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Added example for slider training that will run as is

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Jaret Burkett
2023-07-23 11:24:12 -06:00
parent 434fb22458
commit 9367089d48
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---
# This is in yaml format. You can use json if you prefer
# I like both but yaml is easier to write
# Plus it has comments which is nice for documentation
# This is the config I use on my sliders, It is solid and tested
job: train
config:
# the name will be used to create a folder in the output folder
# it will also replace any [name] token in the rest of this config
name: pet_slider_v1
# folder will be created with name above in folder below
# it can be relative to the project root or absolute
training_folder: "output/LoRA"
device: cuda:0 # cpu, cuda:0, etc
# for tensorboard logging, we will make a subfolder for this job
log_dir: "output/.tensorboard"
# you can stack processes for other jobs, It is not tested with sliders though
# just use one for now
process:
- type: slider # tells runner to run the slider process
# network is the LoRA network for a slider, I recommend to leave this be
network:
# network type lierla is traditional LoRA that works everywhere, only linear layers
type: "lierla"
# rank / dim of the network. Bigger is not always better. Especially for sliders. 8 is good
rank: 8
alpha: 1.0 # just leave it
# training config
train:
# this is also used in sampling. Stick with ddpm unless you know what you are doing
noise_scheduler: "ddpm" # or "ddpm", "lms", "euler_a"
# how many steps to train. More is not always better. I rarely go over 1000
steps: 500
# I have had good results with 4e-4 to 1e-4 at 500 steps
lr: 2e-4
# train the unet. I recommend leaving this true
train_unet: true
# train the text encoder. I don't recommend this unless you have a special use case
# for sliders we are adjusting representation of the concept (unet),
# not the description of it (text encoder)
train_text_encoder: false
# just leave unless you know what you are doing
# also supports "dadaptation" but set lr to 1 if you use that,
# but it learns too fast and I don't recommend it
optimizer: "adamw"
# only constant for now
lr_scheduler: "constant"
# we randomly denoise random num of steps form 1 to this number
# while training. Just leave it
max_denoising_steps: 40
# works great at 1. I do 1 even with my 4090.
batch_size: 1
# bf16 works best if your GPU supports it (modern)
dtype: bf16 # fp32, bf16, fp16
# if you have it, use it. It is faster and better
xformers: true
# I don't recommend using unless you are trying to make a darker lora. Then do 0.1 MAX
# although, the way we train sliders is comparative, so it probably won't work anyway
noise_offset: 0.0
# the model to train the LoRA network on
model:
# huggingface name, relative prom project path, or absolute path to .safetensors or .ckpt
name_or_path: "runwayml/stable-diffusion-v1-5"
is_v2: false # for v2 models
is_v_pred: false # for v-prediction models (most v2 models)
# saving config
save:
dtype: float16 # precision to save. I recommend float16
save_every: 100 # save every this many steps
# sampling config
sample:
# must match train.noise_scheduler, this is not used here
# but may be in future and in other processes
sampler: "ddpm"
# sample every this many steps
sample_every: 20
# image size
width: 512
height: 512
# prompts to use for sampling. Do as many as you want, but it slows down training
# pick ones that will best represent the concept you are trying to adjust
# allows some flags after the prompt
# --m [number] # network multiplier. LoRA weight. -3 for the negative slide, 3 for the positive
# slide are good tests. will inherit sample.network_multiplier if not set
# --n [string] # negative prompt, will inherit sample.neg if not set
# Only 75 tokens allowed currently
prompts:
- "a golden retriever --m -5"
- "a golden retriever --m -3"
- "a golden retriever --m 3"
- "a golden retriever --m 5"
- "calico cat --m -5"
- "calico cat --m -3"
- "calico cat --m 3"
- "calico cat --m 5"
# negative prompt used on all prompts above as default if they don't have one
neg: "cartoon, fake, drawing, illustration, cgi, animated, anime, monochrome"
# seed for sampling. 42 is the answer for everything
seed: 42
# walks the seed so s1 is 42, s2 is 43, s3 is 44, etc
# will start over on next sample_every so s1 is always seed
# works well if you use same prompt but want different results
walk_seed: false
# cfg scale (4 to 10 is good)
guidance_scale: 7
# sampler steps (20 to 30 is good)
sample_steps: 20
# default network multiplier for all prompts
# since we are training a slider, I recommend overriding this with --m [number]
# in the prompts above to get both sides of the slider
network_multiplier: 1.0
# logging information
logging:
log_every: 10 # log every this many steps
use_wandb: false # not supported yet
verbose: false # probably done need unless you are debugging
# slider training config, best for last
slider:
# resolutions to train on. [ width, height ]. This is less important for sliders
# as we are not teaching the model anything it doesn't already know
# but must be a size it understands [ 512, 512 ] for sd_v1.5 and [ 768, 768 ] for sd_v2.1
# you can do as many as you want here
resolutions:
- [ 512, 512 ]
# - [ 512, 768 ]
# - [ 768, 768 ]
# These are the concepts to train on. You can do as many as you want here,
# but they can conflict outweigh each other. Other than experimenting, I recommend
# just doing one for good results
targets:
# target_class is the base concept we are adjusting the representation of
# for example, if we are adjusting the representation of a person, we would use "person"
# if we are adjusting the representation of a cat, we would use "cat" It is not
# a keyword necessarily but what the model understands the concept to represent.
# "person" will affect men, women, children, etc but will not affect cats, dogs, etc
# it is the models base general understanding of the concept and everything it represents
- target_class: "animal"
# positive is the prompt for the positive side of the slider.
# It is the concept that will be excited and amplified in the model when we slide the slider
# to the positive side and forgotten / inverted when we slide
# the slider to the negative side. It is generally best to include the target_class in
# the prompt. You want it to be the extreme of what you want to train on. For example,
# if you want to train on fat people, you would use "an extremely fat, morbidly obese person"
# as the prompt. Not just "fat person"
positive: "cat"
# negative is the prompt for the negative side of the slider and works the same as positive
# it does not necessarily work the same as a negative prompt when generating images
negative: "dog"
# LoRA weight to train this target. I recommend 1.0. Just leave it, it won't work
# how you expect if you change it
multiplier: 1.0
# You can put any information you want here, and it will be saved in the model.
# The below is an example, but you can put your grocery list in it if you want.
# It is saved in the model so be aware of that. The software will include this
# plus some other information for you automatically
meta:
# [name] gets replaced with the name above
name: "[name]"
# version: '1.0'
# creator:
# name: Your Name
# email: your@gmail.com
# website: https://your.website