Training Loss 推导 上面两个小节介绍了扩散模型的前向和逆向过程,直观来讲分别对应从图片到噪声和噪声到图片的两个变化过程,这一小节我们会着重推导扩散模型的训练loss和训练过程。 现已有逆向过程概率p_{\theta}(\mathbf{x}_{0:T}),通过变化可得到逆向过程最终产出的数据似然函数p_{\theta}(\mathbf{x}_{0...
这里来简单说说ddpm的意思,英文全称为Denoising Diffusion Probabilistic Model,中文译为去噪扩散概率模型。 代码流程图 这里我们直接来看论文中给的流程图好了,如下: image-20221231194352866 看到这个图你大概率是懵逼的,我来稍稍为大家解释一下。首先这个图表示整个算法的流程分为了训练阶段(Training)和采样阶段(Sampling)...
defdiffusion_loss_fn(model,x_0,alphas_bar_sqrt,one_minus_alphas_bar_sqrt,n_steps):"""对任意时刻t进行采样计算loss"""batch_size = x_0.shape[0]#对一个batchsize样本生成随机的时刻tt = torch.randint(0,n_steps,size=(batch_size//2,)) t = torch.cat([t,n_steps-1-t],dim=0) t =...
from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer model = AutoModelForMultipleChoice.from_pretrained(model_checkpoint) 1. 2. 3. Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMultipleChoice: ['cls.predictions.bias', 'cls...
Model averaging is a widely used practice in deep learning. The idea is to keep track of a running exponential moving average (EMA) of “recent” weights during training. These weights are not used during the training, but rather at inference time. The thinking is that the raw training weig...
A diffusion model is a type of generative model that adds noise to an image in a series of iterative steps, gradually denoising and transforming a noise vector into an image. It is an alternative to GANs in computer vision tasks, showing promising performance but requiring longer sampling times...
Thanks for your a lot contribution and hard work. Why is the loss of Diffusion model calculated between “RANDOM noise” and “model predicted noise”? Not between “Actual added noise” and “model predicted noise”?
GAN vs diffusion model Let's talk about the benefits of diffusion models, why they're necessary, and their advantages over GANs. Image quality A primary advantage of diffusion models over GANs and VAEs is the ease of training with simple and efficient loss functions and their ability to gener...
Latent Diffusion model 对比传统端到端的深度学习模型，扩散模型的训练过程无疑更为复杂，以 Stable Diffusion 为例，除了扩散模型本身，还有一个 Frozen CLIP Textcoder 来输入 text prompts，以及一个 Autoencoder 实现将高分辨率图像压缩到潜在空间（Latent Space），并在每个 time step 计算 loss。这对训练方案的...
usp=sharing#scrollTo=zOsoqPdXHuL5 def loss_fn(model, x, marginal_prob_std, eps=1e-5): """Compute the loss function. 参考公式 (5) Args: model: A score model. batch: A mini-batch of training data. Returns: loss: A scalar that represents the average loss value across the mini-...