"Install"ボタンを押してインストールします。 Web UI を再起動してください。 使用法 学習した LoRA のモデル(*.pt,*.ckpt,*.safetensors)をsd-webui-additional-networks/models/LoRAに置きます。 Web UI の左下のほうの"Additional Networks"のパネルを開きます。
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EASでKohya_ssを使用してLoRA SDモデルをデプロイする,Platform For AI:このトピックでは、Platform for AI (PAI) のElastic Algorithm Service (EAS) でKohya_ssを使用して、オープンソースのKohya_ssをデプロイし、低ランク適応 (LoRA) モデルをトレーニングする方法につ
help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する", ) return parser if __name__ == "__main__": parser = setup_parser() args = parser.parse_args() ...
datasets])}") # print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ(並列学習、勾配合計含む): {total_batch_size}") accelerator.print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}") ...
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accelerator.print( f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ(並列学習、勾配合計含む): {total_batch_size}" ) accelerator.print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}") ...
f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ(並列学習、勾配合計含む): {total_batch_size}" ) accelerator.print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}") accelerator.print(f" total optimi...
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