The latent cache holds two fp32 posterior tensors per crop/flip variant per
image, pinned on CUDA hosts, so datasets with thousands of images can exhaust
host or pinned memory with no fallback. Estimate the cache size from the first
real encoded latent and fall back to per-step VAE encoding when it exceeds a
4 GiB budget. UNSLOTH_DIFFUSION_FORCE_LATENT_CACHE bypasses the gate; the
existing UNSLOTH_DIFFUSION_NO_LATENT_CACHE opt-out is unchanged.
Replace the with-replacement per-batch index draw in the SDXL and DiT LoRA
trainers with a shared PermutationBatchSampler that visits every image once per
cycle before repeating, so short runs cover the whole dataset. The sampler
reshuffles from the run's rng so the index stream stays seed-deterministic.
Guard the diffusion run detail route against a valid-JSON non-object record,
which previously raised TypeError and returned a 500; it now 404s like the list
path's shape check.
Add regression tests for both.
The latest huggingface-hub release added the Sandboxes feature. Its
bootstrap (_sandbox.py) fetches the static sbx-server binary into /tmp with
an Authorization header and marks it executable, which is exactly the
staged-dropper pattern the scanner hunts, and three while True polling loops
in _sandbox.py / hf_api.py / utils/_http.py match the beaconing heuristic.
All four verified against the official huggingface/huggingface_hub
repository: the snippet is the documented sandbox server injection and the
loops are deadline-style job and sandbox polling. Entries generated with
--write-baseline and reviewed line by line; scan_packages.py huggingface-hub
now exits 0 with the four findings suppressed.
The run detail route built DiffusionTrainingRunDetail(**rec) unguarded, so a
valid-JSON-but-wrong-shape record (hand-edited or an older schema) would 500
instead of reading as absent. Catch ValidationError and 404, matching how the
list route skips malformed records.
A dual-DiT pipeline (Ideogram 4's unconditional tower) placed its second
denoiser resident under the group tier, which defeats the tier since the
pair rarely fits where one alone did not. Stream transformer_2 and
unconditional_transformer alongside the transformer and keep only the
smaller companions resident.
The perf rewrite dropped the bf16 capability guard, so a pre-Ampere CUDA
device (T4/V100/RTX 20xx) would die deep in model load with an opaque dtype
error instead of a clear message. Restores parity with the SDXL trainer.
- Run the trainer's caption discovery in the start route BEFORE freeing GPU
residents, so a missing or uncaptionable dataset 400s without evicting the
loaded chat/Images model.
- sd.cpp unload now waits out a cancelled one-shot generation on the generate
lock before reporting the device free, matching the diffusers backend.
- Clearing a caption that came from metadata.jsonl writes an empty sidecar
tombstone instead of unlinking (both readers treat an existing sidecar as
authoritative), so the cleared label cannot resurface.
- The ControlNet wrapper pipe is only cached while its load is still current,
closing the unload race the model cache already handled.
