然后,在你的Python脚本或交互式环境中,你可以使用以下代码导入diffusers.models.unet_2d_condition模块: python import diffusers.models.unet_2d_condition 从diffusers.models.unet_2d_condition模块中引入Unet2DConditionModel类: 在成功导入模块后,你可以从该模块中引入Unet2DConditionModel类。正确的代码应该是: python...
from diffusers import UNet2DConditionModel model_cls = UNet2DConditionModel path = "config.json" _, ema_kwargs = model_cls.from_config(path, return_unused_kwargs=True) print(ema_kwargs) # Output: {"decay": 0.9999, "inv_gamma": 1.0}: ✅ _, ema_kwargs = model_cls.load_config(pat...
from diffusers import UniPCMultistepScheduler, AutoencoderKL from diffusers import UniPCMultistepScheduler, AutoencoderKL, ControlNetModel from safetensors.torch import load_file from pipeline.pipeline_controlnext import StableDiffusionXLControlNeXtPipeline from models.unet import UNet2DConditionModel, UNET...
目标是归一化不同autoencoder中的latent space值,让其方差趋近于1,确保diffusion model都能work;更多请查看,Explanation of the 0.18215 factor in textual_inversion? · Issue #437 · huggingface/diffusers import torch from torch import nn from torch.nn import functional as F from attention import Self...
unet = UNet2DModel.from_pretrained(pretrained_name_or_path, subfolder="unet") self.noise_scheduler = DDIMScheduler.from_pretrained(pretrained_name_or_path, subfolder="scheduler") def training_step(self, batch, batch_idx: int, optimizer_idx: int): """This function calculate the loss of the...
File "C:\code\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-AnimateAnyone-Evolved\src\models\unet_2d_blocks.py", line 15, in from .transformer_2d import Transformer2DModel File "C:\code\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-AnimateAnyone-Evolved\src\models\transformer_2d...
model=ModelClass(**model_kwargs).to(dtype=torch_dtype,device=device) state_dict=load_state_dict(state_dict_path,torch_dtype=torch_dtype) model.load_state_dict(model.state_dict_converter().from_diffusers(state_dict)) returnmodel defload_model_from_transformers(ModelClass,model_kwargs,state_dict...
device_utils import init_ipex init_ipex() from typing import Union, List, Optional, Dict, Any, Tuple from diffusers.models.unet_2d_condition import UNet2DConditionOutput33 changes: 33 additions & 0 deletions 33 docs/gen_img_README-ja.md Original file line numberDiff line numberDiff line ...
unet_additional_kwargs={ "task_type": "reenact", "use_motion_module": False, "unet_use_temporal_attention": False, "mode": "write", }, ).to(device="cuda", dtype=weight_dtype) denoising_unet = UNet3DConditionModel.from_pretrained_2d( base_model_path, "./pretrained_weights/mm_sd_v...
device = model_output.device if self.resized_size is None: prev_sample = sample + derivative * dt noise = diffusers.schedulers.scheduling_euler_ancestral_discrete.randn_tensor( model_output.shape, dtype=model_output.dtype, device=device, generator=generator ) s_noise = 1.0 else: print("resize...