train in embedding or hypernetwork 1
textual inversion is used to train a custom embedding 2
it finds a descriptive prompt for the model and then makes images that are similar to the user's training data 3
the hypernetwork is a layer that helps stable diffusion learn based on images it has previously generated 4
allowing it to improve and become more accurate with use 5
unlike an embedding training the hypernetwork will really change the model 6
to create embedding 7
choose a unique name for the embedding file 8
write a few words or phrases that describes the subject or artistic style you want to train the model 9
the amount of data you're embedding stores is determined by the number of vectors per token 10
click on create embedding button 11
the name you specify will be created under the embeddings folder once stable diffusion is finished 12
to create hypernetwork 13
enter name to the style, subject or whatever your training on module that process input 14
layer structure 15
you can increase or decrease the number 16
experiment with that 17
you can play around with the activation function 18
drop down menu lists several built-in options to choose 19
layer weights initialization also have drop down menu lists several built-in options to choose 20
layer normalization is not worth it 21
although it will significantly slow down training 22
dropping out can be useful for delaying over training and lifting heavy weights 23
click on create hypernetwork 24
pre-processing your image's source directory 25
where your training images are located 26
destination directory 27
where you want to create processed images and captions 28
width and height 29
change these values to better fits your training images and the size of images you want to generate 30
existing caption text action each training image is described in text captions 31
by selecting keep original size 32
size of the training images will remain as it is 33
check the use blip or deepbooru for caption option if ignore was selected under the existing caption text action 34
create flipped copies box to have stable diffusion horizontally mirror your training images and effectively double your training data set 35
split oversized images option 36
will whenever possible split the image 37
the training image's width and height are determined by the split Image threshold 38
auto focal point crop 39
work best when training and embedding for a particular person and there face 40
auto size crop will automatically crop and resize training images with specified width and height 41
hit the pre-process button when you have all of the settings you want 42
and then wait for stable diffusion to finish 43
check in the destination directory to ensure that blip or deepboooru created the caption accompanied 44
train your embedding or hypernetwork 45
since you have your pre-processed training images 46
captions and embedding or hypernetwork file created 47
it's finally time to train your embedding or hypernetwork in stable diffusion 48
select your embedding or hypernetwork 49
embedding learning rate or hyper Network learning rate 50
theoretically the embedding will learn more quickly the higher this value but speed may affect subject accuracy and quality 51
gradient clipping gradients tend to encapsulate information they collect from the data 52
which also includes long-range dependencies in large text or multi-dimensional data 53
as a result 54
when calculating complex data things can go wrong really quickly and you'll blow your next million dollar model in the process 55
set the batch size to the number of images you want the embedding or hypernetwork to generate using the text prompt you wrote earlier 56
gradient accumulation steps gradient accumulation is a method for training with larger batch sizes then your machine can typically store in memory 57
in the dataset directory place the folder location containing your caption files and pre-processed images that you created earlier 58
log directory 59
stable diffusion will generate images using your embedding or hypernetwork every entered value steps 60
and also save a version of your embedding or hypernetwork every entered value steps 61
prompt template 62
that you have created 63
width and height 64
the desired output images you want 65
do not resize images will skip resizing 66
max steps 67
train until your entered value 68
with generated images using your embedding or hypernetwork every entered value steps 69
and also save a version of your embedding or hypernetwork every entered value steps 70
read parameters previews this tells stable diffusion to use the text prompts you typed earlier 71
ensure you have 72
choose latent sampling method set to deterministic or random 73
random could be better however causes a performance don't have it set to once 74
or the vae won't be accurately tested which could prompt hypernetwork passing while preparing over numerous cycles 75
click train embedding button to start training embedding 76
and click train hypernetwork button to start training hypernetwork