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Trim the comments across the diffusion backend

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Comment-only pass over the Python this PR touches: drop what the code already
says, collapse multi-line explanations that still read on one line, and keep
the reasoning that is not recoverable from the code. No code, docstring
semantics or behaviour changes; verified with an AST comparison against the
previous revision, and the backend suite is unchanged (same 37 environment
failures as before: the API integration tests that need a live keyed server,
the flash-attn install hooks, and the GPU memory fields).
This commit is contained in:
Daniel Han 2026-07-26 20:30:46 +00:00
commit 36df317293
113 changed files with 3389 additions and 4087 deletions
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11
scripts/build_te_prequant_checkpoint.py
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@ -57,8 +57,7 @@ def main(argv = None) -> int:
from core.inference.diffusion_precision import _cast_fp8
from core.inference.diffusion_te_prequant import TE_PREQUANT_FORMAT
# The family is metadata for forensics; detection lives in different modules per
# branch (diffusion_families vs video_families), so resolve best-effort by name.
# Family is forensic metadata; detection differs per branch, so resolve best-effort by name.
family = args.family.strip().lower()
subfolder = args.component if args.config_subfolder is None else args.config_subfolder
@ -70,9 +69,8 @@ def main(argv = None) -> int:
print(f" loading dense encoder from {args.base} (subfolder={subfolder!r}) ...", flush = True)
t0 = time.time()
config = transformers.AutoConfig.from_pretrained(args.base, **from_pretrained_kwargs)
# Prefer the checkpoint's own architecture (what the diffusers pipeline instantiates,
# e.g. Gemma3ForConditionalGeneration); AutoModel.from_config would give the bare base
# class and record a te_class whose state dict the pipeline cannot use.
# Prefer the checkpoint's own architecture; AutoModel.from_config gives the bare base class,
# whose state dict the pipeline cannot use.
arch = (getattr(config, "architectures", None) or [None])[0]
if arch and hasattr(transformers, arch):
encoder_cls_name = arch
@ -104,8 +102,7 @@ def main(argv = None) -> int:
"te_class": encoder_cls_name,
"torch_dtype": args.dtype,
"cast_backend": "diffusers_layerwise",
# str(): torch.__version__ is a TorchVersion object; pickling it into the
# checkpoint makes torch.load(weights_only=True) reject the whole artifact.
# str(): a pickled TorchVersion makes torch.load(weights_only=True) reject the artifact.
"torch_version": str(torch.__version__),
"transformers_version": str(transformers.__version__),
}