max_steps=1875, lr_scheduler_type="constant", optim="paged_adamw_32bit", learning_rate=0.0002, group_by_length=True, bf16=True, warmup_ratio=0.03, max_grad_norm=0.3, ) trainer = AdapterTrainer( model=model, tokenizer=tokenizer, data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=Fa...
self.lr_scheduler_builder = lr_scheduler # Generator and discriminators self.mel_transform = mel_transform self.noise_scheduler_train = DDIMScheduler(num_train_timesteps=1000) self.noise_scheduler_infer = UniPCMultistepScheduler(num_train_timesteps=1000) self.noise_scheduler_infer.set_timesteps(20...
stdhndlr.cpp stdthrow.cpp syncstream.cpp syserror.cpp syserror_import_lib.cpp taskscheduler.cpp thread0.cpp tzdb.cpp ulocale.cpp uncaught_exception.cpp uncaught_exceptions.cpp ushcerr.cpp ushcin.cpp ushclog.cpp ushcout.cpp ushiostr.cpp vector_algorithms.cpp wcerr.cpp wcin.cpp wclog....
正则表达式 1. 使用正则 创建正则表达式有两种方式，一种是以字面量方式创建，另一种是使用RegExp构造...
for i, t in tqdm(enumerate(scheduler.timesteps)): # 准备模型输入:给“带躁”图像加上时间步信息 model_input = scheduler.scale_model_input(x, t) # 预测噪声 with torch.no_grad(): noise_pred = image_pipe.unet(model_input, t)["sample"] ...
from diffusers import DDPMScheduler, UNet2DModel from matplotlib import pyplot as plt device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f'Using device: {device}') # 输出 Using device: cuda 此时会输出运行环境是GPU还是CPU ...
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