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stable-diffusion-webui-dataset-tag-editor
Extension to edit dataset captions for SD web UI by AUTOMATIC1111
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Something that has been on our todo list for a while has been a trophy system to incentivize people to take the effort to review models, here's a bit about that if you're interested or want to provide feedback :)
That's where you lost me. Lora training appeared back in mid December, and the extension to use them appeared in late December, and the earliest Lora I found on civitai after a quick search, was Jan 07.
That's where you lost me. Lora training appeared back in mid December, and the extension to use them appeared in late December, and the earliest Lora I found on civitai after a quick search, was Jan 07.
Automatic1111 got support for LoRas 3 weeks ago (see history of https://github.com/AUTOMATIC1111/stable-diffusion-webui/commits/master/extensions-builtin/Lora/lora.py). Before that they weren't really readily available.
If you haven't come across them yet, these two guides: this and this are good reads, and this one for info about learning rates. Beyond what those guides give info on, there are two points in which I noticed a large increase in my Lora quality- better captioning, and when I resized all the images to have about the same amount of pixels as was being trained. For captioning I have a text file with types of tags I know I'll have to hit- subject (solo, 1girl, 1boy, those early tags), what kind of perspective- portrait, closeup, full body, etc, where the character is looking (looking up, looking to the side, looking at viewer, etc), what the perspective of the viewer is (from above, from below, pov, etc), and I write down common clothing tags for the character. So I have that off to the side, and then I load up this extension for webui. It has a bit of learning curve, but I point it at what pictures I've gotten and get it to interrogate with all the models it offers except blip, and set the confidence threshold to 0.10 so it's spitting out lots of tags. After it interrogates all the pictures, I use the database feature to remove the duplicate tags, and then I save the database so it creates all the text files. Then I go to the "edit caption of selected image" select an image to caption from the left. At that point on the right the top box should be full of tags, and the bottom one should be empty. I look at my checklist from my textfile and start hitting all the areas I need to, which doesn't take long. Then I look up at the top box and read from left to right, top to bottom, one tag a time, and if it's a relevant tag, I type it in the bottom box.
As for the other noticeable increase in quality. Using chainner, I upscaled all my images using an anime focused upscaling model, and then scaled back down to twice their original size. From there I made a chainner workflow that would resize all the images in a directory down to a pixel count I determined, while maintaining whatever ratio the image was in originally. My hypothesis is that if you are training using bucketing, it's best to resize the images to have close to the same pixel count as the resolution you are training at. Supposedly the script does that kind of thing already, but when I did it, and then started increasing the resolution by 64's (576, 640, 704) the detail went up. I had to stop at 704 because that's when I hit the point where my smaller pictures were being increased in size by the script, not decreased in size. I don't know how much this pixel count resizing actually helps, as I haven't tested it extensively yet.