Wizard Settings Reference

The wizard has two modes: Basic (fewer questions, good defaults) and Advanced (all settings exposed). Basic mode settings are shown to everyone. Advanced settings appear only when you select "Advanced" at the start.

Basic Mode Settings

These are always shown regardless of mode.

Required Settings

Setting	Default	What it does	Why you'd change it
Checkpoint directory	From settings or ./checkpoints	Where model weights live	Point to your ACE-Step checkout or custom weights
Model	Interactive picker	Which model variant to train on	Pick base, turbo, sft, or a community fine-tune
Dataset directory	(none)	Folder with preprocessed .pt files	Always required -- your training data
Output directory	(none)	Where adapter weights and logs are saved	Always required -- pick a descriptive name

LoRA Settings

Setting	Default	What it does	Why you'd change it
Rank	64	Number of low-rank dimensions. Higher = more capacity, more VRAM	Lower for quick tests (16), higher for max quality (128). See [[Training Guide]]
Alpha	128	Scaling factor (usually 2x rank). Controls adapter strength	Keep at 2x rank unless experimenting
Dropout	0.1	Dropout on LoRA layers. Prevents overfitting	Increase (0.2-0.3) for very small datasets, decrease (0.05) for large ones
Attention type	both	Which attention layers get adapters: self, cross, or both	self = audio patterns only, cross = text conditioning only. See [[Estimation Guide]] to decide
Target projections	q_proj k_proj v_proj o_proj	Which projections inside each attention block	Use [[Estimation Guide]] results to narrow this down

LoKR Settings (when training LoKR)

Setting	Default	What it does	Why you'd change it
Linear dimension	64	Analogous to LoRA rank	Same guidance as LoRA rank
Linear alpha	128	Scaling factor	Keep at 2x linear dim
Factor	-1	Kronecker factorization factor (-1 = auto)	Leave at auto unless you understand Kronecker factorization
Decompose both	no	Decompose both Kronecker factors	May improve compression at the cost of capacity
Tucker decomposition	no	Use Tucker decomposition	Alternative factorization -- experimental
Scalar scaling	no	Use scalar scaling	Experimental
DoRA weight decompose	no	DoRA-style weight decomposition	Can improve quality in some cases
Attention type	both	Same as LoRA	Same guidance
Target projections	q_proj k_proj v_proj o_proj	Same as LoRA	Same guidance

Training Settings

Setting	Default	What it does	Why you'd change it
Learning rate	1e-4	How fast the optimizer moves. For Prodigy, set to 1.0	Lower (5e-5) for stability, higher (2e-4) for faster convergence
Batch size	1	Samples per step	Increase only if you have VRAM to spare
Gradient accumulation	4	Steps before weight update. Effective batch = batch_size x this	Higher for smoother gradients, lower for faster updates
Max epochs	100	Full passes through the dataset	More for small datasets (200-500), fewer for large (50-100)
Warmup steps	100	LR ramps from 10% to 100% over this many steps	Longer warmup (200+) for stability with large LR
Seed	42	Random seed for reproducibility	Change to get different training runs

CFG Settings (corrected mode only)

Setting	Default	What it does	Why you'd change it
CFG dropout ratio	0.15	Probability of replacing conditions with null embeddings	The base model was trained with 0.15 -- match it. Lower values reduce CFG effectiveness

Logging & Checkpoints

Setting	Default	What it does	Why you'd change it
Save every N epochs	10	Full checkpoint (adapter + optimizer + scheduler)	Lower for safety on long runs, higher to save disk space
Log every N steps	10	TensorBoard loss/LR logging	Lower for more granular curves, higher to reduce overhead
Resume from	(empty)	Path to a checkpoint to resume from	Use after interrupted training. Points to a checkpoint-epoch-N folder

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Advanced Mode Settings

These appear only when "Advanced" is selected at the start of the wizard.

Device & Precision

Setting	Default	What it does	Why you'd change it
Device	auto	GPU selection. Auto picks: CUDA > MPS > XPU > CPU	Multi-GPU: pick cuda:0 or cuda:1. Force CPU for debugging
Precision	auto	Float format. Auto picks: bf16 (CUDA), fp16 (MPS), fp32 (CPU)	Force fp32 for debugging NaN issues. fp16 if your GPU lacks bf16

Optimizer & Scheduler

Setting	Default	What it does	Why you'd change it
Optimizer	adamw	Weight update algorithm	adamw8bit saves VRAM. prodigy auto-tunes LR (set LR to 1.0). adafactor for minimal state
Scheduler	cosine	LR decay curve after warmup	constant for Prodigy. linear for steady decay. constant_with_warmup for flat after ramp

Prodigy note: When you select Prodigy, set the learning rate to 1.0 and the scheduler to constant. Prodigy auto-tunes the actual learning rate internally.

VRAM Savings

Setting	Default	What it does	Why you'd change it
Gradient checkpointing	yes	Recompute activations to save VRAM (~40-60% less, ~10-30% slower)	Disable only if you have abundant VRAM and want max speed
Offload encoder	no	Move encoder/VAE to CPU after setup (~2-4 GB saved)	Enable on tight VRAM budgets (10-16 GB GPUs)

Advanced Training

Setting	Default	What it does	Why you'd change it
Weight decay	0.01	L2 regularization. Prevents overfitting	Increase (0.05) for very small datasets, decrease (0.001) for large
Max grad norm	1.0	Gradient clipping threshold	Increase if you see "gradient clipping" warnings everywhere; decrease for stability
Bias mode	none	Train bias parameters: none, all, lora_only	lora_only may marginally improve quality; all trains all biases

Data Loading

Setting	Default	What it does	Why you'd change it
Workers	4 (Linux), 0 (Windows)	Parallel data loading processes	More workers = faster loading. Windows forces 0 (multiprocessing limitation)
Pin memory	yes	Pin loaded tensors in CPU for faster GPU transfer	Disable if low on system RAM
Prefetch factor	2	Batches each worker prefetches ahead	Higher for faster GPUs that consume data quickly
Persistent workers	yes	Keep workers alive between epochs	Disable if you see memory leaks from data loading

Advanced Logging

Setting	Default	What it does	Why you'd change it
TensorBoard dir	{output}/runs	Where TensorBoard logs go	Custom path if you want all runs in one place
Heavy log every N steps	50	Per-layer gradient norm logging	Lower for debugging training dynamics, higher to reduce overhead
Sample every N epochs	0 (disabled)	Generate audio samples during training	Not yet implemented