[FEAT] DDUF format (#10037)

* load and save dduf archive

* style

* switch to zip uncompressed

* updates

* Update src/diffusers/pipelines/pipeline_utils.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update src/diffusers/pipelines/pipeline_utils.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* first draft

* remove print

* switch to dduf_file for consistency

* switch to huggingface hub api

* fix log

* add a basic test

* Update src/diffusers/configuration_utils.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update src/diffusers/pipelines/pipeline_utils.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update src/diffusers/pipelines/pipeline_utils.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* fix

* fix variant

* change saving logic

* DDUF - Load transformers components manually (#10171)

* update hfh version

* Load transformers components manually

* load encoder from_pretrained with state_dict

* working version with transformers and tokenizer !

* add generation_config case

* fix tests

* remove saving for now

* typing

* need next version from transformers

* Update src/diffusers/configuration_utils.py
Co-authored-by: Lucain <lucain@huggingface.co>

* check path corectly

* Apply suggestions from code review
Co-authored-by: Lucain <lucain@huggingface.co>

* udapte

* typing

* remove check for subfolder

* quality

* revert setup changes

* oups

* more readable condition

* add loading from the hub test

* add basic docs.

* Apply suggestions from code review
Co-authored-by: Lucain <lucain@huggingface.co>

* add example

* add

* make functions private

* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* minor.

* fixes

* fix

* change the precdence of parameterized.

* error out when custom pipeline is passed with dduf_file.

* updates

* fix

* updates

* fixes

* updates

* fix xfail condition.

* fix xfail

* fixes

* sharded checkpoint compat

* add test for sharded checkpoint

* add suggestions

* Update src/diffusers/models/model_loading_utils.py
Co-authored-by: YiYi Xu <yixu310@gmail.com>

* from suggestions

* add class attributes to flag dduf tests

* last one

* fix logic

* remove comment

* revert changes

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Lucain <lucain@huggingface.co>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>

docs/source/en/using-diffusers/other-formats.md

@@ -240,6 +240,46 @@ Benefits of using a single-file layout include:
 1. Easy compatibility with diffusion interfaces such as [ComfyUI](https://github.com/comfyanonymous/ComfyUI) or [Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) which commonly use a single-file layout.
 2. Easier to manage (download and share) a single file.
 
+### DDUF
+
+> [!WARNING]
+> DDUF is an experimental file format and APIs related to it can change in the future.
+
+DDUF (**D**DUF **D**iffusion **U**nified **F**ormat) is a file format designed to make storing, distributing, and using diffusion models much easier. Built on the ZIP file format, DDUF offers a standardized, efficient, and flexible way to package all parts of a diffusion model into a single, easy-to-manage file. It provides a balance between Diffusers multi-folder format and the widely popular single-file format.
+
+Learn more details about DDUF on the Hugging Face Hub [documentation](https://huggingface.co/docs/hub/dduf).
+
+Pass a checkpoint to the `dduf_file` parameter to load it in [`DiffusionPipeline`].
+
+```py
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained(
+    "DDUF/FLUX.1-dev-DDUF", dduf_file="FLUX.1-dev.dduf", torch_dtype=torch.bfloat16
+).to("cuda")
+image = pipe(
+    "photo a cat holding a sign that says Diffusers", num_inference_steps=50, guidance_scale=3.5
+).images[0]
+image.save("cat.png")
+```
+
+To save a pipeline as a `.dduf` checkpoint, use the [`~huggingface_hub.export_folder_as_dduf`] utility, which takes care of all the necessary file-level validations.
+
+```py
+from huggingface_hub import export_folder_as_dduf
+from diffusers import DiffusionPipeline
+import torch 
+
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+
+save_folder = "flux-dev"
+pipe.save_pretrained("flux-dev")
+export_folder_as_dduf("flux-dev.dduf", folder_path=save_folder)
+
+> [!TIP]
+> Packaging and loading quantized checkpoints in the DDUF format is supported as long as they respect the multi-folder structure.
+
 ## Convert layout and files
 
 Diffusers provides many scripts and methods to convert storage layouts and file formats to enable broader support across the diffusion ecosystem.

setup.py

@@ -101,7 +101,7 @@ _deps = [
     "filelock",
     "flax>=0.4.1",
     "hf-doc-builder>=0.3.0",
-    "huggingface-hub>=0.23.2",
+    "huggingface-hub>=0.27.0",
     "requests-mock==1.10.0",
     "importlib_metadata",
     "invisible-watermark>=0.2.0",

src/diffusers/configuration_utils.py

@@ -24,10 +24,10 @@ import os
 import re
 from collections import OrderedDict
 from pathlib import Path
-from typing import Any, Dict, Tuple, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 import numpy as np
-from huggingface_hub import create_repo, hf_hub_download
+from huggingface_hub import DDUFEntry, create_repo, hf_hub_download
 from huggingface_hub.utils import (
     EntryNotFoundError,
     RepositoryNotFoundError,
@@ -347,6 +347,7 @@ class ConfigMixin:
         _ = kwargs.pop("mirror", None)
         subfolder = kwargs.pop("subfolder", None)
         user_agent = kwargs.pop("user_agent", {})
+        dduf_entries: Optional[Dict[str, DDUFEntry]] = kwargs.pop("dduf_entries", None)
 
         user_agent = {**user_agent, "file_type": "config"}
         user_agent = http_user_agent(user_agent)
@@ -358,8 +359,15 @@ class ConfigMixin:
                 "`self.config_name` is not defined. Note that one should not load a config from "
                 "`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
             )
-
-        if os.path.isfile(pretrained_model_name_or_path):
+        # Custom path for now
+        if dduf_entries:
+            if subfolder is not None:
+                raise ValueError(
+                    "DDUF file only allow for 1 level of directory (e.g transformer/model1/model.safetentors is not allowed). "
+                    "Please check the DDUF structure"
+                )
+            config_file = cls._get_config_file_from_dduf(pretrained_model_name_or_path, dduf_entries)
+        elif os.path.isfile(pretrained_model_name_or_path):
             config_file = pretrained_model_name_or_path
         elif os.path.isdir(pretrained_model_name_or_path):
             if subfolder is not None and os.path.isfile(
@@ -426,10 +434,8 @@ class ConfigMixin:
                     f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                     f"containing a {cls.config_name} file"
                 )
-
         try:
-            # Load config dict
-            config_dict = cls._dict_from_json_file(config_file)
+            config_dict = cls._dict_from_json_file(config_file, dduf_entries=dduf_entries)
 
             commit_hash = extract_commit_hash(config_file)
         except (json.JSONDecodeError, UnicodeDecodeError):
@@ -552,9 +558,14 @@ class ConfigMixin:
         return init_dict, unused_kwargs, hidden_config_dict
 
     @classmethod
-    def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
-        with open(json_file, "r", encoding="utf-8") as reader:
-            text = reader.read()
+    def _dict_from_json_file(
+        cls, json_file: Union[str, os.PathLike], dduf_entries: Optional[Dict[str, DDUFEntry]] = None
+    ):
+        if dduf_entries:
+            text = dduf_entries[json_file].read_text()
+        else:
+            with open(json_file, "r", encoding="utf-8") as reader:
+                text = reader.read()
         return json.loads(text)
 
     def __repr__(self):
@@ -616,6 +627,20 @@ class ConfigMixin:
         with open(json_file_path, "w", encoding="utf-8") as writer:
             writer.write(self.to_json_string())
 
+    @classmethod
+    def _get_config_file_from_dduf(cls, pretrained_model_name_or_path: str, dduf_entries: Dict[str, DDUFEntry]):
+        # paths inside a DDUF file must always be "/"
+        config_file = (
+            cls.config_name
+            if pretrained_model_name_or_path == ""
+            else "/".join([pretrained_model_name_or_path, cls.config_name])
+        )
+        if config_file not in dduf_entries:
+            raise ValueError(
+                f"We did not manage to find the file {config_file} in the dduf file. We only have the following files {dduf_entries.keys()}"
+            )
+        return config_file
+
 
 def register_to_config(init):
     r"""

src/diffusers/dependency_versions_table.py

@@ -9,7 +9,7 @@ deps = {
     "filelock": "filelock",
     "flax": "flax>=0.4.1",
     "hf-doc-builder": "hf-doc-builder>=0.3.0",
-    "huggingface-hub": "huggingface-hub>=0.23.2",
+    "huggingface-hub": "huggingface-hub>=0.27.0",
     "requests-mock": "requests-mock==1.10.0",
     "importlib_metadata": "importlib_metadata",
     "invisible-watermark": "invisible-watermark>=0.2.0",

src/diffusers/models/model_loading_utils.py

@@ -20,10 +20,11 @@ import os
 from array import array
 from collections import OrderedDict
 from pathlib import Path
-from typing import List, Optional, Union
+from typing import Dict, List, Optional, Union
 
 import safetensors
 import torch
+from huggingface_hub import DDUFEntry
 from huggingface_hub.utils import EntryNotFoundError
 
 from ..utils import (
@@ -132,7 +133,10 @@ def _fetch_remapped_cls_from_config(config, old_class):
 
 
 def load_state_dict(
-    checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None, disable_mmap: bool = False
+    checkpoint_file: Union[str, os.PathLike],
+    variant: Optional[str] = None,
+    dduf_entries: Optional[Dict[str, DDUFEntry]] = None,
+    disable_mmap: bool = False,
 ):
     """
     Reads a checkpoint file, returning properly formatted errors if they arise.
@@ -144,6 +148,10 @@ def load_state_dict(
     try:
         file_extension = os.path.basename(checkpoint_file).split(".")[-1]
         if file_extension == SAFETENSORS_FILE_EXTENSION:
+            if dduf_entries:
+                # tensors are loaded on cpu
+                with dduf_entries[checkpoint_file].as_mmap() as mm:
+                    return safetensors.torch.load(mm)
             if disable_mmap:
                 return safetensors.torch.load(open(checkpoint_file, "rb").read())
             else:
@@ -284,6 +292,7 @@ def _fetch_index_file(
     revision,
     user_agent,
     commit_hash,
+    dduf_entries: Optional[Dict[str, DDUFEntry]] = None,
 ):
     if is_local:
         index_file = Path(
@@ -309,8 +318,10 @@ def _fetch_index_file(
                 subfolder=None,
                 user_agent=user_agent,
                 commit_hash=commit_hash,
+                dduf_entries=dduf_entries,
             )
-            index_file = Path(index_file)
+            if not dduf_entries:
+                index_file = Path(index_file)
         except (EntryNotFoundError, EnvironmentError):
             index_file = None
 
@@ -319,7 +330,9 @@ def _fetch_index_file(
 
 # Adapted from
 # https://github.com/bghira/SimpleTuner/blob/cea2457ab063f6dedb9e697830ae68a96be90641/helpers/training/save_hooks.py#L64
-def _merge_sharded_checkpoints(sharded_ckpt_cached_folder, sharded_metadata):
+def _merge_sharded_checkpoints(
+    sharded_ckpt_cached_folder, sharded_metadata, dduf_entries: Optional[Dict[str, DDUFEntry]] = None
+):
     weight_map = sharded_metadata.get("weight_map", None)
     if weight_map is None:
         raise KeyError("'weight_map' key not found in the shard index file.")
@@ -332,14 +345,23 @@ def _merge_sharded_checkpoints(sharded_ckpt_cached_folder, sharded_metadata):
     # Load tensors from each unique file
     for file_name in files_to_load:
         part_file_path = os.path.join(sharded_ckpt_cached_folder, file_name)
-        if not os.path.exists(part_file_path):
-            raise FileNotFoundError(f"Part file {file_name} not found.")
+        if dduf_entries:
+            if part_file_path not in dduf_entries:
+                raise FileNotFoundError(f"Part file {file_name} not found.")
+        else:
+            if not os.path.exists(part_file_path):
+                raise FileNotFoundError(f"Part file {file_name} not found.")
 
         if is_safetensors:
-            with safetensors.safe_open(part_file_path, framework="pt", device="cpu") as f:
-                for tensor_key in f.keys():
-                    if tensor_key in weight_map:
-                        merged_state_dict[tensor_key] = f.get_tensor(tensor_key)
+            if dduf_entries:
+                with dduf_entries[part_file_path].as_mmap() as mm:
+                    tensors = safetensors.torch.load(mm)
+                    merged_state_dict.update(tensors)
+            else:
+                with safetensors.safe_open(part_file_path, framework="pt", device="cpu") as f:
+                    for tensor_key in f.keys():
+                        if tensor_key in weight_map:
+                            merged_state_dict[tensor_key] = f.get_tensor(tensor_key)
         else:
             merged_state_dict.update(torch.load(part_file_path, weights_only=True, map_location="cpu"))
 
@@ -360,6 +382,7 @@ def _fetch_index_file_legacy(
     revision,
     user_agent,
     commit_hash,
+    dduf_entries: Optional[Dict[str, DDUFEntry]] = None,
 ):
     if is_local:
         index_file = Path(
@@ -400,6 +423,7 @@ def _fetch_index_file_legacy(
                     subfolder=None,
                     user_agent=user_agent,
                     commit_hash=commit_hash,
+                    dduf_entries=dduf_entries,
                 )
                 index_file = Path(index_file)
                 deprecation_message = f"This serialization format is now deprecated to standardize the serialization format between `transformers` and `diffusers`. We recommend you to remove the existing files associated with the current variant ({variant}) and re-obtain them by running a `save_pretrained()`."

src/diffusers/models/modeling_utils.py

@@ -23,11 +23,11 @@ import re
 from collections import OrderedDict
 from functools import partial, wraps
 from pathlib import Path
-from typing import Any, Callable, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import safetensors
 import torch
-from huggingface_hub import create_repo, split_torch_state_dict_into_shards
+from huggingface_hub import DDUFEntry, create_repo, split_torch_state_dict_into_shards
 from huggingface_hub.utils import validate_hf_hub_args
 from torch import Tensor, nn
 
@@ -607,6 +607,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
         variant = kwargs.pop("variant", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
         quantization_config = kwargs.pop("quantization_config", None)
+        dduf_entries: Optional[Dict[str, DDUFEntry]] = kwargs.pop("dduf_entries", None)
         disable_mmap = kwargs.pop("disable_mmap", False)
 
         allow_pickle = False
@@ -700,6 +701,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             revision=revision,
             subfolder=subfolder,
             user_agent=user_agent,
+            dduf_entries=dduf_entries,
             **kwargs,
         )
         # no in-place modification of the original config.
@@ -776,13 +778,14 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             "revision": revision,
             "user_agent": user_agent,
             "commit_hash": commit_hash,
+            "dduf_entries": dduf_entries,
         }
         index_file = _fetch_index_file(**index_file_kwargs)
         # In case the index file was not found we still have to consider the legacy format.
         # this becomes applicable when the variant is not None.
         if variant is not None and (index_file is None or not os.path.exists(index_file)):
             index_file = _fetch_index_file_legacy(**index_file_kwargs)
-        if index_file is not None and index_file.is_file():
+        if index_file is not None and (dduf_entries or index_file.is_file()):
             is_sharded = True
 
         if is_sharded and from_flax:
@@ -811,6 +814,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
 
             model = load_flax_checkpoint_in_pytorch_model(model, model_file)
         else:
+            # in the case it is sharded, we have already the index
             if is_sharded:
                 sharded_ckpt_cached_folder, sharded_metadata = _get_checkpoint_shard_files(
                     pretrained_model_name_or_path,
@@ -822,10 +826,13 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                     user_agent=user_agent,
                     revision=revision,
                     subfolder=subfolder or "",
+                    dduf_entries=dduf_entries,
                 )
                 # TODO: https://github.com/huggingface/diffusers/issues/10013
-                if hf_quantizer is not None:
-                    model_file = _merge_sharded_checkpoints(sharded_ckpt_cached_folder, sharded_metadata)
+                if hf_quantizer is not None or dduf_entries:
+                    model_file = _merge_sharded_checkpoints(
+                        sharded_ckpt_cached_folder, sharded_metadata, dduf_entries=dduf_entries
+                    )
                     logger.info("Merged sharded checkpoints as `hf_quantizer` is not None.")
                     is_sharded = False
 
@@ -843,6 +850,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                         subfolder=subfolder,
                         user_agent=user_agent,
                         commit_hash=commit_hash,
+                        dduf_entries=dduf_entries,
                     )
 
                 except IOError as e:
@@ -866,6 +874,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                     subfolder=subfolder,
                     user_agent=user_agent,
                     commit_hash=commit_hash,
+                    dduf_entries=dduf_entries,
                 )
 
             if low_cpu_mem_usage:
@@ -887,7 +896,9 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
                     # TODO (sayakpaul,  SunMarc): remove this after model loading refactor
                     else:
                         param_device = torch.device(torch.cuda.current_device())
-                    state_dict = load_state_dict(model_file, variant=variant, disable_mmap=disable_mmap)
+                    state_dict = load_state_dict(
+                        model_file, variant=variant, dduf_entries=dduf_entries, disable_mmap=disable_mmap
+                    )
                     model._convert_deprecated_attention_blocks(state_dict)
 
                     # move the params from meta device to cpu
@@ -983,7 +994,9 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
             else:
                 model = cls.from_config(config, **unused_kwargs)
 
-                state_dict = load_state_dict(model_file, variant=variant, disable_mmap=disable_mmap)
+                state_dict = load_state_dict(
+                    model_file, variant=variant, dduf_entries=dduf_entries, disable_mmap=disable_mmap
+                )
                 model._convert_deprecated_attention_blocks(state_dict)
 
                 model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(

src/diffusers/pipelines/pipeline_loading_utils.py

@@ -12,19 +12,19 @@
 # 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.
-
-
 import importlib
 import os
 import re
 import warnings
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
+import requests
 import torch
-from huggingface_hub import ModelCard, model_info
-from huggingface_hub.utils import validate_hf_hub_args
+from huggingface_hub import DDUFEntry, ModelCard, model_info, snapshot_download
+from huggingface_hub.utils import OfflineModeIsEnabled, validate_hf_hub_args
 from packaging import version
+from requests.exceptions import HTTPError
 
 from .. import __version__
 from ..utils import (
@@ -38,14 +38,16 @@ from ..utils import (
     is_accelerate_available,
     is_peft_available,
     is_transformers_available,
+    is_transformers_version,
     logging,
 )
 from ..utils.torch_utils import is_compiled_module
+from .transformers_loading_utils import _load_tokenizer_from_dduf, _load_transformers_model_from_dduf
 
 
 if is_transformers_available():
     import transformers
-    from transformers import PreTrainedModel
+    from transformers import PreTrainedModel, PreTrainedTokenizerBase
     from transformers.utils import FLAX_WEIGHTS_NAME as TRANSFORMERS_FLAX_WEIGHTS_NAME
     from transformers.utils import SAFE_WEIGHTS_NAME as TRANSFORMERS_SAFE_WEIGHTS_NAME
     from transformers.utils import WEIGHTS_NAME as TRANSFORMERS_WEIGHTS_NAME
@@ -627,6 +629,7 @@ def load_sub_model(
     low_cpu_mem_usage: bool,
     cached_folder: Union[str, os.PathLike],
     use_safetensors: bool,
+    dduf_entries: Optional[Dict[str, DDUFEntry]],
 ):
     """Helper method to load the module `name` from `library_name` and `class_name`"""
 
@@ -663,7 +666,7 @@ def load_sub_model(
             f" any of the loading methods defined in {ALL_IMPORTABLE_CLASSES}."
         )
 
-    load_method = getattr(class_obj, load_method_name)
+    load_method = _get_load_method(class_obj, load_method_name, is_dduf=dduf_entries is not None)
 
     # add kwargs to loading method
     diffusers_module = importlib.import_module(__name__.split(".")[0])
@@ -721,7 +724,10 @@ def load_sub_model(
             loading_kwargs["low_cpu_mem_usage"] = False
 
     # check if the module is in a subdirectory
-    if os.path.isdir(os.path.join(cached_folder, name)):
+    if dduf_entries:
+        loading_kwargs["dduf_entries"] = dduf_entries
+        loaded_sub_model = load_method(name, **loading_kwargs)
+    elif os.path.isdir(os.path.join(cached_folder, name)):
         loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
     else:
         # else load from the root directory
@@ -746,6 +752,22 @@ def load_sub_model(
     return loaded_sub_model
 
 
+def _get_load_method(class_obj: object, load_method_name: str, is_dduf: bool) -> Callable:
+    """
+    Return the method to load the sub model.
+
+    In practice, this method will return the `"from_pretrained"` (or `load_method_name`) method of the class object
+    except if loading from a DDUF checkpoint. In that case, transformers models and tokenizers have a specific loading
+    method that we need to use.
+    """
+    if is_dduf:
+        if issubclass(class_obj, PreTrainedTokenizerBase):
+            return lambda *args, **kwargs: _load_tokenizer_from_dduf(class_obj, *args, **kwargs)
+        if issubclass(class_obj, PreTrainedModel):
+            return lambda *args, **kwargs: _load_transformers_model_from_dduf(class_obj, *args, **kwargs)
+    return getattr(class_obj, load_method_name)
+
+
 def _fetch_class_library_tuple(module):
     # import it here to avoid circular import
     diffusers_module = importlib.import_module(__name__.split(".")[0])
@@ -968,3 +990,70 @@ def _get_ignore_patterns(
             )
 
     return ignore_patterns
+
+
+def _download_dduf_file(
+    pretrained_model_name: str,
+    dduf_file: str,
+    pipeline_class_name: str,
+    cache_dir: str,
+    proxies: str,
+    local_files_only: bool,
+    token: str,
+    revision: str,
+):
+    model_info_call_error = None
+    if not local_files_only:
+        try:
+            info = model_info(pretrained_model_name, token=token, revision=revision)
+        except (HTTPError, OfflineModeIsEnabled, requests.ConnectionError) as e:
+            logger.warning(f"Couldn't connect to the Hub: {e}.\nWill try to load from local cache.")
+            local_files_only = True
+            model_info_call_error = e  # save error to reraise it if model is not cached locally
+
+    if (
+        not local_files_only
+        and dduf_file is not None
+        and dduf_file not in (sibling.rfilename for sibling in info.siblings)
+    ):
+        raise ValueError(f"Requested {dduf_file} file is not available in {pretrained_model_name}.")
+
+    try:
+        user_agent = {"pipeline_class": pipeline_class_name, "dduf": True}
+        cached_folder = snapshot_download(
+            pretrained_model_name,
+            cache_dir=cache_dir,
+            proxies=proxies,
+            local_files_only=local_files_only,
+            token=token,
+            revision=revision,
+            allow_patterns=[dduf_file],
+            user_agent=user_agent,
+        )
+        return cached_folder
+    except FileNotFoundError:
+        # Means we tried to load pipeline with `local_files_only=True` but the files have not been found in local cache.
+        # This can happen in two cases:
+        # 1. If the user passed `local_files_only=True`                    => we raise the error directly
+        # 2. If we forced `local_files_only=True` when `model_info` failed => we raise the initial error
+        if model_info_call_error is None:
+            # 1. user passed `local_files_only=True`
+            raise
+        else:
+            # 2. we forced `local_files_only=True` when `model_info` failed
+            raise EnvironmentError(
+                f"Cannot load model {pretrained_model_name}: model is not cached locally and an error occurred"
+                " while trying to fetch metadata from the Hub. Please check out the root cause in the stacktrace"
+                " above."
+            ) from model_info_call_error
+
+
+def _maybe_raise_error_for_incorrect_transformers(config_dict):
+    has_transformers_component = False
+    for k in config_dict:
+        if isinstance(config_dict[k], list):
+            has_transformers_component = config_dict[k][0] == "transformers"
+            if has_transformers_component:
+                break
+    if has_transformers_component and not is_transformers_version(">", "4.47.1"):
+        raise ValueError("Please upgrade your `transformers` installation to the latest version to use DDUF.")

src/diffusers/pipelines/pipeline_utils.py

@@ -29,10 +29,12 @@ import PIL.Image
 import requests
 import torch
 from huggingface_hub import (
+    DDUFEntry,
     ModelCard,
     create_repo,
     hf_hub_download,
     model_info,
+    read_dduf_file,
     snapshot_download,
 )
 from huggingface_hub.utils import OfflineModeIsEnabled, validate_hf_hub_args
@@ -72,6 +74,7 @@ from .pipeline_loading_utils import (
     CONNECTED_PIPES_KEYS,
     CUSTOM_PIPELINE_FILE_NAME,
     LOADABLE_CLASSES,
+    _download_dduf_file,
     _fetch_class_library_tuple,
     _get_custom_components_and_folders,
     _get_custom_pipeline_class,
@@ -79,6 +82,7 @@ from .pipeline_loading_utils import (
     _get_ignore_patterns,
     _get_pipeline_class,
     _identify_model_variants,
+    _maybe_raise_error_for_incorrect_transformers,
     _maybe_raise_warning_for_inpainting,
     _resolve_custom_pipeline_and_cls,
     _unwrap_model,
@@ -218,6 +222,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
                 repository you want to push to with `repo_id` (will default to the name of `save_directory` in your
                 namespace).
+
             kwargs (`Dict[str, Any]`, *optional*):
                 Additional keyword arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
@@ -531,6 +536,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                     - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
                       saved using
                     [`~DiffusionPipeline.save_pretrained`].
+                    - A path to a *directory* (for example `./my_pipeline_directory/`) containing a dduf file
             torch_dtype (`str` or `torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
                 dtype is automatically derived from the model's weights.
@@ -625,6 +631,8 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             variant (`str`, *optional*):
                 Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
                 loading `from_flax`.
+            dduf_file(`str`, *optional*):
+                Load weights from the specified dduf file.
 
         <Tip>
 
@@ -674,6 +682,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         offload_state_dict = kwargs.pop("offload_state_dict", False)
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
         variant = kwargs.pop("variant", None)
+        dduf_file = kwargs.pop("dduf_file", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
         use_onnx = kwargs.pop("use_onnx", None)
         load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
@@ -722,6 +731,12 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
             )
 
+        if dduf_file:
+            if custom_pipeline:
+                raise NotImplementedError("Custom pipelines are not supported with DDUF at the moment.")
+            if load_connected_pipeline:
+                raise NotImplementedError("Connected pipelines are not supported with DDUF at the moment.")
+
         # 1. Download the checkpoints and configs
         # use snapshot download here to get it working from from_pretrained
         if not os.path.isdir(pretrained_model_name_or_path):
@@ -744,6 +759,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 custom_pipeline=custom_pipeline,
                 custom_revision=custom_revision,
                 variant=variant,
+                dduf_file=dduf_file,
                 load_connected_pipeline=load_connected_pipeline,
                 **kwargs,
             )
@@ -765,7 +781,17 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             )
             logger.warning(warn_msg)
 
-        config_dict = cls.load_config(cached_folder)
+        dduf_entries = None
+        if dduf_file:
+            dduf_file_path = os.path.join(cached_folder, dduf_file)
+            dduf_entries = read_dduf_file(dduf_file_path)
+            # The reader contains already all the files needed, no need to check it again
+            cached_folder = ""
+
+        config_dict = cls.load_config(cached_folder, dduf_entries=dduf_entries)
+
+        if dduf_file:
+            _maybe_raise_error_for_incorrect_transformers(config_dict)
 
         # pop out "_ignore_files" as it is only needed for download
         config_dict.pop("_ignore_files", None)
@@ -943,6 +969,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                     low_cpu_mem_usage=low_cpu_mem_usage,
                     cached_folder=cached_folder,
                     use_safetensors=use_safetensors,
+                    dduf_entries=dduf_entries,
                 )
                 logger.info(
                     f"Loaded {name} as {class_name} from `{name}` subfolder of {pretrained_model_name_or_path}."
@@ -1256,6 +1283,8 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             variant (`str`, *optional*):
                 Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
                 loading `from_flax`.
+            dduf_file(`str`, *optional*):
+                Load weights from the specified DDUF file.
             use_safetensors (`bool`, *optional*, defaults to `None`):
                 If set to `None`, the safetensors weights are downloaded if they're available **and** if the
                 safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
@@ -1296,6 +1325,23 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
         use_onnx = kwargs.pop("use_onnx", None)
         load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
         trust_remote_code = kwargs.pop("trust_remote_code", False)
+        dduf_file: Optional[Dict[str, DDUFEntry]] = kwargs.pop("dduf_file", None)
+
+        if dduf_file:
+            if custom_pipeline:
+                raise NotImplementedError("Custom pipelines are not supported with DDUF at the moment.")
+            if load_connected_pipeline:
+                raise NotImplementedError("Connected pipelines are not supported with DDUF at the moment.")
+            return _download_dduf_file(
+                pretrained_model_name=pretrained_model_name,
+                dduf_file=dduf_file,
+                pipeline_class_name=cls.__name__,
+                cache_dir=cache_dir,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                token=token,
+                revision=revision,
+            )
 
         allow_pickle = False
         if use_safetensors is None:
@@ -1375,7 +1421,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
             allow_patterns += [f"{custom_pipeline}.py"] if f"{custom_pipeline}.py" in filenames else []
             # also allow downloading config.json files with the model
             allow_patterns += [os.path.join(k, "config.json") for k in model_folder_names]
-
             allow_patterns += [
                 SCHEDULER_CONFIG_NAME,
                 CONFIG_NAME,
@@ -1471,7 +1516,6 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
                 user_agent=user_agent,
             )
 
-            # retrieve pipeline class from local file
             cls_name = cls.load_config(os.path.join(cached_folder, "model_index.json")).get("_class_name", None)
             cls_name = cls_name[4:] if isinstance(cls_name, str) and cls_name.startswith("Flax") else cls_name
 

src/diffusers/pipelines/transformers_loading_utils.py

+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# 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.
+import contextlib
+import os
+import tempfile
+from typing import TYPE_CHECKING, Dict
+
+from huggingface_hub import DDUFEntry
+from tqdm import tqdm
+
+from ..utils import is_safetensors_available, is_transformers_available, is_transformers_version
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel, PreTrainedTokenizer
+
+if is_transformers_available():
+    from transformers import PreTrainedModel, PreTrainedTokenizer
+
+if is_safetensors_available():
+    import safetensors.torch
+
+
+def _load_tokenizer_from_dduf(
+    cls: "PreTrainedTokenizer", name: str, dduf_entries: Dict[str, DDUFEntry], **kwargs
+) -> "PreTrainedTokenizer":
+    """
+    Load a tokenizer from a DDUF archive.
+
+    In practice, `transformers` do not provide a way to load a tokenizer from a DDUF archive. This function is a
+    workaround by extracting the tokenizer files from the DDUF archive and loading the tokenizer from the extracted
+    files. There is an extra cost of extracting the files, but of limited impact as the tokenizer files are usually
+    small-ish.
+    """
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        for entry_name, entry in dduf_entries.items():
+            if entry_name.startswith(name + "/"):
+                tmp_entry_path = os.path.join(tmp_dir, *entry_name.split("/"))
+                # need to create intermediary directory if they don't exist
+                os.makedirs(os.path.dirname(tmp_entry_path), exist_ok=True)
+                with open(tmp_entry_path, "wb") as f:
+                    with entry.as_mmap() as mm:
+                        f.write(mm)
+        return cls.from_pretrained(os.path.dirname(tmp_entry_path), **kwargs)
+
+
+def _load_transformers_model_from_dduf(
+    cls: "PreTrainedModel", name: str, dduf_entries: Dict[str, DDUFEntry], **kwargs
+) -> "PreTrainedModel":
+    """
+    Load a transformers model from a DDUF archive.
+
+    In practice, `transformers` do not provide a way to load a model from a DDUF archive. This function is a workaround
+    by instantiating a model from the config file and loading the weights from the DDUF archive directly.
+    """
+    config_file = dduf_entries.get(f"{name}/config.json")
+    if config_file is None:
+        raise EnvironmentError(
+            f"Could not find a config.json file for component {name} in DDUF file (contains {dduf_entries.keys()})."
+        )
+    generation_config = dduf_entries.get(f"{name}/generation_config.json", None)
+
+    weight_files = [
+        entry
+        for entry_name, entry in dduf_entries.items()
+        if entry_name.startswith(f"{name}/") and entry_name.endswith(".safetensors")
+    ]
+    if not weight_files:
+        raise EnvironmentError(
+            f"Could not find any weight file for component {name} in DDUF file (contains {dduf_entries.keys()})."
+        )
+    if not is_safetensors_available():
+        raise EnvironmentError(
+            "Safetensors is not available, cannot load model from DDUF. Please `pip install safetensors`."
+        )
+    if is_transformers_version("<", "4.47.0"):
+        raise ImportError(
+            "You need to install `transformers>4.47.0` in order to load a transformers model from a DDUF file. "
+            "You can install it with: `pip install --upgrade transformers`"
+        )
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        from transformers import AutoConfig, GenerationConfig
+
+        tmp_config_file = os.path.join(tmp_dir, "config.json")
+        with open(tmp_config_file, "w") as f:
+            f.write(config_file.read_text())
+        config = AutoConfig.from_pretrained(tmp_config_file)
+        if generation_config is not None:
+            tmp_generation_config_file = os.path.join(tmp_dir, "generation_config.json")
+            with open(tmp_generation_config_file, "w") as f:
+                f.write(generation_config.read_text())
+            generation_config = GenerationConfig.from_pretrained(tmp_generation_config_file)
+        state_dict = {}
+        with contextlib.ExitStack() as stack:
+            for entry in tqdm(weight_files, desc="Loading state_dict"):  # Loop over safetensors files
+                # Memory-map the safetensors file
+                mmap = stack.enter_context(entry.as_mmap())
+                # Load tensors from the memory-mapped file
+                tensors = safetensors.torch.load(mmap)
+                # Update the state dictionary with tensors
+                state_dict.update(tensors)
+            return cls.from_pretrained(
+                pretrained_model_name_or_path=None,
+                config=config,
+                generation_config=generation_config,
+                state_dict=state_dict,
+                **kwargs,
+            )

src/diffusers/utils/__init__.py

@@ -70,6 +70,7 @@ from .import_utils import (
     is_gguf_available,
     is_gguf_version,
     is_google_colab,
+    is_hf_hub_version,
     is_inflect_available,
     is_invisible_watermark_available,
     is_k_diffusion_available,

src/diffusers/utils/hub_utils.py

@@ -26,6 +26,7 @@ from typing import Dict, List, Optional, Union
 from uuid import uuid4
 
 from huggingface_hub import (
+    DDUFEntry,
     ModelCard,
     ModelCardData,
     create_repo,
@@ -291,9 +292,26 @@ def _get_model_file(
     user_agent: Optional[Union[Dict, str]] = None,
     revision: Optional[str] = None,
     commit_hash: Optional[str] = None,
+    dduf_entries: Optional[Dict[str, DDUFEntry]] = None,
 ):
     pretrained_model_name_or_path = str(pretrained_model_name_or_path)
-    if os.path.isfile(pretrained_model_name_or_path):
+
+    if dduf_entries:
+        if subfolder is not None:
+            raise ValueError(
+                "DDUF file only allow for 1 level of directory (e.g transformer/model1/model.safetentors is not allowed). "
+                "Please check the DDUF structure"
+            )
+        model_file = (
+            weights_name
+            if pretrained_model_name_or_path == ""
+            else "/".join([pretrained_model_name_or_path, weights_name])
+        )
+        if model_file in dduf_entries:
+            return model_file
+        else:
+            raise EnvironmentError(f"Error no file named {weights_name} found in archive {dduf_entries.keys()}.")
+    elif os.path.isfile(pretrained_model_name_or_path):
         return pretrained_model_name_or_path
     elif os.path.isdir(pretrained_model_name_or_path):
         if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)):
@@ -419,6 +437,7 @@ def _get_checkpoint_shard_files(
     user_agent=None,
     revision=None,
     subfolder="",
+    dduf_entries: Optional[Dict[str, DDUFEntry]] = None,
 ):
     """
     For a given model:
@@ -430,11 +449,18 @@ def _get_checkpoint_shard_files(
     For the description of each arg, see [`PreTrainedModel.from_pretrained`]. `index_filename` is the full path to the
     index (downloaded and cached if `pretrained_model_name_or_path` is a model ID on the Hub).
     """
-    if not os.path.isfile(index_filename):
-        raise ValueError(f"Can't find a checkpoint index ({index_filename}) in {pretrained_model_name_or_path}.")
+    if dduf_entries:
+        if index_filename not in dduf_entries:
+            raise ValueError(f"Can't find a checkpoint index ({index_filename}) in {pretrained_model_name_or_path}.")
+    else:
+        if not os.path.isfile(index_filename):
+            raise ValueError(f"Can't find a checkpoint index ({index_filename}) in {pretrained_model_name_or_path}.")
 
-    with open(index_filename, "r") as f:
-        index = json.loads(f.read())
+    if dduf_entries:
+        index = json.loads(dduf_entries[index_filename].read_text())
+    else:
+        with open(index_filename, "r") as f:
+            index = json.loads(f.read())
 
     original_shard_filenames = sorted(set(index["weight_map"].values()))
     sharded_metadata = index["metadata"]
@@ -448,6 +474,8 @@ def _get_checkpoint_shard_files(
             pretrained_model_name_or_path, subfolder=subfolder, original_shard_filenames=original_shard_filenames
         )
         return shards_path, sharded_metadata
+    elif dduf_entries:
+        return shards_path, sharded_metadata
 
     # At this stage pretrained_model_name_or_path is a model identifier on the Hub
     allow_patterns = original_shard_filenames

src/diffusers/utils/import_utils.py

@@ -115,6 +115,13 @@ try:
 except importlib_metadata.PackageNotFoundError:
     _transformers_available = False
 
+_hf_hub_available = importlib.util.find_spec("huggingface_hub") is not None
+try:
+    _hf_hub_version = importlib_metadata.version("huggingface_hub")
+    logger.debug(f"Successfully imported huggingface_hub version {_hf_hub_version}")
+except importlib_metadata.PackageNotFoundError:
+    _hf_hub_available = False
+
 
 _inflect_available = importlib.util.find_spec("inflect") is not None
 try:
@@ -767,6 +774,21 @@ def is_transformers_version(operation: str, version: str):
     return compare_versions(parse(_transformers_version), operation, version)
 
 
+def is_hf_hub_version(operation: str, version: str):
+    """
+    Compares the current Hugging Face Hub version to a given reference with an operation.
+
+    Args:
+        operation (`str`):
+            A string representation of an operator, such as `">"` or `"<="`
+        version (`str`):
+            A version string
+    """
+    if not _hf_hub_available:
+        return False
+    return compare_versions(parse(_hf_hub_version), operation, version)
+
+
 def is_accelerate_version(operation: str, version: str):
     """
     Compares the current Accelerate version to a given reference with an operation.

src/diffusers/utils/testing_utils.py

@@ -478,6 +478,18 @@ def require_bitsandbytes_version_greater(bnb_version):
     return decorator
 
 
+def require_hf_hub_version_greater(hf_hub_version):
+    def decorator(test_case):
+        correct_hf_hub_version = version.parse(
+            version.parse(importlib.metadata.version("huggingface_hub")).base_version
+        ) > version.parse(hf_hub_version)
+        return unittest.skipUnless(
+            correct_hf_hub_version, f"Test requires huggingface_hub with the version greater than {hf_hub_version}."
+        )(test_case)
+
+    return decorator
+
+
 def require_gguf_version_greater_or_equal(gguf_version):
     def decorator(test_case):
         correct_gguf_version = is_gguf_available() and version.parse(

tests/pipelines/allegro/test_allegro.py

@@ -14,6 +14,8 @@
 
 import gc
 import inspect
+import os
+import tempfile
 import unittest
 
 import numpy as np
@@ -24,7 +26,9 @@ from diffusers import AllegroPipeline, AllegroTransformer3DModel, AutoencoderKLA
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
     numpy_cosine_similarity_distance,
+    require_hf_hub_version_greater,
     require_torch_gpu,
+    require_transformers_version_greater,
     slow,
     torch_device,
 )
@@ -297,6 +301,35 @@ class AllegroPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "VAE tiling should not affect the inference results",
         )
 
+    @require_hf_hub_version_greater("0.26.5")
+    @require_transformers_version_greater("4.47.1")
+    def test_save_load_dduf(self):
+        # reimplement because it needs `enable_tiling()` on the loaded pipe.
+        from huggingface_hub import export_folder_as_dduf
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device="cpu")
+        inputs.pop("generator")
+        inputs["generator"] = torch.manual_seed(0)
+
+        pipeline_out = pipe(**inputs)[0].cpu()
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            dduf_filename = os.path.join(tmpdir, f"{pipe.__class__.__name__.lower()}.dduf")
+            pipe.save_pretrained(tmpdir, safe_serialization=True)
+            export_folder_as_dduf(dduf_filename, folder_path=tmpdir)
+            loaded_pipe = self.pipeline_class.from_pretrained(tmpdir, dduf_file=dduf_filename).to(torch_device)
+
+        loaded_pipe.vae.enable_tiling()
+        inputs["generator"] = torch.manual_seed(0)
+        loaded_pipeline_out = loaded_pipe(**inputs)[0].cpu()
+
+        assert np.allclose(pipeline_out, loaded_pipeline_out)
+
 
 @slow
 @require_torch_gpu

tests/pipelines/audioldm/test_audioldm.py

@@ -63,6 +63,8 @@ class AudioLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         ]
     )
 
+    supports_dduf = False
+
     def get_dummy_components(self):
         torch.manual_seed(0)
         unet = UNet2DConditionModel(

tests/pipelines/audioldm2/test_audioldm2.py

@@ -70,6 +70,8 @@ class AudioLDM2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         ]
     )
 
+    supports_dduf = False
+
     def get_dummy_components(self):
         torch.manual_seed(0)
         unet = AudioLDM2UNet2DConditionModel(

tests/pipelines/blipdiffusion/test_blipdiffusion.py

@@ -60,6 +60,8 @@ class BlipDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         "prompt_reps",
     ]
 
+    supports_dduf = False
+
     def get_dummy_components(self):
         torch.manual_seed(0)
         text_encoder_config = CLIPTextConfig(

tests/pipelines/controlnet/test_controlnet.py

@@ -291,6 +291,8 @@ class StableDiffusionMultiControlNetPipelineFastTests(
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
     image_params = frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
 
+    supports_dduf = False
+
     def get_dummy_components(self):
         torch.manual_seed(0)
         unet = UNet2DConditionModel(
@@ -523,6 +525,8 @@ class StableDiffusionMultiControlNetOneModelPipelineFastTests(
     batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
     image_params = frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
 
+    supports_dduf = False
+
     def get_dummy_components(self):
         torch.manual_seed(0)
         unet = UNet2DConditionModel(

tests/pipelines/controlnet/test_controlnet_blip_diffusion.py

@@ -68,6 +68,8 @@ class BlipDiffusionControlNetPipelineFastTests(PipelineTesterMixin, unittest.Tes
         "prompt_reps",
     ]
 
+    supports_dduf = False
+
     def get_dummy_components(self):
         torch.manual_seed(0)
         text_encoder_config = CLIPTextConfig(

tests/pipelines/controlnet/test_controlnet_img2img.py

@@ -198,6 +198,8 @@ class StableDiffusionMultiControlNetPipelineFastTests(
     batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     image_params = frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
 
+    supports_dduf = False
+
     def get_dummy_components(self):
         torch.manual_seed(0)
         unet = UNet2DConditionModel(