技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
training-recipes【数据与研究】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.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
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:
pipelines-and-inference.conversion-and-maintenance.python sub-skills/training-recipes/scripts/dataset_layout_check.py --data-dir DATA --require-captions--conditioning-dir COND for ControlNet/T2I-Adapter/Flux control image pairs.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"--mixed_precision, --gradient_checkpointing, small --train_batch_size, --gradient_accumulation_steps, checkpointing, and a capped --max_train_steps.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.