cb9247e537
- 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. |
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| .. | ||
| assets | ||
| auth | ||
| core | ||
| hub | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| cloudflare_tunnel.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||