Two bugs found while building the hosted checkpoints:
- The builder recorded torch.__version__ (a TorchVersion object) in the
checkpoint metadata, so torch.load(weights_only=True) rejected every
artifact and the loader silently fell back to the dense download.
Record plain strings.
- Re-applying the layerwise fp8 cast to an injected pre-cast encoder
raised on the duplicate hook registration, making quantize_text_encoders
report the engaged cast as failed (status showed no TE quant while the
encoder ran fp8). _cast_fp8 now returns early when the hooks are
already installed.
Also corrects the LTX TE size note: Gemma3-12B stored fp32 (~49 GB), not 27B.
Applies the runtime layerwise fp8 storage cast to a model's dense text
encoder once and saves the cast state dict with baked metadata (format
tag, base_model_id, family, scheme, component, te_class, versions) in
the layout diffusion_te_prequant.py validates. Resolves the encoder
class from the checkpoint's config.architectures so the recorded
te_class matches what the pipeline instantiates. CPU-runnable: the cast
touches storage dtypes only.
