name: training-recipes
description: "Diffusers training recipes for DreamBooth, LoRA, textual
inversion, ControlNet, text-to-image, T2I-Adapter, InstructPix2Pix, SDXL,
Flux, and SD3 examples with dataset and Accelerate safety checks."
disable-model-invocation: true
metadata:
disco-role: operating
license: Apache 2.0
Diffusers Training Recipes
Use this sub-skill when the task is to prepare or adapt Diffusers training examples, build an accelerate launch command, validate a local image dataset, choose between DreamBooth/LoRA/textual inversion/ControlNet/T2I-Adapter/text-to-image recipes, or debug training setup failures.
Route elsewhere:
- Inference-only pipeline usage, loading trained adapters, or generation scripts: use
pipelines-and-inference.
- Maintainer test policy, copied-code updates, or conversion PR mechanics: use
conversion-and-maintenance.
Fast Path
- Identify the recipe and base family from the user request: SD 1.x/2.x, SDXL, SD3, Flux, or a task-specific adapter.
- Validate local datasets before composing commands:
python sub-skills/training-recipes/scripts/dataset_layout_check.py --data-dir DATA --require-captions
- Add
--conditioning-dir COND for ControlNet/T2I-Adapter/Flux control image pairs.
- Generate a dry-run command rather than launching training:
python sub-skills/training-recipes/scripts/training_command_builder.py --recipe dreambooth-lora --model MODEL --dataset DATA --output-dir OUT --instance-prompt "a photo of sks dog"
- Add memory controls before suggesting real runs:
--mixed_precision, --gradient_checkpointing, small --train_batch_size, --gradient_accumulation_steps, checkpointing, and a capped --max_train_steps.
- Require explicit user confirmation before running expensive training, Hub pushes, remote downloads of gated models, or commands that overwrite a non-empty output directory.
References
Bundled Helpers
scripts/training_command_builder.py prints safe accelerate launch commands for common recipes without importing Diffusers or starting training.
scripts/dataset_layout_check.py validates tiny local imagefolder layouts, optional captions, metadata files, and conditioning-image pair counts.