7bf470f8b9
A B200 speed/memory sweep (new scripts/quant_speedmem_bench.py) shows the VAE quant win is a video story. Image AutoencoderKLs are ~0.15-0.26 GB, so fp8 saves ~0.1 GB and only slows their tiny decode (+6-16%); the video Conv3d VAEs are ~2.5 GB and halve to ~1.2 GB at ~2% decode cost. So VAE auto now only engages above a ~1 GB size floor: small image VAEs stay dense (faster decode, no quant quality risk), video VAEs still quantize. An explicit fp8 / fp8_dynamic request skips the gate (opted in). The same sweep confirmed the text-encoder default is already right: fp8_dynamic is E2E-neutral (denoise per-step unchanged; +2% one-time encode) and, by hidden-state cosine vs bf16, marginally more accurate than layerwise fp8 -- so that default is left as is. Tests cover the gate (small skipped, large quantized, explicit bypasses). |
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| .. | ||
| backend | ||
| frontend | ||
| src-tauri | ||
| __init__.py | ||
| install_llama_prebuilt.py | ||
| install_node_prebuilt.py | ||
| install_python_stack.py | ||
| install_sd_cpp_prebuilt.py | ||
| LICENSE.AGPL-3.0 | ||
| node_prebuilt_pins.json | ||
| package-lock.json | ||
| package.json | ||
| setup.bat | ||
| setup.ps1 | ||
| setup.sh | ||
| Unsloth_Studio_Colab.ipynb | ||