解析代码位置:src/diffusers/models/unet_2d_condition.py 1. 简述 1.1 类介绍 先看注释对网络的解释:UNet2DConditionModelis a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep and returns sample shaped output.(UNet2DConditionModel 是一个条件2D UNet模型,它接...
1. unet2dconditionmodel.from_pretrained 的功能 UNet2DConditionModel.from_pretrained 方法的主要功能是从指定的存储库或本地路径加载预训练的 2D 条件 U-Net 模型。U-Net 是一种常用的卷积神经网络架构,特别适用于图像分割任务。在 Diffusion 模型中,U-Net 常被用作去噪网络,用于从噪声图像中恢复出清晰的图像。
我们将首先导入所需的库并加载我们的U-Net模型。 fromdiffusersimportUNet2DConditionModel,LMSDiscreteScheduler## Initializing a schedulerscheduler=LMSDiscreteScheduler(beta_start=0.00085,beta_end=0.012,beta_schedule="scaled_linear",num_train_timesteps=1000)## Initializing the U-Net modelsd_path=r'/media/...
Reproduction from diffusers import UNet2DConditionModel from diffusers.models.lora import LoRALinearLayer class AttnProcessorCustom: def __call__( self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None, scale=1.0, ): print('nothing') def setup_attn_processors(unet)...
stablediffusion/ldm/modules/karlo/diffusers_pipeline.py Line 24 in cf1d67a from ...models import PriorTransformer, UNet2DConditionModel, UNet2DModel But where is the UNet2DConditionModel? I cannot find
input block组成:Res(接收输入x和emb timestep表示成emb,condition表示成emb),MHSA(像素对像素的注意力机制),Downsample mid block:Res,MHSA, Res output block:Res(与input block对应层的输出进行拼接),MHSA,Upsample U-net 第一个模块,time_emb,对输入进来的time_step进行变换, ...
However, Swin-Unet is a model for 2D medical image segmentation, which is not applicable to voxel segmentation of 3D medical images unless a lot of additional work has been performed or some complex adaptation code has been written. TransUnet [21] and TransBTS [30] are a kind of hybrid ...
发现问题:直接运行官方demo ,出错:No module named 'diffusers.models.unet_2d_condition' 分析问题:版本问题 解决问题: from mmagic.apis import MMagicInferencer sd_inferencer = MMagicInferencer(model_name='stable_diffusion') text_prompts = 'A panda is having dinner at KFC' result_out_dir = 'output...
"'{}' object has no attribute '{}'".format( AttributeError: 'UNet2DConditionModel' object has no attribute 'enable_gradient_checkpointing' Given I'm mostly using the default values I'm wondering if I'm just not formatting the file path correctly?
RuntimeError: Error(s) in loading state_dict for UNet2DConditionModel: size mismatch for conv_in.weight: copying a param with shape torch.Size([320, 9, 3, 3]) from checkpoint, the shape in current model is torch.Size([320, 4, 3, 3]). 请问一...