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...
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 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...
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...