safetensors-arch

Tell what a .safetensors file actually is — by reading its tensor table, not its filename and not the metadata the model host attached to it.

$ python -m safetensors_arch models/loras/
base_v10.safetensors  sdxl-checkpoint     2515 tensors; model. + conditioner. + first_stage_model. (full SDXL-family checkpoint)
style-a.safetensors   lora:sdxl           2958 tensors of delta weights; keys carry down_blocks / up_blocks / mid_block (trainer declared stable-diffusion-xl/lora)
style-b.safetensors   lora:dit-adaln      1428 tensors of delta weights; keys carry cross_attn_k_proj (trainer declared anima-preview/lora)

from safetensors_arch import detect, is_compatible

kind, why = detect("some.safetensors")     # ("lora:sdxl", "2958 tensors of ...")
is_compatible("lora:sdxl", "dit-adaln")    # False  -> would silently do nothing
is_compatible("lora:unknown", "dit-adaln") # None   -> not enough information

No dependencies. Reads only the file header, never the weights — classifying a directory of 20 GB checkpoints takes milliseconds.

Run over one real collection of 269 LoRA files, it returns a kind for every one and no unknown, and the split it reports — 169 lora:dit-adaln, 99 lora:sdxl, 1 lora:qwen-image — matches the three folders their owner had filed them into, file for file. That is the useful shape of evidence for a classifier: not that it agrees with itself, but that it agrees with a judgement made independently of it.

Why this exists

Loading a LoRA built for one base-model family onto a different family is a silent failure. Nothing raises. An image comes out, and it looks fine. The LoRA simply contributed nothing.

That is the worst shape a bug can have: silent, reproducible, and it looks like a result. You conclude "this LoRA isn't very good" and move on, and the conclusion is garbage — you never loaded it.

The two things people normally check both lie in practice:

• Filenames lie. A file whose name promised an anime SD model turned out to be a Flux.2 workflow. Names are written by people, for people.
• Vendor metadata lies. A LoRA distributed with baseModel set to one family had the tensor layout of another. The field is self-reported and frequently just wrong.

The tensor table does not lie. An SDXL UNet delta has the UNet block names, in whichever spelling its trainer used — input_blocks / output_blocks / middle_block, or down_blocks / up_blocks / mid_block. A Cosmos-style DiT delta has adaln_modulation. Qwen-Image has transformer_blocks with an add_k_proj text branch. Those names come from the code that produced the weights.

The ordering trap

This is the part worth stealing even if you never use the package.

Some DiT LoRA keys look like this:

diffusion_model.blocks.0.adaln_modulation_cross_attn.1.lora_A.weight

That single key carries both a base-model structure marker (adaln_modulation_cross_attn) and a delta marker (lora_A). If your classifier asks "is this a full model?" before "is this a delta?", every LoRA in that family comes back as a full checkpoint.

Downstream, those files get routed to a loader that cannot open them, and the error message points at the loader — nowhere near the actual cause.

So the order is fixed and load-bearing:

1. Is this an increment? (lora_down, .lora_A, lora_up, lora_te, …)
2. Only then: an increment of what?

tests/test_detect.py::test_dit_lora_is_not_a_checkpoint pins this down.

Design notes

Every answer comes with its reason. detect() returns (kind, why), and the CLI prints both. A classifier you cannot argue with is a classifier you cannot trust — when it is wrong you need to see which marker it matched to know whether to fix the file or fix the rule.

Unknown is a real answer. lora:unknown means "definitely a delta, target family not recognised". It is not folded into a nearby guess. An earlier version of this logic had a fallback that returned a concrete family whenever it saw a checkpoint-style loader, on zero positive evidence — it confidently misfiled a whole family of models, and the confidence was the damaging part.

is_compatible() returns None, not False, when it does not know. The guard exists to stop the definitely wrong pairing. If it blocked everything it did not recognise, it would be useless the first time someone brings a family the table has never seen.

The trainer's own declaration only speaks where the keys do not. Some trainers write modelspec.architecture into the header. It is worth reading — the code that produced the weights wrote it — but it is a claim, and the point of this package is that the tensor table is not. So it never overrules a family the keys named; it is consulted only once the file is established as a delta and no key-based test has recognised the family, where the alternative is lora:unknown. The answer says when it came from there.

Recognised kinds

Adding a family is a couple of lines in detect() plus a test with a synthetic header. Pull requests welcome — especially ones that bring a real file's key list, since that is the only way the table grows correctly.

Prior art

Reading a .safetensors tensor table to work out what a file is has been done before, so that is not the claim here.

• SafetensorsModelInspector classifies by tensor keys across a wide family table — Flux, SDXL, Wan, Hunyuan, Qwen and more — and recognises LoRA/LyCORIS/LoHa/LoKr/DoRA. It is a PyQt6 desktop application, so it wants a GUI stack and a human at the keyboard.
• sai_model_spec_tools is mostly Ruby scripts for writing SAI model-spec metadata into a file, plus one Python script that reads it back and flags a CONTRADICTION DETECTED when the declared base model disagrees with what the keys suggest. Its author calls the scripts basic sketches.

What neither of them answers is the question this package exists for: given this delta and this base model, would attaching them do anything at all? is_compatible() is one call, it returns None rather than guessing when the pair is unfamiliar, and it is the whole reason to prefer a zero-dependency library over a desktop inspector.

Two smaller differences worth stating plainly, since they are the practical ones: this reads only the header, never the weights, so a directory of 20 GB checkpoints classifies in milliseconds; and it imports nothing outside the standard library, so it drops into a build step or a CI job without pulling a GUI toolkit behind it.

The ordering trap above is the part I would most like someone to take even if they use one of the others instead.

Install / test

Not on PyPI. From a clone:

pip install .            # or -e ".[test]" to run the suite
pytest -q

Tests build synthetic safetensors headers in a temp directory, so no model weights are needed to run them.

See DESIGN.md for why it is shaped this way, what was rejected, and what to check before changing it.

License

MIT