Attention: apply_attention_backend now best-effort installs the package an
explicitly requested optional backend needs (sage -> sageattention, flash ->
flash-attn, flash3/flash4 -> kernels, xformers), wheel-only via pip
--only-binary=:all: so a host without a CUDA toolchain never starts a source
build. Gated by UNSLOTH_DIFFUSION_ATTENTION_INSTALL (auto|0), mirroring the
sd.cpp prebuilt installer gate, and only reached after the arch gating in
select_attention_backend, so no install is attempted for a kernel this card
cannot run. Any failure keeps today's native fallback.
Step cache: transformer_cache gains a real auto state (unset or "auto"). At
load the policy engages FBCache when the model's default schedule reaches
FBCACHE_MIN_STEPS = 20 (dev-style 28-step models win ~1.4x; 4-9-step distilled
models never engage, a skipped step costs too much there). generate() then
re-checks the ACTUAL step count and toggles the cache idempotently across the
bar, so one resident load serves both a 28-step and a 4-step request with the
right cache state, and status/resolved provenance follow the toggle. An explicit
off or fbcache request is pinned and never toggled. Compile drops fullgraph when
an auto cache could still engage on a cache-capable transformer, since enabling
FBCache under a fullgraph-compiled transformer would crash.
Verified on GPU: flux.1-schnell load starts uncached (4-step default), engages
fbcache at 24 steps, disengages at 4, re-engages at 28, with images at each
step and the provenance record tracking each transition.
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