alpha: pretrain on 2M dataset, smaller batch size. Limited ability
beta: pretrain on 5.3M dataset, larger batch size. More stable, better ability with only a few information provided.
delta: pretrain on 7.2M dataset, larger batch size. Slightly underfit but better diversity. quality tag introduced.
- rev2: resumed from delta, same dataset, 2 more epoch.

Model arch

This version of DTG is trained from scratch with 400M param LLaMA arch.(In my personal preference I will call it NanoLLaMA) Since it is llama arch. Theoritically it should be able to be used in any LLaMA inference interface.

This repo also provided converted FP16 gguf model and quantized 8bit/6bit gguf models. Basically it is recommended to use llama.cpp or llama-cpp-python to run this model. Which will be very fast.

Format

python3

prompt = f"""
quality: {quality or '<|empty|>'}
rating: {rating or '<|empty|>'}
artist: {artist.strip() or '<|empty|>'}
characters: {characters.strip() or '<|empty|>'}
copyrights: {copyrights.strip() or '<|empty|>'}
aspect ratio: {f"{aspect_ratio:.1f}" or '<|empty|>'}
target: {'<|' + target + '|>' if target else '<|long|>'}
general: {", ".join(special_tags)}, {general.strip().strip(",")}<|input_end|>
"""

for example:

quality: masterpiece
rating: safe
artist: <|empty|>
characters: <|empty|>
copyrights: <|empty|>
aspect ratio: 1.0
target: <|short|>
general: 1girl, solo, dragon girl, dragon horns, dragon tail<|input_end|>

And you may get something like:

rating: safe
artist: <|empty|>
characters: <|empty|>
copyrights: <|empty|>
aspect ratio: 1.0
target: <|short|>
general: 1girl, solo, dragon girl, dragon horns, dragon tail<|input_end|>open mouth, red eyes, long hair, pointy ears, tail, black hair, chinese clothes, simple background, dragon, hair between eyes, horns, china dress, dress, looking at viewer, breasts

Dataset and Training

I use the trainer I implemented in HakuPhi to run the training. with Total 12epoch on 7.2M data. This model have roughly 10~15B token seen.

The dataset is exported by HakuBooru with my danbooru sqlite database. Use the percentile of fav_count on each rating to filter the data. (2M = top 25%, 5.3M = top 75%)

Utilities

HF space: https://huggingface.co/spaces/KBlueLeaf/DTG-demo
Demo for DTG + Kohaku XL Epsilon: https://huggingface.co/spaces/KBlueLeaf/This-Cute-Dragon-Girl-Doesnt-Exist
SD-WebUI Extension: https://github.com/KohakuBlueleaf/z-a1111-sd-webui-dtg
ComfyUI Node: https://github.com/toyxyz/a1111-sd-webui-dtg_comfyui

KBlueLeaf/DanTagGen-delta-rev2

DanTagGen - delta (rev2)

Difference between versions

Model arch

Format

Dataset and Training

Utilities

KBlueLeaf/
DanTagGen-delta-rev2