This page explains how timestep sampling works during training, what the shift parameter actually does, and why Side-Step's approach differs from the upstream community trainer.

TL;DR: shift is an inference-only parameter. It does not affect the training loop. Side-Step's corrected training uses the same continuous timestep sampling as the model's own forward() method. The --shift and --num-inference-steps settings are stored as metadata so you know what values to use when generating audio with your trained adapter.

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What shift does (inference only)

During inference (audio generation), shift warps the timestep schedule used by the diffusion ODE/SDE solver:

t_shifted = shift * t / (1 + (shift - 1) * t)

This formula appears in generate_audio() inside each model variant. It controls how denoising steps are distributed:

• shift=1.0 -- Uniform linear schedule. Steps are evenly spaced from 1.0 to 0.0. This is the standard schedule and requires more steps (typically 50) for good quality. Used by base and sft models.
• shift=3.0 -- Compressed schedule. More denoising happens at the high end (near t=1.0), less at the low end. This allows fewer steps (typically 8) with minimal quality loss. Used by turbo models.

The turbo model also has pre-computed discrete timestep tables for each shift value:

Shift	8-step schedule
1.0	[1.0, 0.875, 0.75, 0.625, 0.5, 0.375, 0.25, 0.125]
2.0	[1.0, 0.933, 0.857, 0.769, 0.667, 0.545, 0.4, 0.222]
3.0	[1.0, 0.955, 0.9, 0.833, 0.75, 0.643, 0.5, 0.3]

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What controls training timesteps

During training, timesteps are sampled from a continuous distribution. All three ACE-Step model variants (turbo, base, sft) define the same sample_t_r() function in their model code:

def sample_t_r(batch_size, device, dtype, data_proportion, timestep_mu, timestep_sigma, use_meanflow):
    t = torch.sigmoid(torch.randn((batch_size,)) * timestep_sigma + timestep_mu)
    r = torch.sigmoid(torch.randn((batch_size,)) * timestep_sigma + timestep_mu)
    t, r = torch.maximum(t, r), torch.minimum(t, r)
    # use_meanflow=False during training -> r = t for all samples
    ...
    return t, r

This is logit-normal sampling: draw from a normal distribution, then pass through a sigmoid to get values in (0, 1). The shape of this distribution is controlled by two parameters from the model's config.json:

Parameter	Typical value	Effect
timestep_mu	-0.4	Shifts the distribution center. Negative values bias toward lower timesteps
timestep_sigma	1.0	Controls spread. Larger values give a wider range of timesteps

Side-Step reads these automatically from each model's config.json at startup. You never need to set them manually.

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Side-Step vs upstream (vanilla) trainer

Side-Step corrected training (fixed mode)

Side-Step's sample_timesteps() function (in timestep_sampling.py) is a line-for-line reimplementation of the model's own sample_t_r():

# Side-Step (timestep_sampling.py)
t = torch.sigmoid(
    torch.randn((batch_size,), device=device, dtype=dtype) * timestep_sigma + timestep_mu
)

This matches the model's native training forward() method exactly, for all three variants. The parameters come from each model's config.json.

Result: Side-Step can correctly train adapters for turbo, base, and sft models because it uses the same timestep distribution the models were originally trained with.

Upstream community trainer

The original ACE-Step community trainer (acestep/training/trainer.py) uses a different approach -- discrete timesteps hardcoded from shift=3.0:

# Upstream trainer (trainer.py)
TURBO_SHIFT3_TIMESTEPS = [1.0, 0.955, 0.9, 0.833, 0.75, 0.643, 0.5, 0.3]

def sample_discrete_timestep(bsz, timesteps_tensor):
    indices = torch.randint(0, timesteps_tensor.shape[0], (bsz,))
    t = timesteps_tensor[indices]
    return t, t

Each training step uniformly picks one of those 8 discrete values. This approach:

1. Does not match how the models were actually trained. The models use continuous logit-normal sampling, not discrete uniform sampling.
2. Is hardcoded for turbo. Those specific timestep values come from shift=3.0 with 8 steps. For base or sft models (which use shift=1.0 and more steps), this schedule is incorrect.
3. Samples from a fundamentally different distribution. Uniform over 8 points vs. continuous logit-normal over the full (0, 1) range.

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What the shift setting does in Side-Step

When you set --shift or configure shift in the wizard, Side-Step:

1. Saves it to the training config JSON alongside your adapter weights
2. Does NOT use it during training -- the training timestep distribution is entirely controlled by timestep_mu and timestep_sigma from the model config

The value is stored so that you (and anyone you share your adapter with) know what shift value to use at inference time when generating audio with the trained LoRA/LoKR.

Recommended values

Model variant	--shift	--num-inference-steps
Turbo	3.0	8
Base	1.0	50
SFT	1.0	50

The wizard auto-detects these from the model you select.

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FAQ

Q: I changed --shift and my training results didn't change. Is this a bug? No. Shift does not affect training. The training timestep distribution comes from the model's own timestep_mu/timestep_sigma parameters, which Side-Step reads automatically.

Q: If shift doesn't affect training, why is it a setting? It's metadata. When you finish training and want to generate audio with your LoRA, you need to know the correct shift value. Storing it with the adapter config prevents guesswork.

Q: Can I train a turbo model and use it with shift=1.0 at inference? Technically yes, but the quality will differ from what the model expects. Also, i don't think it is fully supported by upstream. Use the shift value that matches the model variant you trained on.

Q: Why does the upstream trainer use discrete timesteps? The upstream community trainer was written specifically for the turbo model. It takes the 8 discrete timestep values from the turbo inference schedule and samples uniformly from them. This is a reasonable approximation for turbo but does not match the model's actual training distribution, and it does not work for base or sft models.

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Source references

These are the relevant source locations for anyone who wants to verify:

• Model's native training sampling: sample_t_r() in modeling_acestep_v15_turbo.py (lines 169-194), modeling_acestep_v15_base.py (same), sft/modeling_acestep_v15_base.py (same)
• Model's inference shift warp: generate_audio() in each model file, applies t = shift * t / (1 + (shift - 1) * t)
• Side-Step's corrected sampling: sample_timesteps() in acestep/training_v2/timestep_sampling.py
• Upstream discrete sampling: sample_discrete_timestep() in acestep/training/trainer.py (lines 302-323)

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See Also

• [[Training Guide]] -- Training modes, hyperparameters, and monitoring
• [[The Settings Wizard]] -- All wizard settings explained
• [[VRAM Optimization Guide]] -- GPU tiers and memory management