Merge branch 'main' into flux_ptxla_trillium

Author: sayakpaul

.github/workflows/push_tests.yml

@@ -83,7 +83,7 @@ jobs:
           python utils/print_env.py
       - name: PyTorch CUDA checkpoint tests on Ubuntu
         env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
           # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
           CUBLAS_WORKSPACE_CONFIG: :16:8
         run: |
@@ -137,7 +137,7 @@ jobs:
 
     - name: Run PyTorch CUDA tests
       env:
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        HF_TOKEN: ${{ secrets.DIFFUSERS_HF_HUB_READ_TOKEN }}
         # https://pytorch.org/docs/stable/notes/randomness.html#avoiding-nondeterministic-algorithms
         CUBLAS_WORKSPACE_CONFIG: :16:8
       run: |

docs/source/en/api/pipelines/flux.md

@@ -367,7 +367,7 @@ transformer_8bit = FluxTransformer2DModel.from_pretrained(
 
 pipeline = FluxPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-dev",
-    text_encoder=text_encoder_8bit,
+    text_encoder_2=text_encoder_8bit,
     transformer=transformer_8bit,
     torch_dtype=torch.float16,
     device_map="balanced",

docs/source/en/api/pipelines/hunyuan_video.md

@@ -16,7 +16,7 @@
 
 [HunyuanVideo](https://www.arxiv.org/abs/2412.03603) by Tencent.
 
-*Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at [this https URL](https://github.com/Tencent/HunyuanVideo).*
+*Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at [this https URL](https://github.com/tencent/HunyuanVideo).*
 
 <Tip>
 
@@ -45,14 +45,14 @@ from diffusers.utils import export_to_video
 
 quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
 transformer_8bit = HunyuanVideoTransformer3DModel.from_pretrained(
-    "tencent/HunyuanVideo",
+    "hunyuanvideo-community/HunyuanVideo",
     subfolder="transformer",
     quantization_config=quant_config,
-    torch_dtype=torch.float16,
+    torch_dtype=torch.bfloat16,
 )
 
 pipeline = HunyuanVideoPipeline.from_pretrained(
-    "tencent/HunyuanVideo",
+    "hunyuanvideo-community/HunyuanVideo",
     transformer=transformer_8bit,
     torch_dtype=torch.float16,
     device_map="balanced",

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.

docs/source/en/using-diffusers/text-img2vid.md

@@ -78,10 +78,10 @@ from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
 from diffusers.utils import export_to_video
 
 transformer = HunyuanVideoTransformer3DModel.from_pretrained(
-    "tencent/HunyuanVideo", subfolder="transformer", torch_dtype=torch.bfloat16
+    "hunyuanvideo-community/HunyuanVideo", subfolder="transformer", torch_dtype=torch.bfloat16
 )
 pipe = HunyuanVideoPipeline.from_pretrained(
-  "tencent/HunyuanVideo", transformer=transformer, torch_dtype=torch.float16
+  "hunyuanvideo-community/HunyuanVideo", transformer=transformer, torch_dtype=torch.float16
 )
 
 # reduce memory requirements

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/loaders/lora_pipeline.py

@@ -21,6 +21,7 @@
 from ..utils import (
     USE_PEFT_BACKEND,
     deprecate,
+    get_submodule_by_name,
     is_peft_available,
     is_peft_version,
     is_torch_version,
@@ -1981,10 +1982,17 @@ def _maybe_expand_transformer_param_shape_or_error_(
                 in_features = state_dict[lora_A_weight_name].shape[1]
                 out_features = state_dict[lora_B_weight_name].shape[0]
 
+                # Model maybe loaded with different quantization schemes which may flatten the params.
+                # `bitsandbytes`, for example, flatten the weights when using 4bit. 8bit bnb models
+                # preserve weight shape.
+                module_weight_shape = cls._calculate_module_shape(model=transformer, base_module=module)
+
                 # This means there's no need for an expansion in the params, so we simply skip.
-                if tuple(module_weight.shape) == (out_features, in_features):
+                if tuple(module_weight_shape) == (out_features, in_features):
                     continue
 
+                # TODO (sayakpaul): We still need to consider if the module we're expanding is
+                # quantized and handle it accordingly if that is the case.
                 module_out_features, module_in_features = module_weight.shape
                 debug_message = ""
                 if in_features > module_in_features:
@@ -2080,13 +2088,16 @@ def _maybe_expand_lora_state_dict(cls, transformer, lora_state_dict):
             base_weight_param = transformer_state_dict[base_param_name]
             lora_A_param = lora_state_dict[f"{prefix}{k}.lora_A.weight"]
 
-            if base_weight_param.shape[1] > lora_A_param.shape[1]:
+            # TODO (sayakpaul): Handle the cases when we actually need to expand when using quantization.
+            base_module_shape = cls._calculate_module_shape(model=transformer, base_weight_param_name=base_param_name)
+
+            if base_module_shape[1] > lora_A_param.shape[1]:
                 shape = (lora_A_param.shape[0], base_weight_param.shape[1])
                 expanded_state_dict_weight = torch.zeros(shape, device=base_weight_param.device)
                 expanded_state_dict_weight[:, : lora_A_param.shape[1]].copy_(lora_A_param)
                 lora_state_dict[f"{prefix}{k}.lora_A.weight"] = expanded_state_dict_weight
                 expanded_module_names.add(k)
-            elif base_weight_param.shape[1] < lora_A_param.shape[1]:
+            elif base_module_shape[1] < lora_A_param.shape[1]:
                 raise NotImplementedError(
                     f"This LoRA param ({k}.lora_A.weight) has an incompatible shape {lora_A_param.shape}. Please open an issue to file for a feature request - https://github.com/huggingface/diffusers/issues/new."
                 )
@@ -2098,6 +2109,28 @@ def _maybe_expand_lora_state_dict(cls, transformer, lora_state_dict):
 
         return lora_state_dict
 
+    @staticmethod
+    def _calculate_module_shape(
+        model: "torch.nn.Module",
+        base_module: "torch.nn.Linear" = None,
+        base_weight_param_name: str = None,
+    ) -> "torch.Size":
+        def _get_weight_shape(weight: torch.Tensor):
+            return weight.quant_state.shape if weight.__class__.__name__ == "Params4bit" else weight.shape
+
+        if base_module is not None:
+            return _get_weight_shape(base_module.weight)
+        elif base_weight_param_name is not None:
+            if not base_weight_param_name.endswith(".weight"):
+                raise ValueError(
+                    f"Invalid `base_weight_param_name` passed as it does not end with '.weight' {base_weight_param_name=}."
+                )
+            module_path = base_weight_param_name.rsplit(".weight", 1)[0]
+            submodule = get_submodule_by_name(model, module_path)
+            return _get_weight_shape(submodule.weight)
+
+        raise ValueError("Either `base_module` or `base_weight_param_name` must be provided.")
+
 
 # The reason why we subclass from `StableDiffusionLoraLoaderMixin` here is because Amused initially
 # relied on `StableDiffusionLoraLoaderMixin` for its LoRA support.

src/diffusers/loaders/peft.py

@@ -300,15 +300,17 @@ def load_lora_adapter(self, pretrained_model_name_or_path_or_dict, prefix="trans
             try:
                 inject_adapter_in_model(lora_config, self, adapter_name=adapter_name, **peft_kwargs)
                 incompatible_keys = set_peft_model_state_dict(self, state_dict, adapter_name, **peft_kwargs)
-            except RuntimeError as e:
-                for module in self.modules():
-                    if isinstance(module, BaseTunerLayer):
-                        active_adapters = module.active_adapters
-                        for active_adapter in active_adapters:
-                            if adapter_name in active_adapter:
-                                module.delete_adapter(adapter_name)
-
-                self.peft_config.pop(adapter_name)
+            except Exception as e:
+                # In case `inject_adapter_in_model()` was unsuccessful even before injecting the `peft_config`.
+                if hasattr(self, "peft_config"):
+                    for module in self.modules():
+                        if isinstance(module, BaseTunerLayer):
+                            active_adapters = module.active_adapters
+                            for active_adapter in active_adapters:
+                                if adapter_name in active_adapter:
+                                    module.delete_adapter(adapter_name)
+
+                    self.peft_config.pop(adapter_name)
                 logger.error(f"Loading {adapter_name} was unsucessful with the following error: \n{e}")
                 raise