---

name: training description: "Choose and compose sd-scripts training commands for LoRA/additional networks, DreamBooth/native fine-tuning, Textual Inversion, ControlNet-LLLite, LECO, validation loss, inpainting, and SD/SDXL/SD3/FLUX/Chroma/Lumina/HunyuanImage/Anima model-family training." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

sd-scripts Training

Use this sub-skill when the user needs a training command, training-option diagnosis, or model-family routing for sd-scripts. It is for composing commands and explaining constraints; do not run full training unless the user explicitly provides model weights, dataset paths, compute budget, and permission for GPU/long-running work.

Route First

• Use ../data-preparation for dataset TOML authoring, captioning, bucketing, repeats, subsets, and validation subset layout.
• Use this sub-skill for training script selection, flags, model-family requirements, validation-loss flags, inpainting flags, optimizer/memory choices, and command templates.
• Use ../model-utilities for post-training merge, conversion, metadata inspection, checkpoint extraction, or LoRA application to checkpoints.
• Use ../generation for inference-only image generation, sample prompt syntax for generation scripts, and post-training preview generation outside a training run.

Safe Operating Rules

1. Build commands with placeholders first; do not execute training commands by default.
2. Prefer accelerate launch --num_cpu_threads_per_process 1 <script>.py ... for training scripts.
3. Require concrete paths for the base model, dataset config or training data, and output directory before suggesting a runnable command.
4. Match script and flags to the model family; SD1/2 flags such as --v2, --v_parameterization, --clip_skip, and --max_token_length do not apply to SD3/FLUX-family DiT scripts.
5. Call out expensive/full-training checks as GPU/weights-dependent and skip by default; help-only commands are safer but may still import heavy dependencies.

Script Selection

Goal	Script	Key additions
SD1/SD2 LoRA/additional network	train_network.py	--network_module=networks.lora; add --v2 --v_parameterization for SD2 v-prediction checkpoints only
SDXL LoRA/additional network	sdxl_train_network.py	SDXL checkpoint, --cache_text_encoder_outputs, often --cache_latents; no --v2/--v_parameterization
SD3/SD3.5 LoRA	sd3_train_network.py	MMDiT model plus optional --clip_l, --clip_g, --t5xxl, --vae; --sdpa, --blocks_to_swap
FLUX.1 LoRA	flux_train_network.py	--clip_l, --t5xxl, --ae, --network_module=networks.lora_flux, --guidance_scale 1.0
Chroma LoRA	flux_train_network.py	--model_type=chroma, omit --clip_l, add --guidance_scale 0.0 --apply_t5_attn_mask
Lumina 2 LoRA	lumina_train_network.py	Lumina DiT, --gemma2, --ae; cache text encoder outputs unless training Gemma2
HunyuanImage LoRA	hunyuan_image_train_network.py	Hunyuan DiT/VAE/text encoders; prefer --fp8_scaled over unsupported --fp8_base
Anima LoRA/LoHa/LoKr	anima_train_network.py	Anima/Qwen Image components; optional --compile for per-block torch.compile
DreamBooth/native SD fine-tune	train_db.py	Directory-style DreamBooth dataset; trains base model weights
Fine-tune from metadata/caption files	fine_tune.py	Caption/metadata-driven full model fine-tuning
Textual Inversion	train_textual_inversion.py	Placeholder token initialization and embedding outputs
LECO slider	train_leco.py or sdxl_train_leco.py	--prompts_file is required; parser supports shared training validation
SDXL ControlNet-LLLite	sdxl_train_control_net_lllite.py	Dataset subset needs conditioning_data_dir; set --cond_emb_dim and --network_dim

Command Workflow

1. Identify model family and training kind.
2. Ask for or route to dataset config. For most LoRA/additional network jobs, prefer --dataset_config path/to/dataset.toml.
3. Select network module: networks.lora, networks.lora_flux, networks.loha, or networks.lokr as appropriate.
4. Add model-family assets: FLUX/Chroma need T5XXL and AE; SD3 may need separate CLIP-L/CLIP-G/T5XXL/VAE; Lumina needs Gemma2 and AE; HunyuanImage/Anima need their family-specific components.
5. Add memory settings conservatively: --gradient_checkpointing, --mixed_precision bf16 or fp16, --sdpa or --xformers, --cache_latents, --cache_text_encoder_outputs, --blocks_to_swap, or family-specific fp8 options.
6. Add output/logging/checkpoint flags: --output_dir, --output_name, --save_model_as safetensors, --save_every_n_epochs, optional --logging_dir, --log_with tensorboard or wandb.
7. Add validation flags only when validation data exists: --validation_split, --validate_every_n_steps, --validate_every_n_epochs, --max_validation_steps, --validation_seed.
8. Include sample prompt flags only when the user wants training-time previews and has prompt files/images compatible with the task.

References

• references/commands.md: ready-to-adapt command templates and option clusters.
• references/model-family-guide.md: model-family script requirements, incompatible flags, and memory notes.
• references/troubleshooting.md: common failure modes and targeted fixes.
• scripts/build_training_command.py: safe command-template assembler that prints shell commands without executing training.

Use the Command Builder

Use the bundled builder when the user wants a starter command and has paths available. Run it from this sub-skill directory or with the path used by the host agent to access bundled skill scripts:

python scripts/build_training_command.py \
  --family flux --kind lora \
  --pretrained_model path/to/flux1-dev.safetensors \
  --dataset_config path/to/dataset.toml \
  --output_dir output --output_name my_flux_lora \
  --clip_l path/to/clip_l.safetensors \
  --t5xxl path/to/t5xxl_fp16.safetensors \
  --ae path/to/ae.safetensors

The script prints an accelerate launch ... command; it never starts training.