build(deps): update huggingface-hub requirement from ~=0.26.5 to ~=0.27.0

[dependabot]

Updates the requirements on huggingface-hub to permit the latest version.

Release notes

Sourced from huggingface-hub's releases.

[v0.27.0] DDUF tooling, torch model loading helpers & multiple quality of life improvements and bug fixes

📦 Introducing DDUF tooling

DDUF (DDUF's Diffusion Unified Format) is a single-file format for diffusion models that aims to unify the different model distribution methods and weight-saving formats by packaging all model components into a single file. We will soon have a detailed documentation for that.

The huggingface_hub library now provides tooling to handle DDUF files in Python. It includes helpers to read and export DDUF files, and built-in rules to validate file integrity.

How to write a DDUF file?

>>> from huggingface_hub import export_folder_as_dduf
Export "path/to/FLUX.1-dev" folder as a DDUF file
>>> export_folder_as_dduf("FLUX.1-dev.dduf", folder_path="path/to/FLUX.1-dev")

How to read a DDUF file?

>>> import json
>>> import safetensors.torch
>>> from huggingface_hub import read_dduf_file
Read DDUF metadata (only metadata is loaded, lightweight operation)
>>> dduf_entries = read_dduf_file("FLUX.1-dev.dduf")
Returns a mapping filename <> DDUFEntry
>>> dduf_entries["model_index.json"]
DDUFEntry(filename='model_index.json', offset=66, length=587)
Load the model_index.json content
>>> json.loads(dduf_entries["model_index.json"].read_text())
{'_class_name': 'FluxPipeline', '_diffusers_version': '0.32.0.dev0', '_name_or_path': 'black-forest-labs/FLUX.1-dev', 'scheduler': ['diffusers', 'FlowMatchEulerDiscreteScheduler'], 'text_encoder': ['transformers', 'CLIPTextModel'], 'text_encoder_2': ['transformers', 'T5EncoderModel'], 'tokenizer': ['transformers', 'CLIPTokenizer'], 'tokenizer_2': ['transformers', 'T5TokenizerFast'], 'transformer': ['diffusers', 'FluxTransformer2DModel'], 'vae': ['diffusers', 'AutoencoderKL']}
Load VAE weights using safetensors
>>> with dduf_entries["vae/diffusion_pytorch_model.safetensors"].as_mmap() as mm:
...     state_dict = safetensors.torch.load(mm)

⚠️ Note that this is a very early version of the parser. The API and implementation can evolve in the near future. 👉 More details about the API in the documentation here.

DDUF parser v0.1 by @​Wauplin in #2692

💾 Serialization

Following the introduction of the torch serialization module in 0.22.* and the support of saving torch state dict to disk in 0.24.*, we now provide helpers to load torch state dicts from disk. By centralizing these functionalities in huggingface_hub, we ensure a consistent implementation across the HF ecosystem while allowing external libraries to benefit from standardized weight handling.

>>> from huggingface_hub import load_torch_model, load_state_dict_from_file
load state dict from a single file
>>> state_dict = load_state_dict_from_file("path/to/weights.safetensors")
</tr></table>

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Commits

• 6bfa5dd Release: v0.27.0
• ea8cb06 Release: v0.27.0.rc1
• 875b3a8 Merge branch 'main' into v0.27-release
• c4e6079 [InferenceClient] Keep generate_kwargs instead of generation_parameters (...
• 9100950 Release: v0.27.0.rc0
• 6be2b3e 📝Translating docs to Simplified Chinese (#2705)
• ca3f674 remove context manager when loading shards and handle mlx weights (#2709)
• 4b0b179 DDUF parser v0.1 (#2692)
• b75f8d9 [Serialization] support loading torch state dict from disk (#2687)
• 51b866f Fix test_list_spaces_linked (#2707)
• Additional commits viewable in compare view

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