nf4 loads now enable double quantization (~0.4 bits/param off the frozen base
scales at no fidelity cost), fp8 training uses the rowwise recipe when the
torchao build ships it (per-row scaling confines the DiT activation outliers
that a tensor-wide scale collapses), and the inference quant filter gains a
per-scheme GEMM-tiling divisibility floor (16 for scaled_mm, 32 for MX blocks)
so one ragged Linear cannot crash the first denoise after a clean quantize
pass. plans/fsdp2_diffusion_design.md records the multi-GPU design: bf16/fp8
over FSDP2 with per-block units, LoRA attached before sharding, int8 out of
scope (DTensor over the quantized subclass is undefined), per-family notes.
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