to get weights

curl -L -o output.zip https://replicate.delivery/pbxt/4lrw9387HOaKKR7kHv5YX4N59OSP0ltIcfaYWdm2lHAGVCBIA/output.zip mkdir weights unzip output.zip -d weights

dreambooth-builder

This is a prototype to build a replicate model using existing replicate models as the base.

These concepts might be incorporated into cog/replicate's dreambooth api

motivation

using cog push to build/push a model many times results in a lot of work - downloading / building / ... But each model you build technically just has a difference of the weights.

if we could take an existing popular stable diffusion model, and throw our weights and any customization to predict.py - our image should be much smaller/faster builds/...

we only need to upload our changes (weights & predict.py)

requirements

1. docker must be installed
2. install cog and authenticate
3. the modelname must already exist for your username (go here to create a model on replicate, then use it with build.sh)

usage

0. make sure cog is authenticated
1. run dreambooth trainer on replicate.com - not dreambooth api
2. Download the output.zip and put it into a directory called weights
3. ./build.sh r8.im/username/modelname

todo / open questions

• parse the sha of the pushed model to let you the replicate verison / image
• speed!!! downloading layers doesn't seem useful... is there a way to skip downloading the existing r8.im layers, ... just create new layer(s) and push - as this isn't for running locally
• efficiency?!? is sharing the base image of cog-stable-diffusion helpful? does having the SD2.1 weights in an unused but present layer make things better or worse?
• build these into cog?