detect_quant now reads _quantization_metadata as the authoritative quant
source when present, and resolves marker-file formats from the marked
layers' stored weight dtypes instead of file-wide dtype voting, which
mislabeled fp8 files carrying extra uint8 tensors and had no nvfp4
mapping at all. Schema bump so cached probe entries refresh.
json_helpers does its own error handling and file locking, so the
try/except wrappers, threading locks and isfile checks were dead
weight; probe cache file paths are now defined in paths.py.
fp8_e4m3fn and fp8_e5m2 differ in kernel support, so a bare fp8 token
is not enough to know whether a file runs on a given architecture;
scaled_fp8 derives its token from the detected format.
Local probes read the marker tensor bytes for the true format string
(nvfp4, mxfp8, int8_tensorwise) instead of inferring from weight
dtypes; remote ranged peeks stay dtype-inferred. precision_token maps
quant format or dominant dtype to the short filename token.
Analyzes safetensors/gguf headers without reading weights: curated
tensor-name fingerprints for common families, container kind detection
(model/lora/vae/text-encoder), element-weighted precision over core
tensors, comfy_quant/scaled-fp8/gguf quant detection, lora base
resolution via trainer metadata with shape arbitration, and full
__metadata__ passthrough. Results cached by mtime in
data/model_probe.json.
- analyze_header is pure and shared by remote ranged peeks and local
file reads
- fingerprints verified against the local library and live civitai
headers; unmatched families report unknown rather than guessing
CalamitousFelicitousness