Please read through the entire description (might need to be expanded) and the version change notes as they cover a lot of information about basic use cases and limitations. Thank you!
This is an SD 1.5 LoRA for the character May / Haruka from Pokemon.
Example images were picked from pure 512x512 txt2img results and then re-created at 1024x1024 using txt2img with minimal Hires Fix settings (no upscaler, denoising strength 0.1). This improves faces and other details that are almost impossible to get correctly and consistently at 512x512 (limitation of the technology) while still giving a realistic impression of what it looks like. Pure 512x512 results will have more distorted faces and less detail.
Please see the version change notes for the training and example image generation models as well as the used weights as they might change between versions. Remember that you might need to adjust weights to best suit your use case!
Remember to add the trigger phrase character_pokemon_may (with underscores intact) to your positive prompt.
Note: Since Civitai renames files, you will need to rename the downloaded file to "character_pokemon_may_vX.safetensors" (where X is the current version number) if you want to use the example prompts as-is. Otherwise, change "character_pokemon_may_vX" in the prompts to your local file name. This does not affect the trigger phrase, only the way you reference the LoRA.
The training set contained multiple variations of May's signature look (not including the ones from Emerald and ORAS, sorry) so you might need to put combinations of the following tags in your positive or negative prompts to get the desired results. Some were included with both more generic and specific tags (separated by slashes in this list) and those might need to be combined or used on their own:
bandana
bike shorts
fanny pack
gloves
shirt
shoes
skirt
socks
As the training set contained a few images with floating hair, you might need to add floating hair to the negative prompts if not desired.
Known Limitations / Problems:
The shoes were not present in a lot of images and not a focus for the training so they sadly will not be consistent at all.
Same goes for the fanny pack and the pattern on the bandana (though they tend to be a bit better).
The colors of clothing and accessories can be all over the place, sometimes even swapping around. Especially for closer shots and from behind. Don't really know why yet, I might have to tinker with the training images and/or tags a bit more. Maybe I might have to use explicit color tags in the future. It might also help to just put the colors in the positive prompts for now.
It can be a bit difficult to get images without the bandana or any other headwear, depending on the remaining clothing and framing. In addition to bandana, I would also recommend additional negative prompts such as hat. Maybe even put no headwear into the positive prompts.
It seems very difficult to remove the skirt on its own while keeping the shirt and bike shorts by just using negative prompts. I've tried a lot of things during training but it still does not seem to work consistently. Try adding something like bra or topless to the positive prompts and shirt to the negative prompts and maybe you will get lucky.
Might generate a collar even when the shirt is not present. Adding collar to the negative prompt might help but I've not found a consistent fix yet.
The hairstyle from the side and from behind is very inconsistent and might not look correct due to very limited training data at the moment.
General changes:
Removed unnecessary general tag bag (already had a more specific equivalent, see description)
Changed example weight from ~0.5 to ~0.6 (result of training changes)
Added two more training images and re-cropped a third to maybe help separating the shirt, skirt and bike shorts more (colors still seem to mix a lot though sadly, better to specify explicitly if that happens)
Changes to the training process (with the goal of hopefully making this and my other models more flexible and compatible while reducing overfitting):
Switched from a flat 200 repeats x 1 epoch to 20 repeats x 10 epochs (max) and selecting the best results
Lowered network training alpha from 128 to 64 to hopefully increase the quality of the result a bit
Resized LoRA from rank 128 to rank 64 rank after training (still training with rank 128) to reduce size and "smooth out" some of the training results while not changing much (going down to rank 32 seems to have too much influence on the quality of results, so I kept it at 64)
Used weight: 0.6
Training model: Anything V3
Example image generation model: AbyssOrangeMix2 - Hardcore
Go ahead and upload yours!
Your query returned no results β please try removing some filters or trying a different term.