Round 2 of extensive_testing on 548d99a found no exploitable defect --
has_block's "contains, not is" semantics are shared with minimax_h3's
own equally consequential is_h3() gate, and nothing in this codebase
nests diffusion_model in a wrapper that could trigger a false positive
today. Recorded as a one-line limit rather than left implicit, per the
round's only (non-blocking) suggestion.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Round 1 of extensive_testing on 79ca8cf found is_h3_model() matched on
diffusion_model's bare class name ("MiniMaxH3Model"), inherited
unchanged from v4. This codebase's own core/traits.py.has_block()
documents exactly why that's unsafe for a name this generic: a
same-named class from an unrelated custom node or a future FunPack
model would be silently treated as H3 and sparsified at 0.90 with
H3-specific audio-prefix pinning -- the identical LTX-collision risk
this file's own docstring already warns about, just not guarded
against strongly enough. modules/models/minimax_h3 already qualifies
its own identical check (MODEL_CLASS = the dotted path); this port
carried over the private, weaker duplicate instead of reusing that
pattern.
Switched to core/traits.py's has_block() with the same qualified dotted
name, kept as a local string (not an import of modules/models/minimax_h3
itself, which pulls in comfy_extras at import time -- this file
deliberately stays importable without ComfyUI on the path). Added a
test exercising the exact collision the bare match would have missed:
a class named MiniMaxH3Model from an unrelated module.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Loader-level attention option: mean-pool Q and a smoothed K into
blocks, score them with one small matmul, attend only the top
1-sparsity fraction of key blocks per query block, via a vendored
Triton kernel. Runs alongside the chosen attention backend rather than
replacing it (one override slot; the backend becomes SLA's dense
fall-through for the text refiner, masked calls, and trailing dense
steps), and refuses cleanly -- naming why -- on anything that is not
MiniMax H3 or a machine without CUDA+Triton.
modules/loaders/sla_block_map.py and sla_kernel.py are near-verbatim
ports of v4's vendored files (no FunPack-specific imports, so nothing
to adapt); sla_attention.py is the loader-facing glue (H3 gate,
override composition, per-step wrapper), same content as v4's with a
"not a module" note added to match gguf_support.py's convention.
Wired into FunPackDiffusionModelLoader as six new optional widgets,
following v4's loaders.py wiring but expressed in v5's io.Schema style.
The Triton kernel itself is unexercisable on this Mac (no CUDA) --
matches how real generation is verified in this project (rented GPU,
not local). Ported the CPU-testable half of v4's test suite (the
override contract, the H3 gate, the per-step wrapper); the three
kernel-correctness tests carry the same CUDA+Triton skip guard v4 used.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
