kohya-trainer VS stable-diffusion-webui-dataset-tag-editor

This longer colab notebook: I did use this one (or one of the slight derivatives of it) and got out a safetensors file, but the lora didn't work at all--I'd use it a increase it's weight but I just would see no effect

It isn't what you are asking for (sry) but I struggled with this thing for way too long until I found out about the Kohya Trainer. https://github.com/Linaqruf/kohya-trainer So much easier with a lot of videos by the various YT folks. Standalone WebUI that just works. Life is good here!

It's seem that he is stable diffusion model creators. In that space, it's less knowing about the code and more experimenting on what would happen in the training. The stable diffusion community has repertoire of fine-tuning tools that is accessible for someone who have no single idea on the code behind it, no different than using application like kohya.

Have you tried out one of the colabs from https://github.com/Linaqruf/kohya-trainer ? The colabs themselves are pretty long, but you just have to read each step and then usually push the button to run that cell, then move on to the next one.

** https: //github.com/linaqruf/kohya-trainer**

There are a lot of variables that affect what kind of settings to use, but afaik the best solution to finding the right step count for what your training is still just to save multiple epochs and then run a x/y/z plot comparison. If you can't do that locally because of your 4gb card, you could try using Lora colabs that include inference capabilities.

You seem to be a little confused. You wont find an ipynb of a model. You would reference a model via a content portal like hugginface. If your model is hosted there, you dont have to download it to your computer or gdrive first. You just reference it with the hugginface-style reference, ie runwayml/stable-diffusion-v1-5. Some colabs will let you also reference a URL to pull down the model. Example. https://github.com/Linaqruf/kohya-trainer/blob/main/kohya-LoRA-dreambooth.ipynb. In that case, you can get the direct url to a checkpoint, for example at civit.ai. If you're decent at messing around with code, you can deconstruct that code block to use in a different colab. As for gdrive, it's only a couple dollars to get 100G.

Try this colab to train Lora, it can generate image without the UI too

There are few tools for mass tagging data. Each with their own problems. * stable-diffusion-webui-dataset-tag-editor has good features. But it also has bugs that make it nearly unusable. It is also resource heavy as it runs in the webUI with stable diffusion, and stable diffusion always has models loaded. * BooruDatasetTagManager lacks many useful features.

I tried stable-diffusion-webui-dataset-tag-editor but it has a bug where it would get confused and sometimes swap tags from one image to another ruining everything.

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:

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.

I have been using this: https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor to get tags on some random images (not for a dataset, just for ease of browsing personal photos and such) unfortunately, this exports as a txt file and doesnt know how to do xmp or tag embedding. does anyone know of a way to emb the exported txt file into the image keywords/categories/whatever it supports (based on format) or a quick way to convert it to an xmp sidecar file? not necessarily related to ai generation, but it is related to ai usage. hopefully someone knows the answer or can point me where to find it.

Dataset Tag Editor is perfect for editing large datasets and their caption files. It's been around for a couple months and I only found out about it the other day. I could have saved so much time manually editing hundreds of caption files....

There is one extension I use however: https://github.com/toshiaki1729/stable-diffusion-webui-dataset-tag-editor