from_config(pipeline.scheduler.config) Models 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from diffusers import UNet2DModel import torch # load model repo_id = "google/ddpm-cat-256" model = UNet2DModel.from_pretrai
load_model = UNet2DModel.from_pretrained( input_dir, subfolder="unet") model.register_to_config(**load_model.config) model.load_state_dict(load_model.state_dict()) del load_model 两个回调函数需要用下面的代码来设置。 accelerator.register_save_state_pre_hook(save_model_hook) accelerator.regis...
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, --checkpoint_path将要转换的检查点的路径。 --original_config_file原始架构对应的YAML配置文件。
from diffusers import ControlNet, ControlNetConfig, DiffusionPipeline from PIL import Image import torch # 加载预训练模型配置 config = ControlNetConfig() # 初始化模型 model = ControlNet(config) # 准备训练数据 # 假设你已经有了一个包含文本描述和图像的数据集 train_texts = [...] # 文本描述列表...
from_pretrained( pretrained_model_name_or_path="", torch_dtype=torch.float16, local_files_only=True, use_safetensors=True, variant='fp16', safety_checker=None, ).to("cuda") pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, algorithm_type = 'sde-dpmsolver++'...
5-large"nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16)model_nf4 = SD3Transformer2DModel.from_pretrained( model_id, subfolder="transformer", quantization_config=nf4_config, torch_dtype=torch.bfloat...
from_pretrained( ckpt_id, subfolder="transformer", quantization_config=nf4_config, torch_dtype=torch.bfloat16 ) Then, we use model_nf4 to instantiate the FluxPipeline: from diffusers import FluxPipeline pipeline = StableDiffusion3Pipeline.from_pretrained( ckpt_id, transformer=model_nf4, torch_...
.float() /255.0# 调整张量的维度顺序... hint = detected_map.permute(2,0,1)# 返回生成的提示...returnhint# 创建深度估计器管道>>> depth_estimator = pipeline("depth-estimation")# 加载 Kandinsky V22 先验管道,并指定数据类型为 float16>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(...
pipe = StableDiffusionControlNetPipeline.from_pretrained( model_id, controlnet=controlnet, torch_dtype=torch.float16,)pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)pipe.enable_model_cpu_offload() 超级英雄的瑜伽时间! generator = [torch.Generator(device="cpu").manual_...
controlnet-openpose",torch_dtype=torch.float16)model_id="runwayml/stable-diffusion-v1-5"pipe=StableDiffusionControlNetPipeline.from_pretrained(model_id,controlnet=controlnet,torch_dtype=torch.float16,)pipe.scheduler=UniPCMultistepScheduler.from_config(pipe.scheduler.config)pipe.enable_model_cpu_...