2.2.3 最终Loss 3 总结 Reference 0 前言 当前在深度学习领域,要论除LLM模型之外更受人关注的技术,我想应该是文生图/视频(比如前段时间爆火的Sora),而文生图技术背后所用到的核心技术之一就是Diffusion Model,所以本文就来学习一下DDPM的基本原理,以此作为基础来学习Diffusion Model。应该说Diffusion Model是一个数学...
Average Loss: {epoch_loss/len(train_loader):.4f}") # 4. 设置训练参数 model = SimpleDiffusi...
train(ddpm, net, device=device, ckpt_path=model_path) 按照默认训练配置,在3090上花5分钟不到,训练30~40个epoch即可让网络基本收敛。最终收敛时loss在0.023~0.024左右。 batch size: 512 epoch 0 loss: 0.23103461712201437 elapsed 7.01s epoch 1 loss: 0.0627968365987142 elapsed 13.66s epoch 2 loss: 0.04828...
batch_x = dataset[indices]# Compute the loss.loss = noise_estimation_loss(model, batch_x, alphas_bar_sqrt, one_minus_alphas_bar_sqrt, num_steps)# Before the backward pass, zero all of the network gradientsoptimizer.zero_grad()# Backward pass: compute gradient of the loss with respect to...
(BATCH_SIZE,), device=device).long() x_noisy, noise = forward_diffusion_sample(batch_data, t, device) # 计算得到指定时刻的 加噪后的数据 和 对应的噪声数据 noise_pred = model(x_noisy, t) # 预测对应时刻的噪声 loss = F.mse_loss(noise, noise_pred) # 计算噪声预测的损失值 loss....
def p_losses(denoise_model, x_start, t, noise=None, loss_type="l1"): # 先采样噪声 if noise is None: noise = torch.randn_like(x_start) # 用采样得到的噪声去加噪图片 x_noisy = q_sample(x_start=x_start, t=t, noise=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...
28、CosmicMan: A Text-to-Image Foundation Model for Humans 提出CosmicMan,一种用于生成高保真人体图像的文本到图像基础模型。与当前困在人体图像质量和文本-图像不对齐困境中的通用基础模型不同,CosmicMan能够生成具有细致外貌、合理结构和精确文本-图像对齐的逼真人体图像,同时还提供详细的密集描述。CosmicMan关键在于...
使用Unconditonal model生成图片 生成过程如果直接使用pip默认安装的版本也会出错,报错如下: (忘记记录了,反正也是一个依赖问题,但换环境尝试了没有复现) 安装好依赖版本后,把训练好的模型放在和ldm的配置文件相同文件夹中,改好名字,执行如下命令生成: python scripts/sample_diffusion.py -r models/ldm/lsun_churche...
noise_pred = model(x_noisy, t) # 预测对应时刻的噪声 loss = F.mse_loss(noise, noise_pred) # 计算噪声预测的损失值 loss.backward() optimizer.step() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.