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。
image-training【数据与研究】Choose, edit, validate, and launch KAIR image training configurations for denoising, super-resolution, blind SR, deblocking, PSNR, GAN, DataParallel, and DDP workflows.
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 train or resume KAIR image restoration models, adapt KAIR training option JSON files, choose a training entry script, or diagnose training configuration failures for image workflows.
This sub-skill covers KAIR image training for DnCNN, FDnCNN, FFDNet, DRUNet, USRNet, SRMD, DPSR, MSRResNet, RRDB/RRDBNet, IMDN, BSRGAN-style blind SR, SwinIR image SR/denoising/JPEG deblocking, PSNR-oriented training, GAN training, DataParallel, and DistributedDataParallel.
Do not use this sub-skill for:
../image-testing/SKILL.md.../video-restoration/SKILL.md.../data-preparation/SKILL.md.references/training-workflows.md to choose the KAIR command entry point and option JSON template.references/configuration-reference.md; do not treat the bundled examples as exhaustive source configs.scripts/validate_training_config.py --config <option-json> before launching training. It is read-only and does not import KAIR.gpu_ids, visible devices, --nproc_per_node, and --dist True agree before spending training time.references/training-workflows.md before assuming path.pretrained_netG is honored directly.references/troubleshooting.md.Training commands are intentionally provided as templates to run in the user's own KAIR checkout. Full training is expensive and is not a safe smoke test.