440bec0ec8
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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| .. | ||
| 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 | ||