Fix double timestep shifting in FlowMatchEulerDiscreteScheduler.set_timesteps (#13243)

[jbbqqf]

Fixes #13243.

Summary

FlowMatchEulerDiscreteScheduler.__init__ records self.sigma_max / self.sigma_min after applying the standard `shift * sigmas / (1 + (shift - 1) * sigmas)` transform:

sigmas = timesteps / num_train_timesteps
if not use_dynamic_shifting:
    sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)   # ← shift applied
self.sigmas = sigmas
self.sigma_min = self.sigmas[-1].item()                     # ← post-shift
self.sigma_max = self.sigmas[0].item()                      # ← post-shift

set_timesteps then fed those already-shifted endpoints back into the linspace and applied the same shift again:

timesteps = np.linspace(self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps)
sigmas = timesteps / self.config.num_train_timesteps
...
sigmas = self.shift * sigmas / (1 + (self.shift - 1) * sigmas)   # ← shift applied again

So calling scheduler.set_timesteps(num_train_timesteps) produced a different schedule than the one __init__ had already computed, for the same (num_train_timesteps, num_inference_steps, shift) triple.

Fix

Use the pre-shift [num_train_timesteps, ..., 1] range directly when no custom timesteps/sigmas are provided. The shift is then applied exactly once below. self.sigma_max/self.sigma_min are left as-is so any consumer reading them externally keeps the same public semantics.

Reproduce BEFORE/AFTER yourself (copy-paste)

# --- BEFORE: origin/main, schedules diverge ---
git fetch origin main && git checkout origin/main
python - <<'PY'
import torch
from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
sched = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=3.0)
init_sigmas = sched.sigmas.clone()
sched.set_timesteps(1000)
post_sigmas = sched.sigmas.clone()
print('match (excluding terminal 0):', torch.allclose(init_sigmas, post_sigmas[:-1], atol=1e-5))  # Expected: False
print('init head :', init_sigmas[:3].tolist())
print('post head :', post_sigmas[:3].tolist())
PY

# --- AFTER: this branch ---
git fetch origin pull/<PR>/head:fix && git checkout fix
python - <<'PY'
import torch
from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
sched = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=3.0)
init_sigmas = sched.sigmas.clone()
sched.set_timesteps(1000)
post_sigmas = sched.sigmas.clone()
print('match (excluding terminal 0):', torch.allclose(init_sigmas, post_sigmas[:-1], atol=1e-5))  # Expected: True
PY

I confirmed both runs locally:

• origin/main → match: False (init head [1.0, 0.9997, 0.9993], set_timesteps head [1.0, 0.9991, 0.9982] — visibly different).
• this branch → match: True.

Notes

• Custom timesteps / sigmas paths are unchanged.
• No public API or attribute changes; self.sigma_min/self.sigma_max keep their current values.

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🤖 Disclosure: Authored with assistance from Claude (Anthropic), reproducer run on both refs.