logs/ (a 1.3 MB ComfyUI object_info dump plus stale PID files), temp/ (PR body
and commit message scratch), and async_task_outputs/ (agent task transcripts)
are environment specific runtime artifacts that were committed by accident and
carry stale local state into every checkout. Remove them and gitignore the
directories so they cannot be re-added.
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.
The raw speed_mode string was forwarded to _enable_fp16_accumulation, so a
case-variant like MAX failed the speed_mode != SPEED_MAX check and wrongly
disabled fp16 accumulation on float16 pipelines. Forward the normalized mode
and cover the case-insensitive path in the test.
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.
An unset transformer_quant used to mean off (run the GGUF as-is), so the
hardware ladder only engaged when auto was explicitly chosen and the panel
showed Off as the default. Unset (or auto) now hands the decision to the
ladder: a dense-capable GPU gets at least int8, data-center silicon fp8,
falling back to the GGUF when the device, VRAM, family deny table or disk
cannot take it. An explicit none/off pins GGUF-as-is and is now
expressible in the API (previously only omission meant off, so pinned-off
and unset were indistinguishable); an explicit scheme pins that scheme.
The dense candidate also gains a free-disk gate: with auto as the default
the bf16 base download (up to ~40 GB) must never wedge a nearly-full
model-cache disk, so the candidate is dropped (GGUF build kept) when free
space cannot hold it plus a 10 GiB margin. Unprobeable disk passes.
Frontend: the Dtype select defaults to Auto (fastest for GPU), keeps Off
as an explicit choice, and sends none through instead of omitting it.
Suite: 622 diffusion tests green (default-load test rewritten to the new
contract, explicit-off short-circuit covered), CI-sim green.
resolve_dense_quant_candidate now passes fam.name to
select_transformer_quant_scheme so the policy's proposed scheme honors the
family deny table (qwen-image lands on int8 instead of proposing fp8 that
the execution path would refuse). Test stub updated for the new keyword.
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.
train_precision_modes gates int8/fp8/mxfp8 on has_functional_torchao, and the
Backend CI runner does not install torchao, so the three capability-gating
tests collapsed to nf4/bf16/auto and failed. They exercise the CAPABILITY
gate, not torchao presence: stub the probe functional alongside the CUDA
capability patch. Validated with a torchao-blocked run (22 passed).
The A/B harness measured two regimes. bf16 loads (the Studio default on Ampere+)
are bit-identical with the flag on across all six families, 36/36 same-seed cases,
because the flag only changes fp16 GEMM accumulation. fp16 loads (the pre-Ampere
fallback dtype) show real same-seed drift on the families that genuinely run fp16
GEMMs: SDXL up to 0.050 mean abs diff, FLUX.1 0.028, FLUX.2-klein 0.045, all
finite, no new black frames. qwen-image renders black in fp16 with the flag off
too and z-image fp16 fails in attention, so both are dtype limitations, not
accumulation ones.
So the gate now takes the compute dtype and the speed tier: bf16 engages on any
active tier (provably output-neutral), fp16 engages only under max, the tier that
already trades exactness for measured speed. The deny-list stays empty by
measurement.
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.
fp16 accumulation (torch.backends.cuda.matmul.allow_fp16_accumulation) roughly
doubles fp16 GEMM throughput on consumer tensor cores by keeping the accumulator
in fp16. The flag only affects fp16 GEMMs: bf16-compute DiT families are untouched
by construction, while SDXL's fp16 UNet and any fp16 text encoder or VAE path get
the speedup.
Gate in apply_speed_optims: CUDA target, consumer GPU (datacenter parts keep fp32
accumulation), torch exposes the flag, family not in _FP16_ACCUM_DENY, and the
UNSLOTH_DISABLE_FP16_ACCUM kill switch is unset. The flag is captured in
snapshot_backend_flags and restored on unload like the other process-wide knobs.
_FP16_ACCUM_DENY starts empty: a same-seed A/B harness (off vs on per family at
512 and 1024 with long-prompt and high-guidance stress cases, non-finite, black
frame and drift checks) backs the empty list and populates it if a family ever
overflows.
