Finish the kernel seam across the board so the platform's KernelTable is the
universal solver-dispatch path, not just wan21.
- Loops: ltx2, wan_causal, adapters, adaptive now resolve the flow-match solver
via model.platform.kernels.get(FLOW_MATCH_STEP) instead of importing the numpy
sampler directly (wan21 already did). On CPU this is bit-identical (the cpu
kernel IS the old function); a GPU/accel backend now overrides every loop.
- RL: the FlowGRPO log-prob recompute in unified_rl / joint_multi_rl /
workflow_rl dispatches FLOW_SDE_STEP through the platform, pinned to the SAME
kernel the rollout used (C2 kernel-pinning — otherwise the PPO ratio biases on
a real GPU where rollout and recompute kernels could differ).
- accel backend: add a kind-generic AccelComponent wrapper and override the vae
component too (text_encoder left unregistered to keep the device->cpu fallback
demonstrated), closing the "only dit is overridden" gap.
- tests: vae override assertion; a second-loop (wan-causal chunk rollout) parity
oracle proving accel == cpu bit-identical beyond wan21. README scope updated.
184 tests pass.
Three more capabilities on distinct untested surfaces (167 tests pass, no new runtime primitive).
A — Adapter plane (§9.19): one base + swappable LoRA/ControlNet adapters, selected per request
(DiffusionParams.adapters); AdapterDenoiseLoop applies each active adapter's velocity delta. Per-request
selection changes output, multi-LoRA composes, ControlNet conditions on a control image, mixed-adapter
requests interleave without smearing, hot-swap changes generation, cache key partitions by adapter stack
(the adapter_versions field, previously declared-only). ToyLoRA/ToyControlNet; models/adapters/. 6 tests.
B — Non-linear workflows (§9.17): ParallelWorkflow (fan-out: one input → N models → merged) and
BestOfNWorkflow (generate N → score with the served reward card → return best; inference-time scaling).
The shapes a linear chain can't express. 4 tests.
D — RL→distill flywheel (§9.18): run_flywheel RL-improves the base (NFT), then distills FROM the RL'd model
(DMD2 teacher = RL'd policy) into a faster card, recording the base→rl→distilled provenance chain in
RecipeSpec.parents. The distilled student is measurably closer to the RL'd teacher than the base; the
distilled card serves few-step. training/flywheel.py. 4 tests.
designv4 §9.17–§9.19 + layout/closing/counts (167 tests, 29 files).
SolitaryThinker