Note: This model is a Schnell based model, but it requires CFG scale 3.5 or higher and 20 steps or more. It needs to be used with clip_l_sumeshi_f1s.
My English is terrible, so I use translation tools.
Description
Sumeshi flux.1 S is an experimental anime model to verify if de-distilling and enabling CFG will function. You can use a negative prompt which works to some extent. Since this model uses CFG, it takes about twice as long to generate compared to a regular FLUX model, even with the same number of steps. 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
CFG Scale: 3.5 ~ 7 ( Scale1 does not generate decent outputs. )
Steps: 20 ~ 60 (Not around 4 steps)
(Distilled) Guidance Scale: 0 (Does not work due to Schnell-based model)
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
I added tensors filled with zeros with the guidance_in key to the Schnell model. This tensor is adjusted to the shape of the corresponding key in Dev, as inferred from flux/src/flux/model.py. This is because the trainer did not work properly when these keys were missing if the model name did not include 'schnell'. Since it is filled with zeros, I understand that guidance, like in the Schnell model, will not function. Due to my limited skills and the forceful addition, I'm not sure if this was the correct approach.
Training Details
Basically, the assumption is that the more we learn, the more the network will be reconstructed, the more the distillation will be lifted, and the more CFGs will be available.
