DiffEnc-32-2 trained on CIFAR-10

Checkpoints for an image generation model trained on CIFAR-10.The model was made in Jax. See the github repository for code to load the checkpoints.The model is a variational diffusion model (VDM, https://arxiv.org/abs/2107.00630) with an added trainable time-dependent encoder trained for the article "DiffEnc: Variational Diffusion with a Learned Encoder" (https://arxiv.org/abs/2310.19789).Model uses v-parametrization for the loss. The diffusion model is of size 32 and the encoder is of size 2. That is, the diffusion model uses 32 "down-blocks" in the U-net. See details in article.Model was trained on CIFAR-10 for 2 million steps with a batch size of 128.Random seeds: 1, 2, 13

CIFAR-10 图像生成模型训练的检查点。该模型采用 Jax 构建而成。请参阅 GitHub 仓库以获取加载检查点的代码。模型为变分扩散模型（VDM，https://arxiv.org/abs/2107.00630），并额外添加了一个可训练的时间相关编码器，用于训练文章《DiffEnc：具有学习编码器的变分扩散》（https://arxiv.org/abs/2310.19789）。模型使用 v 参数化来计算损失。扩散模型的大小为 32，编码器的大小为 2，即扩散模型在 U-net 中使用了 32 个“下采样块”。详细内容请参见文章。模型在 CIFAR-10 数据集上训练了 200 万步，批处理大小为 128。随机种子：1, 2, 13。