首先,我们看一下DiT的forward函数的实现。 classDiT(nn.Module):"""Diffusion model with a Transformer backbone."""def__init__(self,input_size=32,patch_size=2,in_channels=4,hidden_size=1152,depth=28,num_heads=16,mlp_ratio=4.0,class_dropout_prob=0.1,num_classes=1000,learn_sigma=True,):supe...
首先,我们看一下DiT的forward函数的实现。 classDiT(nn.Module):"""Diffusion model with a Transformer backbone."""def__init__(self,input_size=32,patch_size=2,in_channels=4,hidden_size=1152,depth=28,num_heads=16,mlp_ratio=4.0,class_dropout_prob=0.1,num_classes=1000,learn_sigma=True,):supe...
learn_sigma=True, ): # 接受多个参数,包括输入图像的大小、图像分块大小、输入通道数、隐藏层维度、模型深度、注意力头数、MLP扩展比例、类别标签的丢弃概率等 super().__init__() self.learn_sigma = learn_sigma self.in_channels = in_channels self.out_channels = in_channels * 2 if learn_sigma e...
例如,使用不同的预测目标可能导致不同的预测结果[33]。在原始的DIT模型中,同时预测噪声和sigma的策略被用于最大化性能[36]。通常,均方误差(MSE)损失函数被用于监督这些目标。 最近,在 Stream 匹配[28; 29]的背景下,速度预测的目标具有更具体的物理意义,代表从噪声到数据的流量。基于此,作者假设监督速度方向可以作...
"learn_sigma": True, "text_states_dim": 1024, "text_states_dim_t5": 2048, "text_len": 77, "text_len_t5": 256, }) hydit_conf = { "G/2": { # Seems to be the main one "unet_config": { "depth" : 40, "num_heads" : 16, "patch_size" : 2, "hidden_size" : 140...
""" List of all HYDiT model types / settings """ from argparse import Namespace hydit_args = Namespace(**{ # normally from argparse "infer_mode": "torch", "norm": "layer", "learn_sigma": True, "text_states_dim": 1024, "text_states_dim_t5": 2048, "text_len": 77, "text_...
Work management: business checklists, work order lists, production and assembly instructions, worker assistance systems, audits in various forms, Six Sigma (6s), 5s, 6s, Gemba Walk, Good Manufacturing Practices (GMP), Standard Operating Procedures (SOP), complaint management ...
A further observation is that my EF mount Sigma 18-200mm f3.5 image stabilised lens clicks loudly when you change the aperture. Loud enough to be clearly audible on a camera mic or even a mic a few feet away in a quiet room. The other thing is that the camera has a fan that runs...
learn_sigma=True, ): # 接受多个参数,包括输入图像的大小、图像分块大小、输入通道数、隐藏层维度、模型深度、注意力头数、MLP扩展比例、类别标签的丢弃概率等 super().__init__() self.learn_sigma = learn_sigma self.in_channels = in_channels ...
self.out_channels = in_channels * 2 if learn_sigma else in_channels self.x_embedder= PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True) self.t_embedder = TimestepEmbedder(hidden_size) self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)num_...