- Free reserved VRAM in the diffusion load worker's failure path: a load-time OOM
never commits _state and the next load's _unload_locked early-returns, so nothing
else reclaimed the half-built pipeline's memory
- Use a monotonic clock for the denoise ETA rate
- Sync _GENERATION_DEFAULTS with the UI table: kontext, flux.2-dev, sdxl-turbo and
SDXL base rows so /v1/images/generations stops falling back to 9 steps / CFG 0
- 400 (not sanitized 500) when /v1/images/generations hits an edit-only model
- Fail fast on pre-Ampere CUDA in the DiT trainer instead of dying in model load
- Run the trainer trust gate in the diffusion training route before freeing GPU
residents so an untrusted base cannot tear down loaded chat/Images models
- Protect native sd.cpp companion VAE/text-encoder repos from cache deletion while
a load is downloading them
- Exempt the task-scoped Images picker from the chat-only GGUF/MLX format gate so
local diffusers pipelines stay selectable on no-GPU hosts
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 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.
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
- list_diffusion_runs skips wrong-shape records and the runs route tolerates
per-record ValidationError so one bad file never breaks the panel
- max_steps: 0 epoch-mode sentinel no longer trips train_steps validation
before epochs are resolved
- numberField keeps an explicit 0 (Seed, LR warmup) instead of falling back
- previous-runs list refetches once more shortly after a terminal status so
the just-finished run appears even if the record write races the fetch
The Windows ROCm torchao import stub satisfies find_spec and even lets
from torchao.quantization import quantize_ succeed, but its quantize_ is a
no-op: auto would pick int8, leave the transformer dense, and disable
compile as if it were quantized. has_functional_torchao imports the exact
symbols the int8 path uses and rejects the stub via its sentinel; both the
auto picker and the /info advertised modes now use it
- Unload the ACTIVE image engine (sd_cpp or diffusers) before diffusion training starts, not just the diffusers singleton
- Count metadata.jsonl captions in dataset summaries so metadata-captioned datasets are not reported as uncaptioned
- Sidecar captions now override metadata rows everywhere (grid edits win); trainer and dataset API agree
- Tag local diffusers image checkpoints with text-to-image so they appear in the Images picker
- Family LoRA targets (_FLUX_TARGETS etc) apply when the config carries the generic defaults; explicit overrides still win
