New version, https://github.com/quickgrid/text-to-image-diffusion.
Implementation of diffusion models in pytorch for custom training. This code is mainly based on this repo.
Models are implemented for 64 x 64 resolution output which are scaled 2x by nearest sampling to 128 x 128 resolution. In DDPM both training and reverse sampling requires around T steps. In DDIM reverse sampling can be done in small number of steps.
Results were upsampled from 64 x 64 trained model output to 128 x 128 by nearest interpolation.
Stanford Cars and CelebA HQ Dataset with 500 reverse diffusion steps. GIF generated by skipping every 20 frames in reverse process.
CelebA HQ dataset with 30-50 reverse diffusion steps. No frames skipped during GIF generation.
Parent folder path should be provided in dataset_path. Inside it must be one or more folder with images. These folders are used as class information.
For fast training it is best to first resize to expected size and remove corrupted, low res images with tools in this repo.
Large Minibatch Training
For gradient accumulation batch_size * accumulation_iters is the actual expected minibatch size. If code batch_size = 2 and accumulation_iters = 16 then minibatch size for gradient calculation is 32.
If required minibatch size is 64 and batch_size = 8 fits in memory then accumulation_iters should be 8.
Resume Training
To resume training checkpoint_path and checkpoint_path_ema should be provided.
Sample Images
This will generate 4 images each with regular and ema model.
trainer.sample(output_name='output', sample_count=4)
Sample Gif
The following will generate out.gif in chosen directory. The pretrained checkpoint paths must be provided to sample.
trainer.sample_gif(
output_name='out',
sample_count=2,
save_path=r'C:\computer_vision\ddpm'
)
| Name | Description |
|---|---|
ddpm.py |
DDPM implementation for testing new features. |
ddim.py |
DDIM implementation for testing new features. |
Models are available in, https://huggingface.co/quickgrid/pytorch-diffusion.
Trained with linear noise schedule and T = 500 noise steps. Only trained for 1 day without waiting for further improvement.
- Match ddpm, ddim variable names, functions and merge code.
- Class conditional generation.
- Classifier Free Guidance (CFG).
- Save EMA step number with checkpoint.
- Add super resolution with unet like imagen for 4X upsampling,
64x64 => 256x256 => 1024x1024. - Train and test with SWA EMA model.
- Add loss to tensorboard.
- Check if overfitting, add validation.
- Convert to channel last mode.
- Transformer encoder block missing layer norm after MHA.
- Move test class to separate file.
- Logging does not print in kaggle.
- Annotated DDPM implementation, https://github.com/quickgrid/paper-implementations/tree/main/pytorch/ddpm.
- DDIM implementation, https://github.com/quickgrid/paper-implementations/tree/main/pytorch/ddim.
- DDPM Implementation, https://www.youtube.com/watch?v=TBCRlnwJtZU.
- DDPM Implementation, https://github.com/dome272/Diffusion-Models-pytorch.
- DDPM paper, https://arxiv.org/pdf/2006.11239.pdf.
- DDIM paper, https://arxiv.org/pdf/2010.02502.pdf.
- Improved DDPM, https://arxiv.org/pdf/2102.09672.pdf.
- Annotated Diffusion, https://huggingface.co/blog/annotated-diffusion.
- Keras DDIM, https://keras.io/examples/generative/ddim/.
- Postional embedding, http://nlp.seas.harvard.edu/annotated-transformer/.
- Attention paper, https://arxiv.org/pdf/1706.03762.pdf.
- Transformers, https://pytorch.org/tutorials/beginner/transformer_tutorial.html.
- Transformer encoder architecture, https://arxiv.org/pdf/2010.11929.pdf.
- UNet architecture, https://arxiv.org/pdf/1505.04597.pdf.
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