clip-interrogator VS stable-diffusion-webui-wd14-tagger

It's image -> words, the inverse of stable diffusion.

see: https://github.com/pharmapsychotic/clip-interrogator

I have been using this on a CPU https://github.com/pharmapsychotic/clip-interrogator, I tried a lot of pre-trained models combinations, all are slow.

I'm addition to controlnet, try the clip interrogator to see how clip would describe the image and then use that language in your prompt. You can try the whole image or cropped portions. There is a colab available if you don't want to run it locally.

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.

If you are interested in this, definitely see if you can get some of the OSS models running and get a feel for how to interrogate them. Maybe see if you can get some mileage out of the CLIP-Interrogator

Next, I used the lists of artists, flavors, mediums, movements, and negatives that are used for the clip-interrogator and pasted these in the chat and told the bot to categorize them accordingly. As you can only paste up to certain characters in single message (4-5K in 3.5 and 6-8K in 4).

Here’s the specific one I’m using (runs in browser)

I really enjoy using the CLIP Interrogator on huggingspaces, but it is often super slow and sometimes straight up breaks. Now it is possible to locally install it, https://github.com/pharmapsychotic/clip-interrogator but I don't know if its viable to run on a laptop with 6gb videocard anyway.

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.