Reported from a rental: manual gain mode with the refinement keys deleted died
with "'TokenRefiner' object has no attribute 'inner'". Learned mode was fine,
because with no key the gains are neutral and the installer returns before
building anything — so only manual, which ignores keys, ever built the wrapper.
Wrapping a module in an nn.Module renames every weight beneath it
(token_refiner.blocks.* -> token_refiner.inner.blocks.*). patch_model applies
object patches BEFORE loading weights, so the names were recorded WITH .inner;
unpatch_model restores weights BEFORE unwrapping, and with several patchers
alive off one shared model — one clone per scene, plus one for the AdaLN gains
— another patcher unwraps first and the restore walks .inner on a real
TokenRefiner.
Both wrappers now patch `<module>.forward` through the same object-patch
mechanism instead of replacing the module, so the tree and the state dict stay
exactly as the checkpoint built them. Neither is an nn.Module any more, and
each captures the original bound method rather than re-reading `.forward` at
call time — ComfyUI restores a patch by writing the previous value back, which
can be a live wrapper, so re-reading would nest a scale per scene.
Reproduced against faithful patch semantics before and after. AdaLN gains had
the identical latent hazard and are fixed the same way.
digital-garbage
68bcf109ff