fix: convert boundary_ratio from timestep space to index space

[Auraithm]

Summary

boundary_ratio=0.9 represents the DiT1/DiT2 switch point at timestep 900. In inference, this value is correctly compared against actual timestep values. However, in training_step, it was incorrectly used as an index fraction for max/min_timestep_boundary.

Due to sigma_shift (flow_match.py L57), the scheduler's timestep array is non-linearly spaced — timestep 900 sits at index ~250 (shift=3), not index 900. Using 0.9 as the index boundary caused DiT1 to be trained on 90% of indices and DiT2 on only 10%, while in inference DiT2 handles the majority of denoising (timestep 900→0).

Fix

Apply inverse sigma_shift to convert boundary_ratio from timestep space to index space before passing it to max/min_timestep_boundary. This aligns training with inference behavior.

• shift=3 → index boundary = 0.25
• shift=5 → index boundary = 0.358 (matches DiffSynth-Studio PR #806)

Reference

• fix training boundary for wan2.2 A14B modelscope/DiffSynth-Studio#806

[tianyilt]

上下文：这里的 boundary 应该是 sigma_shift 前的 index 边界
比如modelscope/DiffSynth-Studio#806

因此不要硬编码：switch_dit_boundary: float = 0.358, #FIXME(dhyu):

[tianyilt]

@Auraithm 最好贴个测试通过的例子

[Auraithm]

测试命令

cd xxx/MOVA && python -c "
import torch
from mova.diffusion.schedulers.flow_match import FlowMatchScheduler
from mova.diffusion.pipelines.mova_train import TimestepConfig, compute_density_for_timestep_sampling

scheduler = FlowMatchScheduler(num_train_timesteps=1000, shift=3.0)
scheduler.set_timesteps(1000, training=True)

boundary_ratio = 0.9
boundary = (scheduler.timesteps >= boundary_ratio * scheduler.num_train_timesteps).sum().item() / scheduler.num_train_timesteps
boundary_idx = int(boundary * 1000)

print(f'boundary = {boundary}, boundary_idx = {boundary_idx}')
print(f'timesteps[{boundary_idx-1}] = {scheduler.timesteps[boundary_idx-1].item():.2f}')
print(f'timesteps[{boundary_idx}] = {scheduler.timesteps[boundary_idx].item():.2f}')

print()
print('--- 偶数步 (DiT1) 采样 100 次 ---')
min_ts, max_ts = float('inf'), float('-inf')
for _ in range(100):
    u = compute_density_for_timestep_sampling('uniform', 1, min_timestep_boundary=0.0, max_timestep_boundary=boundary)
    tid = torch.clamp(torch.floor(u * 1000).long(), 0, boundary_idx - 1).item()
    ts = scheduler.timesteps[tid].item()
    min_ts, max_ts = min(min_ts, ts), max(max_ts, ts)
assert min_ts >= 900
print(f'[PASS] [{min_ts:.2f}, {max_ts:.2f}], 全部 >= 900')

print()
print('--- 奇数步 (DiT2) 采样 100 次 ---')
min_ts, max_ts = float('inf'), float('-inf')
for _ in range(100):
    u = compute_density_for_timestep_sampling('uniform', 1, min_timestep_boundary=boundary, max_timestep_boundary=1.0)
    tid = torch.clamp(torch.floor(u * 1000).long(), boundary_idx, 999).item()
    ts = scheduler.timesteps[tid].item()
    min_ts, max_ts = min(min_ts, ts), max(max_ts, ts)
assert max_ts < 900
print(f'[PASS] [{min_ts:.2f}, {max_ts:.2f}], 全部 < 900')

print()
print('ALL CHECKS PASSED')
"

结果：

Set TORCH_CUDA_ARCH_LIST to 9.0
flash_attn_interface loaded from flash_attn_interface
boundary = 0.251, boundary_idx = 251
timesteps[250] = 900.24
timesteps[251] = 899.76

--- 偶数步 (DiT1) 采样 100 次 ---
[PASS] [900.72, 1000.00], 全部 >= 900

--- 奇数步 (DiT2) 采样 100 次 ---
[PASS] [14.82, 898.32], 全部 < 900

ALL CHECKS PASSED

[yhzx233]

也许这个boundary可以提前算好存为成员变量