Note: This model is a Schnell-based model, but it requires CFG scale of 3 or higher (not guidance scale) with 20 steps or more. It needs to be used with clip_l_nigemono_flux1C.(It's the 234.74 MB file in the menu on the right.)
At this time, this model cannot be generated in WebUI Forge. Please use it with ComfyUI.
My English is terrible, so I use translation tools.
Description
Nigemono_Flux.1_Compact7.5B is an experimental anime model to verify the reduction of parameters in the Flux model. The number of parameters has been reduced from 12B (double blocks 19/ single blocks 38) to 7.5B (double blocks 12/ single blocks 24). You can use a negative prompt which works to some extent. The output is blurred and the style varies depending on the prompt, perhaps because the model has not been fully trained.
Usage
Resolution: Like other Flux models
(Distilled) Guidance Scale: (Distilled) Guidance Scale: 0 (Does not work due to Schnell-based model)
CFG Scale: 6 ~ 9 (recommend 7; scale 1 does not generate decent outputs)
Steps: 20 ~ 100 or and more (recommend 40)
sampler: Euler
scheduler: Simple, Beta
Prompt Format (from Kohaku-XL-Epsilon)
<1girl/1boy/1other/...>, <character>, <series>, <artists>, <general tags>, <quality tags>, <year tags>, <meta tags>, <rating tags>Due to the small amount of training, the <character><series><artists> tags are almost non-functional. As training is focused on girl characters, it may not generate boy or other non-persons well. Since the dataset was created using hakubooru, the prompt format will be the same as the KohakuXL format. However, based on experiments, it is not strictly necessary to follow this format, as it interprets meaning to some extent even in natural language.
Special Tags
Quality Tags: masterpiece, best quality, great quality, good quality, normal quality, low quality, worst quality
Rating Tags: safe, sensitive, nsfw, explicit
Date Tags: newest, recent, mid, early, old
Training
see hugging face
Resources (License)
FLUX.1-schnell (Apache2.0)
danbooru2023-webp-4Mpixel (MIT)
danbooru2023-metadata-database (MIT)
License
Apache2.0
Acknowledgements
black-forest-labs: Thanks for publishing a great open source model.
kohya-ss: Thanks for publishing the essential training scripts and for the quick updates.
Kohaku-Blueleaf: Thanks for the extensive publication of the scripts for the dataset and the various training conditions.