgumentParser, Namespace
+from pathlib import Path
+import importlib.util
+import os
+from ..utils import logging
+from . import BaseDiffusersCLICommand
+
+
+EXPECTED_PARENT_CLASSES = ["PipelineBlock"]
+CONFIG = "config.json"
+
+def conversion_command_factory(args: Namespace):
+    return CustomBlocksCommand(args.block_module_name, args.block_class_name)
+
+
+class CustomBlocksCommand(BaseDiffusersCLICommand):
+    @staticmethod
+    def register_subcommand(parser: ArgumentParser):
+        conversion_parser = parser.add_parser("custom_blocks")
+        conversion_parser.add_argument(
+            "--block_module_name",
+            type=str,
+            default="block.py",
+            help="Module filename in which the custom block will be implemented.",
+        )
+        conversion_parser.add_argument(
+            "--block_class_name", type=str, default=None, help="Name of the custom block. If provided None, we will try to infer it."
+        )
+        conversion_parser.set_defaults(func=conversion_command_factory)
+
+    def __init__(self, block_module_name: str = "block.py", block_class_name: str = None):
+        self.logger = logging.get_logger("diffusers-cli/custom_blocks")
+        self.block_module_name = Path(block_module_name)
+        self.block_class_name = block_class_name
+
+    def run(self):
+        # determine the block to be saved.
+        out = self._get_class_names(self.block_module_name)
+        classes_found = list({cls for cls, _ in out})
+        
+        if self.block_class_name is not None:
+            child_class, parent_class = self._choose_block(out, self.block_class_name)
+            if child_class is None and parent_class is None:
+                raise ValueError(
+                    "`block_class_name` could not be retrieved. Available classes from "
+                    f"{self.block_module_name}:\n{classes_found}"
+                )
+        else:
+            self.logger.info(
+                f"Found classes: {classes_found} will be using {classes_found[0]}. "
+                "If this needs to be changed, re-run the command specifying `block_class_name`."
+            )
+            child_class, parent_class =  out[0][0], out[0][1]
+
+        # dynamically get the custom block and initialize it to call `save_pretrained` in the current directory.
+        # the user is responsible for running it, so I guess that is safe?
+        module_name = f"__dynamic__{self.block_module_name.stem}"
+        spec = importlib.util.spec_from_file_location(module_name, str(self.block_module_name))
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        getattr(module, child_class)().save_pretrained(os.getcwd())
+
+        # or, we could create it manually.
+        # automap = self._create_automap(parent_class=parent_class, child_class=child_class)
+        # with open(CONFIG, "w") as f:
+        #     json.dump(automap, f)
+        with open("requirements.txt", "w") as f:
+            f.write("")
+
+    def _choose_block(self, candidates, chosen=None):
+        for cls, base in candidates:
+            if cls == chosen:
+                return cls, base
+        return None, None
+
+    def _get_class_names(self, file_path):
+        source = file_path.read_text(encoding="utf-8")
+        try:
+            tree = ast.parse(source, filename=file_path)
+        except SyntaxError as e:
+            raise ValueError(f"Could not parse {file_path!r}: {e}") from e
+
+        results: list[tuple[str, str]] = []
+        for node in tree.body:
+            if not isinstance(node, ast.ClassDef):
+                continue
+
+            # extract all base names for this class
+            base_names = [
+                bname for b in node.bases
+                if (bname := self._get_base_name(b)) is not None
+            ]
+
+            # for each allowed base that appears in the class's bases, emit a tuple
+            for allowed in EXPECTED_PARENT_CLASSES:
+                if allowed in base_names:
+                    results.append((node.name, allowed))
+
+        return results
+
+    def _get_base_name(self, node: ast.expr):
+        if isinstance(node, ast.Name):
+            return node.id
+        elif isinstance(node, ast.Attribute):
+            val = self._get_base_name(node.value)
+            return f"{val}.{node.attr}" if val else node.attr
+        return None
+    
+    def _create_automap(self, parent_class, child_class):
+        module = str(self.block_module_name).replace(".py", "").rsplit(".", 1)[-1]
+        auto_map = {f"{parent_class}": f"{module}.{child_class}"}
+        return {"auto_map": auto_map}
+    
@@ -17,6 +17,7 @@
 
 from .env import EnvironmentCommand
 from .fp16_safetensors import FP16SafetensorsCommand
+from .custom_blocks import CustomBlocksCommand
 
 
 def main():
@@ -26,6 +27,7 @@ def main():
     # Register commands
     EnvironmentCommand.register_subcommand(commands_parser)
     FP16SafetensorsCommand.register_subcommand(commands_parser)
+    CustomBlocksCommand.register_subcommand(commands_parser)
 
     # Let's go
     args = parser.parse_args()