- base_precision="mxfp8": torchao MX block-scaled float8 compute on the frozen
base linears (Blackwell sm100+, cuBLAS kernels). Applied after add_adapter like
fp8, never fatal, weights stay bf16 in memory. Measured 1.16x over compiled
bf16 on Z-Image at 1024px batch 4 (16k tokens/step); a wash at small token
counts, so it stays an explicit opt-in and auto never picks it.
- SDXL: regionally compile the U-Net's BasicTransformerBlocks through the same
never-fatal wrapper the DiT trainer uses. 1.35x steady state at 1024px batch 4
with same-seed loss parity (~1e-5 per step) and unchanged peak VRAM; ~30 s
one-time warmup. Steady-state samples/sec now excludes step 1, matching the
DiT trainer.
- /info: mxfp8 advertised only on sm100+; supports_compile now true for sdxl.
- NVFP4 training: not available in torchao 0.16 (no autograd path, no training
recipe), so NVFP4 stays an inference-only quant for now.
193 diffusion backend tests green; frontend build clean.
cb9247e537