Diffusion Clip
This makes it easy to train and apply diffusion moedls to images. The tool being wrapped is gwang-kim/DiffusionCLIP
Docker tooling for gwang-kim/DiffusionCLIP.
The entrypoint is a highly opinionated wrapper on the edit single image operation, to do other things or to override options override the entrypoint.
Args
Example: docker-compose run dc --model_path pretrained/imagenet_cubism_t601.pth --config imagenet.yml --img_path ../working/test.jpg
| Flag | Value |
|---|---|
--model_path | pretrained/${Name of model you put in checkpoints dir} |
--config | Either celeba.yml, imagenet.yml, afqh.yml or `` (read in source repo) |
--img_path | ../working/{Name of file you want to process in working dir} |
Volumes
Be sure to read in the source repo about what needs to go into pretrained (checkpoints) and data.
| Local Path | Purpose |
|---|---|
../../volumes/checkpoints | Pretrained models |
../../volumes/data | Data to train on |
../../volumes/working | Repo to put file into for processing and to get processed files out of |
../../volumes/cache | Python cache |