train — LoRA Fine-Tuning & TTS Training

Train a LoRA adapter for image generation models (FLUX.2-klein, SDXL, Qwen) or a TTS voice model (Piper) from audio datasets.

Two Training Modes

The train command has two completely separate modes with different parameters, backends, and outputs:

Mode	Backend	Dataset Format	Output	See Section
Image (LoRA)	Diffusers + PEFT	image.jpg + image.txt	.safetensors	LoRA Fine-Tuning
Audio (TTS)	Piper (VITS)	metadata.csv + wavs/	.ckpt → .onnx	TTS Training

The mode is auto-detected from --family / --backend flags or the dataset structure:

• --family flux | --family sdxl | --family qwen → Image (LoRA)
• --backend piper | --backend coqui | --backend f5-tts → Audio (TTS)

Quick Reference

# Auto-detect from dataset: images → LoRA, audio csv → TTS
datasety train --input ./dataset --output result

# Force TTS mode (audio parameters)
datasety train --input ./tts_dataset --output voice.ckpt --backend piper --model kontextox/piper-base-us --steps 500

# Force LoRA mode (image parameters)
datasety train --input ./images --output lora.safetensors --family flux --steps 500 --lr 1e-4 --lora-rank 16

---

Image vs Audio Parameters

Important: Parameters are mode-specific. Image parameters (like --lora-rank) do not apply to TTS training, and audio parameters (like --sample-rate) do not apply to LoRA training.

Image (LoRA) Parameters Only

These parameters are used when --family flux, --family sdxl, or --family qwen is set, or when the dataset contains image files:

Option	Description	Default
--family	Model family: flux, sdxl, qwen	auto-detected
--model	HuggingFace repo ID (base model)	black-forest-labs/FLUX.2-klein-base-4B
--output	Output .safetensors path	lora.safetensors
--steps	Number of training steps	100
--lr	Learning rate	1e-4
--lora-rank	LoRA rank (higher = more capacity, larger file)	16
--lora-alpha	LoRA alpha (controls effective learning rate scale)	16.0
--lora-dropout	LoRA dropout rate	0.0
--image-size	Training resolution (square crop, must be divisible by 32 for Qwen)	512
--device	Device: auto, cpu, cuda, mps	auto
--seed	Random seed	42
--save-every	Save checkpoint every N steps	end only
--resume	Resume from a .safetensors checkpoint
--validation-split	Fraction of dataset for validation (0.0–0.5)
--timestep-type	Timestep sampling: sigmoid, lognorm, linear	sigmoid
--caption-dropout	Probability of dropping caption (unconditional training)	0.05
--gradient-checkpointing	Enable gradient checkpointing (saves VRAM)	off
--optimizer	Optimizer: adamw or adamw8bit (requires bitsandbytes)	adamw
--lr-scheduler	LR schedule: constant, cosine, linear	constant
--lr-warmup-steps	Linear warmup steps before target LR	0
--gradient-accumulation-steps	Accumulate gradients over N steps	1
--min-snr-gamma	Min-SNR-γ loss weighting for SDXL (recommended: 5.0)	disabled
--noise-offset	Per-channel noise offset for SDXL (recommended: 0.05–0.1)	0.0

Audio (TTS) Parameters Only

These parameters are used when --backend piper (or coqui, f5-tts) is set, or when the dataset contains metadata.csv:

Option	Description	Default
--backend	TTS backend: piper (coqui, f5-tts planned)	piper
--model	Piper base model (HF repo ID or local path)	(required)
--output	Output directory for .ckpt checkpoints	(required)
--steps	Number of training epochs	100
--sample-rate	Audio sample rate in Hz	22050
--batch-size	Training batch size	32
--accelerator	PyTorch Lightning accelerator: auto, gpu, cpu	auto
--devices	Number of GPUs: auto, 1, 2, -1 (all GPUs)	auto
--test-text	Background inference text to test checkpoints as they drop
--seed	Random seed	42

Shared Parameters (Both Modes)

Option	Description	Default
--input, -i	Dataset directory	required
--steps	Training steps (image) or epochs (audio)	100

---

LoRA Fine-Tuning

Train a LoRA adapter for image generation models from a local dataset of image + caption pairs.

Supported model families: FLUX.2-klein (flow-matching), SDXL (DDPM), and Qwen (flow-matching, image-editing).

datasety train --input ./dataset --output lora.safetensors

Dataset Format

The input directory must contain image files alongside matching .txt caption files:

dataset/
  001.jpg
  001.txt     ← "ohwx person wearing a red jacket"
  002.png
  002.txt     ← "ohwx person smiling outdoors"
  ...

Images are center-cropped to a square and resized to --image-size (default 512 px). Use datasety resize, datasety caption, and the other preparation commands to build the dataset before training.

Base vs Distilled Models

Always use the base (undistilled) model for LoRA training.

Model	Type	Use for
black-forest-labs/FLUX.2-klein-4B	Step-distilled (4–8 steps)	Inference only
black-forest-labs/FLUX.2-klein-9B	Step-distilled (4–8 steps)	Inference only
black-forest-labs/FLUX.2-klein-base-4B	Base (undistilled)	LoRA training ✓
black-forest-labs/FLUX.2-klein-base-9B	Base (undistilled)	LoRA training ✓

The tool will print a warning if you pass a distilled model.

Options

Option	Description	Default
--input, -i	Dataset directory (images + .txt captions)	required
--output, -o	Output LoRA .safetensors path	lora.safetensors
--model, -m	HuggingFace repo ID (base model)	black-forest-labs/FLUX.2-klein-base-4B
--family	Model family: flux, sdxl, qwen	auto-detected
--steps	Number of training steps	100
--lr	Learning rate	1e-4
--lora-rank	LoRA rank	16
--lora-alpha	LoRA alpha	16.0
--lora-dropout	LoRA dropout rate	0.0
--image-size	Training resolution (square crop)	512
--device	auto, cpu, cuda, mps	auto
--seed	Random seed	42
--save-every	Save checkpoint every N steps	end only
--resume	Resume from a LoRA checkpoint (.safetensors)
--validation-split	Fraction of dataset for validation (0.0-0.5)
--timestep-type	Timestep sampling: sigmoid, lognorm, linear	sigmoid
--caption-dropout	Probability of dropping caption (unconditional)	0.05
--gradient-checkpointing	Enable gradient checkpointing (saves VRAM)	off
--optimizer	adamw or adamw8bit (requires bitsandbytes)	adamw
--lr-scheduler	LR schedule: constant, cosine, linear	constant
--lr-warmup-steps	Linear warmup steps before target LR	0
--gradient-accumulation-steps	Accumulate gradients over N steps	1
--min-snr-gamma	Min-SNR-γ loss weighting for SDXL (recommended: 5.0)	disabled
--noise-offset	Per-channel noise offset for SDXL (recommended: 0.05–0.1)	0.0

Training Best Practices

The following defaults reflect ai-toolkit recommendations for fast, stable LoRA convergence:

Technique	Option	Recommended value	Effect
Sigmoid timestep sampling	--timestep-type sigmoid	default	Biases toward mid-timesteps where most learning happens (vs uniform)
Caption dropout	--caption-dropout 0.05	default	5% unconditional steps, improves CFG adherence
8-bit optimizer	--optimizer adamw8bit	opt-in	Halves optimizer state memory; requires bitsandbytes
Cosine LR decay	--lr-scheduler cosine	opt-in	Prevents late-stage oscillation
Warmup	--lr-warmup-steps 50	opt-in	Avoids early large gradient steps
Gradient accumulation	--gradient-accumulation-steps 4	opt-in	Simulates larger batch size without extra VRAM
Gradient checkpointing	--gradient-checkpointing	opt-in	Reduces VRAM ~30% at ~20% speed cost
Min-SNR-γ (SDXL only)	--min-snr-gamma 5.0	opt-in	Stabilises DDPM loss across timesteps
Noise offset (SDXL only)	--noise-offset 0.05	opt-in	Improves dark/bright image generation

Examples

FLUX.2-klein LoRA with all best practices

datasety train \
    --input ./dataset \
    --output ./lora/flux_lora.safetensors \
    --model black-forest-labs/FLUX.2-klein-base-4B \
    --steps 500 \
    --lr 1e-4 \
    --lora-rank 16 \
    --timestep-type sigmoid \
    --caption-dropout 0.05 \
    --optimizer adamw8bit \
    --lr-scheduler cosine \
    --lr-warmup-steps 50 \
    --gradient-accumulation-steps 2 \
    --gradient-checkpointing

FLUX.2-klein LoRA (recommended)

Prepare a dataset first, then train:

# 1. Prepare dataset
datasety resize -i ./raw -o ./dataset -r 512x512
datasety caption -i ./dataset -o ./dataset --trigger-word "ohwx person,"

# 2. Train LoRA on FLUX.2-klein-base-4B (~8 GB VRAM)
datasety train \
    --input ./dataset \
    --output ./lora/flux_lora.safetensors \
    --model black-forest-labs/FLUX.2-klein-base-4B \
    --steps 500 \
    --lr 1e-4 \
    --lora-rank 16

# 3. Use the trained LoRA with synthetic editing
datasety synthetic \
    --input-image photo.jpg \
    --output-image result.png \
    --prompt "ohwx person wearing sunglasses" \
    --lora ./lora/flux_lora.safetensors:0.8

SDXL LoRA

datasety train \
    --input ./dataset \
    --output sdxl_lora.safetensors \
    --model stabilityai/stable-diffusion-xl-base-1.0 \
    --family sdxl \
    --steps 500 \
    --lr 1e-4 \
    --lora-rank 16 \
    --image-size 1024

Qwen Image-Edit LoRA (~30 GB VRAM)

Train a LoRA for Qwen/Qwen-Image-Edit-2511 (or 2509). The dataset needs only image + caption pairs — the same image is used as both source and target (reconstruction training), which teaches the model to preserve identity/style. Image size must be a multiple of 32.

datasety train \
    --input ./dataset \
    --output qwen_lora.safetensors \
    --model Qwen/Qwen-Image-Edit-2511 \
    --steps 500 \
    --lr 5e-5 \
    --lora-rank 16 \
    --image-size 512

The resulting LoRA loads directly with --lora in datasety synthetic:

datasety synthetic \
    --input-image photo.jpg --output-image edited.png \
    --model Qwen/Qwen-Image-Edit-2511 \
    --lora qwen_lora.safetensors:0.8 \
    --prompt "ohwx person wearing a winter hat"

Quick test run (20 steps)

Verify the training loop works before a full run:

datasety train \
    --input ./dataset \
    --output test_lora.safetensors \
    --steps 20 \
    --save-every 10

Resume from checkpoint

datasety train \
    --input ./dataset \
    --output lora.safetensors \
    --resume lora_step200.safetensors \
    --steps 500

Training with validation

datasety train \
    --input ./dataset \
    --output lora.safetensors \
    --steps 500 \
    --validation-split 0.1    # 10% of images held out for validation loss

Save checkpoints during training

datasety train \
    --input ./dataset \
    --output lora.safetensors \
    --steps 1000 \
    --save-every 200    # saves lora_step200.safetensors, lora_step400.safetensors, ...

VRAM Requirements

Model	VRAM	Notes
FLUX.2-klein-base-4B	~8 GB	Default, auto CPU-offload if needed
FLUX.2-klein-base-9B	~18 GB	Higher quality
SDXL	~7 GB	Good for object/style LoRAs
Qwen/Qwen-Image-Edit-2511	~30 GB	Image-editing LoRA, flow-matching

CPU offload is applied automatically when free VRAM is below the required amount.

LoRA Parameters Guide

Parameter	Recommended range	Effect
--lora-rank	4–64	Higher = more capacity, larger file
--lora-alpha	Equal to rank (default)	Controls effective learning rate scale
--steps	100–2000	More steps = more fitting (risk of overfitting)
--lr	1e-5 – 1e-3	Too high causes divergence; too low is slow
--image-size	512 or 1024	Match your target inference resolution

Output

The trained LoRA is saved as a .safetensors file in diffusers-compatible format — keys use the transformer. prefix for FLUX and Qwen models and unet. for SDXL, so the file loads directly with pipeline.load_lora_weights() and with --lora in datasety commands:

datasety synthetic -i ./images -o ./output \
    --prompt "ohwx person in a park" \
    --lora lora.safetensors:0.8

The LoRA weight (:0.8) controls blend strength — typically 0.6–1.0.

Target modules

Model	Trainable modules	File size (rank 16)
FLUX.2-klein-base-4B	to_q, to_k, to_v, to_out.0	~38 MB
FLUX.2-klein-base-9B	to_q, to_k, to_v, to_qkv_mlp_proj	~77 MB
SDXL	to_q, to_k, to_v, to_out.0	~25 MB
Qwen/Qwen-Image-Edit-2511	to_q, to_k, to_v, to_out.0, add_q_proj, add_k_proj, add_v_proj, to_add_out	~45 MB

The 9B FLUX model uses fused to_qkv_mlp_proj projections (single-transformer blocks). Qwen targets both image-stream and text-stream attention projections across 60 transformer blocks.

---

TTS Training (Piper)

Train a Piper TTS model from a dataset produced by datasety audio. Outputs a .ckpt checkpoint that can be exported to .onnx for inference.

Dataset Format

The input directory must contain a Piper/LJSpeech-compatible dataset:

tts_dataset/
├── wavs/
│   ├── utt_0001.wav
│   ├── utt_0002.wav
│   └── ...
└── metadata.csv

utt_0001.wav|Hello world, this is a test.
utt_0002.wav|How are you doing today?

TTS-Specific Options

Option	Description	Default
--backend	TTS backend: piper (coqui, f5-tts planned)	piper
--model	Piper base model (HF repo ID or local path)	(required)
--sample-rate	Audio sample rate in Hz	22050
--batch-size	Training batch size	32
--accelerator	PyTorch Lightning accelerator: auto, gpu, cpu	auto
--devices	Number of GPUs: auto, 1, 2, -1 (all GPUs)	auto
--test-text	Background inference text to test checkpoints as they drop

Piper Auto-Installer

On first run, datasety train automatically:

1. Clones the kontextox/piper1-gpl repository to ~/.cache/datasety/
2. Compiles the monotonic_align Cython extension
3. Installs the Piper Python package and dependencies

No manual compilation required.

Multi-GPU Training

For dual-GPU setups (e.g., 2× L40S), PyTorch Lightning automatically enables Distributed Data Parallel (DDP):

datasety train \
    --input ./tts_dataset \
    --output ./voice_model \
    --backend piper \
    --model "rhasspy/piper-checkpoints:en/en_US/kristin/medium" \
    --steps 1000 \
    --batch-size 32 \
    --accelerator gpu \
    --devices 2

PyTorch Lightning auto-detects and utilizes all available GPUs without extra configuration.

Background Voice Watcher

Pass --test-text to spin up a background daemon that watches for new .ckpt files, exports them to .onnx, and renders a .wav file using your test text. Listen to the model learning in real-time while the GPU keeps training:

datasety train \
    --input ./tts_dataset \
    --output ./voice_model \
    --backend piper \
    --model "rhasspy/piper-checkpoints:en/en_US/kristin/medium" \
    --steps 1000 \
    --test-text "Hello, this is a test of my new voice."

Examples

Basic TTS Training

datasety train \
    --input ./tts_dataset \
    --output ./voice_model \
    --backend piper \
    --model "rhasspy/piper-checkpoints:en/en_US/kristin/medium" \
    --steps 500

Multi-GPU Training with Voice Watcher

datasety train \
    --input ./tts_dataset \
    --output ./voice_model \
    --backend piper \
    --model "rhasspy/piper-checkpoints:en/en_US/kristin/medium" \
    --steps 1000 \
    --batch-size 32 \
    --accelerator gpu \
    --devices 2 \
    --test-text "The quick brown fox jumps over the lazy dog."

Resume Training

datasety train \
    --input ./tts_dataset \
    --output ./voice_model \
    --backend piper \
    --model "rhasspy/piper-checkpoints:en/en_US/kristin/medium" \
    --steps 2000

The trainer automatically resumes from the last checkpoint if one exists in the output directory.