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.
