或者概率分布函数(probability mass function, p.m.f.)。以概率密度函数为例: pθ(x)=e−fθ(x)Zθ 其中,fθ(x)∈R 是一个以 θ 为可学习参数的实值函数(常称为 unnormalized probabilistic model 或者 energy-based model);Zθ 是一个和 θ 相关的归一化常数,作用是保证 ∫pθ(x)dx=1。 为了...
损失函数-Loss Function 训练一个神经网络模型,通常我们需要知道损失函数。在扩散模型中,目标就是在反向马尔可夫链中对训练集找到最大似然估计,这个相当于最小化变分下界的负对数。 最小化交叉熵 为了使方程中的每一项都能解析计算,目标函数可进一步重写为KL散度和熵的组合。 KL散度-KL Divergence KL散度是一个衡量...
“score function”,或者简单的称为“score”,其实就是论文题目中提到的“Gradients of the Data Distribution”,更具体来说其实是概率密度函数的对数的梯度,即: 用来对其进行建模/拟合的模型就叫做——score-based model,记作。和上面直接建模概率密度函数p(x)时不同,score-based model 的优势是不会受到归一化常数...
predicted_noise = denoise_model(x_noisy, t) # 根据加噪了的图片去预测采样的噪声 if loss_type == 'l1': loss = F.l1_loss(noise, predicted_noise) elif loss_type == 'l2': loss = F.mse_loss(noise, predicted_noise) elif loss_type == "huber": loss = F.smooth_l1_loss(noise, pred...
the loss with respect to parametersloss.backward()# Perform gradient clippingtorch.nn.utils.clip_grad_norm_(model.parameters(),1.)# Calling the step function to update the parametersoptimizer.step()# Update the exponential moving averageema.update(model)# Print lossif(t %100==0):print(loss)...
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-...
补充1:GAN生成的头像很逼真,但是多样性远不如diffusion model,在文生图/视频领域已经逐渐被diffusion model取代。 补充2:关于从GAN到AR, VAE 再到 stable diffusion的演变,强推李沐老师和朱老师的全网最佳讲解:【DALL·E 2(内含扩散模型介绍)【论文精读】】 【精准空降到 28:07】 https://www.bilibili.com/vide...
accurate molecules with high binding affinity when the pocket information is fixed. At each time step, the model outputs the (Stein) score, which represents the logarithmic density of the data point. The ELBO objective is derived from these scores and serves as the loss function (See Method)....
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”?
step() ema.update(model) print(f'Epoch {i+1} | Loss {total_loss / (60000/batch_size):.5f}') checkpoint = { 'weights': model.state_dict(), 'optimizer': optimizer.state_dict(), 'ema': ema.state_dict() } torch.save(checkpoint, 'checkpoints/ddpm_checkpoint')...