Phil Wang 164d9be444 use a decorator and take care of sampling in chunks (max_batch_size keyword), in case one is sampling a huge grid of images

2022-05-16 12:34:28 -07:00

..

data

bring in the simple tokenizer released by openai, but also plan on leaving room for custom tokenizer with yttm

2022-04-12 09:23:17 -07:00

dataloaders

allow for overriding use of EMA during sampling in decoder trainer with use_non_ema keyword, also fix some issues with automatic normalization of images and low res conditioning image if latent diffusion is in play

2022-05-16 11:18:30 -07:00

__init__.py

unet_number on decoder trainer only needs to be passed in if there is greater than 1 unet, so that unconditional training of a single ddpm is seamless (experiment in progress locally)

2022-05-16 09:17:17 -07:00

cli.py

add missing import (#56)

2022-05-04 07:42:20 -07:00

dalle2_pytorch.py

allow for overriding use of EMA during sampling in decoder trainer with use_non_ema keyword, also fix some issues with automatic normalization of images and low res conditioning image if latent diffusion is in play

2022-05-16 11:18:30 -07:00

optimizer.py

be able to customize adam eps

2022-05-14 13:55:04 -07:00

tokenizer.py

bring in the simple tokenizer released by openai, but also plan on leaving room for custom tokenizer with yttm

2022-04-12 09:23:17 -07:00

trackers.py

experiment tracker agnostic

2022-05-15 09:56:40 -07:00

trainer.py

use a decorator and take care of sampling in chunks (max_batch_size keyword), in case one is sampling a huge grid of images

2022-05-16 12:34:28 -07:00

vqgan_vae_trainer.py

unet_number on decoder trainer only needs to be passed in if there is greater than 1 unet, so that unconditional training of a single ddpm is seamless (experiment in progress locally)

2022-05-16 09:17:17 -07:00

vqgan_vae.py

remove convnext blocks, they are illsuited for generative work, validated by early experimental results at https://github.com/lucidrains/video-diffusion-pytorch

2022-05-05 07:07:21 -07:00