Automatic update generated bynixpkgs-updatetools. This update was made based on information from passthru.updateScript. meta.description for python311Packages.diffusers is: State-of-the-art diffusion models for image and audio generation in PyTorch meta.homepage for python311Packages.diffusers is:https...
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from IPython.display import clear_output from ui.utils import diffusers_auto_update diffusers_auto_update() clear_output() # 清理很长的内容 print('加载完毕, 请重启内核') 三、运行下面的代码快速体验 这一大章节对应五个小节, 每个小节对应不同的功能, 当要执行某个功能的时候, 执行对应功能的代码即可...
Optional# 从 tqdm 库导入自动选择的进度条支持from tqdm import auto as tqdm_lib# 创建一个线程锁以确保线程安全_lock = threading.Lock()# 定义一个默认的日志处理程序,初始为 None_default_handler:Optional[logging.
AutoencoderKL,# 自动编码器ControlNetModel,# 控制网络模型PriorTransformer,# 先验变换模型UNet2DConditionModel,# 2D 条件 U-Net 模型)# 从调度器导入所需的类from...schedulersimport( DDIMScheduler,# DDIM 调度器DDPMScheduler,# DDPMSchedulerDPMSolverMultistepScheduler,# DPM 多步求解调度器EulerAncestralDiscre...
auto_processor = AutoProcessor.from_pretrained(VIT_PATH) image_encoder = CLIPVisionModelWithProjection.from_pretrained(VIT_PATH) # 从预训练路径加载分词器和文本编码器 tokenizer = CLIPTokenizer.from_pretrained( MODEL_PATH, subfolder="tokenizer", ...
progress_bar.update(1) continue 训练的一开始,函数会从数据的"input"键里取出图像数据。此处的键名是我们之前在数据预处理函数transform_images里写的。 clean_images = batch["input"].to(weight_dtype) 之后函数会设置扩散模型训练中的其他变量,包含随机噪声、时刻。由于本文的重点并不是介绍扩散模型的原理,这段...
from tqdm.autoimporttqdm from pathlibimportPathimportos defget_full_repo_name(model_id:str,organization:str=None,token:str=None):iftoken is None:token=HfFolder.get_token()iforganization is None:username=whoami(token)["name"]returnf"{username}/{model_id}"else:returnf"{organization}/{model_id...
AutoencoderKL: clamp indices of blend_h and blend_v to input size by @kig in #2660 Update README.md by @qwjaskzxl in #2653 [Lora] correct lora saving & loading by @patrickvonplaten in #2655 Add ddim noise comparative analysis pipeline by @aengusng8 in #2665 ...
from diffusers import AutoPipelineForImage2Image from diffusers.utils import load_image, make_image_grid # use from_pipe to avoid consuming additional memory when loading a checkpoint pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_text2image).to("cuda") ...