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DiffEnc-32-2 trained on CIFAR-10

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posted on 2024-04-22, 09:59 authored by Beatrix Miranda Ginn NielsenBeatrix Miranda Ginn Nielsen

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


Funding

Danish Pioneer Centre for AI, DNRF grant number P1

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