039049b05e
Qwen-Image's scheduler skips its static shift under use_dynamic_shifting, so the DiT trainer was drawing UNSHIFTED uniform-schedule sigmas for it (mean sigma 0.50) while inference always runs the exponential mu = log 3 shift plus the shift_terminal 0.02 stretch. Add a flow_shift config lever: "auto" (the new qwen-image default) rebuilds the training sigma table through the scheduler's own time_shift and stretch_shift_to_terminal so the draw matches the inference distribution exactly (mean sigma 0.72); a numeric value applies the standard linear shift s*u/(1+(s-1)*u); 1.0 keeps the historical identity behavior and stays the default for FLUX, Z-Image and Krea 2. The model timestep conditioning follows the shifted sigma, gathered in fp32 so bf16 rounding never skews it. Also wire two opt-in levers with off defaults: cfg_dropout (per-sample empty-prompt conditioning dropout, encoded alongside the captions before the text encoders are freed) and weighting_scheme="bell" (bsmntw-style mid-schedule Gaussian loss weighting normalized to mean 1). Verified with two 80-step rank-8 bf16 LoRA runs on Qwen/Qwen-Image (identity vs auto, same seed): both converge with finite decreasing loss and produce coherent same-seed previews. Unit tests cover the exact transform, the shifted sampling distribution, per-family defaults and config plumbing. |
||
|---|---|---|
| .. | ||
| 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 | ||