)from..modeling_utilsimportModelMixin# 导入模型混合类from.unet_2d_blocksimport( get_down_block,# 导入下采样块的构造函数get_mid_block,# 导入中间块的构造函数get_up_block,# 导入上采样块的构造函数)# 创建一个日志记录器,用于记录模型相关信息logger = logging.get_logger(__name__)# pylint: disable...
Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple. Returns: [`~models.unet_2d.UNet2DOutput`] or `tuple`: If `return_dict` is True, an [`~models.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is returned where the first element is t...
在diffusers/models/unet_2d_blocks.py中,我们可以找到这几个模块类的定义。和原版代码一样,这几个模块的核心组件都是残差块和Transformer块。在Diffusers中,残差块叫做ResnetBlock2D,Transformer块叫做Transformer2DModel。这几个类的执行逻辑和原版仓库的也几乎一样。比如CrossAttnDownBlock2D的定义如下: class CrossAtt...
我们可以对照位于 diffusers/models/unet_2d_condition.py 的SD 的 2D U-Net 类 UNet2DConditionModel 来看一下 SVD 的 U-Net 有何不同。 先来看 __init__ 构造函数。SVD U-Net 几乎就是一个写死了许多参数的特化版 2D U-Net,其构造函数也基本上是 SD 2D U-Net 的构造函数的子集。比如 2D U-Net...
Please use from diffusers.models.unets.unet_2d_blocks import AutoencoderTinyBlock, instead. deprecate("AutoencoderTinyBlock", "0.29", deprecation_message) D:\AI\ComfyUI\venv-comfyui\Lib\site-packages\diffusers\models\unet_2d_blocks.py:254: FutureWarning: UNetMidBlock2D is deprecated and will ...
self.up1 = UnetUp(4 * n_feat, n_feat) self.up2 = UnetUp(2 * n_feat, n_feat) # Initialize the final convolutional layers to map to the same number of channels as the input image self.out = nn.Sequential( nn.Conv2d(2 * n_feat, n_feat, 3, 1, 1), # reduce number of fe...
ModuleNotFoundError: No module named 'diffusers.models.unet_3d_blocks' 这个错误表明Python解释器无法找到名为 diffusers.models.unet_3d_blocks 的模块。这通常是由以下几个原因引起的: 模块路径错误:可能是代码中的导入路径写错了。你可以检查diffusers库的官方文档或源码,确认正确的模块路径。 版本不兼容:如果你...
from .src.models.unet_2d_condition import UNet2DConditionModel File "C:\code\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-AnimateAnyone-Evolved\src\models\unet_2d_condition.py", line 40, in from .unet_2d_blocks import ( File "C:\code\ComfyUI_windows_portable\ComfyUI\custom_nodes\...
导入增加KV的注意力处理器AttnProcessor,# 导入基本的注意力处理器)# 导入嵌入相关的组件from.embeddingsimportTextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps# 导入模型相关的混合类from.modeling_utilsimportModelMixin# 导入UNet的二维块相关组件from.unets.unet_2d_blocks...
[`~models.unets.unet_3d_blocks.UNetMidBlockSpatioTemporal`]. # 注意力头的数量,默认为 (5, 10, 10, 20) num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`): The number of attention heads. # 使用的 dropout 概率,默认为 0.0 ...