This is the multi-page printable view of this section. Click here to print.

Return to the regular view of this page.

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

    FlagValue
    --model_pathpretrained/${Name of model you put in checkpoints dir}
    --configEither 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 PathPurpose
    ../../volumes/checkpointsPretrained models
    ../../volumes/dataData to train on
    ../../volumes/workingRepo to put file into for processing and to get processed files out of
    ../../volumes/cachePython cache