(UNet2DConditionModel 是一个条件2D UNet模型,它接受一个有噪声的样本、条件状态和一个时间步长并返回样本形状的输出。) 类的初始化方法内的主要参数有下列几个: sample_size:输入输出的sizein_channels:输入的通道数out_channels:输出的通道数down_block_types:使用的下采样块元组,默认为("CrossAttnDownBlock2D"...
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。
Describe the bug This error happens with a custom attention processor after setting lora layers Reproduction from diffusers import UNet2DConditionModel from diffusers.models.lora import LoRALinearLayer class AttnProcessorCustom: def __ca...
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/...
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, ...
We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner, erect an interpretable model, and attain segmentation of lung cancer. The specific advancements can be encapsulated as follows: 1) Concentration on the lung ...
in main unet.enable_gradient_checkpointing() File "I:\Git\AI\SDWebUI\venv\lib\site-packages\torch\nn\modules\module.py", line 1207, in __getattr__ raise AttributeError("'{}' object has no attribute '{}'".format( AttributeError: 'UNet2DConditionModel' object has no attribute 'enable_...
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...
text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(model_path, subfolder="text_encoder_2", torch_dtype=torch_dtype, variant=variant,) unet_new = UNet2DConditionModel.from_pretrained(model_path, subfolder="unet", torch_dtype=torch_dtype, variant=variant,)pipe...