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.
The loader used to plan memory from the GGUF file size and only offer the dense
transformer-quant fast path when that plan was already resident, so on a card where
the GGUF forced offload the int8/fp8 build (roughly half the bf16 bytes, or exactly
the quantised size when a pre-quantized checkpoint exists) was never attempted.
diffusion_auto_policy.py is a pure decision layer: a bf16-resident component table
per family (transformer / text encoders / VAE, with base-repo overrides for the
multi-size families), per-scheme size factors with separate steady and transient
(build peak) numbers, and resolve_dense_quant_candidate which the loader now uses to
re-plan memory against the candidate artifact before settling for offload. The
engaged plan is adopted only when the dense build succeeds; the GGUF fallback keeps
its own plan.
Status now carries a resolved provenance record per Advanced control (value, source
auto or explicit, reason) so the UI can label backend decisions.
The always-visible description under the select is gone (the hint tooltip keeps the
full detail) and the no-model gallery placeholder now reads 'Select a diffusion model
to load'.
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.
Falling through to hf_hub_download with a filesystem path as the repo id
raised an opaque HFValidationError; a local dir without the file now
raises FileNotFoundError naming the directory
num_epochs was only int-coerced for the range check, so a string value
from a dict-built config would reach resolve_train_steps' arithmetic;
normalized() now stores the coerced int. Run record reads/writes pass
encoding utf-8 explicitly so non-ASCII prompts survive on Windows
- Trainers emit the pre-clip gradient norm; the service keeps a bounded
grad_norm history and the Train tab renders a Grad Norm chart next to
Loss and LR
- Completed runs show 'Training complete' with a celebratory marker in
the success color instead of a plain status word
- metadata.jsonl caption keys now match on Windows (as_posix relative
paths) in both the trainer discovery and the dataset image records
- RMSNorm eager patch skips installation on torch builds without
F.rms_norm instead of failing at forward time
- GGUF compute description no longer says the GGUF is dequantised: the
INT8/FP8/FP4 modes load the base model's bf16 transformer and quantise
that directly; label no longer wraps in the Advanced panel
torchao 0.17 removed MXLinearConfig from prototype.mx_formats in favour of
MXFP8TrainingOpConfig.from_recipe shared with MoE training. _mxfp8_training_config
tries the 0.16 API first and falls back to the 0.17 one; both feed quantize_.
mxfp8 still degrades to bf16 with a warning when neither import resolves
Krea's release guidance is to train on Krea-2-Raw and run adapters on
Turbo. Raw now leads the krea-2 training bases (Turbo stays available),
both vendor repos are trust-listed, and load_krea2_pipeline fails fast
with an upgrade hint on diffusers older than 0.39 instead of a bare
AttributeError mid-load
