[FEAT] DDUF format

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 @@
     "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 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 @@ def load_config(
         _ = 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 @@ def load_config(
                 "`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 @@ def load_config(
                     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 @@ def extract_init_dict(cls, config_dict, **kwargs):
         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 @@ def to_json_file(self, json_file_path: Union[str, os.PathLike]):
         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 @@
     "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 @@
 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 +345,19 @@ 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):
+        if not os.path.exists(part_file_path) or (dduf_entries and part_file_path not in dduf_entries):
             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 +378,7 @@ 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 +419,7 @@ 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 @@
 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 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         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 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             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 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             "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 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
             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 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     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 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                         subfolder=subfolder,
                         user_agent=user_agent,
                         commit_hash=commit_hash,
+                        dduf_entries=dduf_entries,
                     )
 
                 except IOError as e:
@@ -866,6 +874,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     subfolder=subfolder,
                     user_agent=user_agent,
                     commit_hash=commit_hash,
+                    dduf_entries=dduf_entries,
                 )
 
             if low_cpu_mem_usage:
@@ -887,7 +896,9 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     # 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 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             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(