>#stablediffusion #A1111 #AI #Lora #koyass #sd #sdxl #style #styletraining
also see realistic Character training for SD 1.5 and SDXL
you can download SD 1.5 model (regularized) from
This video is about taring a Style for stable diffusion 1.5 and SDXL with LoRA, its concepts, how it is different from object or character training in terms of data and training parameters, and how can we determine if our style is trained well or not, and what to expect from it.
the principles explained here apply to any style regardless of what it was, art, clothing or anything else.
we will also see difference between using regularization images and without and determine which option produced the better results.
the style I am going to train is a simple black and white sketching style more like hand drawing, which is used in many illustrations.
now this style like many is already learned by SDXL, and could be produced by SD 1.5 with the right prompts, but using a LoRA can make it easier and straight forward.
Now regardless of how useful this style is, the principles are the same to create your own style LoRA.
1- to learn a style we must have large number of different images from different classes that only have the style in common.
2- 100 and up to 400 images are good number for style training, the more the better.
3- lower number of repeats is very important 1 and up to 4 depending on how many images you have, for 400, 1 or 2 is more than enough…1600 steps worked for simple style, a lot more could be required for complex styles, this is different from one dataset to another.
4- captioning must include everything except the style details, style details must be removed from captions.
5- regularization improves results of a style just like with characters.
6- simple styles don’t need more than 32 network dimension for SD 1.5 or 16 for SDXL, complex styles could require a lot more.
7- SDXL contains too many styles already, unlikely that you need to train any new art style!
8- Noise value 0.0357 might be useful for SDXL training in advanced settings.
9- –network_train_unet_only for SDXL didnt improve results in this example, better to test with and without for each dataset
10- Regularization is strongly recommended to increase model flexibility and quality, better to test with and without and choose the better option
11- style is successful if it runs well on weight 1, and could run at higher weights 1 and up to 2, if it doesnt learn the training data too, only the style itself, and can mix with other LoRAs without corrupting their output.
12- styles will affect the output to a certain degree despite how light it is.
other useful info about how stable diffusion works in general and some tips can be seen at
Beginners guide to stable diffusion in Automatic1111 at:
Koyass for lora training https://github.com/bmaltais/kohya_ss