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training-recipes

training-recipes

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.

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Documentation de la Skill

GitHub

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

  1. Identify the recipe and base family from the user request: SD 1.x/2.x, SDXL, SD3, Flux, or a task-specific adapter.
  2. 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.
  3. 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"
  4. 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.
  5. 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.
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