DDPM

One Diffusion model implementation base on Pytorch, feel free to check the full same c++ implementation https://github.com/GlassyWing/TorchDiffusion If you want faster training speed and lower memory usage. (The weight file could be load at any side)

Supported Sampler

• DDPM
• DDIM
• RectifiedFlow

Usage

Train

python train.py --dataset <dataset_dir> -t ddim -s 4

The default will create one experiments folder used to save the checkpoints and log images.

see more with python train.py -h

Inference

Please check that scripts:

1. ddim_test.py
2. ddpm_test.py
3. rectifiedflow_test.py

Generate Example

After 5 days (140k images):

It's trained with this strategy:

Epochs	Approximate Batch Size	Batch Size	accumulation_steps
0-40	64	64	1
40-80	128	64	2
80-120	256	64	4
120-160	512	64	8
160-200	1024	64	16

With this cmd:

python train.py -p <path/to/last_checkpoint> -t 'ddim' -s 4 -b 32 --accum <accumulation_steps>

Tips: You can still accumulate the size of the Batch_size to get better results

QA

1. How long does it take to train to see considerable results?

   About 30min on 3090.

2. Memory usage?

   Image Size: 128 x 128

   Batch Size: 32

   Memory: 3GB

   Vedio Memroy: 17GB