训练集制作:数据质量评估,标签梳理,数据清洗,数据标注,标签清洗,数据增强等。 训练文件配置:预训练模型选择,训练环境配置,训练步数设置,其他超参数设置等。 模型训练:运行SDXL模型/LoRA模型训练脚本,使用TensorBoard监控模型训练等。 模型测试:将训练好的自训练SDXL模型/LoRA模型用于效果评估与消融实验。
LoRA training Textual Inversion training Image generation Model conversion (supports 1.x and 2.x, Stable Diffision ckpt/safetensors and Diffusers) These files do not contain requirements for PyTorch. Because the versions of them depend on your environment. Please install PyTorch at first (see inst...
if block_lrs[i] == 0: # 0のときは学習しない do not optimize when lr is 0 continue params_to_optimize.append({"params": params, "lr": block_lrs[i]}) return params_to_optimize def append_block_lr_to_logs(block_lrs, logs, lr_scheduler, optimizer_type): ...
lora_gui.py modules.txt pyproject.toml requirements.txt requirements_linux.txt requirements_linux_docker.txt requirements_linux_ipex.txt requirements_macos_amd64.txt requirements_macos_arm64.txt requirements_runpod.txt requirements_windows_torch1.txt requirements_windows_torch2.txt sdxl_gen_img.py sd...
networks.lora import LoRANetwork from library.sdxl_original_unet import InferSdxlUNet2DConditionModel from library.original_unet import FlashAttentionFunction from networks.control_net_lllite import ControlNetLLLite from library.utils import GradualLatent, EulerAncestralDiscreteSchedulerGL from library...
networks.lora import LoRANetwork from library.sdxl_original_unet import InferSdxlUNet2DConditionModel from library.original_unet import FlashAttentionFunction from networks.control_net_lllite import ControlNetLLLite from library.utils import GradualLatent, EulerAncestralDiscreteSchedulerGL from library.utils...
networks.lora import LoRANetwork from library.sdxl_original_unet import SdxlUNet2DConditionModel from library.original_unet import FlashAttentionFunction from networks.control_net_lllite import ControlNetLLLite # scheduler: SCHEDULER_LINEAR_START = 0.00085 SCHEDULER_LINEAR_END = 0.0120 SCHEDULER_TIME...