github huggingface/huggingface_hub v0.27.0.rc0
[v0.27.0] DDUF tooling, torch model loading helpers & multiple quality of life improvements and bug fixes
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pre-releaseone day ago

📦 Introducing DDUF tooling

DDUF Banner

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. Check out the DDUF documentation for more details and context.

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")

# Directly load weights into a PyTorch model
>>> model = ... # A PyTorch model
>>> load_torch_model(model, "path/to/checkpoint")

More details in the serialization package reference.

[Serialization] support loading torch state dict from disk by @hanouticelina in #2687

We added a flag to save_torch_state_dict() helper to properly handle model saving in distributed environments, aligning with existing implementations across the Hugging Face ecosystem:

[Serialization] Add is_main_process argument to save_torch_state_dict() by @hanouticelina in #2648

A bug with shared tensor handling reported in transformers#35080 has been fixed:

add argument to pass shared tensors keys to discard by @hanouticelina in #2696

✨ HfApi

The following changes align the client with server-side updates in how security metadata is handled and exposed in the API responses. In particular, The repository security status returned by HfApi().model_info() is now available in the security_repo_status field: 

from huggingface_hub import HfApi

api = HfApi()

model = api.model_info("your_model_id", securityStatus=True)

# get security status info of your model
- security_info = model.securityStatus
+ security_info = model.security_repo_status

🌐 📚 Documentation

Thanks to @miaowumiaomiaowu, more documentation is now available in Chinese! And thanks @13579606 for reviewing these PRs. Check out the result here.

📝Translating docs to Simplified Chinese by @miaowumiaomiaowu in #2689, #2704 and #2705.

💔 Breaking changes

A few breaking changes have been introduced:

  • RepoCardData serialization now preserves None values in nested structures.
  • InferenceClient.image_to_image() now takes a target_size argument instead of height and width arguments. This is has been reflected in the InferenceClient async equivalent as well.
  • InferenceClient.table_question_answering() no longer accepts a parameter argument. This is has been reflected in the InferenceClient async equivalent as well.
  • Due to low usage, list_metrics() has been removed from HfApi.

⏳ Deprecations

Some deprecations have been introduced as well:

  • Legacy token permission checks are deprecated as they are no longer relevant with fine-grained tokens, This includes is_write_action in build_hf_headers(), write_permission=True in login methods. get_token_permission has been deprecated as well.
  • labels argument is deprecated in InferenceClient.zero_shot_classification() and InferenceClient.image_zero_shot_classification(). This is has been reflected in the InferenceClient async equivalent as well.
  • Deprecate is_write_action and write_permission=True when login by @Wauplin in #2632
  • Fix and deprecate get_token_permission by @Wauplin in #2631
  • [Inference Client] fix param docstring and deprecate labels param in zero-shot classification tasks by @hanouticelina in #2668

🛠️ Small fixes and maintenance

😌 QoL improvements

🐛 Bug and typo fixes

🏗️ internal

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