(this is my personal notes mainly on preparing the dataset feel free to comment to bless everyone with your knowledge)
-few images for the dataset? no problem:search for data augmentation like to rotate,change background and crop images (make sure to upscale small images )
you may find this useful: https://github.com/AgaMiko/data-augmentation-review
augementation tool: https://github.com/folkien/pyImageAugmentation
-Ai2Ai training: you may use the results of other models to train your model
EX: LORA trained on DALLE (microsoft version): https://civitai.com/models/123309/dlogo-dall-e-trained-lora
@hk4life SD1.5 LORA trained on SDXL LORA: https://civitai.com/models/128806/real-mario
-when training on images from old animations try to upscale and denoise the images before training unless you want the video compression to be in your results
EX: @Kotoshko original and upscaled versions https://civitai.com/models/129100/freakazoid
-adding flexibility for character lora to do hand stand and lay: add a rotated copy of your images in the dataset
-if there is a clear error with your lora like the images in the dataset are stretched you may want to try and add that error in the training words for example stretched,wide head,wide shoulders,wide body which migh thelp you
-adding negative prompts to the training dataset (this is a method I made with logic not tested yet if you did try this method please comment or mention me to see your results) if you have an image for example a robot you may try to add something like these words to the dataset: Not human, doesn't have flesh,Not animal
you may also try not ugly,not low quality,not poorly drawn
-best upscaler mentioned and tested by different users was Topaz Gigapixel Ai
cons: pretty expensive....
pros: no image limit,different upscaling Ai models,bulk support,can enhance quality without upscaling
if I remember or find more notes I will add it here so make sure to follow and bookmark