sd_dreambooth_extension VS stable-diffusion-webui-wd14-tagger

I am trying to use StableDiffusion via AUTOMATIC1111 with the Dreambooth extension

When I first attempted SD training, I was very frustrated. It wasn't until I found this obscure forum thread on Github that I actually started producing great results with Dreambooth. Because I have such satisfactory results, I'm very reluctant to beat my brains against LoRa and its related training techniques. I gave up trying to train TI embeddings a long time ago. And I never figured out how to train or how to use hypernetworks. I've only been able to get good results with Dreambooth directly because of that thread I linked above. I make LoRas by extracting them from Dreambooth-trained checkpoints. And I have no idea if I'm doing the extractions the right way or not.

Getting the same with automatic1111 and sd_dreambooth extension. Check out more here in the issues log: https://github.com/d8ahazard/sd_dreambooth_extension/issues/1266

It's several moths old and many things have changed. But the spreadsheet available through this thread on Github has been indispensable for me when I train Dreambooth models. I'm astounded no one talks about it. I bring it up all the time. The research presented there should be continued. I'd love to see similar research done for SD v2.1.

Training rate is paramount. Read this Github thread.

https://github.com/d8ahazard/sd_dreambooth_extension/discussions/547/ (in depth training principles understanding)

sd_dreambooth_extension https://github.com/d8ahazard/sd_dreambooth_extension.git main 926ae204 Fri Mar 31 15:12:45 2023 unknown

also the Dreambooth extension supports API (https://github.com/d8ahazard/sd_dreambooth_extension/blob/main/scripts/api.py) so i'm not sure where do you get those news :/

Create an extension like 'send pictures' that uses the WD14 tagger which is way more detailed and has options for nsfw etc. Its used in Automatic1111 and Koyha ss so there's extensions you can probably implement from. https://github.com/toriato/stable-diffusion-webui-wd14-tagger

If anyone is interested I made some changes to toriato's wd14-tagger, now it works also on vladmandic webui, repo here. You can do a new installation, or use your old automatic1111 one changing 3 files, instructions on my repo. The lora files also work (there were some problems in the vlad issue page). I'm not a programmer and it's not perfect though, in fact for now if you don't like the default tagger model you have to change it manually (instructions in the repo), and since it is basically a fork of toriato's version, if there were errors there, there will be here too.

Automatic captioning tools that can be used as an initial point for captions: this tool or this one.

wd14-tagger, to describe anime images and get a prompt idea

Use WD 1.4 tagger (https://github.com/toriato/stable-diffusion-webui-wd14-tagger) to extract prompt words from each frame (threshold 0.65), then use the dataset tag editor (https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor) for batch editing, mainly:

I recommend using EveryDream2. You'll need an 11GB VRAM GPU. There's no need to crop or resize images, just caption them, which can be done automatically with CLIP Interrogator or WD14 taggers. Make sure to add the trigger word for your subject. It's not a Dreambooth script; it's actual training, so it shouldn't be as destructive to the model as Dreambooth. Typically, using an LR of 1e-6 with a cosine scheduler over two epochs and a batch size of 4 works fine. This script supports validation, so you can actually watch in real-time whether the training is going well or if you're overfitting. I got very good results using it.

In my current process, I use CLIP Interrogator to produce a high level caption and wd14 tagger for more granular booru tags. Typically in that order, because you can append the results from the latter to the former. Both tools perform with greater accuracy than the standard interrogators in img2img and give you more flexibility and features as well. You still have to do some manual adjustments, but I generally prefer this process over starting from scratch.