AI代码解释 python ~/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py--checkpoint_pathdreamlikePhotoreal20_dreamlikePhotoreal20.safetensors--dump_pathdreamlikePhotoreal20/--from_safetensors 其中, --
import torch from PIL import Image from diffusers import StableDiffusionImg2ImgPipeline pipe = StableDiffusionImg2ImgPipeline.from_pretrained( pretrained_model_name_or_path="", torch_dtype=torch.float16, local_files_only=True, use_safetensors=True, variant='fp16', safety_checker=None, )....
在保存模型时,脚本同样会先用去噪模型model构建一个流水线,再调用流水线的保存方法save_pretrained将扩散模型的所有组件(去噪模型、噪声调度器)保存下来。 if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1: # save the model unet = accelerator.unwrap_model(model) if args.use_...
utils import export_to_video from PIL import Image # ... 省略其他引用 # 保证乐子,让随机数范围大一些 max_64_bit_int = 2 ** 63 - 1 # ... 省略其他准备工作 # 使用 diffusers 来创建一个 AI Pipeline pipe = StableVideoDiffusionPipeline.from_pretrained( "/app/models/stabilityai/stable-video...
If you wish to use a different scheduler (e.g.: DDIM, LMS, PNDM/PLMS), you can instantiate it before the pipeline and pass it to from_pretrained. from diffusers import LMSDiscreteScheduler pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) prompt = "a photo of an ast...
pipeline_class_name = json.load(f)["_class_name"]# 动态导入对应的管道类pipeline_class =getattr(import_module("diffusers"), pipeline_class_name)# 记录导入的管道类名称self.logger.info(f"Pipeline class imported:{pipeline_class_name}.")# 加载适当的管道pipeline = pipeline_class.from_pretrained(...
StableDiffusion3Pipeline # local_config_path = "config/stable-diffusion-3-medium-diffusers" # pretrained_model_name_or_path = "sd3_model/stableDiffusion3SD3_sd3MediumInclClip.safetensors" # pipe = StableDiffusion3Pipeline.from_single_file( # pretrained_model_name_or_path, # config=local_config...
>>> from diffusers import DDPMParallelScheduler >>> from diffusers import StableDiffusionParadigmsPipeline >>> scheduler = DDPMParallelScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler") >>> pipe = StableDiffusionParadigmsPipeline.from_pretrained( ...
from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16) # We can utilize the enable_group_offload method for Diffusers model implementations pipe.transformer.enable_group_offload( onload_device=onload_device, offload_device=offload_device, offload_type="leaf_level", use_stream=True )...
from diffusers.utils.torch_utils import is_compiled_module # 参考train_controlnet.py 此训练中不涉及unet的训练,只训练encoder和decoder logger = get_logger(__name__) #导入模型 def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str): text_encod...