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---
job: extension
config:
# this name will be the folder and filename name
name: "my_first_flux_lora_v1"
process:
- type: 'sd_trainer'
# root folder to save training sessions/samples/weights
training_folder: "output"
# uncomment to see performance stats in the terminal every N steps
# performance_log_every: 1000
device: cuda:0
# if a trigger word is specified, it will be added to captions of training data if it does not already exist
# alternatively, in your captions you can add [trigger] and it will be replaced with the trigger word
# trigger_word: "p3r5on"
network:
type: "lora"
linear: 16
linear_alpha: 16
save:
dtype: float16 # precision to save
save_every: 500 # save every this many steps
max_step_saves_to_keep: 4 # how many intermittent saves to keep
push_to_hub: false #change this to True to push your trained model to Hugging Face.
# You can either set up a HF_TOKEN env variable or you'll be prompted to log-in
# hf_repo_id: your-username/your-model-slug
# hf_private: true #whether the repo is private or public
datasets:
# datasets are a folder of images. captions need to be txt files with the same name as the image
# for instance image2.jpg and image2.txt. Only jpg, jpeg, and png are supported currently
# images will automatically be resized and bucketed into the resolution specified
# on windows, escape back slashes with another backslash so
# "C:\\path\\to\\images\\folder"
- folder_path: "/workspace/ai-toolkit/images"
caption_ext: "txt"
caption_dropout_rate: 0.05 # will drop out the caption 5% of time
shuffle_tokens: false # shuffle caption order, split by commas
cache_latents_to_disk: true # leave this true unless you know what you're doing
resolution: [512, 768, 1024] # flux enjoys multiple resolutions
train:
batch_size: 1
steps: 1000 # total number of steps to train 500 - 4000 is a good range
gradient_accumulation_steps: 1
train_unet: true
train_text_encoder: false # probably won't work with flux
gradient_checkpointing: true # need the on unless you have a ton of vram
noise_scheduler: "flowmatch" # for training only
optimizer: "adamw8bit"
lr: 0.0004
# skip_first_sample: true
# uncomment to completely disable sampling
# disable_sampling: true
# uncomment to use new vell curved weighting. Experimental but may produce better results
# linear_timesteps: true
# ema will smooth out learning, but could slow it down. Recommended to leave on.
ema_config:
use_ema: true
ema_decay: 0.99
# will probably need this if gpu supports it for flux, other dtypes may not work correctly
dtype: bf16
model:
# huggingface model name or path
name_or_path: "black-forest-labs/FLUX.1-dev"
is_flux: true
quantize: true # run 8bit mixed precision
# low_vram: true # uncomment this if the GPU is connected to your monitors. It will use less vram to quantize, but is slower.
sample:
sampler: "flowmatch" # must match train.noise_scheduler
sample_every: 500 # sample every this many steps
width: 1024
height: 1024
prompts:
- Photo of joslora holding a sign that says 'I LOVE PROMPTS!'
- Professional headshot of joslora in a business suit.
- A happy pilot joslora of a Boeing 747.
- A doctor joslora talking to a patient.
- A chef joslora in the middle of a bustling kitchen, plating a beautifully arranged dish.
- A young joslora with a big grin, holding a large ice cream cone in front of an old-fashioned ice cream parlor.
- A person joslora in a tuxedo, looking directly into the camera with a confident smile, standing on a red carpet at a gala event.
- Person joslora with a bitchin' 80's mullet hairstyle leaning out the window of a pontiac firebird
neg: "" # not used on flux
seed: 42
walk_seed: true
guidance_scale: 4
sample_steps: 20
trigger_word: joslora
# you can add any additional meta info here. [name] is replaced with config name at top
meta:
name: "[name]"
version: '1.0'
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