The prefetch already scopes the file list (no packaged root singles, no
dtype-variant twins, no ONNX/Flax exports), but from_pretrained was then
called with the hub id, and its own snapshot sweep re-downloaded the
skipped files anyway: 24 GB per FLUX.1 repo and 65 GB on FLUX.2-dev, as
found in the blob cache. Return the snapshot dir from the prefetch (keyed
on the pipeline manifest) and hand it to every pipeline-assembly
from_pretrained site; any prefetch failure keeps the hub id and the old
behavior.
The generic Studio config dict path can deliver these flags as strings, and a
non-empty string like "false" is truthy, so an opt-out silently no-ops (the
latent cache still builds, TF32 stays on). Coerce them the same way
gradient_checkpointing already is.
Measured on the fresh linux x64 prebuilt (z-image Q8_0, sd-cli, 192 CPU
threads, 512x512, 9 steps, steady state): sampling 56.1s vs 51.3s (about
9 percent faster), VAE decode unchanged, peak RSS identical. The sd.cpp
engine only serves the no-GPU tier, so the default profile now matches
max: --diffusion-fa plus --diffusion-conv-direct.
A 28-pair accuracy gate on a B200 (same-seed vs the dense bf16 reference)
found per-row fp8 dynamic quant renders EVERY qwen-image frame black
(mean luma 0.0000, SSIM 0.016), reproduced identically with on-the-fly
quantize_ on the dense transformer, so it is the model's activation range,
not a checkpoint artifact. mxfp8 shows real semantic damage at 1024px
(CLIP delta mean 0.0146, worst cases 0.064/0.102) and nvfp4 measures
LPIPS mean 0.51. int8 dynamic (per-token scales) is excellent on Qwen:
LPIPS mean 0.069, SSIM 0.958.
The per-scheme smoke probe only proves the GEMM kernel runs, so it cannot
catch model-level breakage. Add _FAMILY_SCHEME_DENY consulted by
select_transformer_quant_scheme: auto skips denied schemes (Qwen lands on
int8) and an explicit denied request returns None, the same GGUF-fallback
contract as an unsupported scheme. Family is threaded from the three
diffusion.py call sites; existing behavior is unchanged for every other
family. 4 new tests; 529 diffusion tests green; CI-sim green.
spec.forward imports Krea2Pipeline for prepare_position_ids, so the test needs a
real diffusers install; the backend CI matrix runs without one and failed on the
import. Same importorskip guard the sigmas gather test already uses.
The fp16-on-bf16-family refusal in run_dit_lora_training now fires before the heavy
imports, so a host without diffusers gets the real validation error instead of
ModuleNotFoundError. test_in_progress_returns_409_after_validation_passes pins the
resolved device to cuda because the load route only takes the GPU arbiter for non-CPU
loads, which made the ownership assert host-dependent.
